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Create a XML backend for PubMed documents based on the pubmed_parser library

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This commit is contained in:
lucas-morin 2024-12-09 15:50:10 +01:00
parent 7867014d0b
commit 3240955db9
65 changed files with 21161 additions and 105875 deletions
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1
docling/backend/html_backend.py
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@ -305,7 +305,6 @@ class HTMLDocumentBackend(DeclarativeDocumentBackend):
def handle_table(self, element, idx, doc):
"""Handles table tags."""
nested_tables = element.find("table")
if nested_tables is not None:
_log.warn("detected nested tables: skipping for now")

72
docling/backend/xml_backend.py → docling/backend/pubmed_backend.py
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@ -21,7 +21,7 @@ from docling.datamodel.document import InputDocument
_log = logging.getLogger(__name__)
class XMLDocumentBackend(DeclarativeDocumentBackend):
class PubMedDocumentBackend(DeclarativeDocumentBackend):
def __init__(self, in_doc: "InputDocument", path_or_stream: Union[BytesIO, Path]):
super().__init__(in_doc, path_or_stream)
self.path_or_stream = path_or_stream
@ -43,7 +43,7 @@ class XMLDocumentBackend(DeclarativeDocumentBackend):
@classmethod
def supported_formats(cls) -> Set[InputFormat]:
return {InputFormat.XML}
return {InputFormat.PUBMED}
def convert(self) -> DoclingDocument:
# Create empty document
@ -104,7 +104,7 @@ class XMLDocumentBackend(DeclarativeDocumentBackend):
return doc
def add_figure_captions(self, doc: DoclingDocument, xml_components: dict) -> None:
doc.add_heading(parent=None, text="Figures")
doc.add_heading(parent=self.parents["Title"], text="Figures")
for figure_caption_xml_component in xml_components["figure_captions"]:
figure_caption_text = (
figure_caption_xml_component["fig_label"]
@ -115,17 +115,17 @@ class XMLDocumentBackend(DeclarativeDocumentBackend):
label=DocItemLabel.CAPTION, text=figure_caption_text
)
doc.add_picture(
parent=None,
parent=self.parents["Title"],
caption=fig_caption,
)
return
def add_title(self, doc: DoclingDocument, xml_components: dict) -> None:
doc.add_text(
self.parents["Title"] = doc.add_text(
parent=None,
text=xml_components["info"]["full_title"],
label=DocItemLabel.TITLE,
)
)
return
def add_authors(self, doc: DoclingDocument, xml_components: dict) -> None:
@ -152,7 +152,7 @@ class XMLDocumentBackend(DeclarativeDocumentBackend):
authors_affiliations.append(affiliation["name"])
doc.add_text(
parent=None,
parent=self.parents["Title"],
text="; ".join(authors_affiliations),
label=DocItemLabel.PARAGRAPH,
)
@ -163,12 +163,16 @@ class XMLDocumentBackend(DeclarativeDocumentBackend):
xml_components["info"]["abstract"].replace("\n", " ").strip()
)
if abstract_text.strip():
doc.add_text(text=abstract_text, label=DocItemLabel.TEXT)
self.parents["Abstract"] = doc.add_heading(parent=self.parents["Title"], text="Abstract")
doc.add_text(
parent=self.parents["Abstract"],
text=abstract_text,
label=DocItemLabel.TEXT
)
return
def add_main_text(self, doc: DoclingDocument, xml_components: dict) -> None:
sections: list = []
parent = None
for paragraph in xml_components["paragraphs"]:
if ("section" in paragraph) and (paragraph["section"] == ""):
continue
@ -179,7 +183,7 @@ class XMLDocumentBackend(DeclarativeDocumentBackend):
if section in self.parents:
parent = self.parents[section]
else:
parent = None
parent = self.parents["Title"]
self.parents[section] = doc.add_heading(parent=parent, text=section)
@ -189,32 +193,46 @@ class XMLDocumentBackend(DeclarativeDocumentBackend):
if paragraph["section"] in self.parents:
parent = self.parents[paragraph["section"]]
else:
parent = None
parent = self.parents["Title"]
doc.add_text(parent=parent, label=DocItemLabel.TEXT, text=text)
return
def add_references(self, doc: DoclingDocument, xml_components: dict) -> None:
doc.add_heading(parent=None, text="References")
current_list = doc.add_group(label=GroupLabel.LIST, name="list")
self.parents["References"] = doc.add_heading(parent=self.parents["Title"], text="References")
current_list = doc.add_group(
parent=self.parents["References"],
label=GroupLabel.LIST,
name="list"
)
for reference in xml_components["references"]:
reference_text: str = (
reference["name"]
+ ". "
+ reference["article_title"]
+ ". "
+ reference["journal"]
+ " ("
+ reference["year"]
+ ")"
)
reference_text: str = ""
if reference["name"] != "":
reference_text += reference["name"] + ". "
if reference["article_title"] != "":
reference_text += reference["article_title"]
if reference["article_title"][-1] != ".":
reference_text += "."
reference_text += " "
if reference["journal"] != "":
reference_text += reference["journal"]
if reference["year"] != "":
reference_text += " (" + reference["year"] + ")"
if reference_text == "":
print(f"Skipping reference for: {str(self.file)}")
continue
doc.add_list_item(
text=reference_text, enumerated=False, parent=current_list
)
return
def add_tables(self, doc: DoclingDocument, xml_components: dict) -> None:
doc.add_heading(parent=None, text="Tables")
self.parents["Tables"] = doc.add_heading(parent=self.parents["Title"], text="Tables")
for table_xml_component in xml_components["tables"]:
try:
self.add_table(doc, table_xml_component)
@ -292,5 +310,9 @@ class XMLDocumentBackend(DeclarativeDocumentBackend):
label=DocItemLabel.CAPTION,
text=table_xml_component["label"] + " " + table_xml_component["caption"],
)
doc.add_table(data=data, parent=None, caption=table_caption)
doc.add_table(
data=data,
parent=self.parents["Tables"],
caption=table_caption
)
return

6
docling/datamodel/base_models.py
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@ -28,7 +28,7 @@ class InputFormat(str, Enum):
DOCX = "docx"
PPTX = "pptx"
HTML = "html"
XML = "xml"
PUBMED = "pubmed"
IMAGE = "image"
PDF = "pdf"
ASCIIDOC = "asciidoc"
@ -49,7 +49,7 @@ FormatToExtensions: Dict[InputFormat, List[str]] = {
InputFormat.PDF: ["pdf"],
InputFormat.MD: ["md"],
InputFormat.HTML: ["html", "htm", "xhtml"],
InputFormat.XML: ["xml", "nxml"],
InputFormat.PUBMED: ["nxml"],
InputFormat.IMAGE: ["jpg", "jpeg", "png", "tif", "tiff", "bmp"],
InputFormat.ASCIIDOC: ["adoc", "asciidoc", "asc"],
InputFormat.XLSX: ["xlsx"],
@ -66,7 +66,7 @@ FormatToMimeType: Dict[InputFormat, List[str]] = {
"application/vnd.openxmlformats-officedocument.presentationml.presentation",
],
InputFormat.HTML: ["text/html", "application/xhtml+xml"],
InputFormat.XML: ["text/xml", "text/nxml"],
InputFormat.PUBMED: ["application/nxml"],
InputFormat.IMAGE: [
"image/png",
"image/jpeg",

7
docling/datamodel/document.py
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@ -513,6 +513,11 @@ class _DocumentConversionInput(BaseModel):
)
mime = self._mime_from_extension(ext)
# Detect PubMed XML documents
xml_doctype = re.search(r"<!DOCTYPE [^>]+>", content.decode("utf-8")).group()
if "/NLM//DTD JATS" in xml_doctype:
return InputFormat.PUBMED
mime = mime or self._detect_html_xhtml(content)
mime = mime or "text/plain"
@ -525,8 +530,6 @@ class _DocumentConversionInput(BaseModel):
mime = FormatToMimeType[InputFormat.ASCIIDOC][0]
elif ext in FormatToExtensions[InputFormat.HTML]:
mime = FormatToMimeType[InputFormat.HTML][0]
elif ext in FormatToExtensions[InputFormat.XML]:
mime = FormatToMimeType[InputFormat.XML][0]
elif ext in FormatToExtensions[InputFormat.MD]:
mime = FormatToMimeType[InputFormat.MD][0]

10
docling/document_converter.py
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@ -15,7 +15,7 @@ from docling.backend.md_backend import MarkdownDocumentBackend
from docling.backend.msexcel_backend import MsExcelDocumentBackend
from docling.backend.mspowerpoint_backend import MsPowerpointDocumentBackend
from docling.backend.msword_backend import MsWordDocumentBackend
from docling.backend.xml_backend import XMLDocumentBackend
from docling.backend.pubmed_backend import PubMedDocumentBackend
from docling.datamodel.base_models import ConversionStatus, DocumentStream, InputFormat
from docling.datamodel.document import (
ConversionResult,
@ -76,9 +76,9 @@ class HTMLFormatOption(FormatOption):
backend: Type[AbstractDocumentBackend] = HTMLDocumentBackend
class XMLFormatOption(FormatOption):
class PubMedFormatOption(FormatOption):
pipeline_cls: Type = SimplePipeline
backend: Type[AbstractDocumentBackend] = XMLDocumentBackend
backend: Type[AbstractDocumentBackend] = PubMedDocumentBackend
class PdfFormatOption(FormatOption):
@ -110,8 +110,8 @@ _format_to_default_options = {
InputFormat.HTML: FormatOption(
pipeline_cls=SimplePipeline, backend=HTMLDocumentBackend
),
InputFormat.XML: FormatOption(
pipeline_cls=SimplePipeline, backend=XMLDocumentBackend
InputFormat.PUBMED: FormatOption(
pipeline_cls=SimplePipeline, backend=PubMedDocumentBackend
),
InputFormat.IMAGE: FormatOption(
pipeline_cls=StandardPdfPipeline, backend=DoclingParseDocumentBackend

85
tests/data/groundtruth/docling_v2/10-1055-a-2308-2290.nxml.itxt
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@ -1,85 +0,0 @@
item-0 at level 0: unspecified: group _root_
item-1 at level 1: title: The Association between Obstruct ... o-Sample Mendelian Randomization Study
item-2 at level 1: paragraph: Zhihai Huang; Emergency Medicine ... niversity, Zhanjiang, Guangdong, China
item-3 at level 1: text: Background Despite previous obse ... tween OSA and VTE in either direction.
item-4 at level 1: section_header: Introduction
item-5 at level 2: text: Obstructive sleep apnea (OSA) is ... ed with the prevalence of obesity. 3 4
item-6 at level 2: text: There is mounting evidence indic ... potential bidirectional relationship.
item-7 at level 2: text: Although previous observational ... in classical epidemiologic studies. 17
item-8 at level 1: section_header: Data Source and Selection of Instrumental Variables
item-9 at level 2: text: Summary-level data for OSA were ... data sources is provided in Table 1 .
item-10 at level 2: text: The selection criteria for IVs w ... t of IV selection is shown in Fig. 1 .
item-11 at level 1: section_header: Statistical Analysis
item-12 at level 2: text: This study employed the multipli ... indicating significant heterogeneity.
item-13 at level 1: section_header: Instrumental Variable Selection
item-14 at level 2: text: As previously outlined, a total ... lysis are provided in Tables 2 and 3 .
item-15 at level 1: section_header: Effects of OSA on VTE
item-16 at level 2: text: Fig. 2 shows the estimates of th ... to detect any evidence of pleiotropy.
item-17 at level 2: text: The validation analysis using ge ... netic association between OSA and VTE.
item-18 at level 1: section_header: Effects of VTE on OSA
item-19 at level 2: text: We conducted reverse MR analysis ... med the reliability of the MR results.
item-20 at level 1: section_header: Discussion
item-21 at level 2: text: Our findings contradict some pre ... an increased risk of VTE. 29 30 31 32
item-22 at level 2: text: However, these studies were hind ... adjusting for obesity confounding. 36
item-23 at level 2: text: While our MR study did not find ... A and VTE, delving into greater depth.
item-24 at level 1: section_header: Tables
item-26 at level 1: table with [6x7]
item-26 at level 2: caption: Table 1 Information on data sources
item-28 at level 1: table with [47x17]
item-28 at level 2: caption: Table 2 Genetic variants used in the MR analysis
item-30 at level 1: table with [112x11]
item-30 at level 2: caption: Table 3 Genetic variants used in the reverse MR analysis
item-31 at level 1: section_header: Figures
item-33 at level 1: picture
item-33 at level 2: caption: Fig. 1 The flowchart of instrumental variables selection. LD, linkage disequilibrium; SNPs, single-nucleotide polymorphisms; BMI, body mass index; VTE, venous thromboembolism; PE, pulmonary embolism; DVT, deep vein thrombosis; OSA, obstructive sleep apnea; ①, represents OSA (Jiang et al) as the outcome; ②, represents OSA (Campos et al) as the outcome.
item-35 at level 1: picture
item-35 at level 2: caption: Fig. 2 The genetic association of OSA with VTE/PE/DVT. OSA, obstructive sleep apnea; VTE, venous thromboembolism; PE, pulmonary embolism; DVT, deep vein thrombosis; MR, mendelian randomization; IVW, inverse variance weighted; PRESSO, pleiotropy residual sum and outlier; P*, represents P for heterogeneity test; P**, represents P for MR-Egger intercept; P***, represents P for MR-PRESSO global test.
item-37 at level 1: picture
item-37 at level 2: caption: Fig. 3 The genetic association of VTE/PE/DVT with OSA. OSA, obstructive sleep apnea; VTE, venous thromboembolism; PE, pulmonary embolism; DVT, deep vein thrombosis; MR, mendelian randomization; IVW, inverse variance weighted; PRESSO, pleiotropy residual sum and outlier; P*, represents P for heterogeneity test; P**, represents P for MR-Egger intercept; P***, represents P for MR-PRESSO global test.
item-38 at level 1: section_header: References
item-39 at level 1: list: group list
item-40 at level 2: list_item: S H Wang; W S Chen; S E Tang. Be ... ve sleep apnea. Front Pharmacol (2019)
item-41 at level 2: list_item: C R Innes; P T Kelly; M Hlavac; ... pnoea during wakefulness. Sleep (2015)
item-42 at level 2: list_item: C V Senaratna; J L Perret; C J L ... ystematic review. Sleep Med Rev (2017)
item-43 at level 2: list_item: J Bai; H Wen; J Tai. Altered spo ... apnea syndrome. Front Neurosci (2021)
item-44 at level 2: list_item: J M Marin; A Agusti; I Villar. A ... and risk of hypertension. JAMA (2012)
item-45 at level 2: list_item: S Redline; G Yenokyan; D J Gottl ... tudy. Am J Respir Crit Care Med (2010)
item-46 at level 2: list_item: O Mesarwi; A Malhotra. Obstructi ... relationship. J Clin Sleep Med (2020)
item-47 at level 2: list_item: F Piccirillo; S P Crispino; L Bu ... and heart failure. Am J Cardiol (2023)
item-48 at level 2: list_item: K Glise Sandblad; A Rosengren; J ... eople. Res Pract Thromb Haemost (2022)
item-49 at level 2: list_item: R Raj; A Paturi; M A Ahmed; S E ... sm: a systematic review. Cureus (2022)
item-50 at level 2: list_item: C C Lin; J J Keller; J H Kang; T ... Vasc Surg Venous Lymphat Disord (2013)
item-51 at level 2: list_item: Y H Peng; W C Liao; W S Chung. A ... ective cohort study. Thromb Res (2014)
item-52 at level 2: list_item: O Nepveu; C Orione; C Tromeur. A ... from a French cohort. Thromb J (2022)
item-53 at level 2: list_item: O Dabbagh; M Sabharwal; O Hassan ... among females not males. Chest (2010)
item-54 at level 2: list_item: J P Bosanquet; B C Bade; M F Zia ... lation. Clin Appl Thromb Hemost (2011)
item-55 at level 2: list_item: A Xue; L Jiang; Z Zhu. Genome-wi ... ongitudinal changes. Nat Commun (2021)
item-56 at level 2: list_item: B Pu; P Gu; C Zheng; L Ma; X Zhe ... domization analysis. Front Nutr (2022)
item-57 at level 2: list_item: L Jiang; Z Zheng; H Fang; J Yang ... r biobank-scale data. Nat Genet (2021)
item-58 at level 2: list_item: A I Campos; N Ingold; Y Huang. D ... AS analysis with snoring. Sleep (2023)
item-59 at level 2: list_item: R Feng; M Lu; J Xu. Pulmonary em ... omization study. BMC Genom Data (2022)
item-60 at level 2: list_item: R Molenberg; C HL Thio; M W Aalb ... ian randomization study. Stroke (2022)
item-61 at level 2: list_item: S H Wang; B T Keenan; A Wiemken. ... fat. Am J Respir Crit Care Med (2020)
item-62 at level 2: list_item: C Hotoleanu. Association between ... thromboembolism. Med Pharm Rep (2020)
item-63 at level 2: list_item: H Zhao; X Jin. Causal associatio ... randomization study. Front Nutr (2022)
item-64 at level 2: list_item: B Tang; Y Wang; X Jiang. Genetic ... randomization study. Neurology (2022)
item-65 at level 2: list_item: S S Dong; K Zhang; Y Guo. Phenom ... randomization study. Genome Med (2021)
item-66 at level 2: list_item: W Huang; J Xiao; J Ji; L Chen. A ... lian randomization study. eLife (2021)
item-67 at level 2: list_item: X Chen; J Kong; J Pan. Kidney da ... ndomization study. EBioMedicine (2021)
item-68 at level 2: list_item: I Arnulf; M Merino-Andreu; A Per ... nd venous thromboembolism. JAMA (2002)
item-69 at level 2: list_item: K T Chou; C C Huang; Y M Chen. S ... -matched cohort study. Am J Med (2012)
item-70 at level 2: list_item: A Alonso-Fernández; M de la Peña ... monary embolism. Mayo Clin Proc (2013)
item-71 at level 2: list_item: M Ambrosetti; A Lucioni; W Ageno ... nea syndrome?. J Thromb Haemost (2004)
item-72 at level 2: list_item: S Reutrakul; B Mokhlesi. Obstruc ... state of the art review. Chest (2017)
item-73 at level 2: list_item: S Lindström; M Germain; M Crous- ... Randomization study. Hum Genet (2017)
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50254
tests/data/groundtruth/docling_v2/10-1055-a-2308-2290.nxml.json
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286
tests/data/groundtruth/docling_v2/10-1055-a-2308-2290.nxml.md
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@ -1,286 +0,0 @@
# The Association between Obstructive Sleep Apnea and Venous Thromboembolism: A Bidirectional Two-Sample Mendelian Randomization Study
Zhihai Huang; Emergency Medicine Center, Affiliated Hospital of Guangdong Medical University, Zhanjiang, Guangdong, China; Zhenzhen Zheng; Respiratory and Critical Care Medicine, The Second Affiliated Hospital of Guangdong Medical University, Zhanjiang, Guangdong, China; Lingpin Pang; Emergency Medicine Center, Affiliated Hospital of Guangdong Medical University, Zhanjiang, Guangdong, China; Kaili Fu; Respiratory and Critical Care Medicine, The Second Affiliated Hospital of Guangdong Medical University, Zhanjiang, Guangdong, China; Junfen Cheng; Respiratory and Critical Care Medicine, The Second Affiliated Hospital of Guangdong Medical University, Zhanjiang, Guangdong, China; Ming Zhong; Emergency Medicine Center, Affiliated Hospital of Guangdong Medical University, Zhanjiang, Guangdong, China; Lingyue Song; Emergency Medicine Center, Affiliated Hospital of Guangdong Medical University, Zhanjiang, Guangdong, China; Dingyu Guo; Emergency Medicine Center, Affiliated Hospital of Guangdong Medical University, Zhanjiang, Guangdong, China; Qiaoyun Chen; Emergency Medicine Center, Affiliated Hospital of Guangdong Medical University, Zhanjiang, Guangdong, China; Yanxi Li; Emergency Medicine Center, Affiliated Hospital of Guangdong Medical University, Zhanjiang, Guangdong, China; Yongting Lv; Emergency Medicine Center, Affiliated Hospital of Guangdong Medical University, Zhanjiang, Guangdong, China; Riken Chen; Respiratory and Critical Care Medicine, The Second Affiliated Hospital of Guangdong Medical University, Zhanjiang, Guangdong, China; Xishi Sun; Emergency Medicine Center, Affiliated Hospital of Guangdong Medical University, Zhanjiang, Guangdong, China
Background Despite previous observational studies linking obstructive sleep apnea (OSA) to venous thromboembolism (VTE), these findings remain controversial. This study aimed to explore the association between OSA and VTE, including pulmonary embolism (PE) and deep vein thrombosis (DVT), at a genetic level using a bidirectional two-sample Mendelian randomization (MR) analysis. Methods Utilizing summary-level data from large-scale genome-wide association studies in European individuals, we designed a bidirectional two-sample MR analysis to comprehensively assess the genetic association between OSA and VTE. The inverse variance weighted was used as the primary method for MR analysis. In addition, MR–Egger, weighted median, and MR pleiotropy residual sum and outlier (MR-PRESSO) were used for complementary analyses. Furthermore, a series of sensitivity analyses were performed to ensure the validity and robustness of the results. Results The initial and validation MR analyses indicated that genetically predicted OSA had no effects on the risk of VTE (including PE and DVT). Likewise, the reverse MR analysis did not find substantial support for a significant association between VTE (including PE and DVT) and OSA. Supplementary MR methods and sensitivity analyses provided additional confirmation of the reliability of the MR results. Conclusion Our bidirectional two-sample MR analysis did not find genetic evidence supporting a significant association between OSA and VTE in either direction.
## Introduction
Obstructive sleep apnea (OSA) is a prevalent sleep disorder characterized by the recurrent partial or complete obstruction and collapse of the upper airway during sleep, leading to episodes of apneas and hypoventilation. 1 2 Research studies have reported that the prevalence of OSA in the adult population ranges from 9 to 38%, with a higher prevalence observed in males (13–33%) compared to females (6–19%). Moreover, the prevalence of OSA tends to increase with age and is closely associated with the prevalence of obesity. 3 4
There is mounting evidence indicating that OSA serves as an independent risk factor for several cardiovascular diseases, including hypertension, 5 stroke, 6 pulmonary hypertension, 7 and heart failure. 8 Venous thromboembolism (VTE), including deep vein thrombosis (DVT) and pulmonary embolism (PE), is recognized as the third most common cardiovascular disease worldwide. 9 There is evidence suggesting that OSA may also be linked to an increased risk of VTE. 10 For instance, a prospective study involving 15,664 subjects (1,424 subjects with OSA) observed a twofold higher incidence of VTE in patients with OSA compared to non-OSA patients. 11 Similarly, findings from a national retrospective cohort study conducted by Peng and his colleagues indicated that patients with OSA had a 3.50-fold higher risk of DVT and a 3.97-fold higher risk of PE compared to the general population. 12 However, the results of observational studies remain somewhat controversial. A 5-year prospective study involving 2,109 subjects concluded that OSA did not increase the risk of VTE recurrence. 13 Another retrospective analysis involving 1,584 patients, of which 848 were women, revealed an intriguing discovery suggesting that OSA may serve as an independent risk factor for VTE solely in women, rather than in men. 14 Moreover, patients with VTE were found to have a higher prevalence of OSA, 15 suggesting a potential bidirectional relationship.
Although previous observational studies have investigated the potential association between OSA and VTE, elucidating aspects of the association from these studies is challenging due to the limitations of potential confounders and reverse causality bias. Mendelian randomization (MR) is a genetic epidemiological methodology that utilizes genetic variants, such as single-nucleotide polymorphisms (SNPs), as instrumental variables (IVs) to infer the genetic association between exposure and outcome. 16 The advantage of MR analysis lies in the random assignment of genetic variants during meiosis, which effectively circumvents the effects of potential confounders and reverse causality encountered in classical epidemiologic studies. 17
## Data Source and Selection of Instrumental Variables
Summary-level data for OSA were obtained from the GWAS study conducted by Jiang et al on European individuals, which included 2,827 cases and 453,521 controls, covering 11,831,932 SNPs. 18 To ensure the robustness of the findings, additional datasets for OSA were acquired from a GWAS meta-analysis conducted by Campos and colleagues, comprising 25,008 cases of European ancestry and 337,630 controls, involving 9,031,949 SNPs for validation analysis. 19 The study conducted a meta-analysis of GWAS datasets from five cohorts in the United Kingdom, Canada, Australia, the United States, and Finland. These summary-level GWAS statistics for OSA can be accessed from the GWAS Catalog ( https://www.ebi.ac.uk/gwas/downloads ). VTE was defined as a condition comprising PE (blockage of the pulmonary artery or its branches by an embolus) and DVT (formation of a blood clot in a deep vein). The GWAS datasets for VTE (19,372 cases and 357,905 controls), PE (9,243 cases and 367,108 controls), and DVT (9,109 cases and 324,121 controls) were derived from the FinnGen consortium (Release 9, https://r9.finngen.fi/ ). Detailed information regarding the data sources is provided in Table 1 .
The selection criteria for IVs were as follows: (1) the threshold for genome-wide significant SNPs for VTE (including PE and DVT) was set at p  < 5.0 × 10 −8 , while the threshold for OSA was adjusted to p  < 1 × 10 −5 due to the inability to detect OSA-associated SNPs using a significance level of p  < 5.0 × 10 −8 . (2) SNPs with linkage disequilibrium effects ( r 2  < 0.001 within a 10,000-kb window) were excluded to ensure the independence of the selected IVs. (3) The strength of the association between IVs and exposure was measured using the F-statistic [F-statistic = (Beta/SE) 2 ]. 20 SNPs with F-statistics >10 were retained to avoid the effects of weak instrumental bias. (4) During the harmonization process, SNPs that did not match the results were removed, along with palindromic SNPs with ambiguous allele frequencies (0.42–0.58). 21 (5) Previous studies have demonstrated obesity as an established risk factor for OSA and VTE. 22 23 SNPs associated with body mass index were queried and excluded by Phenoscanner (http://www.phenoscanner.medschl.cam.ac.uk/). The flowchart of IV selection is shown in Fig. 1 .
## Statistical Analysis
This study employed the multiplicative random-effects inverse variance weighted (IVW) method as the primary approach for conducting MR analysis to evaluate the genetic association between OSA and VTE. The IVW method meta-analyzes the Wald ratio estimates for each SNP on the outcome, providing precise estimates of causal effects when all selected SNPs are valid IVs. 24 However, the estimates of causal effects from the IVW method may be biased by the influence of pleiotropic IVs. To ensure the validity and robustness of the results, sensitivity analyses were implemented using three additional MR methods, namely MR–Egger, weighted median, and MR pleiotropy residual sum and outlier (MR-PRESSO). The MR–Egger method is able to generate reliable causal estimates even in situations where all IVs are invalid. Additionally, MR–Egger offers an intercept test to detect horizontal pleiotropy, with a significance threshold of p <0.05 indicating the presence of horizontal pleiotropy. 25 In comparison to the IVW and MR–Egger methods, the weighted median method demonstrates greater robustness and provides consistent estimates of causal effects, even when up to 50% of the IVs are invalid instruments. 26 The MR-PRESSO method identifies outliers with potential horizontal pleiotropy and provides estimates after removing the outliers, where p <0.05 for the global test indicates the presence of outliers with horizontal pleiotropy. 27 Furthermore, the Cochran Q test was utilized to examine heterogeneity, with a significance threshold of p <0.05 indicating significant heterogeneity.
## Instrumental Variable Selection
As previously outlined, a total of 13 and 28 SNPs were identified through a rigorous screening process to evaluate the effects of OSA on VTE, PE, and DVT. In the reverse MR analysis, 23, 14, 18, 19, 11, and 13 SNPs were identified to assess the implications of reverse association, respectively. Additional details regarding these genetic variants utilized for MR analysis are provided in Tables 2 and 3 .
## Effects of OSA on VTE
Fig. 2 shows the estimates of the effects for OSA on VTE, PE, and DVT. In the initial MR analysis using the OSA (Jiang et al) dataset, the random-effects IVW method revealed no significant association between OSA and the risk of VTE (odds ratio [OR]: 0.964, 95% confidence interval [CI]: 0.914-1.016, p  = 0.172), PE (OR: 0.929, 95% CI: 0.857–1.006, p  = 0.069), PE (OR: 0.929, 95% CI: 0.857–1.006, p  = 0.069), and DVT (OR: 1.001, 95% CI: 0.936–1.071, p  = 0.973). No heterogeneity was observed using the Cochran Q test (all p * > 0.05). The MR–Egger intercept test (all p ** > 0.05) and the MR-PRESSO global test (all p *** > 0.05) failed to detect any evidence of pleiotropy.
The validation analysis using genetic variants of OSA (Campos et al) yielded similar results. Notably, heterogeneity was observed in the sensitivity analysis for OSA (Campos et al) and VTE ( p * = 0.018). However, considering the random-effects IVW model employed, the level of heterogeneity was deemed acceptable. 28 Despite the presence of outliers suggested by the MR-PRESSO global test ( p  = 0.015), no significant association between OSA and VTE (OR: 1.071, 95% CI: 0.917–1.251, p  = 0.396) was found after excluding an outlier (rs7106583). In addition, none of the three complementary MR methods supported a genetic association between OSA and VTE.
## Effects of VTE on OSA
We conducted reverse MR analysis to further evaluate the effects of VTE (including PE and DVT) on OSA. Both MR analyses yielded consistent results, indicating no significant effects of VTE, PE, and DVT on OSA (see Fig. 3 ). Moreover, the Cochran Q test revealed no heterogeneity (all p * > 0.05), and both the MR–Egger intercept test and the MR-PRESSO global test found no evidence of pleiotropy (all p ** > 0.05 and p *** > 0.05, respectively) (see Fig. 3 ). In summary, a range of sensitivities confirmed the reliability of the MR results.
## Discussion
Our findings contradict some previous observational studies suggesting a link between susceptibility to OSA and an increased risk of VTE. 29 30 31 32
However, these studies were hindered by inadequate consideration of confounding factors, particularly obesity, along with methodological flaws and small sample sizes. Obesity is widely recognized as a significant risk factor for both OSA 33 and VTE. 34 Therefore, it is crucial not to overlook the impact of obesity in striving for a deeper understanding of the potential association between OSA and VTE. Notably, a cohort study involving 31,309 subjects indicated a higher likelihood of VTE development among patients with more severe OSA. Yet, this association disappeared upon adjusting for confounders, notably obesity levels. 35 Thus, it is plausible that the observed association between OSA and VTE could be attributed to obesity confounding. Additionally, Aman and his colleagues' report yielded consistent results, suggesting that OSA does not elevate the risk of VTE after adjusting for obesity confounding. 36
While our MR study did not find evidence supporting a genetic association between OSA and VTE, it remains possible that OSA could influence the onset or progression of VTE. Virchow's triad depicts three major factors inducing VTE: endothelial injury, venous stasis, and hypercoagulability. 37 The pathophysiologic mechanism linking OSA and VTE remains unknown but may be associated with OSA's capacity to affect the three classical mechanistic pathways of Virchow's triad. 38 Intermittent hypoxia, a signature feature of OSA, can induce oxidative stress and activate inflammatory markers, further damaging the vascular endothelium. 39 40 OSA-associated hemodynamic alterations and reduced physical activities may result in venous stasis. 41 A growing number of studies have demonstrated a strong correlation between OSA and hypercoagulability. A retrospective cohort study aimed at assessing coagulation in patients with OSA suggested that patients with moderate to severe OSA experienced elevated markers of blood coagulability, primarily evidenced by shortened prothrombin time, compared to healthy individuals. 42 Two additional studies of thrombotic parameters found that patients with OSA possessed higher levels of the thrombin–antithrombin complex. 43 44 Furthermore, several coagulation factors, such as fibrinogen, coagulation factor VII, coagulation factor XII, and vascular hemophilic factor, which play a crucial role in the coagulation process, are elevated in patients with OSA. 45 Collectively, this evidence supports that patients with OSA are in a state of hypercoagulability, facilitating our understanding of the underlying pathophysiologic mechanisms between OSA and VTE. Considering these potential mechanisms, future large-scale studies are necessary to thoroughly explore the potential association between OSA and VTE, delving into greater depth.
## Tables
Table 1 Information on data sources
| Trait | Sample size | Case | Control | No. of SNPs | Participates | PMID/Link |
|--------------------|---------------|--------|-----------|---------------|-------------------|--------------------------------------------------|
| OSA (Jiang et al) | 456,348 | 2,827 | 453,521 | 11,831,932 | European ancestry | 34737426 |
| OSA (Campos et al) | 362,638 | 25,008 | 337,630 | 9,031,949 | European ancestry | 36525587 |
| VTE | 377,277 | 19,372 | 357,905 | 20,170,236 | European ancestry | FinnGen consortium ( https://www.finngen.fi/fi ) |
| PE | 376,351 | 9,243 | 367,108 | 20,170,202 | European ancestry | FinnGen consortium ( https://www.finngen.fi/fi ) |
| DVT | 333,230 | 9,109 | 324,121 | 20,169,198 | European ancestry | FinnGen consortium ( https://www.finngen.fi/fi ) |
Table 2 Genetic variants used in the MR analysis
| Genetic instruments for OSA (Jiang et al) and their associations with VTE, PE, and DVT | Genetic instruments for OSA (Jiang et al) and their associations with VTE, PE, and DVT | Genetic instruments for OSA (Jiang et al) and their associations with VTE, PE, and DVT | Genetic instruments for OSA (Jiang et al) and their associations with VTE, PE, and DVT | Genetic instruments for OSA (Jiang et al) and their associations with VTE, PE, and DVT | Genetic instruments for OSA (Jiang et al) and their associations with VTE, PE, and DVT | Genetic instruments for OSA (Jiang et al) and their associations with VTE, PE, and DVT | Genetic instruments for OSA (Jiang et al) and their associations with VTE, PE, and DVT | Genetic instruments for OSA (Jiang et al) and their associations with VTE, PE, and DVT | Genetic instruments for OSA (Jiang et al) and their associations with VTE, PE, and DVT | Genetic instruments for OSA (Jiang et al) and their associations with VTE, PE, and DVT | Genetic instruments for OSA (Jiang et al) and their associations with VTE, PE, and DVT | Genetic instruments for OSA (Jiang et al) and their associations with VTE, PE, and DVT | Genetic instruments for OSA (Jiang et al) and their associations with VTE, PE, and DVT | Genetic instruments for OSA (Jiang et al) and their associations with VTE, PE, and DVT | Genetic instruments for OSA (Jiang et al) and their associations with VTE, PE, and DVT | Genetic instruments for OSA (Jiang et al) and their associations with VTE, PE, and DVT |
|--------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------|
| | | | | Exposure: OSA (Jiang et al) | Exposure: OSA (Jiang et al) | Exposure: OSA (Jiang et al) | Exposure: OSA (Jiang et al) | Outcome: VTE | Outcome: VTE | Outcome: VTE | Outcome: PE | Outcome: PE | Outcome: PE | Outcome: DVT | Outcome: DVT | Outcome: DVT |
| | SNP | EA | OA | Beta | SE | p -Value | F-statistic | Beta | SE | p -Value | Beta | SE | p -Value | Beta | SE | p -Value |
| 1 | rs114417992 | C | G | 0.48798 | 0.10793 | 6.15E-06 | 20.4409 | 0.00702 | 0.04378 | 0.87253 | −0.0542 | 0.06211 | 0.3832 | −0.0052 | 0.06334 | 0.93511 |
| 2 | rs115071002 | T | C | −0.3775 | 0.0807 | 2.90E-06 | 21.8836 | −0.0521 | 0.05045 | 0.30146 | 0.0359 | 0.07185 | 0.61729 | −0.0633 | 0.07247 | 0.38241 |
| 3 | rs117025138 | C | G | 0.42795 | 0.09571 | 7.78E-06 | 19.9915 | −0.0149 | 0.05477 | 0.78611 | 0.0087 | 0.07809 | 0.91129 | −0.0338 | 0.07836 | 0.66637 |
| 4 | rs117474005 | T | C | 0.64176 | 0.14138 | 5.64E-06 | 20.6051 | −0.0095 | 0.04108 | 0.81724 | −0.0009 | 0.05862 | 0.98772 | −0.0301 | 0.05891 | 0.60971 |
| 5 | rs139183760 | C | G | 0.82973 | 0.16928 | 9.50E-07 | 24.0262 | 0.06514 | 0.07681 | 0.39643 | 0.04441 | 0.10929 | 0.68448 | 0.04761 | 0.11024 | 0.66585 |
| 6 | rs148047757 | A | G | 0.47481 | 0.10699 | 9.08E-06 | 19.6952 | −0.0522 | 0.0352 | 0.13769 | −0.044 | 0.04999 | 0.37895 | −0.0294 | 0.05078 | 0.562 |
| 7 | rs150798389 | C | A | 0.7875 | 0.17391 | 5.95E-06 | 20.505 | −0.2884 | 0.1435 | 0.04447 | −0.2436 | 0.20053 | 0.22438 | −0.1329 | 0.20679 | 0.52056 |
| 8 | rs16850412 | A | G | 0.19514 | 0.04353 | 7.36E-06 | 20.0977 | 0.02674 | 0.01584 | 0.09145 | 0.04785 | 0.02253 | 0.03368 | 0.0173 | 0.02277 | 0.44739 |
| 9 | rs1911999 | C | T | −0.1312 | 0.02965 | 9.59E-06 | 19.5917 | 0.01759 | 0.01111 | 0.11349 | 0.0376 | 0.01577 | 0.01714 | 0.00223 | 0.01596 | 0.88889 |
| 10 | rs2302012 | A | G | 0.12829 | 0.02871 | 7.88E-06 | 19.9669 | −0.0104 | 0.01076 | 0.33549 | −0.0272 | 0.0153 | 0.07541 | 0.00767 | 0.01545 | 0.61949 |
| 11 | rs35963104 | T | C | 0.16572 | 0.03452 | 1.59E-06 | 23.0393 | −0.0076 | 0.01354 | 0.57685 | −0.02 | 0.01924 | 0.29896 | −0.007 | 0.01942 | 0.71778 |
| 12 | rs60445800 | T | C | 0.29191 | 0.06499 | 7.06E-06 | 20.1758 | −0.0268 | 0.02361 | 0.25672 | −0.0649 | 0.03349 | 0.05277 | 0.0112 | 0.03388 | 0.74095 |
| 13 | rs9587442 | T | C | 0.44308 | 0.09584 | 3.78E-06 | 21.3735 | −0.0385 | 0.03346 | 0.24969 | −0.0101 | 0.04781 | 0.83322 | 0.00182 | 0.04783 | 0.96962 |
| Genetic instruments for OSA (Campos et al) and their associations with VTE, PE, and DVT | Genetic instruments for OSA (Campos et al) and their associations with VTE, PE, and DVT | Genetic instruments for OSA (Campos et al) and their associations with VTE, PE, and DVT | Genetic instruments for OSA (Campos et al) and their associations with VTE, PE, and DVT | Genetic instruments for OSA (Campos et al) and their associations with VTE, PE, and DVT | Genetic instruments for OSA (Campos et al) and their associations with VTE, PE, and DVT | Genetic instruments for OSA (Campos et al) and their associations with VTE, PE, and DVT | Genetic instruments for OSA (Campos et al) and their associations with VTE, PE, and DVT | Genetic instruments for OSA (Campos et al) and their associations with VTE, PE, and DVT | Genetic instruments for OSA (Campos et al) and their associations with VTE, PE, and DVT | Genetic instruments for OSA (Campos et al) and their associations with VTE, PE, and DVT | Genetic instruments for OSA (Campos et al) and their associations with VTE, PE, and DVT | Genetic instruments for OSA (Campos et al) and their associations with VTE, PE, and DVT | Genetic instruments for OSA (Campos et al) and their associations with VTE, PE, and DVT | Genetic instruments for OSA (Campos et al) and their associations with VTE, PE, and DVT | Genetic instruments for OSA (Campos et al) and their associations with VTE, PE, and DVT | Genetic instruments for OSA (Campos et al) and their associations with VTE, PE, and DVT |
| | | | | Exposure: OSA (Campos et al) | Exposure: OSA (Campos et al) | Exposure: OSA (Campos et al) | Exposure: OSA (Campos et al) | Outcome: VTE | Outcome: VTE | Outcome: VTE | Outcome: PE | Outcome: PE | Outcome: PE | Outcome: DVT | Outcome: DVT | Outcome: DVT |
| | SNP | EA | OA | Beta | SE | p -Value | F-statistic | Beta | SE | p -Value | Beta | SE | p -Value | Beta | SE | p -Value |
| 1 | rs10777826 | T | C | −0.0319 | 0.00664 | 1.58E-06 | 23.0496 | 0.00684 | 0.01097 | 0.53296 | 0.01049 | 0.01557 | 0.50053 | 0.01763 | 0.01576 | 0.26318 |
| 2 | rs10878269 | T | C | 0.03308 | 0.0069 | 1.61E-06 | 23.0112 | −0.0208 | 0.01191 | 0.08097 | −0.0262 | 0.01693 | 0.12108 | −0.015 | 0.01711 | 0.37964 |
| 3 | rs111909157 | T | C | −0.1355 | 0.02658 | 3.40E-07 | 26.01 | 0.02664 | 0.04222 | 0.52808 | 0.03995 | 0.05999 | 0.5054 | 0.0364 | 0.06089 | 0.55 |
| 4 | rs116114601 | A | G | −0.0873 | 0.01969 | 9.20E-06 | 19.6692 | −0.0401 | 0.04098 | 0.32779 | −0.0676 | 0.05814 | 0.24516 | −0.0182 | 0.05873 | 0.75679 |
| 5 | rs11989172 | C | G | −0.0378 | 0.00839 | 6.73E-06 | 20.268 | −0.0217 | 0.01283 | 0.09 | −0.039 | 0.01823 | 0.03222 | 0.01058 | 0.01842 | 0.56561 |
| 6 | rs12265404 | A | G | 0.04931 | 0.01041 | 2.17E-06 | 22.4392 | 0.05233 | 0.0166 | 0.00162 | 0.05687 | 0.0233 | 0.01467 | 0.04278 | 0.02358 | 0.06956 |
| 7 | rs12306339 | A | C | −0.0488 | 0.01083 | 6.64E-06 | 20.295 | −0.0051 | 0.01804 | 0.77914 | −0.023 | 0.02561 | 0.37006 | 0.01462 | 0.02593 | 0.57292 |
| 8 | rs13098300 | T | C | 0.03715 | 0.00712 | 1.84E-07 | 27.1962 | 0.00251 | 0.01202 | 0.83434 | 0.0101 | 0.01708 | 0.55432 | 5.55E-05 | 0.01727 | 0.99744 |
| 9 | rs140548601 | C | G | −0.1158 | 0.02428 | 1.85E-06 | 22.7529 | 0.05503 | 0.04711 | 0.24277 | 0.09206 | 0.06692 | 0.16895 | 0.04613 | 0.06762 | 0.49515 |
| 10 | rs143417867 | A | G | −0.3666 | 0.07088 | 2.30E-07 | 26.7599 | −0.1487 | 0.2216 | 0.5021 | 0.15664 | 0.31582 | 0.61991 | −0.0868 | 0.31594 | 0.78353 |
| 11 | rs1942263 | A | G | 0.04569 | 0.01016 | 6.93E-06 | 20.214 | −0.0156 | 0.01713 | 0.36361 | −0.0136 | 0.02436 | 0.57584 | −0.0318 | 0.02468 | 0.19751 |
| 12 | rs2876633 | A | T | −0.0355 | 0.00695 | 3.43E-07 | 25.9896 | −0.0104 | 0.01158 | 0.36765 | −0.0104 | 0.01645 | 0.52845 | 0.0032 | 0.01664 | 0.84772 |
| 13 | rs35847366 | A | G | 0.0545 | 0.01172 | 3.31E-06 | 21.6318 | −0.0365 | 0.01831 | 0.04596 | −0.0383 | 0.02603 | 0.14125 | −0.0511 | 0.02629 | 0.0517 |
| 14 | rs36051007 | T | C | 0.03481 | 0.00716 | 1.14E-06 | 23.6682 | −0.0037 | 0.01095 | 0.73452 | −0.0145 | 0.01557 | 0.35199 | 0.00723 | 0.01573 | 0.64597 |
| 15 | rs3774800 | A | G | −0.0309 | 0.0069 | 7.79E-06 | 19.9898 | 0.00395 | 0.01151 | 0.73124 | −0.0107 | 0.01634 | 0.51218 | 0.0093 | 0.01654 | 0.57396 |
| 16 | rs4542364 | A | G | 0.03028 | 0.00673 | 6.69E-06 | 20.277 | −0.0053 | 0.01084 | 0.6236 | −0.0199 | 0.01541 | 0.19737 | 0.00163 | 0.01559 | 0.91663 |
| 17 | rs4675933 | T | C | −0.0329 | 0.00709 | 3.44E-06 | 21.5482 | 0.00822 | 0.01093 | 0.45187 | 0.00396 | 0.01554 | 0.79863 | 0.01593 | 0.01568 | 0.30957 |
| 18 | rs533143 | T | C | 0.03237 | 0.00732 | 9.73E-06 | 19.5629 | 0.02892 | 0.01429 | 0.04304 | 0.02757 | 0.02031 | 0.1747 | 0.0111 | 0.02054 | 0.58881 |
| 19 | rs60653979 | A | G | 0.03384 | 0.0068 | 6.43E-07 | 24.7805 | 0.01098 | 0.01083 | 0.31063 | −0.0154 | 0.01539 | 0.31844 | 0.02887 | 0.01557 | 0.06364 |
| 20 | rs62559379 | C | G | 0.0706 | 0.01455 | 1.22E-06 | 23.5419 | −0.0163 | 0.02726 | 0.54934 | −0.028 | 0.03871 | 0.46867 | −0.0113 | 0.03915 | 0.77255 |
| 21 | rs7106583 | T | C | 0.03868 | 0.00839 | 4.09E-06 | 21.2244 | −0.0434 | 0.014 | 0.00194 | −0.0205 | 0.02006 | 0.30655 | −0.0414 | 0.0203 | 0.04114 |
| 22 | rs72904209 | T | C | −0.0446 | 0.00983 | 5.67E-06 | 20.5934 | −0.0153 | 0.01617 | 0.34449 | −0.0355 | 0.02292 | 0.1215 | −0.0066 | 0.02327 | 0.77599 |
| 23 | rs73141516 | T | C | 0.06496 | 0.01415 | 4.40E-06 | 21.0865 | 0.0084 | 0.02184 | 0.70062 | −0.0241 | 0.03105 | 0.43797 | 0.03405 | 0.03133 | 0.27717 |
| 24 | rs73164714 | T | C | −0.0695 | 0.01285 | 6.43E-08 | 29.2248 | −0.028 | 0.03721 | 0.45256 | 0.00562 | 0.05276 | 0.91513 | −0.0139 | 0.05319 | 0.79352 |
| 25 | rs7800775 | A | G | 0.03487 | 0.00785 | 8.98E-06 | 19.7136 | 0.00351 | 0.01357 | 0.79598 | 0.00758 | 0.01929 | 0.69414 | −0.0166 | 0.01948 | 0.39528 |
| 26 | rs794999 | A | G | 0.03421 | 0.00764 | 7.64E-06 | 20.0256 | 0.00108 | 0.01258 | 0.93171 | 0.0139 | 0.01786 | 0.43649 | 0.00374 | 0.01807 | 0.83582 |
| 27 | rs9464135 | A | G | −0.0309 | 0.00663 | 3.11E-06 | 21.7436 | −0.0076 | 0.01055 | 0.47151 | 0.01164 | 0.015 | 0.43786 | −0.0375 | 0.01516 | 0.01337 |
| 28 | rs9567762 | A | T | 0.03635 | 0.00823 | 9.92E-06 | 19.5276 | 0.01223 | 0.01084 | 0.25934 | 0.00403 | 0.0154 | 0.7934 | 0.01552 | 0.01557 | 0.31884 |
Table 3 Genetic variants used in the reverse MR analysis
| Genetic instruments for VTE/PE/DVT and their associations with OSA (Jiang et al) | Genetic instruments for VTE/PE/DVT and their associations with OSA (Jiang et al) | Genetic instruments for VTE/PE/DVT and their associations with OSA (Jiang et al) | Genetic instruments for VTE/PE/DVT and their associations with OSA (Jiang et al) | Genetic instruments for VTE/PE/DVT and their associations with OSA (Jiang et al) | Genetic instruments for VTE/PE/DVT and their associations with OSA (Jiang et al) | Genetic instruments for VTE/PE/DVT and their associations with OSA (Jiang et al) | Genetic instruments for VTE/PE/DVT and their associations with OSA (Jiang et al) | Genetic instruments for VTE/PE/DVT and their associations with OSA (Jiang et al) | Genetic instruments for VTE/PE/DVT and their associations with OSA (Jiang et al) | Genetic instruments for VTE/PE/DVT and their associations with OSA (Jiang et al) |
|--------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------|
| | | | | Exposure: VTE | Exposure: VTE | Exposure: VTE | Exposure: VTE | Outcome: OSA (Jiang et al) | Outcome: OSA (Jiang et al) | Outcome: OSA (Jiang et al) |
| | SNP | EA | OA | Beta | SE | p -Value | F-statistic | Beta | SE | p -Value |
| 1 | rs10896706 | A | G | 0.0702142 | 0.0121006 | 6.53E-09 | 33.669456 | −0.0597845 | 0.029345 | 0.0416207 |
| 2 | rs113079063 | T | G | 0.378107 | 0.0507769 | 9.59E-14 | 55.449428 | 0.0050364 | 0.0876134 | 0.954159 |
| 3 | rs114026832 | A | C | 0.773925 | 0.099915 | 9.50E-15 | 59.997944 | 0.0578773 | 0.180543 | 0.748533 |
| 4 | rs114767153 | T | A | −0.20888 | 0.0348173 | 1.98E-09 | 35.991798 | −0.0712189 | 0.0909972 | 0.433833 |
| 5 | rs116997538 | T | C | 0.403288 | 0.0383066 | 6.42E-26 | 110.83665 | −0.067735 | 0.123897 | 0.584581 |
| 6 | rs12054563 | G | A | −0.126677 | 0.0176431 | 6.97E-13 | 51.552027 | 0.0602695 | 0.0663601 | 0.363763 |
| 7 | rs1560711 | T | C | 0.122379 | 0.0141465 | 5.11E-18 | 74.836901 | 0.0310044 | 0.0321024 | 0.334145 |
| 8 | rs174529 | C | T | −0.0686342 | 0.0107211 | 1.54E-10 | 40.982878 | −0.0053417 | 0.0276673 | 0.846904 |
| 9 | rs188337046 | T | C | 0.16048 | 0.0250424 | 1.47E-10 | 41.066712 | 0.178311 | 0.206621 | 0.388145 |
| 10 | rs2066865 | A | G | 0.186112 | 0.0112369 | 1.30E-61 | 274.31889 | 0.0083154 | 0.0313691 | 0.790945 |
| 11 | rs2519785 | G | A | −0.0702991 | 0.0118882 | 3.35E-09 | 34.967721 | 0.0074319 | 0.0297183 | 0.802526 |
| 12 | rs3756011 | A | C | 0.192712 | 0.0105525 | 1.65E-74 | 333.50841 | −0.0026386 | 0.0272831 | 0.922956 |
| 13 | rs57328376 | G | A | 0.0697584 | 0.0109198 | 1.68E-10 | 40.809724 | −0.0101806 | 0.0290533 | 0.726031 |
| 14 | rs576123 | T | C | −0.237396 | 0.0104973 | 3.09E-113 | 511.43633 | 0.00819 | 0.0287779 | 0.775956 |
| 15 | rs5896 | T | C | 0.109291 | 0.0125852 | 3.82E-18 | 75.413406 | 0.0614773 | 0.0388191 | 0.113265 |
| 16 | rs6025 | T | C | 0.873415 | 0.0298388 | 2.42E-188 | 856.79828 | 0.0502217 | 0.0899796 | 0.576745 |
| 17 | rs6060308 | A | G | 0.101587 | 0.0112359 | 1.55E-19 | 81.744876 | 0.0521936 | 0.0308737 | 0.0909227 |
| 18 | rs60681578 | C | A | −0.118392 | 0.0150029 | 2.99E-15 | 62.272211 | 0.0169103 | 0.0390773 | 0.665204 |
| 19 | rs62350309 | G | A | −0.173509 | 0.0181448 | 1.15E-21 | 91.440721 | −0.071956 | 0.0634685 | 0.256909 |
| 20 | rs628094 | A | G | 0.0818781 | 0.0114389 | 8.19E-13 | 51.235029 | 0.0027028 | 0.0302168 | 0.928726 |
| 21 | rs72708961 | C | T | 0.0891913 | 0.0159445 | 2.22E-08 | 31.291269 | −0.0765307 | 0.0367798 | 0.0374539 |
| 22 | rs7772305 | G | A | −0.0726964 | 0.0111586 | 7.28E-11 | 42.443031 | 0.0585778 | 0.0307164 | 0.0565137 |
| 23 | rs78807356 | T | G | 0.541094 | 0.0563616 | 7.96E-22 | 92.167713 | 0.101617 | 0.0796139 | 0.201825 |
| | | | | Exposure: PE | Exposure: PE | Exposure: PE | Exposure: PE | Outcome: OSA (Jiang et al) | Outcome: OSA (Jiang et al) | Outcome: OSA (Jiang et al) |
| | SNP | EA | OA | Beta | SE | p -Value | F-statistic | Beta | SE | p -Value |
| 1 | rs117210485 | A | G | 0.150787 | 0.0228699 | 4.30E-11 | 43.470964 | 0.0214618 | 0.114177 | 0.8509 |
| 2 | rs11758950 | T | C | 0.203947 | 0.0367907 | 2.97E-08 | 30.729716 | 0.0418521 | 0.0821953 | 0.610627 |
| 3 | rs143620474 | A | G | 0.281243 | 0.0512263 | 4.01E-08 | 30.142375 | 0.546819 | 0.155226 | 0.0004271 |
| 4 | rs1481808 | C | T | −0.480929 | 0.0875759 | 3.98E-08 | 30.157318 | −0.164933 | 0.105459 | 0.117828 |
| 5 | rs1560711 | T | C | 0.144704 | 0.0202073 | 8.01E-13 | 51.279584 | 0.0310044 | 0.0321024 | 0.334145 |
| 6 | rs1894692 | A | G | −0.547808 | 0.0457764 | 5.29E-33 | 143.21004 | 0.0002365 | 0.0951533 | 0.998017 |
| 7 | rs2066865 | A | G | 0.227484 | 0.0158067 | 5.85E-47 | 207.11869 | 0.0083154 | 0.0313691 | 0.790945 |
| 8 | rs28584824 | A | C | −0.155264 | 0.0279234 | 2.69E-08 | 30.917541 | −0.0268756 | 0.0782108 | 0.731124 |
| 9 | rs3756011 | A | C | 0.234784 | 0.0149143 | 7.77E-56 | 247.81709 | −0.0026386 | 0.0272831 | 0.922956 |
| 10 | rs62350309 | G | A | −0.202534 | 0.0260372 | 7.33E-15 | 60.507237 | −0.071956 | 0.0634685 | 0.256909 |
| 11 | rs635634 | C | T | −0.239636 | 0.0177935 | 2.43E-41 | 181.37664 | 0.0064596 | 0.0347197 | 0.852404 |
| 12 | rs665082 | C | G | −0.175581 | 0.030484 | 8.42E-09 | 33.175015 | −0.343267 | 0.216405 | 0.112688 |
| 13 | rs77165492 | C | T | 0.209269 | 0.0275462 | 3.03E-14 | 57.714695 | −0.0445618 | 0.0457769 | 0.330327 |
| 14 | rs78807356 | T | G | 0.515784 | 0.0795096 | 8.75E-11 | 42.082022 | 0.101617 | 0.0796139 | 0.201825 |
| | | | | Exposure: DVT | Exposure: DVT | Exposure: DVT | Exposure: DVT | Outcome: OSA (Jiang et al) | Outcome: OSA (Jiang et al) | Outcome: OSA (Jiang et al) |
| | SNP | EA | OA | Beta | SE | p -Value | F-statistic | Beta | SE | p -Value |
| 1 | rs113079063 | T | G | 0.436284 | 0.0717563 | 1.20E-09 | 36.967365 | 0.0050364 | 0.0876134 | 0.954159 |
| 2 | rs116997538 | T | C | 0.466245 | 0.0534583 | 2.74E-18 | 76.067315 | −0.067735 | 0.123897 | 0.584581 |
| 3 | rs13377102 | A | T | −0.233255 | 0.0255094 | 6.02E-20 | 83.610619 | −0.0250186 | 0.0389518 | 0.520681 |
| 4 | rs2066865 | A | G | 0.184507 | 0.0161145 | 2.36E-30 | 131.09678 | 0.0083154 | 0.0313691 | 0.790945 |
| 5 | rs2289252 | T | C | 0.197972 | 0.015135 | 4.26E-39 | 171.09712 | −0.0018411 | 0.0272571 | 0.946148 |
| 6 | rs2519785 | G | A | −0.0982467 | 0.0169973 | 7.46E-09 | 33.409968 | 0.0074319 | 0.0297183 | 0.802526 |
| 7 | rs576123 | T | C | −0.297682 | 0.014983 | 7.70E-88 | 394.73678 | 0.00819 | 0.0287779 | 0.775956 |
| 8 | rs5896 | T | C | 0.141024 | 0.017945 | 3.88E-15 | 61.75884 | 0.0614773 | 0.0388191 | 0.113265 |
| 9 | rs6025 | T | C | 1.10439 | 0.0393903 | 5.71E-173 | 786.07929 | 0.0502217 | 0.0899796 | 0.576745 |
| 10 | rs6060237 | G | A | 0.168453 | 0.0198214 | 1.92E-17 | 72.225216 | 0.0318432 | 0.0414073 | 0.441879 |
| 11 | rs60681578 | C | A | −0.137615 | 0.021627 | 1.98E-10 | 40.489181 | 0.0169103 | 0.0390773 | 0.665204 |
| 12 | rs62350309 | G | A | −0.162704 | 0.0259998 | 3.90E-10 | 39.161241 | −0.071956 | 0.0634685 | 0.256909 |
| 13 | rs666870 | A | G | 0.0924832 | 0.0159069 | 6.10E-09 | 33.802949 | 0.0127968 | 0.0271558 | 0.637472 |
| 14 | rs7308002 | A | G | 0.0978174 | 0.01576 | 5.41E-10 | 38.522974 | −0.0027934 | 0.0275746 | 0.919309 |
| 15 | rs76151810 | A | C | 0.153073 | 0.0273112 | 2.09E-08 | 31.413449 | −0.0018493 | 0.0507256 | 0.970918 |
| 16 | rs7772305 | G | A | −0.100251 | 0.016057 | 4.28E-10 | 38.980608 | 0.0585778 | 0.0307164 | 0.0565137 |
| 17 | rs78807356 | T | G | 0.621447 | 0.0792414 | 4.42E-15 | 61.504078 | 0.101617 | 0.0796139 | 0.201825 |
| 18 | rs9865118 | T | C | 0.0863804 | 0.0151814 | 1.27E-08 | 32.374776 | 0.0363583 | 0.0268338 | 0.175436 |
| Genetic instruments for VTE/PE/DVT and their associations with OSA (Campos et al) | Genetic instruments for VTE/PE/DVT and their associations with OSA (Campos et al) | Genetic instruments for VTE/PE/DVT and their associations with OSA (Campos et al) | Genetic instruments for VTE/PE/DVT and their associations with OSA (Campos et al) | Genetic instruments for VTE/PE/DVT and their associations with OSA (Campos et al) | Genetic instruments for VTE/PE/DVT and their associations with OSA (Campos et al) | Genetic instruments for VTE/PE/DVT and their associations with OSA (Campos et al) | Genetic instruments for VTE/PE/DVT and their associations with OSA (Campos et al) | Genetic instruments for VTE/PE/DVT and their associations with OSA (Campos et al) | Genetic instruments for VTE/PE/DVT and their associations with OSA (Campos et al) | Genetic instruments for VTE/PE/DVT and their associations with OSA (Campos et al) |
| | | | | Exposure: VTE | Exposure: VTE | Exposure: VTE | Exposure: VTE | Outcome: OSA (Campos et al) | Outcome: OSA (Campos et al) | Outcome: OSA (Campos et al) |
| | SNP | EA | OA | Beta | SE | p -Value | F-statistic | Beta | SE | p -Value |
| 1 | rs10896706 | A | G | 0.0702142 | 0.0121006 | 6.53E-09 | 33.669456 | 0.0073376 | 0.0072794 | 0.3136 |
| 2 | rs114767153 | T | A | −0.20888 | 0.0348173 | 1.98E-09 | 35.991798 | −0.0240477 | 0.0220217 | 0.2749 |
| 3 | rs116997538 | T | C | 0.403288 | 0.0383066 | 6.42E-26 | 110.83665 | −0.0202903 | 0.0346251 | 0.558 |
| 4 | rs12054563 | G | A | −0.126677 | 0.0176431 | 6.97E-13 | 51.552027 | −0.0164525 | 0.0159578 | 0.3025 |
| 5 | rs1560711 | T | C | 0.122379 | 0.0141465 | 5.11E-18 | 74.836901 | −0.0033405 | 0.0090041 | 0.7104 |
| 6 | rs174529 | C | T | −0.0686342 | 0.0107211 | 1.54E-10 | 40.982878 | −0.0016235 | 0.0068503 | 0.8124 |
| 7 | rs2066865 | A | G | 0.186112 | 0.0112369 | 1.30E-61 | 274.31889 | −0.0033999 | 0.0077623 | 0.6612 |
| 8 | rs3756011 | A | C | 0.192712 | 0.0105525 | 1.65E-74 | 333.50841 | 0.000575 | 0.0067645 | 0.9326 |
| 9 | rs57328376 | G | A | 0.0697584 | 0.0109198 | 1.68E-10 | 40.809724 | −0.0010062 | 0.0071873 | 0.8885 |
| 10 | rs576123 | T | C | −0.237396 | 0.0104973 | 3.09E-113 | 511.43633 | 0.0183551 | 0.0086786 | 0.03441 |
| 11 | rs5896 | T | C | 0.109291 | 0.0125852 | 3.82E-18 | 75.413406 | 0.020985 | 0.0096527 | 0.02974 |
| 12 | rs6025 | T | C | 0.873415 | 0.0298388 | 2.42E-188 | 856.79828 | 0.0380118 | 0.0218836 | 0.08241 |
| 13 | rs6060308 | A | G | 0.101587 | 0.0112359 | 1.55E-19 | 81.744876 | −0.0009288 | 0.0074901 | 0.9013 |
| 14 | rs60681578 | C | A | −0.118392 | 0.0150029 | 2.99E-15 | 62.272211 | 0.0085067 | 0.0117172 | 0.4678 |
| 15 | rs62350309 | G | A | −0.173509 | 0.0181448 | 1.15E-21 | 91.440721 | 0.0075114 | 0.0152982 | 0.6233 |
| 16 | rs628094 | A | G | 0.0818781 | 0.0114389 | 8.19E-13 | 51.235029 | −0.0022354 | 0.0074021 | 0.7627 |
| 17 | rs72708961 | C | T | 0.0891913 | 0.0159445 | 2.22E-08 | 31.291269 | −0.0170636 | 0.0090957 | 0.06059 |
| 18 | rs7772305 | G | A | −0.0726964 | 0.0111586 | 7.28E-11 | 42.443031 | 0.016709 | 0.0086396 | 0.05311 |
| 19 | rs80137017 | T | C | −0.208902 | 0.0177996 | 8.30E-32 | 137.74147 | 0.0152022 | 0.0099426 | 0.1262 |
| | | | | Exposure: PE | Exposure: PE | Exposure: PE | Exposure: PE | Outcome: OSA (Campos et al) | Outcome: OSA (Campos et al) | Outcome: OSA (Campos et al) |
| | SNP | EA | OA | Beta | SE | p -Value | F-statistic | Beta | SE | p -Value |
| 1 | rs117210485 | A | G | 0.150787 | 0.0228699 | 4.30E-11 | 43.470964 | −0.0346523 | 0.0239146 | 0.1473 |
| 2 | rs143620474 | A | G | 0.281243 | 0.0512263 | 4.01E-08 | 30.142375 | 0.0124988 | 0.0892769 | 0.8889 |
| 3 | rs1481808 | C | T | −0.480929 | 0.0875759 | 3.98E-08 | 30.157318 | −0.0281243 | 0.0269648 | 0.297 |
| 4 | rs1560711 | T | C | 0.144704 | 0.0202073 | 8.01E-13 | 51.279584 | −0.0033405 | 0.0090041 | 0.7104 |
| 5 | rs2066865 | A | G | 0.227484 | 0.0158067 | 5.85E-47 | 207.11869 | −0.0033999 | 0.0077623 | 0.6612 |
| 6 | rs28584824 | A | C | −0.155264 | 0.0279234 | 2.69E-08 | 30.917541 | 0.0324135 | 0.0191569 | 0.09056 |
| 7 | rs3756011 | A | C | 0.234784 | 0.0149143 | 7.77E-56 | 247.81709 | 0.000575 | 0.0067645 | 0.9326 |
| 8 | rs62350309 | G | A | −0.202534 | 0.0260372 | 7.33E-15 | 60.507237 | 0.0075114 | 0.0152982 | 0.6233 |
| 9 | rs635634 | C | T | −0.239636 | 0.0177935 | 2.43E-41 | 181.37664 | 0.0139975 | 0.0096935 | 0.1488 |
| 10 | rs77165492 | C | T | 0.209269 | 0.0275462 | 3.03E-14 | 57.714695 | 0.0013946 | 0.0114311 | 0.9026 |
| 11 | rs80137017 | T | C | −0.230014 | 0.02543 | 1.50E-19 | 81.811776 | 0.0152022 | 0.0099426 | 0.1262 |
| | | | | Exposure: DVT | Exposure: DVT | Exposure: DVT | Exposure: DVT | Outcome: OSA (Campos et al) | Outcome: OSA (Campos et al) | Outcome: OSA (Campos et al) |
| | SNP | EA | OA | Beta | SE | p -Value | F-statistic | Beta | SE | p -Value |
| 1 | rs116997538 | T | C | 0.466245 | 0.0534583 | 2.74E-18 | 76.067315 | −0.0202903 | 0.0346251 | 0.558 |
| 2 | rs13377102 | A | T | −0.233255 | 0.0255094 | 6.02E-20 | 83.610619 | 0.0085579 | 0.0096591 | 0.3759 |
| 3 | rs2066865 | A | G | 0.184507 | 0.0161145 | 2.36E-30 | 131.09678 | −0.0033999 | 0.0077623 | 0.6612 |
| 4 | rs576123 | T | C | −0.297682 | 0.014983 | 7.70E-88 | 394.73678 | 0.0183551 | 0.0086786 | 0.03441 |
| 5 | rs5896 | T | C | 0.141024 | 0.017945 | 3.88E-15 | 61.75884 | 0.020985 | 0.0096527 | 0.02974 |
| 6 | rs6025 | T | C | 1.10439 | 0.0393903 | 5.71E-173 | 786.07929 | 0.0380118 | 0.0218836 | 0.08241 |
| 7 | rs6060237 | G | A | 0.168453 | 0.0198214 | 1.92E-17 | 72.225216 | 0.0060526 | 0.0101724 | 0.5518 |
| 8 | rs60681578 | C | A | −0.137615 | 0.021627 | 1.98E-10 | 40.489181 | 0.0085067 | 0.0117172 | 0.4678 |
| 9 | rs62350309 | G | A | −0.162704 | 0.0259998 | 3.90E-10 | 39.161241 | 0.0075114 | 0.0152982 | 0.6233 |
| 10 | rs666870 | A | G | 0.0924832 | 0.0159069 | 6.10E-09 | 33.802949 | 0.0074616 | 0.0067221 | 0.2669 |
| 11 | rs7308002 | A | G | 0.0978174 | 0.01576 | 5.41E-10 | 38.522974 | −0.0023644 | 0.0068533 | 0.7298 |
| 12 | rs7772305 | G | A | −0.100251 | 0.016057 | 4.28E-10 | 38.980608 | 0.016709 | 0.0086396 | 0.05311 |
| 13 | rs9865118 | T | C | 0.0863804 | 0.0151814 | 1.27E-08 | 32.374776 | −0.0005648 | 0.0066442 | 0.9323 |
## Figures
Fig. 1 The flowchart of instrumental variables selection. LD, linkage disequilibrium; SNPs, single-nucleotide polymorphisms; BMI, body mass index; VTE, venous thromboembolism; PE, pulmonary embolism; DVT, deep vein thrombosis; OSA, obstructive sleep apnea; ①, represents OSA (Jiang et al) as the outcome; ②, represents OSA (Campos et al) as the outcome.
<!-- image -->
Fig. 2 The genetic association of OSA with VTE/PE/DVT. OSA, obstructive sleep apnea; VTE, venous thromboembolism; PE, pulmonary embolism; DVT, deep vein thrombosis; MR, mendelian randomization; IVW, inverse variance weighted; PRESSO, pleiotropy residual sum and outlier; P*, represents P for heterogeneity test; P**, represents P for MR-Egger intercept; P***, represents P for MR-PRESSO global test.
<!-- image -->
Fig. 3 The genetic association of VTE/PE/DVT with OSA. OSA, obstructive sleep apnea; VTE, venous thromboembolism; PE, pulmonary embolism; DVT, deep vein thrombosis; MR, mendelian randomization; IVW, inverse variance weighted; PRESSO, pleiotropy residual sum and outlier; P*, represents P for heterogeneity test; P**, represents P for MR-Egger intercept; P***, represents P for MR-PRESSO global test.
<!-- image -->
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@ -1,76 +0,0 @@
item-0 at level 0: unspecified: group _root_
item-1 at level 1: title: Exploring the Two-Way Link betwe ... o-Sample Mendelian Randomization Study
item-2 at level 1: paragraph: Yang Wang; Vascular Surgery, Sha ... iliated Central Hospital, Jinan, China
item-3 at level 1: text: Background The objective of this ... nd VTE within the European population.
item-4 at level 1: section_header: Introduction
item-5 at level 2: text: Venous thromboembolism (VTE) enc ... risk in clinical settings are crucial.
item-6 at level 2: text: Migraine, characterized by recur ... elationship between VTE and migraines.
item-7 at level 2: text: Mendelian randomization (MR) is ... n traditional epidemiological methods.
item-8 at level 1: section_header: Research Methodology
item-9 at level 2: text: A rigorous bidirectional two-sam ... of the resultant causal inferences. 12
item-10 at level 1: section_header: Data Sources
item-11 at level 2: text: Our SNPs are obtained from large ... in our study can be found in Table 1 .
item-12 at level 2: text: The variances in genetic variati ... ecessity for further ethical approval.
item-13 at level 1: section_header: Filtering Criteria of IVs
item-14 at level 2: text: To select appropriate SNPs as IV ... 0 to minimize weak instrument bias. 19
item-15 at level 1: section_header: Results
item-16 at level 2: text: In the present investigation, we ... analysis results detailed in Table 2 .
item-17 at level 1: section_header: Mendelian Randomization Analysis
item-18 at level 2: text: During the IV screening process, ... ined in our investigation ( Fig. 2D ).
item-19 at level 1: section_header: Reverse Mendelian Randomization Analysis
item-20 at level 2: text: Upon screening for IVs in migrai ... e estimated causal effect ( Fig. 3D ).
item-21 at level 1: section_header: Discussion
item-22 at level 2: text: VTE constitutes a grave health h ... he MR analysis is considered reliable.
item-23 at level 2: text: Our MR analysis discloses a pote ... s in clinical practice is recommended.
item-24 at level 2: text: Our endeavor seeks to offer a pr ... rely a weak risk factor for migraines.
item-25 at level 1: section_header: Tables
item-27 at level 1: table with [3x6]
item-27 at level 2: caption: Table 1 Description of GWAS used for each phenotype
item-29 at level 1: table with [4x7]
item-29 at level 2: caption: Table 2 Mendelian randomization regression causal association results
item-30 at level 1: section_header: Figures
item-32 at level 1: picture
item-32 at level 2: caption: Fig. 1 This figure illustrates the research methodology for the bidirectional Mendelian randomization analysis concerning migraine and VTE. Assumption I: relevance assumption; Assumption II: independence/exchangeability assumption; Assumption III: exclusion restriction assumption.
item-34 at level 1: picture
item-34 at level 2: caption: Fig. 2 This figure explores the correlation between migraine risk and VTE, validating the presence of heterogeneity and pleiotropy. (A) The forest plot displays individual IVs, with each point flanked by lines that depict the 95% confidence interval. The effect of SNPs on the exposure (migraine) is shown along thex-axis, whereas their impact on the outcome (VTE) is presented on they-axis. A fitted line reflects the Mendelian randomization analysis results. (B) A scatter plot visualizes each IV, with the SNP effects on both exposure and outcome similar to that of the forest plot. Again, a fitted line represents the Mendelian randomization results. (C) The funnel plot positions the coefficient βIVfrom the instrumental variable regression on thex-axis to demonstrate the association's strength, while the inverse of its standard error (1/SEIV†) on they-axis indicates the precision of this estimate. (D) A leave-one-out sensitivity analysis is shown on thex-axis, charting the estimated effects from the Mendelian randomization analysis. With each SNP associated with migraine successively excluded, the analysis recalculates the Mendelian randomization effect estimates, culminating with the “all” category that encompasses all considered SNPs. IV, instrumental variable; SNP, single nucleotide polymorphisms; VTE, venous thromboembolism; SE, standard error.†SE is the standard error of β.
item-36 at level 1: picture
item-36 at level 2: caption: Fig. 3 (A–D) This figure presents the relationship between VTE risk and migraine, also verifying heterogeneity and pleiotropy through similar graphic representations as detailed forFig. 2, but with the exposure and outcome reversed—SNPs' effect on VTE and outcome on migraine. SNP, single nucleotide polymorphisms; VTE, venous thromboembolism.
item-37 at level 1: section_header: References
item-38 at level 1: list: group list
item-39 at level 2: list_item: F Khan; T Tritschler; S R Kahn; ... Venous thromboembolism. Lancet (2021)
item-40 at level 2: list_item: J A Heit. Epidemiology of venous thromboembolism. Nat Rev Cardiol (2015)
item-41 at level 2: list_item: . Headache Classification Commit ... rders, 3rd edition. Cephalalgia (2018)
item-42 at level 2: list_item: K Adelborg; S K Szépligeti; L Ho ... based matched cohort study. BMJ (2018)
item-43 at level 2: list_item: K P Peng; Y T Chen; J L Fuh; C H ... tionwide cohort study. Headache (2016)
item-44 at level 2: list_item: S Sacco; A Carolei. Burden of at ... tients with migraine. Neurology (2009)
item-45 at level 2: list_item: A R Folsom; P L Lutsey; J R Misi ... dults. Res Pract Thromb Haemost (2019)
item-46 at level 2: list_item: I Y Elgendy; S E Nadeau; C N Bai ... ease in women. J Am Heart Assoc (2019)
item-47 at level 2: list_item: C A Emdin; A V Khera; S Kathiresan. Mendelian randomization. JAMA (2017)
item-48 at level 2: list_item: T Karlsson; F Hadizadeh; M Rask- ... n analyses. Arthritis Rheumatol (2023)
item-49 at level 2: list_item: T Lawler; S Warren Andersen. Ser ... andomization studies. Nutrients (2023)
item-50 at level 2: list_item: S Burgess; A S Butterworth; J R ... ic predictors. J Clin Epidemiol (2016)
item-51 at level 2: list_item: C Sudlow; J Gallacher; N Allen. ... of middle and old age. PLoS Med (2015)
item-52 at level 2: list_item: M S Lyon; S J Andrews; B Elswort ... summary statistics. Genome Biol (2021)
item-53 at level 2: list_item: M I Kurki; J Karjalainen; P Palt ... ped isolated population. Nature (2023)
item-54 at level 2: list_item: E Sanderson; M M Glymour; M V Ho ... zation. Nat Rev Methods Primers (2022)
item-55 at level 2: list_item: J R Staley; J Blackshaw; M A Kam ... pe associations. Bioinformatics (2016)
item-56 at level 2: list_item: M A Kamat; J A Blackshaw; R Youn ... pe associations. Bioinformatics (2019)
item-57 at level 2: list_item: S Burgess; S G Thompson. Mendeli ... erence Using Genetic Variants. (2021)
item-58 at level 2: list_item: A May; L H Schulte. Chronic migr ... s and treatment. Nat Rev Neurol (2016)
item-59 at level 2: list_item: D W Dodick. Migraine. Lancet (2018)
item-60 at level 2: list_item: A A Khorana; N Mackman; A Falang ... boembolism. Nat Rev Dis Primers (2022)
item-61 at level 2: list_item: E J Bell; A R Folsom; P L Lutsey ... alysis. Diabetes Res Clin Pract (2016)
item-62 at level 2: list_item: S Bhoelan; J Borjas Howard; V Ti ... study. Res Pract Thromb Haemost (2022)
item-63 at level 2: list_item: V Pengo; G Denas. Antiphospholip ... boembolism. Semin Thromb Hemost (2023)
item-64 at level 2: list_item: L Maitrot-Mantelet; M H Horellou ... tional French study. Thromb Res (2014)
item-65 at level 2: list_item: G E Tietjen; S A Collins. Hypercoagulability and migraine. Headache (2018)
item-66 at level 2: list_item: J Schwaiger; S Kiechl; H Stockne ... tients with migraine. Neurology (2008)
item-67 at level 2: list_item: C D Bushnell; M Jamison; A H Jam ... n based case-control study. BMJ (2009)
item-68 at level 2: list_item: W Y Ding; M B Protty; I G Davies ... al fibrillation. Cardiovasc Res (2022)
item-69 at level 2: list_item: R M Gupta; J Hadaya; A Trehan. A ... othelin-1 gene expression. Cell (2017)
item-70 at level 2: list_item: B Kumari; A Prabhakar; A Sahu. E ... lation. Clin Appl Thromb Hemost (2017)
item-71 at level 2: list_item: Y Zhang; J Liu; W Jia. AGEs/RAGE ... thrombosis (DVT). Bioengineered (2021)
item-72 at level 2: list_item: J Padilla; A J Carpenter; N A Da ... Am J Physiol Heart Circ Physiol (2018)
item-73 at level 2: list_item: L Liu; C Jouve; J Sebastien Hulo ... th muscle cells. Cardiovasc Res (2022)
item-74 at level 2: list_item: R Vormittag; P Bencur; C Ay. Low ... romboembolism. J Thromb Haemost (2007)
item-75 at level 2: list_item: H Chun; J H Kurasawa; P Olivares ... plex contacts. J Thromb Haemost (2022)

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# Exploring the Two-Way Link between Migraines and Venous Thromboembolism: A Bidirectional Two-Sample Mendelian Randomization Study
Yang Wang; Vascular Surgery, Shandong Public Health Clinical Center, Shandong University, Jinan, China; Xiaofang Hu; Department of Neurology, Shandong Public Health Clinical Center, Shandong University, Jinan, China; Xiaoqing Wang; Interventional Department, Shandong Public Health Clinical Center, Shandong University, Jinan, China; Lili Li; Interventional Department, Shandong Public Health Clinical Center, Shandong University, Jinan, China; Peng Lou; Vascular Surgery, Shandong Public Health Clinical Center, Shandong University, Jinan, China; Zhaoxuan Liu; Vascular Surgery, Shandong First Medical University affiliated Central Hospital, Jinan, China
Background The objective of this study is to utilize Mendelian randomization to scrutinize the mutual causality between migraine and venous thromboembolism (VTE) thereby addressing the heterogeneity and inconsistency that were observed in prior observational studies concerning the potential interrelation of the two conditions. Methods Employing a bidirectional Mendelian randomization approach, the study explored the link between migraine and VTE, incorporating participants of European descent from a large-scale meta-analysis. An inverse-variance weighted (IVW) regression model, with random-effects, leveraging single nucleotide polymorphisms (SNPs) as instrumental variables was utilized to endorse the mutual causality between migraine and VTE. SNP heterogeneity was evaluated using Cochran's Q-test and to account for multiple testing, correction was implemented using the intercept of the MR-Egger method, and a leave-one-out analysis. Results The IVW model unveiled a statistically considerable causal link between migraine and the development of VTE (odds ratio [OR] = 96.155, 95% confidence interval [CI]: 4.342–2129.458, p = 0.004), implying that migraine poses a strong risk factor for VTE development. Conversely, both IVW and simple model outcomes indicated that VTE poses as a weaker risk factor for migraine (IVW OR = 1.002, 95% CI: 1.000–1.004, p = 0.016). The MR-Egger regression analysis denoted absence of evidence for genetic pleiotropy among the SNPs while the durability of our Mendelian randomization results was vouched by the leave-one-out sensitivity analysis. Conclusion The findings of this Mendelian randomization assessment provide substantiation for a reciprocal causative association between migraine and VTE within the European population.
## Introduction
Venous thromboembolism (VTE) encompasses both deep vein thrombosis and pulmonary embolism, 1 ranking third globally as a prevalent vascular disorder associated with mortality. 2 This increases the mortality risk for patients and compounds the financial burden on health care services. Hence, the ongoing evaluation and assessment of VTE risk in clinical settings are crucial.
Migraine, characterized by recurrent episodes of severe unilateral headaches accompanied by pulsating sensations and autonomic symptoms, affects approximately one billion individuals worldwide. 3 Several research studies indicate an increase in VTE incidence among migraine sufferers. 4 5 6 7 8 Hence, there is a significant need for further investigation to elucidate the causal relationship between VTE and migraines.
Mendelian randomization (MR) is a methodology that utilizes genetic variants as instrumental variables (IVs) to explore the causal association between a modifiable exposure and a disease outcome. 9 By leveraging the random allocation and fixed nature of an individual's alleles at conception, this approach helps alleviate concerns regarding reverse causality and environmental confounders commonly encountered in traditional epidemiological methods.
## Research Methodology
A rigorous bidirectional two-sample MR examination was implemented to probe the causal link between migraine and VTE risk, subsequent to a meticulous screening mechanism. For achieving credible estimations of MR causality, efficacious genetic variances serving as IVs must meet three central postulates: (I) relevance assumption, asserting that variations must demonstrate intimate association with the exposure element; (II) independence/exchangeability assumption, demanding no correlations be exhibited with any measured, unmeasured, or inconspicuous confounding elements germane to the researched correlation of interest; and (III) exclusion restriction assumption, maintaining that the variation affects the outcome exclusively through the exposure, devoid of alternative routes. 10 11 A single nucleotide polymorphism (SNP) refers to a genomic variant where a single nucleotide undergoes alteration at a specific locus within the DNA sequence. SNPs were employed as IVs in this study for estimating causal effects. The study's design is graphically portrayed in Fig. 1 , emphasizing the three fundamental postulates of MR. These postulates are of the utmost importance in affirming the validity of the MR examination and ensuring the reliability of the resultant causal inferences. 12
## Data Sources
Our SNPs are obtained from large-scale genome-wide association studies (GWAS) public databases. The exposure variable for this study was obtained from the largest migraine GWAS meta-analysis conducted by the IEU Open GWAS project, which can be accessed at https://gwas.mrcieu.ac.uk/datasets . 13 14 The outcome variable was derived from the largest VTE GWAS conducted by FinnGen, available at https://www.finngen.fi . 15 A comprehensive overview of the data sources used in our study can be found in Table 1 .
The variances in genetic variations and exposure distributions across diverse ethnicities could potentially result in spurious correlations between genetic variants and exposures. 16 Consequently, the migraine and VTE GWAS for this study were sourced from a homogeneous European populace to circumvent such inaccurate associations. It is crucial to highlight that the data harvested from public databases were current up to March 31, 2023. Given the public nature of all data utilized in our study, there was no necessity for further ethical approval.
## Filtering Criteria of IVs
To select appropriate SNPs as IVs, we followed standard assumptions of MR. First, we performed a screening process using the migraine GWAS summary data, applying a significance threshold of p  < 5 × 10 −8 (Assumption I). To ensure the independence of SNPs and mitigate the effects of linkage disequilibrium, we set the linkage disequilibrium coefficient ( r 2 ) to 0.001 and restricted the width of the linkage disequilibrium region to 10,000 kb. PhenoScanner ( http://www.phenoscanner.medschl.cam.ac.uk/ ) serves as a versatile tool, enabling users to explore genetic variants, genes, and traits linked to a wide spectrum of phenotypes. 17 18 Utilizing PhenoScanner v2, we ruled out SNPs linked with potential confounding constituents and outcomes, thereby addressing assumptions II and III. Subsequently, we extracted the relevant SNPs from the VTE GWAS summary data, ensuring a minimum r 2  > 0.8 and replacing missing SNPs with highly linked SNPs. We excluded SNPs without replacement sites and palindromic SNPs and combined the information from both datasets. Finally, we excluded SNPs directly associated with VTE at a significance level of p  < 5 × 10 −8 and prioritized IVs with an F-statistic [F-statistic = (β/SE)2] > 10 to minimize weak instrument bias. 19
## Results
In the present investigation, we capitalized on a bidirectional two-sample MR analysis in individuals of European descent to scrutinize the potential causative correlation between migraines and VTE risk. Our investigation implies a potential bidirectional pathogenic relationship between migraines and the risk of VTE, as supported by the specific analysis results detailed in Table 2 .
## Mendelian Randomization Analysis
During the IV screening process, it was identified that SNP r10908505 was associated with body mass index (BMI) in VTE. Considering the established association between BMI and VTE, 1 15 this violated Assumption III and the SNP was subsequently excluded. The VTE dataset ultimately consisted of 11 SNPs, with individual SNP F-statistics ranging from 29.76 to 96.77 (all >10), indicating a minimal potential for causal associations to be confounded by weak IV bias ( Supplementary Table S1 , available in the online version). The IVW model revealed that migraine was a statistically significant risk factor for the onset of VTE (odds ratio [OR] = 96.155, 95% confidence interval [CI]: 4.3422–129.458, p  = 0.004) ( Table 2 , Fig. 2A ). The scatter plot ( Fig. 2B ) and funnel plot ( Fig. 2C ) of migraine demonstrated a symmetrical distribution of all included SNPs, suggesting a limited possibility of bias affecting the causal association. The Cochran's Q test, conducted on the MR-Egger regression and the IVW method, yielded statistics of 5.610 and 5.973 ( p  > 0.05), indicating the absence of heterogeneity among the SNPs ( Supplementary Table S2 , available in the online version). These findings suggest a positive correlation between the strength of association between the IVs and migraine, satisfying the assumptions of IV analysis. The MR-Egger regression analysis showed no statistically significant difference from zero for the intercept term ( p  = 0.5617), indicating the absence of genetic pleiotropy among the SNPs ( Supplementary Table S3 , available in the online version). Additionally, the leave-one-out analysis revealed that the inclusion or exclusion of individual SNPs did not substantially impact the estimated causal effects, demonstrating the robustness of the MR results obtained in our investigation ( Fig. 2D ).
## Reverse Mendelian Randomization Analysis
Upon screening for IVs in migraine patients, SNP rs6060308 was excluded due to its association with education 20 21 and violation of Assumption III. The final migraine dataset comprised 13 SNPs, with individual SNP F-statistics ranging from 30.60 to 354.34, all surpassing the threshold of 10 ( Supplementary Table S4 , available in the online version). Both the IVW and simple models supported VTE as a risk factor for migraine. The IVW analysis yielded an OR of 1.002 (95% CI: 1.000–1.004, p  = 0.016), while the simple model yielded an OR of 1.003 (95% CI: 1.000–1.006, p  = 0.047) ( Table 2 , Fig. 3A ). The scatter plot ( Fig. 3B ) and funnel plot ( Fig. 3C ) exhibited symmetrical distributions across all included SNPs, indicating minimal potential for biases affecting the causal association. Heterogeneity among SNPs was observed through the Cochran's Q test of the IVW method and MR-Egger regression, with Q statistics of 18.697 and 20.377, respectively, both with p  < 0.05 ( Supplementary Table S2 , available in the online version). Therefore, careful consideration is necessary for the results obtained from the random-effects IVW method. MR-Egger regression analysis revealed a nonsignificant difference between the intercept term and zero ( p  = 0.3655), suggesting the absence of genetic pleiotropy among the SNPs ( Supplementary Table S3 , available in the online version). Additionally, the leave-one-out analysis demonstrated that the inclusion or exclusion of individual SNPs had no substantial impact on the estimated causal effect ( Fig. 3D ).
## Discussion
VTE constitutes a grave health hazard to patients, necessitating rigorous clinical surveillance. Distinct from common VTE risk factors such as cancer, 22 diabetes, 23 lupus, 24 and antiphospholipid syndrome, 25 migraines remain absent from prevalent VTE guidelines or advisories. The MR findings from our research provide first-of-its-kind evidence of a causal nexus between migraines and VTE in individuals of European descent, signaling that migraines potently predispose individuals to VTE (IVW OR = 96.155, 95% CI: 4.342–2129.458), while VTE presents a weak risk factor for migraines (IVW OR = 1.002, 95% CI: 1.000–1.004). Given the robustness of the IVW analysis, the MR analysis is considered reliable.
Our MR analysis discloses a potential causal association between individuals suffering from migraines and VTE incidence, with a risk rate 96.155 times higher in comparison to nonmigraine sufferers. Previous observational endeavors investigating VTE risk amidst migraine patients have been scant and have yielded discordant outcomes, complicating the provision of clinical directives. 26 27 In a longitudinal inquiry with a 19-year follow-up, Adelborg et al discerned a heightened VTE risk in individuals afflicted with migraines. 4 Peng et al's prospective clinical study unveiled a more than double VTE risk increase in migraine patients during a 4-year follow-up. 5 Schwaiger et al's cohort study, incorporating 574 patients aged 55 to 94, observed a significant escalation in VTE risk among elderly individuals with migraines. 6 28 Bushnell et al uncovered a tripled VTE risk during pregnancy in migraine-affected women. 29 Although these studies validate a potential correlation between migraines and VTE, their persuasiveness is restricted due to other prominent VTE risk factors (such as advanced age and pregnancy) and contradicting findings in existing observational studies. For instance, Folsom et al observed no significant correlation between migraines and VTE risk in elderly individuals, contradicting Schwaiger's conclusion. 7 However, he clarified that the cohort incorporated in his study did not undergo rigorous neurological migraine diagnosis, possibly leading to confounding biases and generating findings that contradict other scholarly endeavors. 7 These contradictions originate from observational studies examining associations rather than causal relationships, invariably involving a confluence of various confounding factors. MR, leveraging SNPs as IVs to ascertain the causal link between migraines and VTE risk, can eliminate other confounding elements resulting in more reliable outcomes. Based on this finding, monitoring VTE risk among migraine patients in clinical practice is recommended.
Our endeavor seeks to offer a preliminary examination of the potential mechanisms underlying the interplay between migraines and VTE. The incidence of VTE habitually involves Virchow's triad, encompassing endothelial damage, venous stasis, and hypercoagulability. 30 On the genetic association front, the SNPs rs9349379 and rs11172113, acting as IVs for migraines, display relevance to the mechanisms underpinning VTE. Prior research earmarks the gene corresponding to rs9349379, PHACTR1 ( Supplementary Table S1 , available in the online version), as a catalyst for the upregulation of EDN1 . 31 Elevated EDN1 expression is associated with increased VTE susceptibility, 32 and EDN1 inhibition can diminish VTE incidence, 33 potentially through Endothelin 1-mediated vascular endothelial inflammation leading to thrombus formation. 34 The SNP rs11172113 corresponds to the gene LRP1 ( Supplementary Table S1 , available in the online version). 35 LRP1 can facilitate the upregulation of FVIII , culminating in an increase in plasma coagulation factor VIII, 36 thereby leading to heightened blood coagulability and an associated elevated VTE risk. 37 While various studies propose divergent mechanisms, they collectively signal that migraines can instigate a hypercoagulable state, thereby promoting the onset of VTE. The SNPs serving as IVs for VTE did not unveil any association with the onset of migraines. This corroborates our MR analysis outcomes, indicating that VTE is merely a weak risk factor for migraines.
## Tables
Table 1 Description of GWAS used for each phenotype
| Variable | Sample size | ID | Population | Database | Year |
|------------|---------------|---------------|--------------|-----------------------|--------|
| Migraine | 337159 | ukb-a-87 | European | IEU Open GWAS project | 2017 |
| VTE | 218792 | finn-b-I9\_VTE | European | FinnGen | 2021 |
Table 2 Mendelian randomization regression causal association results
| Exposures | SNPs (no.) | Methods | β | SE | OR (95% CI) | p |
|-------------|--------------|-------------|-------|-------|-------------------------|-------|
| Migraine | 11 | IVW | 4.566 | 1.58 | 96.155 (4.342–2129.458) | 0.004 |
| VTE | 12 | IVW | 0.002 | 0.001 | 1.002 (1.000–1.004); | 0.016 |
| VTE | 12 | Simple mode | 0.003 | 0.001 | 1.003 (1.000–1.006) | 0.047 |
## Figures
Fig. 1 This figure illustrates the research methodology for the bidirectional Mendelian randomization analysis concerning migraine and VTE. Assumption I: relevance assumption; Assumption II: independence/exchangeability assumption; Assumption III: exclusion restriction assumption.
<!-- image -->
Fig. 2 This figure explores the correlation between migraine risk and VTE, validating the presence of heterogeneity and pleiotropy. (A) The forest plot displays individual IVs, with each point flanked by lines that depict the 95% confidence interval. The effect of SNPs on the exposure (migraine) is shown along thex-axis, whereas their impact on the outcome (VTE) is presented on they-axis. A fitted line reflects the Mendelian randomization analysis results. (B) A scatter plot visualizes each IV, with the SNP effects on both exposure and outcome similar to that of the forest plot. Again, a fitted line represents the Mendelian randomization results. (C) The funnel plot positions the coefficient βIVfrom the instrumental variable regression on thex-axis to demonstrate the association's strength, while the inverse of its standard error (1/SEIV†) on they-axis indicates the precision of this estimate. (D) A leave-one-out sensitivity analysis is shown on thex-axis, charting the estimated effects from the Mendelian randomization analysis. With each SNP associated with migraine successively excluded, the analysis recalculates the Mendelian randomization effect estimates, culminating with the “all” category that encompasses all considered SNPs. IV, instrumental variable; SNP, single nucleotide polymorphisms; VTE, venous thromboembolism; SE, standard error.†SE is the standard error of β.
<!-- image -->
Fig. 3 (A–D) This figure presents the relationship between VTE risk and migraine, also verifying heterogeneity and pleiotropy through similar graphic representations as detailed forFig. 2, but with the exposure and outcome reversed—SNPs' effect on VTE and outcome on migraine. SNP, single nucleotide polymorphisms; VTE, venous thromboembolism.
<!-- image -->
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- K P Peng; Y T Chen; J L Fuh; C H Tang; S J Wang. Association between migraine and risk of venous thromboembolism: a nationwide cohort study. Headache (2016)
- S Sacco; A Carolei. Burden of atherosclerosis and risk of venous thromboembolism in patients with migraine. Neurology (2009)
- A R Folsom; P L Lutsey; J R Misialek; M Cushman. A prospective study of migraine history and venous thromboembolism in older adults. Res Pract Thromb Haemost (2019)
- I Y Elgendy; S E Nadeau; C N Bairey Merz; C J Pepine. Migraine headache: an under-appreciated risk factor for cardiovascular disease in women. J Am Heart Assoc (2019)
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- C Sudlow; J Gallacher; N Allen. UK biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age. PLoS Med (2015)
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- S Burgess; S G Thompson. Mendelian Randomization: Methods for Causal Inference Using Genetic Variants. (2021)
- A May; L H Schulte. Chronic migraine: risk factors, mechanisms and treatment. Nat Rev Neurol (2016)
- D W Dodick. Migraine. Lancet (2018)
- A A Khorana; N Mackman; A Falanga. Cancer-associated venous thromboembolism. Nat Rev Dis Primers (2022)
- E J Bell; A R Folsom; P L Lutsey. Diabetes mellitus and venous thromboembolism: a systematic review and meta-analysis. Diabetes Res Clin Pract (2016)
- S Bhoelan; J Borjas Howard; V Tichelaar. Recurrence risk of venous thromboembolism associated with systemic lupus erythematosus: a retrospective cohort study. Res Pract Thromb Haemost (2022)
- V Pengo; G Denas. Antiphospholipid syndrome in patients with venous thromboembolism. Semin Thromb Hemost (2023)
- L Maitrot-Mantelet; M H Horellou; H Massiou; J Conard; A Gompel; G Plu-Bureau. Should women suffering from migraine with aura be screened for biological thrombophilia?: results from a cross-sectional French study. Thromb Res (2014)
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item-0 at level 0: unspecified: group _root_
item-1 at level 1: title: Differential Effects of Erythrop ... xpression on Venous Thrombosis in Mice
item-2 at level 1: paragraph: Sven Stockhausen; Medizinische K ... ximilians-Universität, Munich, Germany
item-3 at level 1: text: Background Deep vein thrombosis ... and potential therapeutic strategies.
item-4 at level 1: section_header: Introduction
item-5 at level 2: text: Red blood cells (RBCs) are the p ... geted prevention and treatment of DVT.
item-6 at level 2: text: The mechanism of RBC-mediated DV ... DVT has not been conclusively proven.
item-7 at level 2: text: In this project we used a transg ... so identified in this mouse strain. 15
item-8 at level 2: text: The spleen is responsible for RB ... DVT in vivo remains unclear. 20 25 26
item-9 at level 1: section_header: Mouse Model
item-10 at level 2: text: C57BL/6 mice were obtained from ... nich) and were authorized accordingly.
item-11 at level 1: section_header: Statistics
item-12 at level 2: text: Statistical analysis was conduct ... re compared using the chi-square test.
item-13 at level 1: section_header: Chronic EPO Overproduction Leads to Increased DVT in Mice
item-14 at level 2: text: To investigate the impact of chr ... ailable in the online version]). 28 29
item-15 at level 2: text: Based on clinical observations i ... L [available in the online version]).
item-16 at level 1: section_header: High RBC Count Leads to a Decrea ... elet Accumulation in Venous Thrombosis
item-17 at level 2: text: Having observed a correlation be ... y thinner fibrin fibers ( Fig. 3A-C ).
item-18 at level 2: text: To quantify platelet accumulatio ... from EPO transgenic mice ( Fig. 2C ).
item-19 at level 2: text: As mentioned previously, inflamm ... brinogen and platelets were decreased.
item-20 at level 1: section_header: Short-Term Administration of EPO Does Not Foster DVT
item-21 at level 2: text: Due to the significant impact of ... G [available in the online version]).
item-22 at level 2: text: To further investigate the under ... unchanged after 2 weeks of treatment.
item-23 at level 2: text: To analyze the impact of 2-week ... ollowing EPO treatment ( Fig. 3D, E ).
item-24 at level 1: section_header: Splenectomy Does Not Affect Venous Thrombus Formation
item-25 at level 2: text: As the data suggested a qualitat ... [available in the online version]). 13
item-26 at level 2: text: To investigate the role of splen ... obtained from nonsplenectomized mice.
item-27 at level 2: text: Finally, we analyzed the impact ... of enhanced or normal erythropoiesis.
item-28 at level 1: section_header: Discussion
item-29 at level 2: text: Here, we present evidence for a ... ouse model through chronic hypoxia. 37
item-30 at level 2: text: In our analyses, we observed tha ... ing to a 70% reduction in lifespan. 15
item-31 at level 2: text: During the ageing process, RBCs ... easing the risk of DVT formation. 5 71
item-32 at level 2: text: Clearance of RBCs primarily occu ... bsence of the spleen after removal. 15
item-33 at level 2: text: Besides their activating effect ... subjected to short-term EPO injection.
item-34 at level 1: section_header: Figures
item-36 at level 1: picture
item-36 at level 2: caption: Fig. 1 EPO-overexpressing mice experience an increased incidence of DVT formation. (A) Comparison of RBC count between EPO-overexpressing Tg(EPO) mice (n = 12) and control (WT) mice (n = 13). (B) Comparison of platelet count between EPO-overexpressing Tg(EPO) (n = 7) mice and control (WT) mice (n = 5). (C) Comparison of thrombus weight between Tg(EPO) mice (n = 9) and WT (n = 10); mean age in the Tg(EPO) group: 19.8 weeks; mean age in the WT group: 20.1 weeks. (D) Comparison of thrombus incidence between Tg(EPO) mice (n = 9) and WT mice (n = 10); NS = nonsignificant, *p < 0.05, **p < 0.01, ***p < 0.001. DVT, deep vein thrombosis; EPO, erythropoietin; RBC, red blood cell; WT, wild type.
item-38 at level 1: picture
item-38 at level 2: caption: Fig. 2 Chronic overproduction of EPO in mice leads to a decrease in the accumulation of classical drivers of DVT formation, including platelets, neutrophils, and fibrinogen. (A) The proportion of RBC-covered area in the thrombi of EPO-overexpressing Tg(EPO) mice (n = 4) was compared to control (WT) (n = 3) by immunofluorescence staining of cross-sections of the IVC 48 hours after flow reduction. (B) The proportion of fibrinogen-covered area in the thrombi of EPO- overexpressing Tg(EPO) mice (n = 3) was compared to control (WT) (n = 3) using immunofluorescence staining of cross-sections of the IVC 48 hours after flow reduction. (C) The proportion of platelet-covered area in the thrombi of EPO-overexpressing Tg(EPO) mice (n = 3) was compared to control (WT) (n = 3) by immunofluorescence staining of cross-sections of the IVC 48 hours after flow reduction. (D) Quantification of neutrophils was performed by immunofluorescence staining of cross-sections of the IVC 48 hours after flow reduction in EPO-overexpressing Tg(EPO) mice (n = 3) compared to control (WT) (n = 3). (E) Immunofluorescence staining of cross-sections of the IVC 48 hours after flow reduction from EPO-overexpressing Tg(EPO) mice (top) was compared to control (WT) (bottom) for TER119 in red (RBC), CD42b in green (platelets), and Hoechst in blue (DNA). The merged image is on the left, and the single channel image is on the right. Scale bar: 50 µm. (F) Immunofluorescence staining of cross-sections of the IVC 48 hours after flow reduction from EPO-overexpressing Tg(EPO) mice (top) was compared to control (WT) (bottom) for TER119 in red (RBC), fibrinogen in green, and Hoechst in blue (DNA); the merged image is on the left, and single-channel images are on the right. Scale bar: 50 µm.; NS = nonsignificant, *p < 0.05, **p < 0.01, ***p < 0.001. DVT, deep vein thrombosis; EPO, erythropoietin; IVC, inferior vena cava; RBC, red blood cell; WT, wild type.
item-40 at level 1: picture
item-40 at level 2: caption: Fig. 3 The interaction of RBC with fibrinogen leads to the formation of a branched fibrin structure. (A) Immunofluorescence staining of cross-sections of the IVC 48 hours after flow reduction from EPO-overexpressing Tg(EPO) mice (left) was compared to control (WT) for fibrinogen (green). Scare bar: 50 µm. (B) High-resolution confocal images of immunofluorescence staining of cross-sections of the IVC 48 hours after flow reduction from EPO-overexpressing Tg(EPO) mice (left) compared to control (WT) for fibrinogen (green), RBC (red), and DNA (blue). Scale bar: 2 µm. (C) The mean diameter of 2,141 fibrin fibers was measured in cross-sections of thrombi from Tg(EPO) mice (left), and compared to 5,238 fibrin fibers of WT thrombi. (D) The mean diameter of 3,797 fibrin fibers was measured in two cross-sections of thrombi from 2-week EPO-injected mice (left), and compared to 10,920 fibrin fibers of three cross-sections from control (2-week NaCl-injected mice). (E) High-resolution confocal images of immunofluorescence staining of two cross-sections of the IVC 48 hours after flow reduction from 2-week EPO-injected mice (left) were compared to control (2 week NaCl-injected mice) for fibrinogen (green), RBC (red), and DNA (blue). Scale bar: 2 µm. EPO, erythropoietin; IVC, inferior vena cava; RBC, red blood cell; WT, wild type.
item-42 at level 1: picture
item-42 at level 2: caption: Fig. 4 Two-week EPO injection leads to thrombocytopenia without an impact on the bone marrow. (A) RBC count in peripheral blood after 6 × 300 IU EPO treatment of C57Bl/6J mice (n = 10) was compared to control (6 × 30 µL NaCl injection) (n = 9). (B) Platelet count in peripheral blood after 6 × 300 IU EPO treatment of C57Bl/6J mice (n = 10) was compared to control (6 × 30 µL NaCl injection) (n = 10). (C) Area of RBC-positive area in the bone marrow of 6 × 300 IU EPO-treated C57Bl/6J mice (n = 4) was compared to control (6 × 30 µL NaCl injection) (n = 4). (D) Immunofluorescence staining of cross-sections of the bone marrow after 2-week EPO injection (top) was compared to NaCl injection (bottom) stained for TER119 (violet) and Hoechst (white). Scale bar: 100 µm. (E) Number of megakaryocyte count in the bone marrow of 6 × 300 IU EPO-treated C57Bl/6J mice (n = 4) was compared to control (6 × 30 µL NaCl injection) (n = 4). (F) Immunofluorescence staining of cross-sections of the bone marrow after 2-week EPO injection (top) compared to NaCl injection (bottom) stained for CD41 (violet) and Hoechst (white). Scale bar: 100 µm. (G) Platelet large cell ratio in peripheral blood of 6 × 300 IU EPO-treated C57Bl/6J mice (n = 10) compared to control (6 × 30 µL NaCl injection) (n = 9). (H) Thrombus weight of 6 × 300 IU EPO-treated C57Bl/6J mice (n = 10) and NaCl-injected control mice (n = 10). (I) Thrombus incidence of 6 × 300 IU EPO-treated C57Bl/6J mice (n = 10) and NaCl-injected control mice (n = 10). NS = nonsignificant, *p < 0.05, **p < 0.01, ***p < 0.001. EPO, erythropoietin; RBC, red blood cell.
item-44 at level 1: picture
item-44 at level 2: caption: Fig. 5 Splenectomy does not affect the blood count as well as DVT formation. (A) WBC count in C57Bl/6J mice without treatment (n = 5), 48 hours after induction of DVT (n = 7), 5 weeks after splenectomy (n = 3), and 5 weeks after splenectomy with an additional 48-hour induction of DVT (n = 9). (B) Platelet count in C57Bl/6J mice without treatment (n = 6), 48 hours after induction of DVT (n = 6), 5 weeks after splenectomy (n = 3), and 5 weeks after splenectomy with an additional 48-hour induction of DVT (n = 9). (C) RBC count in C57Bl/6J mice without treatment (n = 6), 48 hours after induction of DVT (n = 6), 5 weeks after splenectomy (n = 3), and 5 weeks after splenectomy with an additional 48-hour induction of DVT (n = 9). (D) Thrombus weight in C57Bl6 wild-type mice without splenectomy (n = 6) and with splenectomy (n = 6) (E) Thrombus incidence in C57Bl/6J wild-type mice without splenectomy (n = 6) and with splenectomy (n = 6). (F) Thrombus weight in EPO-overexpressing Tg(EPO) mice without splenectomy (n = 9) and with splenectomy (n = 6) compared to control WT mice without splenectomy (n = 10) and with splenectomy (n = 11). (G) Thrombus incidence in EPO-overexpressing Tg(EPO) mice without splenectomy (n = 9) and with splenectomy (n = 6) compared to control WT mice without splenectomy (n = 10) and with splenectomy (n = 11). NS = nonsignificant, *p < 0.05, **p < 0.01, ***p < 0.001. DVT, deep vein thrombosis; RBC, red blood cell; WT, wild type.
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# Differential Effects of Erythropoietin Administration and Overexpression on Venous Thrombosis in Mice
Sven Stockhausen; Medizinische Klinik und Poliklinik I, Klinikum der Universität München, Ludwig-Maximilians-Universität, Munich, Germany; German Center for Cardiovascular Research (DZHK), partner site Munich Heart Alliance, Munich, Germany; Walter-Brendel Center of Experimental Medicine, Ludwig-Maximilians-Universität, Munich, Germany; Badr Kilani; Medizinische Klinik und Poliklinik I, Klinikum der Universität München, Ludwig-Maximilians-Universität, Munich, Germany; German Center for Cardiovascular Research (DZHK), partner site Munich Heart Alliance, Munich, Germany; Walter-Brendel Center of Experimental Medicine, Ludwig-Maximilians-Universität, Munich, Germany; Irene Schubert; Medizinische Klinik und Poliklinik I, Klinikum der Universität München, Ludwig-Maximilians-Universität, Munich, Germany; German Center for Cardiovascular Research (DZHK), partner site Munich Heart Alliance, Munich, Germany; Walter-Brendel Center of Experimental Medicine, Ludwig-Maximilians-Universität, Munich, Germany; Anna-Lena Steinsiek; Department of cardiology, German Heart Center, Munich, Germany.; Sue Chandraratne; Medizinische Klinik und Poliklinik I, Klinikum der Universität München, Ludwig-Maximilians-Universität, Munich, Germany; German Center for Cardiovascular Research (DZHK), partner site Munich Heart Alliance, Munich, Germany; Walter-Brendel Center of Experimental Medicine, Ludwig-Maximilians-Universität, Munich, Germany; Franziska Wendler; Medizinische Klinik und Poliklinik I, Klinikum der Universität München, Ludwig-Maximilians-Universität, Munich, Germany; German Center for Cardiovascular Research (DZHK), partner site Munich Heart Alliance, Munich, Germany; Walter-Brendel Center of Experimental Medicine, Ludwig-Maximilians-Universität, Munich, Germany; Luke Eivers; Medizinische Klinik und Poliklinik I, Klinikum der Universität München, Ludwig-Maximilians-Universität, Munich, Germany; German Center for Cardiovascular Research (DZHK), partner site Munich Heart Alliance, Munich, Germany; Walter-Brendel Center of Experimental Medicine, Ludwig-Maximilians-Universität, Munich, Germany; Marie-Luise von Brühl; Medizinische Klinik und Poliklinik I, Klinikum der Universität München, Ludwig-Maximilians-Universität, Munich, Germany; German Center for Cardiovascular Research (DZHK), partner site Munich Heart Alliance, Munich, Germany; Walter-Brendel Center of Experimental Medicine, Ludwig-Maximilians-Universität, Munich, Germany; Steffen Massberg; Medizinische Klinik und Poliklinik I, Klinikum der Universität München, Ludwig-Maximilians-Universität, Munich, Germany; German Center for Cardiovascular Research (DZHK), partner site Munich Heart Alliance, Munich, Germany; Walter-Brendel Center of Experimental Medicine, Ludwig-Maximilians-Universität, Munich, Germany; Ilka Ott; Department of cardiology, German Heart Center, Munich, Germany.; Konstantin Stark; Medizinische Klinik und Poliklinik I, Klinikum der Universität München, Ludwig-Maximilians-Universität, Munich, Germany; German Center for Cardiovascular Research (DZHK), partner site Munich Heart Alliance, Munich, Germany; Walter-Brendel Center of Experimental Medicine, Ludwig-Maximilians-Universität, Munich, Germany
Background Deep vein thrombosis (DVT) is a common condition associated with significant mortality due to pulmonary embolism. Despite advanced prevention and anticoagulation therapy, the incidence of venous thromboembolism remains unchanged. Individuals with elevated hematocrit and/or excessively high erythropoietin (EPO) serum levels are particularly susceptible to DVT formation. We investigated the influence of short-term EPO administration compared to chronic EPO overproduction on DVT development. Additionally, we examined the role of the spleen in this context and assessed its impact on thrombus composition. Methods We induced ligation of the caudal vena cava (VCC) in EPO-overproducing Tg(EPO) mice as well as wildtype mice treated with EPO for two weeks, both with and without splenectomy. The effect on platelet circulation time was evaluated through FACS analysis, and thrombus composition was analyzed using immunohistology. Results We present evidence for an elevated thrombogenic phenotype resulting from chronic EPO overproduction, achieved by combining an EPO-overexpressing mouse model with experimental DVT induction. This increased thrombotic state is largely independent of traditional contributors to DVT, such as neutrophils and platelets. Notably, the pronounced prothrombotic effect of red blood cells (RBCs) only manifests during chronic EPO overproduction and is not influenced by splenic RBC clearance, as demonstrated by splenectomy. In contrast, short-term EPO treatment does not induce thrombogenesis in mice. Consequently, our findings support the existence of a differential thrombogenic effect between chronic enhanced erythropoiesis and exogenous EPO administration. Conclusion Chronic EPO overproduction significantly increases the risk of DVT, while short-term EPO treatment does not. These findings underscore the importance of considering EPO-related factors in DVT risk assessment and potential therapeutic strategies.
## Introduction
Red blood cells (RBCs) are the primary carriers oxygen and carbon dioxide in all mammals. Low hemoglobin concentrations in the blood can cause severe oxygen deficiency, leading to ischemia in organs and tissues. At the same time, numerous clinical observations identified elevated hemoglobin levels as an independent risk factor for deep vein thrombosis (DVT) formation. This applies to overproduction of RBCs due to erythropoietin (EPO) administration, as well as foreign blood transfusions. 1 2 3 4 5 6 7 This is also evident in illnesses which exhibit an excessive RBC production such as polycythemia vera or Chuvash polycythemia where a significant increase in thromboembolic complications has been reported. 8 9 In such cases, oral or parenteral anticoagulants are effective preventive measures. However, their use entails significant drawbacks in the form of elevated bleeding risks, which can lead to severe complications. 10 Therefore, it is crucial to identify patients at risk and to expand our understanding of the pathophysiology to enable a more targeted prevention and treatment of DVT.
The mechanism of RBC-mediated DVT formation so far is not fully understood. Essentially, DVT formation is triggered by sterile inflammation. 11 Neutrophils and monocytes deliver tissue factor (TF) to the site of thrombus formation creating a procoagulant environment. 11 However, the contribution of leukocytes to DVT formation may vary depending on the underlying disease and a leukocyte-recruiting property of RBCs in DVT has not been conclusively proven.
In this project we used a transgenic mouse model overexpressing the human EPO gene in an oxygen-independent manner. In these mice the hematocrit is chronically elevated, which leads to several changes. RBCs represent, volumetrically, the largest cellular component in the peripheral blood, thus influencing the viscosity of the blood, fostering cardiovascular events like stroke or ischemic heart disease. 12 RBCs from Tg(EPO) mice show increased flexibility which in turn reduces the viscosity, and protects from thrombus formation. 13 Additionally, excessive NO production has been described. In Tg(EPO) mice, the vasodilative effect of extensive NO release is partly compensated by endothelin. 14 A reduced lifespan of RBCs was also identified in this mouse strain. 15
The spleen is responsible for RBC clearance, which acts as gatekeeper of the state, age, and number of RBCs. 16 The loss of function of the spleen, due to removal, leads to changes in the blood count, the most striking of which is the transient thrombocytosis observed after splenectomy. 17 Even though the platelet count normalizes within weeks, the risk of thromboembolism remains persistently high; however, the mechanism behind this prothrombotic state is unclear. 18 19 20 Previous studies reveal an increase in platelet- and (to a lesser extent) RBC-derived microvesicles in splenectomized patients, which could indicate changes in their life cycle or activation state. 21 At the same time, the levels of negatively charged prothrombotic phospholipids, like phosphatidylserine, in pulmonary embolism increase after splenectomy. 22 23 24 Among others, RBCs can contribute to phosphatidylserine exposure. 25 Old, rigid RBCs with modified phospholipid exposure promote thrombus formation; however, their relevance for DVT in vivo remains unclear. 20 25 26
## Mouse Model
C57BL/6 mice were obtained from Jackson Laboratory. Human EPO-overexpressing mice were generated as previously described. 14 TgN(PDGFBEPO)321Zbz consists of a transgenic mouse line, TgN(PDGFBEPO)321Zbz, expresses human EPO cDNA, and was initially reported by Ruschitzka et al, 14 subsequently named Tg(EPO). The expression is regulated by the platelet-derived growth factor promotor. We used the corresponding WT littermate controls named as WT. 27 Sex- and age-matched groups were used for the experiments with an age limit ranging between 12 and 29 weeks. The mice were housed in a specific-pathogen-free environment in our animal facility. General anesthesia was induced using a mixture of inhaled isoflurane, intravenous fentanyl, medetomidine, and midazolam. All procedures performed on mice were conducted in accordance with local legislation for the protection of animals (Regierung von Oberbayern, Munich) and were authorized accordingly.
## Statistics
Statistical analysis was conducted using GraphPad Prism 5, employing a t -test. Based on clinical observations strongly suggesting an increase in thrombus formation in EPO overproducing mice, a one-sided t -test was performed. 8 9 The normal distribution of the data was confirmed using D'Agostino and Pearson omnibus normality testing. Thrombus incidences between groups were compared using the chi-square test.
## Chronic EPO Overproduction Leads to Increased DVT in Mice
To investigate the impact of chronic erythrocyte overproduction on DVT in mice, we analyzed EPO-overexpressing transgenic Tg(EPO) mice. As expected, this mouse strain exhibited a substantial increase in RBC count ( Fig. 1A ). Additionally, the RBC width coefficient and reticulocyte count were elevated, indicating enhanced RBC production ( Supplementary Fig. S1A, B [available in the online version]). In addition to influencing the RBC lineage, our analyses revealed a significant increase in white blood cell (WBC) count, primarily driven by elevated lymphocyte count ( Supplementary Fig. S1C, E [available in the online version]). However, neutrophils known as major contributors to venous thrombosis showed no significant changes in EPO transgenic mice, while platelet counts were significantly reduced ( Fig. 1B and Supplementary Fig. S1D [available in the online version]). Furthermore, autopsies of the animals confirmed the presence of splenomegaly ( Supplementary Fig. S1F [available in the online version]). 28 29
Based on clinical observations indicating a correlation between high EPO levels and increased incidence of DVT, we utilized an IVC stenosis model to evaluate venous thrombosis in EPO-overexpressing mice. 6 7 Our findings revealed a significant elevation in both the incidence and thrombus weight in Tg(EPO) mice compared to their WT littermates ( Fig. 1C, D ). To determine whether chronic EPO overproduction in transgenic mice affected cardiac function, we assessed parameters such as the left ventricular ejection fraction, fractional shortening, and heart rate, ruling out any alternations ( Supplementary Fig. S1G, H, J [available in the online version]), which aligns with previous publications. 30 Additionally, morphological parameters including left ventricular mass, left ventricular internal diameter end diastole, and inner ventricular end diastolic septum diameter were similar between Tg(EPO) and WT mice ( Supplementary Fig. S1I, K, L [available in the online version]).
## High RBC Count Leads to a Decrease in Platelet Accumulation in Venous Thrombosis
Having observed a correlation between high EPO and hematocrit levels with increased thrombus formation, our aim was to investigate the factors involved in triggering thrombus development through histologic analysis of thrombus composition. In Tg(EPO) mice, the elevated hematocrit levels led to enhanced RBC accumulation within the thrombus, as indicated by the Ter119-covered area measurement ( Fig. 2A ). Given the interaction between RBCs and platelets, which can initiate coagulation activation, we examined the distribution of fibrinogen in relation to RBCs and platelets within the thrombi. 31 Our findings revealed a close association between the fibrinogen signal and RBCs, as well as between the platelet signal and RBCs, indicating interactions among these three factors ( Fig. 2E, F ). However, we observed significantly lower fibrinogen coverage in thrombi from EPO transgenic mice ( Fig. 2B ). Furthermore, the structure of the fibrin meshwork exhibited an overall “looser” morphology with significantly thinner fibrin fibers ( Fig. 3A-C ).
To quantify platelet accumulation in thrombi, we analyzed the CD41-covered area in thrombi of both mouse strains. Consistent with the reduced platelet count in peripheral blood, platelet accumulation was also decreased in thrombi from EPO transgenic mice ( Fig. 2C ).
As mentioned previously, inflammation plays a fundamental role in DVT formation. Therefore, we conducted an analysis to quantify the presence of leukocytes in the thrombus material. Our investigation focused specifically on neutrophils, as they represent the predominant leukocyte population in peripheral blood. Despite observing normal neutrophil counts, we identified a significant reduction in neutrophil recruitment within thrombi from EPO transgenic mice ( Fig. 2D ). In summary, our findings indicate an isolated increase in the number of RBCs within venous thrombi of EPO transgenic mice, while the levels of fibrinogen and platelets were decreased.
## Short-Term Administration of EPO Does Not Foster DVT
Due to the significant impact of chronic EPO overproduction in Tg(EPO) mice on peripheral blood count and its detrimental consequences on DVT formation, we proceeded to analyze the effects of 2-week periodic EPO injections on blood count and subsequent DVT formation in WT mice. Within just 2 weeks, a significant increase of RBC and reticulocyte count in peripheral blood was observed ( Fig. 4A and Supplementary Fig. S2A [available in the online version]). Conversely, platelet count exhibited a notable decrease in EPO-treated mice ( Fig. 4B ). Unlike EPO-overexpressing mice, the leukocyte counts and their differentiation into granulocytes, lymphocytes, and monocytes showed no differences between EPO-treated and nontreated mice ( Supplementary Fig. S2B–E [available in the online version]). Autopsy analyses further revealed a significant enlargement and weight increase of the spleen in EPO-treated mice ( Supplementary Fig. S2F, G [available in the online version]).
To further investigate the underlying cause of thrombocytopenia in EPO-treated mice, we examined the bone marrow composition. Previous studies by Shibata et al demonstrated a reduction in megakaryocytes in Tg(EPO) mice. 32 Therefore, we analyzed the bone marrow composition after 2 weeks of EPO treatment. However, we found no difference in the TER119-covered area, indicating no significant alternation ( Fig. 4C, D ). Similarly, the megakaryocyte count in the bone marrow showed no changes compared to the control group ( Fig. 4E, F ,). In terms of platelet morphology, we observed an increased platelet large cell ratio in the EPO-treated group ( Fig. 4G ). This suggests an elevated production potential of megakaryocytes, as immature platelets tend to have larger cell volumes compared to mature platelets. 33 These findings indicate that our EPO administration protocol enhanced the synthesis capacity of bone marrow stem cells, resulting in augmented erythropoiesis. However, the cellular composition of the bone marrow remained unchanged after 2 weeks of treatment.
To analyze the impact of 2-week EPO treatment on DVT formation, we utilized the IVC stenosis model. Despite similar changes in blood count in Tg(EPO) mice or WT mice after EPO administration, we observed comparable venous thrombus formation between mice treated with EPO for 2 weeks and the control group treated with NaCl ( Fig. 4H, I ). Since we previously observed that only long-term elevation of EPO levels with supraphysiologic hematocrit leads to increased thrombus formation, our focus shifted toward identifying the factors triggering thrombus formation. Therefore, we conducted a histological analysis of thrombus composition. Given the significantly thinner fibrin fibers observed in thrombi from Tg(EPO) mice, we investigated whether similar morphological changes occurred in mice treated with EPO for 2 weeks. Interestingly, the histological examination of the thrombi revealed a comparable thinning of fibrin fibers following EPO treatment ( Fig. 3D, E ).
## Splenectomy Does Not Affect Venous Thrombus Formation
As the data suggested a qualitative change in RBCs in the context of EPO overproduction, we investigated whether splenic clearance of aged RBCs plays a critical role in the increased formation of DVT. In the spleen, aged and damaged RBCs are eliminated, ensuring the presence of young and flexible RBCs. 16 We examined the immediate impact of EPO on spleen morphology. Even a single injection of 300 IU EPO s.c. in mice resulted in a significant increase in spleen weight, despite no difference in blood count compared to the control group ( Supplementary Fig. S2F–H [available in the online version]). This striking phenotype was also observed in mice with chronic EPO overexpression ( Supplementary Fig. S1F [available in the online version]). 13
To investigate the role of splenic RBC clearance in DVT, we performed splenectomy 5 weeks prior to conducting the IVC stenosis model. Firstly, we analyzed the impact of splenectomy on blood cell counts in WT mice 5 weeks postsurgery. We observed an increase in granulocytes and lymphocytes after splenectomy ( Fig. 5A and Supplementary Fig. S3A, B [available in the online version]). Next, we examined the distribution of blood cells in response to DVT development. Similar to nonsplenectomized mice, we observed an increase in WBC count in the peripheral blood ( Fig. 5A ). Additionally, we noted a significant decrease in platelet count in splenectomized mice in response to thrombus development ( Fig. 5B ), which is consistent with the results obtained from nonsplenectomized mice.
Finally, we analyzed the impact of splenectomy on DVT formation in both WT mice and Tg(EPO) mice. Despite changes in blood cell counts and the effects on platelet removal, there was no difference in the incidence and thrombus weight in C57Bl/6 mice ( Fig. 5D, E ). Next, we examined EPO-overexpressing mice, which have been shown to have an increased risk of DVT formation. Despite significant splenomegaly, the incidence of DVT formation remained statistically unchanged after spleen removal ( Fig. 5F, G ). Therefore, splenectomy does not affect thrombus formation in the context of enhanced or normal erythropoiesis.
## Discussion
Here, we present evidence for a differential thrombotic effect of chronic EPO overproduction and short-term external EPO administration. Consistent with clinical observations, chronic overproduction of EPO is associated with an increased risk of DVT formation. This is similar to Chuvash polycythemia where the von-Hippel–Lindau mutation leads to chronic overproduction of hypoxia-induced factors and high EPO levels. 34 In addition to genetically altered EPO production, factors such as residence at high altitudes and naturally increasing EPO secretion also represent risk factors for venous thrombosis and pulmonary thromboembolism. 35 36 These conditions can be mimicked in a mouse model through chronic hypoxia. 37
In our analyses, we observed that short-term administration of EPO does not increase the risk of DVT, in contrast to chronic overproduction of EPO. However, changes in peripheral blood count in response to EPO occur relatively quickly, within 2 weeks of initiating therapy in mice. These changes include elevated levels of hemoglobin and thrombocytopenia, which are consistent with previous studies. 17 22 38 39 40 41 42 43 44 45 In the model of transgenic overexpressing EPO mice, there was an age-dependent progressive decrease in megakaryocyte count in the bone marrow. 32 A similar phenomenon can be observed in mice exposed to chronic hypoxia. 46 It is believed that competition between erythroid and platelet precursors in the stem cell population is responsible for this phenomenon. 38 Despite a similar decrease of peripheral platelet counts, we observed normal megakaryocyte counts in the bone marrow of mice injected with EPO for 2 weeks. We speculate that morphological changes in the bone marrow are long-term consequences of EPO administration. In peripheral blood, we observed a significant increase in the platelet large cell ratio in mice treated with EPO for 2 weeks. This is likely due to an elevated count of reticulated platelets, which has been previously observed in response to EPO treatment. 47 The presence of high levels of reticulated platelets indicates a high synthetic potential of megakaryocytes. Indeed, megakaryocytes possess high-affinity binding sites for EPO resulting in an increase in size, ploidy, and number of megakaryocytes in vitro. 48 49 Young, reticulated platelets are known risk factors for thrombosis, which may counterbalance the overall low platelet count in terms of thrombogenicity. 50 51 52 However, the significant increase in DVT observed in chronic EPO-overexpressing mice is likely attributed to qualitative changes in RBCs. There are several ways in which RBCs can interact with platelets and fibrin. The FAS-L-FAS-R interplay between RBCs and platelets has been shown to enhance DVT formation. 31 Additionally, interactions such as ICAM-4–α1bβ3 integrin and adhesion between RBCs and platelets mediated by GPIb and CD36 have been described. 53 54 As demonstrated in this study, the pronounced prothrombotic effect of RBCs only manifests after several weeks to months of EPO overproduction. Thus, we propose that RBC aging plays a role in this phenomenon. This is supported by the finding that RBCs in our Tg(EPO) mouse model exhibit characteristics of accelerated aging including decreased CD47 expression, leading to a 70% reduction in lifespan. 15
During the ageing process, RBCs not only display increasing amounts of procoagulant phosphatidylserine on their surface but also exhibit heightened osmotic and mechanical fragility, which is also observed in Tg(EPO) mice. 32 55 Fragile RBCs are prone to hemolysis, resulting in the release of ADP and free hemoglobin. Furthermore, hemoglobin directly or indirectly contributes to increased platelet activity, for instance, by forming complexes with nitric oxide (NO). 56 57 58 NO is essential for the survival of Tg(EPO) mice but dispensable for WT mice. 14 Consistent with this, patients with polycythemia vera exhibit platelet hypersensitivity despite normal platelet counts, while plasma haptoglobin concentration, a marker for hemolysis, is decreased. 59 60 61 62 63 64 65 Similarly, chronic subcutaneous EPO administration in hemodialysis patients leads to a prothrombotic phenotype similar to that of polycythemia vera patients. 66 67 68 69 70 Notably, concentrated RBC transfusions result in the rapid clearance of up to 30% of transfused erythrocytes within 24 hours due to their age, thus increasing the risk of DVT formation. 5 71
Clearance of RBCs primarily occurs in the spleen, where tissue-resident macrophages screen for surface markers such as CD47. 72 Subsequently, RBCs are phagocytosed before reaching day 120 of their lifespan. 73 The spleen plays a crucial role in maintaining the shape and membrane resilience of RBCs, acting as a guardian in this regard. 74 However, shortly after splenectomy, the loss of the organ significantly increases the risk of DVT formation. 20 In the long-term basis, we observed no difference in DVT formation after splenectomy, neither in WT mice nor in chronic EPO-overexpressing mice, despite the dramatic increase in macrophage-mediated RBC clearance in these mice. 15 Since RBC clearance occurs primarily in the spleen and liver in mice, we hypothesize that the liver is capable of adequately compensating for the absence of the spleen after removal. 15
Besides their activating effect on platelets, RBCs also directly impact the coagulation system. Previous data demonstrate that following TF activation, RBCs contribute to thrombin generation to a similar extent to platelets. 75 Furthermore, RBCs expose phosphatidylserine, which activates the contact pathway. 31 Notably, the coagulation system in Tg(EPO) mice exhibits normal activity in whole blood adjusted to a physiological hematocrit. 32 Additionally, RBCs express a receptor with properties similar to the α IIb β 3 integrin enabling their interaction with fibrin. 76 This interaction contributes to the formation of a dense fibrin meshwork consisting of thin fibers. 77 Such a structure hinders clot dissolution, leading to slower lysis. 77 In our histological analysis of thrombi, we confirm morphological changes in the fibrin meshwork, resulting in a thinner appearance in both EPO-overexpressing mice and mice subjected to short-term EPO injection.
## Figures
Fig. 1 EPO-overexpressing mice experience an increased incidence of DVT formation. (A) Comparison of RBC count between EPO-overexpressing Tg(EPO) mice (n = 12) and control (WT) mice (n = 13). (B) Comparison of platelet count between EPO-overexpressing Tg(EPO) (n = 7) mice and control (WT) mice (n = 5). (C) Comparison of thrombus weight between Tg(EPO) mice (n = 9) and WT (n = 10); mean age in the Tg(EPO) group: 19.8 weeks; mean age in the WT group: 20.1 weeks. (D) Comparison of thrombus incidence between Tg(EPO) mice (n = 9) and WT mice (n = 10); NS = nonsignificant, *p < 0.05, **p < 0.01, ***p < 0.001. DVT, deep vein thrombosis; EPO, erythropoietin; RBC, red blood cell; WT, wild type.
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Fig. 2 Chronic overproduction of EPO in mice leads to a decrease in the accumulation of classical drivers of DVT formation, including platelets, neutrophils, and fibrinogen. (A) The proportion of RBC-covered area in the thrombi of EPO-overexpressing Tg(EPO) mice (n = 4) was compared to control (WT) (n = 3) by immunofluorescence staining of cross-sections of the IVC 48 hours after flow reduction. (B) The proportion of fibrinogen-covered area in the thrombi of EPO- overexpressing Tg(EPO) mice (n = 3) was compared to control (WT) (n = 3) using immunofluorescence staining of cross-sections of the IVC 48 hours after flow reduction. (C) The proportion of platelet-covered area in the thrombi of EPO-overexpressing Tg(EPO) mice (n = 3) was compared to control (WT) (n = 3) by immunofluorescence staining of cross-sections of the IVC 48 hours after flow reduction. (D) Quantification of neutrophils was performed by immunofluorescence staining of cross-sections of the IVC 48 hours after flow reduction in EPO-overexpressing Tg(EPO) mice (n = 3) compared to control (WT) (n = 3). (E) Immunofluorescence staining of cross-sections of the IVC 48 hours after flow reduction from EPO-overexpressing Tg(EPO) mice (top) was compared to control (WT) (bottom) for TER119 in red (RBC), CD42b in green (platelets), and Hoechst in blue (DNA). The merged image is on the left, and the single channel image is on the right. Scale bar: 50 µm. (F) Immunofluorescence staining of cross-sections of the IVC 48 hours after flow reduction from EPO-overexpressing Tg(EPO) mice (top) was compared to control (WT) (bottom) for TER119 in red (RBC), fibrinogen in green, and Hoechst in blue (DNA); the merged image is on the left, and single-channel images are on the right. Scale bar: 50 µm.; NS = nonsignificant, *p < 0.05, **p < 0.01, ***p < 0.001. DVT, deep vein thrombosis; EPO, erythropoietin; IVC, inferior vena cava; RBC, red blood cell; WT, wild type.
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Fig. 3 The interaction of RBC with fibrinogen leads to the formation of a branched fibrin structure. (A) Immunofluorescence staining of cross-sections of the IVC 48 hours after flow reduction from EPO-overexpressing Tg(EPO) mice (left) was compared to control (WT) for fibrinogen (green). Scare bar: 50 µm. (B) High-resolution confocal images of immunofluorescence staining of cross-sections of the IVC 48 hours after flow reduction from EPO-overexpressing Tg(EPO) mice (left) compared to control (WT) for fibrinogen (green), RBC (red), and DNA (blue). Scale bar: 2 µm. (C) The mean diameter of 2,141 fibrin fibers was measured in cross-sections of thrombi from Tg(EPO) mice (left), and compared to 5,238 fibrin fibers of WT thrombi. (D) The mean diameter of 3,797 fibrin fibers was measured in two cross-sections of thrombi from 2-week EPO-injected mice (left), and compared to 10,920 fibrin fibers of three cross-sections from control (2-week NaCl-injected mice). (E) High-resolution confocal images of immunofluorescence staining of two cross-sections of the IVC 48 hours after flow reduction from 2-week EPO-injected mice (left) were compared to control (2 week NaCl-injected mice) for fibrinogen (green), RBC (red), and DNA (blue). Scale bar: 2 µm. EPO, erythropoietin; IVC, inferior vena cava; RBC, red blood cell; WT, wild type.
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Fig. 4 Two-week EPO injection leads to thrombocytopenia without an impact on the bone marrow. (A) RBC count in peripheral blood after 6 × 300 IU EPO treatment of C57Bl/6J mice (n = 10) was compared to control (6 × 30 µL NaCl injection) (n = 9). (B) Platelet count in peripheral blood after 6 × 300 IU EPO treatment of C57Bl/6J mice (n = 10) was compared to control (6 × 30 µL NaCl injection) (n = 10). (C) Area of RBC-positive area in the bone marrow of 6 × 300 IU EPO-treated C57Bl/6J mice (n = 4) was compared to control (6 × 30 µL NaCl injection) (n = 4). (D) Immunofluorescence staining of cross-sections of the bone marrow after 2-week EPO injection (top) was compared to NaCl injection (bottom) stained for TER119 (violet) and Hoechst (white). Scale bar: 100 µm. (E) Number of megakaryocyte count in the bone marrow of 6 × 300 IU EPO-treated C57Bl/6J mice (n = 4) was compared to control (6 × 30 µL NaCl injection) (n = 4). (F) Immunofluorescence staining of cross-sections of the bone marrow after 2-week EPO injection (top) compared to NaCl injection (bottom) stained for CD41 (violet) and Hoechst (white). Scale bar: 100 µm. (G) Platelet large cell ratio in peripheral blood of 6 × 300 IU EPO-treated C57Bl/6J mice (n = 10) compared to control (6 × 30 µL NaCl injection) (n = 9). (H) Thrombus weight of 6 × 300 IU EPO-treated C57Bl/6J mice (n = 10) and NaCl-injected control mice (n = 10). (I) Thrombus incidence of 6 × 300 IU EPO-treated C57Bl/6J mice (n = 10) and NaCl-injected control mice (n = 10). NS = nonsignificant, *p < 0.05, **p < 0.01, ***p < 0.001. EPO, erythropoietin; RBC, red blood cell.
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Fig. 5 Splenectomy does not affect the blood count as well as DVT formation. (A) WBC count in C57Bl/6J mice without treatment (n = 5), 48 hours after induction of DVT (n = 7), 5 weeks after splenectomy (n = 3), and 5 weeks after splenectomy with an additional 48-hour induction of DVT (n = 9). (B) Platelet count in C57Bl/6J mice without treatment (n = 6), 48 hours after induction of DVT (n = 6), 5 weeks after splenectomy (n = 3), and 5 weeks after splenectomy with an additional 48-hour induction of DVT (n = 9). (C) RBC count in C57Bl/6J mice without treatment (n = 6), 48 hours after induction of DVT (n = 6), 5 weeks after splenectomy (n = 3), and 5 weeks after splenectomy with an additional 48-hour induction of DVT (n = 9). (D) Thrombus weight in C57Bl6 wild-type mice without splenectomy (n = 6) and with splenectomy (n = 6) (E) Thrombus incidence in C57Bl/6J wild-type mice without splenectomy (n = 6) and with splenectomy (n = 6). (F) Thrombus weight in EPO-overexpressing Tg(EPO) mice without splenectomy (n = 9) and with splenectomy (n = 6) compared to control WT mice without splenectomy (n = 10) and with splenectomy (n = 11). (G) Thrombus incidence in EPO-overexpressing Tg(EPO) mice without splenectomy (n = 9) and with splenectomy (n = 6) compared to control WT mice without splenectomy (n = 10) and with splenectomy (n = 11). NS = nonsignificant, *p < 0.05, **p < 0.01, ***p < 0.001. DVT, deep vein thrombosis; RBC, red blood cell; WT, wild type.
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item-0 at level 0: unspecified: group _root_
item-1 at level 1: title: Proposal and Validation of a Cli ... ulation Diagnostic Criteria for Sepsis
item-2 at level 1: paragraph: Kazuma Yamakawa; Department of E ... hi Tokushukai Hospital, Sapporo, Japan
item-3 at level 1: text: Background Japanese Association ... native criteria for sepsis management.
item-4 at level 1: section_header: Introduction
item-5 at level 2: text: Disseminated intravascular coagu ... are widely used in clinical settings.
item-6 at level 2: text: The JAAM DIC criteria have sever ... sis, this burden should be eliminated.
item-7 at level 1: section_header: Study Population
item-8 at level 2: text: This investigation was performed ... ., SOFA score of 2 or more points). 13
item-9 at level 1: section_header: Data Collection and Definitions
item-10 at level 2: text: A case report form was developed ... e was all-cause in-hospital mortality.
item-11 at level 1: section_header: Newly Proposed Modified JAAM-2 DIC Criteria
item-12 at level 2: text: We proposed novel DIC criteria n ... s set at 3 points or more ( Table 2 ).
item-13 at level 1: section_header: Statistical Analysis
item-14 at level 2: text: The overall effectiveness of ant ... along with estimated survival curves.
item-15 at level 1: section_header: Patient Characteristics
item-16 at level 2: text: The patient flow diagram is show ... CU registry in the final study cohort.
item-17 at level 2: text: Baseline characteristics of the ... the anticoagulant and control groups.
item-18 at level 1: section_header: Prognostic Value of the Criteria
item-19 at level 2: text: ROC curves for the original JAAM ... teria was considered to be equivalent.
item-20 at level 1: section_header: Validity of the Criteria in Initiating Anticoagulation
item-21 at level 2: text: Survival curves for the anticoag ... g DIC was considered to be equivalent.
item-22 at level 1: section_header: Clinical Application of the Findings
item-23 at level 2: text: Several different clinical pract ... tion and determining treatment timing.
item-24 at level 2: text: Modification of the JAAM DIC cri ... a totally clinical-friendly approach.
item-25 at level 2: text: We intended to evaluate the seve ... 24 proposed by the ISTH in the future.
item-26 at level 1: section_header: Strengths and Limitations
item-27 at level 2: text: We acknowledge several limitatio ... tis should be conducted in the future.
item-28 at level 1: section_header: Tables
item-30 at level 1: table with [22x2]
item-30 at level 2: caption: Table 1 Underlying diseases targeted by the JAAM-2 DIC criteria
item-32 at level 1: table with [38x4]
item-32 at level 2: caption: Table 3 Baseline characteristics of included sepsis patients in the three datasets
item-33 at level 1: section_header: Figures
item-35 at level 1: picture
item-35 at level 2: caption: Fig. 1 Patient flow for the three datasets used in this study. DIC, disseminated intravascular coagulation; JAAM, Japanese Association for Acute Medicine; ROC, receiver operating characteristic.
item-37 at level 1: picture
item-37 at level 2: caption: Fig. 2 Receiver operating characteristic curves for original JAAM and modified JAAM-2 DIC criteria as predictors of in-hospital mortality. The solid line represents curves for JAAM-2, and the dotted line represents curves for original JAAM. (A) J-Septic DIC dataset, (B) FORECAST dataset, (C) SPICE dataset. DIC, disseminated intravascular coagulation; JAAM, Japanese Association for Acute Medicine.
item-39 at level 1: picture
item-39 at level 2: caption: Fig. 3 Adjusted estimated survival curves according to the original JAAM and modified JAAM-2 DIC status using the J-septic DIC dataset. (A) JAAM DIC score ≤ 3, (B) JAAM DIC score ≥ 4, (C) JAAM-2 DIC score ≤ 2, and (D) JAAM-2 DIC score ≥ 3. The solid line represents patients in the anticoagulant group, and the dotted line represents patients in the control group. DIC, disseminated intravascular coagulation; JAAM, Japanese Association for Acute Medicine.
item-41 at level 1: picture
item-41 at level 2: caption: Fig. 4 Adjusted estimated survival curves according to the original JAAM and modified JAAM-2 DIC status using the FORECAST dataset. (A) JAAM DIC score ≤ 3, (B) JAAM DIC score ≥ 4, (C) JAAM-2 DIC score ≤ 2, and (D) JAAM-2 DIC score ≥ 3. The solid line represents patients in the anticoagulant group, and the dotted line represents patients in the control group. DIC, disseminated intravascular coagulation; JAAM, Japanese Association for Acute Medicine.
item-42 at level 1: section_header: References
item-43 at level 1: list: group list
item-44 at level 2: list_item: M Levi; H Ten Cate. Disseminated ... cular coagulation. N Engl J Med (1999)
item-45 at level 2: list_item: M Hayakawa; S Saito; S Uchino. C ... ing 2011-2013. J Intensive Care (2016)
item-46 at level 2: list_item: S Gando; A Shiraishi; K Yamakawa ... on in severe sepsis. Thromb Res (2019)
item-47 at level 2: list_item: N Kobayashi; T Maekawa; M Takada ... on DIC in Japan. Bibl Haematol (1983)
item-48 at level 2: list_item: F B Taylor; C H Toh; W K Hoots; ... lar coagulation. Thromb Haemost (2001)
item-49 at level 2: list_item: T Iba; Y Umemura; E Watanabe; T ... nd coagulopathy. Acute Med Surg (2019)
item-50 at level 2: list_item: K Yamakawa; J Yoshimura; T Ito; ... pathy in sepsis. Thromb Haemost (2019)
item-51 at level 2: list_item: S Gando; T Iba; Y Eguchi. A mult ... current criteria. Crit Care Med (2006)
item-52 at level 2: list_item: S Gando; D Saitoh; H Ogura. Natu ... ospective survey. Crit Care Med (2008)
item-53 at level 2: list_item: H Ogura; S Gando; T Iba. SIRS-as ... ts with thrombocytopenia. Shock (2007)
item-54 at level 2: list_item: . American College of Chest Phys ... rapies in sepsis. Crit Care Med (1992)
item-55 at level 2: list_item: K M Kaukonen; M Bailey; D Pilche ... ing severe sepsis. N Engl J Med (2015)
item-56 at level 2: list_item: M Singer; C S Deutschman; C W Se ... d Septic Shock (Sepsis-3). JAMA (2016)
item-57 at level 2: list_item: T Abe; H Ogura; A Shiraishi. Cha ... : the FORECAST study. Crit Care (2018)
item-58 at level 2: list_item: T Abe; K Yamakawa; H Ogura. Epid ... m (SPICE-ICU). J Intensive Care (2020)
item-59 at level 2: list_item: R C Bone; R A Balk; F B Cerra. D ... tive therapies in sepsis. Chest (1992)
item-60 at level 2: list_item: J L Vincent; R Moreno; J Takala. ... re Medicine. Intensive Care Med (1996)
item-61 at level 2: list_item: M Levi; C H Toh; J Thachil; H G ... ular coagulation. Br J Haematol (2009)
item-62 at level 2: list_item: H Wada; H Asakura; K Okamoto. Ex ... oagulation in Japan. Thromb Res (2010)
item-63 at level 2: list_item: M Di Nisio; F Baudo; B Cosmi. Di ... Thrombosis (SISET). Thromb Res (2012)
item-64 at level 2: list_item: H Wada; J Thachil; M Di Nisio. G ... ee guidelines. J Thromb Haemost (2013)
item-65 at level 2: list_item: Y Umemura; K Yamakawa; T Kiguchi ... iteria. Clin Appl Thromb Hemost (2016)
item-66 at level 2: list_item: T Iba; M Di Nisio; J Thachil. Re ... ntithrombin activity. Crit Care (2016)
item-67 at level 2: list_item: T Iba; M D Nisio; J H Levy; N Ki ... f a nationwide survey. BMJ Open (2017)
item-68 at level 2: list_item: S Kushimoto; S Gando; D Saitoh. ... psis and trauma. Thromb Haemost (2008)
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# Proposal and Validation of a Clinically Relevant Modification of the Japanese Association for Acute Medicine Disseminated Intravascular Coagulation Diagnostic Criteria for Sepsis
Kazuma Yamakawa; Department of Emergency and Critical Care Medicine, Osaka Medical and Pharmaceutical University, Takatsuki, Japan; Yutaka Umemura; Division of Trauma and Surgical Critical Care, Osaka General Medical Center, Osaka, Japan; Katsunori Mochizuki; Department of Emergency and Critical Care Medicine, Osaka Medical and Pharmaceutical University, Takatsuki, Japan; Department of Emergency and Critical Care Medicine, Azumino Red Cross Hospital, Nagano, Japan; Tadashi Matsuoka; Department of Emergency and Critical Care Medicine, Keio University, Tokyo, Japan; Takeshi Wada; Division of Acute and Critical Care Medicine, Department of Anesthesiology and Critical Care Medicine, Hokkaido University Faculty of Medicine, Sapporo, Japan; Mineji Hayakawa; Division of Acute and Critical Care Medicine, Department of Anesthesiology and Critical Care Medicine, Hokkaido University Faculty of Medicine, Sapporo, Japan; Toshiaki Iba; Department of Emergency and Disaster Medicine, Juntendo University Graduate School of Medicine, Tokyo, Japan; Yasuhiro Ohtomo; National Disaster Medical Center, Tokyo, Japan; Kohji Okamoto; Department of Surgery, Kitakyushu City Yahata Hospital, Kitakyushu, Japan; Toshihiko Mayumi; Department of Intensive Care Unit, Japan Community Healthcare Organization Chukyo Hospital, Nagoya, Japan; Toshiaki Ikeda; Division of Critical Care and Emergency Medicine, Tokyo Medical University Hachioji Medical Center, Tokyo, Japan; Hiroyasu Ishikura; Department of Emergency and Critical Care Medicine, Fukuoka University, Fukuoka, Japan; Hiroshi Ogura; Department of Traumatology and Acute Critical Medicine, Osaka University Graduate School of Medicine, Suita, Japan; Shigeki Kushimoto; Division of Emergency and Critical Care Medicine, Tohoku University Graduate School of Medicine, Sendai, Japan; Daizoh Saitoh; Graduate School of Emergency Medical System, Kokushikan University, Tama, Japan; Satoshi Gando; Division of Acute and Critical Care Medicine, Department of Anesthesiology and Critical Care Medicine, Hokkaido University Faculty of Medicine, Sapporo, Japan; Department of Acute and Critical Care Medicine, Sapporo Higashi Tokushukai Hospital, Sapporo, Japan
Background Japanese Association for Acute Medicine (JAAM) disseminated intravascular coagulation (DIC) criteria were launched nearly 20 years ago. Following the revised conceptual definition of sepsis and subsequent omission of systemic inflammatory response syndrome (SIRS) score from the latest sepsis diagnostic criteria, we omitted the SIRS score and proposed a modified version of JAAM DIC criteria, the JAAM-2 DIC criteria. Objectives To validate and compare performance between new JAAM-2 DIC criteria and conventional JAAM DIC criteria for sepsis. Methods We used three datasets containing adult sepsis patients from a multicenter nationwide Japanese cohort study (J-septic DIC, FORECAST, and SPICE-ICU registries). JAAM-2 DIC criteria omitted the SIRS score and set the cutoff value at ≥3 points. Receiver operating characteristic (ROC) analyses were performed between the two DIC criteria to evaluate prognostic value. Associations between in-hospital mortality and anticoagulant therapy according to DIC status were analyzed using propensity score weighting to compare significance of the criteria in determining introduction of anticoagulants against sepsis. Results Final study cohorts of the datasets included 2,154, 1,065, and 608 sepsis patients, respectively. ROC analysis revealed that curves for both JAAM and JAAM-2 DIC criteria as predictors of in-hospital mortality were almost consistent. Survival curves for the anticoagulant and control groups in the propensity score-weighted prediction model diagnosed using the two criteria were also almost entirely consistent. Conclusion JAAM-2 DIC criteria were equivalent to JAAM DIC criteria regarding prognostic and diagnostic values for initiating anticoagulation. The newly proposed JAAM-2 DIC criteria could be potentially alternative criteria for sepsis management.
## Introduction
Disseminated intravascular coagulation (DIC) is a disorder frequently seen in critically ill patients, especially those with sepsis, that may lead to severe bleeding and organ dysfunction. 1 Because mortality is higher in patients with than without DIC, 2 3 several organizations have put forward DIC scoring systems with the aim of improving the outcome of patients with DIC. The Japanese Ministry of Health and Welfare (JMHW) proposed a criteria for the diagnosis of DIC in 1976. 4 Their criteria involved the evaluation of global coagulation tests, underlying diseases, and clinical symptoms. Thereafter, the subcommittee of the International Society on Thrombosis and Haemostasis (ISTH) proposed a scoring system for overt and non-overt DIC in 2001. 5 However, patients diagnosed according to the JMHW or ISTH DIC criteria are often at high risk of death at the time of diagnosis because of the delay from the onset of coagulopathy. It has been reported that these patients are missing out on the initiation of interventions in the setting of critical illness. 6 7 Thus, the Japanese Association for Acute Medicine (JAAM) proposed another DIC scoring system that aimed to make early diagnosis of DIC in acute diseases possible. 8 9 Now, both the ISTH overt- and JAAM DIC criteria are widely used in clinical settings.
The JAAM DIC criteria have several unique features compared with other DIC criteria, one of which is the inclusion of the systemic inflammatory response syndrome (SIRS) score. Based on the pathophysiological concept, as sepsis-induced DIC is caused by systemic inflammation and subsequent endothelial injury, inclusion of the SIRS score seemed to be reasonable. 10 The SIRS score was introduced as one of the criteria to diagnose sepsis in 1992. 11 In recent years, however, the prognostic relevance of the SIRS score has been questioned, 12 and SIRS criteria have been omitted from the latest definition of sepsis proposed in 2016 13 and are no longer used in clinical practice. Other concerns with including the SIRS score in the DIC criteria were the clinical burden on physicians and inter-observer variability in scoring. To determine the SIRS score, several vital signs need to be assessed and the score calculated. Because the SIRS criteria are now no longer used to diagnose sepsis, this burden should be eliminated.
## Study Population
This investigation was performed using three different datasets extracted from a multicenter nationwide cohort study conducted in Japan. The first dataset, the J-septic DIC dataset, was compiled in 42 intensive care units (ICUs) between January 2011 and December 2013. 2 The second dataset, the FORECAST dataset, was compiled in 59 ICUs between January 2016 and March 2017, 14 and the third dataset, the SPICE dataset, was compiled in 22 ICUs between December 2017 and May 2018. 15 In the first two datasets, patients were eligible for the registry if they were diagnosed as having severe sepsis or septic shock according to the conventional criteria proposed by the American College of Chest Physicians/Society of Critical Care Medicine (ACCP/SCCM) consensus conference in 1991 16 and were 18 years of age or older. In the present analysis, we included as the underlying diseases targeted by the JAAM-2 DIC criteria only those of sepsis patients diagnosed using the Sepsis-3 criteria (i.e., SOFA score of 2 or more points). 13
## Data Collection and Definitions
A case report form was developed for the three datasets used in this study on which the following information was recorded: age, sex, disease severity scores on the day of ICU admission, the source of ICU admission, pre-existing conditions, new organ dysfunction, primary source of infection, and concomitant therapies against sepsis. The severity of illness was evaluated at study entry according to the Acute Physiology and Chronic Health Evaluation (APACHE) II score and SIRS score. The Sequential Organ Failure Assessment (SOFA) score was used to assess organ dysfunction, which was defined as a SOFA subscore ≥2 for each organ. 17 The primary outcome measure was all-cause in-hospital mortality.
## Newly Proposed Modified JAAM-2 DIC Criteria
We proposed novel DIC criteria named the JAAM-2 DIC criteria that were modified from the original JAAM DIC criteria. The underlying diseases targeted by the JAAM-2 DIC criteria, which comply with those of the original JAAM DIC criteria, are shown in Table 1 . 9 The SIRS score component from the JAAM DIC criteria was omitted, and the cutoff value for diagnosing DIC was set at 3 points or more ( Table 2 ).
## Statistical Analysis
The overall effectiveness of anticoagulant therapy on mortality was assessed using a Cox regression model with inverse probability-of-treatment weighting using the propensity scores. The propensity score for receiving anticoagulant therapy was calculated using multivariate logistic regression and included 25 independent variables for the J-septic DIC cohort and 30 variables for the FORECAST cohort, including age, sex, disease severity, source of ICU admission, past medical history of severe conditions, new organ dysfunctions, ICU characteristics, primary source of infection, causal microorganisms, anticoagulant therapy not for DIC, and other therapeutic interventions ( Supplementary Table S1 [available in the online version]). Hazard ratio and estimated 95% confidence interval were calculated along with estimated survival curves.
## Patient Characteristics
The patient flow diagram is shown in Fig. 1 . During the study period, 3,195 consecutive patients fulfilling the inclusion criteria were registered in the J-Septic DIC registry database. After excluding 1,040 patients who met at least one exclusion criterion, we analyzed 2,154 patients in the final study cohort. The anticoagulant group comprised 1,089 patients, and the control group comprised 1,065 patients. Similarly, we enrolled 817 patients from the FORECAST registry and 608 patients from the SPICE-ICU registry in the final study cohort.
Baseline characteristics of the study population are shown in Table 3 , Supplementary Table S2 and S3 (available in the online version). Patient characteristics such as age and sex were similar between the three datasets. After applying an inverse probability of treatment weighting with propensity score, patient characteristics, such as illness severity, as indicated by SOFA, APACHE II, and DIC scores and the rate of new organ dysfunction, were well matched between the anticoagulant and control groups.
## Prognostic Value of the Criteria
ROC curves for the original JAAM and modified JAAM-2 DIC criteria as predictors of in-hospital mortality are shown in Fig. 2 . Consistent with the three different datasets, the curves for both the JAAM and JAAM-2 DIC criteria were almost entirely consistent with each other. These data suggested that in predicting short-term mortality, use of the JAAM DIC and JAAM-2 DIC criteria was considered to be equivalent.
## Validity of the Criteria in Initiating Anticoagulation
Survival curves for the anticoagulant and control groups in the propensity score-weighted prediction model according to DIC status diagnosed using the two criteria are shown in Fig. 3 (J-septic DIC dataset) and Fig. 4 (FORECAST dataset). Consistent with both criteria and both datasets, favorable effects of anticoagulant therapy were observed only in the patient subsets with DIC, whereas differences in mortality between the anticoagulant and control groups in the subsets without DIC were not significant. These findings were consistent between the two datasets and suggested that to determine the optimal target of anticoagulant therapy for sepsis, use of the JAAM DIC and JAAM-2 DIC criteria for diagnosing DIC was considered to be equivalent.
## Clinical Application of the Findings
Several different clinical practice guidelines for DIC have been developed by societies in Britain, 18 Japan, 19 and Italy, 20 along with the harmonized guidance by the ISTH. 21 Some distinct discrepancies in the appraisal of diagnostic criteria for DIC exist between these guidelines. The Japanese and Italian clinical practice guidelines recommend the use of either the JMHW, ISTH, or the JAAM criteria, whereas the British guideline recommends the use of the ISTH criteria. The guidelines do not offer consistent recommendations on diagnosing DIC, and thus, there is currently no definitive agreement as to which of these criteria is superior to the other. The present study does not aim to discuss the diagnostic value of the several DIC criteria because we have no gold standard for DIC diagnosis. No meta-analysis has been conducted so far to compare the prognostic performance among the several available DIC criteria. Nonetheless, we showed that the clinical usefulness of the proposed JAAM-2 DIC criteria was nearly equivalent to that of the traditional JAAM DIC criteria. While a discussion on superiority would be worthless, we showed that the performance of the JAAM-2 scoring system is almost identical to that of the JAAM criteria in terms of mortality prediction and determining treatment timing.
Modification of the JAAM DIC criteria has been discussed in several studies so far. Umemura et al 22 proposed unified DIC criteria involving several hemostatic endothelial molecular markers based on the JAAM DIC criteria and showed that the addition of protein C activity and plasminogen activator inhibitor 1 to the original JAAM DIC criteria resulted in greater prognostic value than the original criteria. Iba et al 23 proposed replacing the SIRS score with antithrombin activity in the JAAM DIC criteria. They validated the proposed criteria using a dataset of 819 sepsis patients and found that using AT-based DIC criteria makes it possible to discriminate a more coagulation disorder-specific population. All of these previous attempts were in addition to or replacements of the other variables instead of the SIRS score, and thus, the burden on clinicians still remained. In the present study, we simply omitted the SIRS score, so this modification of the JAAM DIC criteria should allow a totally clinical-friendly approach.
We intended to evaluate the severity of sepsis by adding “SIRS score ≥ 3”; however, the present study showed the prognosis to be not different without this item included. Therefore, we think it is reasonable to omit the SIRS item from the JAAM criteria. In the present analysis, as the JAAM-2 DIC criteria have been shown to increase clinical simplicity without diminishing any diagnostic performance, the educational activities led by our academic society will aid in the replacement of original JAAM with JAAM-2 DIC criteria in Japan. Furthermore, it will be necessary to verify coherence with the sepsis-induced coagulopathy criteria 24 proposed by the ISTH in the future.
## Strengths and Limitations
We acknowledge several limitations of this study. First, due to its retrospective nature, the anticoagulant intervention was not standardized. The indications for the intervention being examined were dependent on the treatment principles of each hospital or each attending physician. Thus, we used propensity scoring to handle the nonrandomization. Second, this study used sub-group analysis, which might have accidentally generated both false-positive and false-negative results. Finally, this article focused only on patients with sepsis among various underlying diseases of DIC. The original JAAM DIC criteria have been reported to be useful in a variety of underlying diseases. 25 26 27 28 Further validation studies of these novel JAAM-2 DIC criteria targeting other underlying diseases such as trauma, postcardiac arrest, and pancreatitis should be conducted in the future.
## Tables
Table 1 Underlying diseases targeted by the JAAM-2 DIC criteria
| 1. Sepsis/severe infection (any microorganism) | 1. Sepsis/severe infection (any microorganism) |
|-----------------------------------------------------------------------------------|-----------------------------------------------------------------------------------|
| 2. Trauma/burn/surgery | 2. Trauma/burn/surgery |
| 3. Vascular abnormalities | 3. Vascular abnormalities |
| | Large vascular aneurysms |
| | Giant hemangioma |
| | Vasculitis |
| 4. Severe toxic or immunological reactions | 4. Severe toxic or immunological reactions |
| | Snakebite |
| | Recreational drugs |
| | Transfusion reactions |
| | Transplant rejection |
| 5. Malignancy (except bone marrow suppression) | 5. Malignancy (except bone marrow suppression) |
| 6. Obstetric calamities | 6. Obstetric calamities |
| 7. Conditions that may be associated with systemic inflammatory response syndrome | 7. Conditions that may be associated with systemic inflammatory response syndrome |
| | Organ destruction (e.g., severe pancreatitis) |
| | Severe hepatic failure |
| | Ischemia/hypoxia/shock |
| | Heat stroke/malignant syndrome |
| | Fat embolism |
| | Rhabdomyolysis |
| | Others |
| 8. Others | 8. Others |
Table 3 Baseline characteristics of included sepsis patients in the three datasets
| Characteristics | J-septic DIC dataset ( n  = 2,154) | FORECAST dataset ( n  = 817) | SPICE dataset ( n  = 608) |
|--------------------------------------------|--------------------------------------------|--------------------------------------------|--------------------------------------------|
| Age in years | 72 (62–80) | 72 (63–82) | 72 (60–82) |
| Male sex | 1,270 (59%) | 496 (61%) | 350 (58%) |
| Illness severity | Illness severity | Illness severity | Illness severity |
| SIRS score | 3 (2–4) | 3 (2–4) | 3 (2–3) |
| SOFA score | 9 (7–12) | 9 (6–11) | 7 (4.5–10) |
| APACHE II score | 22 (17–28) | 22 (17–29) | 20 (14–27) |
| ISTH overt-DIC score | 4 (2–5) | 3 (2–4) | 2 (0–3) |
| JAAM DIC score | 4 (3–6) | 4 (2–5) | 3 (2–5) |
| Source of ICU admission | Source of ICU admission | Source of ICU admission | Source of ICU admission |
| Emergency department | 1,018 (47%) | 465 (57%) | 350 (58%) |
| Ward | 515 (24%) | 352 (43%) | 258 (42%) |
| Other hospital | 621 (29%) | 352 (43%) | 258 (42%) |
| Pre-existing condition | Pre-existing condition | Pre-existing condition | Pre-existing condition |
| Liver insufficiency | 16 (1%) | 26 (3%) | 26 (4%) |
| Chronic heart failure | 116 (5%) | 104 (13%) | 57 (9%) |
| Chronic respiratory disorder | 85 (4%) | 58 (7%) | 52 (9%) |
| Chronic hemodialysis | 167 (8%) | 52 (6%) | 52 (9%) |
| Immunocompromised | 228 (11%) | 96 (12%) | 38 (6%) |
| New organ dysfunction (SOFA subscores ≥ 2) | New organ dysfunction (SOFA subscores ≥ 2) | New organ dysfunction (SOFA subscores ≥ 2) | New organ dysfunction (SOFA subscores ≥ 2) |
| Respiratory | 1,489 (69%) | 575 (70%) | 370 (61%) |
| Cardiovascular | 1,416 (66%) | 461 (56%) | 254 (42%) |
| Renal | 1,071 (50%) | 413 (51%) | 268 (44%) |
| Hepatic | 383 (18%) | 127 (16%) | 84 (14%) |
| Coagulation | 816 (38%) | 233 (29%) | 127 (21%) |
| Primary source of infection | Primary source of infection | Primary source of infection | Primary source of infection |
| Abdomen | 696 (32%) | 212 (26%) | 120 (20%) |
| Lung | 556 (26%) | 259 (32%) | 203 (33%) |
| Urinary tract | 385 (18%) | 159 (19%) | 102 (17%) |
| Bone/soft tissue | 250 (12%) | 111 (14%) | 90 (15%) |
| Central nervous system | 50 (2%) | 15 (2%) | 15 (2%) |
| Other/unknown | 217 (10%) | 61 (7%) | 78 (13%) |
| Other therapeutic interventions | Other therapeutic interventions | Other therapeutic interventions | Other therapeutic interventions |
| Immunoglobulin | 685 (32%) | 158 (19%) | |
| Low-dose steroids | 539 (25%) | 241 (30%) | |
| Renal replacement therapy | 599 (28%) | 231 (28%) | |
| PMX-DHP | 465 (22%) | 78 (10%) | |
| Surgical intervention | 906 (42%) | 145 (18%) | |
## Figures
Fig. 1 Patient flow for the three datasets used in this study. DIC, disseminated intravascular coagulation; JAAM, Japanese Association for Acute Medicine; ROC, receiver operating characteristic.
<!-- image -->
Fig. 2 Receiver operating characteristic curves for original JAAM and modified JAAM-2 DIC criteria as predictors of in-hospital mortality. The solid line represents curves for JAAM-2, and the dotted line represents curves for original JAAM. (A) J-Septic DIC dataset, (B) FORECAST dataset, (C) SPICE dataset. DIC, disseminated intravascular coagulation; JAAM, Japanese Association for Acute Medicine.
<!-- image -->
Fig. 3 Adjusted estimated survival curves according to the original JAAM and modified JAAM-2 DIC status using the J-septic DIC dataset. (A) JAAM DIC score ≤ 3, (B) JAAM DIC score ≥ 4, (C) JAAM-2 DIC score ≤ 2, and (D) JAAM-2 DIC score ≥ 3. The solid line represents patients in the anticoagulant group, and the dotted line represents patients in the control group. DIC, disseminated intravascular coagulation; JAAM, Japanese Association for Acute Medicine.
<!-- image -->
Fig. 4 Adjusted estimated survival curves according to the original JAAM and modified JAAM-2 DIC status using the FORECAST dataset. (A) JAAM DIC score ≤ 3, (B) JAAM DIC score ≥ 4, (C) JAAM-2 DIC score ≤ 2, and (D) JAAM-2 DIC score ≥ 3. The solid line represents patients in the anticoagulant group, and the dotted line represents patients in the control group. DIC, disseminated intravascular coagulation; JAAM, Japanese Association for Acute Medicine.
<!-- image -->
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item-1 at level 1: title: Exploring Causal Relationships b ... erans: A Mendelian Randomization Study
item-2 at level 1: paragraph: Bihui Zhang; Department of Inter ... versity First Hospital, Beijing, China
item-3 at level 1: text: Background Thromboangiitis oblit ... ective therapeutic strategies for TAO.
item-4 at level 1: section_header: Introduction
item-5 at level 2: text: Thromboangiitis obliterans (TAO) ... d 74%, and 19 and 66%, respectively. 4
item-6 at level 2: text: An immune-mediated response is i ... ew diagnostic and therapeutic avenues.
item-7 at level 2: text: Mendelian randomization (MR) is ... levels on the risk of developing TAO.
item-8 at level 1: section_header: Study Design
item-9 at level 2: text: The current research represents ... numbers GCST90274758 to GCST90274848).
item-10 at level 2: text: Flowchart of the study is shown ... ), and ICD-10 (I73.1) classifications.
item-11 at level 1: section_header: Instrumental Variable Selection
item-12 at level 2: text: We employed comprehensive GWAS s ... (available in the online version). 14
item-13 at level 1: section_header: Statistical Analysis
item-14 at level 2: text: The random-effects inverse varia ... overlap with the FinnGen database. 20
item-15 at level 2: text: Heterogeneity among individual S ... packages in R software version 4.3.2.
item-16 at level 1: section_header: Selection of Instrumental Variables
item-17 at level 2: text: The association between 91 circu ... SNPs, and stem cell factor to 36 SNPs.
item-18 at level 1: section_header: The Causal Role of Inflammation-Related Proteins in TAO
item-19 at level 2: text: Elevated genetically predicted C ... 9) suggested an increased risk of TAO.
item-20 at level 1: section_header: Sensitivity Analysis
item-21 at level 2: text: MR–Egger regression intercepts w ... al relationships, as shown in Fig. 5 .
item-22 at level 1: section_header: MVMR Analysis
item-23 at level 2: text: Fig. 6 reveals that, even after ... stem cell factor ( p  = 0.159) on TAO.
item-24 at level 1: section_header: Discussion
item-25 at level 2: text: C–C motif chemokines, a subfamil ... a biomarker and a therapeutic target.
item-26 at level 2: text: CCL23, also known as myeloid pro ... r investigation into its precise role.
item-27 at level 2: text: GDNF was first discovered as a p ... athways, akin to its action in IBD. 34
item-28 at level 1: section_header: Figures
item-30 at level 1: picture
item-30 at level 2: caption: Fig. 1 The flowchart of the study. The whole workflow of MR analysis. GWAS, genome-wide association study; TAO, thromboangiitis obliterans; SNP, single nucleotide polymorphism; MR, Mendelian randomization.
item-32 at level 1: picture
item-32 at level 2: caption: Fig. 2 Causal relationship between circulating inflammatory proteins and TAO. TAO, thromboangiitis obliterans.
item-34 at level 1: picture
item-34 at level 2: caption: Fig. 3 Scatter plots for the causal association between circulating inflammatory proteins and TAO. TAO, thromboangiitis obliterans.
item-36 at level 1: picture
item-36 at level 2: caption: Fig. 4 Funnel plots of circulating inflammatory proteins.
item-38 at level 1: picture
item-38 at level 2: caption: Fig. 5 Leave-one-out plots for the causal association between circulating inflammatory proteins and TAO. TAO, thromboangiitis obliterans.
item-40 at level 1: picture
item-40 at level 2: caption: Fig. 6 Results from multivariable Mendelian randomization analysis on the impact of circulating inflammatory proteins on TAO, after adjusting for genetically predicted smoking. IVW, inverse variance weighting; TAO, thromboangiitis obliterans.
item-41 at level 1: section_header: References
item-42 at level 1: list: group list
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item-48 at level 2: list_item: M Kobayashi; M Ito; A Nakagawa; ... eritis obliterans). J Vasc Surg (1999)
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# Exploring Causal Relationships between Circulating Inflammatory Proteins and Thromboangiitis Obliterans: A Mendelian Randomization Study
Bihui Zhang; Department of Interventional Radiology and Vascular Surgery, Peking University First Hospital, Beijing, China; Rui He; Department of Plastic Surgery and Burn, Peking University First Hospital, Beijing, China; Ziping Yao; Department of Interventional Radiology and Vascular Surgery, Peking University First Hospital, Beijing, China; Pengyu Li; Department of Interventional Radiology and Vascular Surgery, Peking University First Hospital, Beijing, China; Guochen Niu; Department of Interventional Radiology and Vascular Surgery, Peking University First Hospital, Beijing, China; Ziguang Yan; Department of Interventional Radiology and Vascular Surgery, Peking University First Hospital, Beijing, China; Yinghua Zou; Department of Interventional Radiology and Vascular Surgery, Peking University First Hospital, Beijing, China; Xiaoqiang Tong; Department of Interventional Radiology and Vascular Surgery, Peking University First Hospital, Beijing, China; Min Yang; Department of Interventional Radiology and Vascular Surgery, Peking University First Hospital, Beijing, China
Background Thromboangiitis obliterans (TAO) is a vascular condition characterized by poor prognosis and an unclear etiology. This study employs Mendelian randomization (MR) to investigate the causal impact of circulating inflammatory proteins on TAO. Methods In this MR analysis, summary statistics from a genome-wide association study meta-analysis of 91 inflammation-related proteins were integrated with independently sourced TAO data from the FinnGen consortium's R10 release. Methods such as inverse variance weighting, MR–Egger regression, weighted median approaches, MR-PRESSO, and multivariable MR (MVMR) analysis were utilized. Results The analysis indicated an association between higher levels of C–C motif chemokine 4 and a reduced risk of TAO, with an odds ratio (OR) of 0.44 (95% confidence interval [CI]: 0.29–0.67; p = 1.4 × 10 −4 ; adjusted p = 0.013). Similarly, glial cell line-derived neurotrophic factor exhibited a suggestively protective effect against TAO (OR: 0.43, 95% CI: 0.22–0.81; p = 0.010; adjusted p = 0.218). Conversely, higher levels of C–C motif chemokine 23 were suggestively linked to an increased risk of TAO (OR: 1.88, 95% CI: 1.21–2.93; p = 0.005; adjusted p = 0.218). The sensitivity analysis and MVMR revealed no evidence of heterogeneity or pleiotropy. Conclusion This study identifies C–C motif chemokine 4 and glial cell line-derived neurotrophic factor as potential protective biomarkers for TAO, whereas C–C motif chemokine 23 emerges as a suggestive risk marker. These findings elucidate potential causal relationships and highlight the significance of these proteins in the pathogenesis and prospective therapeutic strategies for TAO.
## Introduction
Thromboangiitis obliterans (TAO), commonly referred to as Buerger's disease, is a distinct nonatherosclerotic, segmental inflammatory disorder that predominantly affects small- and medium-sized arteries and veins in both the upper and lower extremities. 1 TAO, with an annual incidence of 12.6 per 100,000 in the United States, is observed worldwide but is more prevalent in the Middle East and Far East. 1 The disease typically presents in patients <45 years of age. Despite over a century of recognition, advancements in comprehending its etiology, pathophysiology, and optimal treatment strategies have been limited. 2 3 Vascular event-free survival and amputation-free survival rates at 5, 10, and 15 years are reported at 41 and 85%, 23 and 74%, and 19 and 66%, respectively. 4
An immune-mediated response is implicated in TAO pathogenesis. 5 Recent studies have identified a balanced presence of CD4+ and CD8+ T cells near the internal lamina. Additionally, macrophages and S100+ dendritic cells are present in thrombi and intimal layers. 5 6 Elevated levels of diverse cytokines in TAO patients highlight the critical importance of inflammatory and autoimmune mechanisms. 2 7 Nonetheless, the clinical significance of these cytokines is yet to be fully understood, due to the scarcity of comprehensive experimental and clinical studies. Investigating circulating inflammatory proteins could shed light on the biological underpinnings of TAO, offering new diagnostic and therapeutic avenues.
Mendelian randomization (MR) is an approach that leverages genetic variants associated with specific exposures to infer causal relationships between risk factors and disease outcomes. 8 This method, which relies on the random distribution of genetic variants during meiosis, helps minimize confounding factors and biases inherent in environmental or behavioral influences. 9 It is particularly useful in addressing limitations of conventional observational studies and randomized controlled trials, especially for rare diseases like TAO. 10 For a robust MR analysis, three critical assumptions must be met: the genetic variants should be strongly associated with the risk factor, not linked to confounding variables, and affect the outcome solely through the risk factor, excluding any direct causal pathways. 10 In the present study, a MR was employed to evaluate the impact of genetically proxied inflammatory protein levels on the risk of developing TAO.
## Study Design
The current research represents a MR analysis conducted in accordance with STROBE-MR guidelines. 11 Genetic variants associated with circulating inflammatory proteins were identified from a comprehensive genome-wide meta-analysis, which analyzed 91 plasma proteins in a sample of 14,824 individuals of European descent, spanning 11 distinct cohorts. 12 This study utilized the Olink Target-96 Inflammation immunoassay panel to focus on 92 inflammation-related proteins. However, due to assay issues, brain-derived neurotrophic factor was subsequently removed from the panel by Olink, resulting in the inclusion of 91 proteins in the analysis. Protein quantitative trait locus (pQTL) mapping was employed to determine genetic impacts on these inflammation-related proteins. The data on these 91 plasma inflammatory proteins, including the pQTL findings, are accessible in the EBI GWAS Catalog (accession numbers GCST90274758 to GCST90274848).
Flowchart of the study is shown in Fig. 1 . Summary statistics for TAO in the genome-wide association study (GWAS) were derived from the FinnGen consortium R10 release (finngen\_R10\_I9\_THROMBANG). Launched in 2017, the FinnGen study is a comprehensive nationwide effort combining genetic information from Finnish biobanks with digital health records from national registries. 13 The GWAS included a substantial cohort of 412,181 Finnish participants, analyzing 21,311,942 variants, with TAO cases (114) and controls (381,977) identified according to International Classification of Diseases (ICD)-8 (44310), ICD-9 (4431A), and ICD-10 (I73.1) classifications.
## Instrumental Variable Selection
We employed comprehensive GWAS summary statistics for 91 inflammation-related proteins to select genetic instruments. The criteria for eligibility included: (1) single nucleotide polymorphisms (SNPs) must exhibit a genome-wide significant association with each protein ( p  < 5.0 × 10 −6 ); (2) SNPs should be independently associated with the exposure, meaning they must not be in linkage disequilibrium (defined as r 2  < 0.01, distance > 10,000 kb) with other SNPs for the same exposure; (3) the chosen genetic instruments must account for at least 0.1% of the exposure variance, ensuring sufficient strength for the genetic instrumental variables (IVs) to assess a causal effect. For each exposure, we harmonized IVs to ensure compatibility and consistency between different data sources and variables. Since smoking is a well-accepted risk factor for TAO, SNPs that were associated with smoking or thrombo-associated events were deleted for MR due to the PhenoScanner V2 database (http://www.phenoscanner.medschl.cam.ac.uk/), details are shown in Supplementary Table S1 (available in the online version). 14
## Statistical Analysis
The random-effects inverse variance weighted (IVW) method was used as the primary MR method to estimate the causal relationships between circulating inflammatory proteins and TAO. The IVW method offers a consistent estimate of the causal effect of exposure on the outcome, under the assumption that each genetic variant meets the IV criteria. 15 16 For sensitivity analysis, multiple methods, including MR–Egger regression, MR pleiotropy Residual Sum and Outlier (MR-PRESSO), and weighted median approaches, were employed in this study to examine the robustness of results. An adaptation of MR–Egger regression is capable of identifying certain violations of standard IV assumptions, providing an adjusted estimate that is unaffected by these issues. This method also measures the extent of directional pleiotropy and serves as a robustness check. 17 The weighted median is consistent even when up to 50% of the information comes from invalid IVs. 18 For SNPs numbering more than three, MRPRESSO was employed to identify and adjust for horizontal pleiotropy. This method can pinpoint horizontal pleiotropic outliers among SNPs and deliver results matching those from IVW when outliers are absent. 19 Leave-one-out analysis was conducted to determine if significant findings were driven by a single SNP. To mitigate potential pleiotropic effects attributable to smoking, a multivariable MR (MVMR) analysis incorporating adjustments for genetically predicted smoking behaviors was conducted. The GWAS data pertaining to smoking were sourced from the EBI GWAS Catalog (GCST90029014), ensuring no sample overlap with the FinnGen database. 20
Heterogeneity among individual SNP-based estimates was assessed using Cochran's Q value. In instances with only one SNP for the exposure, the Wald ratio method was applied, dividing the SNP–outcome association estimate by the SNP–exposure association estimate to determine the causal link. The F-statistic was estimated to evaluate the strength of each instrument, with an F-statistic greater than 10 indicating a sufficiently strong instrument. 21 False discovery rate (FDR) correction was conducted by the Benjamini–Hochberg method, with a FDR of adjusted p  < 0.1. A suggestive association was considered when p  < 0.05 but adjusted p ≥ 0.1. All analyses were two-sided and performed using the TwoSampleMR (version 0.5.8), MendelianRandomization (version 0.9.0), and MRPRESSO (version 1.0) packages in R software version 4.3.2.
## Selection of Instrumental Variables
The association between 91 circulating inflammatory proteins and TAO through the IVW method is detailed in Supplementary Table S2 (available in the online version). After an extensive quality control review, 173 SNPs associated with six circulating inflammation-related proteins were identified as IVs for TAO. Notably, C–C motif chemokine 23 (CCL23) levels were linked to 30 SNPs, C–C motif chemokine 25 to 37 SNPs, C–C motif chemokine 28 to 21 SNPs, C–C motif chemokine 4 (CCL4) to 27 SNPs, glial cell line-derived neurotrophic factor (GDNF) to 22 SNPs, and stem cell factor to 36 SNPs.
## The Causal Role of Inflammation-Related Proteins in TAO
Elevated genetically predicted CCL4 levels were linked to a decreased TAO risk, as shown in Fig. 2 . Specifically, each unit increase in the genetically predicted level of CCL4 was associated with an odds ratio (OR) of 0.44 (95% confidence interval [CI]: 0.29–0.67; p  = 1.4 × 10 −4 ; adjusted p  = 0.013) for TAO. Similarly, levels of C–C motif chemokine 28 (OR: 0.33; 95% CI: 0.12–0.91; p  = 0.034; adjusted p  = 0.579), GDNF (OR: 0.43, 95% CI: 0.22–0.81; p  = 0.010; adjusted p  = 0.218), and stem cell factor (OR: 0.49, 95% CI: 0.29–0.84; p  = 0.009; adjusted p  = 0.218) also showed a suggestive inverse association with TAO, as depicted in Fig. 2 . Conversely, higher levels of genetically predicted CCL23 (OR: 1.88, 95% CI: 1.21–2.93; p  = 0.005; adjusted p  = 0.218) and C–C motif chemokine 25 (OR: 1.44, 95% CI: 1.01–2.06; p  = 0.046; adjusted p  = 0.579) suggested an increased risk of TAO.
## Sensitivity Analysis
MR–Egger regression intercepts were not significantly different from zero, suggesting no horizontal pleiotropy (all intercept p  > 0.05), as depicted in Fig. 2 . The MR-PRESSO test also found no pleiotropic outliers among these SNPs ( p  > 0.05), further corroborating the absence of pleiotropy. Consistency with these findings was confirmed by the weighted median approach. Scatter plots illustrating the genetic associations with circulating inflammatory proteins and TAO are presented in Fig. 3 . Cochran's Q test detected no heterogeneity among the genetic IVs for the measured levels (all p  > 0.1). Additionally, funnel plots showed no significant asymmetry, suggesting negligible publication bias and directional horizontal pleiotropy ( Fig. 4 ). The robustness of these causal estimates was further validated by a leave-one-out analysis, demonstrating that no single IV disproportionately influenced the observed causal relationships, as shown in Fig. 5 .
## MVMR Analysis
Fig. 6 reveals that, even after adjusting for genetically predicted smoking, the level of CCL4 still exerts a direct protective influence against TAO (IVW: OR= 0.54, p  = 0.009; MR–Egger: p  = 0.013, intercept p  = 0.843). The level of CCL23 is suggestively associated with an increased risk of TAO (IVW: OR = 2.24, p  = 0.011; MR–Egger: p  = 0.019, intercept p  = 0.978). GDNF levels show a suggestively protective effect against TAO (IVW: OR = 0.315, p  = 0.016; MR–Egger: p  = 0.020, intercept p  = 0.634). However, no significant direct impacts were observed for the levels of C–C motif chemokine 25 ( p  = 0.079), C–C motif chemokine 28 ( p  = 0.179), or stem cell factor ( p  = 0.159) on TAO.
## Discussion
C–C motif chemokines, a subfamily of small, secreted proteins, engage with G protein-coupled chemokine receptors on the cell surface, which are distinguished by directly juxtaposed cysteines. 22 Their renowned function is to orchestrate cell migration, particularly of leukocytes, playing crucial roles in both protective and destructive immune and inflammatory responses. 23 CCL4, also known as the macrophage inflammatory protein, is a significant member of the CC chemokine family. This protein, encoded by the CCL4 gene in humans, interacts with CCR5 and is identified as a pivotal human immunodeficiency virus-suppressive factor secreted by CD8+ T-cells. 24 Additionally, its involvement has been increasingly recognized in cardiovascular diseases. 23 While CCL4 exhibits a protective effect in Type 1 diabetes mellitus patients, it is also found to be elevated in conditions such as atherosclerosis and myocardial infarction. 23 CCL4's ability to activate PI3K and MAPK signaling pathways and inhibit the NF-κB pathway contributes to the enhanced proliferation of porcine uterine luminal epithelial cells. 25 This mechanism may elucidate CCL4's protective role in TAO, as demonstrated in this study (OR: 0.44; 95% CI: 0.29–0.67; p  = 1.4 × 10 −4 ; adjusted p  = 0.013), highlighting its potential as both a biomarker and a therapeutic target.
CCL23, also known as myeloid progenitor inhibitory factor-1, represents another key member of the CC chemokine subfamily. It plays a role in the inflammatory process, capable of inhibiting the release of polymorphonuclear leukocytes from the bone marrow. 26 As a relatively novel chemokine, CCL23's biological significance remains partially unexplored. 27 Circulating CCL23 exhibited a continuous increase from baseline to 24 hours in ischemic stroke patients and could predict the clinical outcome after 3 months. 28 Elevated blood levels of CCL23 have been linked with antineutrophil cytoplasmic antibody-associated vasculitis. 29 Although its mechanisms are largely uncharted, CCL23 is known to facilitate the chemotaxis of human THP-1 monocytes, increase adhesion molecule CD11c expression, and stimulate MMP-2 release from THP-1 monocytes. 30 Moreover, CCL23 can enhance leucocyte trafficking and direct the migration of monocytes, macrophages, dendritic cells, and T lymphocytes. 27 This study posits CCL23 as a suggestive risk factor for TAO (OR: 1.88, 95% CI: 1.21–2.93; p  = 0.005; adjusted p  = 0.218), warranting further investigation into its precise role.
GDNF was first discovered as a potent survival factor for midbrain dopaminergic neurons and has shown promise in preserving these neurons in animal models of Parkinson's disease. 31 Recent studies have further elucidated GDNF's significance in neuronal safeguarding and cerebral recuperation. 32 Additionally, GDNF has been implicated in inflammatory bowel disease (IBD), where it bolsters the integrity of the intestinal epithelial barrier and facilitates wound repair, while also exerting an immunomodulatory influence. 33 34 In our study, GDNF is identified as a potential protective agent against TAO, with an OR of 0.43 (95% CI: 0.22–0.81; p  = 0.010; adjusted p  = 0.218). It is postulated that GDNF's protective mechanism in TAO may involve the inhibition of apoptosis through the activation of MAPK and AKT pathways, akin to its action in IBD. 34
## Figures
Fig. 1 The flowchart of the study. The whole workflow of MR analysis. GWAS, genome-wide association study; TAO, thromboangiitis obliterans; SNP, single nucleotide polymorphism; MR, Mendelian randomization.
<!-- image -->
Fig. 2 Causal relationship between circulating inflammatory proteins and TAO. TAO, thromboangiitis obliterans.
<!-- image -->
Fig. 3 Scatter plots for the causal association between circulating inflammatory proteins and TAO. TAO, thromboangiitis obliterans.
<!-- image -->
Fig. 4 Funnel plots of circulating inflammatory proteins.
<!-- image -->
Fig. 5 Leave-one-out plots for the causal association between circulating inflammatory proteins and TAO. TAO, thromboangiitis obliterans.
<!-- image -->
Fig. 6 Results from multivariable Mendelian randomization analysis on the impact of circulating inflammatory proteins on TAO, after adjusting for genetically predicted smoking. IVW, inverse variance weighting; TAO, thromboangiitis obliterans.
<!-- image -->
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- C E Hughes; R JB Nibbs. A guide to chemokines and their receptors. FEBS J (2018)
- T T Chang; J W Chen. Emerging role of chemokine CC motif ligand 4 related mechanisms in diabetes mellitus and cardiovascular disease: friends or foes?. Cardiovasc Diabetol (2016)
- S G Irving; P F Zipfel; J Balke. Two inflammatory mediator cytokine genes are closely linked and variably amplified on chromosome 17q. Nucleic Acids Res (1990)
- W Lim; H Bae; F W Bazer; G Song. Characterization of C-C motif chemokine ligand 4 in the porcine endometrium during the presence of the maternal-fetal interface. Dev Biol (2018)
- C H Shih; S F van Eeden; Y Goto; J C Hogg. CCL23/myeloid progenitor inhibitory factor-1 inhibits production and release of polymorphonuclear leukocytes and monocytes from the bone marrow. Exp Hematol (2005)
- D Karan. CCL23 in balancing the act of endoplasmic reticulum stress and antitumor immunity in hepatocellular carcinoma. Front Oncol (2021)
- A Simats; T García-Berrocoso; A Penalba. CCL23: a new CC chemokine involved in human brain damage. J Intern Med (2018)
- M Brink; E Berglin; A J Mohammad. Protein profiling in presymptomatic individuals separates myeloperoxidase-antineutrophil cytoplasmic antibody and proteinase 3-antineutrophil cytoplasmic antibody vasculitides. Arthritis Rheumatol (2023)
- C S Kim; J H Kang; H R Cho. Potential involvement of CCL23 in atherosclerotic lesion formation/progression by the enhancement of chemotaxis, adhesion molecule expression, and MMP-2 release from monocytes. Inflamm Res (2011)
- M Saarma; H Sariola. Other neurotrophic factors: glial cell line-derived neurotrophic factor (GDNF). Microsc Res Tech (1999)
- Z Zhang; G Y Sun; S Ding. Glial cell line-derived neurotrophic factor and focal ischemic stroke. Neurochem Res (2021)
- H Chen; T Han; L Gao; D Zhang. The involvement of glial cell-derived neurotrophic factor in inflammatory bowel disease. J Interferon Cytokine Res (2022)
- M Meir; S Flemming; N Burkard; J Wagner; C T Germer; N Schlegel. The glial cell-line derived neurotrophic factor: a novel regulator of intestinal barrier function in health and disease. Am J Physiol Gastrointest Liver Physiol (2016)

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item-12 at level 2: list_item: MN Seleh; MB Khazaeli; RH Wheele ... etastatic melanoma.. Cancer Res (1992)

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# Bilateral External Ophthalmoplegia Induced by Herpes Zoster Ophthalmicus
Fumitaka Shimizu; Department of Neurology and Clinical Neuroscience, Yamaguchi University Graduate School of Medicine, Japan
## References
- T Shima; K Yamashita; K Furuta; . Right-sided herpes zoster ophthalmicus complicated by bilateral third, fourth, and sixth cranial nerve palsies and syndrome of inappropriate antidiuretic hormone secretion.. Intern Med (2024)
- N Yuki. Infectious origins of, and molecular mimicry in, Guillain-Barré and Fisher syndromes.. Lancet Infect Dis (2001)
- AC Rizzo; M Ulivi; N Brunelli; . A case of Miller Fisher syndrome associated with preceding herpes zoster ophthalmicus.. Muscle Nerve (2017)
- CC Wang; JC Shiang; JT Chen; SH Lin. Syndrome of inappropriate secretion of antidiuretic hormone associated with localized herpes zoster ophthalmicus.. J Gen Intern Med (2011)
- S Fujiwara; Y Manabe; Y Nakano; Y Omote; H Narai; K Abe. A case of Miller-Fisher syndrome with syndrome of inappropriate secretion of antidiuretic hormone.. Case Rep Neurol (2021)
- A Benkirane; F London. Miller Fisher syndrome complicated by inappropriate secretion of antidiuretic hormone: a case report.. Acta Neurol Belg (2022)
- T Murakami; A Yoshihara; S Kikuchi; M Yasuda; A Hoshi; Y Ugawa. A patient with Fisher syndrome and pharyngeal-cervical-brachial variant of Guillain-Barré syndrome having a complication of SIADH.. Rinsho Shinkeigaku (2013)
- MN Seleh; MB Khazaeli; RH Wheeler; . Phase I trial of the murine monoclonal anti-GD2 antibody 14G2a in metastatic melanoma.. Cancer Res (1992)

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item-0 at level 0: unspecified: group _root_
item-1 at level 1: title: An In-depth Single-center Retros ... ion Patients with and without Diabetes
item-2 at level 1: paragraph: Sho Hitomi; Division of Cardiolo ... icine, Iwate Medical University, Japan
item-3 at level 1: text: Objective This study examined va ... ial, particularly in patients with DM.
item-4 at level 1: section_header: Introduction
item-5 at level 2: text: Diabetes mellitus (DM) is widely ... also been employed in such cases (1).
item-6 at level 2: text: Acute myocardial infarction (AMI ... found to have DM as a comorbidity (2).
item-7 at level 2: text: With the widespread adoption of ... o be worse within this population (6).
item-8 at level 1: section_header: Study population
item-9 at level 2: text: The study population comprised p ... he 3rd universal definition of MI (7).
item-10 at level 2: text: AMI was diagnosed based on the e ... hen biomarker values were unavailable.
item-11 at level 1: section_header: Definition
item-12 at level 2: text: The definition of each parameter ... 73 m2 upon admission (12) or dialysis.
item-13 at level 1: section_header: Patient and clinical characteristics
item-14 at level 2: text: The participants were stratified ... s on admission between the two groups.
item-15 at level 1: section_header: Patient management and overall in-hospital outcomes
item-16 at level 2: text: A detailed comparison of the pat ... e frequently than in the non-DM group.
item-17 at level 2: text: Regarding mechanical support, pa ... fidence interval: 1.19-2.93, p=0.007).
item-18 at level 1: section_header: An in-depth analysis of the caus ... hospital deaths and predictive factors
item-19 at level 2: text: The in-hospital mortality rate o ... cal complications remained high (65%).
item-20 at level 2: text: Factors potentially associated w ... g the surviving and deceased patients.
item-21 at level 2: text: The predictors of in-hospital mo ... ” and “a history of stroke” (p=0.006).
item-22 at level 1: section_header: Discussion
item-23 at level 2: text: In Japan, there is a limited amo ... itable for in-depth research analyses.
item-24 at level 2: text: Although previous studies have d ... ion to further improve survival rates.
item-25 at level 2: text: In the DM group, we observed a h ... ures between the DM and non-DM groups.
item-26 at level 2: text: CS continues to be a significant ... outcomes of patients experiencing CS.
item-27 at level 1: section_header: Study limitations
item-28 at level 2: text: Several limitations associated w ... nclude this parameter in our analysis.
item-29 at level 1: section_header: Tables
item-31 at level 1: table with [31x9]
item-31 at level 2: caption: Table 1. A Comparison of Baseline Clinical Characteristics between the DM or Non-DM Groups.
item-33 at level 1: table with [13x9]
item-33 at level 2: caption: Table 2. A Comparison of Patient Management between the Two Groups.
item-35 at level 1: table with [20x13]
item-35 at level 2: caption: Table 3. Comparisons of Clinical Characteristics between AMI Patients Who Survived and Those Who Died from Both the DM and Non-DM Groups.
item-37 at level 1: table with [12x13]
item-37 at level 2: caption: Table 4. Univariate Analyses for In-hospital Death.
item-39 at level 1: table with [12x13]
item-39 at level 2: caption: Table 5. Multivariate Analyses for In-hospital Death.
item-40 at level 1: section_header: Figures
item-42 at level 1: picture
item-42 at level 2: caption: Figure 1. Thirty-day cumulative survival rates in patients with acute myocardial infarction, stratified by DM status. Patients with DM (shown in red) had a significantly lower survival rate than those without DM (shown in blue).
item-44 at level 1: picture
item-44 at level 2: caption: Figure 2. Pie charts illustrating the causes of death in each group reveal differences. In the DM group, a higher percentage of non-cardiac deaths, such as infections, malignancies, strokes, and multiple organ failures, was observed than in the non-DM group (32% vs. 14%, respectively), with a statistically significant difference (p=0.046). Among the six cases of mechanical complications in patients who died in the DM group, there were two cases of ventricular septal rupture (VSR) and four cases of free-wall rupture (FWR). In the non-DM group, out of the 12 cases of mechanical complications in deceased patients, there were 4 cases of VSR, 7 of FMR, and 1 of papillary muscle rupture (PMR).
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# An In-depth Single-center Retrospective Assessment of In-hospital Outcomes in Acute Myocardial Infarction Patients with and without Diabetes
Sho Hitomi; Division of Cardiology, Department of Internal Medicine, Iwate Medical University, Japan; Yorihiko Koeda; Division of Cardiology, Department of Internal Medicine, Iwate Medical University, Japan; Kengo Tosaka; Division of Cardiology, Department of Internal Medicine, Iwate Medical University, Japan; Nozomu Kanehama; Division of Cardiology, Department of Internal Medicine, Iwate Medical University, Japan; Masanobu Niiyama; Department of Cardiology, Japanese Red Cross Hachinohe Hospital, Japan; Masaru Ishida; Division of Cardiology, Department of Internal Medicine, Iwate Medical University, Japan; Tomonori Itoh; Division of Cardiology, Department of Internal Medicine, Iwate Medical University, Japan; Yoshihiro Morino; Division of Cardiology, Department of Internal Medicine, Iwate Medical University, Japan
Objective This study examined variations in in-hospital mortality causes and identified independent mortality predictors among patients with acute myocardial infarction (AMI) with and without diabetes mellitus (DM). Methods We examined factors influencing in-hospital mortality in a single-center retrospective observational study. Separate multivariate analyses were conducted for both groups to identify independent predictors of in-hospital mortality. Patients This study included consecutive patients admitted to Iwate Medical University Hospital between January 2012 and December 2017 with a diagnosis of AMI. Results Of 1,140 patients meeting the AMI criteria (average age: 68.2±12.8 years old, 75% men), 408 (35.8%) had diabetes. The DM group had a 1.87-times higher 30-day mortality rate, a lower prevalence of ST-elevated MI (56.6% vs. 65.3% in non-DM, p=0.004), and more frequent non-cardiac causes of death (32% vs. 14% in non-DM, p=0.046) than the non-DM group. Independent predictors of in-hospital mortality in both groups were cardiogenic shock (CS) [DM: hazard ratio (HR) 6.59, 95% confidence interval (CI) 2.90-14.95; non-DM: HR 4.42, 95% CI 1.99-9.77] and renal dysfunction (DM: HR 5.64, 95% CI 1.59-20.04; non-DM: HR 5.92, 95% CI 1.79-19.53). Among patients with DM, a history of stroke was an additional independent predictor of in-hospital mortality (HR 2.59, 95% CI 1.07-6.31). Conclusion Notable disparities were identified in the causes of death and predictive factors of mortality between these two groups of patients with AMI. To further improve AMI outcomes, individualized management and prioritizing non-cardiac comorbidities during hospitalization may be crucial, particularly in patients with DM.
## Introduction
Diabetes mellitus (DM) is widely recognized as being associated with poor clinical scenarios across various facets of ischemic heart disease. Indeed, it is a significant risk factor for coronary disease. Furthermore, atherosclerotic changes in coronary arteries tend to exhibit a more extensive distribution in individuals with DM than in those without DM. Following revascularization, a higher occurrence of restenosis or major adverse clinical events is expected in patients with DM than in those without DM during the follow-up period. Drug-eluting stents have also been employed in such cases (1).
Acute myocardial infarction (AMI) has remained a significant contributor to mortality worldwide. Based on an observational multicenter registry in Japan, 36.4% of AMI patients were found to have DM as a comorbidity (2).
With the widespread adoption of primary coronary intervention (PCI), AMI mortality has substantially declined. The current nationwide registry database in Japan has indicated that the mortality rate could be reduced to less than 3% if AMI patients were to receive primary PCI (3). However, a German study that compared outcomes between 2005 and 2021 highlighted that the rates of in-hospital death remained significantly higher in myocardial infarction (MI) patients with DM than in those without DM, despite an overall reduction in in-hospital mortality (4). Even with the contemporary utilization of primary PCI, patients with DM still experience higher in-hospital mortality than those without DM (5), and the long-term prognosis has also been observed to be worse within this population (6).
## Study population
The study population comprised patients admitted to Iwate Medical University Hospital between January 2012 and December 2017 due to AMI, specifically those who met the criteria outlined in the 3rd universal definition of MI (7).
AMI was diagnosed based on the evidence of myocardial necrosis in patients with acute myocardial ischemia in a clinical setting. The criteria for detection included the presence of a rise and/or fall in cardiac biomarker values, with at least one value exceeding the 99th percentile upper reference limit. In addition, at least one of the following conditions had to be met: 1) symptoms of ischemia; 2) new or presumed new significant ST-segment-T wave changes or new left bundle branch block; 3) development of a pathological Q wave on an electrocardiogram (ECG); 4) imaging evidence of new loss of viable myocardium or new regional wall motion abnormality; and 5) identification of an intracoronary thrombus by angiography or autopsy. Furthermore, in accordance with the classification of MI (types 1 to 5) as defined by the 3rd universal definition (7), the patients were classified as either type 1, spontaneous MI; type 2, MI secondary to an ischemia imbalance; or MI resulting in death when biomarker values were unavailable.
## Definition
The definition of each parameter used in this study was established by referring to previous studies that are widely regarded as representative in the field. Hypertension was defined (in accordance with the ACC/AHA Stage 2 hypertension guidelines) as a systolic blood pressure of ≥140 mmHg, a diastolic blood pressure of ≥90 mmHg upon admission, or the use of antihypertensive medication (8). Diabetes was defined as a blood sugar level ≥200 mg/dL upon admission, an HbA1c level of ≥6.5%, or the administration of diabetes medication (9). For cases that did not meet this definition, fasting blood sugar, daily blood sugar fluctuation, and glucose tolerance tests were not conducted. Dyslipidemia was defined in line with the guidelines in Japan as low-density lipoprotein (LDL)-cholesterol ≥140 mg/dL or high-density lipoprotein (HDL)-cholesterol <40 mg/dL (10) and included a total cholesterol level of ≥240 mg/dL or the administration of lipid-lowering drugs. A history of ischemic heart disease was defined as a history of AMI or revascularization (PCI or CABG). A current smoking habit was defined as smoking within the year prior to admission. A history of stroke was defined as any past stroke that required hospitalization, including cerebral infarction and intracranial hemorrhaging. Consequently, incidental asymptomatic lacunar infarctions identified on imaging were excluded. Atrial fibrillation was defined as any history of treatment, regardless of whether it was chronic or paroxysmal, or any evidence of atrial fibrillation found on previous Holter monitoring or a 12-lead ECG. Cases of transient paroxysmal atrial fibrillation observed during hospitalization without a previous record were not included. However, those with consistent atrial fibrillation waveforms upon admission, even without prior records, were included. Obesity was defined as a body mass index (BMI) ≥25.0 kg/m2 or higher upon admission (11). Renal dysfunction was defined as an estimated glomerular filtration rate (eGFR) of <60 mL/min/1.73 m2 upon admission (12) or dialysis.
## Patient and clinical characteristics
The participants were stratified into two groups based on the presence of DM. Table 1 shows a comparison of the baseline clinical characteristics between the DM and non-DM groups. The DM group had a significantly higher BMI and a higher prevalence of hyperlipidemia than the non-DM group. In contrast, a current smoking habit and hypertension were significantly more prevalent in the non-DM group than in the DM group. Regarding the history of major vascular diseases, a history of coronary artery disease or stroke was significantly more frequent in the DM group than in the non-DM group. Importantly, the frequency of cardiac arrest on admission was significantly higher in the DM group than in the non-DM group, despite no apparent differences in systolic and diastolic blood pressure values on admission between the two groups.
## Patient management and overall in-hospital outcomes
A detailed comparison of the patient management strategies is presented in Table 2. In the DM group, emergent coronary angiography and PCI were performed significantly less frequently than in the non-DM group. In addition, the prevalence of lesions involving the left main coronary artery was significantly higher in the DM group than in the non-DM group. Furthermore, patients in the DM group underwent CABG significantly more frequently than in the non-DM group.
Regarding mechanical support, patients in the DM group received significantly more frequent treatments with mechanical ventilation, intra-aortic balloon pump, and veno-atrial extracorporeal membrane oxygenation than in the non-DM group. As a result, in-hospital mortality was significantly higher and the length of hospital stay significantly longer in the DM group than in the non-DM group. The Kaplan-Meier survival curve of in-hospital mortality (within 30 days after admission), illustrated in Fig. 1, shows a significant difference between the two groups. The HR for in-hospital mortality in the DM group was 1.87 (95% confidence interval: 1.19-2.93, p=0.007).
## An in-depth analysis of the causes of in-hospital deaths and predictive factors
The in-hospital mortality rate of the 1,140 patients included in this study was 6.6%. A comparison of the causes of in-hospital death between the two groups is shown in Fig. 2. Nearly half of the causes were attributed to cardiogenic shock (CS) in both groups. However, the remaining causes of death appeared to differ between the two groups, particularly in terms of mechanical complications, infection, and malignant disease. When comparing the causes of death, a higher proportion of non-cardiac deaths (including infections, malignancies, strokes, and multiple organ failures) were observed in the DM group than in the non-DM group (32% vs. 14%, p=0.046, respectively). Deaths due to mechanical complications were more frequent in the non-DM group than in the DM group; however, the difference was not statistically significant (DM: 16% vs. non-DM: 32%, p=0.119). When examining the relationship between the timing of death and its causes, it was found that, for both groups, the majority of deaths until the third clinical day were predominantly due to CS or mechanical complications. However, regarding the causes of death after the tenth clinical day, in the DM group, the proportion of deaths attributed to CS or mechanical complications was <30%, with a greater number of patients dying from other causes, such as lethal arrhythmias, cerebral infarction, infections, and malignancies. In contrast, in the non-DM group, the proportion of patients dying from CS or mechanical complications remained high (65%).
Factors potentially associated with in-hospital mortality were individually compared between those who survived and those who died in both the DM and non-DM groups (Table 3). In the DM group, statistically significant differences were observed in the age, hypertension, current smoking habit, history of stroke, Killip status, ejection fraction on admission, renal dysfunction (eGFR <60 mL/min/1.73 m2), and serum BNP levels between the survival and in-hospital death subgroups. However, these factors showed slight variation in the non-DM group. Interestingly, the sex, history of atrial fibrillation, and ST elevation were also found to be significantly different factors in the non-DM group. Notably, in the non-DM group, a history of stroke no longer had a significant impact on in-hospital death when comparing the surviving and deceased patients.
The predictors of in-hospital mortality for both the DM and non-DM groups were analyzed separately using univariate analyses, and the results are shown in Table 4. Any factors found to be statistically significant in the univariate analyses in either group were included in the Cox's proportional hazards model to identify independent predictors of in-hospital death, as shown in Table 5. Consequently, CS, renal dysfunction, and a history of stroke independently predicted in-hospital mortality in the DM group. Conversely, CS and renal dysfunction were independent predictors of in-hospital mortality in the non-DM group. Furthermore, in the non-DM group, patients who underwent revascularization (emergency PCI or CABG) had a lower risk of in-hospital mortality than those who did not, but this difference was not statistically significant in the DM group. An interaction test for in-hospital death using a two-way analysis of variance showed that there was an interaction between “DM” and “a history of stroke” (p=0.006).
## Discussion
In Japan, there is a limited amount of research regarding predictors of in-hospital mortality for AMI based on the presence or absence of DM or differences in the specific causes of death. Although we have conducted large-scale studies related to AMI in Japan, such as the JROAD registry (13) and the J-PCI Registry (3), these registry surveys are limited in terms of available parameters, and it is speculated that they may not be suitable for in-depth research analyses.
Although previous studies have documented an adverse prognosis in AMI patients with comorbid diabetes mellitus (5,14,15), our study adds clarity by demonstrating a substantial impact on mortality. Notably, we identified an HR of 1.87 for mortality, even in a cohort in which nearly 85% of the patients underwent invasive strategies. Cardiovascular deaths (including those due to CS, mechanical complications, and lethal arrhythmias) accounted for nearly 85% of in-hospital fatalities in the non-DM group. Conversely, they constituted only 68% of in-hospital deaths in the DM group, signifying a higher prevalence of noncardiovascular causes of mortality in this population. Infections, strokes, and malignancies have emerged as direct causes of death in this subgroup. Considering the systemic nature of disorders in patients with DM, these results are not surprising, but they underscore the importance of comprehensive care or systemic management for this population to further improve survival rates.
In the DM group, we observed a higher prevalence of an advanced Killip status upon admission and a greater frequency of mechanical cardiac support devices than in the non-DM group. Consequently, the patients' conditions tended to deteriorate further from the time of admission than in the non-DM group. Nevertheless, the DM group exhibited lower actual rates of both coronary angiography and revascularization than the non-DM group, similar to a previous report (16). The lower frequency of ST-elevation MI and an increased possibility of asymptomatic patients, concerns related to an impaired renal function, as well as the use of contrast agents may partially explain the lower rate of emergent angiography. The higher frequency of left main coronary artery involvement in the DM group than in the non-DM group led to the reduced use of PCI and increased use of CABG. These factors are hypothesized to not only influence the lower frequency of revascularization procedures but also explain the divergent prognostic outcomes of these procedures between the DM and non-DM groups.
CS continues to be a significant factor influencing mortality in patients with AMI, as supported by numerous previous studies (17-19). However, our findings revealed that there was no marked difference in the in-hospital mortality between DM and non-DM patients with CS, consistent with a previous study (20). Because the management of CS remains a paramount concern in both groups, alternative approaches, such as the utilization of left ventricle unloading devices (21,22) or intracoronary supersaturated oxygen therapy (23), should be explored to enhance the outcomes of patients experiencing CS.
## Study limitations
Several limitations associated with the present study warrant mention. In this study, diabetes was defined according to criteria established from prior AMI research and existing literature. Intraday glucose variability and oral glucose tolerance tests were not performed, potentially leading to some cases of diabetes or impaired glucose tolerance being categorized as nondiabetic. This limitation was inherent to this study. Furthermore, despite the availability of the latest 4th version of the universal definition (24), we chose to apply the 3rd version of the universal definition (7) in this study. This decision was made because the 3rd definition was used during the recruitment period. However, it is important to acknowledge that the composition of the enrolled patient population may not have substantially differed; therefore, we employed the 3rd universal definition (25). Finally, the door-to-balloon time is a well-established mortality parameter in AMI patients (26). However, our study had a substantial proportion of NSTEMI patients (approximately 40%); therefore, we did not include this parameter in our analysis.
## Tables
Table 1. A Comparison of Baseline Clinical Characteristics between the DM or Non-DM Groups.
| Variables | | Total (n=1140) | | DM (n=408) | | Non-DM (n=732) | | p value |
|-----------------------------------|----|-------------------|----|--------------------|----|-------------------|----|-----------|
| Age (years) | | 68.2±12.8 | | 68.3±12.1 | | 68.0±13.1 | | 0.977 |
| Sex (male) | | 76.0% | | 74.8% | | 76.6% | | 0.475 |
| BMI (kg/m2) | | 24.4±4.0 | | 25.0±4.2 | | 24.0±3.8 | | <0.001 |
| Obesity (BMI≥25) | | 38.1% | | 45.6% | | 34.6% | | <0.001 |
| DM | | 35.8% | | 100% | | 0 | | |
| Hypertension | | 69.9% | | 78.7% | | 64.9% | | <0.001 |
| Dyslipidemia | | 51.3% | | 62.8% | | 44.8% | | <0.001 |
| Current smoker | | 34.9% | | 34.4% | | 35.2% | | 0.784 |
| History of CAD | | 13.5% | | 21.4% | | 9.0% | | <0.001 |
| History of stroke | | 11.5% | | 15.3% | | 9.4% | | 0.005 |
| History of atrial fibrillation | | 7.9% | | 9.1% | | 7.2% | | 0.266 |
| CPA on admission | | 5.3% | | 7.2% | | 4.2% | | 0.029 |
| Systolic BP on admission (mmHg) | | 145±34 | | 144±34 | | 145±34 | | 0.517 |
| Diastolic BP on admission (mmHg) | | 85±21 | | 83±21 | | 86±22 | | 0.027 |
| HR on admission (bpm) | | 81±19 | | 82±20 | | 81±19 | | 0.105 |
| STEMI | | 62.2% | | 56.6% | | 65.3% | | 0.004 |
| Killip I-IV (%) | | 71.6/14.3/5.5/8.6 | | 64.2/16.3/7.9/11.6 | | 75.8/13.1/4.1/6.9 | | <0.001 |
| LVEF (%) | | 51.4±19.7 | | 49.6±28.9 | | 52.3±11.6 | | <0.001 |
| Serum creatinine (mg/dL) | | 1.24±1.65 | | 1.57±2.12 | | 1.06±1.29 | | 0.008 |
| eGFR (mL/min/1.73 m2) | | 68.0±28.5 | | 64.3±33.0 | | 70.1±25.5 | | 0.003 |
| Renal dysfuncti on (eGFR<60) | | 36.9% | | 44.4% | | 32.8% | | <0.001 |
| Hemodialysis or CAPD | | 4.4% | | 8.1% | | 2.3% | | <0.001 |
| Blood glucose (mg/dL) | | 163±78 | | 207±97 | | 140±50 | | <0.001 |
| Hemoglobin A1c (%) | | 6.1±1.5 | | 7.1±1.5 | | 5.6±0.5 | | <0.001 |
| Triglyceride (mg/dL) | | 127±111 | | 138±146 | | 120±84 | | 0.002 |
| Total cholesterol (mg/dL) | | 186±45 | | 179±49 | | 189±42 | | <0.001 |
| LDL-cholesterol (mg/dL) | | 115±37 | | 109±37 | | 119±37 | | <0.001 |
| HDL-cholesterol (mg/dL) | | 47±14 | | 45±15 | | 48±14 | | <0.001 |
| L/H ratio | | 2.6±1.0 | | 2.5±1.0 | | 2.6±1.0 | | 0.144 |
| Brain natriuretic peptide (pg/mL) | | 381±800 | | 511±975 | | 308±673 | | 0.001 |
Table 2. A Comparison of Patient Management between the Two Groups.
| Variables | | Total (n=1,140) | | DM (n=408) | | Non-DM (n=732) | | p value |
|-------------------------------------|----|-------------------|----|--------------|----|------------------|----|-----------|
| Emergency coronary angiography | | 87.8% | | 84.2% | | 89.7% | | 0.007 |
| Lesion of left main trunk | | 9.7% | | 13.9% | | 7.3% | | 0.001 |
| Multivessel coronary artery disease | | 59.6% | | 71.4% | | 53.0% | | <0.001 |
| Emergency PCI | | 78.4% | | 73.8% | | 81.0% | | 0.004 |
| Slow-flow or no-reflow post PCI | | 14.2% | | 16.1% | | 13.1% | | 0.203 |
| Coronary artery bypass grafting | | 9.2% | | 12.3% | | 7.5% | | 0.008 |
| Respirator | | 10.5% | | 14.2% | | 8.5% | | 0.004 |
| Intra-aortic balloon pumping | | 11.4% | | 16.7% | | 8.4% | | <0.001 |
| VA-ECMO | | 2.0% | | 3.3% | | 1.3% | | 0.023 |
| Peak creatine kinase (IU/L) | | 2,198±2,758 | | 2,073±2,850 | | 2,269±2,705 | | 0.003 |
| Hospitalization days | | 19±48 | | 20±22 | | 18±57 | | 0.002 |
| In-hospital mortality | | 6.6% | | 9.3% | | 5.1% | | 0.005 |
Table 3. Comparisons of Clinical Characteristics between AMI Patients Who Survived and Those Who Died from Both the DM and Non-DM Groups.
| Variables | | DM (n=408) | DM (n=408) | DM (n=408) | DM (n=408) | DM (n=408) | | Non-DM (n=732) | Non-DM (n=732) | Non-DM (n=732) | Non-DM (n=732) | Non-DM (n=732) |
|-------------------------------------------|----|-------------------|--------------|--------------------------|--------------|--------------|----|-------------------|------------------|--------------------------|------------------|------------------|
| Variables | | Survivors (n=370) | | In-hospital death (n=38) | | p value | | Survivors (n=695) | | In-hospital death (n=37) | | p value |
| Age (years) | | 67.9±12.3 | | 72.8±9.6 | | 0.025 | | 67.6±13.0 | | 75.7±12.7 | | <0.001 |
| Sex (male) | | 74.3% | | 78.9% | | 0.532 | | 77.6% | | 59.5% | | 0.011 |
| Obesity (BMI≥25) | | 46.1% | | 40.5% | | 0.520 | | 35.1% | | 24.2% | | 0.199 |
| Hypertension | | 80.3% | | 63.2% | | 0.014 | | 64.0% | | 82.9% | | 0.022 |
| Dyslipidemia | | 63.3% | | 57.9% | | 0.510 | | 45.1% | | 40.0% | | 0.556 |
| Current smoker | | 35.9% | | 14.3% | | 0.020 | | 36.1% | | 14.3% | | 0.018 |
| History of CAD | | 21.9% | | 16.2% | | 0.422 | | 8.7% | | 14.3% | | 0.263 |
| History of stroke | | 13.8% | | 28.6% | | 0.022 | | 9.7% | | 3.2% | | 0.227 |
| History of atrial fibrillation | | 8.4% | | 15.8% | | 0.130 | | 6.1% | | 27.8% | | <0.001 |
| Cardiogenic shock (Killip IV) | | 6.5% | | 62.2% | | <0.001 | | 4.5% | | 52.8% | | <0.001 |
| STEMI | | 55.4% | | 68.4% | | 0.123 | | 64.2% | | 86.1% | | 0.007 |
| LVEF (%) | | 49.5±12.0 | | 50.8±89.3 | | <0.001 | | 52.7±11.4 | | 44.2±13.8 | | <0.001 |
| Blood glucose (mg/dL) | | 200±88 | | 282±149 | | 0.001 | | 128±27 | | 133±47 | | 0.252 |
| Renal dysfunction (eGFR<60) | | 40.5% | | 81.6% | | <0.001 | | 39.6% | | 84.8% | | <0.001 |
| Brain natriuretic peptide (pg/mL) | | 463.6±935.3 | | 972.4±1225.5 | | <0.001 | | 279.8±625.9 | | 834.9±1146.0 | | <0.001 |
| Revascularization (emergency PCI or CABG) | | 82.7% | | 73.7% | | 0.169 | | 86.3% | | 62.2% | | <0.001 |
| - Emergency PCI | | 74.1% | | 71.1% | | 0.689 | | 82.4% | | 54.1% | | <0.001 |
| - CABG | | 13.2% | | 2.6% | | 0.057 | | 7.3% | | 10.8% | | 0.435 |
Table 4. Univariate Analyses for In-hospital Death.
| Variables | | DM (n=408) | DM (n=408) | DM (n=408) | DM (n=408) | DM (n=408) | | Non-DM (n=732) | Non-DM (n=732) | Non-DM (n=732) | Non-DM (n=732) | Non-DM (n=732) |
|-------------------------------------------|----|--------------|--------------|--------------|--------------|--------------|----|------------------|------------------|------------------|------------------|------------------|
| Variables | | HR | | 95%CI | | p value | | HR | | 95%CI | | p value |
| Age (years) | | 1.03 | | (0.99-1.06) | | 0.061 | | 1.05 | | (1.02-1.08) | | 0.002 |
| Sex (female) | | 0.77 | | (0.35-1.69) | | 0.517 | | 2.10 | | (1.09-4.06) | | 0.028 |
| Hypertension | | 0.45 | | (0.23-0.87) | | 0.017 | | 2.45 | | (1.02-5.91) | | 0.046 |
| History of stroke | | 1.90 | | (0.91-3.97) | | 0.087 | | 0.26 | | (0.36-1.93) | | 0.263 |
| History of atrial fibrillation | | 1.90 | | (0.79-4.58) | | 0.15 | | 4.07 | | (1.93-8.55) | | <0.001 |
| Cardiogenic shock (Killip IV) | | 8.84 | | (4.49-17.41) | | <0.001 | | 10.82 | | (5.48-21.38) | | <0.001 |
| STEMI | | 1.79 | | (0.90-3.57) | | 0.098 | | 2.96 | | (1.15-7.63) | | 0.025 |
| LVEF (%) | | 1.00 | | (0.99-1.01) | | 0.19 | | 0.96 | | (0.94-0.99) | | 0.002 |
| Renal dysfunction (eGFR<60) | | 4.30 | | (1.89-9.86) | | <0.001 | | 11.32 | | (4.39-29.20) | | <0.001 |
| Revascularization (emergency PCI or CABG) | | 0.61 | | (0.30-1.25) | | 0.178 | | 0.23 | | (0.12-0.45) | | <0.001 |
Table 5. Multivariate Analyses for In-hospital Death.
| Variables | | DM (n=408) | DM (n=408) | DM (n=408) | DM (n=408) | DM (n=408) | | Non-DM (n=732) | Non-DM (n=732) | Non-DM (n=732) | Non-DM (n=732) | Non-DM (n=732) |
|-------------------------------------------|----|--------------|--------------|--------------|--------------|--------------|----|------------------|------------------|------------------|------------------|------------------|
| Variables | | HR | | 95%CI | | p value | | HR | | 95%CI | | p value |
| Age (years) | | 0.99 | | (0.95-1.03) | | 0.497 | | 1.01 | | (0.96-1.04) | | 0.980 |
| Sex (female) | | 0.78 | | (0.26-2.38) | | 0.668 | | 1.40 | | (0.63-3.01) | | 0.406 |
| Hypertension | | 0.81 | | (0.34-1.93) | | 0.634 | | 1.38 | | (0.53-3.58) | | 0.506 |
| History of stroke | | 2.59 | | (1.07-6.31) | | 0.036 | | 0.34 | | (0.045-2.61) | | 0.302 |
| History of atrial fibrillation | | 1.60 | | (0.54-4.76) | | 0.396 | | 2.07 | | (0.83-5.19) | | 0.119 |
| Cardiogenic shock (Killip IV) | | 6.59 | | (2.90-14.95) | | <0.001 | | 4.42 | | (1.99-9.77) | | <0.001 |
| STEMI | | 1.85 | | (0.71-4.80) | | 0.206 | | 2.46 | | (0.89-6.71) | | 0.080 |
| LVEF (%) | | 0.98 | | (0.95-1.01) | | 0.160 | | 0.99 | | (0.96-1.02) | | 0.354 |
| Renal dysfunction (eGFR<60) | | 5.64 | | (1.59-20.04) | | 0.008 | | 5.92 | | (1.79-19.53) | | 0.004 |
| Revascularization (emergency PCI or CABG) | | 0.66 | | (0.28-1.58) | | 0.350 | | 0.24 | | (0.10-0.56) | | <0.001 |
## Figures
Figure 1. Thirty-day cumulative survival rates in patients with acute myocardial infarction, stratified by DM status. Patients with DM (shown in red) had a significantly lower survival rate than those without DM (shown in blue).
<!-- image -->
Figure 2. Pie charts illustrating the causes of death in each group reveal differences. In the DM group, a higher percentage of non-cardiac deaths, such as infections, malignancies, strokes, and multiple organ failures, was observed than in the non-DM group (32% vs. 14%, respectively), with a statistically significant difference (p=0.046). Among the six cases of mechanical complications in patients who died in the DM group, there were two cases of ventricular septal rupture (VSR) and four cases of free-wall rupture (FWR). In the non-DM group, out of the 12 cases of mechanical complications in deceased patients, there were 4 cases of VSR, 7 of FMR, and 1 of papillary muscle rupture (PMR).
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## References
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- T Sato; T Ono; Y Morimoto; . Five-year clinical outcomes after implantation of sirolimus-eluting stents in patients with and without diabetes mellitus.. Cardiovasc Interv Ther (2012)
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- PK Whelton; RM Carey; WS Aronow; . 2017 ACC/AHA/AAPA/ABC/ACPM/AGS/APhA/ASH/ASPC/NMA/PCNA guideline for the prevention, detection, evaluation, and management of high blood pressure in adults: executive summary: a report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines.. Hypertension (2018)
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item-0 at level 0: unspecified: group _root_
item-1 at level 1: title: The Diversity of Neurological Co ... trospective Case Series of 26 Patients
item-2 at level 1: paragraph: Joe Nemoto; Department of Neurol ... ity Graduate School of Medicine, Japan
item-3 at level 1: text: Objective This study clarified a ... to the distribution of herpes zoster.
item-4 at level 1: section_header: Introduction
item-5 at level 2: text: Varicella zoster virus (VZV) is ... to a single condition remains unclear.
item-6 at level 1: section_header: The diagnosis of herpes zoster
item-7 at level 2: text: The diagnosis of herpes zoster w ... ≥2 from the nadir period to discharge.
item-8 at level 1: section_header: Characteristics of neurological ... rbances in patients with herpes zoster
item-9 at level 2: text: Seventeen (65%) patients receive ... reated and non-steroid-treated groups.
item-10 at level 1: section_header: Relationship between neurological manifestations and herpes zoster
item-11 at level 2: text: The associations between neurolo ... ents with meningitis and encephalitis.
item-12 at level 1: section_header: Neurological disability at nadir
item-13 at level 2: text: Seventeen (65%) patients had sev ... ted to neurological severity (p=0.36).
item-14 at level 1: section_header: Recovery from severe neurological disability
item-15 at level 2: text: Of the 17 severely disabled pati ... e not associated with a good recovery.
item-16 at level 1: section_header: Discussion
item-17 at level 2: text: The proportion of patients with ... e, further investigation is warranted.
item-18 at level 2: text: Our study found that limb paraly ... tant spread across the nervous system.
item-19 at level 2: text: This study showed that increased ... ociated with the neurological outcome.
item-20 at level 2: text: Whether or not steroids are effe ... cal complications associated with VZV.
item-21 at level 1: section_header: Tables
item-23 at level 1: table with [8x13]
item-23 at level 2: caption: Table 1. Association of Location of Herpes Zoster with Neurological Phenotypes.
item-25 at level 1: table with [19x9]
item-25 at level 2: caption: Table 2. Clinical Characteristics of Patients with Severe Neurological Disability Subsequent to Herpes Zoster.
item-27 at level 1: table with [19x9]
item-27 at level 2: caption: Table 3. Comparison between the Clinical Characteristics of Patients with and without Good Recovery from Severely Disabled Condition at Nadir.
item-28 at level 1: section_header: References
item-29 at level 1: list: group list
item-30 at level 2: list_item: K Shiraki; N Toyama; T Daikoku; ... zoster.. Open Forum Infect Dis (2017)
item-31 at level 2: list_item: J Nemoto; T Kanda. Varicella-zos ... al nervous system.. Brain Nerve (2022)
item-32 at level 2: list_item: T Lenfant; AS L'Honneur; B Ranqu ... rebrospinal fluid.. Brain Behav (2022)
item-33 at level 2: list_item: A Mirouse; R Sonneville; K Razaz ... hort study.. Ann Intensive Care (2022)
item-34 at level 2: list_item: Y Ohtsu; Y Susaki; K Noguchi. Ab ... Eur J Drug Metab Pharmacokinet (2018)
item-35 at level 2: list_item: S Takeshima; Y Shiga; T Himeno; ... insho Shinkeigaku (Clin Neurol) (2017)
item-36 at level 2: list_item: L Martinez-Almoyna; T De Broucke ... t version).. Rev Neurol (Paris) (2019)
item-37 at level 2: list_item: T Solomon; BD Michael; PE Smith; ... National Guidelines.. J Infect (2012)
item-38 at level 2: list_item: MA Nagel; D Jones; A Wyborny. Va ... d pathogenesis.. J Neuroimmunol (2017)

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# The Diversity of Neurological Complications Associated with Herpes Zoster: A Retrospective Case Series of 26 Patients
Joe Nemoto; Department of Neurology, Tokuyama Central Hospital, Japan; Department of Neurology and Clinical Neuroscience, Yamaguchi University Graduate School of Medicine, Japan; Jun-ichi Ogasawara; Department of Neurology, Tokuyama Central Hospital, Japan; Michiaki Koga; Department of Neurology and Clinical Neuroscience, Yamaguchi University Graduate School of Medicine, Japan
Objective This study clarified a variety of neurological phenotypes associated with varicella-zoster virus (VZV) reactivation. Methods This retrospective single-center study included consecutive patients with herpes zoster accompanied by neurological disturbances from April 2016 to September 2022. A comparative analysis was performed to examine whether or not the neurological phenotype and severity were associated with the distribution of herpes zoster, clinical and laboratory findings, and treatments. Results Twenty-six patients with a median age of 74 years old were enrolled. None of the patients had been vaccinated against herpes zoster. Of the 26 patients, 14 (54%) developed monoparesis, 5 (19%) developed meningitis, 5 (19%) developed encephalitis, 1 (4%) developed paraplegia, and 1 (4%) developed bladder and rectal problems. Monoparesis of the upper limb is associated with herpes zoster involving the cervical and thoracic dermatomes, whereas meningitis and encephalitis often occur in patients with herpes zoster in the trigeminal and thoracic dermatomes. Neurological disability was generally severe [modified Rankin Scale (mRS) score ≥3] on admission [17 of 26 (65%) patients]. Good recovery after admission was associated with a lower mRS value before the onset of neurological disability, clinical meningitis, and elevated cell counts and protein levels in the cerebrospinal fluid. Good recoveries were observed in patients with herpes zoster in the trigeminal or thoracic dermatomes more frequently than in other dermatomes. Conclusion This study revealed that VZV-related neurological complications are heterogeneous, commonly leading to severe disability and poor outcomes, and that neurological phenotypes and outcomes are related to the distribution of herpes zoster.
## Introduction
Varicella zoster virus (VZV) is a common pathogen that causes herpes zoster. Most Japanese people are thought to be in a state of latent VZV infection, and one-third of the Japanese population develops herpes zoster associated with reactivation of the virus by 80 years old (1). In addition to herpes zoster, reactivation of VZV is associated with various neurological disorders, including radiculitis, meningitis, and encephalitis (2). The extent of neurological disability varies from person to person; however, neurological recovery is generally incomplete in patients with neurological complications (3). Why neurological phenotypes and extent of neurological disability vary in relation to a single condition remains unclear.
## The diagnosis of herpes zoster
The diagnosis of herpes zoster was performed by primary care physicians, dermatologists, or neurologists and was based on visual inspection of the skin rash with or without polymerase chain reaction (PCR) confirmation from swab specimens, although the reactivation of VZV was confirmed by PCR of CSF specimens in previous studies (3,4). Herpes zoster has been observed in the trigeminal, cervical, thoracic, lumbar, and sacral dermatomes. Patients taking immunosuppressant medications and those with a history of diabetes were considered immunocompromised. The following neurological complications were observed: monoparesis, meningitis, encephalitis, paraplegia, and bladder/rectal disturbance. Monoparesis was defined as monoparesis without meningeal irritation, ongoing disturbance of consciousness, or seizures (3). Meningitis was defined as meningeal irritation without ongoing disturbance of consciousness, seizures, or limb paresis (3). Encephalitis was defined as an ongoing disturbance of consciousness or seizures irrespective of meningeal irritation (3). Paraplegia was defined as paraplegia without upper limb paresis, meningeal irritation, ongoing disturbance of consciousness, or seizures. Bladder/rectal disturbances were defined as new-onset subjective bladder or defecation symptoms without paresis, meningeal irritation, ongoing disturbance of consciousness, and seizures. Severe neurological disability was defined as an mRS score ≥3. The nadir period was defined as the period of the most severe neurological symptoms assessed by a neurologist. Good neurological recovery was defined as a decrease in the mRS score of ≥2 from the nadir period to discharge.
## Characteristics of neurological disturbances in patients with herpes zoster
Seventeen (65%) patients received oral antiviral agents before their first visit to the Department of Neurology. Of these patients, eight received amenamevir, an oral antiviral agent that was thought to be unable to cross the blood-brain barrier (5). Every patient received intravenous acyclovir after admission, and intravenous methylprednisolone pulse therapy (1,000 mg/day for 3 days) or oral prednisolone (1 mg/kg body weight/day for 5 days) was administered to 18 (69%) patients. There were no significant differences in the age, sex, CSF findings, or mRS score at each point between the steroid-treated and non-steroid-treated groups.
## Relationship between neurological manifestations and herpes zoster
The associations between neurological phenotypes and herpes zoster distribution are shown in Table 1. Of note, herpes zoster was detected in the cervical and thoracic dermatomes of all 12 patients with upper limb monoparesis, whereas it was detected in the lumbar and sacral dermatomes of all patients with lower limb monoparesis, paraplegia, or bladder/rectal disturbances. Furthermore, herpes zoster was confirmed in the trigeminal or thoracic dermatomes of all patients with meningitis and encephalitis.
## Neurological disability at nadir
Seventeen (65%) patients had severe disability (mRS score ≥3) at the nadir. Patients with severe disabilities had longer hospitalization durations than those without severe disabilities (p=0.023). Other clinical characteristics, including the age, immunocompromised condition, mRS value before onset, CSF findings, and type of therapy, were not significantly associated with severe disability at nadir (Table 2). Extensive distribution of herpes zoster (≥2 areas among 5) was identified in 6 (35%) severely disabled patients but was not significantly related to neurological severity (p=0.36).
## Recovery from severe neurological disability
Of the 17 severely disabled patients, 8 (47%) showed a good neurological recovery by discharge (defined as a decrease in the mRS score of ≥2 from nadir to discharge) (Table 3). Elevated CSF cell counts and protein levels were significantly associated with a good recovery (p=0.038 and p=0.027, respectively). Furthermore, all 3 severely disabled patients with meningitis were determined to have obtained a good recovery, but the p value was not significant (p=0.082). More patients with herpes zoster located in the trigeminal or thoracic dermatomes showed a better recovery than those with herpes zoster in other dermatomes (70% vs. 14%; p=0.0498). None of the patients who had been treated with steroids showed a good recovery. The frequency of a good recovery was significantly lower in the steroid-treated group than in the non-steroid-treated group (p=0.029). Other clinical characteristics, including the age, immunocompromised status, and mRS before the onset, were not associated with a good recovery.
## Discussion
The proportion of patients with specific neurological phenotypes associated with VZV infections has been investigated in French populations, and investigators found that 23% of patients exhibited monoparesis and cranial nerve palsy, 38% exhibited meningitis, and 39% exhibited encephalitis (3). In contrast, we found that a smaller proportion of VZV-infected patients in the Japanese population had meningitis (19%) or encephalitis (19%). Although this discrepancy might reflect racial or geographic differences between populations, we believe that the discrepancy between the results might be accounted for by the differences between the study inclusion criteria for patients. The French investigators only included patients in whom VZV reactivation had been confirmed by PCR of CSF specimens, whereas our study also included patients in whom CSF specimens were not tested by PCR. The difference between the criteria may have biased the results regarding the proportions of neurological phenotypes. Patients with monoparesis might have been excluded from the French study by not conducting a CSF analysis, leading to a larger proportion of patients with encephalitis or meningitis. It is also possible that the proportions of neurological phenotypes differ depending on the characteristics of the VZV infecting the hosts. Therefore, further investigation is warranted.
Our study found that limb paralysis was closely associated with the distribution of prior herpes zoster episodes in patients, which suggests that reactivation of VZV in the dorsal root ganglia affects the nearby motor neurons. In contrast, all patients with encephalitis and meningitis in our study had herpes zoster within the trigeminal, thoracic, or both dermatomes. This result is compatible with a previous report that herpes zoster is frequently located within the trigeminal or thoracic dermatomes in patients with VZV meningitis [6 of 9 (67%) patients] (6). Why inflammation can spread from the thoracic dorsal root ganglion to the distant cerebrum and meninges remains unclear. There may be a particular mechanism underlying distant spread across the nervous system.
This study showed that increased cell counts and CSF protein levels, decreased mRS values before the onset, and no use of steroids were associated with a good neurological recovery in our study patients, which is partly consistent with previous findings (3,4). We also discovered a novel relationship between the neurological recovery and herpes zoster location, possibly due in part to the fact that patients with a meningitis phenotype often have herpes zoster in the trigeminal or thoracic dermatomes and also often show a good recovery. Further research involving a larger study population is needed to clarify whether or not the distribution of herpes zoster is associated with the neurological outcome.
Whether or not steroids are effective in the treatment of neurological complications associated with VZV reactivation remains unclear. In our study, none of the steroid-treated patients showed a good recovery; however, steroids were often used in severe cases [13 of 18 patients (72%)], indicating that the effectiveness of steroids could not be adequately evaluated in our study. The Association of British Neurologists has recommended corticosteroids be used to treat VZV encephalitis in patients with concomitant vasculitis. However, the French Federation of Neurology does not recommend treatment with corticosteroids, irrespective of vasculitis (7,8). Steroids may have an effect on the treatment of VZV-associated vasculopathy (9). Randomized controlled trials should be conducted to determine the efficacy of steroids for neurological complications associated with VZV.
## Tables
Table 1. Association of Location of Herpes Zoster with Neurological Phenotypes.
| Neurological phenotype | | | | Herpes zoster | Herpes zoster | Herpes zoster | Herpes zoster | Herpes zoster | Herpes zoster | Herpes zoster | Herpes zoster | Herpes zoster |
|----------------------------|----|------------|----|------------------|-----------------|-----------------|-----------------|-----------------|-----------------|-----------------|-----------------|-----------------|
| Neurological phenotype | | | | Trigeminal (n=3) | | Cervical (n=10) | | Thoracic (n=13) | | Lumbar (n=6) | | Sacral (n=2) |
| Monoparesis | | U/E (n=12) | | 0 | | 9 | | 6 | | 1 | | 0 |
| | | L/E (n=2) | | 0 | | 0 | | 0 | | 2 | | 1 |
| Meningitis | | (n=5) | | 1 | | 0 | | 4 | | 0 | | 0 |
| Encephalitis | | (n=5) | | 2 | | 1 | | 3 | | 1 | | 0 |
| Paraplegia | | (n=1) | | 0 | | 0 | | 0 | | 1 | | 1 |
| Bladder/rectal disturbance | | (n=1) | | 0 | | 0 | | 0 | | 1 | | 0 |
Table 2. Clinical Characteristics of Patients with Severe Neurological Disability Subsequent to Herpes Zoster.
| | | | | Neurological disability at nadir | Neurological disability at nadir | Neurological disability at nadir | | p value |
|-----------------------------------------------------------------------|-----------------------------------------------------------------------|-----------------------------------------------------------------------|----|------------------------------------|------------------------------------|------------------------------------|----|-----------|
| | | | | Severe (n=17)a | | Not severe (n=9) | | p value |
| Median age (range) | | | | 79 (23-84) | | 68 (31-83) | | NS |
| Herpes zoster | | Trigeminal, n (%) | | 3 (100%) | | 0 (0%) | | NS |
| | | Cervical, n (%) | | 7 (70%) | | 3 (30%) | | NS |
| | | Thoracic, n (%) | | 7 (54%) | | 6 (46%) | | NS |
| | | Lumbar, n (%) | | 5 (83%) | | 1 (17%) | | NS |
| | | Sacral, n (%) | | 2 (100%) | | 0 (0%) | | NS |
| Immunocompromised condition, n (%) | Immunocompromised condition, n (%) | Immunocompromised condition, n (%) | | 6 (60%) | | 4 (40%) | | NS |
| Median mRS before onset (range) | Median mRS before onset (range) | Median mRS before onset (range) | | 0 (0-4) | | 0 (0-0) | | NS |
| Median days from onset of neurological symptom to first visit (range) | Median days from onset of neurological symptom to first visit (range) | Median days from onset of neurological symptom to first visit (range) | | 9 (0-38) | | 6 (1-54) | | NS |
| Neurological phenotype | | Monoparesis, n (%) | | 8 (57%) | | 6 (43%) | | NS |
| | | Meningitis, n (%) | | 3 (60%) | | 2 (40%) | | NS |
| | | Encephalitis, n (%) | | 5 (100%) | | 0 (0%) | | NS |
| CSF | | Cell count (/μL), median (range) | | 22 (0-371) | | 10 (0-202) | | NS |
| | | Protein (mg/dL), median (range) | | 75 (42-217) | | 56 (38-173) | | NS |
| Therapy | | Acyclovir, n (%) | | 17 (100%) | | 9 (100%) | | NS |
| | | Steroid, n (%) | | 13 (76%) | | 5 (56%) | | NS |
| Median hospitalization days (range) | | | | 27 (6-77) | | 18 (14-70) | | 0.023 |
Table 3. Comparison between the Clinical Characteristics of Patients with and without Good Recovery from Severely Disabled Condition at Nadir.
| | | | | Neurological recovery | Neurological recovery | Neurological recovery | | p value |
|-----------------------------------------------------------------------|-----------------------------------------------------------------------|-----------------------------------------------------------------------|----|-------------------------|-------------------------|-------------------------|----|-----------|
| | | | | Good (n=8)a | | Poor (n=9) | | p value |
| Median age (range) | | | | 77 (23-84) | | 79 (69-83) | | NS |
| Herpes zoster | | Trigeminal, n (%) | | 2 (67%) | | 1 (33%) | | NS |
| | | Cervical, n (%) | | 2 (29%) | | 5 (71%) | | NS |
| | | Thoracic, n (%) | | 5 (71%) | | 2 (29%) | | NS |
| | | Lumbar, n (%) | | 1 (20%) | | 4 (80%) | | NS |
| | | Sacral, n (%) | | 0 (0%) | | 2 (100%) | | NS |
| Immunocompromised condition, n (%) | Immunocompromised condition, n (%) | Immunocompromised condition, n (%) | | 2 (33%) | | 4 (67%) | | NS |
| Median mRS before onset (range) | Median mRS before onset (range) | Median mRS before onset (range) | | 0 (0-1) | | 0 (0-4) | | NS |
| Median days from onset of neurological symptom to first visit (range) | Median days from onset of neurological symptom to first visit (range) | Median days from onset of neurological symptom to first visit (range) | | 4 (0-33) | | 11 (0-38) | | NS |
| Neurological phenotype | | Monoparesis, n (%) | | 2 (25%) | | 6 (75%) | | NS |
| | | Meningitis, n (%) | | 3 (100%) | | 0 (0%) | | NS |
| | | Encephalitis, n (%) | | 3 (60%) | | 2 (40%) | | NS |
| CSF | | Cell count (/μL), median (range) | | 51 (8-371) | | 9 (0-328) | | 0.038 |
| | | Protein (mg/dL), median (range) | | 116 (69-217) | | 62 (42-205) | | 0.027 |
| Therapy | | Acyclovir, n (%) | | 8 (100%) | | 9 (100%) | | NS |
| | | Steroid, n (%) | | 4 (50%) | | 9 (100%) | | 0.029 |
| Median hospitalization days (range) | | | | 24 (15-49) | | 39 (6-77) | | NS |
## References
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- J Nemoto; T Kanda. Varicella-zoster virus infection in the central nervous system.. Brain Nerve (2022)
- T Lenfant; AS L'Honneur; B Ranque; . Neurological complications of varicella zoster virus reactivation: prognosis, diagnosis, and treatment of 72 patients with positive PCR in the cerebrospinal fluid.. Brain Behav (2022)
- A Mirouse; R Sonneville; K Razazi; . Neurologic outcome of VZV encephalitis one year after ICU admission: a multicenter cohort study.. Ann Intensive Care (2022)
- Y Ohtsu; Y Susaki; K Noguchi. Absorption, distribution, metabolism, and excretion of the novel helicase-primase inhibitor, amenamevir (ASP2151), in rodents.. Eur J Drug Metab Pharmacokinet (2018)
- S Takeshima; Y Shiga; T Himeno; . Clinical, epidemiological and etiological studies of adult aseptic meningitis: report of 11 cases with varicella zoster virus meningitis.. Rinsho Shinkeigaku (Clin Neurol) (2017)
- L Martinez-Almoyna; T De Broucker; A Mailles; JP Stahl. Management of infectious encephalitis in adults: highlights from the French guidelines (short version).. Rev Neurol (Paris) (2019)
- T Solomon; BD Michael; PE Smith; . Management of suspected viral encephalitis in adults - Association of British Neurologists and British Infection Association National Guidelines.. J Infect (2012)
- MA Nagel; D Jones; A Wyborny. Varicella zoster virus vasculopathy: the expanding clinical spectrum and pathogenesis.. J Neuroimmunol (2017)

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item-0 at level 0: unspecified: group _root_
item-1 at level 1: title: Atypical Hemolytic Uremic Syndro ... ofactor Protein (CD46) Genetic Variant
item-2 at level 1: paragraph: Kosuke Mochizuki; Department of ... Kansai Electric Power Hospital, Japan
item-3 at level 1: text: Atypical hemolytic uremic syndro ... equired for the manifestation of aHUS.
item-4 at level 1: section_header: Introduction
item-5 at level 2: text: In the Kidney Disease: Improving ... f “secondary aHUS” have been excluded.
item-6 at level 2: text: Pathogenic genetic variants in a ... t abnormality could not be identified.
item-7 at level 1: section_header: Case Report
item-8 at level 2: text: The laboratory data are shown in ... 469 U/L, and haptoglobin was 23 mg/dL.
item-9 at level 2: text: The patient was found to have su ... he patient was discharged on day X+26.
item-10 at level 2: text: We outsourced next-generation se ... aHUS triggered by acute pancreatitis.
item-11 at level 1: section_header: Discussion
item-12 at level 2: text: In the present case, the patient ... idered a limitation of this case (14).
item-13 at level 2: text: Several triggers are thought to ... the manifestation of aHUS is unclear.
item-14 at level 2: text: In the present case, aHUS develo ... as CFH, CFI, C3, and THBD (13,17,22).
item-15 at level 2: text: The patient in this case respond ... US relapse is triggered by some event.
item-16 at level 1: section_header: Tables
item-18 at level 1: table with [10x15]
item-18 at level 2: caption: Table 1. Progress of Lab-date in This Case.
item-20 at level 1: table with [9x9]
item-20 at level 2: caption: Table 2. Profiles of the Patients with MCP P165S Variant.
item-21 at level 1: section_header: Figures
item-23 at level 1: picture
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item-40 at level 2: list_item: M Riedl; F Fakhouri; Quintrec M ... pproaches.. Semin Thromb Hemost (2014)
item-41 at level 2: list_item: F Fakhouri; L Roumenina; F Provo ... ne mutations.. J Am Soc Nephrol (2010)
item-42 at level 2: list_item: JM Campistol; M Arias; G Ariceta ... consensus document.. Nefrologia (2015)
item-43 at level 2: list_item: Clech A Le; N Simon-Tillaux; F P ... netic risk factors.. Kidney Int (2019)
item-44 at level 2: list_item: J Barish; P Kopparthy; B Fletche ... que presentation.. BMJ Case Rep (2019)
item-45 at level 2: list_item: T Kajiyama; M Fukuda; Y Rikitake ... eatitis: a case report.. Cureus (2023)
item-46 at level 2: list_item: O Taton; M Delhaye; P Stordeur; ... zumab.. Acta Gastroenterol Belg (2016)
item-47 at level 2: list_item: M Noris; J Caprioli; E Bresin; . ... enotype.. Clin J Am Soc Nephrol (2010)

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# Atypical Hemolytic Uremic Syndrome Triggered by Acute Pancreatitis in a Patient with a Membrane Cofactor Protein (CD46) Genetic Variant
Kosuke Mochizuki; Department of Nephrology, Kansai Electric Power Hospital, Japan; Naohiro Toda; Department of Nephrology, Kansai Electric Power Hospital, Japan; Masaaki Fujita; Department of Rheumatology, Kansai Electric Power Hospital, Japan; Satoshi Kurahashi; Department of Nephrology, Kansai Electric Power Hospital, Japan; Hisako Hirashima; Department of Nephrology, Kansai Electric Power Hospital, Japan; Kazuki Yoshioka; Department of Hematology, Kansai Electric Power Hospital, Japan; Tomoya Kitagawa; Department of Hematology, Kansai Electric Power Hospital, Japan; Akira Ishii; Department of Nephrology, Kansai Electric Power Hospital, Japan; Toshiyuki Komiya; Department of Nephrology, Kansai Electric Power Hospital, Japan
Atypical hemolytic uremic syndrome (aHUS) is a type of HUS. We herein report a case of aHUS triggered by pancreatitis in a patient with a heterozygous variant of membrane cofactor protein ( MCP ; P165S), a complement-related gene. Plasma exchange therapy and hemodialysis improved thrombocytopenia and anemia without leading to end-stage kidney disease. This MCP heterozygous variant was insufficient to cause aHUS on its own. Pancreatitis, in addition to a genetic background with a MCP heterozygous variant, led to the manifestation of aHUS. This case supports the “multiple hit theory” that several factors are required for the manifestation of aHUS.
## Introduction
In the Kidney Disease: Improving Global Outcomes (KDIGO) classification, TMA is classified into four categories: STEC-HUS induced by Shiga toxin-producing Escherichia coli (STEC) infection, thrombotic thrombocytopenic purpura (TTP), “primary aHUS,” and “secondary aHUS.” TTP is caused by a marked deficiency in a disintegrin-like and metalloproteinase with thrombospondin type 1 motifs 13 (ADAMTS-13) activity, either because of genetic abnormalities or acquired autoantibodies. Conversely, more than 90% of HUS cases are STEC-HUS, and the remaining 10% are aHUS, which does not involve STEC infection (1,2). The term “primary aHUS” is used when an underlying abnormality of the alternative pathway of complement is strongly suspected, and other causes of “secondary aHUS” have been excluded.
Pathogenic genetic variants in a patient with aHUS include complement factor H (CFH), membrane cofactor protein (MCP; CD46), complement factor I (CFI), complement 3 (C3), complement factor B (CFB), thrombomodulin (THBD), complement factor H related protein 1 (CFHR1), complement factor H related protein 5 (CFHR5), and diacylglycerol kinase epsilon (DGKE) (3-6). Fujisawa et al. reported that, in an analysis of 118 aHUS patients in Japan, the frequencies of C3, CFH, MCP and DGKE genetic abnormalities were 32 (27%), 10 (8%), 5 (4%), and 1 (0.8%), respectively, and the frequency of anti-CFH antibodies was 20 (17%). Unidentified genetic abnormalities were reported in 36 patients (30%) (7). However, even in some patients, a complement abnormality could not be identified.
## Case Report
The laboratory data are shown in Table 1. After EUS-FNA, the patient complained of left-sided abdominal pain, and the pancreatic amylase level was found to have increased to 1,547 U/L. The patient's vital signs were as follows: body temperature, 37.5°C; and blood pressure, 136/74 mmHg. Contrast-enhanced computed tomography (CT) revealed a contrast-impaired area of the pancreatic tail and increased peripancreatic fatty tissue density (Figure A). The patient was diagnosed with acute pancreatitis after EUS-FNA. Treatment was started with fasting, analgesia with acetaminophen, treatment with nafamostat-mesylate, and large extracellular fluid replacement of 4,000 mL/day. Serum creatinine increased from 0.75 to 1.33 mg/dL, and the estimated glomerular filtration rate (eGFR) decreased from 87 to 46.5, indicating acute kidney injury. The urinary protein excretion was 6.15 g/gCr, and hematuria (3+) was observed. Blood samples showed progression of anemia from Hb 15.5 to 12.4 mg/dL, and platelets decreased from 313,000 to 5,000/μL. Schistocytes were detected at a rate of 50-70/2,000 red blood cells (RBCs), lactate dehydrogenase (LDH) levels were elevated to 2,469 U/L, and haptoglobin was 23 mg/dL.
The patient was found to have suffered a relapse of inflammation and showed elevated pancreatic enzymes on day X+7. CT showed pancreatic swelling and hypo-absorptive areas in the peripancreatic, gastric fold, and transverse colonic mesentery, suggesting the formation of walled-off necrosis (WON) (Figure B). The WON improved with antibiotic treatment. After confirming no recurrence of postprandial abdominal pain, the patient was discharged on day X+26.
We outsourced next-generation sequencing of complement-related genetic abnormalities to the KAZUSA DNA Research Institute. In the laboratory, CFH, CFI, MCP, C3, CFB, THBD, DGKE, and CFHR5 were analyzed using next-generation sequencing hybridization capture methods for rare single nucleotide substitutions and deletions with an allele frequency of <0.5%. In addition, complement function tests (sheep erythrocyte hemolysis test) and anti-CFH antibody tests were performed. The sheep erythrocyte hemolysis test was performed because it is useful for detecting genetic mutations in CFH and anti-CFH antibodies. The results showed the absence of anti-CFH antibodies (8). Genetic testing revealed a 493-base cytosine (C) to thymine (T) mutation in MCP (MCP P165S), a complement-related gene responsible for atypical HUS, resulting in a missense variant of proline-to-serine substitution and a heterozygous mutation in MCP. Based on the MCP findings, we considered this case to potentially be primary aHUS triggered by acute pancreatitis.
## Discussion
In the present case, the patient had a missense variant of MCP (MCP P165S). A previous report indicated a 50% reduction in MCP expression in leukocytes in patients with the MCP P165S variant (9). Another study reported that a 50% reduction in MCP expression leads to a decrease in the binding ability of C3b to <50% compared to normal MCP expression (10). The MCP P165S variant is not registered in Clinvar but is categorized as “likely pathogenic” according to the The American College of Medical Genetics and Genomics guidelines (ACMG guidelines) (11). Esparaza-Gordillio et al. reported families with the same genetic variant as this case and found that patients with only the MCP variant did not manifest aHUS, whereas those with coexisting the CFI mutation and MCPggaac single-nucleotide variant haplotype block, another variant of MCP, did manifest aHUS (9,12,13). The clinical characteristics of the patients with the MCP P165S variant are summarized in Table 2. In the present case, only the protein-coding region exons of CFH, CFI, CD46, C3, CFB, THBD, and DGKE and their intron boundaries were searched using the next-generation sequencer, and the multiple ligation-dependent probe amplification (MLPA) method was not used to detect structural variants. The lack of a genetic analysis using MLPA is considered a limitation of this case (14).
Several triggers are thought to be involved in the manifestation of aHUS, in addition to genetic mutations, including infection, pregnancy, transplantation, metabolic diseases, vasculitis, and pancreatitis, called the “multiple hit hypothesis” (13,15-17). Among the reported triggers, cases of aHUS triggered by pancreatitis are rare, accounting for just 3% (4/110) of secondary aHUS cases (18-21). Previously reported cases of aHUS triggered by pancreatitis were not specifically investigated for complement-related gene mutations, so whether or not complement-related gene mutations other than pancreatitis have any effects on the manifestation of aHUS is unclear.
In the present case, aHUS developed in a patient with a genetic variant (MCP P165S) that originally did not meet the threshold for aHUS development due to pancreatitis after EUS-FNA. This shows that several triggers, not just genetic mutations, are involved in the development of aHUS, supporting the “multiple hit hypothesis.” Patients with MCP variants have a better prognosis and lower probability of developing end-stage kidney disease (ESKD) and death than patients with other genetic mutations, such as CFH, CFI, C3, and THBD (13,17,22).
The patient in this case responded well to plasma exchange therapy and PE. He recovered his kidney function, supporting the relatively good prognosis of patients with MCP mutations compared to other aHUS-related gene mutations. Although the efficacy of eculizumab as a treatment for aHUS has been described in recent years, eculizumab was not used in this case, as the patient had a good clinical course with plasma exchange therapy and HD (17,19,21). In addition, infection with WON after pancreatitis was suspected on day X+7; therefore, it was difficult to use eculizumab, which produces immunosuppressive effects. However, the use of eculizumab should be considered in the future if aHUS relapse is triggered by some event.
## Tables
Table 1. Progress of Lab-date in This Case.
| Variable | | DAY0 | | DAY2 | | DAY3 | | DAY4 | | DAY7 | | DAY11 | | DAY14 |
|---------------------|----|--------|----|--------|----|--------|----|--------|----|--------|----|---------|----|---------|
| WBC (×103/μL) | | 54 | | 112 | | 105 | | 107 | | 210 | | 171 | | 77 |
| Haemoglobin (g/dL) | | 15.5 | | 12.4 | | 10.5 | | 8.8 | | 7.2 | | 7.9 | | 8.3 |
| Platelets (×104/μL) | | 31.3 | | 0.5 | | 2.1 | | 1.6 | | 19.6 | | 63.8 | | 83 |
| Cr (mg/dL) | | 0.75 | | 1.33 | | 1.51 | | 1.67 | | 1.34 | | 1 | | 0.98 |
| LDH (U/L) | | 151 | | 2,469 | | 2,513 | | 2,024 | | 550 | | 447 | | 362 |
| T-Bil (mg/dL) | | 0.77 | | 4.35 | | 4.57 | | 4.49 | | 1.98 | | 1 | | 0.82 |
| P-Amy (U/L) | | 34 | | 1,632 | | 1,080 | | 302 | | 196 | | 134 | | 173 |
| CRP (mg/dL) | | 0.47 | | 16.76 | | 25.95 | | 15.21 | | 21.22 | | 5.52 | | 1.43 |
| Haptoglobin (mg/dL) | | 23 | | | | | | <10 | | | | | | 88 |
Table 2. Profiles of the Patients with MCP P165S Variant.
| patient | MCP mutation | Other gene variant | sex | Age at onset (yr) | Triggers | onset of aHUS | outcome at first episode | reference |
|-----------|----------------|----------------------|-------|---------------------|--------------|-----------------|----------------------------|-------------|
| This case | P165S | | M | 49 | pancreatitis | + | Remission | |
| HUS68 | P165S | CFI(T538X) MCP ggaac | F | 57 | n.a. | + | Remission | (9) |
| HUS84 | P165S | CFI(T538X) MCP ggaac | F | 41 | No trigger | + | Remission | (9) |
| III-10 | P165S | | M | 52 | | - | | (9) |
| III-11 | P165S | CFI(T538X) | M | 54 | | - | | (9) |
| IV-1 | P165S | CFI(T538X) | F | 37 | | - | | (9) |
| IV-2 | P165S | CFI(T538X) | M | 35 | | - | | (9) |
| IV-3 | P165S | | F | 30 | | - | | (9) |
## Figures
Figure. Abdominal computed tomography (CT) findings in this patient. (A) Day X+2 after performing endoscopic ultrasound-guided fine needle aspiration (EUS-FNA). The pancreatic body tail has a mildly reduced contrast effect in the arterial phase, consistent with the findings of acute pancreatitis. (B) Day X+7. CT indicates a low-absorption area suspected of being walled-off necrosis (WON) around the peripancreatic to the gastric body and gastric antrum.
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item-0 at level 0: unspecified: group _root_
item-1 at level 1: title: Characterization of TSET, an anc ... idespread membrane trafficking complex
item-2 at level 1: paragraph: Jennifer Hirst; Cambridge Instit ... mbridge , Cambridge , United Kingdom
item-3 at level 1: text: The heterotetrameric AP and F-CO ... p://dx.doi.org/10.7554/eLife.02866.002
item-4 at level 1: section_header: Introduction
item-5 at level 2: text: The evolution of eukaryotes some ... mbers of the AP/COPI subunit families.
item-6 at level 1: section_header: Diagrams of APs and F-COPI.
item-7 at level 2: text: (A) Structures of the assembled ... ements 1–4, Figure 1—source data 1, 2.
item-8 at level 1: section_header: Summary table of all subunits identified using reverse HHpred.
item-9 at level 2: text: The lighter shading indicates wh ... GI). The new complex is called ‘TSET’.
item-10 at level 1: section_header: The search for novel AP-related complexes
item-11 at level 2: text: Because we were unable to find a ... -based searching (Hirst et al., 2011).
item-12 at level 2: text: In addition to known proteins, o ... ly members are only 14.63% identical).
item-13 at level 1: section_header: TSET: a new trafficking complex
item-14 at level 2: text: To determine whether the four ne ... xcess, probably due to overexpression.
item-15 at level 2: text: Interestingly, two of the other ... r the newly identified heterotetramer.
item-16 at level 2: text: The other three proteins in the ... embrane and/or endosomal compartments.
item-17 at level 1: section_header: Characterisation of the TSET complex in Dictyostelium.
item-18 at level 2: text: (A) Western blots of axenic D. d ... nts 1 and 2, Figure 2; Videos 1 and 2.
item-19 at level 1: section_header: Characterisation of the TSET complex in Dictyostelium
item-20 at level 2: text: One of the key properties of coa ... lasma membrane) from a cytosolic pool.
item-21 at level 2: text: Silencing TPLATE in Arabidopsis ... ive without a functional TSET complex.
item-22 at level 2: text: Very recently, the discoverers o ... stelium produce a very mild phenotype.
item-23 at level 1: section_header: TSET is ancient and widespread in eukaryotes
item-24 at level 2: text: When TPLATE was discovered in Ar ... complex was present prior to the LECA.
item-25 at level 2: text: Although TSET is clearly ancient ... stinct from the known heterotetramers.
item-26 at level 2: text: Phylogenetic analysis of the TTR ... a et al., 2010; Asensio et al., 2013).
item-27 at level 2: text: Although TSET is deduced to have ... sted in our pre-opisthokont ancestors.
item-28 at level 1: section_header: Distribution of TSET subunits.
item-29 at level 2: text: (A) Coulson plot showing the dis ... data 1, Figure 3—figure supplement 1.
item-30 at level 1: section_header: Evolution of TSET.
item-31 at level 2: text: (A) Simplified diagram of the co ... also Figure 4—figure supplements 1–10.
item-32 at level 1: section_header: Phylogenetic analysis of TPLATE, ... obustly excluded from the β-COP clade.
item-33 at level 2: text: In this and all other figure sup ... es are denoted by symbols (see inset).
item-34 at level 1: section_header: Conclusions
item-35 at level 2: text: TSET is the latest addition to a ... nts) adding lineage-specific function.
item-36 at level 2: text: Studies on the muniscins may hel ... sms as the clathrin pathway took over.
item-37 at level 2: text: Thus, our bioinformatics tool, r ... to the history of the eukaryotic cell.
item-38 at level 1: section_header: Construction of the ‘reverse HHpred’ database
item-39 at level 2: text: The proteomes of various organis ... able to find the entire TSET complex.
item-40 at level 1: section_header: Data assimilation
item-41 at level 2: text: The large adaptor subunits share ... ptin), a shared homology is suggested.
item-42 at level 1: section_header: Dictyostelium: the search for TSPOON and TCUP
item-43 at level 2: text: While searching for genes encodi ... . purpureum TCUP) (www.dictybase.org).
item-44 at level 1: section_header: Dictyostelium expression constructs
item-45 at level 2: text: The σ-like (TSPOON) coding seque ... omoter (pDT61 and pDT58 respectively).
item-46 at level 1: section_header: Dictyostelium cell culture and transformation
item-47 at level 2: text: All of the methods used for cell ... at Bio-protocol (Hirst et al., 2015).
item-48 at level 2: text: D. discoideum Ax2-derived strain ... medium containing 10 µg/ml blasicidin.
item-49 at level 1: section_header: Dictyostelium microscopy and fractionation
item-50 at level 2: text: Cells were transformed with GFP ... N-GFP and A15_TSPOON expressing cells.
item-51 at level 2: text: For fractionation, cells express ... ody against GFP (Seaman et al., 2009).
item-52 at level 1: section_header: Dictyostelium pulldowns and proteomics
item-53 at level 2: text: Pulldowns were performed using D ... tham, MA) (Antrobus and Borner, 2011).
item-54 at level 2: text: Proteins that came down in the n ... were then log-transformed and plotted.
item-55 at level 1: section_header: Dictyostelium gene disruption
item-56 at level 2: text: The TSPOON disruption plasmid wa ... nding sites in pLPBLP, yielding pDT70.
item-57 at level 2: text: Growth of control vs mutant stra ... ing the resultant plaques (Kay, 1982).
item-58 at level 1: section_header: Endocytosis assays
item-59 at level 2: text: Membrane uptake was measured in ... time is 1/slope of the initial phase.
item-60 at level 2: text: Fluid phase uptake was measured ... otein content (Traynor and Kay, 2007).
item-61 at level 1: section_header: Comparative genomics
item-62 at level 2: text: Sequences from Arabidopsis thali ... t generator v1.5 (Field et al., 2013).
item-63 at level 1: section_header: Phylogenetic analysis
item-64 at level 2: text: Identified sequences were combin ... are available in Supplementary file 1.
item-65 at level 1: section_header: Homology modeling
item-66 at level 2: text: The Phyre v2.0 web server (Kelle ... alized using MacPyMOL (www.pymol.org).
item-67 at level 1: section_header: Figures
item-69 at level 1: picture
item-69 at level 2: caption: Figure 1. Diagrams of APs and F-COPI. (A) Structures of the assembled complexes. All six complexes are heterotetramers; the individual subunits are called adaptins in the APs (e.g., γ-adaptin) and COPs in COPI (e.g., γ-COP). The two large subunits in each complex are structurally similar to each other. They are arranged with their N-terminal domains in the core of the complex, and these domains are usually (but not always) followed by a flexible linker and an appendage domain. The medium subunits consist of an N-terminal longin-related domain followed by a C-terminal μ homology domain (MHD). The small subunits consist of a longin-related domain only. (B) Jpred secondary structure predictions of some of the known subunits (all from Homo sapiens), together with new family members from Dictyostelium discoideum (Dd) and Arabidopsis thaliana (At). See also Figure 1—figure supplements 1–4, Figure 1—source data 1, 2. DOI:http://dx.doi.org/10.7554/eLife.02866.003
item-71 at level 1: picture
item-71 at level 2: caption: Figure 1—figure supplement 1. PDB entries used to search for adaptor-related proteins. DOI:http://dx.doi.org/10.7554/eLife.02866.006
item-73 at level 1: picture
item-73 at level 2: caption: Figure 1—figure supplement 2. Summary table of all subunits identified using reverse HHpred. The lighter shading indicates where an orthologue was found either below the arbitrary cut-off, by using NCBI BLAST (see Figure 1—figure supplement 3), or by searching a genomic database (e.g., AP-1 μ1 |Naegr1|35900|, JGI). The new complex is called ‘TSET’. DOI:http://dx.doi.org/10.7554/eLife.02866.007
item-75 at level 1: picture
item-75 at level 2: caption: Figure 1—figure supplement 3. Subunits that failed to be identified using reverse HHpred, but were identified by homology searching using NCBI BLAST. DOI:http://dx.doi.org/10.7554/eLife.02866.008
item-77 at level 1: picture
item-77 at level 2: caption: Figure 1—figure supplement 4. TSET orthologues in different species. The orthologues were identified by reverse HHpred, except for those in italics, which were found by BLAST searching (NCBI) using closely related organisms. TTRAY1 and TTRAY2 were initially identified by proteomics in a complex with TSET, but could also have been predicted by reverse HHpred as closely related to β′-COP using the PDB structure, 3mkq_A. In all other organisms TTRAY1 and TTRAY2 were identified by NCBI BLAST (italics). Note that orthologues of TSAUCER in P. patens, and TTRAY 2 in M. pusilla were identified in Phytozome, which is a genomic database hosted by Joint Genome Institute (JGI). Note orthologues of TCUP in D. purpureum and TSPOON in D. discoideum were identified by searching genomic sequences using closely related sequences, and have been manually appended in DictyBase. In these cases corresponding sequences are not at present found at NCBI. Whilst S. moellendorffii and V. vinifera were included in the reverse HHpred database, they were not included in the Coulson plot. DOI:http://dx.doi.org/10.7554/eLife.02866.009
item-79 at level 1: picture
item-79 at level 2: caption: Figure 1—figure supplement 5. Identification of ENTH/ANTH domain proteins and the AP complexes with which they associate, using reverse HHpred. Reverse HHpred searches were initiated using the key words ‘epsin’ or ‘ENTH’. The PDB structures used were: 1eyh_A (Chain A, Crystal Structure Of The Epsin N-Terminal Homology (Enth) Domain At 1.56 Angstrom Resolution); 1inz_A (Chain A, Solution Structure Of The Epsin N-Terminal Homology (Enth) Domain Of Human Epsin); 1xgw_A (Chain A, The Crystal Structure Of Human Enthoprotin N-Terminal Domain); 3onk_A (Chain A, Yeast Ent3_enth Domain), and the output was assimilated in Excel as described for the adaptors. The identity of the hits was determined using NCBI BLAST searching. Note that all of the organisms that have lost AP-4 have also lost its binding partner, tepsin. DOI:http://dx.doi.org/10.7554/eLife.02866.010
item-81 at level 1: picture
item-81 at level 2: caption: Figure 2. Characterisation of the TSET complex in Dictyostelium. (A) Western blots of axenic D. discoideum expressing either GFP-tagged small subunit (σ-like) or free GFP, under the control of the Actin15 promoter, labelled with anti-GFP. The Ax2 parental cell strain was included as a control, and an antibody against the AP-2α subunit was used to demonstrate that equivalent amounts of protein were loaded. (B) Coomassie blue-stained gel of GFP-tagged small subunit and associated proteins immunoprecipitated with anti-GFP. The GFP-tagged protein is indicated with a red asterix. (C) iBAQ ratios (an estimate of molar ratios) for the proteins that consistently coprecipitated with the GFP-tagged small subunit. All appear to be equimolar with each other, and the higher ratios for the small (σ-like/TSPOON) subunit and GFP are likely to be a consequence of their overexpression, which we also saw in a repeat experiment in which we used the small subunit's own promoter (Figure 2—figure supplement 1). (D) Predicted structure of the N-terminal portion of D. discoideum TTRAY1, shown as a ribbon diagram. (E) Stills from live cell imaging of cells expressing either TSPOON-GFP or free GFP, using TIRF microscopy. The punctate labelling in the TSPOON-GFP-expressing cells indicates that some of the construct is associated with the plasma membrane. See Videos 1 and 2. (F) Western blots of extracts from cells expressing either TSPOON-GFP or free GFP. The post-nuclear supernatants (PNS) were centrifuged at high speed to generate supernatant (cytosol) and pellet fractions. Equal protein loadings were probed with anti-GFP. Whereas the GFP was exclusively cytosolic, a substantial proportion of TSPOON-GFP fractionated into the membrane-containing pellet. (G) Mean generation time (MGT) for control (Ax2) and TSPOON knockout cells. The knockout cells grew slightly faster than the control. (H) Differentiation of the Ax2 control strain and two TSPOON knockout strains (1725 and 1727). All three strains produced fruiting bodies upon starvation. (I) Assay for fluid phase endocytosis. The control and knockout strains took up FITC-dextran at similar rates. (J) Assay for endocytosis of membrane, labelled with FM1-43, showing the time taken to internalise the entire surface area. The knockout strains took significantly longer than the control (*p<0.05; **p<0.01). See also Figure 2—figure supplements 1 and 2, Figure 2; Videos 1 and 2. DOI:http://dx.doi.org/10.7554/eLife.02866.011
item-83 at level 1: picture
item-83 at level 2: caption: Figure 2—figure supplement 1. Further characterisation of Dictyostelium TSET. (A) iBAQ ratios for the proteins that coprecipitated with TSPOON-GFP, normalized to the median abundance of all proteins across five experiments. ND = not detected. (B) Fluorescence and phase contrast micrographs of cells expressing GFP-tagged TSPOON under the control of its own promoter (Prom-TSPOON-GFP). The construct appears mainly cytosolic. (C) Homology modeling of TTRAYs from A. thaliana, D. discoideum, and N. gruberi, revealing two β-propeller domains followed by an α-solenoid. (D) Disruption of the TSPOON gene. PCR was used to amplify either the wild-type TSPOON gene (in Ax2) or the disrupted TSPOON gene. The resulting products were either left uncut (U) or digested with SmaI (S), which should not cut the wild-type gene, but should cleave the disrupted gene into three bands. Several clones are shown, including HM1725 (200/1 A1). (E) Spore viability after detergent treatment was used to test for integrity of the cellulosic spore and the ability to hatch in a timely manner. The control (Ax2) strain and the knockout (HM1725) strain both showed good viability. (F) Expansion rate of plaques on bacterial lawns. The rates for control (Ax2) and knockout (HM1725, 1727, and 1728) strains were similar initially, but by 2 days the control plaques were larger. (G) Micrographs of plaques from control and knockout strains. DOI:http://dx.doi.org/10.7554/eLife.02866.012
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item-85 at level 2: caption: Figure 2—figure supplement 2. Distribution of secG. DOI:http://dx.doi.org/10.7554/eLife.02866.013
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item-87 at level 2: caption: Figure 2—figure supplement 3. Distribution of vacuolins. DOI:http://dx.doi.org/10.7554/eLife.02866.014
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item-89 at level 2: caption: Video 1. Related to Figure 2. TIRF microscopy of D. discoideum expressing TSPOON-GFP, expressed off its own promoter in TSPOON knockout cells. One frame was collected every second. Dynamic puncta can be seen, indicating that the construct forms patches at the plasma membrane. DOI:http://dx.doi.org/10.7554/eLife.02866.015
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item-91 at level 2: caption: Video 2. Related to Figure 2. TIRF microscopy of D. discoideum expressing free GFP, driven by the Actin15 promoter in TSPOON knockout cells. One frame was collected every second. The signal is diffuse and cytosolic. DOI:http://dx.doi.org/10.7554/eLife.02866.016
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item-93 at level 2: caption: Figure 3. Distribution of TSET subunits. (A) Coulson plot showing the distribution of TSET in a diverse set of representative eukaryotes. Presence of the entire complex in at least four supergroups suggests its presence in the last eukaryotic common ancestor (LECA) with frequent secondary loss. Solid sectors indicate sequences identified and classified using BLAST and HMMer. Empty sectors indicate taxa in which no significant orthologues were identified. Filled sectors in the Holozoa and Fungi represent F-BAR domain-containing FCHo and Syp1, respectively. Taxon name abbreviations are inset. Names in bold indicate taxa with all six components. (B) Deduced evolutionary history of TSET as present in the LECA but independently lost multiple times, either partially or completely. See also Figure 3—source data 1, Figure 3—figure supplement 1. DOI:http://dx.doi.org/10.7554/eLife.02866.017
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item-95 at level 2: caption: Figure 3—figure supplement 1. Models used for phylogenetic analyses. WC = with COPI; WOC = without COPI. DOI:http://dx.doi.org/10.7554/eLife.02866.019
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item-97 at level 2: caption: Figure 4. Evolution of TSET. (A) Simplified diagram of the concatenated tree for TSET, APs, and COPI, based on Figure 4—figure supplement 8. Numbers indicate posterior probabilities for MrBayes and PhyloBayes and maxium-likelihood bootstrap values for PhyML and RAxML, in that order. (B) Schematic diagram of TSET. (C) Possible evolution of the three families of heterotetramers: TSET, APs, and COPI. We propose that the earliest ancestral complex was a likely a heterotrimer or a heterohexamer formed from two identical heterotrimers, containing large (red), small (yellow), and scaffolding (blue) subunits. All three of these proteins were composed of known ancient building blocks of the membrane-trafficking system (Vedovato et al., 2009): α-solenoid domains in both the large and scaffolding subunits; two β-propellers in the scaffolding subunit; and a longin domain forming the small subunit. The gene encoding the large subunit then duplicated and mutated to generate the two distinct types of large subunits (red and magenta), and the gene encoding the small subunit also duplicated and mutated (yellow and orange), with one of the two proteins (orange) acquiring a μ homology domain (MHD) to form the ancestral heterotetramer, as proposed by Boehm and Bonifacino (12). However, the scaffolding subunit remained a homodimer. Upon diversification into three separate families, the scaffolding subunit duplicated independently in TSET and COPI, giving rise to TTRAY1 and TTRAY2 in TSET, and to α- and β′-COP in COPI. COPI also acquired a new subunit, ε-COP (purple). The scaffolding subunit may have been lost in the ancestral AP complex, as indicated in the diagram; however, AP-5 is tightly associated with two other proteins, SPG11 and SPG15, and the relationship of SPG11 and SPG15 to TTRAY/B-COPI remains unresolved, so it is possible that SPG11 and SPG15 are highly divergent descendants of the original scaffolding subunits. The other AP complexes are free heterotetramers when in the cytosol, but membrane-associated AP-1 and AP-2 interact with another scaffold, clathrin; and AP-3 has also been proposed to interact transiently with a protein with similar architecture, Vps41 (Rehling et al., 1999; Cabrera et al., 2010; Asensio et al., 2013). So far no scaffold has been proposed for AP-4. Although the order of emergence of TSET and COP relative to adaptins is unresolved, our most recent analyses indicate that, contrary to previous reports (Hirst et al., 2011), AP-5 diverged basally within the adaptin clade, followed by AP-3, AP-4, and APs 1 and 2, all prior to the LECA. This still suggests a primordial bridging of the secretory and phagocytic systems prior to emergence of a trans-Golgi network. The muniscins arose much later, in ancestral opisthokonts, from a translocation of the TSET MHD-encoding sequence to a position immediately downstream from an F-BAR domain-encoding sequence. Another translocation occurred in plants, where an SH3 domain-coding sequence was inserted at the 3′ end of the TSAUCER-coding sequence. See also Figure 4—figure supplements 1–10. DOI:http://dx.doi.org/10.7554/eLife.02866.020
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item-99 at level 2: caption: Figure 4—figure supplement 1. Phylogenetic analysis of TPLATE, β-COP, and β-adaptin, with TPLATE robustly excluded from the β-COP clade. In this and all other figure supplements to Figure 4, AP subunits are boxed in blue, F-COPI subunits are boxed in red, and subunits of TSET are boxed in yellow. Node support for critical nodes is shown. Numbers indicate Bayesian posterior probabilities (MrBayes) and bootstrap support from Maximum-likelihood analysis (RAxML). Support values for other nodes are denoted by symbols (see inset). DOI:http://dx.doi.org/10.7554/eLife.02866.021
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item-101 at level 2: caption: Figure 4—figure supplement 2. Phylogenetic analysis of TPLATE and β-adaptin subunits (β-COP removed) showing, with weak support, that TPLATE is excluded from the adaptin clade. DOI:http://dx.doi.org/10.7554/eLife.02866.022
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item-103 at level 2: caption: Figure 4—figure supplement 3. Phylogenetic analysis of TSAUCER, γ-COP, and γαδεζ-adaptin subunits, with TCUP robustly excluded from the γ-COP clade, and weakly excluded from the adaptin clade. DOI:http://dx.doi.org/10.7554/eLife.02866.023
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item-105 at level 2: caption: Figure 4—figure supplement 4. Phylogenetic analysis of TSAUCER and γαδεζ-adaptin subunits (γ-COP removed), showing weak support for the exclusion of TSAUCER from the adaptin clade. DOI:http://dx.doi.org/10.7554/eLife.02866.024
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item-107 at level 2: caption: Figure 4—figure supplement 5. Phylogenetic analysis of TCUP, δ-COP, and μ-adaptin subunits, with TSAUCER robustly excluded from the δ-COP clade and weakly excluded from the adaptin clade. DOI:http://dx.doi.org/10.7554/eLife.02866.025
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item-109 at level 2: caption: Figure 4—figure supplement 6. Phylogenetic analysis of TCUP and μ-adaptin subunits (δ-COP removed), showing weak support for the exclusion of TCUP from the adaptin clade. DOI:http://dx.doi.org/10.7554/eLife.02866.026
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item-111 at level 2: caption: Figure 4—figure supplement 7. Phylogenetic analysis of TSPOON with ζ-COP and σ–adaptin subunits with moderate support for the exclusion of TSPOON from both the COPI and adaptin clades, in addition to moderate support for the monophyly of the TSPOON clade. DOI:http://dx.doi.org/10.7554/eLife.02866.027
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item-113 at level 2: caption: Figure 4—figure supplement 8. TSET is a phylogenetically distinct lineage from F-COPI and the AP complexes. Phylogenetic analysis of the heterotetrameric complexes: F-COPI (orange), TSET (purple), and AP (magenta, blue, red, green, and yellow for 5, 3, 1, 2, and 4, respectively), shows strong, weak, and moderate support for clades of each complex, respectively. Node support for critical nodes is shown. Numbers indicate Bayesian posterior probabilities (MrBayes and PhyloBayes) and bootstrap support from Maximum-likelihood analysis (PhyML and RAXML). Support values for other nodes are denoted by symbols (see inset). DOI:http://dx.doi.org/10.7554/eLife.02866.028
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item-115 at level 2: caption: Figure 4—figure supplement 9. Phylogenetic analysis of TTRAY1, TTRAY2, α-COP, and β′-COP. TTRAYs 1 and 2, and COPI α and β′, arose from separate gene duplications, indicating that the ancestral complex had only one such protein, although possibly present as two identical copies. Phylogenetic analysis of α- and β′-COPI (red), and TTRAYs 1 and 2 (yellow), shows a well supported COPI clade excluding all of the TTRAY1 and 2 sequences, suggesting that the duplications giving rise to these proteins occurred independently, and the utilization of two different outer coat members occurred through convergent evolution. Node support for critical nodes is shown. Numbers indicate Bayesian posterior probabilities (MrBayes) and bootstrap support from Maximum-likelihood analysis (RAxML). Support values for other nodes are denoted by symbols (see inset). DOI:http://dx.doi.org/10.7554/eLife.02866.029
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item-117 at level 2: caption: Figure 4—figure supplement 10. Muniscin family members identified by reverse HHpred, using the following PDB structures. 2V0O_A (Chain A, Fcho2 F-Bar Domain); 3 G9H_A (Chain A, Crystal Structure Of The C-Terminal Mu Homology Domain Of Syp1); 3G9G_A (Chain A, Crystal Structure Of The N-Terminal EfcF-Bar Domain Of Syp1). DOI:http://dx.doi.org/10.7554/eLife.02866.030
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# Characterization of TSET, an ancient and widespread membrane trafficking complex
Jennifer Hirst; Cambridge Institute for Medical Research , University of Cambridge , Cambridge , United Kingdom; Alexander Schlacht; Department of Cell Biology , University of Alberta , Edmonton , Canada; John P Norcott; Department of Engineering , University of Cambridge , Cambridge , United Kingdom; David Traynor; Cell Biology , MRC Laboratory of Molecular Biology , Cambridge , United Kingdom; Gareth Bloomfield; Cell Biology , MRC Laboratory of Molecular Biology , Cambridge , United Kingdom; Robin Antrobus; Cambridge Institute for Medical Research , University of Cambridge , Cambridge , United Kingdom; Robert R Kay; Cell Biology , MRC Laboratory of Molecular Biology , Cambridge , United Kingdom; Joel B Dacks; Department of Cell Biology , University of Alberta , Edmonton , Canada; Margaret S Robinson; Cambridge Institute for Medical Research , University of Cambridge , Cambridge , United Kingdom
The heterotetrameric AP and F-COPI complexes help to define the cellular map of modern eukaryotes. To search for related machinery, we developed a structure-based bioinformatics tool, and identified the core subunits of TSET, a 'missing link' between the APs and COPI. Studies in Dictyostelium indicate that TSET is a heterohexamer, with two associated scaffolding proteins. TSET is non-essential in Dictyostelium , but may act in plasma membrane turnover, and is essentially identical to the recently described TPLATE complex, TPC. However, whereas TPC was reported to be plant-specific, we can identify a full or partial complex in every eukaryotic supergroup. An evolutionary path can be deduced from the earliest origins of the heterotetramer/scaffold coat to its multiple manifestations in modern organisms, including the mammalian muniscins, descendants of the TSET medium subunits. Thus, we have uncovered the machinery for an ancient and widespread pathway, which provides new insights into early eukaryotic evolution. DOI: http://dx.doi.org/10.7554/eLife.02866.001 eLife digest Eukaryotes make up almost all of the life on Earth that we can see around us, and include organisms as diverse as animals, fungi, plants, slime moulds, and seaweeds. The defining feature of eukaryotes is that, unlike nearly all bacteria, they have membrane-bound compartments—such as the nucleus—within their cells. Moving molecules, such as proteins, between these compartments is essential for living eukaryotic cells, and these molecules are usually trafficked inside membrane-bound packages called vesicles. Two similar sets of protein complexes—each containing four different subunits—ensure that the molecules are packaged inside the correct vesicles. However, it is not clear how these two protein complexes (called the AP complexes and the COPI complex) are related to each other, and when and where they originated in the history of life. Now, Hirst, Schlacht et al. have discovered a new—but very ancient–protein complex that they refer to as the ‘missing link’ between the AP and COPI complexes. The four subunits inside this new complex were found by searching for proteins with shapes that were similar to those of the AP and COPI proteins, rather than just searching for proteins with similar sequences of amino acids. This approach identified related protein subunits in groups as diverse as plants and slime moulds, which suggests that this protein complex evolved in the earliest of the eukaryotes. The four subunits identified in a slime mould were confirmed to interact, and also shown to bind to the plasma membrane of living cells. One of the subunits had already been named TPLATE, so Hirst, Schlacht et al. decided to call the complex TSET; the other three subunits were named TSAUCER, TCUP and TSPOON, and two other proteins that interacted with the complex were both called TTRAY. While most of the TSET complex itself has been lost from humans and other animals, one of subunit appears to have evolved into a family of proteins that help molecules get into cells. The discovery of TSET reveals another major player in vesicle-trafficking that is not only important for our understanding of how modern eukaryotes work, but also how ancient eukaryotes evolved. DOI: http://dx.doi.org/10.7554/eLife.02866.002
## Introduction
The evolution of eukaryotes some 2 billion years ago radically changed the biosphere, giving rise to nearly all visible life on Earth. Key to this transition was the ability to generate intracellular membrane compartments and the trafficking pathways that interconnect them, mediated in part by the heterotetrameric adaptor complexes, APs 1–5 and COPI (Dacks et al., 2008; Field and Dacks, 2009; Hirst et al., 2011; Koumandou et al., 2013). In mammals, APs 1 and 2 and COPI are essential for viability, while mutations in the other APs cause severe genetic disorders (Boehm and Bonifacino, 2002; Hirst et al., 2013). The AP and COPI complexes share a similar architecture, due to common ancestry predating the last eukaryotic common ancestor (LECA). All six complexes consist of two large subunits of ∼100 kD, a medium subunit of ∼50 kD, and a small subunit of ∼20 kD (Figure 1A). Their function is to select cargo for packaging into transport vesicles, and together with membrane-deforming scaffolding proteins such as clathrin and the COPI B-subcomplex, they facilitate the trafficking of proteins and lipids between membrane compartments in the secretory and endocytic pathways. The recent discovery of the evolutionarily ancient AP-5 complex, found on late endosomes and lysosomes, added a new dimension to models of the endomembrane system, and raised the possibility that other undetected membrane-trafficking complexes might exist (Hirst et al., 2011). Therefore, we set out in search of additional members of the AP/COPI subunit families.
## Diagrams of APs and F-COPI.
(A) Structures of the assembled complexes. All six complexes are heterotetramers; the individual subunits are called adaptins in the APs (e.g., γ-adaptin) and COPs in COPI (e.g., γ-COP). The two large subunits in each complex are structurally similar to each other. They are arranged with their N-terminal domains in the core of the complex, and these domains are usually (but not always) followed by a flexible linker and an appendage domain. The medium subunits consist of an N-terminal longin-related domain followed by a C-terminal μ homology domain (MHD). The small subunits consist of a longin-related domain only. (B) Jpred secondary structure predictions of some of the known subunits (all from Homo sapiens), together with new family members from Dictyostelium discoideum (Dd) and Arabidopsis thaliana (At). See also Figure 1—figure supplements 1–4, Figure 1—source data 1, 2.
## Summary table of all subunits identified using reverse HHpred.
The lighter shading indicates where an orthologue was found either below the arbitrary cut-off, by using NCBI BLAST (see Figure 1—figure supplement 3), or by searching a genomic database (e.g., AP-1 μ1 |Naegr1|35900|, JGI). The new complex is called ‘TSET’.
## The search for novel AP-related complexes
Because we were unable to find any promising candidates for new AP/COPI-related machinery using sequence-based searches, we developed a more sensitive tool, designed to search for structural similarity rather than sequence similarity. Using HHpred to analyse every protein in the RefSeq database from 15 organisms, covering a broad span of eukaryotic diversity, we built a ‘reverse HHpred’ database. This database contains potential homologues for >300,000 different proteins (http://reversehhpred.cimr.cam.ac.uk), and can be searched with structures from the Protein Data Bank (PDB). As proof of principle, we used this database to identify all four subunits of the AP-5 complex (Figure 1—figure supplements 1 and 2; Figure 1—source data 1, 2), even though in our previous study only the medium subunit was initially detectable by bioinformatics-based searching (Hirst et al., 2011).
In addition to known proteins, our reverse HHpred database revealed novel candidates for each of the four subunit families, with orthologues present in diverse eukaryotes including plants and Dictyostelium (Figure 1—figure supplements 2–4, Figure 1—source data 1, 2). Secondary structure predictions confirmed that the new family members have similar folds to their counterparts in the AP complexes and COPI (Figure 1B). Only one of these proteins had been characterised functionally: TPLATE (NP\_186827.2), an Arabidopsis protein related to the AP β subunits and β-COP, found in a microscopy-based screen for proteins involved in mitosis and localised to the cell plate (Van Damme et al., 2006; Van Damme et al., 2011). There is some variability between orthologous subunits in different organisms: for instance, Arabidopsis has added an SH3 domain to the C-terminal end of its ‘γαδεζ’ large subunit, while Dictyostelium has lost the μ homology domain (MHD) at the end of its medium subunit; and in general there seems to be much less selective pressure on these genes than on those encoding other AP/COPI family members (e.g., the AP-1 β1 subunits are 58.01% identical in Dictyostelium and Arabidopsis, while the new β family members are only 14.63% identical).
## TSET: a new trafficking complex
To determine whether the four new candidate subunits identified in our searches actually form a complex, we transformed D. discoideum with a GFP-tagged version of its small (σ-like) subunit (Figure 2A), and then used anti-GFP to immunoprecipitate the construct and any associated proteins from cell extracts (Figure 2B). Precipitates were analysed by mass spectrometry, yielding ten proteins considered to be specifically immunoprecipitated (Figure 2—figure supplement 1a). Two of these were the small subunit itself and its GFP tag. Three others were the remaining candidate subunits: XP\_639969.1 (the β-like subunit), XP\_640471.1 (the γαδεζ-like subunit), and XP\_629998.1 (the μ-like subunit), confirming their presence in a complex. Quantification by iBAQ indicated that these three proteins were present in the immunoprecipitate at approximately equimolar levels (Figure 2C, Figure 2—figure supplement 1A), while the small subunit and GFP tag were in ∼15-fold molar excess, probably due to overexpression.
Interestingly, two of the other proteins in the immunoprecipitate, also approximately equimolar to the three coprecipitating subunits, were XP\_642289.1 and XP\_637150.1. Both proteins are predicted to consist of two N-terminal β-propeller domains followed by an α-solenoid (Figure 2D, Figure 2—figure supplement 1C). This type of architecture is found in several coat components, including clathrin heavy chain, SPG11 (associated with AP-5), the α-COP and β′-COP subunits of the COPI coat (B-COPI), and the Sec31 subunit of the COPII coat (Devos et al., 2004). HHpred analyses show that the closest matches for both XP\_642289.1 and XP\_637150.1 are β'-COP, followed by α-COP. Probable orthologues of XP\_642289.1 and XP\_637150.1 can be found in other organisms that have the four core subunits (Figure 1—figure supplement 4). Because proteins with this architecture often act as a coat for transport vesicles, we hypothesize that these proteins may provide a scaffold for the newly identified heterotetramer.
The other three proteins in the immunoprecipitate, secG and vacuolins A and B, appear to be less widespread taxonomically (Figure 2—figure supplement 2 and 3), but are nonetheless suggestive of function. SecG is related to the plasma membrane- and endosome-associated ARNO/cytohesin family of Arf GEFs in animal cells (Shina et al., 2010), and also appears to be equimolar with the core complex. Vacuolins are members of the SPFH (stomatin-prohibitin-flotillin-HflC/K) superfamily. They have been shown to associate with the late vacuole just before exocytosis and also with the plasma membrane (Rauchenberger et al., 1997; Gotthardt et al., 2002), and to contribute to vacuole function (Jenne et al., 1998). However, the amounts of coprecipitating vacuolins were more variable, suggesting that they are less tightly associated with the complex (Figure 2—figure supplement 1A). Thus, like TPLATE, both SecG and the vacuolins have been implicated in membrane traffic, acting at the plasma membrane and/or endosomal compartments.
## Characterisation of the TSET complex in Dictyostelium.
(A) Western blots of axenic D. discoideum expressing either GFP-tagged small subunit (σ-like) or free GFP, under the control of the Actin15 promoter, labelled with anti-GFP. The Ax2 parental cell strain was included as a control, and an antibody against the AP-2α subunit was used to demonstrate that equivalent amounts of protein were loaded. (B) Coomassie blue-stained gel of GFP-tagged small subunit and associated proteins immunoprecipitated with anti-GFP. The GFP-tagged protein is indicated with a red asterix. (C) iBAQ ratios (an estimate of molar ratios) for the proteins that consistently coprecipitated with the GFP-tagged small subunit. All appear to be equimolar with each other, and the higher ratios for the small (σ-like/TSPOON) subunit and GFP are likely to be a consequence of their overexpression, which we also saw in a repeat experiment in which we used the small subunit's own promoter (Figure 2—figure supplement 1). (D) Predicted structure of the N-terminal portion of D. discoideum TTRAY1, shown as a ribbon diagram. (E) Stills from live cell imaging of cells expressing either TSPOON-GFP or free GFP, using TIRF microscopy. The punctate labelling in the TSPOON-GFP-expressing cells indicates that some of the construct is associated with the plasma membrane. See Videos 1 and 2. (F) Western blots of extracts from cells expressing either TSPOON-GFP or free GFP. The post-nuclear supernatants (PNS) were centrifuged at high speed to generate supernatant (cytosol) and pellet fractions. Equal protein loadings were probed with anti-GFP. Whereas the GFP was exclusively cytosolic, a substantial proportion of TSPOON-GFP fractionated into the membrane-containing pellet. (G) Mean generation time (MGT) for control (Ax2) and TSPOON knockout cells. The knockout cells grew slightly faster than the control. (H) Differentiation of the Ax2 control strain and two TSPOON knockout strains (1725 and 1727). All three strains produced fruiting bodies upon starvation. (I) Assay for fluid phase endocytosis. The control and knockout strains took up FITC-dextran at similar rates. (J) Assay for endocytosis of membrane, labelled with FM1-43, showing the time taken to internalise the entire surface area. The knockout strains took significantly longer than the control (*p<0.05; **p<0.01). See also Figure 2—figure supplements 1 and 2, Figure 2; Videos 1 and 2.
## Characterisation of the TSET complex in Dictyostelium
One of the key properties of coat proteins is their ability to cycle on and off membranes. Although by widefield fluorescence microscopy TSPOON-GFP looked diffuse and cytosolic (Figure 2—figure supplement 1B), TIRF imaging showed a punctate pattern, especially in the cells with lower expression, indicating that some of the construct is associated with the plasma membrane (Figure 2E, Figure 2; Video 1). In contrast, free GFP appeared to be entirely cytosolic (Figure 2E, Figure 2; Video 2). In addition, high speed centrifugation of a post-nuclear supernatant showed a substantial amount of TSPOON-GFP coming down in the membrane-containing pellet, in contrast to free GFP, which was exclusively in the supernatant (Figure 2F). These findings indicate that like other coat proteins, the complex is transiently recruited onto a membrane (specifically, the plasma membrane) from a cytosolic pool.
Silencing TPLATE in Arabidopsis produces a very severe phenotype, with impaired growth and differentiation, thought to be caused by defects in clathrin-mediated endocytosis (Van Damme et al., 2006; Van Damme et al., 2011). To investigate the function of TSET in Dictyostelium, we disrupted the TSPOON gene by replacing most of the coding sequence with a selectable marker (Figure 2—figure supplement 1D). Surprisingly, the resulting knockout cells grew at least as fast a control axenic strain (Figure 2G shows the mean generation time); and differentiation also appeared normal, with fruiting bodies forming under appropriate stimuli (Figure 2H). Uptake of FITC-dextran, an assay for fluid phase endocytosis, was unimpaired in the TSPOON knockout cells (Figure 2I); however, uptake of FM1-43, a membrane marker, was slower than in the control (Figure 2J shows the time taken to internalise the entire surface area), indicating that TSET plays a role in plasma membrane turnover, consistent with studies on Arabidopsis. Nevertheless, it is clear that in contrast to Arabidopsis, Dictyostelium can thrive without a functional TSET complex.
Very recently, the discoverers of TPLATE used tandem affinity purification to identify TPLATE binding partners, and found the Arabidopsis orthologues of the TSET components that we identified independently in the present study (Gadeyne et al., 2014). The Arabidopsis pulldowns did not contain any proteins resembling secG or the vacuolins, supporting our hypothesis that these proteins are add-ons to the core heterohexamer. However, Arabidopsis TSET is associated with two additional proteins containing EH domains, which we did not find in our Dictyostelium pulldowns. Some of the Arabidopsis pulldowns also brought down components of the machinery for clathrin-mediated endocytosis, including clathrin itself. Although we also found clathrin and associated proteins in our Dictyostelium immunoprecipitates, these proteins were equally abundant in control immunoprecipitates from non-GFP-expressing cells, indicating that they were contaminants. The differences in proteins that coprecipitate with TSET in the two organisms are probably a reflection of functional differences: TSET knockouts in Arabidopsis are lethal and knockdowns profoundly affect clathrin-mediated endocytosis, while TSET knockdowns in Dictyostelium produce a very mild phenotype.
## TSET is ancient and widespread in eukaryotes
When TPLATE was discovered in Arabidopsis, it was reported to be unique to plant species (Van Damme et al., 2006; Van Damme et al., 2011). Similarly, in the more recent Arabidopsis study, the authors concluded that the complex was plant-specific (Gadeyne et al., 2014). However, these conclusions were based on analyses of plants, yeast, and humans only. Our identification and characterization of homologues of all six subunits in Dictyostelium discoideum, as well as their presence in the excavate Naegleria gruberi, suggested that the evolutionary distribution was much more extensive. In depth homology searching identified orthologues in genomes from across the broad diversity of eukaryotes (Figure 3, Figure 3—source data 1, Figure 3—figure supplement 1), strongly suggesting that the complex was present prior to the LECA.
Although TSET is clearly ancient, its relationship to the other heterotetrameric complexes was unclear from homology searching alone. Consequently, after analyses of the individual subunits (Figure 4—figure supplements 1–7), we performed a phylogenetic analysis on the concatenated set of the four core subunits for direct comparison of TSET with the other AP and COPI complexes (Figure 4A, Figure 4—figure supplement 8). This provided moderate support for TSET as a clade, but strong resolution excluding it from the APs and COPI, as well as backbone resolution between the heterotetramer clades. Thus, TSET is clearly an ancient component of the eukaryotic membrane-trafficking system, distinct from the known heterotetramers.
Phylogenetic analysis of the TTRAYs and their closest relatives, β′-COP and α-COP (Figure 4—figure supplement 9), showed that the paralogues are due to ancient duplications in the TSET and COPI families respectively, which occurred prior to the divergence of the LECA. Together, these findings imply that the ancestor of the TSET, COPI, and AP complexes was a heterohexamer rather than a heterotetramer, consisting of five different proteins, with the two scaffolding proteins present as two identical copies (Figure 4B,C). These scaffolding subunits then duplicated independently in COPI and TSET. The ancestral AP complex may have lost its original scaffolding subunits, although AP-5, the first AP to branch away, is closely associated with SPG11, a β-propeller + α-solenoid protein whose relationship to the TTRAYs and B-COPI is as yet unclear. None of the other APs has any closely associated proteins with this architecture, but AP-1 and AP-2 transiently interact with clathrin, and there may also be a transient association between AP-3 and another β-propeller + α-solenoid protein, Vps41 (Rehling et al., 1999; Cabrera et al., 2010; Asensio et al., 2013).
Although TSET is deduced to have been present in LECA, the complex appears to have been entirely or partially lost in various lineages (Figure 3B). None of the subunits has a full orthologue in opisthokonts (animals and fungi), indicating secondary loss in the line leading to humans. However, the C-terminal domain of TCUP is homologous to the C-terminal domains of the muniscins, opisthokont-specific proteins (Gadeyne et al., 2014) (Figure 4—figure supplement 10). This suggests that in opisthokonts, the TCUP gene retained its 3′ end, which then combined with a new 5′ end encoding an F-BAR domain to generate the muniscin family (Figure 4C). These include the vertebrate proteins FCHo1/2 and the yeast protein Syp1, important players in the endocytic pathway (Reider et al., 2009; Henne et al., 2010; Cocucci et al., 2012; Umasankar et al., 2012; Mayers et al., 2013). The muniscins constitute one of eight families of MHD proteins in humans, and the only family whose evolutionary origin was unexplained until now. The present study indicates not only that the muniscins are homologous to TCUP, but also that they are the sole surviving remnants of the full TSET complex that existed in our pre-opisthokont ancestors.
## Distribution of TSET subunits.
(A) Coulson plot showing the distribution of TSET in a diverse set of representative eukaryotes. Presence of the entire complex in at least four supergroups suggests its presence in the last eukaryotic common ancestor (LECA) with frequent secondary loss. Solid sectors indicate sequences identified and classified using BLAST and HMMer. Empty sectors indicate taxa in which no significant orthologues were identified. Filled sectors in the Holozoa and Fungi represent F-BAR domain-containing FCHo and Syp1, respectively. Taxon name abbreviations are inset. Names in bold indicate taxa with all six components. (B) Deduced evolutionary history of TSET as present in the LECA but independently lost multiple times, either partially or completely. See also Figure 3—source data 1, Figure 3—figure supplement 1.
## Evolution of TSET.
(A) Simplified diagram of the concatenated tree for TSET, APs, and COPI, based on Figure 4—figure supplement 8. Numbers indicate posterior probabilities for MrBayes and PhyloBayes and maxium-likelihood bootstrap values for PhyML and RAxML, in that order. (B) Schematic diagram of TSET. (C) Possible evolution of the three families of heterotetramers: TSET, APs, and COPI. We propose that the earliest ancestral complex was a likely a heterotrimer or a heterohexamer formed from two identical heterotrimers, containing large (red), small (yellow), and scaffolding (blue) subunits. All three of these proteins were composed of known ancient building blocks of the membrane-trafficking system (Vedovato et al., 2009): α-solenoid domains in both the large and scaffolding subunits; two β-propellers in the scaffolding subunit; and a longin domain forming the small subunit. The gene encoding the large subunit then duplicated and mutated to generate the two distinct types of large subunits (red and magenta), and the gene encoding the small subunit also duplicated and mutated (yellow and orange), with one of the two proteins (orange) acquiring a μ homology domain (MHD) to form the ancestral heterotetramer, as proposed by Boehm and Bonifacino (12). However, the scaffolding subunit remained a homodimer. Upon diversification into three separate families, the scaffolding subunit duplicated independently in TSET and COPI, giving rise to TTRAY1 and TTRAY2 in TSET, and to α- and β′-COP in COPI. COPI also acquired a new subunit, ε-COP (purple). The scaffolding subunit may have been lost in the ancestral AP complex, as indicated in the diagram; however, AP-5 is tightly associated with two other proteins, SPG11 and SPG15, and the relationship of SPG11 and SPG15 to TTRAY/B-COPI remains unresolved, so it is possible that SPG11 and SPG15 are highly divergent descendants of the original scaffolding subunits. The other AP complexes are free heterotetramers when in the cytosol, but membrane-associated AP-1 and AP-2 interact with another scaffold, clathrin; and AP-3 has also been proposed to interact transiently with a protein with similar architecture, Vps41 (Rehling et al., 1999; Cabrera et al., 2010; Asensio et al., 2013). So far no scaffold has been proposed for AP-4. Although the order of emergence of TSET and COP relative to adaptins is unresolved, our most recent analyses indicate that, contrary to previous reports (Hirst et al., 2011), AP-5 diverged basally within the adaptin clade, followed by AP-3, AP-4, and APs 1 and 2, all prior to the LECA. This still suggests a primordial bridging of the secretory and phagocytic systems prior to emergence of a trans-Golgi network. The muniscins arose much later, in ancestral opisthokonts, from a translocation of the TSET MHD-encoding sequence to a position immediately downstream from an F-BAR domain-encoding sequence. Another translocation occurred in plants, where an SH3 domain-coding sequence was inserted at the 3′ end of the TSAUCER-coding sequence. See also Figure 4—figure supplements 1–10.
## Phylogenetic analysis of TPLATE, β-COP, and β-adaptin, with TPLATE robustly excluded from the β-COP clade.
In this and all other figure supplements to Figure 4, AP subunits are boxed in blue, F-COPI subunits are boxed in red, and subunits of TSET are boxed in yellow. Node support for critical nodes is shown. Numbers indicate Bayesian posterior probabilities (MrBayes) and bootstrap support from Maximum-likelihood analysis (RAxML). Support values for other nodes are denoted by symbols (see inset).
## Conclusions
TSET is the latest addition to a growing set of trafficking proteins that have ancient distributions, but are frequently lost (Schlacht et al., 2014), or in the case of TSET reduced perhaps with neofunctionalization (Figure 3). This is consistent with the uneven distribution of the individual components (in contrast to the all-or-nothing distribution of AP-5), the additional apparently lineage-specific binding partners in Dictyostelium, and the acquisition of extra domains (e.g., F-BAR in opisthokonts and SH3 in plants) adding lineage-specific function.
Studies on the muniscins may help to explain the different phenotypes of TSET knockouts in Dictyostelium and Arabidopsis. Like Arabidopsis TSET, the muniscins interact with EH domain-containing proteins and participate in clathrin-mediated endocytosis (Reider et al., 2009; Henne et al., 2010; Cocucci et al., 2012; Umasankar et al., 2012; Mayers et al., 2013). Dictyostelium has lost its TCUP MHD, and it seems likely that concomitant with this loss, it also lost some of TSET's binding partners and functions. Nevertheless, we suspect that TSET may predate clathrin-mediated endocytosis, for two reasons. First, AP-1 and AP-2, the two AP complexes that function together with clathrin, are the most recent additions to the AP family (Figure 4A); and second, TSET already has its own β-propeller + α-solenoid scaffold, so it is not clear why it would need clathrin as well. Thus, the interaction between TSET and the clathrin pathway may have evolved considerably later than TSET itself, although still pre-LECA. It is tempting to speculate that TSET was part of the original endocytic machinery, which then became redundant in some organisms as the clathrin pathway took over.
Thus, our bioinformatics tool, reverse HHpred, is able to find novel homologues of known proteins, and could potentially be used to identify new players both in membrane traffic and in other pathways (Figure 1—figure supplement 5). Using this tool, we were able to find the four core subunits of an ancient complex belonging to the same family as the APs and COPI. This ancient complex, TSET, is therefore both the answer to the question of the origin of the last set of MHD proteins in humans, and a major new piece of the puzzle to be incorporated alongside the other membrane-trafficking machinery, as we delve into the history of the eukaryotic cell.
## Construction of the ‘reverse HHpred’ database
The proteomes of various organisms (detailed in Figure 1—figure supplement 2) were downloaded from the National Center for Biotechnology Information archives at ftp://ftp.ncbi.nih.gov/refseq/release/. The *.protein.faa.gz files obtained were then split into separate files, each containing one protein sequence. These were stored such that each directory contained information from only one species (the total number of protein ‘faa’ files searched for each organism were: Arabidopsis thaliana, 35270; Caenorhabditis elegans, 23903; Dictyostelium discoideum, 13262; Dictyostelium purpureum, 12399; Drosophila melanogaster, 22256; Giardia lamblia, 6502; Homo sapiens, 32977; Micromonas pusilla, 10269; Mus musculus, 29897; Naegleria gruberi , 15756; Physcomitrella patens, 35893; Saccharomyces cerevisiae, 5882; Schizosaccharomyces pombe, 5004; Selaginella moellendorffii, 31312; Vitis vinifera, 23492; Volvox carteri, 14429). The latest protein data bank (pdb70), which contains all publicly available 3D structures of proteins, was downloaded from the Gene Center Munich, Ludwig-Maximilians-Universität (LMU) Munich via their web site at: ftp://toolkit.lmb.uni-muenchen.de/pub/HHsearch/databases/hhsearch\_dbs/. The linux rpm version 2.0.11 of the hhsuite software was downloaded from the same website at ftp://toolkit.lmb.uni-muenchen.de/pub/HH-suite/releases/. Each of the faa files was then compared to the pdb70 databank using the hhsearch program from the above suite. The files were tested using the default parameters. Once each protein sequence was tested, the output file was parsed and the hits were extracted and then inserted into a mysql database. The database is searchable by keywords in PDB entries, and therefore is limited to searches where the structure of a given domain structure has been solved. The database is accessible using the link http://reversehhpred.cimr.cam.ac.uk, and searches can be initiated using keywords. Should the link become unavailable, or if you are interested in hosting this yourself please email jpn25@cam.ac.uk for more information. A conceptually similar database, ‘BackPhyre’, has independently been generated, using Phyre (Kelley and Sternberg, 2009) rather than HHpred as a starting point to identify homologues of known proteins based on predicted structural similarities. Like reverse HHpred, BackPhyre is able to find three of the four TSET subunits in Arabidopsis; however, the only eukaryotes represented in BackPhyre are A. thaliana, D. melanogaster, H. sapiens, M. musculus, P. falciparum, and S. cerevisiae; and without additional organisms, such as D. discoideum and N. gruberi, we would not have been able to find the entire TSET complex.
## Data assimilation
The large adaptor subunits share sequence and structure homology, as do the medium and small subunits. Therefore, we were able to combine searches for novel large subunits, or for medium/small subunits. Using the key words ‘clathrin’, ‘adaptor’, ‘adapter’, ‘adaptin’, ‘AP1’, ‘AP2’, ‘AP3’, ‘AP4’, we searched in PDB for solved structures of any large or medium/small subunit in a given organism (11 solved structures for the large subunits and six solved structures for the medium/small subunits were used to initiate searches [Figure 1—figure supplement 1]). These structures span different domains found within the subunits. For each search, a list was output of any proteins found to contain structural homology. Included in this information are the precise amino acids encompassing the region of similarity, the probability score, and most importantly the ‘result number’. A protein with a ‘result number’ of ‘1’ means that there was no other structure in the PDB database that it is more like. Since multiple structures for the various subunits were used, we could also factor in the number of times a particular protein was identified in a search (‘repeats’). These parameters were used as key pieces of evidence to determine how likely a hit in these searches would be. Once the primary data were outputted, all other manipulations were performed in Excel. For the large subunits there were 11 data sets (the 11 structures used to search for homologues), and for the medium/small subunits there were six data sets. The data manipulation was standardised at this point, and the following steps performed to assimilate the data. The data sets were sorted by result number to preclude anything with a result number of >50 (this means that there are 49 other structures in the PDB database that this protein is more similar to). Duplicates, where a protein was identified in multiple searches, were removed with the highest ranking (in ‘result’ terms) kept, and the number of times it was identified recorded in a new column (‘repeats’). The results were the ordered with the lowest ‘Result number’ and the highest ‘Probability’ to give a final list of proteins (Figure 1—source data 1, 2). Generally only proteins with a ‘Result number’ <10, ‘Probability’ >50%, at least 100 amino acids of homology (‘thstt’ to ‘thend’), and ‘Repeats’ at least two times were considered to be real hits. For ease of visualisation, only proteins with Result number <10 or ‘Repeats’ >2 are shown, and other proteins of interest (e.g., FCHo1, Syp1) with Result number <10 that did not fit the criteria listed above are greyed out. The ‘IDs’ have been deduced using NCBI BLAST searches, and have not been experimentally verified. Where the identity is ambiguous (such as the identity of a β-adaptin), a shared homology is suggested.
## Dictyostelium: the search for TSPOON and TCUP
While searching for genes encoding potential components of the complex in four dictyostelid genomes, we could find complete sets in Polysphondylium pallidum and Dictyostelium fasciculatum, but one component each was missing in the databases of predicted proteins of D. discoideum (σ-like subunit) and D. purpureum (μ-like subunit). We identified these genes by tblastn (Camacho et al., 2009), using the most closely related orthologous sequence as query and the chromosomal sequences as target. Gene models were created and refined using the Artemis tool (Carver et al., 2012). These two genes have been given the DictyBase IDs DDB\_G0350235 (D. discoideum TSPOON) and DPU0040472 (D. purpureum TCUP) (www.dictybase.org).
## Dictyostelium expression constructs
The σ-like (TSPOON) coding sequence (CDS) was synthesised (GeneCust) with a BglII restriction site inserted at its 5′ end, its stop codon removed, and a SpeI site inserted at its 3' end, then cloned into pBluescript KSII and sequenced. The CDS was then transferred into a derivative of pDM1005 (Veltman et al., 2009) as a BglII/SpeI fragment, placing GFP at the C terminus, with expression driven from the constitutive actin15 promoter, to generate plasmid pJH101. In addition, the TSPOON promoter and the first 105 bases of the CDS were amplified from Ax2 gemonic DNA by PCR, using primers (5′TATCTCGAGCGTCTTCATCTTCACTATCATTTAATG-3′) and (5′-TAAAAGCTTTTCATATTCACTCTGTTTCTCGTC-3′). The product was cut with XhoI/HindIII, and the 536-bp fragment cloned into the pBluescript KSII plasmid already containing the TSPOON CDS, via the XhoI site in the vector and the silent HindIII site introduced at nucleotide +97 of the TSPOON CDS during its synthesis. The resulting promoter-driven TSPOON CDS was removed by digestion with XhoI/SpeI and inserted into the corresponding sites of pDM323 and pDM450, resulting in expression constructs containing the TSPOON CDS with GFP fused at its C terminus and driven by its own promoter (pDT61 and pDT58 respectively).
## Dictyostelium cell culture and transformation
All of the methods used for cell biological studies on Dictyostelium are described in detail at Bio-protocol (Hirst et al., 2015).
D. discoideum Ax2-derived strains were grown and maintained in HL5 medium (Formedium) containing 200 µg/ml dihydrostreptomycin on tissue culture treated plastic dishes, or shaken at 180 rpm, at 22°C (Kay, 1987). Cells were transformed with expression constructs (30 µg/4 × 106 cells) by electroporation using previously described methods (Knecht and Pang, 1995). Transformants were selected and maintained in axenic medium supplemented with 60 µg/ml hygromycin (pDT58 and pJH101) and 20 µg/ml G418 (pDT61 and Actin15\_GTP; Traynor and Kay, 2007). For the TSPOON knockout, 17.5 µg of the blasticidin disruption cassette, freed from pDT70 by digestion with ApaI and SacII, was added to 4 × 106 Ax2 cells before electroporation. Transformants were selected and maintained in HL5 medium containing 10 µg/ml blasicidin.
## Dictyostelium microscopy and fractionation
Cells were transformed with GFP driven by the actin 15 promoter (A15\_GFP; Traynor and Kay, 2007), or with TSPOON-GFP driven by either the actin 15 promoter (A15\_TSPOON -GFP) or its own promoter (promoter\_TSPOON-GFP). For microscopy, the cells were washed in KK2 (16.5 mM KH2PO4, 3.8 mM K2HPO4, 2 mM MgSO4) at 2 × 107/ml and then transferred into glass bottom dishes (MatTek, Ashland, MA) at 1 × 106/cm2. They were either imaged immediately (vegetative) or allowed to starve for a further 6–8 hr (developed) before imaging live on a Zeiss Axiovert 200 inverted microscope (Carl Zeiss, Jena, Germany) using a Zeiss Plan Achromat 63 × oil immersion objective (numerical aperture 1.4), an OCRA-ER2 camera (Hamamatsu, Hamamatsu, Japan), and Improvision Openlab software (PerkinElmer, Waltham, MA). Various treatments including with or without starvation, fixation, pre-fixation saponin treatment did not reveal obvious membrane-associated labelling in cells expressing either promoter\_TSPOON-GFP and A15\_TSPOON expressing cells.
For fractionation, cells expressing A15\_GFP or promoter\_TSPOON-GFP were grown until they reached a density of 2–4 × 106/ml in selective media, and by microscopy >50% of cells were expressing GFP. Starting with a maximum of 8 × 108 cells, the cells were washed in KK2 buffer and then pelleted at 600 × g for 3 min. The cells were resuspended in PBS with a protease inhibitor cocktail (Roche), lysed by 8 strokes of a motorized Potter–Elvehjem homogenizer followed by 5 strokes through a 21-g needle, and centrifuged at 4100 × g for 32 min to get rid of nuclei and unbroken cells. The postnuclear supernatant was then centrifuged at 50,000 rpm (135,700 × g RCFmax) for 30 min in a TLA-110 rotor (Beckman Coulter) to recover the membrane pellet. The cytosolic supernatant and pellet were run on pre-cast NUPAGE 4–12% BisTris Gels (Novex) at equal protein loadings, and Western blots were probed with an antibody against GFP (Seaman et al., 2009).
## Dictyostelium pulldowns and proteomics
Pulldowns were performed using Dictyostelium discoideum stably expressing TSPOON-GFP under a constitutive (A15\_ TSPOON-GFP) and its own promoter (prom\_TSPOON-GFP). Similar results were found with both cell lines regardless of the promoter. Non-transformed cells were used as a control. Cells were grown until they reached a density of 2–4 × 106/ml in selective media, and by microscopy >50% of cells were expressing GFP. Starting with a maximum of 8 × 108 cells, they were pelleted by centrifugation (600×g for 2 min) and washed twice in KK2 buffer before being resuspended at 2 × 107 cells/ml in KK2 buffer and starved for 4–6 hr at 22°C by shaking at 180 rpm. The cells were then pelleted at 600×g for 3 min and then lysed in 4 ml PBS 1% TX100 plus protease inhibitor cocktail tablet (Roche) for 10 min on ice, and then spun 20,000×g 15 min to get rid of debris and insoluble material. By protein assay the resulting lysate contained 10–15 mg total protein. The lysates were pre-cleared using PA-sepharose 30 min, and then immunoprecipitated using anti-GFP overnight with rotation at 4°C. PA-sepharose was added for 60 min and then the antibody complexes washed with PBS 1%TX100 followed by PBS before elution from beads with 100 mM Tris, 2% SDS 60°C for 10 min. The eluted proteins were precipitated with acetone overnight at −20°C, recovered by spinning 15,000×g 5 min and then resuspending in sample buffer. The samples were run on pre-cast NUPAGE 4–12% BisTris Gels (Novex), stained with SimplyBlue Safe Stain (Invitrogen) and then cut into 8 gel slices. Each gel slice was processed by filter-aided sample preparation solution digest, and the sample was analyzed by liquid chromatography–tandem mass spectrometry in an Orbitrap mass spectrometer (Thermo Scientific; Waltham, MA) (Antrobus and Borner, 2011).
Proteins that came down in the non-transformed control were eliminated, as were any proteins with less than 5 identified peptides, proteins that did not consistently coimmunoprecipitate in three independent experiments, or proteins of very low abundance compared with the bait (i.e., molar ratios of <0.002). The remaining ten proteins were considered to be specifically immunoprecipitated. Normalized peptide intensities were used to estimate the relative abundance of the specific interactors (iBAQ method; Schwanhäusser et al., 2011). For each protein, the values from all five repeats were plotted, including the bait protein and GFP which are clearly overrepresented by overexpression. The relative abundances of proteins were normalized to the median abundance of all proteins across each experiment (i.e., median set to 1.0) and values were then log-transformed and plotted.
## Dictyostelium gene disruption
The TSPOON disruption plasmid was constructed by inserting regions amplified by PCR from upstream and downstream of the TSPOON gene into both side of the blasticidin-resistance cassette in pLPBLP (Faix et al., 2004). The primer pair used to amplify the 5′ region was TCP1 (5′-ACTGGGCCCTGATGTTTACCTCTCTTTGGGTCATCCCATTCTATAC-3′) with σ-TCP2 (5′-AAAAAGCTTTATTACCATTGTTATTGGTAATTAACAAACTATTGATC-3′) and for the 3′ homology TCP3 (5′-A CCGCGGCCGCATAATTCAAAGAGGTCATTTAGATCAAGTTCAATTAG-3′) with TCP4 (5′-CCTCCGCGGCTTCAGGCATTGGTTCAACTTCTTGATTATTCTCAAC -3'). The PCR products were inserted as ApaI/HindIII and NotI/SacII fragments into the corresponding sites in pLPBLP, yielding pDT70.
Growth of control vs mutant strains was assayed in HL5 medium, by calculating the mean generation time, and on Klebsiella aerogenes bacterial lawns, by monitoring the expansion of a spot of 104 cells. Spore viability was also assayed, both with and without detergent treatment, by clonally diluting spores on bacterial lawns and counting the resultant plaques (Kay, 1982).
## Endocytosis assays
Membrane uptake was measured in real time at 22°C with 2 × 106 cells in 1 ml of KK2C containing 10 µM FM1-43 (Life Technologies). Briefly, a 2-ml fluorimeter cuvette containing 0.9 ml of KK2C plus 11 µM FM1-43 was placed in the fluorimeter (PerkinElmer LS50B) with stirring set on high. The uptake was initiated by the addition of 100 µl cells at 2 × 107/ml in KK2C and data collected every 1.2 s at an excitation of 470 nm (slit width 5 nm) and emission of 570 nm (slit width 10 nm) for up to 360 s. The uptake curves were biphasic and the data were normalized against the initial rise in fluorescence, when the cells were first added to the FM1-43, as this essentially corresponds to the dye incorporation into the plasma membrane only (Aguado-Velasco and Bretscher, 1999). The uptake rate was calculated from linear regression of the initial linear phase of the uptake using GraphPad Prism software. The surface area uptake time is 1/slope of the initial phase.
Fluid phase uptake was measured at 22°C using FITC-dextran 70 kDa (Sigma FD-70) by adding 2 mg/ml (final) to cells (1 × 107/ml) in filtered HL5 medium that was shaken at 180 rpm. Duplicate 0.5 ml samples were taken at each time point and diluted in 1 ml of ice-cold HL5 in a microcentrifuge tube held on iced water. Cells were pelleted, the supernatant aspirated, and the pellet washed twice by centrifugation in 1.5 ml ice-cold wash buffer (KK2C plus 0.5%wt/vol BSA) before being lysed in 1 ml of buffer [100 mM Tris–HCl, 0.2% (vol/vol) Triton X-100, pH 8.6] and fluorescence then determined (excitation 490 nm, slit width 2.5 nm; emission 520 nm, slit width 10 nm). Data were normalized to protein content (Traynor and Kay, 2007).
## Comparative genomics
Sequences from Arabidopsis thaliana, Dictyostelium discoideum, and Naegleria gruberi were obtained with our new reverse HHpred tool. These sequences were used to build HMMs for each subunit using HMMer v3.1b1 (http://hmmer.org). HMMs were used to search the protein databases for the organisms in Figure 3A (see Figure 3—source data 1 for the location of each genomic database). Sequences identified as potential homologues were verified through reciprocal BLAST into the genomes of each of the original three sequences. Sequences were considered homologues if they retrieved the correct orthologue as the reciprocal best hit in at least one of the reference genomes, with an e-value at least two orders of magnitude better than the next best hit. New sequences were incorporated into the HMM prior to searching a new genome in order to increase the sensitivity and specificity of the HMM. Genomic protein databases were also searched by BLAST using the closest related organism with an identified sequence as the reference genome. Nucleotide databases (scaffolds or contigs) were also searched using tblastn to ensure that no sequences were missed resulting from incomplete protein databases. The distribution of TSET components is displayed in Coulson plot format using the Coulson plot generator v1.5 (Field et al., 2013).
## Phylogenetic analysis
Identified sequences were combined with the adaptin and COPI sequences from Hirst et al. (2011) into subunit-specific data sets with the intention of concatenation. Data sets were aligned using MUSCLE v3.6 (Edgar, 2004) and masked and trimmed using Mesquite v2.75. Phylogenetic analysis was carried out using MrBayes v.3.2.2 (Ronquist and Huelsenbeck, 2003) and RAxML v7.6.3 (Stamatakis, 2006), hosted on the CIPRES web portal (Miller et al., 2010). MrBayes was run using a mixed model with the gamma parameter until convergence (splits frequencey of 0.1). RAxML was run under the LG + F + CAT model (Lartillot et al., 2009) and bootstrapped with 100 pseudoreplicates. The resulting trees were visualized using FigTree v1.4. Initial data sets were run and long branches were removed. Data sets were then re-aligned and re-run as above. Opisthokont adaptin and COPI sequences were also removed from all data sets except from the TCUP alignment. Data sets were realigned and new phylogenetic analyses were carried out. Remaining sequences were used for concatenation. Sequences were aligned and trimmed, as above, and concatenated using Geneious v7.0.6. Subsequent phylogenetic analysis was carried using PhyloBayes v3.3 (Lartillot et al., 2009) under the LG + CAT model until a splits frequency of 0.1 and 100 sampling points was achieved, and PhyML v3.0, with model testing carried out using ProtTest v3.3. MrBayes and RAxML were used as above. Raw phylogenetic trees were converted into figures using Adobe Illustrator CS4. The models of amino acid sequence evolution are provided in Figure 3—figure supplement 1. The database identifiers of all sequences and their abbreviations and figure annotations are provided in Figure 3—source data 1. All alignments are available in Supplementary file 1.
## Homology modeling
The Phyre v2.0 web server (Kelley and Sternberg, 2009) was used to predict the 3D structures of each TTRAY from A. thaliana, D. discoideum, and N. gruberi. Default settings were used for structural predictions, and structures were visualized using MacPyMOL (www.pymol.org).
## Figures
Figure 1. Diagrams of APs and F-COPI. (A) Structures of the assembled complexes. All six complexes are heterotetramers; the individual subunits are called adaptins in the APs (e.g., γ-adaptin) and COPs in COPI (e.g., γ-COP). The two large subunits in each complex are structurally similar to each other. They are arranged with their N-terminal domains in the core of the complex, and these domains are usually (but not always) followed by a flexible linker and an appendage domain. The medium subunits consist of an N-terminal longin-related domain followed by a C-terminal μ homology domain (MHD). The small subunits consist of a longin-related domain only. (B) Jpred secondary structure predictions of some of the known subunits (all from Homo sapiens), together with new family members from Dictyostelium discoideum (Dd) and Arabidopsis thaliana (At). See also Figure 1—figure supplements 1–4, Figure 1—source data 1, 2. DOI:http://dx.doi.org/10.7554/eLife.02866.003
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Figure 1—figure supplement 1. PDB entries used to search for adaptor-related proteins. DOI:http://dx.doi.org/10.7554/eLife.02866.006
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Figure 1—figure supplement 2. Summary table of all subunits identified using reverse HHpred. The lighter shading indicates where an orthologue was found either below the arbitrary cut-off, by using NCBI BLAST (see Figure 1—figure supplement 3), or by searching a genomic database (e.g., AP-1 μ1 |Naegr1|35900|, JGI). The new complex is called ‘TSET’. DOI:http://dx.doi.org/10.7554/eLife.02866.007
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Figure 1—figure supplement 3. Subunits that failed to be identified using reverse HHpred, but were identified by homology searching using NCBI BLAST. DOI:http://dx.doi.org/10.7554/eLife.02866.008
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Figure 1—figure supplement 4. TSET orthologues in different species. The orthologues were identified by reverse HHpred, except for those in italics, which were found by BLAST searching (NCBI) using closely related organisms. TTRAY1 and TTRAY2 were initially identified by proteomics in a complex with TSET, but could also have been predicted by reverse HHpred as closely related to β′-COP using the PDB structure, 3mkq\_A. In all other organisms TTRAY1 and TTRAY2 were identified by NCBI BLAST (italics). Note that orthologues of TSAUCER in P. patens, and TTRAY 2 in M. pusilla were identified in Phytozome, which is a genomic database hosted by Joint Genome Institute (JGI). Note orthologues of TCUP in D. purpureum and TSPOON in D. discoideum were identified by searching genomic sequences using closely related sequences, and have been manually appended in DictyBase. In these cases corresponding sequences are not at present found at NCBI. Whilst S. moellendorffii and V. vinifera were included in the reverse HHpred database, they were not included in the Coulson plot. DOI:http://dx.doi.org/10.7554/eLife.02866.009
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Figure 1—figure supplement 5. Identification of ENTH/ANTH domain proteins and the AP complexes with which they associate, using reverse HHpred. Reverse HHpred searches were initiated using the key words ‘epsin’ or ‘ENTH’. The PDB structures used were: 1eyh\_A (Chain A, Crystal Structure Of The Epsin N-Terminal Homology (Enth) Domain At 1.56 Angstrom Resolution); 1inz\_A (Chain A, Solution Structure Of The Epsin N-Terminal Homology (Enth) Domain Of Human Epsin); 1xgw\_A (Chain A, The Crystal Structure Of Human Enthoprotin N-Terminal Domain); 3onk\_A (Chain A, Yeast Ent3\_enth Domain), and the output was assimilated in Excel as described for the adaptors. The identity of the hits was determined using NCBI BLAST searching. Note that all of the organisms that have lost AP-4 have also lost its binding partner, tepsin. DOI:http://dx.doi.org/10.7554/eLife.02866.010
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Figure 2. Characterisation of the TSET complex in Dictyostelium. (A) Western blots of axenic D. discoideum expressing either GFP-tagged small subunit (σ-like) or free GFP, under the control of the Actin15 promoter, labelled with anti-GFP. The Ax2 parental cell strain was included as a control, and an antibody against the AP-2α subunit was used to demonstrate that equivalent amounts of protein were loaded. (B) Coomassie blue-stained gel of GFP-tagged small subunit and associated proteins immunoprecipitated with anti-GFP. The GFP-tagged protein is indicated with a red asterix. (C) iBAQ ratios (an estimate of molar ratios) for the proteins that consistently coprecipitated with the GFP-tagged small subunit. All appear to be equimolar with each other, and the higher ratios for the small (σ-like/TSPOON) subunit and GFP are likely to be a consequence of their overexpression, which we also saw in a repeat experiment in which we used the small subunit's own promoter (Figure 2—figure supplement 1). (D) Predicted structure of the N-terminal portion of D. discoideum TTRAY1, shown as a ribbon diagram. (E) Stills from live cell imaging of cells expressing either TSPOON-GFP or free GFP, using TIRF microscopy. The punctate labelling in the TSPOON-GFP-expressing cells indicates that some of the construct is associated with the plasma membrane. See Videos 1 and 2. (F) Western blots of extracts from cells expressing either TSPOON-GFP or free GFP. The post-nuclear supernatants (PNS) were centrifuged at high speed to generate supernatant (cytosol) and pellet fractions. Equal protein loadings were probed with anti-GFP. Whereas the GFP was exclusively cytosolic, a substantial proportion of TSPOON-GFP fractionated into the membrane-containing pellet. (G) Mean generation time (MGT) for control (Ax2) and TSPOON knockout cells. The knockout cells grew slightly faster than the control. (H) Differentiation of the Ax2 control strain and two TSPOON knockout strains (1725 and 1727). All three strains produced fruiting bodies upon starvation. (I) Assay for fluid phase endocytosis. The control and knockout strains took up FITC-dextran at similar rates. (J) Assay for endocytosis of membrane, labelled with FM1-43, showing the time taken to internalise the entire surface area. The knockout strains took significantly longer than the control (*p<0.05; **p<0.01). See also Figure 2—figure supplements 1 and 2, Figure 2; Videos 1 and 2. DOI:http://dx.doi.org/10.7554/eLife.02866.011
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Figure 2—figure supplement 1. Further characterisation of Dictyostelium TSET. (A) iBAQ ratios for the proteins that coprecipitated with TSPOON-GFP, normalized to the median abundance of all proteins across five experiments. ND = not detected. (B) Fluorescence and phase contrast micrographs of cells expressing GFP-tagged TSPOON under the control of its own promoter (Prom-TSPOON-GFP). The construct appears mainly cytosolic. (C) Homology modeling of TTRAYs from A. thaliana, D. discoideum, and N. gruberi, revealing two β-propeller domains followed by an α-solenoid. (D) Disruption of the TSPOON gene. PCR was used to amplify either the wild-type TSPOON gene (in Ax2) or the disrupted TSPOON gene. The resulting products were either left uncut (U) or digested with SmaI (S), which should not cut the wild-type gene, but should cleave the disrupted gene into three bands. Several clones are shown, including HM1725 (200/1 A1). (E) Spore viability after detergent treatment was used to test for integrity of the cellulosic spore and the ability to hatch in a timely manner. The control (Ax2) strain and the knockout (HM1725) strain both showed good viability. (F) Expansion rate of plaques on bacterial lawns. The rates for control (Ax2) and knockout (HM1725, 1727, and 1728) strains were similar initially, but by 2 days the control plaques were larger. (G) Micrographs of plaques from control and knockout strains. DOI:http://dx.doi.org/10.7554/eLife.02866.012
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Figure 2—figure supplement 2. Distribution of secG. DOI:http://dx.doi.org/10.7554/eLife.02866.013
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Figure 2—figure supplement 3. Distribution of vacuolins. DOI:http://dx.doi.org/10.7554/eLife.02866.014
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Video 1. Related to Figure 2. TIRF microscopy of D. discoideum expressing TSPOON-GFP, expressed off its own promoter in TSPOON knockout cells. One frame was collected every second. Dynamic puncta can be seen, indicating that the construct forms patches at the plasma membrane. DOI:http://dx.doi.org/10.7554/eLife.02866.015
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Video 2. Related to Figure 2. TIRF microscopy of D. discoideum expressing free GFP, driven by the Actin15 promoter in TSPOON knockout cells. One frame was collected every second. The signal is diffuse and cytosolic. DOI:http://dx.doi.org/10.7554/eLife.02866.016
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Figure 3. Distribution of TSET subunits. (A) Coulson plot showing the distribution of TSET in a diverse set of representative eukaryotes. Presence of the entire complex in at least four supergroups suggests its presence in the last eukaryotic common ancestor (LECA) with frequent secondary loss. Solid sectors indicate sequences identified and classified using BLAST and HMMer. Empty sectors indicate taxa in which no significant orthologues were identified. Filled sectors in the Holozoa and Fungi represent F-BAR domain-containing FCHo and Syp1, respectively. Taxon name abbreviations are inset. Names in bold indicate taxa with all six components. (B) Deduced evolutionary history of TSET as present in the LECA but independently lost multiple times, either partially or completely. See also Figure 3—source data 1, Figure 3—figure supplement 1. DOI:http://dx.doi.org/10.7554/eLife.02866.017
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Figure 3—figure supplement 1. Models used for phylogenetic analyses. WC = with COPI; WOC = without COPI. DOI:http://dx.doi.org/10.7554/eLife.02866.019
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Figure 4. Evolution of TSET. (A) Simplified diagram of the concatenated tree for TSET, APs, and COPI, based on Figure 4—figure supplement 8. Numbers indicate posterior probabilities for MrBayes and PhyloBayes and maxium-likelihood bootstrap values for PhyML and RAxML, in that order. (B) Schematic diagram of TSET. (C) Possible evolution of the three families of heterotetramers: TSET, APs, and COPI. We propose that the earliest ancestral complex was a likely a heterotrimer or a heterohexamer formed from two identical heterotrimers, containing large (red), small (yellow), and scaffolding (blue) subunits. All three of these proteins were composed of known ancient building blocks of the membrane-trafficking system (Vedovato et al., 2009): α-solenoid domains in both the large and scaffolding subunits; two β-propellers in the scaffolding subunit; and a longin domain forming the small subunit. The gene encoding the large subunit then duplicated and mutated to generate the two distinct types of large subunits (red and magenta), and the gene encoding the small subunit also duplicated and mutated (yellow and orange), with one of the two proteins (orange) acquiring a μ homology domain (MHD) to form the ancestral heterotetramer, as proposed by Boehm and Bonifacino (12). However, the scaffolding subunit remained a homodimer. Upon diversification into three separate families, the scaffolding subunit duplicated independently in TSET and COPI, giving rise to TTRAY1 and TTRAY2 in TSET, and to α- and β′-COP in COPI. COPI also acquired a new subunit, ε-COP (purple). The scaffolding subunit may have been lost in the ancestral AP complex, as indicated in the diagram; however, AP-5 is tightly associated with two other proteins, SPG11 and SPG15, and the relationship of SPG11 and SPG15 to TTRAY/B-COPI remains unresolved, so it is possible that SPG11 and SPG15 are highly divergent descendants of the original scaffolding subunits. The other AP complexes are free heterotetramers when in the cytosol, but membrane-associated AP-1 and AP-2 interact with another scaffold, clathrin; and AP-3 has also been proposed to interact transiently with a protein with similar architecture, Vps41 (Rehling et al., 1999; Cabrera et al., 2010; Asensio et al., 2013). So far no scaffold has been proposed for AP-4. Although the order of emergence of TSET and COP relative to adaptins is unresolved, our most recent analyses indicate that, contrary to previous reports (Hirst et al., 2011), AP-5 diverged basally within the adaptin clade, followed by AP-3, AP-4, and APs 1 and 2, all prior to the LECA. This still suggests a primordial bridging of the secretory and phagocytic systems prior to emergence of a trans-Golgi network. The muniscins arose much later, in ancestral opisthokonts, from a translocation of the TSET MHD-encoding sequence to a position immediately downstream from an F-BAR domain-encoding sequence. Another translocation occurred in plants, where an SH3 domain-coding sequence was inserted at the 3′ end of the TSAUCER-coding sequence. See also Figure 4—figure supplements 1–10. DOI:http://dx.doi.org/10.7554/eLife.02866.020
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Figure 4—figure supplement 1. Phylogenetic analysis of TPLATE, β-COP, and β-adaptin, with TPLATE robustly excluded from the β-COP clade. In this and all other figure supplements to Figure 4, AP subunits are boxed in blue, F-COPI subunits are boxed in red, and subunits of TSET are boxed in yellow. Node support for critical nodes is shown. Numbers indicate Bayesian posterior probabilities (MrBayes) and bootstrap support from Maximum-likelihood analysis (RAxML). Support values for other nodes are denoted by symbols (see inset). DOI:http://dx.doi.org/10.7554/eLife.02866.021
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Figure 4—figure supplement 2. Phylogenetic analysis of TPLATE and β-adaptin subunits (β-COP removed) showing, with weak support, that TPLATE is excluded from the adaptin clade. DOI:http://dx.doi.org/10.7554/eLife.02866.022
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Figure 4—figure supplement 3. Phylogenetic analysis of TSAUCER, γ-COP, and γαδεζ-adaptin subunits, with TCUP robustly excluded from the γ-COP clade, and weakly excluded from the adaptin clade. DOI:http://dx.doi.org/10.7554/eLife.02866.023
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Figure 4—figure supplement 4. Phylogenetic analysis of TSAUCER and γαδεζ-adaptin subunits (γ-COP removed), showing weak support for the exclusion of TSAUCER from the adaptin clade. DOI:http://dx.doi.org/10.7554/eLife.02866.024
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Figure 4—figure supplement 5. Phylogenetic analysis of TCUP, δ-COP, and μ-adaptin subunits, with TSAUCER robustly excluded from the δ-COP clade and weakly excluded from the adaptin clade. DOI:http://dx.doi.org/10.7554/eLife.02866.025
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Figure 4—figure supplement 6. Phylogenetic analysis of TCUP and μ-adaptin subunits (δ-COP removed), showing weak support for the exclusion of TCUP from the adaptin clade. DOI:http://dx.doi.org/10.7554/eLife.02866.026
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Figure 4—figure supplement 7. Phylogenetic analysis of TSPOON with ζ-COP and σ–adaptin subunits with moderate support for the exclusion of TSPOON from both the COPI and adaptin clades, in addition to moderate support for the monophyly of the TSPOON clade. DOI:http://dx.doi.org/10.7554/eLife.02866.027
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Figure 4—figure supplement 8. TSET is a phylogenetically distinct lineage from F-COPI and the AP complexes. Phylogenetic analysis of the heterotetrameric complexes: F-COPI (orange), TSET (purple), and AP (magenta, blue, red, green, and yellow for 5, 3, 1, 2, and 4, respectively), shows strong, weak, and moderate support for clades of each complex, respectively. Node support for critical nodes is shown. Numbers indicate Bayesian posterior probabilities (MrBayes and PhyloBayes) and bootstrap support from Maximum-likelihood analysis (PhyML and RAXML). Support values for other nodes are denoted by symbols (see inset). DOI:http://dx.doi.org/10.7554/eLife.02866.028
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Figure 4—figure supplement 9. Phylogenetic analysis of TTRAY1, TTRAY2, α-COP, and β′-COP. TTRAYs 1 and 2, and COPI α and β′, arose from separate gene duplications, indicating that the ancestral complex had only one such protein, although possibly present as two identical copies. Phylogenetic analysis of α- and β′-COPI (red), and TTRAYs 1 and 2 (yellow), shows a well supported COPI clade excluding all of the TTRAY1 and 2 sequences, suggesting that the duplications giving rise to these proteins occurred independently, and the utilization of two different outer coat members occurred through convergent evolution. Node support for critical nodes is shown. Numbers indicate Bayesian posterior probabilities (MrBayes) and bootstrap support from Maximum-likelihood analysis (RAxML). Support values for other nodes are denoted by symbols (see inset). DOI:http://dx.doi.org/10.7554/eLife.02866.029
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Figure 4—figure supplement 10. Muniscin family members identified by reverse HHpred, using the following PDB structures. 2V0O\_A (Chain A, Fcho2 F-Bar Domain); 3 G9H\_A (Chain A, Crystal Structure Of The C-Terminal Mu Homology Domain Of Syp1); 3G9G\_A (Chain A, Crystal Structure Of The N-Terminal EfcF-Bar Domain Of Syp1). DOI:http://dx.doi.org/10.7554/eLife.02866.030
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item-0 at level 0: unspecified: group _root_
item-1 at level 1: title: KRAB-zinc finger protein gene ex ... retrotransposons in the murine lineage
item-2 at level 2: paragraph: Gernot Wolf; The Eunice Kennedy ... tes of Health Bethesda United States
item-3 at level 2: section_header: Abstract
item-4 at level 3: text: The Krüppel-associated box zinc ... edundant role restricting TE activity.
item-5 at level 2: section_header: Introduction
item-6 at level 3: text: Nearly half of the human and mou ... s are active beyond early development.
item-7 at level 3: text: TEs, especially long terminal re ... f evolutionarily young KRAB-ZFP genes.
item-8 at level 2: section_header: Mouse KRAB-ZFPs target retrotransposons
item-9 at level 3: text: We analyzed the RNA expression p ... duplications (Kauzlaric et al., 2017).
item-10 at level 3: text: To determine the binding sites o ... ctive in the early embryo (Figure 1A).
item-11 at level 3: text: We generally observed that KRAB- ... responsible for this silencing effect.
item-12 at level 3: text: To further test the hypothesis t ... t easily evade repression by mutation.
item-13 at level 3: text: Our KRAB-ZFP ChIP-seq dataset al ... ntirely shift the mode of DNA binding.
item-14 at level 2: section_header: KRAB-ZFP binding to ETn retrotransposons.
item-15 at level 3: text: (A) Comparison of the PBSLys1,2 ... (adapted from Legiewicz et al., 2010).
item-16 at level 2: section_header: KRAB-ZFP genes clusters in the m ... that were investigated in this study.
item-17 at level 3: text: * Number of protein-coding KRAB- ... ChIP-seq was performed in this study.
item-18 at level 2: section_header: Genetic deletion of KRAB-ZFP gen ... leads to retrotransposon reactivation
item-19 at level 3: text: The majority of KRAB-ZFP genes a ... ung et al., 2014; Deniz et al., 2018).
item-20 at level 2: section_header: KRAB-ZFP cluster deletions license TE-borne enhancers
item-21 at level 3: text: We next used our RNA-seq dataset ... vating effects of TEs on nearby genes.
item-22 at level 3: text: While we generally observed that ... he internal region and not on the LTR.
item-23 at level 2: section_header: ETn retrotransposition in Chr4-cl KO and WT mice
item-24 at level 3: text: IAP, ETn/ETnERV and MuLV/RLTR4 r ... s may contribute to reduced viability.
item-25 at level 3: text: We reasoned that retrotransposon ... Tn insertions at a high recovery rate.
item-26 at level 3: text: Using this dataset, we first con ... nsertions in our pedigree (Figure 4A).
item-27 at level 3: text: To validate some of the novel ET ... ess might have truncated this element.
item-28 at level 3: text: Besides novel ETn insertions tha ... tions (Figure 4—figure supplement 3D).
item-29 at level 3: text: Finally, we asked whether there ... s clearly also play an important role.
item-30 at level 2: section_header: Confirmation of novel ETn insertions identified by capture-seq.
item-31 at level 3: text: (A) PCR validation of novel ETn ... color gradient indicates log10(RPM+1).
item-32 at level 2: section_header: Discussion
item-33 at level 3: text: C2H2 zinc finger proteins, about ... ) depending upon their insertion site.
item-34 at level 2: section_header: Cell lines and transgenic mice
item-35 at level 3: text: Mouse ES cells and F9 EC cells w ... KO/KO and KO/WT (B6/129 F2) offspring.
item-36 at level 2: section_header: Generation of KRAB-ZFP expressing cell lines
item-37 at level 3: text: KRAB-ZFP ORFs were PCR-amplified ... led and further expanded for ChIP-seq.
item-38 at level 2: section_header: CRISPR/Cas9 mediated deletion of KRAB-ZFP clusters and an MMETn insertion
item-39 at level 3: text: All gRNAs were expressed from th ... PCR genotyping (Supplementary file 3).
item-40 at level 2: section_header: ChIP-seq analysis
item-41 at level 3: text: For ChIP-seq analysis of KRAB-ZF ... 010 or Khil et al., 2012 respectively.
item-42 at level 3: text: ChIP-seq libraries were construc ... were re-mapped using Bowtie (--best).
item-43 at level 2: section_header: Luciferase reporter assays
item-44 at level 3: text: For KRAB-ZFP repression assays, ... after transfection as described above.
item-45 at level 2: section_header: RNA-seq analysis
item-46 at level 3: text: Whole RNA was purified using RNe ... lemented in the R function p.adjust().
item-47 at level 2: section_header: Reduced representation bisulfite sequencing (RRBS-seq)
item-48 at level 3: text: For RRBS-seq analysis, Chr4-cl W ... h sample were considered for analysis.
item-49 at level 2: section_header: Retrotransposition assay
item-50 at level 3: text: The retrotransposition vectors p ... were stained with Amido Black (Sigma).
item-51 at level 2: section_header: Capture-seq screen
item-52 at level 3: text: To identify novel retrotransposo ... assembly using the Unicycler software.
item-53 at level 2: section_header: Tables
item-54 at level 3: table with [9x5]
item-54 at level 4: caption: Table 1. * Number of protein-coding KRAB-ZFP genes identified in a previously published screen (Imbeault et al., 2017) and the ChIP-seq data column indicates the number of KRAB-ZFPs for which ChIP-seq was performed in this study.
item-55 at level 3: table with [31x5]
item-55 at level 4: caption: Key resources table
item-56 at level 2: section_header: Figures
item-57 at level 2: picture
item-57 at level 3: caption: Figure 1. Genome-wide binding patterns of mouse KRAB-ZFPs. (A) Probability heatmap of KRAB-ZFP binding to TEs. Blue color intensity (main field) corresponds to -log10 (adjusted p-value) enrichment of ChIP-seq peak overlap with TE groups (Fisher’s exact test). The green/red color intensity (top panel) represents mean KAP1 (GEO accession: GSM1406445) and H3K9me3 (GEO accession: GSM1327148) enrichment (respectively) at peaks overlapping significantly targeted TEs (adjusted p-value<1e-5) in WT ES cells. (B) Summarized ChIP-seq signal for indicated KRAB-ZFPs and previously published KAP1 and H3K9me3 in WT ES cells across 127 intact ETn elements. (C) Heatmaps of KRAB-ZFP ChIP-seq signal at ChIP-seq peaks. For better comparison, peaks for all three KRAB-ZFPs were called with the same parameters (p<1e-10, peak enrichment >20). The top panel shows a schematic of the arrangement of the contact amino acid composition of each zinc finger. Zinc fingers are grouped and colored according to similarity, with amino acid differences relative to the five consensus fingers highlighted in white.
item-58 at level 2: picture
item-58 at level 3: caption: Figure 1—figure supplement 1. ES cell-specific expression of KRAB-ZFP gene clusters. (A) Heatmap showing expression patterns of mouse KRAB-ZFPs in 40 mouse tissues and cell lines (ENCODE). Heatmap colors indicate gene expression levels in log2 transcripts per million (TPM). The asterisk indicates a group of 30 KRAB-ZFPs that are exclusively expressed in ES cells. (B) Physical location of the genes encoding for the 30 KRAB-ZFPs that are exclusively expressed in ES cells. (C) Phylogenetic (Maximum likelihood) tree of the KRAB domains of mouse KRAB-ZFPs. KRAB-ZFPs encoded on the gene clusters on chromosome 2 and 4 are highlighted. The scale bar at the bottom indicates amino acid substitutions per site.
item-59 at level 2: picture
item-59 at level 3: caption: Figure 1—figure supplement 2. KRAB-ZFP binding motifs and their repression activity. (A) Comparison of computationally predicted (bottom) and experimentally determined (top) KRAB-ZFP binding motifs. Only significant pairs are shown (FDR < 0.1). (B) Luciferase reporter assays to confirm KRAB-ZFP repression of the identified target sites. Bars show the luciferase activity (normalized to Renilla luciferase) of reporter plasmids containing the indicated target sites cloned upstream of the SV40 promoter. Reporter plasmids were co-transfected into 293 T cells with a Renilla luciferase plasmid for normalization and plasmids expressing the targeting KRAB-ZFP. Normalized mean luciferase activity (from three replicates) is shown relative to luciferase activity of the reporter plasmid co-transfected with an empty pcDNA3.1 vector.
item-60 at level 2: picture
item-60 at level 3: caption: Figure 1—figure supplement 3. KRAB-ZFP binding to ETn retrotransposons. (A) Comparison of the PBSLys1,2 sequence with Zfp961 binding motifs in nonrepetitive peaks (Nonrep) and peaks at ETn elements. (B) Retrotransposition assays of original (ETnI1-neoTNF and MusD2-neoTNF Ribet et al., 2004) and modified reporter vectors where the Rex2 or Gm13051 binding motifs where removed. Schematic of reporter vectors are displayed at the top. HeLa cells were transfected as described in the Materials and Methods section and neo-resistant colonies, indicating retrotransposition events, were selected and stained. (C) Stem-loop structure of the ETn RNA export signal, the Gm13051 motif on the corresponding DNA is marked with red circles, the part of the motif that was deleted is indicated with grey crosses (adapted from Legiewicz et al., 2010).
item-61 at level 2: picture
item-61 at level 3: caption: Figure 2. Retrotransposon reactivation in KRAB-ZFP cluster KO ES cells. (A) RNA-seq analysis of TE expression in five KRAB-ZFP cluster KO ES cells. Green and grey squares on top of the panel represent KRAB-ZFPs with or without ChIP-seq data, respectively, within each deleted gene cluster. Reactivated TEs that are bound by one or several KRAB-ZFPs are indicated by green squares in the panel. Significantly up- and downregulated elements (adjusted p-value<0.05) are highlighted in red and green, respectively. (B) Differential KAP1 binding and H3K9me3 enrichment at TE groups (summarized across all insertions) in Chr2-cl and Chr4-cl KO ES cells. TE groups targeted by one or several KRAB-ZFPs encoded within the deleted clusters are highlighted in blue (differential enrichment over the entire TE sequences) and red (differential enrichment at TE regions that overlap with KRAB-ZFP ChIP-seq peaks). (C) DNA methylation status of CpG sites at indicated TE groups in WT and Chr4-cl KO ES cells grown in serum containing media or in hypomethylation-inducing media (2i + Vitamin C). P-values were calculated using paired t-test.
item-62 at level 2: picture
item-62 at level 3: caption: Figure 2—figure supplement 1. Epigenetic changes at TEs and TE-borne enhancers in KRAB-ZFP cluster KO ES cells. (A) Differential analysis of summative (all individual insertions combined) H3K9me3 enrichment at TE groups in Chr10-cl, Chr13.1-cl and Chr13.2-cl KO ES cells. TE groups targeted by one or several KRAB-ZFPs encoded within the deleted clusters are highlighted in orange (differential enrichment over the entire TE sequences) and red (differential enrichment at TE regions that overlap with KRAB-ZFP ChIP-seq peaks). (B) Top: Schematic view of the Cd59a/Cd59b locus with a 5’ truncated ETn insertion. ChIP-seq (Input subtracted from ChIP) data for overexpressed epitope-tagged Gm13051 (a Chr4-cl KRAB-ZFP) in F9 EC cells, and re-mapped KAP1 (GEO accession: GSM1406445) and H3K9me3 (GEO accession: GSM1327148) in WT ES cells are shown together with RNA-seq data from Chr4-cl WT and KO ES cells (mapped using Bowtie (-a -m 1 --strata -v 2) to exclude reads that cannot be uniquely mapped). Bottom: Transcriptional activity of a 5 kb fragment with or without fragments of the ETn insertion was tested by luciferase reporter assay in Chr4-cl WT and KO ES cells.
item-63 at level 2: picture
item-63 at level 3: caption: Figure 3. TE-dependent gene activation in KRAB-ZFP cluster KO ES cells. (A) Differential gene expression in Chr2-cl and Chr4-cl KO ES cells. Significantly up- and downregulated genes (adjusted p-value<0.05) are highlighted in red and green, respectively, KRAB-ZFP genes within the deleted clusters are shown in blue. (B) Correlation of TEs and gene deregulation. Plots show enrichment of TE groups within 100 kb of up- and downregulated genes relative to all genes. Significantly overrepresented LTR and LINE groups (adjusted p-value<0.1) are highlighted in blue and red, respectively. (C) Schematic view of the downstream region of Chst1 where a 5’ truncated ETn insertion is located. ChIP-seq (Input subtracted from ChIP) data for overexpressed epitope-tagged Gm13051 (a Chr4-cl KRAB-ZFP) in F9 EC cells, and re-mapped KAP1 (GEO accession: GSM1406445) and H3K9me3 (GEO accession: GSM1327148) in WT ES cells are shown together with RNA-seq data from Chr4-cl WT and KO ES cells (mapped using Bowtie (-a -m 1 --strata -v 2) to exclude reads that cannot be uniquely mapped). (D) RT-qPCR analysis of Chst1 mRNA expression in Chr4-cl WT and KO ES cells with or without the CRISPR/Cas9 deleted ETn insertion near Chst1. Values represent mean expression (normalized to Gapdh) from three biological replicates per sample (each performed in three technical replicates) in arbitrary units. Error bars represent standard deviation and asterisks indicate significance (p<0.01, Student’s t-test). n.s.: not significant. (E) Mean coverage of ChIP-seq data (Input subtracted from ChIP) in Chr4-cl WT and KO ES cells over 127 full-length ETn insertions. The binding sites of the Chr4-cl KRAB-ZFPs Rex2 and Gm13051 are indicated by dashed lines.
item-64 at level 2: picture
item-64 at level 3: caption: Figure 4. ETn retrotransposition in Chr4-cl KO mice. (A) Pedigree of mice used for transposon insertion screening by capture-seq in mice of different strain backgrounds. The number of novel ETn insertions (only present in one animal) are indicated. For animals whose direct ancestors have not been screened, the ETn insertions are shown in parentheses since parental inheritance cannot be excluded in that case. Germ line insertions are indicated by asterisks. All DNA samples were prepared from tail tissues unless noted (-S: spleen, -E: ear, -B:Blood) (B) Statistical analysis of ETn insertion frequency in tail tissue from 30 Chr4-cl KO, KO/WT and WT mice that were derived from one Chr4-c KO x KO/WT and two Chr4-cl KO/WT x KO/WT matings. Only DNA samples that were collected from juvenile tails were considered for this analysis. P-values were calculated using one-sided Wilcoxon Rank Sum Test. In the last panel, KO, WT and KO/WT mice derived from all matings were combined for the statistical analysis.
item-65 at level 2: picture
item-65 at level 3: caption: Figure 4—figure supplement 1. Birth statistics of KRAB-ZFP cluster KO mice and TE reactivation in adult tissues. (A) Birth statistics of Chr4- and Chr2-cl mice derived from KO/WT x KO/WT matings in different strain backgrounds. (B) RNA-seq analysis of TE expression in Chr2- (left) and Chr4-cl (right) KO tissues. TE groups with the highest reactivation phenotype in ES cells are shown separately. Significantly up- and downregulated elements (adjusted p-value<0.05) are highlighted in red and green, respectively. Experiments were performed in at least two biological replicates.
item-66 at level 2: picture
item-66 at level 3: caption: Figure 4—figure supplement 2. Identification of polymorphic ETn and MuLV retrotransposon insertions in Chr4-cl KO and WT mice. Heatmaps show normalized capture-seq read counts in RPM (Read Per Million) for identified polymorphic ETn (A) and MuLV (B) loci in different mouse strains. Only loci with strong support for germ line ETn or MuLV insertions (at least 100 or 3000 ETn or MuLV RPM, respectively) in at least two animals are shown. Non-polymorphic insertion loci with high read counts in all screened mice were excluded for better visibility. The sample information (sample name and cell type/tissue) is annotated at the bottom, with the strain information indicated by color at the top. The color gradient indicates log10(RPM+1).
item-67 at level 2: picture
item-67 at level 3: caption: Figure 4—figure supplement 3. Confirmation of novel ETn insertions identified by capture-seq. (A) PCR validation of novel ETn insertions in genomic DNA of three littermates (IDs: T09673, T09674 and T00436) and their parents (T3913 and T3921). Primer sequences are shown in Supplementary file 3. (B) ETn capture-seq read counts (RPM) at putative novel somatic (loci identified exclusively in one single animal), novel germ line (loci identified in several littermates) insertions, and at B6 reference ETn elements. (C) Heatmap shows capture-seq read counts (RPM) of a Chr4-cl KO mouse (ID: C6733) as determined in different tissues. Each row represents a novel ETn locus that was identified in at least one tissue. The color gradient indicates log10(RPM+1). (D) Heatmap shows the capture-seq RPM in technical replicates using the same Chr4-cl KO DNA sample (rep1/rep2) or replicates with DNA samples prepared from different sections of the tail from the same mouse at different ages (tail1/tail2). Each row represents a novel ETn locus that was identified in at least one of the displayed samples. The color gradient indicates log10(RPM+1).
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# KRAB-zinc finger protein gene expansion in response to active retrotransposons in the murine lineage
Gernot Wolf; The Eunice Kennedy Shriver National Institute of Child Health and Human Development, The National Institutes of Health Bethesda United States; Alberto de Iaco; School of Life Sciences, École Polytechnique Fédérale de Lausanne (EPFL) Lausanne Switzerland; Ming-An Sun; The Eunice Kennedy Shriver National Institute of Child Health and Human Development, The National Institutes of Health Bethesda United States; Melania Bruno; The Eunice Kennedy Shriver National Institute of Child Health and Human Development, The National Institutes of Health Bethesda United States; Matthew Tinkham; The Eunice Kennedy Shriver National Institute of Child Health and Human Development, The National Institutes of Health Bethesda United States; Don Hoang; The Eunice Kennedy Shriver National Institute of Child Health and Human Development, The National Institutes of Health Bethesda United States; Apratim Mitra; The Eunice Kennedy Shriver National Institute of Child Health and Human Development, The National Institutes of Health Bethesda United States; Sherry Ralls; The Eunice Kennedy Shriver National Institute of Child Health and Human Development, The National Institutes of Health Bethesda United States; Didier Trono; School of Life Sciences, École Polytechnique Fédérale de Lausanne (EPFL) Lausanne Switzerland; Todd S Macfarlan; The Eunice Kennedy Shriver National Institute of Child Health and Human Development, The National Institutes of Health Bethesda United States
## Abstract
The Krüppel-associated box zinc finger protein (KRAB-ZFP) family diversified in mammals. The majority of human KRAB-ZFPs bind transposable elements (TEs), however, since most TEs are inactive in humans it is unclear whether KRAB-ZFPs emerged to suppress TEs. We demonstrate that many recently emerged murine KRAB-ZFPs also bind to TEs, including the active ETn, IAP, and L1 families. Using a CRISPR/Cas9-based engineering approach, we genetically deleted five large clusters of KRAB-ZFPs and demonstrate that target TEs are de-repressed, unleashing TE-encoded enhancers. Homozygous knockout mice lacking one of two KRAB-ZFP gene clusters on chromosome 2 and chromosome 4 were nonetheless viable. In pedigrees of chromosome 4 cluster KRAB-ZFP mutants, we identified numerous novel ETn insertions with a modest increase in mutants. Our data strongly support the current model that recent waves of retrotransposon activity drove the expansion of KRAB-ZFP genes in mice and that many KRAB-ZFPs play a redundant role restricting TE activity.
## Introduction
Nearly half of the human and mouse genomes consist of transposable elements (TEs). TEs shape the evolution of species, serving as a source for genetic innovation (Chuong et al., 2016; Frank and Feschotte, 2017). However, TEs also potentially harm their hosts by insertional mutagenesis, gene deregulation and activation of innate immunity (Maksakova et al., 2006; Kano et al., 2007; Brodziak et al., 2012; Hancks and Kazazian, 2016). To protect themselves from TE activity, host organisms have developed a wide range of defense mechanisms targeting virtually all steps of the TE life cycle (Dewannieux and Heidmann, 2013). In tetrapods, KRAB zinc finger protein (KRAB-ZFP) genes have amplified and diversified, likely in response to TE colonization (Thomas and Schneider, 2011; Najafabadi et al., 2015; Wolf et al., 2015a; Wolf et al., 2015b; Imbeault et al., 2017). Conventional ZFPs bind DNA using tandem arrays of C2H2 zinc finger domains, each capable of specifically interacting with three nucleotides, whereas some zinc fingers can bind two or four nucleotides and include DNA backbone interactions depending on target DNA structure (Patel et al., 2018). This allows KRAB-ZFPs to flexibly bind to large stretches of DNA with high affinity. The KRAB domain binds the corepressor KAP1, which in turn recruits histone modifying enzymes including the NuRD histone deacetylase complex and the H3K9-specific methylase SETDB1 (Schultz et al., 2002; Sripathy et al., 2006), which induces persistent and heritable gene silencing (Groner et al., 2010). Deletion of KAP1 (Rowe et al., 2010) or SETDB1 (Matsui et al., 2010) in mouse embryonic stem (ES) cells induces TE reactivation and cell death, but only minor phenotypes in differentiated cells, suggesting KRAB-ZFPs are most important during early embryogenesis where they mark TEs for stable epigenetic silencing that persists through development. However, SETDB1-containing complexes are also required to repress TEs in primordial germ cells (Liu et al., 2014) and adult tissues (Ecco et al., 2016), indicating KRAB-ZFPs are active beyond early development.
TEs, especially long terminal repeat (LTR) retrotransposons, also known as endogenous retroviruses (ERVs), can affect expression of neighboring genes through their promoter and enhancer functions (Macfarlan et al., 2012; Wang et al., 2014; Thompson et al., 2016). KAP1 deletion in mouse ES cells causes rapid gene deregulation (Rowe et al., 2013), indicating that KRAB-ZFPs may regulate gene expression by recruiting KAP1 to TEs. Indeed, Zfp809 knock-out (KO) in mice resulted in transcriptional activation of a handful of genes in various tissues adjacent to ZFP809-targeted VL30-Pro elements (Wolf et al., 2015b). It has therefore been speculated that KRAB-ZFPs bind to TE sequences to domesticate them for gene regulatory innovation (Ecco et al., 2017). This idea is supported by the observation that many human KRAB-ZFPs target TE groups that have lost their coding potential millions of years ago and that KRAB-ZFP target sequences within TEs are in some cases under purifying selection (Imbeault et al., 2017). However, there are also clear signs of an evolutionary arms-race between human TEs and KRAB-ZFPs (Jacobs et al., 2014), indicating that some KRAB-ZFPs may limit TE mobility for stretches of evolutionary time, prior to their ultimate loss from the genome or adaptation for other regulatory functions. Here we use the laboratory mouse, which has undergone a recent expansion of the KRAB-ZFP family, to determine the in vivo requirement of the majority of evolutionarily young KRAB-ZFP genes.
## Mouse KRAB-ZFPs target retrotransposons
We analyzed the RNA expression profiles of mouse KRAB-ZFPs across a wide range of tissues to identify candidates active in early embryos/ES cells. While the majority of KRAB-ZFPs are expressed at low levels and uniformly across tissues, a group of KRAB-ZFPs are highly and almost exclusively expressed in ES cells (Figure 1—figure supplement 1A). About two thirds of these KRAB-ZFPs are physically linked in two clusters on chromosome 2 (Chr2-cl) and 4 (Chr4-cl) (Figure 1—figure supplement 1B). These two clusters encode 40 and 21 KRAB-ZFP annotated genes, respectively, which, with one exception on Chr4-cl, do not have orthologues in rat or any other sequenced mammals (Supplementary file 1). The KRAB-ZFPs within these two genomic clusters also group together phylogenetically (Figure 1—figure supplement 1C), indicating these gene clusters arose by a series of recent segmental gene duplications (Kauzlaric et al., 2017).
To determine the binding sites of the KRAB-ZFPs within these and other gene clusters, we expressed epitope-tagged KRAB-ZFPs using stably integrating vectors in mouse embryonic carcinoma (EC) or ES cells (Table 1, Supplementary file 1) and performed chromatin immunoprecipitation followed by deep sequencing (ChIP-seq). We then determined whether the identified binding sites are significantly enriched over annotated TEs and used the non-repetitive peak fraction to identify binding motifs. We discarded 7 of 68 ChIP-seq datasets because we could not obtain a binding motif or a target TE and manual inspection confirmed low signal to noise ratio. Of the remaining 61 KRAB-ZFPs, 51 significantly overlapped at least one TE subfamily (adjusted p-value<1e-5). Altogether, 81 LTR retrotransposon, 18 LINE, 10 SINE and one DNA transposon subfamilies were targeted by at least one of the 51 KRAB-ZFPs (Figure 1A and Supplementary file 1). Chr2-cl KRAB-ZFPs preferably bound IAPEz retrotransposons and L1-type LINEs, while Chr4-cl KRAB-ZFPs targeted various retrotransposons, including the closely related MMETn (hereafter referred to as ETn) and ETnERV (also known as MusD) elements (Figure 1A). ETn elements are non-autonomous LTR retrotransposons that require trans-complementation by the fully coding ETnERV elements that contain Gag, Pro and Pol genes (Ribet et al., 2004). These elements have accumulated to ~240 and~100 copies in the reference C57BL/6 genome, respectively, with ~550 solitary LTRs (Baust et al., 2003). Both ETn and ETnERVs are still active, generating polymorphisms and mutations in several mouse strains (Gagnier et al., 2019). The validity of our ChIP-seq screen was confirmed by the identification of binding motifs - which often resembled the computationally predicted motifs (Figure 1—figure supplement 2A) - for the majority of screened KRAB-ZFPs (Supplementary file 1). Moreover, predicted and experimentally determined motifs were found in targeted TEs in most cases (Supplementary file 1), and reporter repression assays confirmed KRAB-ZFP induced silencing for all the tested sequences (Figure 1—figure supplement 2B). Finally, we observed KAP1 and H3K9me3 enrichment at most of the targeted TEs in wild type ES cells, indicating that most of these KRAB-ZFPs are functionally active in the early embryo (Figure 1A).
We generally observed that KRAB-ZFPs present exclusively in mouse target TEs that are restricted to the mouse genome, indicating KRAB-ZFPs and their targets emerged together. For example, several mouse-specific KRAB-ZFPs in Chr2-cl and Chr4-cl target IAP and ETn elements which are only found in the mouse genome and are highly active. This is the strongest data to date supporting that recent KRAB-ZFP expansions in these young clusters is a response to recent TE activity. Likewise, ZFP599 and ZFP617, both conserved in Muroidea, bind to various ORR1-type LTRs which are present in the rat genome (Supplementary file 1). However, ZFP961, a KRAB-ZFP encoded on a small gene cluster on chromosome 8 that is conserved in Muroidea targets TEs that are only found in the mouse genome (e.g. ETn), a paradox we have previously observed with ZFP809, which also targets TEs that are evolutionarily younger than itself (Wolf et al., 2015b). The ZFP961 binding site is located at the 5’ end of the internal region of ETn and ETnERV elements, a sequence that usually contains the primer binding site (PBS), which is required to prime retroviral reverse transcription. Indeed, the ZFP961 motif closely resembles the PBSLys1,2 (Figure 1—figure supplement 3A), which had been previously identified as a KAP1-dependent target of retroviral repression (Yamauchi et al., 1995; Wolf et al., 2008). Repression of the PBSLys1,2 by ZFP961 was also confirmed in reporter assays (Figure 1—figure supplement 2B), indicating that ZFP961 is likely responsible for this silencing effect.
To further test the hypothesis that KRAB-ZFPs target sites necessary for retrotransposition, we utilized previously generated ETn and ETnERV retrotransposition reporters in which we mutated KRAB-ZFP binding sites (Ribet et al., 2004). Whereas the ETnERV reporters are sufficient for retrotransposition, the ETn reporter requires ETnERV genes supplied in trans. We tested and confirmed that the REX2/ZFP600 and GM13051 binding sites within these TEs are required for efficient retrotransposition (Figure 1—figure supplement 3B). REX2 and ZFP600 both bind a target about 200 bp from the start of the internal region (Figure 1B), a region that often encodes the packaging signal. GM13051 binds a target coding for part of a highly structured mRNA export signal (Legiewicz et al., 2010) near the 3’ end of the internal region of ETn (Figure 1—figure supplement 3C). Both signals are characterized by stem-loop intramolecular base-pairing in which a single mutation can disrupt loop formation. This indicates that at least some KRAB-ZFPs evolved to bind functionally essential target sequences which cannot easily evade repression by mutation.
Our KRAB-ZFP ChIP-seq dataset also provided unique insights into the emergence of new KRAB-ZFPs and binding patterns. The Chr4-cl KRAB-ZFPs REX2 and ZFP600 bind to the same target within ETn but with varying affinity (Figure 1C). Comparison of the amino acids responsible for DNA contact revealed a high similarity between REX2 and ZFP600, with the main differences at the most C-terminal zinc fingers. Additionally, we found that GM30910, another KRAB-ZFP encoded in the Chr4-cl, also shows a strong similarity to both KRAB-ZFPs yet targets entirely different groups of TEs (Figure 1C and Supplementary file 1). Together with previously shown data (Ecco et al., 2016), this example highlights how addition of a few new zinc fingers to an existing array can entirely shift the mode of DNA binding.
## KRAB-ZFP binding to ETn retrotransposons.
(A) Comparison of the PBSLys1,2 sequence with Zfp961 binding motifs in nonrepetitive peaks (Nonrep) and peaks at ETn elements. (B) Retrotransposition assays of original (ETnI1-neoTNF and MusD2-neoTNF Ribet et al., 2004) and modified reporter vectors where the Rex2 or Gm13051 binding motifs where removed. Schematic of reporter vectors are displayed at the top. HeLa cells were transfected as described in the Materials and Methods section and neo-resistant colonies, indicating retrotransposition events, were selected and stained. (C) Stem-loop structure of the ETn RNA export signal, the Gm13051 motif on the corresponding DNA is marked with red circles, the part of the motif that was deleted is indicated with grey crosses (adapted from Legiewicz et al., 2010).
## KRAB-ZFP genes clusters in the mouse genome that were investigated in this study.
* Number of protein-coding KRAB-ZFP genes identified in a previously published screen (Imbeault et al., 2017) and the ChIP-seq data column indicates the number of KRAB-ZFPs for which ChIP-seq was performed in this study.
## Genetic deletion of KRAB-ZFP gene clusters leads to retrotransposon reactivation
The majority of KRAB-ZFP genes are harbored in large, highly repetitive clusters that have formed by successive complex segmental duplications (Kauzlaric et al., 2017), rendering them inaccessible to conventional gene targeting. We therefore developed a strategy to delete entire KRAB-ZFP gene clusters in ES cells (including the Chr2-cl and Chr4-cl as well as two clusters on chromosome 13 and a cluster on chromosome 10) using two CRISPR/Cas9 gRNAs targeting unique regions flanking each cluster, and short single-stranded repair oligos with homologies to both sides of the projected cut sites. Using this approach, we generated five cluster KO ES cell lines in at least two biological replicates and performed RNA sequencing (RNA-seq) to determine TE expression levels. Strikingly, four of the five cluster KO ES cells exhibited distinct TE reactivation phenotypes (Figure 2A). Chr2-cl KO resulted in reactivation of several L1 subfamilies as well as RLTR10 (up to more than 100-fold as compared to WT) and IAPEz ERVs. In contrast, the most strongly upregulated TEs in Chr4-cl KO cells were ETn/ETnERV (up to 10-fold as compared to WT), with several other ERV groups modestly reactivated. ETn/ETnERV elements were also upregulated in Chr13.2-cl KO ES cells while the only upregulated ERVs in Chr13.1-cl KO ES cells were MMERVK10C elements (Figure 2A). Most reactivated retrotransposons were targeted by at least one KRAB-ZFP that was encoded in the deleted cluster (Figure 2A and Supplementary file 1), indicating a direct effect of these KRAB-ZFPs on TE expression levels. Furthermore, we observed a loss of KAP1 binding and H3K9me3 at several TE subfamilies that are targeted by at least one KRAB-ZFP within the deleted Chr2-cl and Chr4-cl (Figure 2B, Figure 2—figure supplement 1A), including L1, ETn and IAPEz elements. Using reduced representation bisulfite sequencing (RRBS-seq), we found that a subset of KRAB-ZFP bound TEs were partially hypomethylated in Chr4-cl KO ES cells, but only when grown in genome-wide hypomethylation-inducing conditions (Blaschke et al., 2013; Figure 2C and Supplementary file 2). These data are consistent with the hypothesis that KRAB-ZFPs/KAP1 are not required to establish DNA methylation, but under certain conditions they protect specific TEs and imprint control regions from genome-wide demethylation (Leung et al., 2014; Deniz et al., 2018).
## KRAB-ZFP cluster deletions license TE-borne enhancers
We next used our RNA-seq datasets to determine the effect of KRAB-ZFP cluster deletions on gene expression. We identified 195 significantly upregulated and 130 downregulated genes in Chr4-cl KO ES cells, and 108 upregulated and 59 downregulated genes in Chr2-cl KO ES cells (excluding genes on the deleted cluster) (Figure 3A). To address whether gene deregulation in Chr2-cl and Chr4-cl KO ES cells is caused by nearby TE reactivation, we determined whether genes near certain TE subfamilies are more frequently deregulated than random genes. We found a strong correlation of gene upregulation and TE proximity for several TE subfamilies, of which many became transcriptionally activated themselves (Figure 3B). For example, nearly 10% of genes that are located within 100 kb (up- or downstream of the TSS) of an ETn element are upregulated in Chr4-cl KO ES cells, as compared to 0.8% of all genes. In Chr2-cl KO ES cells, upregulated genes were significantly enriched near various LINE groups but also IAPEz-int and RLTR10-int elements, indicating that TE-binding KRAB-ZFPs in these clusters limit the potential activating effects of TEs on nearby genes.
While we generally observed that TE-associated gene reactivation is not caused by elongated or spliced transcription starting at the retrotransposons, we did observe that the strength of the effect of ETn elements on gene expression is stronger on genes in closer proximity. About 25% of genes located within 20 kb of an ETn element, but only 5% of genes located at a distance between 50 and 100 kb from the nearest ETn insertion, become upregulated in Chr4-cl KO ES cells. Importantly however, the correlation is still significant for genes that are located at distances between 50 and 100 kb from the nearest ETn insertion, indicating that ETn elements can act as long-range enhancers of gene expression in the absence of KRAB-ZFPs that target them. To confirm that Chr4-cl KRAB-ZFPs such as GM13051 block ETn-borne enhancers, we tested the ability of a putative ETn enhancer to activate transcription in a reporter assay. For this purpose, we cloned a 5 kb fragment spanning from the GM13051 binding site within the internal region of a truncated ETn insertion to the first exon of the Cd59a gene, which is strongly activated in Chr4-cl KO ES cells (Figure 2—figure supplement 1B). We observed strong transcriptional activity of this fragment which was significantly higher in Chr4-cl KO ES cells. Surprisingly, this activity was reduced to background when the internal segment of the ETn element was not included in the fragment, suggesting the internal segment of the ETn element, but not its LTR, contains a Chr4-cl KRAB-ZFP sensitive enhancer. To further corroborate these findings, we genetically deleted an ETn element that is located about 60 kb from the TSS of Chst1, one of the top-upregulated genes in Chr4-cl KO ES cells (Figure 3C). RT-qPCR analysis revealed that the Chst1 upregulation phenotype in Chr4-cl KO ES cells diminishes when the ETn insertion is absent, providing direct evidence that a KRAB-ZFP controlled ETn-borne enhancer regulates Chst1 expression (Figure 3D). Furthermore, ChIP-seq confirmed a general increase of H3K4me3, H3K4me1 and H3K27ac marks at ETn elements in Chr4-cl KO ES cells (Figure 3E). Notably, enhancer marks were most pronounced around the GM13051 binding site near the 3’ end of the internal region, confirming that the enhancer activity of ETn is located on the internal region and not on the LTR.
## ETn retrotransposition in Chr4-cl KO and WT mice
IAP, ETn/ETnERV and MuLV/RLTR4 retrotransposons are highly polymorphic in inbred mouse strains (Nellåker et al., 2012), indicating that these elements are able to mobilize in the germ line. Since these retrotransposons are upregulated in Chr2-cl and Chr4-cl KO ES cells, we speculated that these KRAB-ZFP clusters evolved to minimize the risks of insertional mutagenesis by retrotransposition. To test this, we generated Chr2-cl and Chr4-cl KO mice via ES cell injection into blastocysts, and after germ line transmission we genotyped the offspring of heterozygous breeding pairs. While the offspring of Chr4-cl KO/WT parents were born close to Mendelian ratios in pure C57BL/6 and mixed C57BL/6 129Sv matings, one Chr4-cl KO/WT breeding pair gave birth to significantly fewer KO mice than expected (p-value=0.022) (Figure 4—figure supplement 1A). Likewise, two out of four Chr2-cl KO breeding pairs on mixed C57BL/6 129Sv matings failed to give birth to a single KO offspring (p-value<0.01) while the two other mating pairs produced KO offspring at near Mendelian ratios (Figure 4—figure supplement 1A). Altogether, these data indicate that KRAB-ZFP clusters are not absolutely essential in mice, but that genetic and/or epigenetic factors may contribute to reduced viability.
We reasoned that retrotransposon activation could account for the reduced viability of Chr2-cl and Chr4-cl KO mice in some matings. However, since only rare matings produced non-viable KO embryos, we instead turned to the viable KO mice to assay for increased transposon activity. RNA-seq in blood, brain and testis revealed that, with a few exceptions, retrotransposons upregulated in Chr2 and Chr4 KRAB-ZFP cluster KO ES cells are not expressed at higher levels in adult tissues (Figure 4—figure supplement 1B). Likewise, no strong transcriptional TE reactivation phenotype was observed in liver and kidney of Chr4-cl KO mice (data not shown) and ChIP-seq with antibodies against H3K4me1, H3K4me3 and H3K27ac in testis of Chr4-cl WT and KO mice revealed no increase of active histone marks at ETn elements or other TEs (data not shown). This indicates that Chr2-cl and Chr4-cl KRAB-ZFPs are primarily required for TE repression during early development. This is consistent with the high expression of these KRAB-ZFPs uniquely in ES cells (Figure 1—figure supplement 1A). To determine whether retrotransposition occurs at a higher frequency in Chr4-cl KO mice during development, we screened for novel ETn (ETn/ETnERV) and MuLV (MuLV/RLTR4\_MM) insertions in viable Chr4-cl KO mice. For this purpose, we developed a capture-sequencing approach to enrich for ETn/MuLV DNA and flanking sequences from genomic DNA using probes that hybridize with the 5’ and 3’ ends of ETn and MuLV LTRs prior to deep sequencing. We screened genomic DNA samples from a total of 76 mice, including 54 mice from ancestry-controlled Chr4-cl KO matings in various strain backgrounds, the two ES cell lines the Chr4-cl KO mice were generated from, and eight mice from a Chr2-cl KO mating which served as a control (since ETn and MuLVs are not activated in Chr2-cl KO ES cells) (Supplementary file 4). Using this approach, we were able to enrich reads mapping to ETn/MuLV LTRs about 2,000-fold compared to genome sequencing without capture. ETn/MuLV insertions were determined by counting uniquely mapped reads that were paired with reads mapping to ETn/MuLV elements (see materials and methods for details). To assess the efficiency of the capture approach, we determined what proportion of a set of 309 largely intact (two LTRs flanking an internal sequence) reference ETn elements could be identified using our sequencing data. 95% of these insertions were called with high confidence in the majority of our samples (data not shown), indicating that we are able to identify ETn insertions at a high recovery rate.
Using this dataset, we first confirmed the polymorphic nature of both ETn and MuLV retrotransposons in laboratory mouse strains (Figure 4—figure supplement 2A), highlighting the potential of these elements to retrotranspose. To identify novel insertions, we filtered out insertions that were supported by ETn/MuLV-paired reads in more than one animal. While none of the 54 ancestry-controlled mice showed a single novel MuLV insertion, we observed greatly varying numbers of up to 80 novel ETn insertions in our pedigree (Figure 4A).
To validate some of the novel ETn insertions, we designed specific PCR primers for five of the insertions and screened genomic DNA of the mice in which they were identified as well as their parents. For all tested insertions, we were able to amplify their flanking sequence and show that these insertions are absent in their parents (Figure 4—figure supplement 3A). To confirm their identity, we amplified and sequenced three of the novel full-length ETn insertions. Two of these elements (Genbank accession: MH449667-68) resembled typical ETnII elements with identical 5’ and 3’ LTRs and target site duplications (TSD) of 4 or 6 bp, respectively. The third sequenced element (MH449669) represented a hybrid element that contains both ETnI and MusD (ETnERV) sequences. Similar insertions can be found in the B6 reference genome; however, the identified novel insertion has a 2.5 kb deletion of the 5’ end of the internal region. Additionally, the 5’ and 3’ LTR of this element differ in one nucleotide near the start site and contain an unusually large 248 bp TSD (containing a SINE repeat) indicating that an improper integration process might have truncated this element.
Besides novel ETn insertions that were only identified in one specific animal, we also observed three ETn insertions that could be detected in several siblings but not in their parents or any of the other screened mice. This strongly indicates that these retrotransposition events occurred in the germ line of the parents from which they were passed on to some of their offspring. One of these germ line insertions was evidently passed on from the offspring to the next generation (Figure 4A). As expected, the read numbers supporting these novel germ line insertions were comparable to the read numbers that were found in the flanking regions of annotated B6 ETn insertions (Figure 4—figure supplement 3B). In contrast, virtually all novel insertions that were only found in one animal were supported by significantly fewer reads (Figure 4—figure supplement 3B). This indicates that these elements resulted from retrotransposition events in the developing embryo and not in the zygote or parental germ cells. Indeed, we detected different sets of insertions in various tissues from the same animal (Figure 4—figure supplement 3C). Even between tail samples that were collected from the same animal at different ages, only a fraction of the new insertions were present in both samples, while technical replicates from the same genomic DNA samples showed a nearly complete overlap in insertions (Figure 4—figure supplement 3D).
Finally, we asked whether there were more novel ETn insertions in mice lacking the Chr4-cl relative to their wild type and heterozygous littermates in our pedigree. Interestingly, only one out of the eight Chr4-cl KO mice in a pure C57BL/6 strain background and none of the eight offspring from a Chr2-cl mating carried a single novel ETn insertion (Figure 4A). When crossing into a 129Sv background for a single generation before intercrossing heterozygous mice (F1), we observed 4 out of 8 Chr4-cl KO mice that contained at least one new ETn insertion, whereas none of 3 heterozygous mice contained any insertions. After crossing to the 129Sv background for a second generation (F2), we determined the number of novel ETn insertions in the offspring of one KO/WT x KO and two KO/WT x KO/WT matings, excluding all samples that were not derived from juvenile tail tissue. Only in the offspring of the KO/WT x KO mating, we observed a statistically significant higher average number of ETn insertions in KO vs. KO/WT animals (7.3 vs. 29.6, p=0.045, Figure 4B). Other than that, only a non-significant trend towards greater average numbers of ETn insertions in KO (11 vs. 27.8, p=0.192, Figure 4B) was apparent in one of the WT/KO x KO/WT matings whereas no difference in ETn insertion numbers between WT and KO mice could be observed in the second mating WT/KO x KO/WT (26 vs. 31, p=0.668, Figure 4B). When comparing all KO with all WT and WT/KO mice from these three matings, a trend towards more ETn insertions in KO remained but was not supported by strong significance (26 vs. 13, p=0.057, Figure 4B). Altogether, we observed a high variability in the number of new ETn insertions in both KO and WT but our data suggest that the Chr4-cl KRAB-ZFPs may have a modest effect on ETn retrotransposition rates in some mouse strains but other genetic and epigenetic effects clearly also play an important role.
## Confirmation of novel ETn insertions identified by capture-seq.
(A) PCR validation of novel ETn insertions in genomic DNA of three littermates (IDs: T09673, T09674 and T00436) and their parents (T3913 and T3921). Primer sequences are shown in Supplementary file 3. (B) ETn capture-seq read counts (RPM) at putative novel somatic (loci identified exclusively in one single animal), novel germ line (loci identified in several littermates) insertions, and at B6 reference ETn elements. (C) Heatmap shows capture-seq read counts (RPM) of a Chr4-cl KO mouse (ID: C6733) as determined in different tissues. Each row represents a novel ETn locus that was identified in at least one tissue. The color gradient indicates log10(RPM+1). (D) Heatmap shows the capture-seq RPM in technical replicates using the same Chr4-cl KO DNA sample (rep1/rep2) or replicates with DNA samples prepared from different sections of the tail from the same mouse at different ages (tail1/tail2). Each row represents a novel ETn locus that was identified in at least one of the displayed samples. The color gradient indicates log10(RPM+1).
## Discussion
C2H2 zinc finger proteins, about half of which contain a KRAB repressor domain, represent the largest DNA-binding protein family in mammals. Nevertheless, most of these factors have not been investigated using loss-of-function studies. The most comprehensive characterization of human KRAB-ZFPs revealed a strong preference to bind TEs (Imbeault et al., 2017; Najafabadi et al., 2015) yet their function remains unknown. In humans, very few TEs are capable of retrotransposition yet many of them, often tens of million years old, are bound by KRAB-ZFPs. While this suggests that human KRAB-ZFPs mainly serve to control TE-borne enhancers and may have potentially transcription-independent functions, we were interested in the biological significance of KRAB-ZFPs in restricting potentially active TEs. The mouse is an ideal model for such studies since the mouse genome contains several active TE families, including IAP, ETn and L1 elements. We found that many of the young KRAB-ZFPs present in the genomic clusters of KRAB-ZFPs on chromosomes 2 and 4, which are highly expressed in a restricted pattern in ES cells, bound redundantly to these three active TE families. In several cases, KRAB-ZFPs bound to functionally constrained sequence elements we and others have demonstrated to be necessary for retrotransposition, including PBS and viral packaging signals. Targeting such sequences may help the host defense system keep pace with rapidly evolving mouse transposons. This provides strong evidence that many young KRAB-ZFPs are indeed expanding in response to TE activity. But do these young KRAB-ZFP genes limit the mobilization of TEs? Despite the large number of polymorphic ETn elements in mouse strains (Nellåker et al., 2012) and several reports of phenotype-causing novel ETn germ line insertions, no new ETn insertions were reported in recent screens of C57BL/6 mouse genomes (Richardson et al., 2017; Gagnier et al., 2019), indicating that the overall rate of ETn germ line mobilization in inbred mice is rather low. We have demonstrated that Chr4-cl KRAB-ZFPs control ETn/ETnERV expression in ES cells, but this does not lead to widespread ETn mobility in viable C57BL/6 mice. In contrast, we found numerous novel, including several germ line, ETn insertions in both WT and Chr4-cl KO mice in a C57BL/6 129Sv mixed genetic background, with generally more insertions in KO mice and in mice with more 129Sv DNA. This is consistent with a report detecting ETn insertions in FVB.129 mice (Schauer et al., 2018). Notably, there was a large variation in the number of new insertions in these mice, possibly caused by hyperactive polymorphic ETn insertions that varied from individual to individual, epigenetic variation at ETn insertions between individuals and/or the general stochastic nature of ETn mobilization. Furthermore, recent reports have suggested that KRAB-ZFP gene content is distinct in different strains of laboratory mice (Lilue et al., 2018; Treger et al., 2019), and reduced KRAB-ZFP gene content could contribute to increased activity in individual mice. Although we have yet to find obvious phenotypes in the mice carrying new insertions, novel ETn germ line insertions have been shown to cause phenotypes from short tails (Lugani et al., 2013; Semba et al., 2013; Vlangos et al., 2013) to limb malformation (Kano et al., 2007) and severe morphogenetic defects including polypodia (Lehoczky et al., 2013) depending upon their insertion site.
## Cell lines and transgenic mice
Mouse ES cells and F9 EC cells were cultivated as described previously (Wolf et al., 2015b) unless stated otherwise. Chr4-cl KO ES cells originate from B6;129‐ Gt(ROSA)26Sortm1(cre/ERT)Nat/J mice (Jackson lab), all other KRAB-ZFP cluster KO ES cell lines originate from JM8A3.N1 C57BL/6N-Atm1Brd ES cells (KOMP Repository). Chr2-cl KO and WT ES cells were initially grown in serum-containing media (Wolf et al., 2015b) but changed to 2i media (De Iaco et al., 2017) for several weeks before analysis. To generate Chr4-cl and Chr2-cl KO mice, the cluster deletions were repeated in B6 ES (KOMP repository) or R1 (Nagy lab) ES cells, respectively, and heterozygous clones were injected into B6 albino blastocysts. Chr2-cl KO mice were therefore kept on a mixed B6/Svx129/Sv-CP strain background while Chr4-cl KO mice were initially derived on a pure C57BL/6 background. For capture-seq screens, Chr4-cl KO mice were crossed with 129 × 1/SvJ mice (Jackson lab) to produce the founder mice for Chr4-cl KO and WT (B6/129 F1) offspring. Chr4-cl KO/WT (B6/129 F1) were also crossed with 129 × 1/SvJ mice to get Chr4-cl KO/WT (B6/129 F1) mice, which were intercrossed to give rise to the parents of Chr4-cl KO/KO and KO/WT (B6/129 F2) offspring.
## Generation of KRAB-ZFP expressing cell lines
KRAB-ZFP ORFs were PCR-amplified from cDNA or synthesized with codon-optimization (Supplementary file 1), and stably expressed with 3XFLAG or 3XHA tags in F9 EC or ES cells using Sleeping beauty transposon-based (Wolf et al., 2015b) or lentiviral expression vectors (Imbeault et al., 2017; Supplementary file 1). Cells were selected with puromycin (1 µg/ml) and resistant clones were pooled and further expanded for ChIP-seq.
## CRISPR/Cas9 mediated deletion of KRAB-ZFP clusters and an MMETn insertion
All gRNAs were expressed from the pX330-U6-Chimeric\_BB-CBh-hSpCas9 vector (RRID:Addgene\_42230) and nucleofected into 106 ES cells using Amaxa nucleofection in the following amounts: 5 µg of each pX330-gRNA plasmid, 1 µg pPGK-puro and 500 pmoles single-stranded repair oligos (Supplementary file 3). One day after nucleofection, cells were kept under puromycin selection (1 µg/ml) for 24 hr. Individual KO and WT clones were picked 7–8 days after nucleofection and expanded for PCR genotyping (Supplementary file 3).
## ChIP-seq analysis
For ChIP-seq analysis of KRAB-ZFP expressing cells, 5–10 × 107 cells were crosslinked and immunoprecipitated with anti-FLAG (Sigma-Aldrich Cat# F1804, RRID:AB\_262044) or anti-HA (Abcam Cat# ab9110, RRID:AB\_307019 or Covance Cat# MMS-101P-200, RRID:AB\_10064068) antibody using one of two previously described protocols (O'Geen et al., 2010; Imbeault et al., 2017) as indicated in Supplementary file 1. H3K9me3 distribution in Chr4-cl, Chr10-cl, Chr13.1-cl and Chr13.2-cl KO ES cells was determined by native ChIP-seq with anti-H3K9me3 serum (Active Motif Cat# 39161, RRID:AB\_2532132) as described previously (Karimi et al., 2011). In Chr2-cl KO ES cells, H3K9me3 and KAP1 ChIP-seq was performed as previously described (Ecco et al., 2016). In Chr4-cl KO and WT ES cells KAP1 binding was determined by endogenous tagging of KAP1 with C-terminal GFP (Supplementary file 3), followed by FACS to enrich for GFP-positive cells and ChIP with anti-GFP (Thermo Fisher Scientific Cat# A-11122, RRID:AB\_221569) using a previously described protocol (O'Geen et al., 2010). For ChIP-seq analysis of active histone marks, cross-linked chromatin from ES cells or testis (from two-week old mice) was immunoprecipitated with antibodies against H3K4me3 (Abcam Cat# ab8580, RRID:AB\_306649), H3K4me1 (Abcam Cat# ab8895, RRID:AB\_306847) and H3K27ac (Abcam Cat# ab4729, RRID:AB\_2118291) following the protocol developed by O'Geen et al., 2010 or Khil et al., 2012 respectively.
ChIP-seq libraries were constructed and sequenced as indicated in Supplementary file 4. Reads were mapped to the mm9 genome using Bowtie (RRID:SCR\_005476; settings: --best) or Bowtie2 (Langmead and Salzberg, 2012) as indicated in Supplementary file 4. Under these settings, reads that map to multiple genomic regions are assigned to the top-scored match and, if a set of equally good choices is encountered, a pseudo-random number is used to choose one location. Peaks were called using MACS14 (RRID:SCR\_013291) under high stringency settings (p<1e-10, peak enrichment >20) (Zhang et al., 2008). Peaks were called both over the Input control and a FLAG or HA control ChIP (unless otherwise stated in Supplementary file 4) and only peaks that were called in both settings were kept for further analysis. In cases when the stringency settings did not result in at least 50 peaks, the settings were changed to medium (p<1e-10, peak enrichment >10) or low (p<1e-5, peak enrichment >10) stringency (Supplementary file 4). For further analysis, all peaks were scaled to 200 bp regions centered around the peak summits. The overlap of the scaled peaks to each repeat element in UCSC Genome Browser (RRID:SCR\_005780) were calculated by using the bedfisher function (settings: -f 0.25) from BEDTools (RRID:SCR\_006646). The right-tailed p-values between pair-wise comparison of each ChIP-seq peak and repeat element were extracted, and then adjusted using the Benjamini-Hochberg approach implemented in the R function p.adjust(). Binding motifs were determined using only nonrepetitive (<10% repeat content) peaks with MEME (Bailey et al., 2009). MEME motifs were compared with in silico predicted motifs (Najafabadi et al., 2015) using Tomtom (Bailey et al., 2009) and considered as significantly overlapping with a False Discovery Rate (FDR) below 0.1. To find MEME and predicted motifs in repetitive peaks, we used FIMO (Bailey et al., 2009). Differential H3K9me3 and KAP1 distribution in WT and Chr2-cl or Chr4-cl KO ES cells at TEs was determined by counting ChIP-seq reads overlapping annotated insertions of each TE group using BEDTools (MultiCovBed). Additionally, ChIP-seq reads were counted at the TE fraction that was bound by Chr2-cl or Chr4-cl KRAB-ZFPs (overlapping with 200 bp peaks). Count tables were concatenated and analyzed using DESeq2 (Love et al., 2014). The previously published ChIP-seq datasets for KAP1 (Castro-Diaz et al., 2014) and H3K9me3 (Dan et al., 2014) were re-mapped using Bowtie (--best).
## Luciferase reporter assays
For KRAB-ZFP repression assays, double-stranded DNA oligos containing KRAB-ZFP target sequences (Supplementary file 3) were cloned upstream of the SV40 promoter of the pGL3-Promoter vector (Promega) between the restriction sites for NheI and XhoI. 33 ng of reporter vectors were co-transfected (Lipofectamine 2000, Thermofisher) with 33 ng pRL-SV40 (Promega) for normalization and 33 ng of transient KRAB-ZFP expression vectors (in pcDNA3.1) or empty pcDNA3.1 into 293 T cells seeded one day earlier in 96-well plates. Cells were lysed 48 hr after transfection and luciferase/Renilla luciferase activity was measured using the Dual-Luciferase Reporter Assay System (Promega). To measure the transcriptional activity of the MMETn element upstream of the Cd59a gene, fragments of varying sizes (Supplementary file 3) were cloned into the promoter-less pGL3-basic vector (Promega) using NheI and NcoI sites. 70 ng of reporter vectors were cotransfected with 30 ng pRL-SV40 into feeder-depleted Chr4-cl WT and KO ES cells, seeded into a gelatinized 96-well plate 2 hr before transfection. Luciferase activity was measured 48 hr after transfection as described above.
## RNA-seq analysis
Whole RNA was purified using RNeasy columns (Qiagen) with on column DNase treatment or the High Pure RNA Isolation Kit (Roche) (Supplementary file 4). Tissues were first lysed in TRIzol reagent (ThermoFisher) and RNA was purified after the isopropanol precipitation step using RNeasy columns (Qiagen) with on column DNase treatment. Libraries were generated using the SureSelect Strand-Specific RNA Library Prep kit (Agilent) or Illumina’s TruSeq RNA Library Prep Kit (with polyA selection) and sequenced as 50 or 100 bp paired-end reads on an Illumina HiSeq2500 (RRID:SCR\_016383) or HiSeq3000 (RRID:SCR\_016386) machine (Supplementary file 4). RNA-seq reads were mapped to the mouse genome (mm9) using Tophat (RRID:SCR\_013035; settings: --I 200000 g 1) unless otherwise stated. These settings allow each mappable read to be reported once, in case the read maps to multiple locations equally well, one match is randomly chosen. For differential transposon expression, mapped reads that overlap with TEs annotated in Repeatmasker (RRID:SCR\_012954) were counted using BEDTools MultiCovBed (setting: -split). Reads mapping to multiple fragments that belong to the same TE insertion (as indicated by the repeat ID) were summed up. Only transposons with a total of at least 20 (for two biological replicates) or 30 (for three biological replicates) mapped reads across WT and KO samples were considered for differential expression analysis. Transposons within the deleted KRAB-ZFP cluster were excluded from the analysis. Read count tables were used for differential expression analysis with DESeq2 (RRID:SCR\_015687). For differential gene expression analysis, reads overlapping with gene exons were counted using HTSeq-count and analyzed using DESeq2. To test if KRAB-ZFP peaks are significantly enriched near up- or down-regulated genes, a binomial test was performed. Briefly, the proportion of the peaks that are located within a certain distance up- or downstream to the TSS of genes was determined using the windowBed function of BED tools. The probability p in the binomial distribution was estimated as the fraction of all genes overlapped with KRAB-ZFP peaks. Then, given n which is the number of specific groups of genes, and x which is the number of this group of genes overlapped with peaks, the R function binom.test() was used to estimate the p-value based on right-tailed Binomial test. Finally, the adjusted p-values were determined separately for LTR and LINE retrotransposon groups using the Benjamini-Hochberg approach implemented in the R function p.adjust().
## Reduced representation bisulfite sequencing (RRBS-seq)
For RRBS-seq analysis, Chr4-cl WT and KO ES cells were grown in either standard ES cell media containing FCS or for one week in 2i media containing vitamin C as described previously (Blaschke et al., 2013). Genomic DNA was purified from WT and Chr4-cl KO ES cells using the Quick-gDNA purification kit (Zymo Research) and bisulfite-converted with the NEXTflex Bisulfite-Seq Kit (Bio Scientific) using Msp1 digestion to fragment DNA. Libraries were sequenced as 50 bp paired-end reads on an Illumina HiSeq. The reads were processed using Trim Galore (--illumina --paired –rrbs) to trim poor quality bases and adaptors. Additionally, the first 5 nt of R2 and the last 3 nt of R1 and R2 were trimmed. Reads were then mapped to the reference genome (mm9) using Bismark (Krueger and Andrews, 2011) to extract methylation calling results. The CpG methylation pattern for each covered CpG dyads (two complementary CG dinucleotides) was calculated using a custom script (Source code 1: get\_CpG\_ML.pl). For comparison of CpG methylation between WT and Chr4-cl KO ES cells (in serum or 2i + Vitamin C conditions) only CpG sites with at least 10-fold coverage in each sample were considered for analysis.
## Retrotransposition assay
The retrotransposition vectors pCMV-MusD2, pCMV-MusD2-neoTNF and pCMV-ETnI1-neoTNF (Ribet et al., 2004) were a kind gift from Dixie Mager. To partially delete the Gm13051 binding site within pCMV-MusD2-neoTNF, the vector was cut with KpnI and re-ligated using a repair oligo, leaving a 24 bp deletion within the Gm13051 binding site. The Rex2 binding site in pCMV-ETnI1-neoTNF was deleted by cutting the vector with EcoRI and XbaI followed by re-ligation using two overlapping PCR products, leaving a 45 bp deletion while maintaining the rest of the vector unchanged (see Supplementary file 3 for primer sequences). For MusD retrotransposition assays, 5 × 104 HeLa cells (ATCC CCL-2) were transfected in a 24-well dish with 100 ng pCMV-MusD2-neoTNF or pCMV-MusD2-neoTNF (ΔGm13051-m) using Lipofectamine 2000. For ETn retrotransposition assays, 50 ng of pCMV-ETnI1-neoTNF or pCMV-ETnI1-neoTNF (ΔRex2) vectors were cotransfected with 50 ng pCMV-MusD2 to provide gag and pol proteins in trans. G418 (0.6 mg/ml) was added five days after transfection and cells were grown under selection until colonies were readily visible by eye. G418-resistant colonies were stained with Amido Black (Sigma).
## Capture-seq screen
To identify novel retrotransposon insertions, genomic DNA from various tissues (Supplementary file 4) was purified and used for library construction with target enrichment using the SureSelectQXT Target Enrichment kit (Agilent). Custom RNA capture probes were designed to hybridize with the 120 bp 5’ ends of the 5’ LTRs and the 120 bp 3’ ends of the 3’ LTR of about 600 intact (internal region flanked by two LTRs) MMETn/RLTRETN retrotransposons or of 140 RLTR4\_MM/RLTR4 retrotransposons that were upregulated in Chr4-cl KO ES cells (Figure 4—source data 2). Enriched libraries were sequenced on an Illumina HiSeq as paired-end 50 bp reads. R1 and R2 reads were mapped to the mm9 genome separately, using settings that only allow non-duplicated, uniquely mappable reads (Bowtie -m 1 --best --strata; samtools rmdup -s) and under settings that allow multimapping and duplicated reads (Bowtie --best). Of the latter, only reads that overlap (min. 50% of read) with RLTRETN, MMETn-int, ETnERV-int, ETnERV2-int or ETnERV3-int repeats (ETn) or RLTR4, RLTR4\_MM-int or MuLV-int repeats (RLTR4) were kept. Only uniquely mappable reads whose paired reads were overlapping with the repeats mentioned above were used for further analysis. All ETn- and RLTR4-paired reads were then clustered (as bed files) using BEDTools (bedtools merge -i -n -d 1000) to receive a list of all potential annotated and non-annotated new ETn or RLTR4 insertion sites and all overlapping ETn- or RLTR4-paired reads were counted for each sample at each locus. Finally, all regions that were located within 1 kb of an annotated RLTRETN, MMETn-int, ETnERV-int, ETnERV2-int or ETnERV3-int repeat as well as regions overlapping with previously identified polymorphic ETn elements (Nellåker et al., 2012) were removed. Genomic loci with at least 10 reads per million unique ETn- or RLTR4-paired reads were considered as insertion sites. To qualify for a de-novo insertion, we allowed no called insertions in any of the other screened mice at the locus and not a single read at the locus in the ancestors of the mouse. Insertions at the same locus in at least two siblings from the same offspring were considered as germ line insertions, if the insertion was absent in the parents and mice who were not direct descendants from these siblings. Full-length sequencing of new ETn insertions was done by Sanger sequencing of short PCR products in combination with Illumina sequencing of a large PCR product (Supplementary file 3), followed by de-novo assembly using the Unicycler software.
## Tables
Table 1. * Number of protein-coding KRAB-ZFP genes identified in a previously published screen (Imbeault et al., 2017) and the ChIP-seq data column indicates the number of KRAB-ZFPs for which ChIP-seq was performed in this study.
| Cluster | Location | Size (Mb) | # of KRAB-ZFPs* | ChIP-seq data |
|-----------|------------|-------------|-------------------|-----------------|
| Chr2 | Chr2 qH4 | 3.1 | 40 | 17 |
| Chr4 | Chr4 qE1 | 2.3 | 21 | 19 |
| Chr10 | Chr10 qC1 | 0.6 | 6 | 1 |
| Chr13.1 | Chr13 qB3 | 1.2 | 6 | 2 |
| Chr13.2 | Chr13 qB3 | 0.8 | 26 | 12 |
| Chr8 | Chr8 qB3.3 | 0.1 | 4 | 4 |
| Chr9 | Chr9 qA3 | 0.1 | 4 | 2 |
| Other | - | - | 248 | 4 |
Key resources table
| Reagent type (species) or resource | Designation | Source or reference | Identifiers | Additional information |
|------------------------------------------|----------------------------------------|-----------------------------------|-------------------------------------|------------------------------------------------------|
| Strain, strain background (Mus musculus) | 129 × 1/SvJ | The Jackson Laboratory | 000691 | Mice used to generate mixed strain Chr4-cl KO mice |
| Cell line (Homo-sapiens) | HeLa | ATCC | ATCC CCL-2 | |
| Cell line (Mus musculus) | JM8A3.N1 C57BL/6N-Atm1Brd | KOMP Repository | PL236745 | B6 ES cells used to generate KO cell lines and mice |
| Cell line (Mus musculus) | B6;129‐ Gt(ROSA)26Sortm1(cre/ERT)Nat/J | The Jackson Laboratory | 004847 | ES cells used to generate KO cell lines and mice |
| Cell line (Mus musculus) | R1 ES cells | Andras Nagy lab | R1 | 129 ES cells used to generate KO cell lines and mice |
| Cell line (Mus musculus) | F9 Embryonic carcinoma cells | ATCC | ATCC CRL-1720 | |
| Antibody | Mouse monoclonal ANTI-FLAG M2 antibody | Sigma-Aldrich | Cat# F1804, RRID:AB\_262044 | ChIP (1 µg/107 cells) |
| Antibody | Rabbit polyclonal anti-HA | Abcam | Cat# ab9110, RRID:AB\_307019 | ChIP (1 µg/107 cells) |
| Antibody | Mouse monoclonal anti-HA | Covance | Cat# MMS-101P-200, RRID:AB\_10064068 | |
| Antibody | Rabbit polyclonal anti-H3K9me3 | Active Motif | Cat# 39161, RRID:AB\_2532132 | ChIP (3 µl/107 cells) |
| Antibody | Rabbit polyclonal anti-GFP | Thermo Fisher Scientific | Cat# A-11122, RRID:AB\_221569 | ChIP (1 µg/107 cells) |
| Antibody | Rabbit polyclonal anti- H3K4me3 | Abcam | Cat# ab8580, RRID:AB\_306649 | ChIP (1 µg/107 cells) |
| Antibody | Rabbit polyclonal anti- H3K4me1 | Abcam | Cat# ab8895, RRID:AB\_306847 | ChIP (1 µg/107 cells) |
| Antibody | Rabbit polyclonal anti- H3K27ac | Abcam | Cat# ab4729, RRID:AB\_2118291 | ChIP (1 µg/107 cells) |
| Recombinant DNA reagent | pCW57.1 | Addgene | RRID:Addgene\_41393 | Inducible lentiviral expression vector |
| Recombinant DNA reagent | pX330-U6-Chimeric\_BB-CBh-hSpCas9 | Addgene | RRID:Addgene\_42230 | CRISPR/Cas9 expression construct |
| Sequence-based reagent | Chr2-cl KO gRNA.1 | This paper | Cas9 gRNA | GCCGTTGCTCAGTCCAAATG |
| Sequenced-based reagent | Chr2-cl KO gRNA.2 | This paper | Cas9 gRNA | GATACCAGAGGTGGCCGCAAG |
| Sequenced-based reagent | Chr4-cl KO gRNA.1 | This paper | Cas9 gRNA | GCAAAGGGGCTCCTCGATGGA |
| Sequence-based reagent | Chr4-cl KO gRNA.2 | This paper | Cas9 gRNA | GTTTATGGCCGTGCTAAGGTC |
| Sequenced-based reagent | Chr10-cl KO gRNA.1 | This paper | Cas9 gRNA | GTTGCCTTCATCCCACCGTG |
| Sequenced-based reagent | Chr10-cl KO gRNA.2 | This paper | Cas9 gRNA | GAAGTTCGACTTGGACGGGCT |
| Sequenced-based reagent | Chr13.1-cl KO gRNA.1 | This paper | Cas9 gRNA | GTAACCCATCATGGGCCCTAC |
| Sequenced-based reagent | Chr13.1-cl KO gRNA.2 | This paper | Cas9 gRNA | GGACAGGTTATAGGTTTGAT |
| Sequenced-based reagent | Chr13.2-cl KO gRNA.1 | This paper | Cas9 gRNA | GGGTTTCTGAGAAACGTGTA |
| Sequenced-based reagent | Chr13.2-cl KO gRNA.2 | This paper | Cas9 gRNA | GTGTAATGAGTTCTTATATC |
| Commercial assay or kit | SureSelectQXT Target Enrichment kit | Agilent | G9681-90000 | |
| Software, algorithm | Bowtie | http://bowtie-bio.sourceforge.net | RRID:SCR\_005476 | |
| Software, algorithm | MACS14 | https://bio.tools/macs | RRID:SCR\_013291 | |
| Software, algorithm | Tophat | https://ccb.jhu.edu | RRID:SCR\_013035 | |
## Figures
Figure 1. Genome-wide binding patterns of mouse KRAB-ZFPs. (A) Probability heatmap of KRAB-ZFP binding to TEs. Blue color intensity (main field) corresponds to -log10 (adjusted p-value) enrichment of ChIP-seq peak overlap with TE groups (Fisher’s exact test). The green/red color intensity (top panel) represents mean KAP1 (GEO accession: GSM1406445) and H3K9me3 (GEO accession: GSM1327148) enrichment (respectively) at peaks overlapping significantly targeted TEs (adjusted p-value<1e-5) in WT ES cells. (B) Summarized ChIP-seq signal for indicated KRAB-ZFPs and previously published KAP1 and H3K9me3 in WT ES cells across 127 intact ETn elements. (C) Heatmaps of KRAB-ZFP ChIP-seq signal at ChIP-seq peaks. For better comparison, peaks for all three KRAB-ZFPs were called with the same parameters (p<1e-10, peak enrichment >20). The top panel shows a schematic of the arrangement of the contact amino acid composition of each zinc finger. Zinc fingers are grouped and colored according to similarity, with amino acid differences relative to the five consensus fingers highlighted in white.
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Figure 1—figure supplement 1. ES cell-specific expression of KRAB-ZFP gene clusters. (A) Heatmap showing expression patterns of mouse KRAB-ZFPs in 40 mouse tissues and cell lines (ENCODE). Heatmap colors indicate gene expression levels in log2 transcripts per million (TPM). The asterisk indicates a group of 30 KRAB-ZFPs that are exclusively expressed in ES cells. (B) Physical location of the genes encoding for the 30 KRAB-ZFPs that are exclusively expressed in ES cells. (C) Phylogenetic (Maximum likelihood) tree of the KRAB domains of mouse KRAB-ZFPs. KRAB-ZFPs encoded on the gene clusters on chromosome 2 and 4 are highlighted. The scale bar at the bottom indicates amino acid substitutions per site.
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Figure 1—figure supplement 2. KRAB-ZFP binding motifs and their repression activity. (A) Comparison of computationally predicted (bottom) and experimentally determined (top) KRAB-ZFP binding motifs. Only significant pairs are shown (FDR < 0.1). (B) Luciferase reporter assays to confirm KRAB-ZFP repression of the identified target sites. Bars show the luciferase activity (normalized to Renilla luciferase) of reporter plasmids containing the indicated target sites cloned upstream of the SV40 promoter. Reporter plasmids were co-transfected into 293 T cells with a Renilla luciferase plasmid for normalization and plasmids expressing the targeting KRAB-ZFP. Normalized mean luciferase activity (from three replicates) is shown relative to luciferase activity of the reporter plasmid co-transfected with an empty pcDNA3.1 vector.
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Figure 1—figure supplement 3. KRAB-ZFP binding to ETn retrotransposons. (A) Comparison of the PBSLys1,2 sequence with Zfp961 binding motifs in nonrepetitive peaks (Nonrep) and peaks at ETn elements. (B) Retrotransposition assays of original (ETnI1-neoTNF and MusD2-neoTNF Ribet et al., 2004) and modified reporter vectors where the Rex2 or Gm13051 binding motifs where removed. Schematic of reporter vectors are displayed at the top. HeLa cells were transfected as described in the Materials and Methods section and neo-resistant colonies, indicating retrotransposition events, were selected and stained. (C) Stem-loop structure of the ETn RNA export signal, the Gm13051 motif on the corresponding DNA is marked with red circles, the part of the motif that was deleted is indicated with grey crosses (adapted from Legiewicz et al., 2010).
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Figure 2. Retrotransposon reactivation in KRAB-ZFP cluster KO ES cells. (A) RNA-seq analysis of TE expression in five KRAB-ZFP cluster KO ES cells. Green and grey squares on top of the panel represent KRAB-ZFPs with or without ChIP-seq data, respectively, within each deleted gene cluster. Reactivated TEs that are bound by one or several KRAB-ZFPs are indicated by green squares in the panel. Significantly up- and downregulated elements (adjusted p-value<0.05) are highlighted in red and green, respectively. (B) Differential KAP1 binding and H3K9me3 enrichment at TE groups (summarized across all insertions) in Chr2-cl and Chr4-cl KO ES cells. TE groups targeted by one or several KRAB-ZFPs encoded within the deleted clusters are highlighted in blue (differential enrichment over the entire TE sequences) and red (differential enrichment at TE regions that overlap with KRAB-ZFP ChIP-seq peaks). (C) DNA methylation status of CpG sites at indicated TE groups in WT and Chr4-cl KO ES cells grown in serum containing media or in hypomethylation-inducing media (2i + Vitamin C). P-values were calculated using paired t-test.
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Figure 2—figure supplement 1. Epigenetic changes at TEs and TE-borne enhancers in KRAB-ZFP cluster KO ES cells. (A) Differential analysis of summative (all individual insertions combined) H3K9me3 enrichment at TE groups in Chr10-cl, Chr13.1-cl and Chr13.2-cl KO ES cells. TE groups targeted by one or several KRAB-ZFPs encoded within the deleted clusters are highlighted in orange (differential enrichment over the entire TE sequences) and red (differential enrichment at TE regions that overlap with KRAB-ZFP ChIP-seq peaks). (B) Top: Schematic view of the Cd59a/Cd59b locus with a 5’ truncated ETn insertion. ChIP-seq (Input subtracted from ChIP) data for overexpressed epitope-tagged Gm13051 (a Chr4-cl KRAB-ZFP) in F9 EC cells, and re-mapped KAP1 (GEO accession: GSM1406445) and H3K9me3 (GEO accession: GSM1327148) in WT ES cells are shown together with RNA-seq data from Chr4-cl WT and KO ES cells (mapped using Bowtie (-a -m 1 --strata -v 2) to exclude reads that cannot be uniquely mapped). Bottom: Transcriptional activity of a 5 kb fragment with or without fragments of the ETn insertion was tested by luciferase reporter assay in Chr4-cl WT and KO ES cells.
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Figure 3. TE-dependent gene activation in KRAB-ZFP cluster KO ES cells. (A) Differential gene expression in Chr2-cl and Chr4-cl KO ES cells. Significantly up- and downregulated genes (adjusted p-value<0.05) are highlighted in red and green, respectively, KRAB-ZFP genes within the deleted clusters are shown in blue. (B) Correlation of TEs and gene deregulation. Plots show enrichment of TE groups within 100 kb of up- and downregulated genes relative to all genes. Significantly overrepresented LTR and LINE groups (adjusted p-value<0.1) are highlighted in blue and red, respectively. (C) Schematic view of the downstream region of Chst1 where a 5’ truncated ETn insertion is located. ChIP-seq (Input subtracted from ChIP) data for overexpressed epitope-tagged Gm13051 (a Chr4-cl KRAB-ZFP) in F9 EC cells, and re-mapped KAP1 (GEO accession: GSM1406445) and H3K9me3 (GEO accession: GSM1327148) in WT ES cells are shown together with RNA-seq data from Chr4-cl WT and KO ES cells (mapped using Bowtie (-a -m 1 --strata -v 2) to exclude reads that cannot be uniquely mapped). (D) RT-qPCR analysis of Chst1 mRNA expression in Chr4-cl WT and KO ES cells with or without the CRISPR/Cas9 deleted ETn insertion near Chst1. Values represent mean expression (normalized to Gapdh) from three biological replicates per sample (each performed in three technical replicates) in arbitrary units. Error bars represent standard deviation and asterisks indicate significance (p<0.01, Student’s t-test). n.s.: not significant. (E) Mean coverage of ChIP-seq data (Input subtracted from ChIP) in Chr4-cl WT and KO ES cells over 127 full-length ETn insertions. The binding sites of the Chr4-cl KRAB-ZFPs Rex2 and Gm13051 are indicated by dashed lines.
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Figure 4. ETn retrotransposition in Chr4-cl KO mice. (A) Pedigree of mice used for transposon insertion screening by capture-seq in mice of different strain backgrounds. The number of novel ETn insertions (only present in one animal) are indicated. For animals whose direct ancestors have not been screened, the ETn insertions are shown in parentheses since parental inheritance cannot be excluded in that case. Germ line insertions are indicated by asterisks. All DNA samples were prepared from tail tissues unless noted (-S: spleen, -E: ear, -B:Blood) (B) Statistical analysis of ETn insertion frequency in tail tissue from 30 Chr4-cl KO, KO/WT and WT mice that were derived from one Chr4-c KO x KO/WT and two Chr4-cl KO/WT x KO/WT matings. Only DNA samples that were collected from juvenile tails were considered for this analysis. P-values were calculated using one-sided Wilcoxon Rank Sum Test. In the last panel, KO, WT and KO/WT mice derived from all matings were combined for the statistical analysis.
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Figure 4—figure supplement 1. Birth statistics of KRAB-ZFP cluster KO mice and TE reactivation in adult tissues. (A) Birth statistics of Chr4- and Chr2-cl mice derived from KO/WT x KO/WT matings in different strain backgrounds. (B) RNA-seq analysis of TE expression in Chr2- (left) and Chr4-cl (right) KO tissues. TE groups with the highest reactivation phenotype in ES cells are shown separately. Significantly up- and downregulated elements (adjusted p-value<0.05) are highlighted in red and green, respectively. Experiments were performed in at least two biological replicates.
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Figure 4—figure supplement 2. Identification of polymorphic ETn and MuLV retrotransposon insertions in Chr4-cl KO and WT mice. Heatmaps show normalized capture-seq read counts in RPM (Read Per Million) for identified polymorphic ETn (A) and MuLV (B) loci in different mouse strains. Only loci with strong support for germ line ETn or MuLV insertions (at least 100 or 3000 ETn or MuLV RPM, respectively) in at least two animals are shown. Non-polymorphic insertion loci with high read counts in all screened mice were excluded for better visibility. The sample information (sample name and cell type/tissue) is annotated at the bottom, with the strain information indicated by color at the top. The color gradient indicates log10(RPM+1).
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Figure 4—figure supplement 3. Confirmation of novel ETn insertions identified by capture-seq. (A) PCR validation of novel ETn insertions in genomic DNA of three littermates (IDs: T09673, T09674 and T00436) and their parents (T3913 and T3921). Primer sequences are shown in Supplementary file 3. (B) ETn capture-seq read counts (RPM) at putative novel somatic (loci identified exclusively in one single animal), novel germ line (loci identified in several littermates) insertions, and at B6 reference ETn elements. (C) Heatmap shows capture-seq read counts (RPM) of a Chr4-cl KO mouse (ID: C6733) as determined in different tissues. Each row represents a novel ETn locus that was identified in at least one tissue. The color gradient indicates log10(RPM+1). (D) Heatmap shows the capture-seq RPM in technical replicates using the same Chr4-cl KO DNA sample (rep1/rep2) or replicates with DNA samples prepared from different sections of the tail from the same mouse at different ages (tail1/tail2). Each row represents a novel ETn locus that was identified in at least one of the displayed samples. The color gradient indicates log10(RPM+1).
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item-0 at level 0: unspecified: group _root_
item-1 at level 1: title: Risk factors associated with fai ... s: Results of a multi-country analysis
item-2 at level 2: paragraph: Clara R. Burgert-Brucker; Global ... shington, DC, United States of America
item-3 at level 2: section_header: Abstract
item-4 at level 3: text: Achieving elimination of lymphat ... ine prevalence and/or lower elevation.
item-5 at level 2: section_header: Introduction
item-6 at level 3: text: Lymphatic filariasis (LF), a dis ... 8 countries remain endemic for LF [3].
item-7 at level 3: text: The road to elimination as a pub ... t elimination be officially validated.
item-8 at level 3: text: Pre-TAS include at least one sen ... me of day that blood can be taken [5].
item-9 at level 2: section_header: Methods
item-10 at level 3: text: Building on previous work, we de ... available global geospatial data sets.
item-11 at level 2: section_header: Data sources
item-12 at level 3: text: Information on baseline prevalen ... publicly available sources (Table 1).
item-13 at level 2: section_header: Outcome and covariate variables
item-14 at level 3: text: The outcome of interest for this ... r than or equal to 1% Mf or 2% Ag [4].
item-15 at level 3: text: Potential covariates were derive ... is and the final categorizations used.
item-16 at level 2: section_header: Baseline prevalence
item-17 at level 3: text: Baseline prevalence can be assum ... (2) using the cut-off of <10% or ≥10%.
item-18 at level 2: section_header: Agent
item-19 at level 3: text: In terms of differences in trans ... dazole (DEC-ALB)] from the MDA domain.
item-20 at level 2: section_header: Environment
item-21 at level 3: text: LF transmission intensity is inf ... dicates a higher level of “greenness.”
item-22 at level 3: text: We included the socio-economic v ... proxy for socio-economic status [33].
item-23 at level 3: text: Finally, all or parts of distric ... s were co-endemic with onchocerciasis.
item-24 at level 2: section_header: MDA
item-25 at level 3: text: Treatment effectiveness depends ... esent a threat to elimination [41,42].
item-26 at level 2: section_header: Statistical analysis and modeling
item-27 at level 3: text: Sensitivity analysis was perform ... ot have been truly LF-endemic [43,44].
item-28 at level 2: section_header: Results
item-29 at level 3: text: The overall pre-TAS pass rate fo ... ts had baseline prevalences below 20%.
item-30 at level 3: text: Fig 3 shows the unadjusted analy ... overage, and sufficient rounds of MDA.
item-31 at level 3: text: Fig 4 shows the risk ratio resul ... of failing pre-TAS (95% CI 1.95–4.83).
item-32 at level 3: text: Sensitivity analyses were conduc ... gnified by large confidence intervals.
item-33 at level 3: text: Overall 74 districts in the data ... or 51% of all the failures (38 of 74).
item-34 at level 2: section_header: Discussion
item-35 at level 3: text: This paper reports for the first ... ctors associated with TAS failure [7].
item-36 at level 3: text: Though diagnostic test used was ... FTS was more sensitive than ICT [45].
item-37 at level 3: text: Elevation was the only environme ... ich impact vector chances of survival.
item-38 at level 3: text: The small number of failures ove ... search has shown the opposite [15,16].
item-39 at level 3: text: All other variables included in ... are not necessary to lower prevalence.
item-40 at level 3: text: Limitations to this study includ ... reducing LF prevalence [41,48,51–53].
item-41 at level 2: section_header: Tables
item-42 at level 3: table with [18x8]
item-42 at level 4: caption: Table 1 Categorization of potential factors influencing pre-TAS results.
item-43 at level 3: table with [11x6]
item-43 at level 4: caption: Table 2 Adjusted risk ratios for pre-TAS failure from log-binomial model sensitivity analysis.
item-44 at level 2: section_header: Figures
item-45 at level 2: picture
item-45 at level 3: caption: Fig 1 Number of pre-TAS by country.
item-46 at level 2: picture
item-46 at level 3: caption: Fig 2 District-level baseline prevalence by country.
item-47 at level 2: picture
item-47 at level 3: caption: Fig 3 Percent pre-TAS failure by each characteristic (unadjusted).
item-48 at level 2: picture
item-48 at level 3: caption: Fig 4 Adjusted risk ratios for pre-TAS failure with 95% Confidence Interval from log-binomial model.
item-49 at level 2: picture
item-49 at level 3: caption: Fig 5 Analysis of failures by model combinations.
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# Risk factors associated with failing pre-transmission assessment surveys (pre-TAS) in lymphatic filariasis elimination programs: Results of a multi-country analysis
Clara R. Burgert-Brucker; Global Health Division, RTI International, Washington, DC, United States of America; Kathryn L. Zoerhoff; Global Health Division, RTI International, Washington, DC, United States of America; Maureen Headland; Global Health Division, RTI International, Washington, DC, United States of America; Global Health, Population, and Nutrition, FHI 360, Washington, DC, United States of America; Erica A. Shoemaker; Global Health Division, RTI International, Washington, DC, United States of America; Rachel Stelmach; Global Health Division, RTI International, Washington, DC, United States of America; Mohammad Jahirul Karim; Department of Disease Control, Ministry of Health and Family Welfare, Dhaka, Bangladesh; Wilfrid Batcho; National Control Program of Communicable Diseases, Ministry of Health, Cotonou, Benin; Clarisse Bougouma; Lymphatic Filariasis Elimination Program, Ministère de la Santé, Ouagadougou, Burkina Faso; Roland Bougma; Lymphatic Filariasis Elimination Program, Ministère de la Santé, Ouagadougou, Burkina Faso; Biholong Benjamin Didier; National Onchocerciasis and Lymphatic Filariasis Control Program, Ministry of Health, Yaounde, Cameroon; Nko'Ayissi Georges; National Onchocerciasis and Lymphatic Filariasis Control Program, Ministry of Health, Yaounde, Cameroon; Benjamin Marfo; Neglected Tropical Diseases Programme, Ghana Health Service, Accra, Ghana; Jean Frantz Lemoine; Ministry of Health, Port-au-Prince, Haiti; Helena Ullyartha Pangaribuan; National Institute Health Research & Development, Ministry of Health, Jakarta, Indonesia; Eksi Wijayanti; National Institute Health Research & Development, Ministry of Health, Jakarta, Indonesia; Yaya Ibrahim Coulibaly; Filariasis Unit, International Center of Excellence in Research, Faculty of Medicine and Odontostomatology, Bamako, Mali; Salif Seriba Doumbia; Filariasis Unit, International Center of Excellence in Research, Faculty of Medicine and Odontostomatology, Bamako, Mali; Pradip Rimal; Epidemiology and Disease Control Division, Department of Health Service, Kathmandu, Nepal; Adamou Bacthiri Salissou; Programme Onchocercose et Filariose Lymphatique, Ministère de la Santé, Niamey, Niger; Yukaba Bah; National Neglected Tropical Disease Program, Ministry of Health and Sanitation, Freetown, Sierra Leone; Upendo Mwingira; Neglected Tropical Disease Control Programme, National Institute for Medical Research, Dar es Salaam, Tanzania; Andreas Nshala; IMA World Health/Tanzania NTD Control Programme, Uppsala University, & TIBA Fellow, Dar es Salaam, Tanzania; Edridah Muheki; Programme to Eliminate Lymphatic Filariasis, Ministry of Health, Kampala, Uganda; Joseph Shott; Division of Neglected Tropical Diseases, Office of Infectious Diseases, Bureau for Global Health, USAID, Washington, DC, United States of America; Violetta Yevstigneyeva; Division of Neglected Tropical Diseases, Office of Infectious Diseases, Bureau for Global Health, USAID, Washington, DC, United States of America; Egide Ndayishimye; Global Health, Population, and Nutrition, FHI 360, Washington, DC, United States of America; Margaret Baker; Global Health Division, RTI International, Washington, DC, United States of America; John Kraemer; Global Health Division, RTI International, Washington, DC, United States of America; Georgetown University, Washington, DC, United States of America; Molly Brady; Global Health Division, RTI International, Washington, DC, United States of America
## Abstract
Achieving elimination of lymphatic filariasis (LF) as a public health problem requires a minimum of five effective rounds of mass drug administration (MDA) and demonstrating low prevalence in subsequent assessments. The first assessments recommended by the World Health Organization (WHO) are sentinel and spot-check sites—referred to as pre-transmission assessment surveys (pre-TAS)—in each implementation unit after MDA. If pre-TAS shows that prevalence in each site has been lowered to less than 1% microfilaremia or less than 2% antigenemia, the implementation unit conducts a TAS to determine whether MDA can be stopped. Failure to pass pre-TAS means that further rounds of MDA are required. This study aims to understand factors influencing pre-TAS results using existing programmatic data from 554 implementation units, of which 74 (13%) failed, in 13 countries. Secondary data analysis was completed using existing data from Bangladesh, Benin, Burkina Faso, Cameroon, Ghana, Haiti, Indonesia, Mali, Nepal, Niger, Sierra Leone, Tanzania, and Uganda. Additional covariate data were obtained from spatial raster data sets. Bivariate analysis and multilinear regression were performed to establish potential relationships between variables and the pre-TAS result. Higher baseline prevalence and lower elevation were significant in the regression model. Variables statistically significantly associated with failure (p-value ≤0.05) in the bivariate analyses included baseline prevalence at or above 5% or 10%, use of Filariasis Test Strips (FTS), primary vector of Culex , treatment with diethylcarbamazine-albendazole, higher elevation, higher population density, higher enhanced vegetation index (EVI), higher annual rainfall, and 6 or more rounds of MDA. This paper reports for the first time factors associated with pre-TAS results from a multi-country analysis. This information can help countries more effectively forecast program activities, such as the potential need for more rounds of MDA, and prioritize resources to ensure adequate coverage of all persons in areas at highest risk of failing pre-TAS. Author summary Achieving elimination of lymphatic filariasis (LF) as a public health problem requires a minimum of five rounds of mass drug administration (MDA) and being able to demonstrate low prevalence in several subsequent assessments. LF elimination programs implement sentinel and spot-check site assessments, called pre-TAS, to determine whether districts are eligible to implement more rigorous population-based surveys to determine whether MDA can be stopped or if further rounds are required. Reasons for failing pre-TAS are not well understood and have not previously been examined with data compiled from multiple countries. For this analysis, we analyzed data from routine USAID and WHO reports from Bangladesh, Benin, Burkina Faso, Cameroon, Ghana, Haiti, Indonesia, Mali, Nepal, Niger, Sierra Leone, Tanzania, and Uganda. In a model that included multiple variables, high baseline prevalence and lower elevation were significant. In models comparing only one variable to the outcome, the following were statistically significantly associated with failure: higher baseline prevalence at or above 5% or 10%, use of the FTS, primary vector of Culex , treatment with diethylcarbamazine-albendazole, lower elevation, higher population density, higher Enhanced Vegetation Index, higher annual rainfall, and six or more rounds of mass drug administration. These results can help national programs plan MDA more effectively, e.g., by focusing resources on areas with higher baseline prevalence and/or lower elevation.
## Introduction
Lymphatic filariasis (LF), a disease caused by parasitic worms transmitted to humans by mosquito bite, manifests in disabling and stigmatizing chronic conditions including lymphedema and hydrocele. To eliminate LF as a public health problem, the World Health Organization (WHO) recommends two strategies: reducing transmission through annual mass drug administration (MDA) and reducing suffering through ensuring the availability of morbidity management and disability prevention services to all patients [1]. For the first strategy, eliminating LF as a public health problem is defined as a ‘reduction in measurable prevalence in infection in endemic areas below a target threshold at which further transmission is considered unlikely even in the absence of MDA’ [2]. As of 2018, 14 countries have eliminated LF as a public health problem while 58 countries remain endemic for LF [3].
The road to elimination as a public health problem has several milestones. First, where LF prevalence at baseline has exceeded 1% as measured either through microfilaremia (Mf) or antigenemia (Ag), MDA is implemented and treatment coverage is measured in all implementation units, which usually correspond to districts. Implementation units must complete at least five rounds of ‘effective’ treatment, i.e. treatment with a minimum coverage of 65% of the total population. Then, WHO recommends sentinel and spot-check site assessments—referred to as pre-transmission assessment surveys (pre-TAS)—in each implementation unit to determine whether prevalence in each site is less than 1% Mf or less than 2% Ag [4]. Next, if these thresholds are met, national programs can progress to the first transmission assessment survey (TAS). The TAS is a population-based cluster or systematic survey of six- and seven-year-old children to assess whether transmission has fallen below the threshold at which infection is believed to persist. TAS is conducted at least three times, with two years between each survey. TAS 1 results determine if it is appropriate to stop MDA or whether further rounds are required. Finally, when TAS 2 and 3 also fall below the set threshold in every endemic implementation unit nationwide and morbidity criteria have been fulfilled, the national program submits a dossier to WHO requesting that elimination be officially validated.
Pre-TAS include at least one sentinel and one spot-check site per one million population. Sentinel sites are established at the start of the program in villages where LF prevalence was believed to be relatively high. Spot-check sites are villages not previously tested but purposively selected as potentially high-risk areas due to original high prevalence, low coverage during MDA, high vector density, or other factors [4]. At least six months after MDA implementation, data are collected from a convenience sample of at least 300 people over five years old in each site. Originally, Mf was recommended as the indicator of choice for pre-TAS, assessed by blood smears taken at the time of peak parasite periodicity [4]. WHO later recommended the use of circulating filarial antigen rapid diagnostic tests, BinaxNow immunochromatographic card tests (ICTs), and after 2016, Alere Filariasis Test Strips (FTS), because they are more sensitive, easier to implement, and more flexible about time of day that blood can be taken [5].
## Methods
Building on previous work, we delineated five domains of variables that could influence pre-TAS outcomes: prevalence, agent, environment, MDA, and pre-TAS implementation (Table 1) [6–8]. We prioritized key concepts that could be measured through our data or captured through publicly available global geospatial data sets.
## Data sources
Information on baseline prevalence, MDA coverage, the number of MDA rounds, and pre-TAS information (month and year of survey, district, site name, and outcome) was gathered through regular reporting for the USAID-funded NTD programs (ENVISION, END in Africa, and END in Asia). These data were augmented by other reporting data such as the country’s dossier data annexes, the WHO Preventive Chemotherapy and Transmission Control Databank, and WHO reporting forms. Data were then reviewed by country experts, including the Ministry of Health program staff and implementing program staff, and updated as necessary. Data on vectors were also obtained from country experts. The district geographic boundaries were matched to geospatial shapefiles from the ENVISION project geospatial data repository, while other geospatial data were obtained through publicly available sources (Table 1).
## Outcome and covariate variables
The outcome of interest for this analysis was whether a district passed or failed the pre-TAS. Failure was defined as any district that had at least one sentinel or spot-check site with a prevalence higher than or equal to 1% Mf or 2% Ag [4].
Potential covariates were derived from the available data for each factor in the domain groups listed in Table 1. New dichotomous variables were created for all variables that had multiple categories or were continuous for ease of interpretation in models and use in program decision-making. Cut-points for continuous variables were derived from either a priori knowledge or through exploratory analysis considering the mean or median value of the dataset, looking to create two groups of similar size with logical cut-points (e.g. rounding numbers to whole numbers). All the variables derived from publicly available global spatial raster datasets were summarized to the district level in ArcGIS Pro using the “zonal statistics” tool. The final output used the continuous value measuring the mean pixel value for the district for all variables except geographic area. Categories for each variable were determined by selecting the mean or median dataset value or cut-off used in other relevant literature [7]. The following section describes the variables that were included in the final analysis and the final categorizations used.
## Baseline prevalence
Baseline prevalence can be assumed as a proxy for local transmission conditions [14] and correlates with prevalence after MDA [14–20]. Baseline prevalence for each district was measured by either blood smears to measure Mf or rapid diagnostic tests to measure Ag. Other studies have modeled Mf and Ag prevalence separately, due to lack of a standardized correlation between the two, especially at pre-MDA levels [21,22]. However, because WHO mapping guidance states that MDA is required if either Mf or Ag is ≥1% and there were not enough data to model each separately, we combined baseline prevalence values regardless of diagnostic test used. We created two variables for use in the analysis (1) using the cut-off of <5% or ≥5% (dataset median value of 5%) and (2) using the cut-off of <10% or ≥10%.
## Agent
In terms of differences in transmission dynamics by agent, research has shown that Brugia spp. are more susceptible to the anti-filarial drug regimens than Wuchereria bancrofti parasites [23]. Thus, we combined districts reporting B. malayi and B. timori and compared them to areas with W. bancrofti or mixed parasites. Two variables from other domains were identified in exploratory analyses to be highly colinear with the parasite, and thus we considered them in the same group of variables for the final regression models. These were variables delineating vectors (Anopheles or Mansonia compared to Culex) from the environmental domain and drug package [ivermectin-albendazole (IVM-ALB) compared to diethylcarbamazine-albendazole (DEC-ALB)] from the MDA domain.
## Environment
LF transmission intensity is influenced by differing vector transmission dynamics, including vector biting rates and competence, and the number of individuals with microfilaria [21,24,25]. Since vector data are not always available, previous studies have explored whether environmental variables associated with vector density, such as elevation, rainfall, and temperature, can be used to predict LF prevalence [8,21,26–31]. We included the district area and elevation in meters as geographic variables potentially associated with transmission intensity. In addition, within the climate factor, we included Enhanced Vegetation Index (EVI) and rainfall variables. EVI measures vegetation levels, or “greenness,” where a higher index value indicates a higher level of “greenness.”
We included the socio-economic variable of population density, as it has been positively associated with LF prevalence in some studies [8,27,29], but no significant association has been found in others [30]. Population density could be correlated with vector, as in eastern African countries LF is mostly transmitted by Culex in urban areas and by Anopheles in rural areas [32]. Additionally, inclusion of the satellite imagery of nighttime lights data is another a proxy for socio-economic status [33].
Finally, all or parts of districts that are co-endemic with onchocerciasis may have received multiple rounds of MDA with ivermectin before LF MDA started, which may have lowered LF prevalence in an area [34–36]. Thus, we included a categorical variable to distinguish if districts were co-endemic with onchocerciasis.
## MDA
Treatment effectiveness depends upon both drug efficacy (ability to kill adult worms, ability to kill Mf, drug resistance, drug quality) and implementation of MDA (coverage, compliance, number of rounds) [14,16]. Ivermectin is less effective against adult worms than DEC, and therefore it is likely that Ag reduction is slower in areas using ivermectin instead of DEC in MDA [37]. Models also have shown that MDA coverage affects prevalence, although coverage has been defined in various ways, such as median coverage, number of rounds, or individual compliance [14–16,20,38–40]. Furthermore, systematic non-compliance, or population sub-groups which consistently refuse to take medicines, has been shown to represent a threat to elimination [41,42].
## Statistical analysis and modeling
Sensitivity analysis was performed for the final log-binomial model to test for the validity of results under different parameters by excluding some sub-sets of districts from the dataset and rerunning the model. This analysis was done to understand the robustness of the model when (1) excluding all districts in Cameroon, (2) including only districts in Africa, (3) including only districts with W. bancrofti parasite, and (4) including only districts with Anopheles as the primary vector. The sensitivity analysis excluding Cameroon was done for two reasons. First, Cameroon had the most pre-TAS results included, but no failures. Second, 70% of the Cameroon districts included in the analysis are co-endemic for loiasis. Given that diagnostic tests used in LF mapping have since been shown to cross-react with loiasis, there is some concern that these districts might not have been truly LF-endemic [43,44].
## Results
The overall pre-TAS pass rate for the districts included in this analysis was 87% (74 failures in 554 districts). Nearly 40% of the 554 districts were from Cameroon (134) and Tanzania (87) (Fig 1). No districts in Bangladesh, Cameroon, Mali, or Uganda failed a pre-TAS in this data set; over 25% of districts in Burkina Faso, Ghana, Haiti, Nepal, and Sierra Leone failed pre-TAS in this data set. Baseline prevalence varied widely within and between the 13 countries. Fig 2 shows the highest, lowest, and median baseline prevalence in the study districts by country. Burkina Faso had the highest median baseline prevalence at 52% and Burkina Faso, Tanzania, and Ghana all had at least one district with a very high baseline of over 70%. In Mali, Indonesia, Benin, and Bangladesh, all districts had baseline prevalences below 20%.
Fig 3 shows the unadjusted analysis for key variables by pre-TAS result. Variables statistically significantly associated with failure (p-value ≤0.05) included higher baseline prevalence at or above 5% or 10%, FTS diagnostic test, primary vector of Culex, treatment with DEC-ALB, higher elevation, higher population density, higher EVI, higher annual rainfall, and six or more rounds of MDA. Variables that were not significantly associated with pre-TAS failure included diagnostic method used (Ag or Mf), parasite, co-endemicity for onchocerciasis, median MDA coverage, and sufficient rounds of MDA.
Fig 4 shows the risk ratio results with their corresponding confidence intervals. In a model with interaction between baseline and diagnostic test the baseline parameter was significant while diagnostic test and the interaction term were not. Districts with high baseline had a statistically significant (p-value ≤0.05) 2.52 times higher risk of failure (95% CI 1.37–4.64) compared to those with low baseline prevalence. The FTS diagnostic test or ICT diagnostic test alone were not significant nor was the interaction term. Additionally, districts with an elevation below 350 meters had a statistically significant (p-value ≤0.05) 3.07 times higher risk of failing pre-TAS (95% CI 1.95–4.83).
Sensitivity analyses were conducted using the same model with different subsets of the dataset including (1) all districts except for districts in Cameroon (134 total with no failures), (2) only districts in Africa, (3) only districts with W. bancrofti, and (4) only districts with Anopheles as primary vector. The results of the sensitivity models (Table 2) indicate an overall robust model. High baseline and lower elevation remained significant across all the models. The ICT diagnostic test used remains insignificant across all models. The FTS diagnostic test was positively significant in model 1 and negatively significant in model 4. The interaction term of baseline prevalence and FTS diagnostic test was significant in three models though the estimate was unstable in the W. bancrofti-only and Anopheles-only models (models 3 and 4 respectively), as signified by large confidence intervals.
Overall 74 districts in the dataset failed pre-TAS. Fig 5 summarizes the likelihood of failure by variable combinations identified in the log-binomial model. For those districts with a baseline prevalence ≥10% that used a FTS diagnostic test and have an average elevation below 350 meters (Combination C01), 87% of the 23 districts failed. Of districts with high baseline that used an ICT diagnostic test and have a low average elevation (C02) 45% failed. Overall, combinations with high baseline and low elevation C01, C02, and C04 accounted for 51% of all the failures (38 of 74).
## Discussion
This paper reports for the first time factors associated with pre-TAS results from a multi-country analysis. Variables significantly associated with failure were higher baseline prevalence and lower elevation. Districts with a baseline prevalence of 10% or more were at 2.52 times higher risk to fail pre-TAS in the final log-binomial model. In the bivariate analysis, baseline prevalence above 5% was also significantly more likely to fail compared to lower baselines, which indicates that the threshold for higher baseline prevalence may be as little as 5%, similar to what was found in Goldberg et al., which explored ecological and socioeconomic factors associated with TAS failure [7].
Though diagnostic test used was selected for the final log-binomial model, neither category (FTS or ICT) were significant after interaction with high baseline. FTS alone is significant in the bivariate analysis compared to ICT or Mf. This result is not surprising given previous research which found that FTS was more sensitive than ICT [45].
Elevation was the only environmental domain variable selected for the final log-binomial model during the model selection process, with areas of lower elevation (<350m) found to be at 3.07 times higher risk to fail pre-TAS compared to districts with a higher elevation. Similar results related to elevation were found in previous studies [8,31], including Goldberg et al. [7], who used a cutoff of 200 meters. Elevation likely also encompasses some related environmental concepts, such as vector habitat, greenness (EVI), or rainfall, which impact vector chances of survival.
The small number of failures overall prevented the inclusion of a large number of variables in the final log-binomial model. However, other variables that are associated with failure as identified in the bivariate analyses, such as Culex vector, higher population density, higher EVI, higher rainfall and more rounds of MDA, should not be discounted when making programmatic decisions. Other models have shown that Culex as the predominant vector in a district, compared to Anopheles, results in more intense interventions needed to reach elimination [24,41]. Higher population density, which was also found to predict TAS failure [7], could be related to different vector species’ transmission dynamics in urban areas, as well as the fact that MDAs are harder to conduct and to accurately measure in urban areas [46,47]. Both higher enhanced vegetation index (>0.3) and higher rainfall (>700 mm per year) contribute to expansion of vector habitats and population. Additionally, having more than five rounds of MDA before pre-TAS was also statistically significantly associated with higher failure in the bivariate analysis. It is unclear why higher number of rounds is associated with first pre-TAS failure given that other research has shown the opposite [15,16].
All other variables included in this analysis were not significantly associated with pre-TAS failure in our analysis. Goldberg et al. found Brugia spp. to be significantly associated with failure, but our results did not. This is likely due in part to the small number of districts with Brugia spp. in our dataset (6%) compared to 46% in the Goldberg et al. article [7]. MDA coverage levels were not significantly associated with pre-TAS failure, likely due to the lack of variance in the coverage data since WHO guidance dictates a minimum of five rounds of MDA with ≥65% epidemiological coverage to be eligible to implement pre-TAS. It should not be interpreted as evidence that high MDA coverage levels are not necessary to lower prevalence.
Limitations to this study include data sources, excluded data, unreported data, misassigned data, and aggregation of results at the district level. The main data sources for this analysis were programmatic data, which may be less accurate than data collected specifically for research purposes. This is particularly true of the MDA coverage data, where some countries report data quality challenges in areas of instability or frequent population migration. Even though risk factors such as age, sex, compliance with MDA, and use of bednets have been shown to influence infection in individuals [40,48–50], we could not include factors from the human host domain in our analysis, as data sets were aggregated at site level and did not include individual information. In addition, vector control data were not universally available across the 13 countries and thus were not included in the analysis, despite studies showing that vector control has an impact on reducing LF prevalence [41,48,51–53].
## Tables
Table 1 Categorization of potential factors influencing pre-TAS results.
| Domain | Factor | Covariate | Description | Reference Group | Summary statistic | Temporal Resolution | Source |
|------------------------|-----------------------|-------------------------------|-----------------------------------------------------------------|----------------------|---------------------|-----------------------|--------------------|
| Prevalence | Baseline prevalence | 5% cut off | Maximum reported mapping or baseline sentinel site prevalence | <5% | Maximum | Varies | Programmatic data |
| Prevalence | Baseline prevalence | 10% cut off | Maximum reported mapping or baseline sentinel site prevalence | <10% | Maximum | Varies | Programmatic data |
| Agent | Parasite | Parasite | Predominate parasite in district | W. bancrofti & mixed | Binary value | 2018 | Programmatic data |
| Environment | Vector | Vector | Predominate vector in district | Anopheles & Mansonia | Binary value | 2018 | Country expert |
| Environment | Geography | Elevation | Elevation measured in meters | >350 | Mean | 2000 | CGIAR-CSI SRTM [9] |
| Environment | Geography | District area | Area measured in km2 | >2,500 | Maximum sum | Static | Programmatic data |
| Environment | Climate | EVI | Enhanced vegetation index | > 0.3 | Mean | 2015 | MODIS [10] |
| Environment | Climate | Rainfall | Annual rainfall measured in mm | ≤ 700 | Mean | 2015 | CHIRPS [11] |
| Environment | Socio-economic | Population density | Number of people per km2 | ≤ 100 | Mean | 2015 | WorldPop [12] |
| Environment | Socio-economic | Nighttime lights | Nighttime light index from 0 to 63 | >1.5 | Mean | 2015 | VIIRS [13] |
| Environment | Co-endemicity | Co-endemic for onchocerciasis | Part or all of district is also endemic for onchocerciases | Non-endemic | Binary value | 2018 | Programmatic data |
| MDA | Drug efficacy | Drug package | DEC-ALB or IVM-ALB | DEC-ALB | Binary value | 2018 | Programmatic data |
| MDA | Implementation of MDA | Coverage | Median MDA coverage for last 5 rounds | ≥ 65% | Median | Varies | Programmatic data |
| MDA | Implementation of MDA | Sufficient rounds | Number of rounds of sufficient (≥ 65% coverage) in last 5 years | ≥ 3 | Count | Varies | Programmatic data |
| MDA | Implementation of MDA | Number of rounds | Maximum number of recorded rounds of MDA | ≥ 6 | Maximum | Varies | Programmatic data |
| Pre-TAS implementation | Quality of survey | Diagnostic method | Using Mf or Ag | Mf | Binary value | Varies | Programmatic data |
| Pre-TAS implementation | Quality of survey | Diagnostic test | Using Mf, ICT, or FTS | Mf | Categorical | Varies | Programmatic data |
Table 2 Adjusted risk ratios for pre-TAS failure from log-binomial model sensitivity analysis.
| | | (1) | (2) | (3) | (4) |
|---------------------------------------------|------------------|----------------------------|--------------------------|--------------------------------------|---------------------------------|
| | Full Model | Without Cameroon districts | Only districts in Africa | Only W. bancrofti parasite districts | Only Anopheles vector districts |
| Number of Failures | 74 | 74 | 44 | 72 | 46 |
| Number of total districts | (N = 554) | (N = 420) | (N = 407) | (N = 518) | (N = 414) |
| Covariate | RR (95% CI) | RR (95% CI) | RR (95% CI) | RR (95% CI) | RR (95% CI) |
| Baseline prevalence > = 10% & used FTS test | 2.38 (0.96–5.90) | 1.23 (0.52–2.92) | 14.52 (1.79–117.82) | 2.61 (1.03–6.61) | 15.80 (1.95–127.67) |
| Baseline prevalence > = 10% & used ICT test | 0.80 (0.20–3.24) | 0.42 (0.11–1.68) | 1.00 (0.00–0.00) | 0.88 (0.21–3.60) | 1.00 (0.00–0.00) |
| +Used FTS test | 1.16 (0.52–2.59) | 2.40 (1.12–5.11) | 0.15 (0.02–1.11) | 1.03 (0.45–2.36) | 0.13 (0.02–0.96) |
| +Used ICT test | 0.92 (0.32–2.67) | 1.47 (0.51–4.21) | 0.33 (0.04–2.54) | 0.82 (0.28–2.43) | 0.27 (0.03–2.04) |
| +Baseline prevalence > = 10% | 2.52 (1.37–4.64) | 2.42 (1.31–4.47) | 2.03 (1.06–3.90) | 2.30 (1.21–4.36) | 2.01 (1.07–3.77) |
| Elevation < 350m | 3.07 (1.95–4.83) | 2.21 (1.42–3.43) | 4.68 (2.22–9.87) | 3.04 (1.93–4.79) | 3.76 (1.92–7.37) |
## Figures
Fig 1 Number of pre-TAS by country.
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Fig 2 District-level baseline prevalence by country.
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Fig 3 Percent pre-TAS failure by each characteristic (unadjusted).
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Fig 4 Adjusted risk ratios for pre-TAS failure with 95% Confidence Interval from log-binomial model.
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Fig 5 Analysis of failures by model combinations.
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item-0 at level 0: unspecified: group _root_
item-1 at level 1: title: Potential to reduce greenhouse g ... cattle systems in subtropical regions
item-2 at level 2: paragraph: Henrique M. N. Ribeiro-Filho; De ... , California, United States of America
item-3 at level 2: section_header: Abstract
item-4 at level 3: text: Carbon (C) footprint of dairy pr ... uce the C footprint to a small extent.
item-5 at level 2: section_header: Introduction
item-6 at level 3: text: Greenhouse gas (GHG) emissions f ... suitable for food crop production [4].
item-7 at level 3: text: Considering the key role of live ... anagement to mitigate the C footprint.
item-8 at level 3: text: In subtropical climate zones, co ... t in tropical pastures (e.g. [17–19]).
item-9 at level 3: text: It has been shown that dairy cow ... sions from crop and reduced DM intake.
item-10 at level 2: section_header: Materials and methods
item-11 at level 3: text: An LCA was developed according t ... 90816 - https://www.udesc.br/cav/ceua.
item-12 at level 2: section_header: System boundary
item-13 at level 3: text: The goal of the study was to ass ... n were outside of the system boundary.
item-14 at level 2: section_header: Functional unit
item-15 at level 3: text: The functional unit was one kilo ... tein according to NRC [20] as follows:
item-16 at level 3: text: ECM = Milk production × (0.0929 ... characteristics described in Table 1.
item-17 at level 2: section_header: Data sources and livestock system description
item-18 at level 3: text: Using experimental data, three s ... med during an entire lactation period.
item-19 at level 2: section_header: Impact assessment
item-20 at level 3: text: The CO2e emissions were calculat ... 65 for CO2, CH4 and N2O, respectively.
item-21 at level 2: section_header: Diets composition
item-22 at level 3: text: The DM intake of each ingredient ... collected throughout the experiments.
item-23 at level 2: section_header: GHG emissions from crop and pasture production
item-24 at level 3: text: GHG emission factors used for of ... onsume 70% of pastures during grazing.
item-25 at level 3: text: Emissions from on-farm feed prod ... factors described by Rotz et al. [42].
item-26 at level 2: section_header: Animal husbandry
item-27 at level 3: text: The CH4 emissions from enteric f ... 1) = 13.8 + 0.185 × NDF (% DM intake).
item-28 at level 2: section_header: Manure from confined cows and urine and dung from grazing animals
item-29 at level 3: text: The CH4 emission from manure (kg ... for dietary GE per kg of DM (MJ kg-1).
item-30 at level 3: text: The OM digestibility was estimat ... h were 31%, 26% and 46%, respectively.
item-31 at level 3: text: The N2O-N emissions from urine a ... using the IPCC [38] emission factors.
item-32 at level 2: section_header: Farm management
item-33 at level 3: text: Emissions due to farm management ... crop and pasture production’ section.
item-34 at level 3: text: The amount of fuel use for manur ... me that animals stayed on confinement.
item-35 at level 3: text: The emissions from fuel were est ... × kg CO2e (kg machinery mass)-1 [42].
item-36 at level 3: text: Emissions from electricity for m ... ws in naturally ventilated barns [47].
item-37 at level 3: text: The lower impact of emissions fr ... greater than 5% of total C footprint.
item-38 at level 3: text: Emissions from farm management d ... gas and hard coal, respectively [46].
item-39 at level 2: section_header: Co-product allocation
item-40 at level 3: text: The C footprint for milk produce ... directly assigned to milk production.
item-41 at level 2: section_header: Sensitivity analysis
item-42 at level 3: text: A sensitivity index was calculat ... ses a similar change in the footprint.
item-43 at level 2: section_header: Greenhouse gas emissions
item-44 at level 3: text: Depending on emission factors us ... more than 5% of overall GHG emissions.
item-45 at level 3: text: Considering IPCC emission factor ... the C footprint of the dairy systems.
item-46 at level 3: text: The similarity of C footprint be ... of TMR was replaced by pasture access.
item-47 at level 3: text: The lower C footprint in scenari ... r, averaging 0.004 kg N2O-N kg-1 [37].
item-48 at level 2: section_header: Overall greenhouse gas emissions ... attle systems under various scenarios.
item-49 at level 3: text: TMR = ad libitum TMR intake, 75T ... lectricity = 0.205 kg CO2e kWh-1 [46].
item-50 at level 2: section_header: Methane emissions
item-51 at level 3: text: The enteric CH4 intensity was si ... ], which did not happen in this study.
item-52 at level 3: text: The lack of difference in enteri ... same scenarios as in this study [26].
item-53 at level 2: section_header: Sensitivity of the C footprint.
item-54 at level 3: text: Sensitivity index = percentage c ... lectricity = 0.205 kg CO2e kWh-1 [46].
item-55 at level 2: section_header: Emissions from excreta and feed production
item-56 at level 3: text: Using IPCC emission factors for ... may not be captured by microbes [65].
item-57 at level 3: text: Using local emission factors for ... be revised for the subtropical region.
item-58 at level 3: text: Emissions for feed production de ... act, particularly in confinements [9].
item-59 at level 2: section_header: Greenhouse gas emissions (GHG) f ... ed production in dairy cattle systems.
item-60 at level 3: text: TMR = ad libitum TMR intake, 75T ... uestered CO2-C from perennial pasture.
item-61 at level 2: section_header: Further considerations
item-62 at level 3: text: The potential for using pasture ... g ECM)-1 in case of foot lesions [72].
item-63 at level 3: text: Grazing lands may also improve b ... hange of CO2 would be negligible [76].
item-64 at level 2: section_header: Tables
item-65 at level 3: table with [13x3]
item-65 at level 4: caption: Table 1 Descriptive characteristics of the herd.
item-66 at level 3: table with [21x11]
item-66 at level 4: caption: Table 2 Dairy cows' diets in different scenarios.
item-67 at level 3: table with [9x5]
item-67 at level 4: caption: Table 3 GHG emission factors for Off- and On-farm feed production.
item-68 at level 3: table with [28x5]
item-68 at level 4: caption: Table 4 GHG emissions from On-farm feed production.
item-69 at level 3: table with [12x4]
item-69 at level 4: caption: Table 5 Factors for major resource inputs in farm management.
item-70 at level 2: section_header: Figures
item-71 at level 2: picture
item-71 at level 3: caption: Fig 1 Overview of the milk production system boundary considered in the study.
item-72 at level 2: picture
item-72 at level 3: caption: Fig 2 Overall greenhouse gas emissions in dairy cattle systems under various scenarios. TMR = ad libitum TMR intake, 75TMR = 75% of ad libitum TMR intake with access to pasture, 50TMR = 50% of ad libitum TMR intake with access to pasture. (a) N2O emission factors for urine and dung from IPCC [38], feed production emission factors from Table 3 without accounting for sequestered CO2-C from perennial pasture, production of electricity = 0.73 kg CO2e kWh-1 [41]. (b) N2O emission factors for urine and dung from IPCC [38], feed production emission factors from Table 3 without accounting for sequestered CO2-C from perennial pasture, production of electricity = 0.205 kg CO2e kWh-1 [46]; (c) N2O emission factors for urine and dung from local data [37], feed production EF from Table 4 without accounting for sequestered CO2-C from perennial pasture, production of electricity = 0.205 kg CO2e kWh-1 [46]. (d) N2O emission factors for urine and dung from local data [37], feed production emission factors from Table 4 accounting for sequestered CO2-C from perennial pasture, production of electricity = 0.205 kg CO2e kWh-1 [46].
item-73 at level 2: picture
item-73 at level 3: caption: Fig 3 Sensitivity of the C footprint. Sensitivity index = percentage change in C footprint for a 10% change in the given emission source divided by 10% of. (a) N2O emission factors for urine and dung from IPCC [38], feed production emission factors from Table 3, production of electricity = 0.73 kg CO2e kWh-1 [41]. (b) N2O emission factors for urine and dung from IPCC [38], feed production emission factors from Table 3, production of electricity = 0.205 kg CO2e kWh-1 [46]; (c) N2O emission factors for urine and dung from local data [37], feed production EF from Table 4 without accounting sequestered CO2-C from perennial pasture, production of electricity = 0.205 kg CO2e kWh-1 [46]. (d) N2O emission factors for urine and dung from local data [37], feed production emission factors from Table 4 accounting sequestered CO2-C from perennial pasture, production of electricity = 0.205 kg CO2e kWh-1 [46].
item-74 at level 2: picture
item-74 at level 3: caption: Fig 4 Greenhouse gas emissions (GHG) from manure and feed production in dairy cattle systems. TMR = ad libitum TMR intake, 75TMR = 75% of ad libitum TMR intake with access to pasture, 50TMR = 50% of ad libitum TMR intake with access to pasture. (a) N2O emission factors for urine and dung from IPCC [38]. (b) Feed production emission factors from Table 3. (c) N2O emission factors for urine and dung from local data [37]. (d) Feed production emission factors from Table 4 accounting sequestered CO2-C from perennial pasture.
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# Potential to reduce greenhouse gas emissions through different dairy cattle systems in subtropical regions
Henrique M. N. Ribeiro-Filho; Department of Animal Science, University of California, Davis, California, United States of America; Programa de Pós-graduação em Ciência Animal, Universidade do Estado de Santa Catarina, Lages, Santa Catarina, Brazil; Maurício Civiero; Programa de Pós-graduação em Ciência Animal, Universidade do Estado de Santa Catarina, Lages, Santa Catarina, Brazil; Ermias Kebreab; Department of Animal Science, University of California, Davis, California, United States of America
## Abstract
Carbon (C) footprint of dairy production, expressed in kg C dioxide (CO 2 ) equivalents (CO 2 e) (kg energy-corrected milk (ECM)) -1 , encompasses emissions from feed production, diet management and total product output. The proportion of pasture on diets may affect all these factors, mainly in subtropical climate zones, where cows may access tropical and temperate pastures during warm and cold seasons, respectively. The aim of the study was to assess the C footprint of a dairy system with annual tropical and temperate pastures in a subtropical region. The system boundary included all processes up to the animal farm gate. Feed requirement during the entire life of each cow was based on data recorded from Holstein × Jersey cow herds producing an average of 7,000 kg ECM lactation -1 . The milk production response as consequence of feed strategies (scenarios) was based on results from two experiments (warm and cold seasons) using lactating cows from the same herd. Three scenarios were evaluated: total mixed ration (TMR) ad libitum intake, 75, and 50% of ad libitum TMR intake with access to grazing either a tropical or temperate pasture during lactation periods. Considering IPCC and international literature values to estimate emissions from urine/dung, feed production and electricity, the C footprint was similar between scenarios, averaging 1.06 kg CO 2 e (kg ECM) -1 . Considering factors from studies conducted in subtropical conditions and actual inputs for on-farm feed production, the C footprint decreased 0.04 kg CO 2 e (kg ECM) -1 in scenarios including pastures compared to ad libitum TMR. Regardless of factors considered, emissions from feed production decreased as the proportion of pasture went up. In conclusion, decreasing TMR intake and including pastures in dairy cow diets in subtropical conditions have the potential to maintain or reduce the C footprint to a small extent.
## Introduction
Greenhouse gas (GHG) emissions from livestock activities represent 10–12% of global emissions [1], ranging from 5.5–7.5 Gt CO2 equivalents (CO2e) yr-1, with almost 30% coming from dairy cattle production systems [2]. However, the livestock sector supply between 13 and 17% of calories and between 28 and 33% of human edible protein consumption globally [3]. Additionally, livestock produce more human-edible protein per unit area than crops when land is unsuitable for food crop production [4].
Considering the key role of livestock systems in global food security, several technical and management interventions have been investigated to mitigate methane (CH4) emissions from enteric fermentation [5], animal management [6] and manure management [7]. CH4 emissions from enteric fermentation represents around 34% of total emissions from livestock sector, which is the largest source [2]. Increasing proportions of concentrate and digestibility of forages in the diet have been proposed as mitigation strategies [1,5]. In contrast, some life cycle assessment (LCA) studies of dairy systems in temperate regions [8–11] have identified that increasing concentrate proportion may increase carbon (C) footprint due to greater resource use and pollutants from the production of feed compared to forage. Thus, increasing pasture proportion on dairy cattle systems may be an alternative management to mitigate the C footprint.
In subtropical climate zones, cows may graze tropical pastures rather than temperate pastures during the warm season [12]. Some important dairy production areas, such as southern Brazil, central to northern Argentina, Uruguay, South Africa, New Zealand and Australia, are located in these climate zones, having more than 900 million ha in native, permanent or temporary pastures, producing almost 20% of global milk production [13]. However, due to a considerable inter-annual variation in pasture growth rates [14,15], the interest in mixed systems, using total mixed ration (TMR) + pasture has been increasing [16]. Nevertheless, to our best knowledge, studies conducted to evaluate milk production response in dairy cow diets receiving TMR and pastures have only been conducted in temperate pastures and not in tropical pastures (e.g. [17–19]).
It has been shown that dairy cows receiving TMR-based diets may not decrease milk production when supplemented with temperate pastures in a vegetative growth stage [18]. On the other hand, tropical pastures have lower organic matter digestibility and cows experience reduced dry matter (DM) intake and milk yield compared to temperate pastures [20,21]. A lower milk yield increases the C footprint intensity [22], offsetting an expected advantage through lower GHG emissions from crop and reduced DM intake.
## Materials and methods
An LCA was developed according to the ISO standards [23,24] and Food and Agriculture Organization of the United Nations (FAO) Livestock Environmental Assessment Protocol guidelines [25]. All procedures were approved by the ‘Comissão de Ética no Uso de Animais’ (CEUA/UDESC) on September 15, 2016—Approval number 4373090816 - https://www.udesc.br/cav/ceua.
## System boundary
The goal of the study was to assess the C footprint of annual tropical and temperate pastures in lactating dairy cow diets. The production system was divided into four main processes: (i) animal husbandry, (ii) manure management and urine and dung deposited by grazing animals, (iii) production of feed ingredients and (iv) farm management (Fig 1). The study boundary included all processes up to the animal farm gate (cradle to gate), including secondary sources such as GHG emissions during the production of fuel, electricity, machinery, manufacturing of fertilizer, pesticides, seeds and plastic used in silage production. Fuel combustion and machinery (manufacture and repairs) for manure handling and electricity for milking and confinement were accounted as emissions from farm management. Emissions post milk production were assumed to be similar for all scenarios, therefore, activities including milk processing, distribution, retail or consumption were outside of the system boundary.
## Functional unit
The functional unit was one kilogram of energy-corrected milk (ECM) at the farm gate. All processes in the system were calculated based on one kilogram ECM. The ECM was calculated by multiplying milk production by the ratio of the energy content of the milk to the energy content of standard milk with 4% fat and 3.3% true protein according to NRC [20] as follows:
ECM = Milk production × (0.0929 × fat% + 0.0588× true protein% + 0.192) / (0.0929 × (4%) + 0.0588 × (3.3%) + 0.192), where fat% and protein% are fat and protein percentages in milk, respectively. The average milk production and composition were recorded from the University of Santa Catarina State (Brazil) herd, considering 165 lactations between 2009 and 2018. The herd is predominantly Holstein × Jersey cows, with key characteristics described in Table 1.
## Data sources and livestock system description
Using experimental data, three scenarios were evaluated during the lactation period: ad libitum TMR intake, and 75, and 50% of ad libitum TMR intake with access to grazing either an annual tropical or temperate pasture as a function of month ([26], Civiero et al., in press). From April to October (210 days) cows accessed an annual temperate pasture (ryegrass), and from November to beginning of February (95 days) cows grazed an annual tropical pasture (pearl-millet). The average annual reduction in ECM production in dairy cows with access to pastures is 3%. This value was assumed during an entire lactation period.
## Impact assessment
The CO2e emissions were calculated by multiplying the emissions of CO2, CH4 and N2O by their 100-year global warming potential (GWP100), based on IPCC assessment report 5 (AR5; [27]). The values of GWP100 are 1, 28 and 265 for CO2, CH4 and N2O, respectively.
## Diets composition
The DM intake of each ingredient throughout the entire life of animals during lactation periods was calculated for each scenario: cows receiving only TMR, cows receiving 75% of TMR with annual pastures and cows receiving 50% of TMR with annual pastures (Table 2). In each of other phases of life (calf, heifer, dry cow), animals received the same diet, including a perennial tropical pasture (kikuyu grass, Pennisetum clandestinum). The DM intake of calves, heifers and dry cows was calculated assuming 2.8, 2.5 and 1.9% body weight, respectively [20]. In each case, the actual DM intake of concentrate and corn silage was recorded, and pasture DM intake was estimated by the difference between daily expected DM intake and actual DM intake of concentrate and corn silage. For lactating heifers and cows, TMR was formulated to meet the net energy for lactation (NEL) and metabolizable protein (MP) requirements of experimental animals, according to [28]. The INRA system was used because it is possible to estimate pasture DM intake taking into account the TMR intake, pasture management and the time of access to pasture using the GrazeIn model [29], which was integrated in the software INRAtion 4.07 (https://www.inration.educagri.fr/fr/forum.php). The nutrient intake was calculated as a product of TMR and pasture intake and the nutrient contents of TMR and pasture, respectively, which were determined in feed samples collected throughout the experiments.
## GHG emissions from crop and pasture production
GHG emission factors used for off- and on-farm feed production were based on literature values, and are presented in Table 3. The emission factor used for corn grain is the average of emission factors observed in different levels of synthetic N fertilization [30]. The emission factor used for soybean is based on Brazilian soybean production [31]. The emissions used for corn silage, including feed processing (cutting, crushing and mixing), and annual or perennial grass productions were 3300 and 1500 kg CO2e ha-1, respectively [32]. The DM production (kg ha-1) of corn silage and pastures were based on regional and locally recorded data [33–36], assuming that animals are able to consume 70% of pastures during grazing.
Emissions from on-farm feed production (corn silage and pasture) were estimated using primary and secondary sources based on the actual amount of each input (Table 4). Primary sources were direct and indirect N2O-N emissions from organic and synthetic fertilizers and crop/pasture residues, CO2-C emissions from lime and urea applications, as well as fuel combustion. The direct N2O-N emission factor (kg (kg N input)-1) is based on a local study performed previously [37]. For indirect N2O-N emissions (kg N2O-N (kg NH3-N + NOx)-1), as well as CO2-C emissions from lime + urea, default values proposed by IPCC [38] were used. For perennial pastures, a C sequestration of 0.57 t ha-1 was used based on a 9-year study conducted in southern Brazil [39]. Due to the use of conventional tillage, no C sequestration was considered for annual pastures. The amount of fuel required was 8.9 (no-tillage) and 14.3 L ha-1 (disking) for annual tropical and temperate pastures, respectively [40]. The CO2 from fuel combustion was 2.7 kg CO2 L-1 [41]. Secondary sources of emissions during the production of fuel, machinery, fertilizer, pesticides, seeds and plastic for ensilage were estimated using emission factors described by Rotz et al. [42].
## Animal husbandry
The CH4 emissions from enteric fermentation intensity (g (kg ECM)-1) was a function of estimated CH4 yield (g (kg DM intake)-1), actual DM intake and ECM. The enteric CH4 yield was estimated as a function of neutral detergent fiber (NDF) concentration on total DM intake, as proposed by Niu et al. [43], where: CH4 yield (g (kg DM intake)-1) = 13.8 + 0.185 × NDF (% DM intake).
## Manure from confined cows and urine and dung from grazing animals
The CH4 emission from manure (kg (kg ECM)-1) was a function of daily CH4 emission from manure (kg cow-1) and daily ECM (kg cow-1). The daily CH4 emission from manure was estimated according to IPCC [38], which considered daily volatile solid (VS) excreted (kg DM cow-1) in manure. The daily VS was estimated as proposed by Eugène et al. [44] as: VS = NDOMI + (UE × GE) × (OM/18.45), where: VS = volatile solid excretion on an organic matter (OM) basis (kg day-1), NDOMI = non-digestible OM intake (kg day-1): (1- OM digestibility) × OM intake, UE = urinary energy excretion as a fraction of GE (0.04), GE = gross energy intake (MJ day-1), OM = organic matter (g), 18.45 = conversion factor for dietary GE per kg of DM (MJ kg-1).
The OM digestibility was estimated as a function of chemical composition, using equations published by INRA [21], which takes into account the effects of digestive interactions due to feeding level, the proportion of concentrate and rumen protein balance on OM digestibility. For scenarios where cows had access to grazing, the amount of calculated VS were corrected as a function of the time at pasture. The biodegradability of manure factor (0.13 for dairy cows in Latin America) and methane conversion factor (MCF) values were taken from IPCC [38]. The MCF values for pit storage below animal confinements (> 1 month) were used for the calculation, taking into account the annual average temperature (16.6ºC) or the average temperatures during the growth period of temperate (14.4ºC) or tropical (21ºC) annual pastures, which were 31%, 26% and 46%, respectively.
The N2O-N emissions from urine and feces were estimated considering the proportion of N excreted as manure and storage or as urine and dung deposited by grazing animals. These proportions were calculated based on the proportion of daily time that animals stayed on pasture (7 h/24 h = 0.29) or confinement (1−0.29 = 0.71). For lactating heifers and cows, the total amount of N excreted was calculated by the difference between N intake and milk N excretion. For heifers and non-lactating cows, urinary and fecal N excretion were estimated as proposed by Reed et al. [45] (Table 3: equations 10 and 12, respectively). The N2O emissions from stored manure as well as urine and dung during grazing were calculated based on the conversion of N2O-N emissions to N2O emissions, where N2O emissions = N2O-N emissions × 44/28. The emission factors were 0.002 kg N2O-N (kg N)-1 stored in a pit below animal confinements, and 0.02 kg N2O-N (kg of urine and dung)-1 deposited on pasture [38]. The indirect N2O emissions from storage manure and urine and dung deposits on pasture were also estimated using the IPCC [38] emission factors.
## Farm management
Emissions due to farm management included those from fuel and machinery for manure handling and electricity for milking and confinement (Table 5). Emissions due to feed processing such as cutting, crushing, mixing and distributing, as well as secondary sources of emissions during the production of fuel, machinery, fertilizer, pesticides, seeds and plastic for ensilage were included in ‘Emissions from crop and pasture production’ section.
The amount of fuel use for manure handling were estimated taking into consideration the amount of manure produced per cow and the amounts of fuel required for manure handling (L diesel t-1) [42]. The amount of manure was estimated from OM excretions (kg cow-1), assuming that the manure has 8% ash on DM basis and 60% DM content. The OM excretions were calculated by NDOMI × days in confinement × proportion of daily time that animals stayed on confinement.
The emissions from fuel were estimated considering the primary (emissions from fuel burned) and secondary (emissions for producing and transporting fuel) emissions. The primary emissions were calculated by the amount of fuel required for manure handling (L) × (kg CO2e L-1) [41]. The secondary emissions from fuel were calculated by the amount of fuel required for manure handling × emissions for production and transport of fuel (kg CO2e L-1) [41]. Emissions from manufacture and repair of machinery for manure handling were estimated by manure produced per cow (t) × (kg machinery mass (kg manure)-1 × 10−3) [42] × kg CO2e (kg machinery mass)-1 [42].
Emissions from electricity for milking and confinement were estimated using two emission factors (kg CO2 kWh-1). The first one is based on United States electricity matrix [41], and was used as a reference of an electricity matrix with less hydroelectric power than the region under study. The second is based on the Brazilian electricity matrix [46]. The electricity required for milking activities is 0.06 kWh (kg milk produced)-1 [47]. The annual electricity use for lighting was 75 kWh cow-1, which is the value considered for lactating cows in naturally ventilated barns [47].
The lower impact of emissions from farm management is in agreement with other studies conducted in Europe [9, 62] and USA [42, 55], where the authors found that most emissions in dairy production systems are from enteric fermentation, feed production and emissions from excreta. As emissions from fuel for on-farm feed production were accounted into the ‘emissions from crop and pasture production’, total emissions from farm management were not greater than 5% of total C footprint.
Emissions from farm management dropped when the emission factor for electricity generation was based on the Brazilian matrix. In this case, the emission factor for electricity generation (0.205 kg CO2e kWh-1 [46]) is much lower than that in a LCA study conducted in US (0.73 kg CO2e kWh-1 [42]). This apparent discrepancy is explained because in 2016, almost 66% of the electricity generated in Brazil was from hydropower, which has an emission factor of 0.074 kg CO2e kWh-1 against 0.382 and 0.926 kg CO2e kWh-1 produced by natural gas and hard coal, respectively [46].
## Co-product allocation
The C footprint for milk produced in the system was calculated using a biophysical allocation approach, as recommended by the International Dairy Federation [49], and described by Thoma et al. [48]. Briefly, ARmilk = 1–6.04 × BMR, where: ARmilk is the allocation ratio for milk and BMR is cow BW at the time of slaughter (kg) + calf BW sold (kg) divided by the total ECM produced during cow`s entire life (kg). The ARmilk were 0.854 and 0.849 for TMR and TMR with both pasture scenarios, respectively. The ARmilk was applied to the whole emissions, except for the electricity consumed for milking (milking parlor) and refrigerant loss, which was directly assigned to milk production.
## Sensitivity analysis
A sensitivity index was calculated as described by Rotz et al. [42]. The sensitivity index was defined for each emission source as the percentage change in the C footprint for a 10% change in the given emission source divided by 10%. Thus, a value near 0 indicates a low sensitivity, whereas an index near or greater than 1 indicates a high sensitivity because a change in this value causes a similar change in the footprint.
## Greenhouse gas emissions
Depending on emission factors used for calculating emissions from urine and dung (IPCC or local data) and feed production (Tables 3 or 4), the C footprint was similar (Fig 2A and 2B) or decreased by 0.04 kg CO2e (kg ECM)-1 (Fig 2C and 2D) in scenarios that included pastures compared to ad libitum TMR intake. Due to differences in emission factors, the overall GHG emission values ranged from 0.92 to 1.04 kg CO2e (kg ECM)-1 for dairy cows receiving TMR exclusively, and from 0.88 to 1.04 kg CO2e (kg ECM)-1 for cows with access to pasture. Using IPCC emission factors [38], manure emissions increased as TMR intake went down (Fig 2A and 2B). However, using local emission factors for estimating N2O-N emissions [37], manure emissions decreased as TMR intake went down (Fig 2C and 2D). Regardless of emission factors used (Tables 3 or 4), emissions from feed production decreased to a small extent as the proportion of TMR intake decreased. Emissions from farm management did not contribute more than 5% of overall GHG emissions.
Considering IPCC emission factors for N2O emissions from urine and dung [38] and those from Table 3, the C footprint ranged from 0.99 to 1.04 kg CO2e (kg ECM)-1, and was close to those reported under confined based systems in California [49], Canada [50], China [8], Ireland [9], different scenarios in Australia [51,52] and Uruguay [11], which ranged from 0.98 to 1.16 kg CO2e (kg ECM)-1. When local emission factors for N2O emissions from urine and dung [37] and those from Table 4 were taking into account, the C footprint for scenarios including pasture, without accounting for sequestered CO2-C from perennial pasture—0.91 kg CO2e (kg ECM)-1—was lower than the range of values described above. However, these values were still greater than high-performance confinement systems in UK and USA [53] or grass based dairy systems in Ireland [9,53] and New Zealand [8,54], which ranged from 0.52 to 0.89 kg CO2e (kg ECM)-1. Regardless of which emission factor was used, we found a lower C footprint in all conditions compared to scenarios with lower milk production per cow or in poor conditions of manure management, which ranged from 1.4 to 2.3 kg CO2e (kg ECM)-1 [8,55]. Thus, even though differences between studies may be partially explained by various assumptions (e.g., emission factors, co-product allocation, methane emissions estimation, sequestered CO2-C, etc.), herd productivity and manure management were systematically associated with the C footprint of the dairy systems.
The similarity of C footprint between different scenarios using IPCC [38] for estimating emissions from manure and for emissions from feed production (Table 3) was a consequence of the trade-off between greater manure emissions and lower emissions to produce feed, as the proportion of pasture in diets increased. Additionally, the small negative effect of pasture on ECM production also contributed to the trade-off. The impact of milk production on the C footprint was reported in a meta-analysis comprising 30 studies from 15 different countries [22]. As observed in this study (Fig 2A and 2B) the authors reported no significant difference between the C footprint of pasture-based vs. confinement systems. However, they observed that an increase of 1000 kg cow-1 (5000 to 6000 kg ECM) reduced the C footprint by 0.12 kg CO2e (kg ECM)-1, which may explain an apparent discrepancy between our study and an LCA performed in south Brazilian conditions [56]. Their study compared a confinement and a grazing-based dairy system with annual average milk production of 7667 and 5535 kg cow, respectively. In this study, the same herd was used in all systems, with an annual average milk production of around 7000 kg cow-1. Experimental data showed a reduction not greater than 3% of ECM when 50% of TMR was replaced by pasture access.
The lower C footprint in scenarios with access to pasture, when local emission factors [37] were used for N2O emissions from urine and dung and for feed production (Table 4), may also be partially attributed to the small negative effect of pasture on ECM production. Nevertheless, local emission factors for urine and dung had a great impact on scenarios including pastures compared to ad libitum TMR intake. Whereas the IPCC [38] considers an emission of 0.02 kg N2O-N (kg N)-1 for urine and dung from grazing animals, experimental evidence shows that it may be up to five times lower, averaging 0.004 kg N2O-N kg-1 [37].
## Overall greenhouse gas emissions in dairy cattle systems under various scenarios.
TMR = ad libitum TMR intake, 75TMR = 75% of ad libitum TMR intake with access to pasture, 50TMR = 50% of ad libitum TMR intake with access to pasture. (a) N2O emission factors for urine and dung from IPCC [38], feed production emission factors from Table 3 without accounting for sequestered CO2-C from perennial pasture, production of electricity = 0.73 kg CO2e kWh-1 [41]. (b) N2O emission factors for urine and dung from IPCC [38], feed production emission factors from Table 3 without accounting for sequestered CO2-C from perennial pasture, production of electricity = 0.205 kg CO2e kWh-1 [46]; (c) N2O emission factors for urine and dung from local data [37], feed production EF from Table 4 without accounting for sequestered CO2-C from perennial pasture, production of electricity = 0.205 kg CO2e kWh-1 [46]. (d) N2O emission factors for urine and dung from local data [37], feed production emission factors from Table 4 accounting for sequestered CO2-C from perennial pasture, production of electricity = 0.205 kg CO2e kWh-1 [46].
## Methane emissions
The enteric CH4 intensity was similar between different scenarios (Fig 2), showing the greatest sensitivity index, with values ranging from 0.53 to 0.62, which indicate that for a 10% change in this source, the C footprint may change between 5.3 and 6.2% (Fig 3). The large effect of enteric CH4 emissions on the whole C footprint was expected, because the impact of enteric CH4 on GHG emissions of milk production in different dairy systems has been estimated to range from 44 to 60% of the total CO2e [50,52,57,58]. However, emissions in feed production may be the most important source of GHG when emission factors for producing concentrate feeds are greater than 0.7 kg CO2e kg-1 [59], which did not happen in this study.
The lack of difference in enteric CH4 emissions in different systems can be explained by the narrow range of NDF content in diets (<4% difference). This non-difference is due to the lower NDF content of annual temperate pastures (495 g (kg DM)-1) compared to corn silage (550 g (kg DM)-1). Hence, an expected, increase NDF content with decreased concentrate was partially offset by an increase in the pasture proportion relatively low in NDF. This is in agreement with studies conducted in southern Brazil, which have shown that the actual enteric CH4 emissions may decrease with inclusion of temperate pastures in cows receiving corn silage and soybean meal [60] or increase enteric CH4 emissions when dairy cows grazing a temperate pasture was supplemented with corn silage [61]. Additionally, enteric CH4 emissions did not differ between dairy cows receiving TMR exclusively or grazing a tropical pasture in the same scenarios as in this study [26].
## Sensitivity of the C footprint.
Sensitivity index = percentage change in C footprint for a 10% change in the given emission source divided by 10% of. (a) N2O emission factors for urine and dung from IPCC [38], feed production emission factors from Table 3, production of electricity = 0.73 kg CO2e kWh-1 [41]. (b) N2O emission factors for urine and dung from IPCC [38], feed production emission factors from Table 3, production of electricity = 0.205 kg CO2e kWh-1 [46]; (c) N2O emission factors for urine and dung from local data [37], feed production EF from Table 4 without accounting sequestered CO2-C from perennial pasture, production of electricity = 0.205 kg CO2e kWh-1 [46]. (d) N2O emission factors for urine and dung from local data [37], feed production emission factors from Table 4 accounting sequestered CO2-C from perennial pasture, production of electricity = 0.205 kg CO2e kWh-1 [46].
## Emissions from excreta and feed production
Using IPCC emission factors for N2O emissions from urine and dung [38] and those from Table 3, CH4 emissions from manure decreased 0.07 kg CO2e (kg ECM)-1, but N2O emissions from manure increased 0.09 kg CO2e (kg ECM)-1, as TMR intake was restricted to 50% ad libitum (Fig 4A). Emissions for pastures increased by 0.06 kg CO2e (kg ECM)-1, whereas emissions for producing concentrate feeds and corn silage decreased by 0.09 kg CO2e (kg ECM)-1, as TMR intake decreased (Fig 4B). In this situation, the lack of difference in calculated C footprints of different systems was also due to the greater emissions from manure, and offset by lower emissions from feed production with inclusion of pasture in lactating dairy cow diets. The greater N2O-N emissions from manure with pasture was a consequence of higher N2O-N emissions due to greater CP content and N urine excretion, as pasture intake increased. The effect of CP content on urine N excretion has been shown by several authors in lactating dairy cows [62–64]. For instance, by decreasing CP content from 185 to 152 g (kg DM)-1, N intake decreased by 20% and urine N excretion by 60% [62]. In this study, the CP content for lactating dairy cows ranged from 150 g (kg DM)-1 on TMR system to 198 g (kg DM)-1 on 50% TMR with pasture. Additionally, greater urine N excretion is expected with greater use of pasture. This occurs because protein utilization in pastures is inefficient, as the protein in fresh forages is highly degradable in the rumen and may not be captured by microbes [65].
Using local emission factors for N2O emissions from urine and dung [37] and those from Table 4, reductions in CH4 emissions from stocked manure, when pastures were included on diets, did not offset by increases in N2O emissions from excreta (Fig 4C). In this case, total emissions from manure (Fig 4C) and feed production (Fig 4D) decreased with the inclusion of pasture. The impact of greater CP content and N urine excretion with increased pasture intake was offset by the much lower emission factors used for N2O emissions from urine and dung. As suggested by other authors [66,67], these results show that IPCC default value may need to be revised for the subtropical region.
Emissions for feed production decreased when pasture was included due to the greater emission factor for corn grain production compared to pastures. Emissions from concentrate and silage had at least twice the sensitivity index compared to emissions from pastures. The amount of grain required per cow in a lifetime decreased from 7,300 kg to 4,000 kg when 50% of TMR was replaced by pasture access. These results are in agreement with other studies which found lower C footprint, as concentrate use is reduced and/or pasture is included [9,68,69]. Moreover, it has been demonstrated that in intensive dairy systems, after enteric fermentation, feed production is the second main contributor to C footprint [50]. There is potential to decrease the environmental impact of dairy systems by reducing the use of concentrate ingredients with high environmental impact, particularly in confinements [9].
## Greenhouse gas emissions (GHG) from manure and feed production in dairy cattle systems.
TMR = ad libitum TMR intake, 75TMR = 75% of ad libitum TMR intake with access to pasture, 50TMR = 50% of ad libitum TMR intake with access to pasture. (a) N2O emission factors for urine and dung from IPCC [38]. (b) Feed production emission factors from Table 3. (c) N2O emission factors for urine and dung from local data [37]. (d) Feed production emission factors from Table 4 accounting sequestered CO2-C from perennial pasture.
## Further considerations
The potential for using pasture can reduce the C footprint because milk production kept pace with animal confinement. However, if milk production is to decrease with lower TMR intake and inclusion of pasture [19], the C footprint would be expected to increase. Lorenz et al. [22] showed that an increase in milk yield from 5,000 to 6,000 kg ECM reduced the C footprint by 0.12 kg CO2e (kg ECM)-1, whereas an increase from 10,000 to 11,000 kg ECM reduced the C footprint by only 0.06 kg CO2e (kg ECM)-1. Hence, the impact of increasing milk production on decreasing C footprint is not linear, and mitigation measures, such as breeding for increased genetic yield potential and increasing concentrate ratio in the diet, are potentially harmful for animal’s health and welfare [70]. For instance, increasing concentrate ratio potentially increases the occurrence of subclinical ketosis and foot lesions, and C footprint may increase by 0.03 kg CO2e (kg ECM)-1 in subclinical ketosis [71] and by 0.02 kg CO2e (kg ECM)-1 in case of foot lesions [72].
Grazing lands may also improve biodiversity [73]. Strategies such as zero tillage may increase stocks of soil C [74]. This study did not consider C sequestration during the growth of annual pastures, because it was assumed these grasses were planted with tillage, having a balance between C sequestration and C emissions [38]. Considering the C sequestration from no-tillage perennial pasture, the amount of C sequestration will more than compensates for C emitted. These results are in agreement with other authors who have shown that a reduction or elimination of soil tillage increases annual soil C sequestration in subtropical areas by 0.5 to 1.5 t ha-1 [75]. If 50% of tilled areas were under perennial grasslands, 1.0 t C ha-1 would be sequestered, further reducing the C footprint by 0.015 and 0.025 kg CO2e (kg ECM)-1 for the scenarios using 75 and 50% TMR, respectively. Eliminating tillage, the reduction on total GHG emissions would be 0.03 and 0.05 kg CO2e (kg ECM)-1 for 75 and 50% TMR, respectively. However, this approach may be controversial because lands which have been consistently managed for decades have approached steady state C storage, so that net exchange of CO2 would be negligible [76].
## Tables
Table 1 Descriptive characteristics of the herd.
| Item | Unit | Average |
|-------------------------------|-----------|-----------|
| Milking cows | # | 165 |
| Milk production | kg year-1 | 7,015 |
| Milk fat | % | 4.0 |
| Milk protein | % | 3.3 |
| Length of lactation | days | 305 |
| Body weight | kg | 553 |
| Lactations per cow | # | 4 |
| Replacement rate | % | 25 |
| Cull rate | % | 25 |
| First artificial insemination | months | 16 |
| Weaned | days | 60 |
| Mortality | % | 3.0 |
Table 2 Dairy cows' diets in different scenarios.
| | Calf | Calf | Pregnant/dry | Pregnant/dry | Lactation | Lactation | Lactation | Weighted average | Weighted average | Weighted average |
|-----------------------------------|-----------------------------------|-----------------------------------|-----------------------------------|-----------------------------------|-----------------------------------|-----------------------------------|-----------------------------------|-----------------------------------|-----------------------------------|-----------------------------------|
| | 0–12 mo | 12-AI mo | Heifer | Cow | TMR | TMR75 | TMR50 | TMR | TMR75 | TMR50 |
| Days | 360 | 120 | 270 | 180 | 1220 | 1220 | 1220 | | | |
| DM intake, kg d-1 | 3.35 | 6.90 | 10.4 | 11.0 | 18.7 | 17.2 | 17.0 | 13.8 | 12.9 | 12.8 |
| Ingredients, g (kg DM)-1 | Ingredients, g (kg DM)-1 | Ingredients, g (kg DM)-1 | Ingredients, g (kg DM)-1 | Ingredients, g (kg DM)-1 | Ingredients, g (kg DM)-1 | Ingredients, g (kg DM)-1 | Ingredients, g (kg DM)-1 | Ingredients, g (kg DM)-1 | Ingredients, g (kg DM)-1 | Ingredients, g (kg DM)-1 |
| Ground corn | 309 | 145 | 96.3 | - | 257 | 195 | 142 | 218 | 183 | 153 |
| Soybean meal | 138 | 22 | 26.7 | - | 143 | 105 | 76.1 | 109 | 88.0 | 71.0 |
| Corn silage | 149 | 290 | 85.6 | - | 601 | 451 | 326 | 393 | 308 | 237 |
| Ann temperate pasture | 184 | 326 | 257 | - | - | 185 | 337 | 81.3 | 186 | 273 |
| Ann tropical pasture | - | - | 107 | - | - | 63 | 119 | 13.4 | 49.1 | 81.0 |
| Perenn tropical pasture | 219 | 217 | 428 | 1000 | - | - | - | 186 | 186 | 186 |
| Chemical composition, g (kg DM)-1 | Chemical composition, g (kg DM)-1 | Chemical composition, g (kg DM)-1 | Chemical composition, g (kg DM)-1 | Chemical composition, g (kg DM)-1 | Chemical composition, g (kg DM)-1 | Chemical composition, g (kg DM)-1 | Chemical composition, g (kg DM)-1 | Chemical composition, g (kg DM)-1 | Chemical composition, g (kg DM)-1 | Chemical composition, g (kg DM)-1 |
| Organic matter | 935 | 924 | 913 | 916 | 958 | 939 | 924 | 943 | 932 | 924 |
| Crude protein | 216 | 183 | 213 | 200 | 150 | 170 | 198 | 175 | 186 | 202 |
| Neutral detergent fibre | 299 | 479 | 518 | 625 | 382 | 418 | 449 | 411 | 431 | 449 |
| Acid detergent fibre | 127 | 203 | 234 | 306 | 152 | 171 | 187 | 174 | 185 | 194 |
| Ether extract | 46.5 | 30.4 | 28.6 | 25.0 | 31.8 | 31.1 | 30.4 | 33.2 | 32.8 | 32.4 |
| Nutritive value | Nutritive value | Nutritive value | Nutritive value | Nutritive value | Nutritive value | Nutritive value | Nutritive value | Nutritive value | Nutritive value | Nutritive value |
| OM digestibility, % | 82.1 | 77.9 | 77.1 | 71.9 | 72.4 | 75.0 | 77.2 | 74.8 | 76.3 | 77.6 |
| NEL, Mcal (kg DM)-1 | 1.96 | 1.69 | 1.63 | 1.44 | 1.81 | 1.78 | 1.74 | 1.8 | 1.8 | 1.7 |
| MP, g (kg DM)-1 | 111 | 93.6 | 97.6 | 90.0 | 95.0 | 102 | 102 | 97.5 | 102 | 101 |
Table 3 GHG emission factors for Off- and On-farm feed production.
| Feed | DM yield (kg ha-1) | Emission factor | Unita | References |
|------------------|----------------------|-------------------|----------------------|--------------|
| Off-farm | | | | |
| Corn grain | 7,500 | 0.316 | kg CO2e (kg grain)-1 | [30] |
| Soybean | 2,200 | 0.186 | kg CO2e (kg grain)-1 | [31] |
| On-farm | | | | |
| Corn silageb | 16,000 | 0.206 | kg CO2e (kg DM)-1 | [32,33] |
| Annual ryegrassc | 9,500 | 0.226 | kg CO2e (kg DM)-1 | [32,34] |
| Pearl milletd | 11,000 | 0.195 | kg CO2e (kg DM)-1 | [32,35] |
| Kikuyu grasse | 9,500 | 0.226 | kg CO2e (kg DM)-1 | [32,36] |
Table 4 GHG emissions from On-farm feed production.
| Item | Corn silage | Annual temperate pasture | Annual tropical pasture | Perennial tropical pasture |
|-------------------------------------------|---------------|----------------------------|---------------------------|------------------------------|
| DM yield, kg ha-1 | 16000 | 9500 | 11000 | 9500 |
| Direct N2O emissions to air | | | | |
| N organic fertilizer, kg ha-1a | 150 | 180 | 225 | 225 |
| N synthetic fertilizer | - | 20 | 25 | 25 |
| N from residual DM, kg ha-1b | 70 | 112 | 129 | 112 |
| Emission fator, kg N2O-N (kg N)-1c | 0.002 | 0.002 | 0.002 | 0.002 |
| kg N2O ha-1 from direct emissions | 0.69 | 0.98 | 1.19 | 1.14 |
| Indirect N2O emissions to air | | | | |
| kg NH3-N+NOx-N (kg organic N)-1b | 0.2 | 0.2 | 0.2 | 0.2 |
| kg NH3-N+NOx-N (kg synthetic N)-1b | 0.1 | 0.1 | 0.1 | 0.1 |
| kg N2O-N (kg NH3-N+NOx-N)-1b | 0.01 | 0.01 | 0.01 | 0.01 |
| kg N2O ha-1 from NH3+NOx volatilized | 0.47 | 0.60 | 0.75 | 0.75 |
| Indirect N2O emissions to soil | | | | |
| kg N losses by leaching (kg N)-1b | 0.3 | 0.3 | 0.3 | 0.3 |
| kg N2O-N (kg N leaching)-1 | 0.0075 | 0.0075 | 0.0075 | 0.0075 |
| kg N2O ha-1 from N losses by leaching | 0.78 | 1.10 | 1.34 | 1.28 |
| kg N2O ha-1 (direct + indirect emissions) | 1.94 | 2.68 | 3.28 | 3.16 |
| kg CO2e ha-1 from N20 emissionsd | 514 | 710 | 869 | 838 |
| kg CO2 ha-1 from lime+ureab | 515 | 721 | 882 | 852 |
| kg CO2 ha-1 from diesel combustione | 802 | 38 | 23 | 12 |
| kg CO2e from secondary sourcesf | 516 | 205 | 225 | 284 |
| Total CO2e emitted, kg ha-1 | 1833 | 964 | 1130 | 1148 |
| Emission factor, kg CO2e (kg DM)-1g | 0.115 | 0.145 | 0.147 | 0.173 |
| Carbon sequestered, kg ha-1h | - | - | - | 570 |
| Sequestered CO2-C, kg ha-1 | - | - | - | 1393 |
| kg CO2e ha-1 (emitted—sequestered) | 1833 | 964 | 1130 | -245 |
| Emission factor, kg CO2e (kg DM)-1i | 0.115 | 0.145 | 0.147 | -0.037 |
Table 5 Factors for major resource inputs in farm management.
| Item | Factor | Unita | References |
|------------------------------------------|----------|-------------------|--------------|
| Production and transport of diesel | 0.374 | kg CO2e L-1 | [41] |
| Emissions from diesel fuel combustion | 2.637 | kg CO2e L-1 | [41] |
| Production of electricityb | 0.73 | kg CO2e kWh-1 | [41] |
| Production of electricity (alternative)c | 0.205 | kg CO2e kWh-1 | [46] |
| Production of machinery | 3.54 | kg CO2e (kg mm)-1 | [42] |
| Manure handling | | | |
| Fuel for manure handling | 0.600 | L diesel tonne-1 | [42] |
| Machinery for manure handling | 0.17 | kg mm kg-1 | [42] |
| Milking and confinement | | | |
| Electricity for milking | 0.06 | kWh (kg milk)-1 | [47] |
| Electricity for lightingd | 75 | kWh cow-1 | [47] |
## Figures
Fig 1 Overview of the milk production system boundary considered in the study.
<!-- image -->
Fig 2 Overall greenhouse gas emissions in dairy cattle systems under various scenarios. TMR = ad libitum TMR intake, 75TMR = 75% of ad libitum TMR intake with access to pasture, 50TMR = 50% of ad libitum TMR intake with access to pasture. (a) N2O emission factors for urine and dung from IPCC [38], feed production emission factors from Table 3 without accounting for sequestered CO2-C from perennial pasture, production of electricity = 0.73 kg CO2e kWh-1 [41]. (b) N2O emission factors for urine and dung from IPCC [38], feed production emission factors from Table 3 without accounting for sequestered CO2-C from perennial pasture, production of electricity = 0.205 kg CO2e kWh-1 [46]; (c) N2O emission factors for urine and dung from local data [37], feed production EF from Table 4 without accounting for sequestered CO2-C from perennial pasture, production of electricity = 0.205 kg CO2e kWh-1 [46]. (d) N2O emission factors for urine and dung from local data [37], feed production emission factors from Table 4 accounting for sequestered CO2-C from perennial pasture, production of electricity = 0.205 kg CO2e kWh-1 [46].
<!-- image -->
Fig 3 Sensitivity of the C footprint. Sensitivity index = percentage change in C footprint for a 10% change in the given emission source divided by 10% of. (a) N2O emission factors for urine and dung from IPCC [38], feed production emission factors from Table 3, production of electricity = 0.73 kg CO2e kWh-1 [41]. (b) N2O emission factors for urine and dung from IPCC [38], feed production emission factors from Table 3, production of electricity = 0.205 kg CO2e kWh-1 [46]; (c) N2O emission factors for urine and dung from local data [37], feed production EF from Table 4 without accounting sequestered CO2-C from perennial pasture, production of electricity = 0.205 kg CO2e kWh-1 [46]. (d) N2O emission factors for urine and dung from local data [37], feed production emission factors from Table 4 accounting sequestered CO2-C from perennial pasture, production of electricity = 0.205 kg CO2e kWh-1 [46].
<!-- image -->
Fig 4 Greenhouse gas emissions (GHG) from manure and feed production in dairy cattle systems. TMR = ad libitum TMR intake, 75TMR = 75% of ad libitum TMR intake with access to pasture, 50TMR = 50% of ad libitum TMR intake with access to pasture. (a) N2O emission factors for urine and dung from IPCC [38]. (b) Feed production emission factors from Table 3. (c) N2O emission factors for urine and dung from local data [37]. (d) Feed production emission factors from Table 4 accounting sequestered CO2-C from perennial pasture.
<!-- image -->
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item-0 at level 0: unspecified: group _root_
item-1 at level 1: title: Comparison of written reports of ... asis on magnetic resonance mammography
item-2 at level 1: paragraph: Sabine Malur; Department of Gyne ... ch-Schiller University, Jena, Germany.
item-3 at level 1: text: Patients with abnormal breast fi ... to that of MR mammography (ie 94.6%).
item-4 at level 1: section_header: Introduction
item-5 at level 2: text: Mammography and sonography are t ... aphy and mammography are combined [3].
item-6 at level 2: text: It is generally accepted that MR ... cal and multicentric invasive disease.
item-7 at level 1: section_header: Results
item-8 at level 2: text: All patients underwent breast su ... ents was 58 years (range 19-85 years).
item-9 at level 2: text: The sensitivity of MR mammograph ... all three methods (P < 0.05; Table 3).
item-10 at level 2: text: Mammography was false-negative i ... aphy or mammography were nonsuspected.
item-11 at level 1: section_header: Discussion
item-12 at level 2: text: When the validity of individual ... %, respectively [6,7,8,9,10,15,16,17].
item-13 at level 2: text: The performance of mammography, ... sensitivity of all imaging procedures.
item-14 at level 2: text: When combinations of all three t ... ammography alone (94.6% versus 94.6%).
item-15 at level 2: text: The majority of false-positive r ... and may mimic invasive cancer [16,18].
item-16 at level 2: text: Ten out of 185 (5.4%) malignant ... cinomas had a low microvessel density.
item-17 at level 2: text: Multifocality of breast cancers ... was shown to be unifocal by histology.
item-18 at level 2: text: Kramer et al [24] reported that ... tric cancers were diagnosed correctly.
item-19 at level 2: text: Carcinoma in situ is identified ... es without early contrast enhancement.
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"orig": "Comparison of written reports of mammography, sonography and magnetic resonance mammography for preoperative evaluation of breast lesions, with special emphasis on magnetic resonance mammography",
"text": "Comparison of written reports of mammography, sonography and magnetic resonance mammography for preoperative evaluation of breast lesions, with special emphasis on magnetic resonance mammography"
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"orig": "Sabine Malur; Department of Gynecology, Friedrich-Schiller University, Jena, Germany.; Susanne Wurdinger; Institute for Diagnostic and Interventional Radiology, Friedrich-Schiller University, Jena, Germany.; Andreas Moritz; Department of Gynecology, Friedrich-Schiller University, Jena, Germany.; Wolfgang Michels; Department of Gynecology, Friedrich-Schiller University, Jena, Germany.; Achim Schneider; Department of Gynecology, Friedrich-Schiller University, Jena, Germany.",
"text": "Sabine Malur; Department of Gynecology, Friedrich-Schiller University, Jena, Germany.; Susanne Wurdinger; Institute for Diagnostic and Interventional Radiology, Friedrich-Schiller University, Jena, Germany.; Andreas Moritz; Department of Gynecology, Friedrich-Schiller University, Jena, Germany.; Wolfgang Michels; Department of Gynecology, Friedrich-Schiller University, Jena, Germany.; Achim Schneider; Department of Gynecology, Friedrich-Schiller University, Jena, Germany."
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"orig": "Patients with abnormal breast findings ( n = 413) were examined by mammography, sonography and magnetic resonance (MR) mammography; 185 invasive cancers, 38 carcinoma in situ and 254 benign tumours were confirmed histologically. Sensitivity for mammography was 83.7%, for sonography it was 89.1% and for MR mammography it was 94.6% for invasive cancers. In 42 patients with multifocal invasive cancers, multifocality had been detected by mammography and sonography in 26.2%, and by MR mammography in 66.7%. In nine patients with multicentric cancers, detection rates were 55.5, 55.5 and 88.8%, respectively. Carcinoma in situ was diagnosed by mammography in 78.9% and by MR mammography in 68.4% of patients. Combination of all three diagnostic methods lead to the best results for detection of invasive cancer and multifocal disease. However, sensitivity of mammography and sonography combined was identical to that of MR mammography (ie 94.6%).",
"text": "Patients with abnormal breast findings ( n = 413) were examined by mammography, sonography and magnetic resonance (MR) mammography; 185 invasive cancers, 38 carcinoma in situ and 254 benign tumours were confirmed histologically. Sensitivity for mammography was 83.7%, for sonography it was 89.1% and for MR mammography it was 94.6% for invasive cancers. In 42 patients with multifocal invasive cancers, multifocality had been detected by mammography and sonography in 26.2%, and by MR mammography in 66.7%. In nine patients with multicentric cancers, detection rates were 55.5, 55.5 and 88.8%, respectively. Carcinoma in situ was diagnosed by mammography in 78.9% and by MR mammography in 68.4% of patients. Combination of all three diagnostic methods lead to the best results for detection of invasive cancer and multifocal disease. However, sensitivity of mammography and sonography combined was identical to that of MR mammography (ie 94.6%)."
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"orig": "Introduction",
"text": "Introduction",
"level": 1
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"orig": "Mammography and sonography are the standard imaging techniques for detection and evaluation of breast disease [1]. Mammography is the most established screening modality [2]. Especially in young women and women with dense breasts, sonography appears superior to mammography, and differentiation between solid tumours and cysts is easier. Sensitivity and specificity of sonography or mammography are higher if sonography and mammography are combined [3].",
"text": "Mammography and sonography are the standard imaging techniques for detection and evaluation of breast disease [1]. Mammography is the most established screening modality [2]. Especially in young women and women with dense breasts, sonography appears superior to mammography, and differentiation between solid tumours and cysts is easier. Sensitivity and specificity of sonography or mammography are higher if sonography and mammography are combined [3]."
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"orig": "It is generally accepted that MR mammography is the most sensitive technique for diagnosis of breast cancer, whereas the reported specificity of MR mammography varies [4,5,6,7,8,9,10,11,12]. In those studies, MR mammography was performed and evaluated by highly specialized radiologists in a research setting. It was therefore the purpose of the present prospective study to compare the validity of MR mammography with mammography and sonography in clinical routine practice. Findings for the three diagnostic methods documented on routine reports that were available to the surgeon preoperatively formed the basis of this comparison. Special emphasis was placed on the identification of multifocal and multicentric invasive disease.",
"text": "It is generally accepted that MR mammography is the most sensitive technique for diagnosis of breast cancer, whereas the reported specificity of MR mammography varies [4,5,6,7,8,9,10,11,12]. In those studies, MR mammography was performed and evaluated by highly specialized radiologists in a research setting. It was therefore the purpose of the present prospective study to compare the validity of MR mammography with mammography and sonography in clinical routine practice. Findings for the three diagnostic methods documented on routine reports that were available to the surgeon preoperatively formed the basis of this comparison. Special emphasis was placed on the identification of multifocal and multicentric invasive disease."
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"orig": "Results",
"text": "Results",
"level": 1
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"orig": "All patients underwent breast surgery and all abnormal lesions identified by mammography, sonography or MR mammography were surgically removed. A total of 477 breast lesions were examined histologically, revealing the presence of 185 invasive cancers, 38 carcinomata in situ and 254 benign lesions (fibroadenoma, papilloma, intraductal or adenoid ductal hyperplasia, cystic mastopathia). There were four patients with malignant lesions in both breasts. In 42 patients multifocal tumours and in nine patients multicentric tumors were found on histological examination. Among the 185 invasive lesions, 178 were primary cancers, five were recurrences, one was metastatic and one was an angiosarcoma. The majority of invasive breast cancers were staged as pT1c (44%). Six per cent of tumors were detected in stage pT1a, 18% in stage pT1b, 25% in stage pT2, 3% in stage pT3 and 4% in stage pT4. The distribution of histopathological tumour types is shown in Table 1. The mean age of patients was 58 years (range 19-85 years).",
"text": "All patients underwent breast surgery and all abnormal lesions identified by mammography, sonography or MR mammography were surgically removed. A total of 477 breast lesions were examined histologically, revealing the presence of 185 invasive cancers, 38 carcinomata in situ and 254 benign lesions (fibroadenoma, papilloma, intraductal or adenoid ductal hyperplasia, cystic mastopathia). There were four patients with malignant lesions in both breasts. In 42 patients multifocal tumours and in nine patients multicentric tumors were found on histological examination. Among the 185 invasive lesions, 178 were primary cancers, five were recurrences, one was metastatic and one was an angiosarcoma. The majority of invasive breast cancers were staged as pT1c (44%). Six per cent of tumors were detected in stage pT1a, 18% in stage pT1b, 25% in stage pT2, 3% in stage pT3 and 4% in stage pT4. The distribution of histopathological tumour types is shown in Table 1. The mean age of patients was 58 years (range 19-85 years)."
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"orig": "The sensitivity of MR mammography was significantly higher than those of mammography and sonography (P < 0.005 and P < 0.05; Table 2). The specificity of sonography was significantly higher than those of mammography and MR mammography (P < 0.05 and P < 0.005; Table 2). The negative predictive values for sonography and MR mammography were significantly higher than that of mammography (P < 0.05 and P < 0.005; Table 2). With regard to accuracy, no significant difference between the three modalities was found (Table 2). Combining of all three diagnostic methods yielded the best results for detection of cancer (P < 0.005; Table 3). The sensitivity and negative predictive value for the combination of mammography and MR mammography, and the combination of sonography and MR mammography were significantly higher than those for the combination of mammograpy and sonography (P < 0.05; Table 3). The highest result for accuracy was seen for a combination of all three methods (P < 0.05; Table 3).",
"text": "The sensitivity of MR mammography was significantly higher than those of mammography and sonography (P < 0.005 and P < 0.05; Table 2). The specificity of sonography was significantly higher than those of mammography and MR mammography (P < 0.05 and P < 0.005; Table 2). The negative predictive values for sonography and MR mammography were significantly higher than that of mammography (P < 0.05 and P < 0.005; Table 2). With regard to accuracy, no significant difference between the three modalities was found (Table 2). Combining of all three diagnostic methods yielded the best results for detection of cancer (P < 0.005; Table 3). The sensitivity and negative predictive value for the combination of mammography and MR mammography, and the combination of sonography and MR mammography were significantly higher than those for the combination of mammograpy and sonography (P < 0.05; Table 3). The highest result for accuracy was seen for a combination of all three methods (P < 0.05; Table 3)."
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"orig": "Mammography was false-negative in 30 out of 184 invasive cancers, sonography was false-negative in 20 out of 185 cancers, and 10 out of 185 invasive cancers were missed by MR mammography. The majority of false-negative findings was found in stage1 disease, ductal carcinoma and grade 3 tumors (Table 4). Of 10 invasive cancers missed by MR mammography, eight were found by mammography and sonography. By all three techniques, one invasive ductal carcinoma (pT1b) was misinterpreted as fibroadenoma. In another patient, a microinvasive lobular carcinoma of 5 mm diameter was not detected with mammography and MR mammography, whereas sonography detected a solid, benign tumour. MR mammography identified 10 invasive cancers (5.2%) that were missed by mammography and sonography, whereas one invasive cancer was found by mammography alone. By sonography alone, not a single case of invasive disease was detected when MR mammography or mammography were nonsuspected.",
"text": "Mammography was false-negative in 30 out of 184 invasive cancers, sonography was false-negative in 20 out of 185 cancers, and 10 out of 185 invasive cancers were missed by MR mammography. The majority of false-negative findings was found in stage1 disease, ductal carcinoma and grade 3 tumors (Table 4). Of 10 invasive cancers missed by MR mammography, eight were found by mammography and sonography. By all three techniques, one invasive ductal carcinoma (pT1b) was misinterpreted as fibroadenoma. In another patient, a microinvasive lobular carcinoma of 5 mm diameter was not detected with mammography and MR mammography, whereas sonography detected a solid, benign tumour. MR mammography identified 10 invasive cancers (5.2%) that were missed by mammography and sonography, whereas one invasive cancer was found by mammography alone. By sonography alone, not a single case of invasive disease was detected when MR mammography or mammography were nonsuspected."
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"$ref": "#/texts/14"
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"$ref": "#/texts/15"
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"$ref": "#/texts/16"
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"orig": "Discussion",
"text": "Discussion",
"level": 1
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"orig": "When the validity of individual diagnostic methods for detection of invasive breast cancer was analyzed, the sensitivity and specificity of mammography ranged from 79.9 to 89% and from 64 to 93.5%, respectively [6,7,13]; for sonography from 67.6 to 96% and from 93 to 97.7%, respectively [13,14]; and for MR mammography from 91 to 98.9 and from 20 to 97.4%, respectively [6,7,8,9,10,15,16,17].",
"text": "When the validity of individual diagnostic methods for detection of invasive breast cancer was analyzed, the sensitivity and specificity of mammography ranged from 79.9 to 89% and from 64 to 93.5%, respectively [6,7,13]; for sonography from 67.6 to 96% and from 93 to 97.7%, respectively [13,14]; and for MR mammography from 91 to 98.9 and from 20 to 97.4%, respectively [6,7,8,9,10,15,16,17]."
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"orig": "The performance of mammography, sonography and MR mammography was compared in three large series (Table 5). The present results are similar with regard to sensitivity and specificity for the detection of malignant breast lesions, with MR mammography reaching the highest sensitivity of all imaging procedures.",
"text": "The performance of mammography, sonography and MR mammography was compared in three large series (Table 5). The present results are similar with regard to sensitivity and specificity for the detection of malignant breast lesions, with MR mammography reaching the highest sensitivity of all imaging procedures."
},
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"self_ref": "#/texts/13",
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"orig": "When combinations of all three technique were analyzed, mammography and sonography (standard method) had a sensitivity of 83% and a specificity of 92% for detection of malignant disease [3]. A combination of mammography, sonography and MR mammography (combined method) showed a sensitivity of 95% and a specificity of 64% [3]. For nonpalpable lesions, sensitivity increased from 73% by the standard method to 82% for the combined method. Specificity for the standard method (89%) was higher than that for the combined method (71%). For palpable lesions a sensitivity of 85% for the standard and 98% for the combined method was achieved, whereas specificity for the standard method was 100% compared with 45% for the combined methods [3]. The positive predictive value was 94% for the standard and 80% for the combined methods, and the negative predictive values were 78 and 89%, respectively [3]. In the present study, we also found the highest sensitivity, specificity, and positive and negative predictive values for the combination of all three methods. Combination of mammography and sonography was as sensitive as MR mammography alone (94.6% versus 94.6%).",
"text": "When combinations of all three technique were analyzed, mammography and sonography (standard method) had a sensitivity of 83% and a specificity of 92% for detection of malignant disease [3]. A combination of mammography, sonography and MR mammography (combined method) showed a sensitivity of 95% and a specificity of 64% [3]. For nonpalpable lesions, sensitivity increased from 73% by the standard method to 82% for the combined method. Specificity for the standard method (89%) was higher than that for the combined method (71%). For palpable lesions a sensitivity of 85% for the standard and 98% for the combined method was achieved, whereas specificity for the standard method was 100% compared with 45% for the combined methods [3]. The positive predictive value was 94% for the standard and 80% for the combined methods, and the negative predictive values were 78 and 89%, respectively [3]. In the present study, we also found the highest sensitivity, specificity, and positive and negative predictive values for the combination of all three methods. Combination of mammography and sonography was as sensitive as MR mammography alone (94.6% versus 94.6%)."
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"orig": "The majority of false-positive results for invasive cancer by MR mammography (80 out of 439) were caused by papillomas, intraductal hyperplasia grade 2 or 3, or fibroadenomas in the present series. These lesions have a good blood supply and may mimic invasive cancer [16,18].",
"text": "The majority of false-positive results for invasive cancer by MR mammography (80 out of 439) were caused by papillomas, intraductal hyperplasia grade 2 or 3, or fibroadenomas in the present series. These lesions have a good blood supply and may mimic invasive cancer [16,18]."
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"self_ref": "#/texts/15",
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"orig": "Ten out of 185 (5.4%) malignant lesions were classified as false-negative by MR mammography. On histology, the majority of false-negative invasive cancers were lobular cancers (four out of 10). Bone et al [18] reported false-negative results in 11 out of 155 readings, with the majority being lobular cancers on histology. Lack of tumour-induced neovascularity may explain such findings. In particular, invasive lobular cancers infiltrate the normal tissue with columns of single cells, and receive adequate oxygenation without the requirement for increased vascularization [19]. Buadu et al [11] found that lobular and mucinous carcinomas had a low microvessel density.",
"text": "Ten out of 185 (5.4%) malignant lesions were classified as false-negative by MR mammography. On histology, the majority of false-negative invasive cancers were lobular cancers (four out of 10). Bone et al [18] reported false-negative results in 11 out of 155 readings, with the majority being lobular cancers on histology. Lack of tumour-induced neovascularity may explain such findings. In particular, invasive lobular cancers infiltrate the normal tissue with columns of single cells, and receive adequate oxygenation without the requirement for increased vascularization [19]. Buadu et al [11] found that lobular and mucinous carcinomas had a low microvessel density."
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"self_ref": "#/texts/16",
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"orig": "Multifocality of breast cancers can be recognized adequately by MR mammography [20,21]. Boetes et al [22] reported that all 61 multifocal cancers were detected by MR mammography, compared with 31% by mammography and 38% by sonography. Esserman et al [20] detected multifocality by MR mammography in 100% (10 out of 10) versus 44% (four out of nine) by mammography. Relevant changes in therapy due to additional multicentric and contralateral tumour findings by MR mammography occur in 18% of patients as compared with conventional imaging [23]. We found a detection rate of multifocality of 66.7% by MR mammography, as compared with 26.2% by mammography and sonography. However, in 16 patients multifocal invasive disease as diagnosed by MR mammography was shown to be unifocal by histology.",
"text": "Multifocality of breast cancers can be recognized adequately by MR mammography [20,21]. Boetes et al [22] reported that all 61 multifocal cancers were detected by MR mammography, compared with 31% by mammography and 38% by sonography. Esserman et al [20] detected multifocality by MR mammography in 100% (10 out of 10) versus 44% (four out of nine) by mammography. Relevant changes in therapy due to additional multicentric and contralateral tumour findings by MR mammography occur in 18% of patients as compared with conventional imaging [23]. We found a detection rate of multifocality of 66.7% by MR mammography, as compared with 26.2% by mammography and sonography. However, in 16 patients multifocal invasive disease as diagnosed by MR mammography was shown to be unifocal by histology."
},
{
"self_ref": "#/texts/17",
"parent": {
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"orig": "Kramer et al [24] reported that MR mammography yielded the highest sensitivity for detection of multicentricity as compared with mammography and sonography (89, 66 and 79%, respectively) in 38 patients. These findings are comparable with the present results, in which eight out of nine multicentric cancers were diagnosed correctly.",
"text": "Kramer et al [24] reported that MR mammography yielded the highest sensitivity for detection of multicentricity as compared with mammography and sonography (89, 66 and 79%, respectively) in 38 patients. These findings are comparable with the present results, in which eight out of nine multicentric cancers were diagnosed correctly."
},
{
"self_ref": "#/texts/18",
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"orig": "Carcinoma in situ is identified by mammography through the presence of suspicious microcalcifications. Suspicious microcalcifications are more frequent in intraductal than in infiltrating cancers [25], which was also observed in the present series. Mammography showed a detection rate for carcinoma in situ of 78.9%, as compared with 65.8% by MR mammography; the combination of mammography and MR mammography lead to a detection rate of 86.4%. Fischer et al [26] reported that carcinoma in situ was identified by MR mammography in 25 out of 35 patients (72%); three ductal carcinomata in situ were detected by MR mammography exclusively. Sittek et al [27] reported that 14 out of 20 carcinomata in situ (70%) were correctly diagnosed by MR mammography on the basis of focal increase of signal intensity. Those authors concluded that carcinoma in situ is not reliably detected by MR mammography because of lack of a uniform pattern of enhancement. Esserman et al [20] reported a detection rate of 43% for ductal carcinoma in situ by MR mammography. Among 36 woman with carcinoma in situ, Gilles et al [28] demonstrated two cases without early contrast enhancement.",
"text": "Carcinoma in situ is identified by mammography through the presence of suspicious microcalcifications. Suspicious microcalcifications are more frequent in intraductal than in infiltrating cancers [25], which was also observed in the present series. Mammography showed a detection rate for carcinoma in situ of 78.9%, as compared with 65.8% by MR mammography; the combination of mammography and MR mammography lead to a detection rate of 86.4%. Fischer et al [26] reported that carcinoma in situ was identified by MR mammography in 25 out of 35 patients (72%); three ductal carcinomata in situ were detected by MR mammography exclusively. Sittek et al [27] reported that 14 out of 20 carcinomata in situ (70%) were correctly diagnosed by MR mammography on the basis of focal increase of signal intensity. Those authors concluded that carcinoma in situ is not reliably detected by MR mammography because of lack of a uniform pattern of enhancement. Esserman et al [20] reported a detection rate of 43% for ductal carcinoma in situ by MR mammography. Among 36 woman with carcinoma in situ, Gilles et al [28] demonstrated two cases without early contrast enhancement."
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"self_ref": "#/texts/19",
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"prov": [],
"orig": "References",
"text": "References",
"level": 1
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"self_ref": "#/texts/20",
"parent": {
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"children": [],
"label": "list_item",
"prov": [],
"orig": "SG Orel; RH Troupin. Nonmammographic imaging of the breast: current issues and future prospects.. Semin Roentgenol (1993)",
"text": "SG Orel; RH Troupin. Nonmammographic imaging of the breast: current issues and future prospects.. Semin Roentgenol (1993)",
"enumerated": false,
"marker": "-"
},
{
"self_ref": "#/texts/21",
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"label": "list_item",
"prov": [],
"orig": "K Kerlikowske; D Grady; SM Rubin; C Sandrock; VL Ernster. Efficancy of screening mammography.. JAMA (1995)",
"text": "K Kerlikowske; D Grady; SM Rubin; C Sandrock; VL Ernster. Efficancy of screening mammography.. JAMA (1995)",
"enumerated": false,
"marker": "-"
},
{
"self_ref": "#/texts/22",
"parent": {
"$ref": "#/groups/0"
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"children": [],
"label": "list_item",
"prov": [],
"orig": "M M\u00fcller-Schimpfle; P Stoll; W Stern; S Kutz; F Dammann; CD Claussen. Do mammography, sonography and MR mammography have a diagnostic benefit compared with mammography and sonography?. AJR Am J Roentgenol (1997)",
"text": "M M\u00fcller-Schimpfle; P Stoll; W Stern; S Kutz; F Dammann; CD Claussen. Do mammography, sonography and MR mammography have a diagnostic benefit compared with mammography and sonography?. AJR Am J Roentgenol (1997)",
"enumerated": false,
"marker": "-"
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{
"self_ref": "#/texts/23",
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"label": "list_item",
"prov": [],
"orig": "SH Heywang; D Hahn; H Schmidt; I Krischke; W Eiermann; R Bassermann; J Lissner. MR imaging of the breast using gadolinium-DTPA.. J Comput Assist Tomogr (1986)",
"text": "SH Heywang; D Hahn; H Schmidt; I Krischke; W Eiermann; R Bassermann; J Lissner. MR imaging of the breast using gadolinium-DTPA.. J Comput Assist Tomogr (1986)",
"enumerated": false,
"marker": "-"
},
{
"self_ref": "#/texts/24",
"parent": {
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"children": [],
"label": "list_item",
"prov": [],
"orig": "WA Kaiser; E Zeitler. MR mammography: first clinical results [in German].. R\u00f6ntgenpraxis (1985)",
"text": "WA Kaiser; E Zeitler. MR mammography: first clinical results [in German].. R\u00f6ntgenpraxis (1985)",
"enumerated": false,
"marker": "-"
},
{
"self_ref": "#/texts/25",
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"label": "list_item",
"prov": [],
"orig": "GM Kacl; P Liu; JF Debatin; E Garzoli; RF Caduff; GP Krestin. Detection of breast cancer with conventional mammography and contrast-enhanced MR imaging.. Eur Radiol (1998)",
"text": "GM Kacl; P Liu; JF Debatin; E Garzoli; RF Caduff; GP Krestin. Detection of breast cancer with conventional mammography and contrast-enhanced MR imaging.. Eur Radiol (1998)",
"enumerated": false,
"marker": "-"
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"self_ref": "#/texts/26",
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# Comparison of written reports of mammography, sonography and magnetic resonance mammography for preoperative evaluation of breast lesions, with special emphasis on magnetic resonance mammography
Sabine Malur; Department of Gynecology, Friedrich-Schiller University, Jena, Germany.; Susanne Wurdinger; Institute for Diagnostic and Interventional Radiology, Friedrich-Schiller University, Jena, Germany.; Andreas Moritz; Department of Gynecology, Friedrich-Schiller University, Jena, Germany.; Wolfgang Michels; Department of Gynecology, Friedrich-Schiller University, Jena, Germany.; Achim Schneider; Department of Gynecology, Friedrich-Schiller University, Jena, Germany.
Patients with abnormal breast findings ( n = 413) were examined by mammography, sonography and magnetic resonance (MR) mammography; 185 invasive cancers, 38 carcinoma in situ and 254 benign tumours were confirmed histologically. Sensitivity for mammography was 83.7%, for sonography it was 89.1% and for MR mammography it was 94.6% for invasive cancers. In 42 patients with multifocal invasive cancers, multifocality had been detected by mammography and sonography in 26.2%, and by MR mammography in 66.7%. In nine patients with multicentric cancers, detection rates were 55.5, 55.5 and 88.8%, respectively. Carcinoma in situ was diagnosed by mammography in 78.9% and by MR mammography in 68.4% of patients. Combination of all three diagnostic methods lead to the best results for detection of invasive cancer and multifocal disease. However, sensitivity of mammography and sonography combined was identical to that of MR mammography (ie 94.6%).
## Introduction
Mammography and sonography are the standard imaging techniques for detection and evaluation of breast disease [1]. Mammography is the most established screening modality [2]. Especially in young women and women with dense breasts, sonography appears superior to mammography, and differentiation between solid tumours and cysts is easier. Sensitivity and specificity of sonography or mammography are higher if sonography and mammography are combined [3].
It is generally accepted that MR mammography is the most sensitive technique for diagnosis of breast cancer, whereas the reported specificity of MR mammography varies [4,5,6,7,8,9,10,11,12]. In those studies, MR mammography was performed and evaluated by highly specialized radiologists in a research setting. It was therefore the purpose of the present prospective study to compare the validity of MR mammography with mammography and sonography in clinical routine practice. Findings for the three diagnostic methods documented on routine reports that were available to the surgeon preoperatively formed the basis of this comparison. Special emphasis was placed on the identification of multifocal and multicentric invasive disease.
## Results
All patients underwent breast surgery and all abnormal lesions identified by mammography, sonography or MR mammography were surgically removed. A total of 477 breast lesions were examined histologically, revealing the presence of 185 invasive cancers, 38 carcinomata in situ and 254 benign lesions (fibroadenoma, papilloma, intraductal or adenoid ductal hyperplasia, cystic mastopathia). There were four patients with malignant lesions in both breasts. In 42 patients multifocal tumours and in nine patients multicentric tumors were found on histological examination. Among the 185 invasive lesions, 178 were primary cancers, five were recurrences, one was metastatic and one was an angiosarcoma. The majority of invasive breast cancers were staged as pT1c (44%). Six per cent of tumors were detected in stage pT1a, 18% in stage pT1b, 25% in stage pT2, 3% in stage pT3 and 4% in stage pT4. The distribution of histopathological tumour types is shown in Table 1. The mean age of patients was 58 years (range 19-85 years).
The sensitivity of MR mammography was significantly higher than those of mammography and sonography (P < 0.005 and P < 0.05; Table 2). The specificity of sonography was significantly higher than those of mammography and MR mammography (P < 0.05 and P < 0.005; Table 2). The negative predictive values for sonography and MR mammography were significantly higher than that of mammography (P < 0.05 and P < 0.005; Table 2). With regard to accuracy, no significant difference between the three modalities was found (Table 2). Combining of all three diagnostic methods yielded the best results for detection of cancer (P < 0.005; Table 3). The sensitivity and negative predictive value for the combination of mammography and MR mammography, and the combination of sonography and MR mammography were significantly higher than those for the combination of mammograpy and sonography (P < 0.05; Table 3). The highest result for accuracy was seen for a combination of all three methods (P < 0.05; Table 3).
Mammography was false-negative in 30 out of 184 invasive cancers, sonography was false-negative in 20 out of 185 cancers, and 10 out of 185 invasive cancers were missed by MR mammography. The majority of false-negative findings was found in stage1 disease, ductal carcinoma and grade 3 tumors (Table 4). Of 10 invasive cancers missed by MR mammography, eight were found by mammography and sonography. By all three techniques, one invasive ductal carcinoma (pT1b) was misinterpreted as fibroadenoma. In another patient, a microinvasive lobular carcinoma of 5 mm diameter was not detected with mammography and MR mammography, whereas sonography detected a solid, benign tumour. MR mammography identified 10 invasive cancers (5.2%) that were missed by mammography and sonography, whereas one invasive cancer was found by mammography alone. By sonography alone, not a single case of invasive disease was detected when MR mammography or mammography were nonsuspected.
## Discussion
When the validity of individual diagnostic methods for detection of invasive breast cancer was analyzed, the sensitivity and specificity of mammography ranged from 79.9 to 89% and from 64 to 93.5%, respectively [6,7,13]; for sonography from 67.6 to 96% and from 93 to 97.7%, respectively [13,14]; and for MR mammography from 91 to 98.9 and from 20 to 97.4%, respectively [6,7,8,9,10,15,16,17].
The performance of mammography, sonography and MR mammography was compared in three large series (Table 5). The present results are similar with regard to sensitivity and specificity for the detection of malignant breast lesions, with MR mammography reaching the highest sensitivity of all imaging procedures.
When combinations of all three technique were analyzed, mammography and sonography (standard method) had a sensitivity of 83% and a specificity of 92% for detection of malignant disease [3]. A combination of mammography, sonography and MR mammography (combined method) showed a sensitivity of 95% and a specificity of 64% [3]. For nonpalpable lesions, sensitivity increased from 73% by the standard method to 82% for the combined method. Specificity for the standard method (89%) was higher than that for the combined method (71%). For palpable lesions a sensitivity of 85% for the standard and 98% for the combined method was achieved, whereas specificity for the standard method was 100% compared with 45% for the combined methods [3]. The positive predictive value was 94% for the standard and 80% for the combined methods, and the negative predictive values were 78 and 89%, respectively [3]. In the present study, we also found the highest sensitivity, specificity, and positive and negative predictive values for the combination of all three methods. Combination of mammography and sonography was as sensitive as MR mammography alone (94.6% versus 94.6%).
The majority of false-positive results for invasive cancer by MR mammography (80 out of 439) were caused by papillomas, intraductal hyperplasia grade 2 or 3, or fibroadenomas in the present series. These lesions have a good blood supply and may mimic invasive cancer [16,18].
Ten out of 185 (5.4%) malignant lesions were classified as false-negative by MR mammography. On histology, the majority of false-negative invasive cancers were lobular cancers (four out of 10). Bone et al [18] reported false-negative results in 11 out of 155 readings, with the majority being lobular cancers on histology. Lack of tumour-induced neovascularity may explain such findings. In particular, invasive lobular cancers infiltrate the normal tissue with columns of single cells, and receive adequate oxygenation without the requirement for increased vascularization [19]. Buadu et al [11] found that lobular and mucinous carcinomas had a low microvessel density.
Multifocality of breast cancers can be recognized adequately by MR mammography [20,21]. Boetes et al [22] reported that all 61 multifocal cancers were detected by MR mammography, compared with 31% by mammography and 38% by sonography. Esserman et al [20] detected multifocality by MR mammography in 100% (10 out of 10) versus 44% (four out of nine) by mammography. Relevant changes in therapy due to additional multicentric and contralateral tumour findings by MR mammography occur in 18% of patients as compared with conventional imaging [23]. We found a detection rate of multifocality of 66.7% by MR mammography, as compared with 26.2% by mammography and sonography. However, in 16 patients multifocal invasive disease as diagnosed by MR mammography was shown to be unifocal by histology.
Kramer et al [24] reported that MR mammography yielded the highest sensitivity for detection of multicentricity as compared with mammography and sonography (89, 66 and 79%, respectively) in 38 patients. These findings are comparable with the present results, in which eight out of nine multicentric cancers were diagnosed correctly.
Carcinoma in situ is identified by mammography through the presence of suspicious microcalcifications. Suspicious microcalcifications are more frequent in intraductal than in infiltrating cancers [25], which was also observed in the present series. Mammography showed a detection rate for carcinoma in situ of 78.9%, as compared with 65.8% by MR mammography; the combination of mammography and MR mammography lead to a detection rate of 86.4%. Fischer et al [26] reported that carcinoma in situ was identified by MR mammography in 25 out of 35 patients (72%); three ductal carcinomata in situ were detected by MR mammography exclusively. Sittek et al [27] reported that 14 out of 20 carcinomata in situ (70%) were correctly diagnosed by MR mammography on the basis of focal increase of signal intensity. Those authors concluded that carcinoma in situ is not reliably detected by MR mammography because of lack of a uniform pattern of enhancement. Esserman et al [20] reported a detection rate of 43% for ductal carcinoma in situ by MR mammography. Among 36 woman with carcinoma in situ, Gilles et al [28] demonstrated two cases without early contrast enhancement.
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item-0 at level 0: unspecified: group _root_
item-1 at level 1: section_header: Introduction
item-2 at level 2: text: Cancer therapeutics have made re ... eding risk resulting from its use [7].
item-3 at level 2: text: Chemotherapy-induced cardiotoxic ... the onset and progression of AF [11].
item-4 at level 2: text: The mitochondrial quality survei ... alance of mitochondrial dynamics [17].
item-5 at level 2: text: A previous study found that PI3K ... pathogenesis of ibrutinib-mediated AF.
item-6 at level 1: section_header: Ibrutinib increases AF susceptib ... d promotes mitochondrial fragmentation
item-7 at level 2: text: To assess the proarrhythmic pote ... nce of atrial mitochondria (Fig. S1I).
item-8 at level 1: section_header: Ibrutinib induces AF by impairin ... ction and structure of atrial myocytes
item-9 at level 2: text: We investigated the molecular me ... mitochondrial structure and function.
item-10 at level 1: section_header: Ibrutinib disrupts mitochondrial ... own-regulating atrial AKAP1 expression
item-11 at level 2: text: As GO and KEGG enrichment analys ... ed expression of DRP1 (Fig. 2D and E).
item-12 at level 2: text: We computed the activity score o ... between ibrutinib and AKAP1 (Fig. 2J).
item-13 at level 1: section_header: AKAP1 down-regulation mediates DRP1 dephosphorylation in HL-1 myocytes
item-14 at level 2: text: We investigated the interaction ... ondrial fission marker (Fig. 3B to F).
item-15 at level 1: section_header: Ibrutinib-induced AKAP1 depletio ... olarization via DRP1 dephosphorylation
item-16 at level 2: text: Mitochondrial dynamic imbalance ... l morphology to normal (Fig. 4A to E).
item-17 at level 2: text: Western blot analysis showed tha ... AKAP1 overexpression (Fig. 4O and P).
item-18 at level 1: section_header: AKAP1 deficiency impairs mitocho ... tabolic reprogramming in HL-1 myocytes
item-19 at level 2: text: Mitochondrial bioenergetics and ... rial fission inhibitor (Fig. 5A to F).
item-20 at level 2: text: Impaired mitochondrial respirati ... -treated with Mdivi-1 (Fig. 5L and M).
item-21 at level 1: section_header: AKAP1 down-regulation is involve ... is, oxidative stress, and inflammation
item-22 at level 2: text: Insufficient energy production c ... r Mdivi-1 intervention (Fig. 6A to C).
item-23 at level 2: text: We next elucidated the regulator ... Mdivi-1 intervention (Fig. 6F and G).
item-24 at level 2: text: Mitochondrial dysfunction not on ... i-1 cotreatment groups (Fig. 6I to K).
item-25 at level 1: section_header: AKAP1 overexpression prevents ib ... S and restoring mitochondrial function
item-26 at level 2: text: To verify whether AKAP1 up-regul ... y AKAP1 overexpression (Fig. 7F to I).
item-27 at level 2: text: MitoSOX staining revealed that m ... inflammatory response (Fig. 7L to P).
item-28 at level 1: section_header: Discussion
item-29 at level 2: text: Mitochondrial damage serves as a ... t for exploring preventive approaches.
item-30 at level 2: text: AKAP1, a scaffolding protein, an ... sturbances through off-target effects.
item-31 at level 2: text: Recent studies have investigated ... pathogenesis of ibrutinib-induced AF.
item-32 at level 2: text: Our study utilized high-throughp ... xorubicin-induced cardiotoxicity [27].
item-33 at level 2: text: Excessive fission depletes ATPs ... forms a pathological substrate for AF.
item-34 at level 2: text: In this study, we discovered tha ... oxicity of tyrosine kinase inhibitors.
item-35 at level 2: text: Increasing evidence suggests tha ... ators for AKAP1 are not yet available.
item-36 at level 1: section_header: Animal model
item-37 at level 2: text: C57BL/6J mice (8 to 10 weeks, we ... d libitum throughout the study period.
item-38 at level 1: section_header: Cell culture and lentivirus transfection
item-39 at level 2: text: For stable AKAP1 overexpression, ... ailed in the related prior study [53].
item-40 at level 1: section_header: In vivo electrophysiology study
item-41 at level 2: text: Electrophysiological studies on ... pisodes of each animal was documented.
item-42 at level 1: section_header: Echocardiography
item-43 at level 2: text: The Vevo 2100 Imaging System (FU ... ted the echocardiographic examination.
item-44 at level 1: section_header: Transmission electron microscopy
item-45 at level 2: text: Retrograde aortic perfusion with ... s described in our previous study [21]
item-46 at level 1: section_header: Single-cell RNA-seq analysis
item-47 at level 2: text: The single-cell transcriptome da ... pathway activities of specific cells.
item-48 at level 1: section_header: Confocal imaging
item-49 at level 2: text: Fluorescent images were obtained ... ntibodies used for immunofluorescence.
item-50 at level 2: text: MitoTracker Red (0.5 μM, 30 min) ... he MitoSOX Red (5 μM, 20 min) Reagent.
item-51 at level 1: section_header: Isolation of primary atrial myocytes and atrial mitochondria
item-52 at level 2: text: Atrial myocytes were isolated us ... anual (Thermo Fisher Scientific, USA).
item-53 at level 1: section_header: Western blot analysis
item-54 at level 2: text: Atrial tissues or cultured cell ... the antibodies documented in Table S1.
item-55 at level 1: section_header: mtDNA copy number and quantitative PCR
item-56 at level 2: text: The mitochondrial DNA copy numbe ... analysis of the respective sequences.
item-57 at level 1: section_header: Figures
item-59 at level 1: picture
item-59 at level 2: caption: Fig. 1. Ibrutinib induces atrial fibrillation (AF) by impairing mitochondrial function and structures of atrial myocytes. (A and B) Expression heatmap and volcano map of differentially expressed atrial proteins, illustrating that 170 proteins were up-regulated and 208 were down-regulated in ibrutinib-treated mice compared with control mice. N = 3 independent mice per group. (C to H) Gene Ontology and KEGG enrichment analysis of the 378 differentially expressed proteins in atrial tissue. (I to L) GSEA of differentially expressed proteins following ibrutinib treatment.
item-61 at level 1: picture
item-61 at level 2: caption: Fig. 2. Ibrutinib disrupts mitochondrial homeostasis by down-regulating atrial AKAP1 expression. (A) Volcano plot labeled with the top mitochondria-related differentially expressed proteins. (B) Expression of marker genes for cell-type annotation is indicated on the DotPlot. (C) Uniform manifold approximation and projection representation of all single cells color-coded for their assigned major cell type. (D and E) Featureplots showing the expression patterns of AKAP1 and DRP1 in atrial tissues from AF and control groups. (F to H) Analysis of the correlation between AKAP1 expression and activity score of mitochondria-related pathways. (I) Atrial pan-subpopulation correlation analysis of AKAP1 expression and OXPHOS pathway activity. (J) Molecular docking analysis of the interaction between ibrutinib and AKAP1.
item-63 at level 1: picture
item-63 at level 2: caption: Fig. 3. AKAP1 binding mediates DRP1 phosphorylation in atrial myocytes. (A) Immunofluorescence imaging of AKAP1 and DRP1 in ibrutinib-treated atrial myocytes. (B to F) Proteins were isolated from atrial myocytes. Western blotting was used to assess AKAP1, DRP1, FIS1, and phosphorylated DRP1 expression levels. ***P < 0.001. ns, not significant.
item-65 at level 1: picture
item-65 at level 2: caption: Fig. 4. Ibrutinib-induced AKAP1 depletion promotes mitochondrial fission and membrane depolarization via DRP1 dephosphorylation. (A to E) MitoTracker staining for labeling mitochondria in atrial myocytes in vitro. Average mitochondrial length, ratio of fragmented/tubular mitochondria, and mitochondrial morphological parameters were determined. (F to N) Western blot analysis of mitochondrial DRP1 (mito-DRP1), total DRP1 (t-DRP1), phosphorylated DRP1, FIS1, MFN1, and OPA1 in HL-1 cells. VDAC1 was used as a loading control for mitochondrial proteins. (O) Red-to-green ratio of JC-1 fluorescence intensity. (P) Atrial myocytes loaded with JC-1 to analyze changes in mitochondrial membrane potential. N = 8 independent cell samples per group. **P < 0.01, ***P < 0.001.
item-67 at level 1: picture
item-67 at level 2: caption: Fig. 5. AKAP1 deficiency impairs mitochondrial respiratory function and promotes metabolic reprogramming in atrial myocytes. (A to F) Analysis of HL-1 mitochondrial bioenergetics using the Seahorse XFe96 Analyzer. OCR measurements were taken continuously from baseline and after the sequential addition of 2 mM oligomycin, 1 mM FCCP, and 0.5 mM R/A to measure basal respiration, maximal respiration, spare respiratory capacity, proton leak, and ATP-production levels. (G to J) Total and individual rates of ATP production as mediated by glycolysis or mitochondrial metabolism in HL-1 cells transduced with control and LV-Akap1. (K) Ratio between ATP produced by OXPHOS and that by glycolysis in HL-1 cells transduced with control and LV-Akap1. (L and M) ELISA analysis of mitochondrial respiration complex I/III activities. N = 8 independent cell samples per group. *P < 0.05, **P < 0.01, ***P < 0.001.
item-69 at level 1: picture
item-69 at level 2: caption: Fig. 6. AKAP1 down-regulation in impaired mitochondrial biogenesis, oxidative stress, and inflammation. (A to C) qPCR analysis of mitochondrial biogenesis parameters (mtDNA copy number, PGC-1α, and NRF1). (D and E) ELISA analysis of redox balance biomarkers, including MDA and GSH contents. (F and G) MitoSOX staining of mitochondrial ROS in HL-1 cells. (H) Visual GSEA of inflammation response pathway in ibrutinib-treated myocytes. (I to K) ELISA of atrial inflammatory biomarkers, including TNF-α, IL-6, and IL-18. N = 8 independent cell samples per group. *P < 0.05, **P < 0.01, ***P < 0.001.
item-71 at level 1: picture
item-71 at level 2: caption: Fig. 7. AKAP1 overexpression prevents ibrutinib-induced AF by improving MQS and restoring mitochondrial function. (A) Simultaneous recordings of surface ECG following intraesophageal burst pacing. (B) Quantification of AF time. (C) AF inducibility in control and ibrutinib-treated mice with and without AAV-Akap1 transfection. (D and E) Myocardial fibrosis was assessed in each group through Sirius Red staining. The proportion of fibrotic tissue to myocardial tissue was quantified for each group. (F to I) Western blot analysis of total DRP1 (t-DRP1), phosphorylated DRP1, MFN1, and NRF1 in atrial tissues. (J and K) Images and quantification of isolated atrial myocytes loaded with the mitochondrial ROS indicator MitoSox Red. (L to P) ELISA of MQS/redox/inflammation biomarkers, including complex I/III, MDA, GSH, and IL-18. N = 8 mice per group. *P < 0.05, **P < 0.01, ***P < 0.001.
item-73 at level 1: picture
item-73 at level 2: caption: Fig. 8. This schematic illustrates the proposed mechanism by which ibrutinib promotes atrial fibrillation (AF). Ibrutinib treatment leads to the downregulation of A-kinase anchoring protein 1 (AKAP1), triggering mitochondrial quality surveillance (MQS) impairment. This results in enhanced translocation of dynamin-related protein 1 (DRP1) to the mitochondria, driving mitochondrial fission. Concurrently, metabolic reprogramming is characterized by increased glycolysis and decreased oxidative phosphorylation (OXPHOS), alongside reduced mitochondrial biogenesis. These mitochondrial dysfunctions elevate oxidative stress and inflammatory responses, contributing to atrial metabolic and structural remodeling, ultimately leading to ibrutinib-induced AF.
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## Introduction
Cancer therapeutics have made remarkable progress over time, significantly prolonging the survival period of cancer patients. However, the accompanying increased risk of cardiovascular diseases has emerged as a crucial factor affecting the prognosis of cancer survivors [1–3]. Anticancer drugs have exhibited considerable cardiotoxicity, which greatly limits their clinical applications. Consequently, cardio-oncology has become a field of common concern for both oncologists and cardiologists. Ibrutinib, an efficacious Bruton’s tyrosine kinase (BTK) inhibitor endorsed by the US Food and Drug Administration for treating chronic lymphocytic leukemia, mantle cell lymphoma, and so on [4,5], is linked to elevated risks of atrial fibrillation (AF) and hemorrhagic complications. A 10-fold increase in the incidence of AF has been reported in the ibrutinib arm [4]. Long-term studies on the use of ibrutinib have revealed AF rates of up to 16% with an average observation period of 28 months [6]. Managing AF associated with ibrutinib use is complex, primarily due to the necessity of balancing the benefits of anticoagulation that it affords against the bleeding risk resulting from its use [7].
Chemotherapy-induced cardiotoxicity involves various mechanisms. Mitochondrial damage is a critical event in cardiotoxicity induced by chemotherapy drugs, such as doxorubicin and sunitinib [8,9]. This damage manifests as imbalanced mitochondrial dynamics, suppressed respiratory function, and so on [10]. Because AF is associated with rapid atrial activation rates that require a high energy supply from mitochondria, metabolic remodeling centered on mitochondrial dysfunction is identified as the chief factor driving the onset and progression of AF [11].
The mitochondrial quality surveillance (MQS) system is closely linked to maintaining mitochondrial homeostasis and restoring any damage to the organelle. The optimal mitochondrial physiological function can be maintained by repairing or removing the damaged mitochondria using regulating MQS factors, such as mitochondrial fission/fusion balance, bioenergetics, and biogenesis. Although studies have explored the role of an impaired MQS system in both cardiovascular diseases and chemotherapy-induced cardiotoxicity like doxorubicin [11–13], the role of an altered MQS system and its upstream regulatory mechanisms in ibrutinib-induced AF have remained largely unexplored. A-kinase anchoring protein 1 (AKAP1) is regarded as a crucial regulator of MQS including mitochondrial metabolism and dynamics. By anchoring the related proteins on the mitochondrial outer membrane (MOM), AKAP1 integrates multiple key pathways to influence mitochondrial morphology, function, and cellular fate. On one hand, AKAP1 promotes mitochondrial respiration and increases the adenosine triphosphate (ATP) synthesis rate by facilitating the phosphorylation of essential mitochondrial proteins like NDUFS4 and cytochrome c oxidase [14–16]. Furthermore, AKAP1 modulates the phosphorylation and dephosphorylation-mediated activation of dynamin-related protein 1 (DRP1), a key protein in mitochondrial fission, through interactions with various binding partners on the MOM, thereby exerting regulatory effects on the balance of mitochondrial dynamics [17].
A previous study found that PI3K-Akt pathway inhibition could cause ibrutinib-induced AF [18]. Additionally, Xiao et al. [19] proposed that the C-terminal Src kinase may be a regulatory target in ibrutinib-induced AF. We have previously identified the pathological phenotypes of calcium dysregulation and structural remodeling in the atrial tissue of mouse model with ibrutinib-induced AF [20]. We further reported excessive production of reactive oxygen species (ROS) and the resulting oxidative stress in atrial myocytes as a potential mechanism for ibrutinib-induced AF [21]. Damaged mitochondria markedly contribute to the generation of ROS, and the disruption of the MQS system is a common toxicological mechanism underlying the cardiotoxicity of anticancer drugs. We herein explore alterations in the atrial MQS system and the related regulatory targets in the pathogenesis of ibrutinib-mediated AF.
## Ibrutinib increases AF susceptibility and promotes mitochondrial fragmentation
To assess the proarrhythmic potential of ibrutinib, we conducted intraesophageal burst pacing and recorded electrocardiograms (ECGs) in both ibrutinib-treated and control groups. The former exhibited a significantly greater incidence of AF than that of the control group. The duration of burst-pacing-induced AF was longer in the ibrutinib-treated mice (Fig. S1A to C). Echocardiographic evaluation demonstrated that ibrutinib changed the atrial structure. The left atrium (LA) diameter and area of the ibrutinib group were larger than those of the control group (Fig. S1D to F). We further assessed pathological changes in the atria and observed a significant increase in atrial fibrosis in ibrutinib-treated mice (Fig. S1G and H). The ultrastructure of atrial myocytes was observed through transmission electron microscopy (TEM). The mitochondria in the atrial myocytes of the ibrutinib-treated group had a fragmented and punctuated appearance, with significantly shorter lengths than those in the control group, indicating that ibrutinib may disrupt the dynamic balance of atrial mitochondria (Fig. S1I).
## Ibrutinib induces AF by impairing the mitochondrial function and structure of atrial myocytes
We investigated the molecular mechanisms underlying the ibrutinib-induced increased risk of AF. A proteomic analysis was conducted to compare the atrial protein expressions of both the ibrutinib-treated and control groups. The ibrutinib treatment modified the expression of 378 proteins in the murine atrial tissue (|log2 fold change| > 1; P-adj < 0.05). Of these, 170 proteins were up-regulated and 208 were down-regulated in the ibrutinib group (Fig. 1A and B). A Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis revealed that mitochondrial metabolic pathways, including oxidative phosphorylation (OXPHOS) and the tricarboxylic acid (TCA) cycle, were significantly altered by the ibrutinib treatment. A Gene Ontology (GO) biological process enrichment analysis showed that the ibrutinib treatment primarily affected proteins involved in the mitochondrial fission, mitochondrial organization, and cellular respiration. Similarly, a GO cellular component enrichment analysis indicated that ibrutinib mainly influenced the proteins located in mitochondrial respiratory chain, mitochondrial membrane, and matrix of mitochondria (Fig. 1C to F). The Gene Set Enrichment Analysis (GSEA) plot demonstrated that ibrutinib up-regulated mitochondrial fission and ROS pathway but impaired OXPHOS and mitochondrial biogenesis (Fig. 1I to L). Hence, ibrutinib intervention appeared to modify the expression of atrial proteins responsible for mitochondrial structure and function.
## Ibrutinib disrupts mitochondrial homeostasis by down-regulating atrial AKAP1 expression
As GO and KEGG enrichment analysis revealed that differentially expressed proteins were primarily associated with mitochondrial structural and functional changes, we annotated highly significant mitochondria-related differentially expressed proteins (−(log10 P) > 3) in the volcano map. AKAP1 and DRP1 had markedly differential expressions in the ibrutinib group. AKAP1 is crucial for regulating the MQS system. It can maintain the mitochondrial dynamic equilibrium by maintaining DRP1 phosphorylation at Ser637. Hence, off-target effects on AKAP1 could be responsible for ibrutinib-induced AF. Next, we investigated the expression and molecular functions of the aforementioned candidate proteins using single-cell transcriptome data from atrial tissue of patients with AF and control patients. Employing uniform manifold approximation and projection, we performed a hierarchical clustering of cardiac cells from the atrial tissues of patients with the AF and control groups. Sorting the data based on the differential expression of established lineage markers revealed that the atrial tissues comprised 10 major cell types (Fig. 2A to C). The featureplots showed that, in contrast to control samples, atrial tissues from AF patients exhibited decreased expression of AKAP1, especially in the cardiomyocyte subset. Conversely, most cell subsets of the atrial tissue from AF patients displayed increased expression of DRP1 (Fig. 2D and E).
We computed the activity score of the mitochondria-related pathways in the cardiomyocyte subset of the AF group using the AUCell method. The AKAP1 expression was extracted from the AF scRNA-seq dataset, and correlation analysis was conducted with activity scores of mitochondria-related pathways (Fig. 2F to H). The AKAP1 expression was positively correlated with the activities of mitochondrial biogenesis and mitochondrial fusion and negatively with mitochondrial fission. Hence, AKAP1 deficiency could be responsible for excessive mitochondrial fission observed in ibrutinib-treated mice. A pan-subpopulation analysis of the correlation between AKAP1 expression and the OXPHOS activity score in the scRNA-seq dataset of AF patients showed that AKAP1 was significantly associated with mitochondrial metabolism in most atrial subpopulations (Fig. 2I). To further investigate this relationship, we stratified the cardiomyocytes in the subset based on AKAP1 expression. Using the median AKAP1 expression value as a threshold, we categorized the cardiomyocytes into high AKAP1 expression (hiAKAP1) and low AKAP1 expression (loAKAP1) groups (Fig. S2A). Differential expression analysis was performed between these groups, followed by GO and KEGG enrichment analyses. The results revealed that differentially expressed genes were predominantly enriched in biological process terms related to mitochondrial function. These included mitochondrial electron transport, OXPHOS, mitochondrial depolarization, and mitochondrial membrane organization. In terms of cellular process, the genes were enriched in categories such as mitochondrial respiratory chain complex and MOM. KEGG pathway analysis highlighted enrichment in OXPHOS and the TCA cycle (Fig. S2B to E). GSEA provided additional insights. Compared to the hiAKAP1 group, the loAKAP1 cardiomyocyte subgroup exhibited enhanced activation of mitochondrial fission events. Conversely, the OXPHOS pathway, representative of mitochondrial metabolism, was significantly suppressed in the loAKAP1 group (Fig. S2F and G). We performed molecular docking (MD) to elucidate the potential off-target binding interactions between AKAP1 and ibrutinib molecules. The results revealed a binding energy of −6.79 kcal/mol for the protein–ligand complex, suggesting a strong binding effect between ibrutinib and AKAP1 (Fig. 2J).
## AKAP1 down-regulation mediates DRP1 dephosphorylation in HL-1 myocytes
We investigated the interaction between AKAP1 and DRP1 and its impact on the mitochondrial dynamic to elucidate the mechanism underlying AKAP1-deficiency-mediated mitochondrial fragmentation. Confocal immunofluorescence imaging demonstrated that ibrutinib treatment decreased the AKAP1 expression (red) and increased the DRP1 expression (green) in HL-1 cells (Fig. 3A). DRP1 phosphorylation is a downstream consequence of the presence of AKAP1 on the outer mitochondrial membrane (OMM). DRP1 possesses an N-terminal guanosine triphosphatase (GTPase) domain. It hydrolyzes guanosine triphosphate to generate the energy necessary for fission. AKAP1, when anchored to protein kinase A, phosphorylates DRP1 at Ser637 within the GTPase effector domain, thus inhibiting its GTPase activity and attenuating mitochondrial fission. A reduction in AKAP1 expression enhances the dephosphorylation of DRP1 at Ser637, facilitating mitochondrial fission. Therefore, we wanted to know whether AKAP1 promotes mitochondrial fission by dephosphorylating DRP1. Western blot analysis revealed that ibrutinib dephosphorylated DRP1 at Ser637 in HL-1 cells, without affecting the phosphorylation levels at Ser616, and simultaneously increased the expression of FIS1, a mitochondrial fission marker (Fig. 3B to F).
## Ibrutinib-induced AKAP1 depletion promotes mitochondrial fission and membrane depolarization via DRP1 dephosphorylation
Mitochondrial dynamic imbalance is regarded as both the initiator and facilitator of mitochondrial dysfunction. We quantitatively assessed ibrutinib-induced changes in mitochondrial morphology by considering the fragmented mitochondria in the atrial tissues of ibrutinib-treated mice and the proteomics enrichment analysis results. To further investigate the mechanism underlying AKAP1-deficiency-mediated mitochondrial fission, the HL-1 cardiomyocytes were infected with LV-Akap1 before ibrutinib treatment. MitoTracker staining demonstrated that ibrutinib exposure formed small, round, and fragmented mitochondria in atrial myocytes, indicating enhanced mitochondrial fission. To quantitatively evaluate alterations in atrial mitochondrial fragmentation, we measured the aspect ratio and form factor of mitochondria in both the ibrutinib and control groups. The former displayed a significant decrease in AR and FF parameters than that in the control group. However, transfection with LV-Akap1 partially restored the mitochondrial morphology to normal (Fig. 4A to E).
Western blot analysis showed that ibrutinib exposure led to a significant increase in mitochondrial fission-related proteins (DRP1 and FIS1) and a decrease in mitochondrial fusion-associated proteins (MFN1 and OPA1). Importantly, compared to the modest increase in total DRP1 (t-DRP1), the expression of mitochondria-localized DRP1 (mito-DRP1) was remarkably elevated in the ibrutinib-treated group, along with a significant dephosphorylation of DRP1 at Ser637. Transfecting HL-1 cells with LV-Akap1 considerably increased the phosphorylation level of DRP1 at Ser637. Furthermore, without significant changes in total DRP1, LV-Akap1 transfection diminished DRP1 translocation to mitochondria and enhanced the expressions of MFN1 and OPA1 (Fig. 4F to N). The mitochondrial membrane potential determines the mitochondrial function. Excessive mitochondrial fission can depolarize the mitochondrial membrane potential, which was restored to near-normal levels by AKAP1 overexpression (Fig. 4O and P).
## AKAP1 deficiency impairs mitochondrial respiratory function and promotes metabolic reprogramming in HL-1 myocytes
Mitochondrial bioenergetics and dynamics are interconnected. A disruption in the balance between fusion and fission can impair mitochondrial bioenergetics, diminishing ATP production. We investigated whether ibrutinib disturbs mitochondrial bioenergetics homeostasis by interfering with AKAP1. We conducted a Seahorse Mito Stress test to assess mitochondrial respiration. The results demonstrated that ibrutinib stimulation significantly impaired basal respiration, maximal respiration, spare respiratory capacity, and ATP production, which were attenuated by transduction with LV-Akap1 or cotreatment with Mdivi-1, a mitochondrial fission inhibitor (Fig. 5A to F).
Impaired mitochondrial respiration is linked to metabolic reprogramming. As mitochondrial OXPHOS declines, the real-time ATP rate assay was utilized to assess whether the ibrutinib-treated cells displayed adaptive reprogramming toward the glycolytic pathway. The assay simultaneously measured the basal ATP-production rates from mitochondrial respiration (oxygen consumption rate [OCR]) and glycolysis (extracellular acidification rate). The ibrutinib-treated myocytes had a significantly reduced total ATP-production rate, primarily owing to the inability of the elevated glycoATP rate to compensate for the mitoATP rate deficiency. AKAP1 overexpression or Mdivi-1 cotreatment effectively reversed the metabolic reprogramming phenotype. The degree of this alteration can be determined by the extent of reduction in the ATP index ratio (mitoATP production rate/glycoATP production rate) in ibrutinib-treated myocytes. This reduction was partially restored by AKAP1 overexpression or Mdivi-1 intervention (Fig. 5G to K). These results are consistent with the finding that the activity of mitochondrial ETC complex I/III was compromised in the ibrutinib group but improved to near-normal levels in ibrutinib-treated cells transduced with LV-Akap1 or co-treated with Mdivi-1 (Fig. 5L and M).
## AKAP1 down-regulation is involved in impaired mitochondrial biogenesis, oxidative stress, and inflammation
Insufficient energy production could be associated with defective mitochondrial biogenesis, a process crucial for replenishing number of healthy mitochondria. Ibrutinib treatment reduced the levels of PGC-1 and NRF1, 2 essential regulators of mitochondrial biogenesis. It also reduced the mtDNA copy number. However, this inhibitory effect of ibrutinib on mitochondrial biogenesis was ameliorated by AKAP1 overexpression or Mdivi-1 intervention (Fig. 6A to C).
We next elucidated the regulatory role of AKAP1 in the mitochondrial redox balance in ibrutinib-treated myocytes. Representative MitoSOX staining images showed that ibrutinib promotes excessive mitochondrial ROS production. AKAP1 overexpression significantly diminished the mitoROS level, also observed in the Mdivi-1 cotreatment group. Ibrutinib-treated myocytes exhibited reduced glutathione (GSH) and elevated malondialdehyde (MDA) levels, which were restored by LV-Akap1 transduction and Mdivi-1 intervention (Fig. 6F and G).
Mitochondrial dysfunction not only disturbs energy metabolism but also triggers inflammatory responses through an intricate immunometabolic crosstalk. Given the observed atrial fibrosis in animal models, GSEA of inflammatory response pathway indicated that inflammation activation is presented in ibrutinib-treated myocytes (Fig. 6H). We performed enzyme-linked immunosorbent assay (ELISA) to assess alterations in the levels of inflammatory factors. The ibrutinib group exhibited elevated levels of tumor necrosis factor-α (TNF-α), interleukin-6 (IL-6), and IL-18. However, the AKAP1 overexpression significantly down-regulated these cytokines. IL-18 was reversed in both AKAP1 overexpression and Mdivi-1 cotreatment groups (Fig. 6I to K).
## AKAP1 overexpression prevents ibrutinib-induced AF by improving MQS and restoring mitochondrial function
To verify whether AKAP1 up-regulation could mitigate the AF risk and restore the MQS system in ibrutinib-treated mice models, we established an in vivo AKAP1 overexpression model using adeno-associated virus (AAV), with AAV-GFP as the control group. The ibrutinib-treated mice exhibited a higher AF incidence and prolonged AF duration than those of control mice. ECG measurements revealed that the ibrutinib-treated group with AKAP1 overexpression had diminished AF duration and incidence (Fig. 7A to C). AKAP1 overexpression also ameliorated atrial fibrosis (Fig. 7D and E). We validated the ibrutinib-induced Drp1 Ser637 dephosphorylation and expression levels of mitochondrial fission/fusion markers. The ibrutinib treatment reduced DRP1 phosphorylation at Ser637 and simultaneously decreased the MFN1 (fusion marker) and NRF1 (biogenesis marker) levels. These effects were reversed by AKAP1 overexpression (Fig. 7F to I).
MitoSOX staining revealed that mitochondrial ROS production was significantly elevated in ibrutinib-treated mice, which normalized following AKAP1 overexpression (Fig. 7J and K). This finding was supported by the ibrutinib-induced reduction in GSH contents and an increase in MDA contents, which were also reversed by AKAP1 overexpression. Similar trends were observed for the activity of mitochondrial complex I/III. These data confirmed the regulatory role of AKAP1 in mitochondrial bioenergetics in ibrutinib-induced AF. Moreover, the IL-18 level was up-regulated in the ibrutinib-treated group. The AKAP1 overexpression could normalize the inflammatory response (Fig. 7L to P).
## Discussion
Mitochondrial damage serves as a major determinant in the pathogenesis of chemotherapeutic agents’ cardiotoxic effects. We employed gene-editing technologies in animal models and HL-1 cells and showed that ibrutinib increases the AF risk by disrupting the AKAP1-regulated MQS. Our study has several key findings. First, ibrutinib-treated mice exhibited decreased AKAP1 expression, which was closely associated with an increased risk of AF. Second, down-regulated AKAP1 disturbs the MQS system in atrial myocytes, as evidenced by the increased mitochondrial fission caused by enhanced Drp1 Ser637 dephosphorylation and OMM translocation, which is accompanied by impaired mitochondrial biogenesis and mitochondrial metabolic reprogramming in bioenergetics. Third, AKAP1 deficiency in atrial cardiomyocytes dysregulates the MQS, which further promotes oxidative stress and inflammatory activation, resulting in increased atrial fibrosis. Importantly, the AKAP1 overexpression effectively restores mitochondrial homeostasis and alleviates oxidative stress and inflammation-induced fibrosis in the ibrutinib-treated mouse model (Fig. 8). Our results offer novel perspectives on the molecular mechanisms connecting impaired MQS with electrophysiological alterations in ibrutinib-induced AF, indicating that AKAP1 could be a promising target for exploring preventive approaches.
AKAP1, a scaffolding protein, anchors to the OMM with its inserted mitochondrial targeting sequence. By recruiting signaling proteins to the OMM, AKAP1 serves as a mitochondrial signaling hub regulating mitochondrial homeostasis [22,23]. AKAP1 deficiency in retinal ganglion cells causes mitochondrial fragmentation by promoting Drp1 Ser637 dephosphorylation [24]. Previous studies on diabetic cardiomyopathy have revealed that AKAP1 down-regulation can compromise mitochondrial respiration by impeding NDUFS1 translocation into mitochondria and promoting mitochondrial ROS generation [25]. We employed quantitative proteomics to investigate alterations in protein expression following ibrutinib treatment. Enrichment analysis revealed that mitochondria-related pathways were enriched with differentially expressed proteins. The volcano plot showed that AKAP1 was one of the most significantly down-regulated candidates among the mitochondria-related proteins. We validated atrial AKAP1 down-regulation based on AF single-cell transcriptome data. We observed a significant correlation between AKAP1 expression and the activity score of MQS-related pathways, including mitochondrial fission, biogenesis, and OXPHOS in cardiomyocyte subpopulations. Our study is the first to provide a comprehensive understanding of ibrutinib-mediated atrial MQS disturbances through off-target effects.
Recent studies have investigated ROS-related mechanisms in ibrutinib-induced AF. Jiang et al. [20] reported ibrutinib-related atrial calcium handling disorders caused by increased RyR2 Ser2814 phosphorylation. Our recent research has further revealed that enhanced ibrutinib-mediated oxidative stress may increase the Ser2814 phosphorylation of RyR2. This observation is supported by the finding that antioxidant interventions effectively reduced calcium overload and inflammation-induced atrial fibrosis, thereby decreasing the ibrutinib-induced AF risk [21]. During mitochondrial electron transfer, electrons can leak and react with oxygen to create superoxide anions, making mitochondria a major source of ROS. Disruptions in electron transport in damaged mitochondria increase electron leakage, causing ROS bursts. However, as a crucial source of intracellular ROS, the role of mitochondrial damage in ibrutinib-induced AF has not been thoroughly investigated. Through GSEA of proteomics, we found that mitochondrial fission and ROS generation pathways were activated and OXPHOS and mitochondrial biogenesis processes were suppressed in the ibrutinib group. Hence, MQS impairment may be critical for the pathogenesis of ibrutinib-induced AF.
Our study utilized high-throughput sequencing along with genetic editing to investigate the mechanism of ibrutinib-induced mitochondrial damage leading to AF, with a focus on AKAP1-mediated MQS disturbance. The mitochondrial dynamic imbalance is crucial for cardio-oncology. As highly dynamic organelles, mitochondria continuously cycle between fission and fusion events to adapt to environmental demands. During mitochondrial fission, dephosphorylation of DRP1 at Ser637 promotes its recruitment to mitochondria [26]. The recruited DRP1 forms ring-like structures on OMM, constricting the organelle and producing 2 daughter mitochondria. Excessive mitochondrial fission from increased DRP1 Ser616 phosphorylation has also been found to be responsible for the pathogenesis of doxorubicin-induced cardiotoxicity [27].
Excessive fission depletes ATPs by regulating mitochondrial metabolic reprogramming, along with suppressed biogenesis, ultimately disrupting the MQS system. Metabolic reprogramming, a cellular self-regulatory mechanism in response to environmental changes, has been demonstrated to play a crucial role in the pathophysiology of both cardiometabolic and neoplastic diseases [28–31]. This adaptive process has gained increasing recognition and understanding in the field of cardio-oncology, highlighting its role in the complex interplay between cancer therapeutics and cardiovascular health. Sunitinib disturbs mitochondrial bioenergetics by reducing the complex I activities, accompanied by an increased generation of mitochondrial ROS and decreased uptake of substrates, such as nucleosides [32]. A down-regulation of mitochondrial biogenesis markers, including Nrf2, PPARα, and PGC-1α expression, has been reported in the animal models of doxorubicin-induced cardiotoxicity and some other cardiovascular disorders [13,33]. In the mechanism of cardiotoxicity induced by anticancer drugs, the suppression of mitochondrial biogenesis is accompanied by an increase in ROS generation [34]. Interestingly, this dual phenomenon has also been observed in the pathophysiology of AF [35]. This imbalance in mitochondrial homeostasis limits the capacity of atrial energy production and increases ROS generation because of a reduction in the electron transfer efficiency owing to excessive mitochondrial fission in naive daughter mitochondria [36]. The increased ROS levels can attack adjacent intact mitochondria, resulting in a self-perpetuating cycle of ROS-induced ROS production [37]. ROS overproduction and inflammation activation exhibit reciprocal stimulation, creating a vicious loop that forms a pathological substrate for AF.
In this study, we discovered that ibrutinib can disrupt the mitochondrial homeostasis in atrial cardiomyocytes. Indeed, the cardiotoxic side effects exhibited by a series of tyrosine kinase inhibitors are closely associated with the mitochondrial homeostasis disturbance. Sunitinib, a widely used vascular endothelial growth factor receptor tyrosine kinase inhibitor for the treatment of metastatic renal cell carcinoma, has been reported to have common cardiotoxicity with an incidence rate of 1% to 10% [1]. Mechanistic studies have revealed that sunitinib can induce myocardial apoptosis [38], which is manifested as the disruption of mitochondrial network structures [39], reduced efficiency of electron transfer, and the accompanying decrease in ATP production [8]. In H9c2 cardiac cell models exposed to sunitinib for 24 h and mouse models treated by gavage for 2 weeks, the collapse of mitochondrial membrane potential and elevated mitochondrial ROS can be observed. Intervention with mito-TEMPO can effectively alleviate mitochondrial dysfunction. Sorafenib, a multitargeted tyrosine kinase inhibitor, has emerged as a first-line treatment for patients with advanced hepatocellular carcinoma and renal cell carcinoma. Evidence suggests that at the therapeutically relevant doses, sorafenib can directly compromise the mitochondrial function of H9c2 cardiomyocytes [40]. The mechanism underlying this cardiotoxicity may be attributed to sorafenib’s role as an effective ferroptosis inducer [41]. Ferroptosis is typically associated with substantial alterations in mitochondrial morphology, characterized by increased mitochondrial membrane density and reduced mitochondrial cristae [42]. Furthermore, sorafenib has been shown to activate the c-Jun N-terminal kinase downstream pathways, leading to disruption of mitochondrial respiration and induction of apoptosis [43]. Imatinib, which functions through inhibiting the activity of BCR-ABL tyrosine kinase, has become the standard treatment for Philadelphia chromosome-positive chronic myeloid leukemia. However, reports from both in vivo and in vitro studies have highlighted its potential cardiotoxicity. Ultrastructural analysis of cardiac tissue from patients undergoing imatinib therapy has revealed the presence of pleomorphic mitochondria with disrupted cristae. In vitro experiments have provided further insights into the mechanisms of this cardiotoxicity. Cardiomyocytes exposed to imatinib for 24 h demonstrate activation of mitochondrial death pathways, triggered by PERK-eIF2α signaling. This process is accompanied by accumulation of mitochondrial superoxide and disruption of mitochondrial membrane potential [44]. These findings suggest that mitochondrial homeostasis dysregulation may represent a shared pathophysiological mechanism underlying the cardiotoxicity associated with tyrosine kinase inhibitors. This insight opens up new avenues for therapeutic strategies aimed at mitigating the cardiac side effects of these drugs. Future research focusing on maintaining mitochondrial homeostasis could potentially yield promising interventions to reduce the cardiotoxicity of tyrosine kinase inhibitors.
Increasing evidence suggests that targeting mitochondrial homeostasis in the heart could help manage both AF and cardio-oncology. Dipeptidyl peptidase-4 (DPP-4) inhibitors demonstrate upstream preventive effects on AF by enhancing mitochondrial biogenesis [45]. A preclinical study showed that DPP-4 inhibitors can alleviate doxorubicin-induced myocyte deaths [46]. A reduced risk of AF can be observed in patients receiving sodium-glucose co-transporter 2 (SGLT2) inhibitors [47]. The electrophysiological protective actions of SGLT2 inhibitors may be associated with metabolic regulation via AMPK activation, resulting in decreased mitochondrial fission and suppressed ROS generation [48]. Recent research emphasizes the therapeutic promise of SGLT2 inhibitors in cardio-oncology. TriNetX data analysis demonstrated that SGLT2 inhibitors significantly lowered the risk of heart failure worsening and AF in patients with cancer treatment-related cardiac dysfunction [49], which is tied to pharmacological actions of attenuating oxidative stress and stimulating mitochondrial biogenesis [50,51]. AKAP1 overexpression in ibrutinib-treated mice can markedly enhance mitochondrial homeostasis and ultimately decrease the AF risk. Indeed, the AKAP1 overexpression strategy has demonstrated therapeutic benefits by restoring mitochondrial homeostasis in several diseases, including diabetic kidney disease, glaucoma, and diabetic cardiomyopathy [24,25,52]. However, specific pharmacological activators for AKAP1 are not yet available.
## Animal model
C57BL/6J mice (8 to 10 weeks, weighing 20 to 25 g) were acquired from the Animal Centre at Anzhen Hospital in Beijing, China. The protocols were sanctioned by the Committee for Animal Care and Use of Anzhen Hospital. Following a previously described method, the mice were given daily intraperitoneal doses of ibrutinib at a dose of 30 mg·kg−1·day−1 and were assessed after 14 days of treatment [21]. Mice that received intraperitoneal injections of phosphate-buffered saline (PBS) served as the vehicle control. The biological function of AKAP1 was investigated via checking the cardiac-specific overexpression of AKAP1 in mice transfected with adeno-associated virus 9 (AAV-9). Purified AAV-9 encoding Akap1 was provided by BrainVTA (Wuhan, China) Co. Ltd. The mice were allocated into 4 different groups: AAV9-cTNT-EGFP-Con, AAV9-cTNT-Akap1-Con, AAV9-cTNT-EGFP-Ibru, and AAV9-cTNT-Akap1-Ibru. Each mouse was intravenously injected with 100 μl of either 5.0 to 6.0 × 1013 GC/ml AAV9-cTNT-Akap1 or 5.0 to 6.0 × 1013 GC/ml AAV9-cTNT-EGFP in the tail. All mice were maintained in specific pathogen-free environments with a 12-h light/dark cycle, regulated temperature, and humidity. Food and water were available ad libitum throughout the study period.
## Cell culture and lentivirus transfection
For stable AKAP1 overexpression, HL-1 cells were transduced with either a negative control lentivirus (LV-NC) or an Akap1 overexpression lentivirus (LV-Akap1). The Akap1 overexpression lentivirus was designed, synthesized, and sequence-verified by Obio Technology Corp. (Shanghai, China). To mimic ibrutinib exposure on the atrial myocytes, the HL-1 cells were treated with 1 μM of ibrutinib for 24 h before experimental determinations, as detailed in the related prior study [53].
## In vivo electrophysiology study
Electrophysiological studies on the mice were conducted following previously described methods [21]. To be brief, mice were anesthetized via the intraperitoneal injection of 1% pentobarbital sodium. ECGs were recorded from surface limb leads. The electrode was introduced through the esophagus in close proximity to the LA, to capture ECGs with PowerLab and LabChart 7 software. Rapid pacing (10 ms at 30 Hz) was employed to evaluate susceptibility to pacing-induced AF. The AF phenotype was identified by distinguishing it from sinus rhythm phenotype when the ECGs demonstrated an absence of distinct P waves. We used this characteristic to differentiate AF from sinus rhythm. The restoration of sinus rhythm was confirmed by observing the detectable P waves. Duration of AF episodes of each animal was documented.
## Echocardiography
The Vevo 2100 Imaging System (FUJIFILM VisualSonics, Inc.) was employed to assess cardiac structural and functional parameters [21]. Initially, mice from each group were anesthetized using ether. Following effective sedation, the mice were laid in the supine position, their fur was trimmed, and their skin was cleansed before examining the atrial structural indicators. Ultrasound assessments were conducted to measure the LA diameter and area. A technician blinded to the grouping of the mice conducted the echocardiographic examination.
## Transmission electron microscopy
Retrograde aortic perfusion with 2.5% glutaraldehyde was employed to fix left atrial tissue samples from each group. Subsequently, the myocardial tissues of the mice were processed according to standard protocols and examined using TEM as described in our previous study [21]
## Single-cell RNA-seq analysis
The single-cell transcriptome dataset (GSE161016, PRJNA815461) from the Gene Expression Omnibus database, which includes data from the atrial tissue of healthy controls and patients with persistent AF, was utilized in this study [54,55]. Single-cell RNA-seq data of the atrial appendage tissue from the patients with AF were acquired from PRJNA815461, while that of the atrial tissue from the control group was obtained from 2 patients who had no prior history of coronary artery disease (GSE16101). The Seurat R package was employed to preprocess the raw data. Single cells were filtered (cells expressing a range of 500 to 4,000 unique genes and less than 10% mitochondrial transcripts were retained) for the subsequent analysis. Uniform manifold approximation and projection was used to group and visualize the cells, allowing for a clear differentiation and visualization of cell clusters. Statistically significant cell marker genes (adjusted P < 0.05) were identified and utilized to classify the clustered cells into their respective cell types. The FeaturePlot function was then used for gene expression distributions. Furthermore, the R package AUCell was utilized to assess the mitochondria-related pathway activities of specific cells.
## Confocal imaging
Fluorescent images were obtained with a 40× oil immersion objective on the laser scanning confocal microscope operating in line-scan mode (400 Hz). Cells were plated on gelatin-coated cytospin slides, allowed to air dry, and fixed with paraformaldehyde for 30 min at 20°C. Following a PBS wash, the cells were permeabilized for 15 min with 0.3% Triton X-100 and then blocked at room temperature with 10% bovine serum albumin for 1 h. Then, cells were incubated overnight at 4°C with primary antibodies, followed by the treatment with fluorescence-labeled secondary antibodies for 1 h. The nuclei were stained using 4′,6-diamidino-2-phenylindole. Table S1 lists the antibodies used for immunofluorescence.
MitoTracker Red (0.5 μM, 30 min) was employed to analyze mitochondrial morphology. The form factor and aspect ratio of mitochondria were quantified based on the number of branches, branch junctions, and the total branch length in the mitochondrial skeleton [56]. JC-1 dye (1 μM, 30 min) was employed to evaluate mitochondrial membrane potential, with the ratio of red fluorescence (aggregated) to green fluorescence (monomeric) indicating the mitochondrial membrane potential. Mitochondrial superoxide production was evaluated using the MitoSOX Red (5 μM, 20 min) Reagent.
## Isolation of primary atrial myocytes and atrial mitochondria
Atrial myocytes were isolated using the established enzyme digestion protocol utilizing the Langendorff retrograde perfusion technique [57]. To promote adherence, the isolated atrial myocytes were seeded onto a laminin-coated dish. More detailed methodological information of atrial myocytes isolation can be found in the Supplementary Materials. In this study, mitochondria were isolated from atrial myocytes using a mitochondrial isolation kit (Thermo Fisher Scientific, USA). The first step involved collecting the atrial myocytes and centrifuging them at 700 g for 5 min at 4 °C to remove the supernatant. The cell pellet was then washed twice with pre-chilled 1×PBS. Subsequently, the cells were resuspended in mitochondrial isolation buffer and subjected to gentle mechanical disruption on ice to lyse the cells. The lysed suspension underwent differential centrifugation: an initial low-speed spin to remove intact cells and larger debris, followed by a high-speed spin to pellet the mitochondria. The resulting mitochondrial samples were either used immediately or stored at −80 °C until further Western blot analysis. The entire procedure was carried out under cold conditions to ensure the integrity and functionality of the mitochondria. For more detailed information, please refer to the manufacturer’s manual (Thermo Fisher Scientific, USA).
## Western blot analysis
Atrial tissues or cultured cell specimens were analyzed using a previously described Western blot protocol. In brief, the samples were lysed in ice-cold RIPA lysis buffer containing 10 mM of Tris-Cl (pH 8.0), 1 mM of EDTA, 1% Triton X-100, 0.1% sodium deoxycholate, 0.1% SDS, 150 mM of NaCl, 1 mM of phenylmethylsulfonyl fluoride, and 0.02 mg/ml each of aprotinin, leupeptin, and pepstatin. The lysates were sonicated and clarified by centrifugation. Protein concentration was determined using a Bradford assay. Then, sodium dodecyl sulfate–polyacrylamide gel electrophoresis was employed to separate 50 to 100 mg of proteins per lane. The resolved proteins were then transferred to a polyvinylidene difluoride membrane and subsequently incubated with the antibodies documented in Table S1.
## mtDNA copy number and quantitative PCR
The mitochondrial DNA copy number was determined by comparing the cytochrome c oxidase subunit 3 (Cox3) mtDNA gene with the UCP2 nuclear gene using quantitative polymerase chain reaction (qPCR). To quantify mtDNA mRNA, Trizol reagent (Life Technologies Inc., Carlsbad, CA, USA) was used to extract total RNA, which was then reverse-transcribed using oligo (dT)-primed cDNA. Table S2 provides the primers utilized for qPCR analysis of the respective sequences.
## Figures
Fig. 1. Ibrutinib induces atrial fibrillation (AF) by impairing mitochondrial function and structures of atrial myocytes. (A and B) Expression heatmap and volcano map of differentially expressed atrial proteins, illustrating that 170 proteins were up-regulated and 208 were down-regulated in ibrutinib-treated mice compared with control mice. N = 3 independent mice per group. (C to H) Gene Ontology and KEGG enrichment analysis of the 378 differentially expressed proteins in atrial tissue. (I to L) GSEA of differentially expressed proteins following ibrutinib treatment.
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Fig. 2. Ibrutinib disrupts mitochondrial homeostasis by down-regulating atrial AKAP1 expression. (A) Volcano plot labeled with the top mitochondria-related differentially expressed proteins. (B) Expression of marker genes for cell-type annotation is indicated on the DotPlot. (C) Uniform manifold approximation and projection representation of all single cells color-coded for their assigned major cell type. (D and E) Featureplots showing the expression patterns of AKAP1 and DRP1 in atrial tissues from AF and control groups. (F to H) Analysis of the correlation between AKAP1 expression and activity score of mitochondria-related pathways. (I) Atrial pan-subpopulation correlation analysis of AKAP1 expression and OXPHOS pathway activity. (J) Molecular docking analysis of the interaction between ibrutinib and AKAP1.
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Fig. 3. AKAP1 binding mediates DRP1 phosphorylation in atrial myocytes. (A) Immunofluorescence imaging of AKAP1 and DRP1 in ibrutinib-treated atrial myocytes. (B to F) Proteins were isolated from atrial myocytes. Western blotting was used to assess AKAP1, DRP1, FIS1, and phosphorylated DRP1 expression levels. ***P < 0.001. ns, not significant.
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Fig. 4. Ibrutinib-induced AKAP1 depletion promotes mitochondrial fission and membrane depolarization via DRP1 dephosphorylation. (A to E) MitoTracker staining for labeling mitochondria in atrial myocytes in vitro. Average mitochondrial length, ratio of fragmented/tubular mitochondria, and mitochondrial morphological parameters were determined. (F to N) Western blot analysis of mitochondrial DRP1 (mito-DRP1), total DRP1 (t-DRP1), phosphorylated DRP1, FIS1, MFN1, and OPA1 in HL-1 cells. VDAC1 was used as a loading control for mitochondrial proteins. (O) Red-to-green ratio of JC-1 fluorescence intensity. (P) Atrial myocytes loaded with JC-1 to analyze changes in mitochondrial membrane potential. N = 8 independent cell samples per group. **P < 0.01, ***P < 0.001.
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Fig. 5. AKAP1 deficiency impairs mitochondrial respiratory function and promotes metabolic reprogramming in atrial myocytes. (A to F) Analysis of HL-1 mitochondrial bioenergetics using the Seahorse XFe96 Analyzer. OCR measurements were taken continuously from baseline and after the sequential addition of 2 mM oligomycin, 1 mM FCCP, and 0.5 mM R/A to measure basal respiration, maximal respiration, spare respiratory capacity, proton leak, and ATP-production levels. (G to J) Total and individual rates of ATP production as mediated by glycolysis or mitochondrial metabolism in HL-1 cells transduced with control and LV-Akap1. (K) Ratio between ATP produced by OXPHOS and that by glycolysis in HL-1 cells transduced with control and LV-Akap1. (L and M) ELISA analysis of mitochondrial respiration complex I/III activities. N = 8 independent cell samples per group. *P < 0.05, **P < 0.01, ***P < 0.001.
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Fig. 6. AKAP1 down-regulation in impaired mitochondrial biogenesis, oxidative stress, and inflammation. (A to C) qPCR analysis of mitochondrial biogenesis parameters (mtDNA copy number, PGC-1α, and NRF1). (D and E) ELISA analysis of redox balance biomarkers, including MDA and GSH contents. (F and G) MitoSOX staining of mitochondrial ROS in HL-1 cells. (H) Visual GSEA of inflammation response pathway in ibrutinib-treated myocytes. (I to K) ELISA of atrial inflammatory biomarkers, including TNF-α, IL-6, and IL-18. N = 8 independent cell samples per group. *P < 0.05, **P < 0.01, ***P < 0.001.
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Fig. 7. AKAP1 overexpression prevents ibrutinib-induced AF by improving MQS and restoring mitochondrial function. (A) Simultaneous recordings of surface ECG following intraesophageal burst pacing. (B) Quantification of AF time. (C) AF inducibility in control and ibrutinib-treated mice with and without AAV-Akap1 transfection. (D and E) Myocardial fibrosis was assessed in each group through Sirius Red staining. The proportion of fibrotic tissue to myocardial tissue was quantified for each group. (F to I) Western blot analysis of total DRP1 (t-DRP1), phosphorylated DRP1, MFN1, and NRF1 in atrial tissues. (J and K) Images and quantification of isolated atrial myocytes loaded with the mitochondrial ROS indicator MitoSox Red. (L to P) ELISA of MQS/redox/inflammation biomarkers, including complex I/III, MDA, GSH, and IL-18. N = 8 mice per group. *P < 0.05, **P < 0.01, ***P < 0.001.
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Fig. 8. This schematic illustrates the proposed mechanism by which ibrutinib promotes atrial fibrillation (AF). Ibrutinib treatment leads to the downregulation of A-kinase anchoring protein 1 (AKAP1), triggering mitochondrial quality surveillance (MQS) impairment. This results in enhanced translocation of dynamin-related protein 1 (DRP1) to the mitochondria, driving mitochondrial fission. Concurrently, metabolic reprogramming is characterized by increased glycolysis and decreased oxidative phosphorylation (OXPHOS), alongside reduced mitochondrial biogenesis. These mitochondrial dysfunctions elevate oxidative stress and inflammatory responses, contributing to atrial metabolic and structural remodeling, ultimately leading to ibrutinib-induced AF.
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<article xmlns:mml="http://www.w3.org/1998/Math/MathML" article-type="research-article" xml:lang="en" dtd-version="1.3"><?properties open_access?><processing-meta base-tagset="archiving" mathml-version="3.0" table-model="xhtml" tagset-family="jats"><restricted-by>pmc</restricted-by></processing-meta><front><journal-meta><journal-id journal-id-type="nlm-ta">Thromb Haemost</journal-id><journal-id journal-id-type="iso-abbrev">Thromb Haemost</journal-id><journal-id journal-id-type="doi">10.1055/s-00035024</journal-id><journal-title-group><journal-title>Thrombosis and Haemostasis</journal-title></journal-title-group><issn pub-type="ppub">0340-6245</issn><issn pub-type="epub">2567-689X</issn><publisher><publisher-name>Georg Thieme Verlag KG</publisher-name><publisher-loc>R&#x000fc;digerstra&#x000df;e 14, 70469 Stuttgart, Germany</publisher-loc></publisher></journal-meta>
<article-meta><article-id pub-id-type="pmid">38657649</article-id><article-id pub-id-type="pmc">11518614</article-id>
<article-id pub-id-type="doi">10.1055/a-2313-0311</article-id><article-id pub-id-type="publisher-id">TH-23-06-0235</article-id><article-categories><subj-group><subject>Stroke, Systemic or Venous Thromboembolism</subject></subj-group></article-categories><title-group><article-title>Exploring the Two-Way Link between Migraines and Venous Thromboembolism: A Bidirectional Two-Sample Mendelian Randomization Study</article-title></title-group><contrib-group><contrib contrib-type="author"><contrib-id contrib-id-type="orcid">http://orcid.org/0000-0003-4357-7451</contrib-id><name><surname>Wang</surname><given-names>Yang</given-names></name><xref rid="AF23060235-1" ref-type="aff">1</xref></contrib><contrib contrib-type="author"><name><surname>Hu</surname><given-names>Xiaofang</given-names></name><xref rid="AF23060235-2" ref-type="aff">2</xref></contrib><contrib contrib-type="author"><name><surname>Wang</surname><given-names>Xiaoqing</given-names></name><xref rid="AF23060235-3" ref-type="aff">3</xref></contrib><contrib contrib-type="author"><name><surname>Li</surname><given-names>Lili</given-names></name><xref rid="AF23060235-3" ref-type="aff">3</xref></contrib><contrib contrib-type="author"><name><surname>Lou</surname><given-names>Peng</given-names></name><xref rid="AF23060235-1" ref-type="aff">1</xref></contrib><contrib contrib-type="author"><name><surname>Liu</surname><given-names>Zhaoxuan</given-names></name><xref rid="AF23060235-4" ref-type="aff">4</xref><xref rid="CO23060235-1" ref-type="author-notes"/></contrib></contrib-group><aff id="AF23060235-1"><label>1</label><institution>Vascular Surgery, Shandong Public Health Clinical Center, Shandong University, Jinan, China</institution></aff><aff id="AF23060235-2"><label>2</label><institution>Department of Neurology, Shandong Public Health Clinical Center, Shandong University, Jinan, China</institution></aff><aff id="AF23060235-3"><label>3</label><institution>Interventional Department, Shandong Public Health Clinical Center, Shandong University, Jinan, China</institution></aff><aff id="AF23060235-4"><label>4</label><institution>Vascular Surgery, Shandong First Medical University affiliated Central Hospital, Jinan, China</institution></aff><author-notes><corresp id="CO23060235-1"><bold>Address for correspondence </bold>Zhaoxuan Liu, MD <institution>Vascular Surgery, Shandong first Medical University affiliated Central Hospital</institution><addr-line>No. 105 Jiefang Road, Jinan city</addr-line><country>China</country><email>liuxin2014@hotmail.com</email></corresp></author-notes><pub-date pub-type="epub"><day>20</day><month>5</month><year>2024</year></pub-date><pub-date pub-type="collection"><month>11</month><year>2024</year></pub-date><pub-date pub-type="pmc-release"><day>1</day><month>5</month><year>2024</year></pub-date><volume>124</volume><issue>11</issue><fpage>1053</fpage><lpage>1060</lpage><history><date date-type="received"><day>10</day><month>6</month><year>2023</year></date><date date-type="accepted"><day>10</day><month>4</month><year>2024</year></date></history><permissions><copyright-statement>
The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution-NonDerivative-NonCommercial License, permitting copying and reproduction so long as the original work is given appropriate credit. Contents may not be used for commercial purposes, or adapted, remixed, transformed or built upon. (
<uri xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="https://creativecommons.org/licenses/by-nc-nd/4.0/">https://creativecommons.org/licenses/by-nc-nd/4.0/</uri>
)
</copyright-statement><copyright-year>2024</copyright-year><copyright-holder>The Author(s).</copyright-holder><license><ali:license_ref xmlns:ali="http://www.niso.org/schemas/ali/1.0/" specific-use="textmining" content-type="ccbyncndlicense">https://creativecommons.org/licenses/by-nc-nd/4.0/</ali:license_ref><license-p>This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License, which permits unrestricted reproduction and distribution, for non-commercial purposes only; and use and reproduction, but not distribution, of adapted material for non-commercial purposes only, provided the original work is properly cited.</license-p></license></permissions><abstract abstract-type="graphical"><p><bold>Background</bold>
&#x02003;The objective of this study is to utilize Mendelian randomization to scrutinize the mutual causality between migraine and venous thromboembolism (VTE) thereby addressing the heterogeneity and inconsistency that were observed in prior observational studies concerning the potential interrelation of the two conditions.
</p><p><bold>Methods</bold>
&#x02003;Employing a bidirectional Mendelian randomization approach, the study explored the link between migraine and VTE, incorporating participants of European descent from a large-scale meta-analysis. An inverse-variance weighted (IVW) regression model, with random-effects, leveraging single nucleotide polymorphisms (SNPs) as instrumental variables was utilized to endorse the mutual causality between migraine and VTE. SNP heterogeneity was evaluated using Cochran's Q-test and to account for multiple testing, correction was implemented using the intercept of the MR-Egger method, and a leave-one-out analysis.
</p><p><bold>Results</bold>
&#x02003;The IVW model unveiled a statistically considerable causal link between migraine and the development of VTE (odds ratio [OR]&#x02009;=&#x02009;96.155, 95% confidence interval [CI]: 4.342&#x02013;2129.458,
<italic>p</italic>
&#x02009;=&#x02009;0.004), implying that migraine poses a strong risk factor for VTE development. Conversely, both IVW and simple model outcomes indicated that VTE poses as a weaker risk factor for migraine (IVW OR&#x02009;=&#x02009;1.002, 95% CI: 1.000&#x02013;1.004,
<italic>p</italic>
&#x02009;=&#x02009;0.016). The MR-Egger regression analysis denoted absence of evidence for genetic pleiotropy among the SNPs while the durability of our Mendelian randomization results was vouched by the leave-one-out sensitivity analysis.
</p><p><bold>Conclusion</bold>
&#x02003;The findings of this Mendelian randomization assessment provide substantiation for a reciprocal causative association between migraine and VTE within the European population.
</p><graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="10-1055-a-2313-0311-i23060235-toc.jpg"/></abstract><kwd-group><title>Keyword</title><kwd>Mendelian randomization</kwd><kwd>migraine</kwd><kwd>venous thromboembolism</kwd><kwd>bidirectional</kwd><kwd>causality</kwd></kwd-group></article-meta></front><body><sec><title>Introduction</title><p>
Venous thromboembolism (VTE) encompasses both deep vein thrombosis and pulmonary embolism,
<xref rid="JR23060235-1" ref-type="bibr">1</xref>
ranking third globally as a prevalent vascular disorder associated with mortality.
<xref rid="JR23060235-2" ref-type="bibr">2</xref>
This increases the mortality risk for patients and compounds the financial burden on health care services. Hence, the ongoing evaluation and assessment of VTE risk in clinical settings are crucial.
</p><p>
Migraine, characterized by recurrent episodes of severe unilateral headaches accompanied by pulsating sensations and autonomic symptoms, affects approximately one billion individuals worldwide.
<xref rid="JR23060235-3" ref-type="bibr">3</xref>
Several research studies indicate an increase in VTE incidence among migraine sufferers.
<xref rid="JR23060235-4" ref-type="bibr">4</xref>
<xref rid="JR23060235-5" ref-type="bibr">5</xref>
<xref rid="JR23060235-6" ref-type="bibr">6</xref>
<xref rid="JR23060235-7" ref-type="bibr">7</xref>
<xref rid="JR23060235-8" ref-type="bibr">8</xref>
Hence, there is a significant need for further investigation to elucidate the causal relationship between VTE and migraines.
</p><p>
Mendelian randomization (MR) is a methodology that utilizes genetic variants as instrumental variables (IVs) to explore the causal association between a modifiable exposure and a disease outcome.
<xref rid="JR23060235-9" ref-type="bibr">9</xref>
By leveraging the random allocation and fixed nature of an individual's alleles at conception, this approach helps alleviate concerns regarding reverse causality and environmental confounders commonly encountered in traditional epidemiological methods.
</p><p>In order to mitigate confounding factors and ensure robust outcomes, this investigation adopts a pioneering approach by utilizing MR to explore the genetic-level causal correlation between migraine and VTE. To the best of our knowledge, no previous study has employed this method to examine the association between these two pathological conditions, thereby lending an innovative and cutting-edge aspect to this research.</p></sec><sec><title>Materials and Methods</title><sec><title>Research Methodology</title><p>
A rigorous bidirectional two-sample MR examination was implemented to probe the causal link between migraine and VTE risk, subsequent to a meticulous screening mechanism. For achieving credible estimations of MR causality, efficacious genetic variances serving as IVs must meet three central postulates: (I) relevance assumption, asserting that variations must demonstrate intimate association with the exposure element; (II) independence/exchangeability assumption, demanding no correlations be exhibited with any measured, unmeasured, or inconspicuous confounding elements germane to the researched correlation of interest; and (III) exclusion restriction assumption, maintaining that the variation affects the outcome exclusively through the exposure, devoid of alternative routes.
<xref rid="JR23060235-10" ref-type="bibr">10</xref>
<xref rid="JR23060235-11" ref-type="bibr">11</xref>
A single nucleotide polymorphism (SNP) refers to a genomic variant where a single nucleotide undergoes alteration at a specific locus within the DNA sequence. SNPs were employed as IVs in this study for estimating causal effects. The study's design is graphically portrayed in
<xref rid="FI23060235-1" ref-type="fig">Fig. 1</xref>
, emphasizing the three fundamental postulates of MR. These postulates are of the utmost importance in affirming the validity of the MR examination and ensuring the reliability of the resultant causal inferences.
<xref rid="JR23060235-12" ref-type="bibr">12</xref>
</p><fig id="FI23060235-1"><label>Fig. 1</label><caption><p>
This figure illustrates the research methodology for the bidirectional Mendelian randomization analysis concerning migraine and VTE. Assumption I: relevance assumption; Assumption II: independence/exchangeability assumption; Assumption III: exclusion restriction assumption.
</p></caption><graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="10-1055-a-2313-0311-i23060235-1"/></fig></sec><sec><title>Data Sources</title><p>
Our SNPs are obtained from large-scale genome-wide association studies (GWAS) public databases. The exposure variable for this study was obtained from the largest migraine GWAS meta-analysis conducted by the IEU Open GWAS project, which can be accessed at
<uri xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="https://gwas.mrcieu.ac.uk/datasets">https://gwas.mrcieu.ac.uk/datasets</uri>
.
<xref rid="JR23060235-13" ref-type="bibr">13</xref>
<xref rid="JR23060235-14" ref-type="bibr">14</xref>
The outcome variable was derived from the largest VTE GWAS conducted by FinnGen, available at
<uri xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="https://www.finngen.fi">https://www.finngen.fi</uri>
.
<xref rid="JR23060235-15" ref-type="bibr">15</xref>
A comprehensive overview of the data sources used in our study can be found in
<xref rid="TB23060235-1" ref-type="table">Table 1</xref>
.
</p><table-wrap id="TB23060235-1"><label>Table 1</label><caption><title>Description of GWAS used for each phenotype</title></caption><table rules="all"><colgroup align="left" span="6"><col align="left" width="12.86%"/><col align="left" width="15.08%"/><col align="left" width="19.62%"/><col align="left" width="15.22%"/><col align="left" width="27.7%"/><col align="left" width="9.54%"/></colgroup><thead><tr><th align="left" valign="bottom">Variable</th><th align="left" valign="bottom">Sample size</th><th align="left" valign="bottom">ID</th><th align="left" valign="bottom">Population</th><th align="left" valign="bottom">Database</th><th align="left" valign="bottom">Year</th></tr></thead><tbody><tr><td align="left" valign="top">Migraine</td><td align="left" valign="top">337159</td><td align="left" valign="top">ukb-a-87</td><td align="left" valign="top">European</td><td align="left" valign="top">IEU Open GWAS project</td><td align="left" valign="top">2017</td></tr><tr><td align="left" valign="top">VTE</td><td align="left" valign="top">218792</td><td align="left" valign="top">finn-b-I9_VTE</td><td align="left" valign="top">European</td><td align="left" valign="top">FinnGen</td><td align="left" valign="top">2021</td></tr></tbody></table><table-wrap-foot><fn id="FN23060235-2"><p>Abbreviations: GWAS, genome-wide association studies; VTE, venous thromboembolism.</p></fn><fn id="FN23060235-3"><p>Note: Basic information of GWAS for migraine and VTE is displayed in this table.</p></fn></table-wrap-foot></table-wrap><p>
The variances in genetic variations and exposure distributions across diverse ethnicities could potentially result in spurious correlations between genetic variants and exposures.
<xref rid="JR23060235-16" ref-type="bibr">16</xref>
Consequently, the migraine and VTE GWAS for this study were sourced from a homogeneous European populace to circumvent such inaccurate associations. It is crucial to highlight that the data harvested from public databases were current up to March 31, 2023. Given the public nature of all data utilized in our study, there was no necessity for further ethical approval.
</p></sec><sec><title>Filtering Criteria of IVs</title><p>
To select appropriate SNPs as IVs, we followed standard assumptions of MR. First, we performed a screening process using the migraine GWAS summary data, applying a significance threshold of
<italic>p</italic>
&#x02009;&#x0003c;&#x02009;5&#x02009;&#x000d7;&#x02009;10
<sup>&#x02212;8</sup>
(Assumption I). To ensure the independence of SNPs and mitigate the effects of linkage disequilibrium, we set the linkage disequilibrium coefficient (
<italic>r</italic>
<sup>2</sup>
) to 0.001 and restricted the width of the linkage disequilibrium region to 10,000&#x02009;kb. PhenoScanner (
<uri xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="http://www.phenoscanner.medschl.cam.ac.uk/">http://www.phenoscanner.medschl.cam.ac.uk/</uri>
) serves as a versatile tool, enabling users to explore genetic variants, genes, and traits linked to a wide spectrum of phenotypes.
<xref rid="JR23060235-17" ref-type="bibr">17</xref>
<xref rid="JR23060235-18" ref-type="bibr">18</xref>
Utilizing PhenoScanner v2, we ruled out SNPs linked with potential confounding constituents and outcomes, thereby addressing assumptions II and III. Subsequently, we extracted the relevant SNPs from the VTE GWAS summary data, ensuring a minimum
<italic>r</italic>
<sup>2</sup>
&#x02009;&#x0003e;&#x02009;0.8 and replacing missing SNPs with highly linked SNPs. We excluded SNPs without replacement sites and palindromic SNPs and combined the information from both datasets. Finally, we excluded SNPs directly associated with VTE at a significance level of
<italic>p</italic>
&#x02009;&#x0003c;&#x02009;5&#x02009;&#x000d7;&#x02009;10
<sup>&#x02212;8</sup>
and prioritized IVs with an F-statistic [F-statistic&#x02009;=&#x02009;(&#x003b2;/SE)2]&#x02009;&#x0003e;&#x02009;10 to minimize weak instrument bias.
<xref rid="BR23060235-19" ref-type="bibr">19</xref>
</p></sec><sec><title>Statistical Analysis</title><p>For our analysis, we employed the inverse-variance weighted (IVW) random-effects regression model to assess the causal relationship between migraine and VTE, utilizing SNPs as IVs. This approach allowed us to directly calculate the causal effect using summary data, eliminating the need for individual-level data. To assess SNP heterogeneity, we conducted Cochran's Q test and, in the presence of heterogeneity, relied on the results of the IVW model. To examine the presence of pleiotropy, we utilized the MR-Egger method and conducted leave-one-out analysis. All statistical analyses were performed using the TwoSampleMR package in R 4.2.2 software, with a significance level set at &#x003b1;&#x02009;=&#x02009;0.05.</p></sec></sec><sec><title>Results</title><p>
In the present investigation, we capitalized on a bidirectional two-sample MR analysis in individuals of European descent to scrutinize the potential causative correlation between migraines and VTE risk. Our investigation implies a potential bidirectional pathogenic relationship between migraines and the risk of VTE, as supported by the specific analysis results detailed in
<xref rid="TB23060235-2" ref-type="table">Table 2</xref>
.
</p><table-wrap id="TB23060235-2"><label>Table 2</label><caption><title>Mendelian randomization regression causal association results</title></caption><table rules="all"><colgroup align="left" span="7"><col align="left" width="13.04%"/><col align="left" width="13.04%"/><col align="left" width="15.94%"/><col align="left" width="8.7%"/><col align="left" width="8.7%"/><col align="left" width="28.98%"/><col align="left" width="11.6%"/></colgroup><thead><tr><th align="left" valign="bottom">Exposures</th><th align="left" valign="bottom">SNPs (no.)</th><th align="left" valign="bottom">Methods</th><th align="left" valign="bottom">&#x003b2;</th><th align="left" valign="bottom">SE</th><th align="left" valign="bottom">OR (95% CI)</th><th align="left" valign="bottom">
<italic>p</italic>
</th></tr></thead><tbody><tr><td align="left" valign="top">Migraine</td><td align="left" valign="top">11</td><td align="left" valign="top">IVW</td><td align="left" valign="top">4.566</td><td align="left" valign="top">1.580</td><td align="left" valign="top">96.155 (4.342&#x02013;2129.458)</td><td align="left" valign="top">0.004</td></tr><tr><td align="left" rowspan="2" valign="top">VTE</td><td align="left" rowspan="2" valign="top">12</td><td align="left" valign="top">IVW</td><td align="left" valign="top">0.002</td><td align="left" valign="top">0.001</td><td align="left" valign="top">1.002 (1.000&#x02013;1.004);</td><td align="left" valign="top">0.016</td></tr><tr><td align="left" valign="top">Simple mode</td><td align="left" valign="top">0.003</td><td align="left" valign="top">0.001</td><td align="left" valign="top">1.003 (1.000&#x02013;1.006)</td><td align="left" valign="top">0.047</td></tr></tbody></table><table-wrap-foot><fn id="FN23060235-4"><p>Abbreviations: CI: confidence interval; IVW, inverse variance weighting; OR, odds ratio; SE, standard error; SNPs, single nucleotide polymorphisms; VTE, venous thromboembolism.</p></fn><fn id="FN23060235-5"><p>Note: This table displays the causal relationship between migraine leading to VTE and VTE leading to migraine.</p></fn></table-wrap-foot></table-wrap><sec><title>Mendelian Randomization Analysis</title><p>
During the IV screening process, it was identified that SNP r10908505 was associated with body mass index (BMI) in VTE. Considering the established association between BMI and VTE,
<xref rid="JR23060235-1" ref-type="bibr">1</xref>
<xref rid="JR23060235-15" ref-type="bibr">15</xref>
this violated Assumption III and the SNP was subsequently excluded. The VTE dataset ultimately consisted of 11 SNPs, with individual SNP F-statistics ranging from 29.76 to 96.77 (all &#x0003e;10), indicating a minimal potential for causal associations to be confounded by weak IV bias (
<xref rid="SM23060235-1" ref-type="supplementary-material">Supplementary Table S1</xref>
, available in the online version). The IVW model revealed that migraine was a statistically significant risk factor for the onset of VTE (odds ratio [OR]&#x02009;=&#x02009;96.155, 95% confidence interval [CI]: 4.3422&#x02013;129.458,
<italic>p</italic>
&#x02009;=&#x02009;0.004) (
<xref rid="TB23060235-2" ref-type="table">Table 2</xref>
,
<xref rid="FI23060235-2" ref-type="fig">Fig. 2A</xref>
). The scatter plot (
<xref rid="FI23060235-2" ref-type="fig">Fig. 2B</xref>
) and funnel plot (
<xref rid="FI23060235-2" ref-type="fig">Fig. 2C</xref>
) of migraine demonstrated a symmetrical distribution of all included SNPs, suggesting a limited possibility of bias affecting the causal association. The Cochran's Q test, conducted on the MR-Egger regression and the IVW method, yielded statistics of 5.610 and 5.973 (
<italic>p</italic>
&#x02009;&#x0003e;&#x02009;0.05), indicating the absence of heterogeneity among the SNPs (
<xref rid="SM23060235-1" ref-type="supplementary-material">Supplementary Table S2</xref>
, available in the online version). These findings suggest a positive correlation between the strength of association between the IVs and migraine, satisfying the assumptions of IV analysis. The MR-Egger regression analysis showed no statistically significant difference from zero for the intercept term (
<italic>p</italic>
&#x02009;=&#x02009;0.5617), indicating the absence of genetic pleiotropy among the SNPs (
<xref rid="SM23060235-1" ref-type="supplementary-material">Supplementary Table S3</xref>
, available in the online version). Additionally, the leave-one-out analysis revealed that the inclusion or exclusion of individual SNPs did not substantially impact the estimated causal effects, demonstrating the robustness of the MR results obtained in our investigation (
<xref rid="FI23060235-2" ref-type="fig">Fig. 2D</xref>
).
</p><fig id="FI23060235-2"><label>Fig. 2</label><caption><p>
This figure explores the correlation between migraine risk and VTE, validating the presence of heterogeneity and pleiotropy. (
<bold>A</bold>
) The forest plot displays individual IVs, with each point flanked by lines that depict the 95% confidence interval. The effect of SNPs on the exposure (migraine) is shown along the
<italic>x</italic>
-axis, whereas their impact on the outcome (VTE) is presented on the
<italic>y</italic>
-axis. A fitted line reflects the Mendelian randomization analysis results. (
<bold>B</bold>
) A scatter plot visualizes each IV, with the SNP effects on both exposure and outcome similar to that of the forest plot. Again, a fitted line represents the Mendelian randomization results. (
<bold>C</bold>
) The funnel plot positions the coefficient &#x003b2;
<sub>IV</sub>
from the instrumental variable regression on the
<italic>x</italic>
-axis to demonstrate the association's strength, while the inverse of its standard error (1/SE
<sub>IV</sub>
<sup>&#x02020;</sup>
) on the
<italic>y</italic>
-axis indicates the precision of this estimate. (
<bold>D</bold>
) A leave-one-out sensitivity analysis is shown on the
<italic>x</italic>
-axis, charting the estimated effects from the Mendelian randomization analysis. With each SNP associated with migraine successively excluded, the analysis recalculates the Mendelian randomization effect estimates, culminating with the &#x0201c;all&#x0201d; category that encompasses all considered SNPs. IV, instrumental variable; SNP, single nucleotide polymorphisms; VTE, venous thromboembolism; SE, standard error.
<sup>&#x02020;</sup>
SE is the standard error of &#x003b2;.
</p></caption><graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="10-1055-a-2313-0311-i23060235-2"/></fig></sec><sec><title>Reverse Mendelian Randomization Analysis</title><p>
Upon screening for IVs in migraine patients, SNP rs6060308 was excluded due to its association with education
<xref rid="JR23060235-20" ref-type="bibr">20</xref>
<xref rid="JR23060235-21" ref-type="bibr">21</xref>
and violation of Assumption III. The final migraine dataset comprised 13 SNPs, with individual SNP F-statistics ranging from 30.60 to 354.34, all surpassing the threshold of 10 (
<xref rid="SM23060235-1" ref-type="supplementary-material">Supplementary Table S4</xref>
, available in the online version). Both the IVW and simple models supported VTE as a risk factor for migraine. The IVW analysis yielded an OR of 1.002 (95% CI: 1.000&#x02013;1.004,
<italic>p</italic>
&#x02009;=&#x02009;0.016), while the simple model yielded an OR of 1.003 (95% CI: 1.000&#x02013;1.006,
<italic>p</italic>
&#x02009;=&#x02009;0.047) (
<xref rid="TB23060235-2" ref-type="table">Table 2</xref>
,
<xref rid="FI23060235-3" ref-type="fig">Fig. 3A</xref>
). The scatter plot (
<xref rid="FI23060235-3" ref-type="fig">Fig. 3B</xref>
) and funnel plot (
<xref rid="FI23060235-3" ref-type="fig">Fig. 3C</xref>
) exhibited symmetrical distributions across all included SNPs, indicating minimal potential for biases affecting the causal association. Heterogeneity among SNPs was observed through the Cochran's Q test of the IVW method and MR-Egger regression, with Q statistics of 18.697 and 20.377, respectively, both with
<italic>p</italic>
&#x02009;&#x0003c;&#x02009;0.05 (
<xref rid="SM23060235-1" ref-type="supplementary-material">Supplementary Table S2</xref>
, available in the online version). Therefore, careful consideration is necessary for the results obtained from the random-effects IVW method. MR-Egger regression analysis revealed a nonsignificant difference between the intercept term and zero (
<italic>p</italic>
&#x02009;=&#x02009;0.3655), suggesting the absence of genetic pleiotropy among the SNPs (
<xref rid="SM23060235-1" ref-type="supplementary-material">Supplementary Table S3</xref>
, available in the online version). Additionally, the leave-one-out analysis demonstrated that the inclusion or exclusion of individual SNPs had no substantial impact on the estimated causal effect (
<xref rid="FI23060235-3" ref-type="fig">Fig. 3D</xref>
).
</p><fig id="FI23060235-3"><label>Fig. 3</label><caption><p>
(
<bold>A&#x02013;D</bold>
) This figure presents the relationship between VTE risk and migraine, also verifying heterogeneity and pleiotropy through similar graphic representations as detailed for
<xref rid="FI23060235-2" ref-type="fig">Fig. 2</xref>
, but with the exposure and outcome reversed&#x02014;SNPs' effect on VTE and outcome on migraine. SNP, single nucleotide polymorphisms; VTE, venous thromboembolism.
</p></caption><graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="10-1055-a-2313-0311-i23060235-3"/></fig></sec></sec><sec><title>Discussion</title><p>
VTE constitutes a grave health hazard to patients, necessitating rigorous clinical surveillance. Distinct from common VTE risk factors such as cancer,
<xref rid="JR23060235-22" ref-type="bibr">22</xref>
diabetes,
<xref rid="JR23060235-23" ref-type="bibr">23</xref>
lupus,
<xref rid="JR23060235-24" ref-type="bibr">24</xref>
and antiphospholipid syndrome,
<xref rid="JR23060235-25" ref-type="bibr">25</xref>
migraines remain absent from prevalent VTE guidelines or advisories. The MR findings from our research provide first-of-its-kind evidence of a causal nexus between migraines and VTE in individuals of European descent, signaling that migraines potently predispose individuals to VTE (IVW OR&#x02009;=&#x02009;96.155, 95% CI: 4.342&#x02013;2129.458), while VTE presents a weak risk factor for migraines (IVW OR&#x02009;=&#x02009;1.002, 95% CI: 1.000&#x02013;1.004). Given the robustness of the IVW analysis, the MR analysis is considered reliable.
</p><p>
Our MR analysis discloses a potential causal association between individuals suffering from migraines and VTE incidence, with a risk rate 96.155 times higher in comparison to nonmigraine sufferers. Previous observational endeavors investigating VTE risk amidst migraine patients have been scant and have yielded discordant outcomes, complicating the provision of clinical directives.
<xref rid="JR23060235-26" ref-type="bibr">26</xref>
<xref rid="JR23060235-27" ref-type="bibr">27</xref>
In a longitudinal inquiry with a 19-year follow-up, Adelborg et al discerned a heightened VTE risk in individuals afflicted with migraines.
<xref rid="JR23060235-4" ref-type="bibr">4</xref>
Peng et al's prospective clinical study unveiled a more than double VTE risk increase in migraine patients during a 4-year follow-up.
<xref rid="JR23060235-5" ref-type="bibr">5</xref>
Schwaiger et al's cohort study, incorporating 574 patients aged 55 to 94, observed a significant escalation in VTE risk among elderly individuals with migraines.
<xref rid="JR23060235-6" ref-type="bibr">6</xref>
<xref rid="JR23060235-28" ref-type="bibr">28</xref>
Bushnell et al uncovered a tripled VTE risk during pregnancy in migraine-affected women.
<xref rid="JR23060235-29" ref-type="bibr">29</xref>
Although these studies validate a potential correlation between migraines and VTE, their persuasiveness is restricted due to other prominent VTE risk factors (such as advanced age and pregnancy) and contradicting findings in existing observational studies. For instance, Folsom et al observed no significant correlation between migraines and VTE risk in elderly individuals, contradicting Schwaiger's conclusion.
<xref rid="JR23060235-7" ref-type="bibr">7</xref>
However, he clarified that the cohort incorporated in his study did not undergo rigorous neurological migraine diagnosis, possibly leading to confounding biases and generating findings that contradict other scholarly endeavors.
<xref rid="JR23060235-7" ref-type="bibr">7</xref>
These contradictions originate from observational studies examining associations rather than causal relationships, invariably involving a confluence of various confounding factors. MR, leveraging SNPs as IVs to ascertain the causal link between migraines and VTE risk, can eliminate other confounding elements resulting in more reliable outcomes. Based on this finding, monitoring VTE risk among migraine patients in clinical practice is recommended.
</p><p>The reverse MR analysis reveals that compared to non-VTE patients, those with VTE among individuals of European ancestry exhibit a marginally heightened susceptibility to migraines, with a relative risk of 1.02 (as per the IVW method). This discovery concurs with the existing void in research on migraines among VTE patients. Thus, even with slightly increased risks of migraines in VTE patients, we do not advocate for heightened concern regarding the emergence of migraines among this patient group.</p><p>
Our endeavor seeks to offer a preliminary examination of the potential mechanisms underlying the interplay between migraines and VTE. The incidence of VTE habitually involves Virchow's triad, encompassing endothelial damage, venous stasis, and hypercoagulability.
<xref rid="JR23060235-30" ref-type="bibr">30</xref>
On the genetic association front, the SNPs rs9349379 and rs11172113, acting as IVs for migraines, display relevance to the mechanisms underpinning VTE. Prior research earmarks the gene corresponding to rs9349379,
<italic>PHACTR1</italic>
(
<xref rid="SM23060235-1" ref-type="supplementary-material">Supplementary Table S1</xref>
, available in the online version), as a catalyst for the upregulation of
<italic>EDN1</italic>
.
<xref rid="JR23060235-31" ref-type="bibr">31</xref>
Elevated
<italic>EDN1</italic>
expression is associated with increased VTE susceptibility,
<xref rid="JR23060235-32" ref-type="bibr">32</xref>
and
<italic>EDN1</italic>
inhibition can diminish VTE incidence,
<xref rid="JR23060235-33" ref-type="bibr">33</xref>
potentially through Endothelin 1-mediated vascular endothelial inflammation leading to thrombus formation.
<xref rid="JR23060235-34" ref-type="bibr">34</xref>
The SNP rs11172113 corresponds to the gene
<italic>LRP1</italic>
(
<xref rid="SM23060235-1" ref-type="supplementary-material">Supplementary Table S1</xref>
, available in the online version).
<xref rid="JR23060235-35" ref-type="bibr">35</xref>
<italic>LRP1</italic>
can facilitate the upregulation of
<italic>FVIII</italic>
, culminating in an increase in plasma coagulation factor VIII,
<xref rid="JR23060235-36" ref-type="bibr">36</xref>
thereby leading to heightened blood coagulability and an associated elevated VTE risk.
<xref rid="JR23060235-37" ref-type="bibr">37</xref>
While various studies propose divergent mechanisms, they collectively signal that migraines can instigate a hypercoagulable state, thereby promoting the onset of VTE. The SNPs serving as IVs for VTE did not unveil any association with the onset of migraines. This corroborates our MR analysis outcomes, indicating that VTE is merely a weak risk factor for migraines.
</p><sec><title>Strengths and Limitations</title><p>This study possesses several notable strengths. First, it fulfills all three assumptions of MR, minimizing the influence of confounding factors and addressing the limitations inherent in observational studies, thus yielding more robust findings. We carefully excluded two SNPs (rs10908505 and rs6060308) that could potentially impact the results. In addition, we employed PhenoScanner v2 to comprehensively probe confounding variables related to VTE, as described earlier, and factors associated with migraines, such as acute migraine medication overuse, obesity, depression, stress, and alcohol, leading to the subsequent exclusion of relevant SNPs. This ensured that the selected SNPs specifically captured the causal effects and eliminated potential confounding effects from polygenic associations with disease susceptibility. Second, the study sample was restricted to individuals of European descent, minimizing the potential bias introduced by population heterogeneity and enhancing the internal validity of the findings. Third, the use of strongly correlated SNPs (F &#x0003e;&#x0003e; 10) in both migraine &#x02192; VTE and VTE &#x02192; migraine analyses enhances the validity of the IVs. Finally, this study pioneers the hypothesis of a plausible causal association between VTE and an elevated susceptibility to migraines, offering novel insights into their potential relationship.</p><p>
Nonetheless, it is essential to acknowledge the presence of several limitations in this study that warrant consideration. First, the study participants were predominantly of European ancestry, which, although it avoids the influence of ethnicity on the results, limits the generalizability of the findings to other ethnic groups. Second, our study solely establishes the causal relationship between migraine and VTE risk without elucidating the underlying mechanisms. Third, we only selected SNPs that met the stringent genome-wide significance level (
<italic>p</italic>
&#x02009;&#x0003c;&#x02009;5&#x02009;&#x000d7;&#x02009;10
<sup>&#x02212;8</sup>
), potentially excluding truly relevant variations that did not reach this threshold. Lastly, as our MR analysis relies on publicly available summary statistics data, the lack of detailed clinical information hinders subgroup analysis.
</p></sec></sec><sec><title>Conclusion</title><p>In essence, the bidirectional Mendelian randomization analysis, conducted within the European populace, indicates the presence of a relatively strong causal correlation between migraines and VTE, while the causative relationship between VTE and migraines appears exceedingly faint. These deductions imply the need for rigorous monitoring of VTE in individuals of European descent suffering from migraines, thus requiring synergistic effort between general physicians and neurologists. Nevertheless, VTE patients should refrain from undue worries concerning the incidence of migraines. Further, we stress the importance of harnessing information gleaned from a wide array of observational studies and controlled experiments to strengthen the credibility of drawn causal inferences. This is pivotal in establishing a mutual corroboration with the results obtained through MR analysis. Hence, the exigency for additional stringent observational studies and comprehensive laboratory research prevails to corroborate the conclusions made in this study.</p></sec></body><back><sec><boxed-text content-type="backinfo"><p>
<bold>What is known about this topic?</bold>
</p><list list-type="bullet"><list-item><p>Previous research has not definitively established whether migraine is a risk factor for VTE, and observational studies have contradictory findings.</p></list-item><list-item><p>Previous studies do not provide evidence for the prevention of VTE occurrence in patients with migraines.</p></list-item></list><p>
<bold>What does this paper add?</bold>
</p><list list-type="bullet"><list-item><p>Migraine has been identified as a strong-risk factor for VTE.</p></list-item><list-item><p>VTE has been identified as a weak-risk factor for migraine.</p></list-item><list-item><p>For the first time, genetic evidence has been presented to emphasize the importance of preventing VTE occurrence in individuals with migraines.</p></list-item></list></boxed-text></sec><fn-group><fn fn-type="COI-statement" id="d35e118"><p><bold>Conflict of Interest</bold> None declared.</p></fn></fn-group><sec sec-type="supplementary-material"><title>Supplementary Material</title><supplementary-material id="SM23060235-1"><media xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="10-1055-a-2313-0311-s23060235.pdf"><caption><p>Supplementary Material</p></caption><caption><p>Supplementary Material</p></caption></media></supplementary-material></sec><ref-list><title>References</title><ref id="JR23060235-1"><label>1</label><mixed-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Khan</surname><given-names>F</given-names></name><name><surname>Tritschler</surname><given-names>T</given-names></name><name><surname>Kahn</surname><given-names>S R</given-names></name><name><surname>Rodger</surname><given-names>M A</given-names></name></person-group><article-title>Venous thromboembolism</article-title><source>Lancet</source><year>2021</year><volume>398</volume>(10294):<fpage>64</fpage><lpage>77</lpage><pub-id pub-id-type="pmid">33984268</pub-id>
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<article-id pub-id-type="doi">10.1055/s-0043-1775965</article-id><article-id pub-id-type="publisher-id">TH-22-08-0374</article-id><article-categories><subj-group><subject>Stroke, Systemic or Venous Thromboembolism</subject></subj-group></article-categories><title-group><article-title>Differential Effects of Erythropoietin Administration and Overexpression on Venous Thrombosis in Mice</article-title></title-group><contrib-group><contrib contrib-type="author"><contrib-id contrib-id-type="orcid">http://orcid.org/0000-0001-6555-6622</contrib-id><name><surname>Stockhausen</surname><given-names>Sven</given-names></name><xref rid="AF22080374-1" ref-type="aff">1</xref><xref rid="AF22080374-2" ref-type="aff">2</xref><xref rid="AF22080374-3" ref-type="aff">3</xref></contrib><contrib contrib-type="author"><name><surname>Kilani</surname><given-names>Badr</given-names></name><xref rid="AF22080374-1" ref-type="aff">1</xref><xref rid="AF22080374-2" ref-type="aff">2</xref><xref rid="AF22080374-3" ref-type="aff">3</xref></contrib><contrib contrib-type="author"><name><surname>Schubert</surname><given-names>Irene</given-names></name><xref rid="AF22080374-1" ref-type="aff">1</xref><xref rid="AF22080374-2" ref-type="aff">2</xref><xref rid="AF22080374-3" ref-type="aff">3</xref></contrib><contrib contrib-type="author"><name><surname>Steinsiek</surname><given-names>Anna-Lena</given-names></name><xref rid="AF22080374-4" ref-type="aff">4</xref></contrib><contrib contrib-type="author"><name><surname>Chandraratne</surname><given-names>Sue</given-names></name><xref rid="AF22080374-1" ref-type="aff">1</xref><xref rid="AF22080374-2" ref-type="aff">2</xref><xref rid="AF22080374-3" ref-type="aff">3</xref></contrib><contrib contrib-type="author"><name><surname>Wendler</surname><given-names>Franziska</given-names></name><xref rid="AF22080374-1" ref-type="aff">1</xref><xref rid="AF22080374-2" ref-type="aff">2</xref><xref rid="AF22080374-3" ref-type="aff">3</xref></contrib><contrib contrib-type="author"><name><surname>Eivers</surname><given-names>Luke</given-names></name><xref rid="AF22080374-1" ref-type="aff">1</xref><xref rid="AF22080374-2" ref-type="aff">2</xref><xref rid="AF22080374-3" ref-type="aff">3</xref></contrib><contrib contrib-type="author"><name><surname>von Br&#x000fc;hl</surname><given-names>Marie-Luise</given-names></name><xref rid="AF22080374-1" ref-type="aff">1</xref><xref rid="AF22080374-2" ref-type="aff">2</xref><xref rid="AF22080374-3" ref-type="aff">3</xref></contrib><contrib contrib-type="author"><name><surname>Massberg</surname><given-names>Steffen</given-names></name><xref rid="AF22080374-1" ref-type="aff">1</xref><xref rid="AF22080374-2" ref-type="aff">2</xref><xref rid="AF22080374-3" ref-type="aff">3</xref></contrib><contrib contrib-type="author"><name><surname>Ott</surname><given-names>Ilka</given-names></name><xref rid="AF22080374-4" ref-type="aff">4</xref></contrib><contrib contrib-type="author"><name><surname>Stark</surname><given-names>Konstantin</given-names></name><xref rid="AF22080374-1" ref-type="aff">1</xref><xref rid="AF22080374-2" ref-type="aff">2</xref><xref rid="AF22080374-3" ref-type="aff">3</xref><xref rid="CO22080374-1" ref-type="author-notes"/></contrib></contrib-group><aff id="AF22080374-1"><label>1</label><institution>Medizinische Klinik und Poliklinik I, Klinikum der Universit&#x000e4;t M&#x000fc;nchen, Ludwig-Maximilians-Universit&#x000e4;t, Munich, Germany</institution></aff><aff id="AF22080374-2"><label>2</label><institution>German Center for Cardiovascular Research (DZHK), partner site Munich Heart Alliance, Munich, Germany</institution></aff><aff id="AF22080374-3"><label>3</label><institution>Walter-Brendel Center of Experimental Medicine, Ludwig-Maximilians-Universit&#x000e4;t, Munich, Germany</institution></aff><aff id="AF22080374-4"><label>4</label><institution>Department of cardiology, German Heart Center, Munich, Germany.</institution></aff><author-notes><corresp id="CO22080374-1"><bold>Address for correspondence </bold>Konstantin Stark <institution>Medizinische Klinik und Poliklinik I, Ludwig-Maximilians-University</institution><addr-line>Marchioninistr. 15, 81377 Munich</addr-line><country>Germany</country><email>Konstantin.stark@med.uni-muenchen.de</email></corresp></author-notes><pub-date pub-type="epub"><day>16</day><month>10</month><year>2023</year></pub-date><pub-date pub-type="collection"><month>11</month><year>2024</year></pub-date><pub-date pub-type="pmc-release"><day>1</day><month>10</month><year>2023</year></pub-date><volume>124</volume><issue>11</issue><fpage>1027</fpage><lpage>1039</lpage><history><date date-type="received"><day>17</day><month>9</month><year>2022</year></date><date date-type="accepted"><day>06</day><month>8</month><year>2023</year></date></history><permissions><copyright-statement>
The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution-NonDerivative-NonCommercial License, permitting copying and reproduction so long as the original work is given appropriate credit. Contents may not be used for commercial purposes, or adapted, remixed, transformed or built upon. (
<uri xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="https://creativecommons.org/licenses/by-nc-nd/4.0/">https://creativecommons.org/licenses/by-nc-nd/4.0/</uri>
)
</copyright-statement><copyright-year>2023</copyright-year><copyright-holder>The Author(s).</copyright-holder><license><ali:license_ref xmlns:ali="http://www.niso.org/schemas/ali/1.0/" specific-use="textmining" content-type="ccbyncndlicense">https://creativecommons.org/licenses/by-nc-nd/4.0/</ali:license_ref><license-p>This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License, which permits unrestricted reproduction and distribution, for non-commercial purposes only; and use and reproduction, but not distribution, of adapted material for non-commercial purposes only, provided the original work is properly cited.</license-p></license></permissions><abstract abstract-type="graphical"><p><bold>Background</bold>
&#x02003;Deep vein thrombosis (DVT) is a common condition associated with significant mortality due to pulmonary embolism. Despite advanced prevention and anticoagulation therapy, the incidence of venous thromboembolism remains unchanged. Individuals with elevated hematocrit and/or excessively high erythropoietin (EPO) serum levels are particularly susceptible to DVT formation. We investigated the influence of short-term EPO administration compared to chronic EPO overproduction on DVT development. Additionally, we examined the role of the spleen in this context and assessed its impact on thrombus composition.
</p><p><bold>Methods</bold>
&#x02003;We induced ligation of the caudal vena cava (VCC) in EPO-overproducing Tg(EPO) mice as well as wildtype mice treated with EPO for two weeks, both with and without splenectomy. The effect on platelet circulation time was evaluated through FACS analysis, and thrombus composition was analyzed using immunohistology.
</p><p><bold>Results</bold>
&#x02003;We present evidence for an elevated thrombogenic phenotype resulting from chronic EPO overproduction, achieved by combining an EPO-overexpressing mouse model with experimental DVT induction. This increased thrombotic state is largely independent of traditional contributors to DVT, such as neutrophils and platelets. Notably, the pronounced prothrombotic effect of red blood cells (RBCs) only manifests during chronic EPO overproduction and is not influenced by splenic RBC clearance, as demonstrated by splenectomy. In contrast, short-term EPO treatment does not induce thrombogenesis in mice. Consequently, our findings support the existence of a differential thrombogenic effect between chronic enhanced erythropoiesis and exogenous EPO administration.
</p><p><bold>Conclusion</bold>
&#x02003;Chronic EPO overproduction significantly increases the risk of DVT, while short-term EPO treatment does not. These findings underscore the importance of considering EPO-related factors in DVT risk assessment and potential therapeutic strategies.
</p><graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="10-1055-s-0043-1775965-i22080374-toc.jpg"/></abstract><kwd-group><title>Keywords</title><kwd>deep vein thrombosis</kwd><kwd>red blood cells</kwd><kwd>spleen</kwd><kwd>erythropoietin</kwd><kwd>sterile inflammation</kwd><kwd>fibrin</kwd></kwd-group><funding-group><award-group><funding-source>Deutsche Forschungsgemeinschaft</funding-source><award-id>1123</award-id></award-group><award-group><funding-source>Deutsche Forschungsgemeinschaft</funding-source><award-id>914</award-id></award-group><award-group><funding-source>Deutsche Gesellschaft f&#x000fc;r Kardiologie-Herz und Kreislaufforschung</funding-source><award-id>DGK09/2020</award-id></award-group><award-group><funding-source>ERC starting grant</funding-source><award-id>947611</award-id></award-group><award-group><funding-source>F&#x000f6;rderprogramm f&#x000fc;r Forschung und Lehre</funding-source><award-id>1123</award-id></award-group><funding-statement><bold>Funding</bold>
This project is supported by the ERC starting grant T-MEMORE project 947611 (K.S.). This study was supported by the Deutsche Forschungsgemeinschaft through the collaborative research center 1123 project A07 (K.S., S.M.) and the collaborative research center 914 project B02 (K.S., S.M.). We thank for the support by the grant from the Deutsche Gesellschaft f&#x000fc;r Kardiologie project DGK09/2020 and the F&#x000f6;rderprogramm f&#x000fc;r Forschung und Lehre (F&#x000f6;FoLe) project 1123 (S.S).
</funding-statement></funding-group></article-meta></front><body><sec><title>Introduction</title><p>
Red blood cells (RBCs) are the primary carriers oxygen and carbon dioxide in all mammals. Low hemoglobin concentrations in the blood can cause severe oxygen deficiency, leading to ischemia in organs and tissues. At the same time, numerous clinical observations identified elevated hemoglobin levels as an independent risk factor for deep vein thrombosis (DVT) formation. This applies to overproduction of RBCs due to erythropoietin (EPO) administration, as well as foreign blood transfusions.
<xref rid="JR22080374-1" ref-type="bibr">1</xref>
<xref rid="JR22080374-2" ref-type="bibr">2</xref>
<xref rid="JR22080374-3" ref-type="bibr">3</xref>
<xref rid="JR22080374-4" ref-type="bibr">4</xref>
<xref rid="JR22080374-5" ref-type="bibr">5</xref>
<xref rid="JR22080374-6" ref-type="bibr">6</xref>
<xref rid="JR22080374-7" ref-type="bibr">7</xref>
This is also evident in illnesses which exhibit an excessive RBC production such as polycythemia vera or Chuvash polycythemia where a significant increase in thromboembolic complications has been reported.
<xref rid="JR22080374-8" ref-type="bibr">8</xref>
<xref rid="JR22080374-9" ref-type="bibr">9</xref>
In such cases, oral or parenteral anticoagulants are effective preventive measures. However, their use entails significant drawbacks in the form of elevated bleeding risks, which can lead to severe complications.
<xref rid="JR22080374-10" ref-type="bibr">10</xref>
Therefore, it is crucial to identify patients at risk and to expand our understanding of the pathophysiology to enable a more targeted prevention and treatment of DVT.
</p><p>
The mechanism of RBC-mediated DVT formation so far is not fully understood. Essentially, DVT formation is triggered by sterile inflammation.
<xref rid="JR22080374-11" ref-type="bibr">11</xref>
Neutrophils and monocytes deliver tissue factor (TF) to the site of thrombus formation creating a procoagulant environment.
<xref rid="JR22080374-11" ref-type="bibr">11</xref>
However, the contribution of leukocytes to DVT formation may vary depending on the underlying disease and a leukocyte-recruiting property of RBCs in DVT has not been conclusively proven.
</p><p>
In this project we used a transgenic mouse model overexpressing the human EPO gene in an oxygen-independent manner. In these mice the hematocrit is chronically elevated, which leads to several changes. RBCs represent, volumetrically, the largest cellular component in the peripheral blood, thus influencing the viscosity of the blood, fostering cardiovascular events like stroke or ischemic heart disease.
<xref rid="JR22080374-12" ref-type="bibr">12</xref>
RBCs from Tg(EPO) mice show increased flexibility which in turn reduces the viscosity, and protects from thrombus formation.
<xref rid="JR22080374-13" ref-type="bibr">13</xref>
Additionally, excessive NO production has been described. In Tg(EPO) mice, the vasodilative effect of extensive NO release is partly compensated by endothelin.
<xref rid="JR22080374-14" ref-type="bibr">14</xref>
A reduced lifespan of RBCs was also identified in this mouse strain.
<xref rid="JR22080374-15" ref-type="bibr">15</xref>
</p><p>
The spleen is responsible for RBC clearance, which acts as gatekeeper of the state, age, and number of RBCs.
<xref rid="JR22080374-16" ref-type="bibr">16</xref>
The loss of function of the spleen, due to removal, leads to changes in the blood count, the most striking of which is the transient thrombocytosis observed after splenectomy.
<xref rid="JR22080374-17" ref-type="bibr">17</xref>
Even though the platelet count normalizes within weeks, the risk of thromboembolism remains persistently high; however, the mechanism behind this prothrombotic state is unclear.
<xref rid="JR22080374-18" ref-type="bibr">18</xref>
<xref rid="JR22080374-19" ref-type="bibr">19</xref>
<xref rid="JR22080374-20" ref-type="bibr">20</xref>
Previous studies reveal an increase in platelet- and (to a lesser extent) RBC-derived microvesicles in splenectomized patients, which could indicate changes in their life cycle or activation state.
<xref rid="JR22080374-21" ref-type="bibr">21</xref>
At the same time, the levels of negatively charged prothrombotic phospholipids, like phosphatidylserine, in pulmonary embolism increase after splenectomy.
<xref rid="JR22080374-22" ref-type="bibr">22</xref>
<xref rid="JR22080374-23" ref-type="bibr">23</xref>
<xref rid="JR22080374-24" ref-type="bibr">24</xref>
Among others, RBCs can contribute to phosphatidylserine exposure.
<xref rid="JR22080374-25" ref-type="bibr">25</xref>
Old, rigid RBCs with modified phospholipid exposure promote thrombus formation; however, their relevance for DVT in vivo remains unclear.
<xref rid="JR22080374-20" ref-type="bibr">20</xref>
<xref rid="JR22080374-25" ref-type="bibr">25</xref>
<xref rid="JR22080374-26" ref-type="bibr">26</xref>
</p><p>
In this study, we investigated the effect
<bold>s</bold>
of short-term EPO administration compared to chronic intrinsic EPO overproduction and the interference with RBC clearance on experimental venous thrombosis. We found that chronic intrinsic EPO overproduction resulted in excessive venous thrombosis. In this setting, platelets and leukocytes were reduced in thrombi, while RBC accumulation was markedly increased. In contrast, short-term EPO administration had no effect on DVT. Interference with RBC clearance by splenectomy had no effect on DVT, either in cases of chronic EPO overproduction or in wild-type (WT) mice. In summary, our data indicate that only long-term and excessively increased EPO levels affect DVT formation in mice, independent of splenic clearance of RBCs.
</p></sec><sec><title>Methods</title><sec><title>Mouse Model</title><p>
C57BL/6 mice were obtained from Jackson Laboratory. Human EPO-overexpressing mice were generated as previously described.
<xref rid="JR22080374-14" ref-type="bibr">14</xref>
TgN(PDGFBEPO)321Zbz consists of a transgenic mouse line, TgN(PDGFBEPO)321Zbz, expresses human EPO cDNA, and was initially reported by Ruschitzka et al,
<xref rid="JR22080374-14" ref-type="bibr">14</xref>
subsequently named Tg(EPO). The expression is regulated by the platelet-derived growth factor promotor. We used the corresponding WT littermate controls named as WT.
<xref rid="JR22080374-27" ref-type="bibr">27</xref>
Sex- and age-matched groups were used for the experiments with an age limit ranging between 12 and 29 weeks. The mice were housed in a specific-pathogen-free environment in our animal facility. General anesthesia was induced using a mixture of inhaled isoflurane, intravenous fentanyl, medetomidine, and midazolam. All procedures performed on mice were conducted in accordance with local legislation for the protection of animals (Regierung von Oberbayern, Munich) and were authorized accordingly.
</p></sec><sec><title>Stenosis of the Inferior Vena Cava</title><p>The operation was carried out under general anesthesia. The procedure involved median laparotomy to access the inferior vena cava (IVC). A suture was placed around the IVC and ligated below the renal vein. To prevent complete stasis, a placeholder with a diameter of about 0.5&#x02009;mm was inserted into the loop and subsequently removed after tightening. Side branches of the IVC were not ligated. As result, intravascular flow reduction occurred, ultimately leading to thrombus formation. Thrombus quantification was conducted by removing the IVC (segment between the renal vein and the confluence of the common iliac veins). The incidence and weight of the thrombus were documented.</p></sec><sec><title>Acute and Chronic EPO Experiments</title><p>To analyze the effect of short-term EPO administration on DVT formation, we subcutaneously (s.c.) injected 300&#x02009;IU (10&#x02009;IU/&#x000b5;L) EPO (Epoetin alfa HEXAL) three times a week into the gluteal region of C57Bl/6J mice purchased from the Jackson Laboratory. The injections were carried out for a duration of 2 weeks resulting in a total of six EPO treatments. After the completion of the 2-week EPO administration, there was a gap of 2 days before ligating the IVC. The ligation procedure was performed when the mice were 18 weeks old. The control group consisted of age- and sex-matched mice that received the same volume (30 &#x000b5;L) of a 0.9% NaCl solution per dosage.</p></sec><sec><title>Ultrasound Analysis of Myocardial Performance</title><p>The cardiac ultrasound analysis was conducted using a Vevo 2100 Imaging System (Visualsonics). To ensure sufficient tolerance during the investigation, short-term inhalation anesthesia (Isofluran CP, cp pharma) was administered during the investigation. Subsequently, the mice were then positioned on their back, and transthoracic echocardiography was performed.</p></sec><sec><title>Intracardial Blood Withdrawal</title><p>Blood was collected from adequately anesthesized mice through cardiac puncture using a syringe containing citrate as an anticoagulant.</p></sec><sec><title>Blood Cell Counts</title><p>Blood cell counts were determined in citrated blood using an automated cell counter (ABX Micros ES60, Horiba ABX).</p></sec><sec><title>Splenectomy</title><p>To remove the spleen, the mice were anesthetized as previously described. A lateral subcostal incision was made, followed by ligation and cutting of the splenic vessels. Subsequently, the spleen was removed and the surgical wound was closed with sutures. The organ removal procedure was performed 5 weeks prior to subsequent experiments, such as ligation of the IVC.</p></sec><sec><title>Immunofluorescence Staining of Frozen Sections</title><p>After harvesting thrombi, the organic material was embedded in OCT, rapidly frozen in liquid nitrogen, and stored at &#x02212;80&#x000b0;C. Subsequently, 5&#x02009;&#x000b5;m slides were sectioned using a cryotome (CryoStar NX70 Kryostat, Thermo Fisher Scientific). The staining procedure began with a fixation step using 4% ethanol-free formaldehyde (Thermo Fisher; #28908), followed by blocking with goat serum (Thermo Fisher; #50062Z). The following antibodies were used: CD41 (clone: MWReg30, BD Bioscience; #12-0411-83; isotype: rat IgG1), Fibrin(-ogen) (clone: polyclonal; DAKO; #A0080; isotype: rabbit IgG), Ly6G (clone: 1A8, Thermo Fisher; #12-9668-82; isotype: rat IgG2a), MPO (polyclonal, Dako; #A0398; isotype: rabbit IgG), TER119 (clone: TER-119; Thermo Fisher; #12-5921-83; isotype: rat IgG2b). Alexa-labeled secondary antibodies were used to induce fluorescence (Invitrogen; #A11007; #A11034). Nuclei were marked using Hoechst (ThermoFisher; #H3570). Image acquisition was performed on an AxioImager M2 (Carl Zeiss Microscopy) using corresponding AxioVision SE65 software. Near-field analysis of fibrin fibers was captured on an inverted Zeiss LSM 880 confocal microscope in AiryScan Super Resolution (SR) Mode (magnification, &#x000d7;63 objective, with 5 to 6 random images acquired per thrombus). Further structural analysis of the fibrin fibers was conducted using Imaris (Oxford instruments). To quantify neutrophil extracellular traps, we identified DNA protrusions (Hoechst-positive) originating from Ly6G-positive cells and covered by MPO.</p></sec><sec><title>Statistics</title><p>
Statistical analysis was conducted using GraphPad Prism 5, employing a
<italic>t</italic>
-test. Based on clinical observations strongly suggesting an increase in thrombus formation in EPO overproducing mice, a one-sided
<italic>t</italic>
-test was performed.
<xref rid="JR22080374-8" ref-type="bibr">8</xref>
<xref rid="JR22080374-9" ref-type="bibr">9</xref>
The normal distribution of the data was confirmed using D'Agostino and Pearson omnibus normality testing. Thrombus incidences between groups were compared using the chi-square test.
</p></sec></sec><sec><title>Results</title><sec><title>Chronic EPO Overproduction Leads to Increased DVT in Mice</title><p>
To investigate the impact of chronic erythrocyte overproduction on DVT in mice, we analyzed EPO-overexpressing transgenic Tg(EPO) mice. As expected, this mouse strain exhibited a substantial increase in RBC count (
<xref rid="FI22080374-1" ref-type="fig">Fig. 1A</xref>
). Additionally, the RBC width coefficient and reticulocyte count were elevated, indicating enhanced RBC production (
<xref rid="SM22080374-1" ref-type="supplementary-material">Supplementary Fig. S1A, B</xref>
[available in the online version]). In addition to influencing the RBC lineage, our analyses revealed a significant increase in white blood cell (WBC) count, primarily driven by elevated lymphocyte count (
<xref rid="SM22080374-1" ref-type="supplementary-material">Supplementary Fig. S1C, E</xref>
[available in the online version]). However, neutrophils known as major contributors to venous thrombosis showed no significant changes in EPO transgenic mice, while platelet counts were significantly reduced (
<xref rid="FI22080374-1" ref-type="fig">Fig. 1B</xref>
and
<xref rid="SM22080374-1" ref-type="supplementary-material">Supplementary Fig. S1D</xref>
[available in the online version]). Furthermore, autopsies of the animals confirmed the presence of splenomegaly (
<xref rid="SM22080374-1" ref-type="supplementary-material">Supplementary Fig. S1F</xref>
[available in the online version]).
<xref rid="JR22080374-28" ref-type="bibr">28</xref>
<xref rid="JR22080374-29" ref-type="bibr">29</xref>
</p><fig id="FI22080374-1"><label>Fig. 1</label><caption><p>
EPO-overexpressing mice experience an increased incidence of DVT formation. (
<bold>A</bold>
) Comparison of RBC count between EPO-overexpressing Tg(EPO) mice (
<italic>n</italic>
&#x02009;=&#x02009;12) and control (WT) mice (
<italic>n</italic>
&#x02009;=&#x02009;13). (
<bold>B</bold>
) Comparison of platelet count between EPO-overexpressing Tg(EPO) (
<italic>n</italic>
&#x02009;=&#x02009;7) mice and control (WT) mice (
<italic>n</italic>
&#x02009;=&#x02009;5). (
<bold>C</bold>
) Comparison of thrombus weight between Tg(EPO) mice (
<italic>n</italic>
&#x02009;=&#x02009;9) and WT (
<italic>n</italic>
&#x02009;=&#x02009;10); mean age in the Tg(EPO) group: 19.8 weeks; mean age in the WT group: 20.1 weeks. (
<bold>D</bold>
) Comparison of thrombus incidence between Tg(EPO) mice (
<italic>n</italic>
&#x02009;=&#x02009;9) and WT mice (
<italic>n</italic>
&#x02009;=&#x02009;10); NS&#x02009;=&#x02009;nonsignificant, *
<italic>p</italic>
&#x02009;&#x0003c;&#x02009;0.05, **
<italic>p</italic>
&#x02009;&#x0003c;&#x02009;0.01, ***
<italic>p</italic>
&#x02009;&#x0003c;&#x02009;0.001. DVT, deep vein thrombosis; EPO, erythropoietin; RBC, red blood cell; WT, wild type.
</p></caption><graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="10-1055-s-0043-1775965-i22080374-1"/></fig><p>
Based on clinical observations indicating a correlation between high EPO levels and increased incidence of DVT, we utilized an IVC stenosis model to evaluate venous thrombosis in EPO-overexpressing mice.
<xref rid="JR22080374-6" ref-type="bibr">6</xref>
<xref rid="JR22080374-7" ref-type="bibr">7</xref>
Our findings revealed a significant elevation in both the incidence and thrombus weight in Tg(EPO) mice compared to their WT littermates (
<xref rid="FI22080374-1" ref-type="fig">Fig. 1C, D</xref>
). To determine whether chronic EPO overproduction in transgenic mice affected cardiac function, we assessed parameters such as the left ventricular ejection fraction, fractional shortening, and heart rate, ruling out any alternations (
<xref rid="SM22080374-1" ref-type="supplementary-material">Supplementary Fig. S1G, H, J</xref>
[available in the online version]), which aligns with previous publications.
<xref rid="JR22080374-30" ref-type="bibr">30</xref>
Additionally, morphological parameters including left ventricular mass, left ventricular internal diameter end diastole, and inner ventricular end diastolic septum diameter were similar between Tg(EPO) and WT mice (
<xref rid="SM22080374-1" ref-type="supplementary-material">Supplementary Fig. S1I, K, L</xref>
[available in the online version]).
</p></sec><sec><title>High RBC Count Leads to a Decrease in Platelet Accumulation in Venous Thrombosis</title><p>
Having observed a correlation between high EPO and hematocrit levels with increased thrombus formation, our aim was to investigate the factors involved in triggering thrombus development through histologic analysis of thrombus composition. In Tg(EPO) mice, the elevated hematocrit levels led to enhanced RBC accumulation within the thrombus, as indicated by the Ter119-covered area measurement (
<xref rid="FI22080374-2" ref-type="fig">Fig. 2A</xref>
). Given the interaction between RBCs and platelets, which can initiate coagulation activation, we examined the distribution of fibrinogen in relation to RBCs and platelets within the thrombi.
<xref rid="JR22080374-31" ref-type="bibr">31</xref>
Our findings revealed a close association between the fibrinogen signal and RBCs, as well as between the platelet signal and RBCs, indicating interactions among these three factors (
<xref rid="FI22080374-2" ref-type="fig">Fig. 2E, F</xref>
). However, we observed significantly lower fibrinogen coverage in thrombi from EPO transgenic mice (
<xref rid="FI22080374-2" ref-type="fig">Fig. 2B</xref>
). Furthermore, the structure of the fibrin meshwork exhibited an overall &#x0201c;looser&#x0201d; morphology with significantly thinner fibrin fibers (
<xref rid="FI22080374-3" ref-type="fig">Fig. 3A-C</xref>
).
</p><fig id="FI22080374-2"><label>Fig. 2</label><caption><p>
Chronic overproduction of EPO in mice leads to a decrease in the accumulation of classical drivers of DVT formation, including platelets, neutrophils, and fibrinogen. (
<bold>A</bold>
) The proportion of RBC-covered area in the thrombi of EPO-overexpressing Tg(EPO) mice (
<italic>n</italic>
&#x02009;=&#x02009;4) was compared to control (WT) (
<italic>n</italic>
&#x02009;=&#x02009;3) by immunofluorescence staining of cross-sections of the IVC 48&#x02009;hours after flow reduction. (
<bold>B</bold>
) The proportion of fibrinogen-covered area in the thrombi of EPO- overexpressing Tg(EPO) mice (
<italic>n</italic>
&#x02009;=&#x02009;3) was compared to control (WT) (
<italic>n</italic>
&#x02009;=&#x02009;3) using immunofluorescence staining of cross-sections of the IVC 48&#x02009;hours after flow reduction. (
<bold>C</bold>
) The proportion of platelet-covered area in the thrombi of EPO-overexpressing Tg(EPO) mice (
<italic>n</italic>
&#x02009;=&#x02009;3) was compared to control (WT) (
<italic>n</italic>
&#x02009;=&#x02009;3) by immunofluorescence staining of cross-sections of the IVC 48&#x02009;hours after flow reduction. (
<bold>D</bold>
) Quantification of neutrophils was performed by immunofluorescence staining of cross-sections of the IVC 48&#x02009;hours after flow reduction in EPO-overexpressing Tg(EPO) mice (
<italic>n</italic>
&#x02009;=&#x02009;3) compared to control (WT) (
<italic>n</italic>
&#x02009;=&#x02009;3). (
<bold>E</bold>
) Immunofluorescence staining of cross-sections of the IVC 48&#x02009;hours after flow reduction from EPO-overexpressing Tg(EPO) mice (top) was compared to control (WT) (bottom) for TER119 in red (RBC), CD42b in green (platelets), and Hoechst in blue (DNA). The merged image is on the left, and the single channel image is on the right. Scale bar: 50&#x02009;&#x000b5;m. (
<bold>F</bold>
) Immunofluorescence staining of cross-sections of the IVC 48&#x02009;hours after flow reduction from EPO-overexpressing Tg(EPO) mice (top) was compared to control (WT) (bottom) for TER119 in red (RBC), fibrinogen in green, and Hoechst in blue (DNA); the merged image is on the left, and single-channel images are on the right. Scale bar: 50&#x02009;&#x000b5;m.; NS&#x02009;=&#x02009;nonsignificant, *
<italic>p</italic>
&#x02009;&#x0003c;&#x02009;0.05, **
<italic>p</italic>
&#x02009;&#x0003c;&#x02009;0.01, ***
<italic>p</italic>
&#x02009;&#x0003c;&#x02009;0.001. DVT, deep vein thrombosis; EPO, erythropoietin; IVC, inferior vena cava; RBC, red blood cell; WT, wild type.
</p></caption><graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="10-1055-s-0043-1775965-i22080374-2"/></fig><fig id="FI22080374-3"><label>Fig. 3</label><caption><p>
The interaction of RBC with fibrinogen leads to the formation of a branched fibrin structure. (
<bold>A</bold>
) Immunofluorescence staining of cross-sections of the IVC 48&#x02009;hours after flow reduction from EPO-overexpressing Tg(EPO) mice (left) was compared to control (WT) for fibrinogen (green). Scare bar: 50&#x02009;&#x000b5;m. (
<bold>B</bold>
) High-resolution confocal images of immunofluorescence staining of cross-sections of the IVC 48&#x02009;hours after flow reduction from EPO-overexpressing Tg(EPO) mice (left) compared to control (WT) for fibrinogen (green), RBC (red), and DNA (blue). Scale bar: 2&#x02009;&#x000b5;m. (
<bold>C</bold>
) The mean diameter of 2,141 fibrin fibers was measured in cross-sections of thrombi from Tg(EPO) mice (left), and compared to 5,238 fibrin fibers of WT thrombi. (
<bold>D</bold>
) The mean diameter of 3,797 fibrin fibers was measured in two cross-sections of thrombi from 2-week EPO-injected mice (left), and compared to 10,920 fibrin fibers of three cross-sections from control (2-week NaCl-injected mice). (
<bold>E</bold>
) High
<bold>-</bold>
resolution confocal images of immunofluorescence staining of two cross-sections of the IVC 48&#x02009;hours after flow reduction from 2-week EPO-injected mice (left) were compared to control (2 week NaCl-injected mice) for fibrinogen (green), RBC (red), and DNA (blue). Scale bar: 2&#x02009;&#x000b5;m. EPO, erythropoietin; IVC, inferior vena cava; RBC, red blood cell; WT, wild type.
</p></caption><graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="10-1055-s-0043-1775965-i22080374-3"/></fig><p>
To quantify platelet accumulation in thrombi, we analyzed the CD41-covered area in thrombi of both mouse strains. Consistent with the reduced platelet count in peripheral blood, platelet accumulation was also decreased in thrombi from EPO transgenic mice (
<xref rid="FI22080374-2" ref-type="fig">Fig. 2C</xref>
).
</p><p>
As mentioned previously, inflammation plays a fundamental role in DVT formation. Therefore, we conducted an analysis to quantify the presence of leukocytes in the thrombus material. Our investigation focused specifically on neutrophils, as they represent the predominant leukocyte population in peripheral blood. Despite observing normal neutrophil counts, we identified a significant reduction in neutrophil recruitment within thrombi from EPO transgenic mice (
<xref rid="FI22080374-2" ref-type="fig">Fig. 2D</xref>
). In summary, our findings indicate an isolated increase in the number of RBCs within venous thrombi of EPO transgenic mice, while the levels of fibrinogen and platelets were decreased.
</p></sec><sec><title>Short-Term Administration of EPO Does Not Foster DVT</title><p>
Due to the significant impact of chronic EPO overproduction in Tg(EPO) mice on peripheral blood count and its detrimental consequences on DVT formation, we proceeded to analyze the effects of 2-week periodic EPO injections on blood count and subsequent DVT formation in WT mice. Within just 2 weeks, a significant increase of RBC and reticulocyte count in peripheral blood was observed (
<xref rid="FI22080374-4" ref-type="fig">Fig. 4A</xref>
and
<xref rid="SM22080374-1" ref-type="supplementary-material">Supplementary Fig. S2A</xref>
[available in the online version]). Conversely, platelet count exhibited a notable decrease in EPO-treated mice (
<xref rid="FI22080374-4" ref-type="fig">Fig. 4B</xref>
). Unlike EPO-overexpressing mice, the leukocyte counts and their differentiation into granulocytes, lymphocytes, and monocytes showed no differences between EPO-treated and nontreated mice (
<xref rid="SM22080374-1" ref-type="supplementary-material">Supplementary Fig. S2B&#x02013;E</xref>
[available in the online version]). Autopsy analyses further revealed a significant enlargement and weight increase of the spleen in EPO-treated mice (
<xref rid="SM22080374-1" ref-type="supplementary-material">Supplementary Fig. S2F, G</xref>
[available in the online version]).
</p><fig id="FI22080374-4"><label>Fig. 4</label><caption><p>
Two-week EPO injection leads to thrombocytopenia without an impact on the bone marrow. (
<bold>A</bold>
) RBC count in peripheral blood after 6&#x02009;&#x000d7;&#x02009;300&#x02009;IU EPO treatment of C57Bl/6J mice (
<italic>n</italic>
&#x02009;=&#x02009;10) was compared to control (6&#x02009;&#x000d7;&#x02009;30 &#x000b5;L NaCl injection) (
<italic>n</italic>
&#x02009;=&#x02009;9). (
<bold>B</bold>
) Platelet count in peripheral blood after 6&#x02009;&#x000d7;&#x02009;300&#x02009;IU EPO treatment of C57Bl/6J mice (
<italic>n</italic>
&#x02009;=&#x02009;10) was compared to control (6&#x02009;&#x000d7;&#x02009;30 &#x000b5;L NaCl injection) (
<italic>n</italic>
&#x02009;=&#x02009;10). (
<bold>C</bold>
) Area of RBC-positive area in the bone marrow of 6&#x02009;&#x000d7;&#x02009;300&#x02009;IU EPO
<bold>-</bold>
treated C57Bl/6J mice (
<italic>n</italic>
&#x02009;=&#x02009;4) was compared to control (6&#x02009;&#x000d7;&#x02009;30 &#x000b5;L NaCl injection) (
<italic>n</italic>
&#x02009;=&#x02009;4). (
<bold>D</bold>
) Immunofluorescence staining of cross-sections of the bone marrow after 2-week EPO injection (top) was compared to NaCl injection (bottom) stained for TER119 (violet) and Hoechst (white). Scale bar: 100&#x02009;&#x000b5;m. (
<bold>E</bold>
) Number of megakaryocyte count in the bone marrow of 6&#x02009;&#x000d7;&#x02009;300&#x02009;IU EPO-treated C57Bl/6J mice (
<italic>n</italic>
&#x02009;=&#x02009;4) was compared to control (6&#x02009;&#x000d7;&#x02009;30 &#x000b5;L NaCl injection) (
<italic>n</italic>
&#x02009;=&#x02009;4). (
<bold>F</bold>
) Immunofluorescence staining of cross-sections of the bone marrow after 2-week EPO injection (top) compared to NaCl injection (bottom) stained for CD41 (violet) and Hoechst (white). Scale bar: 100&#x02009;&#x000b5;m. (
<bold>G</bold>
) Platelet large cell ratio in peripheral blood of 6&#x02009;&#x000d7;&#x02009;300&#x02009;IU EPO
<bold>-</bold>
treated C57Bl/6J mice (
<italic>n</italic>
&#x02009;=&#x02009;10) compared to control (6&#x02009;&#x000d7;&#x02009;30 &#x000b5;L NaCl injection) (
<italic>n</italic>
&#x02009;=&#x02009;9). (
<bold>H</bold>
) Thrombus weight of 6&#x02009;&#x000d7;&#x02009;300&#x02009;IU EPO
<bold>-</bold>
treated C57Bl/6J mice (
<italic>n</italic>
&#x02009;=&#x02009;10) and NaCl-injected control mice (
<italic>n</italic>
&#x02009;=&#x02009;10). (
<bold>I</bold>
) Thrombus incidence of 6&#x02009;&#x000d7;&#x02009;300&#x02009;IU EPO-treated C57Bl/6J mice (
<italic>n</italic>
&#x02009;=&#x02009;10) and NaCl-injected control mice (
<italic>n</italic>
&#x02009;=&#x02009;10). NS&#x02009;=&#x02009;nonsignificant, *
<italic>p</italic>
&#x02009;&#x0003c;&#x02009;0.05, **
<italic>p</italic>
&#x02009;&#x0003c;&#x02009;0.01, ***
<italic>p</italic>
&#x02009;&#x0003c;&#x02009;0.001. EPO, erythropoietin; RBC, red blood cell.
</p></caption><graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="10-1055-s-0043-1775965-i22080374-4"/></fig><p>
To further investigate the underlying cause of thrombocytopenia in EPO-treated mice, we examined the bone marrow composition. Previous studies by Shibata et al demonstrated a reduction in megakaryocytes in Tg(EPO) mice.
<xref rid="JR22080374-32" ref-type="bibr">32</xref>
Therefore, we analyzed the bone marrow composition after 2 weeks of EPO treatment. However, we found no difference in the TER119-covered area, indicating no significant alternation (
<xref rid="FI22080374-4" ref-type="fig">Fig. 4C, D</xref>
). Similarly, the megakaryocyte count in the bone marrow showed no changes compared to the control group (
<xref rid="FI22080374-4" ref-type="fig">Fig. 4E, F</xref>
,). In terms of platelet morphology, we observed an increased platelet large cell ratio in the EPO-treated group (
<xref rid="FI22080374-4" ref-type="fig">Fig. 4G</xref>
). This suggests an elevated production potential of megakaryocytes, as immature platelets tend to have larger cell volumes compared to mature platelets.
<xref rid="JR22080374-33" ref-type="bibr">33</xref>
These findings indicate that our EPO administration protocol enhanced the synthesis capacity of bone marrow stem cells, resulting in augmented erythropoiesis. However, the cellular composition of the bone marrow remained unchanged after 2 weeks of treatment.
</p><p>
To analyze the impact of 2-week EPO treatment on DVT formation, we utilized the IVC stenosis model. Despite similar changes in blood count in Tg(EPO) mice or WT mice after EPO administration, we observed comparable venous thrombus formation between mice treated with EPO for 2 weeks and the control group treated with NaCl (
<xref rid="FI22080374-4" ref-type="fig">Fig. 4H, I</xref>
). Since we previously observed that only long-term elevation of EPO levels with supraphysiologic hematocrit leads to increased thrombus formation, our focus shifted toward identifying the factors triggering thrombus formation. Therefore, we conducted a histological analysis of thrombus composition. Given the significantly thinner fibrin fibers observed in thrombi from Tg(EPO) mice, we investigated whether similar morphological changes occurred in mice treated with EPO for 2 weeks. Interestingly, the histological examination of the thrombi revealed a comparable thinning of fibrin fibers following EPO treatment (
<xref rid="FI22080374-3" ref-type="fig">Fig. 3D, E</xref>
).
</p><p>In contrast to chronic EPO overproduction in Tg(EPO) mice, short-term administration of EPO does not increase the incidence of DVT, despite similar changes in blood cell counts. Therefore, the quantitative changes in blood count alone cannot explain the increased thrombosis observed in the presence of EPO overexpression in Tg(EPO) mice.</p></sec><sec><title>Splenectomy Does Not Affect Venous Thrombus Formation</title><p>
As the data suggested a qualitative change in RBCs in the context of EPO overproduction, we investigated whether splenic clearance of aged RBCs plays a critical role in the increased formation of DVT. In the spleen, aged and damaged RBCs are eliminated, ensuring the presence of young and flexible RBCs.
<xref rid="JR22080374-16" ref-type="bibr">16</xref>
We examined the immediate impact of EPO on spleen morphology. Even a single injection of 300&#x02009;IU EPO s.c. in mice resulted in a significant increase in spleen weight, despite no difference in blood count compared to the control group (
<xref rid="SM22080374-1" ref-type="supplementary-material">Supplementary Fig. S2F&#x02013;H</xref>
[available in the online version]). This striking phenotype was also observed in mice with chronic EPO overexpression (
<xref rid="SM22080374-1" ref-type="supplementary-material">Supplementary Fig. S1F</xref>
[available in the online version]).
<xref rid="JR22080374-13" ref-type="bibr">13</xref>
</p><p>
To investigate the role of splenic RBC clearance in DVT, we performed splenectomy 5 weeks prior to conducting the IVC stenosis model. Firstly, we analyzed the impact of splenectomy on blood cell counts in WT mice 5 weeks postsurgery. We observed an increase in granulocytes and lymphocytes after splenectomy (
<xref rid="FI22080374-5" ref-type="fig">Fig. 5A</xref>
and
<xref rid="SM22080374-1" ref-type="supplementary-material">Supplementary Fig. S3A, B</xref>
[available in the online version]). Next, we examined the distribution of blood cells in response to DVT development. Similar to nonsplenectomized mice, we observed an increase in WBC count in the peripheral blood (
<xref rid="FI22080374-5" ref-type="fig">Fig. 5A</xref>
). Additionally, we noted a significant decrease in platelet count in splenectomized mice in response to thrombus development (
<xref rid="FI22080374-5" ref-type="fig">Fig. 5B</xref>
), which is consistent with the results obtained from nonsplenectomized mice.
</p><fig id="FI22080374-5"><label>Fig. 5</label><caption><p>
Splenectomy does not affect the blood count as well as DVT formation. (
<bold>A</bold>
) WBC count in C57Bl/6J mice without treatment (
<italic>n</italic>
&#x02009;=&#x02009;5), 48&#x02009;hours after induction of DVT (
<italic>n</italic>
&#x02009;=&#x02009;7), 5 weeks after splenectomy (
<italic>n</italic>
&#x02009;=&#x02009;3), and 5 weeks after splenectomy with an additional 48-hour induction of DVT (
<italic>n</italic>
&#x02009;=&#x02009;9). (
<bold>B</bold>
) Platelet count in C57Bl/6J mice without treatment (
<italic>n</italic>
&#x02009;=&#x02009;6), 48&#x02009;hours after induction of DVT (
<italic>n</italic>
&#x02009;=&#x02009;6), 5 weeks after splenectomy (
<italic>n</italic>
&#x02009;=&#x02009;3), and 5 weeks after splenectomy with an additional 48-hour induction of DVT (
<italic>n</italic>
&#x02009;=&#x02009;9). (
<bold>C</bold>
) RBC count in C57Bl/6J mice without treatment (
<italic>n</italic>
&#x02009;=&#x02009;6), 48&#x02009;hours after induction of DVT (
<italic>n</italic>
&#x02009;=&#x02009;6), 5 weeks after splenectomy (
<italic>n</italic>
&#x02009;=&#x02009;3), and 5 weeks after splenectomy with an additional 48-hour induction of DVT (
<italic>n</italic>
&#x02009;=&#x02009;9). (
<bold>D</bold>
) Thrombus weight in C57Bl6 wild-type mice without splenectomy (
<italic>n</italic>
&#x02009;=&#x02009;6) and with splenectomy (
<italic>n</italic>
&#x02009;=&#x02009;6) (
<bold>E</bold>
) Thrombus incidence in C57Bl/6J wild-type mice without splenectomy (
<italic>n</italic>
&#x02009;=&#x02009;6) and with splenectomy (
<italic>n</italic>
&#x02009;=&#x02009;6). (
<bold>F</bold>
) Thrombus weight in EPO-overexpressing Tg(EPO) mice without splenectomy (
<italic>n</italic>
&#x02009;=&#x02009;9) and with splenectomy (
<italic>n</italic>
&#x02009;=&#x02009;6) compared to control WT mice without splenectomy (
<italic>n</italic>
&#x02009;=&#x02009;10) and with splenectomy (
<italic>n</italic>
&#x02009;=&#x02009;11). (
<bold>G</bold>
) Thrombus incidence in EPO-overexpressing Tg(EPO) mice without splenectomy (
<italic>n</italic>
&#x02009;=&#x02009;9) and with splenectomy (
<italic>n</italic>
&#x02009;=&#x02009;6) compared to control WT mice without splenectomy (
<italic>n</italic>
&#x02009;=&#x02009;10) and with splenectomy (
<italic>n</italic>
&#x02009;=&#x02009;11). NS&#x02009;=&#x02009;nonsignificant, *
<italic>p</italic>
&#x02009;&#x0003c;&#x02009;0.05, **
<italic>p</italic>
&#x02009;&#x0003c;&#x02009;0.01, ***
<italic>p</italic>
&#x02009;&#x0003c;&#x02009;0.001. DVT, deep vein thrombosis; RBC, red blood cell; WT, wild type.
</p></caption><graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="10-1055-s-0043-1775965-i22080374-5"/></fig><p>
Finally, we analyzed the impact of splenectomy on DVT formation in both WT mice and Tg(EPO) mice. Despite changes in blood cell counts and the effects on platelet removal, there was no difference in the incidence and thrombus weight in C57Bl/6 mice (
<xref rid="FI22080374-5" ref-type="fig">Fig. 5D, E</xref>
). Next, we examined EPO-overexpressing mice, which have been shown to have an increased risk of DVT formation. Despite significant splenomegaly, the incidence of DVT formation remained statistically unchanged after spleen removal (
<xref rid="FI22080374-5" ref-type="fig">Fig. 5F, G</xref>
). Therefore, splenectomy does not affect thrombus formation in the context of enhanced or normal erythropoiesis.
</p></sec></sec><sec><title>Discussion</title><p>
Here, we present evidence for a differential thrombotic effect of chronic EPO overproduction and short-term external EPO administration. Consistent with clinical observations, chronic overproduction of EPO is associated with an increased risk of DVT formation. This is similar to Chuvash polycythemia where the von-Hippel&#x02013;Lindau mutation leads to chronic overproduction of hypoxia-induced factors and high EPO levels.
<xref rid="JR22080374-34" ref-type="bibr">34</xref>
In addition to genetically altered EPO production, factors such as residence at high altitudes and naturally increasing EPO secretion also represent risk factors for venous thrombosis and pulmonary thromboembolism.
<xref rid="JR22080374-35" ref-type="bibr">35</xref>
<xref rid="JR22080374-36" ref-type="bibr">36</xref>
These conditions can be mimicked in a mouse model through chronic hypoxia.
<xref rid="JR22080374-37" ref-type="bibr">37</xref>
</p><p>Therefore, it is highly probable that EPO and RBC play significant roles in DVT formation. In fact, our data suggest that qualitative changes in RBC, rather than solely quantitative changes, are responsible for the increased occurrence of venous thrombus formation.</p><p>
In our analyses, we observed that short-term administration of EPO does not increase the risk of DVT, in contrast to chronic overproduction of EPO. However, changes in peripheral blood count in response to EPO occur relatively quickly, within 2 weeks of initiating therapy in mice. These changes include elevated levels of hemoglobin and thrombocytopenia, which are consistent with previous studies.
<xref rid="JR22080374-17" ref-type="bibr">17</xref>
<xref rid="JR22080374-22" ref-type="bibr">22</xref>
<xref rid="JR22080374-38" ref-type="bibr">38</xref>
<xref rid="JR22080374-39" ref-type="bibr">39</xref>
<xref rid="JR22080374-40" ref-type="bibr">40</xref>
<xref rid="JR22080374-41" ref-type="bibr">41</xref>
<xref rid="JR22080374-42" ref-type="bibr">42</xref>
<xref rid="JR22080374-43" ref-type="bibr">43</xref>
<xref rid="JR22080374-44" ref-type="bibr">44</xref>
<xref rid="JR22080374-45" ref-type="bibr">45</xref>
In the model of transgenic overexpressing EPO mice, there was an age-dependent progressive decrease in megakaryocyte count in the bone marrow.
<xref rid="JR22080374-32" ref-type="bibr">32</xref>
A similar phenomenon can be observed in mice exposed to chronic hypoxia.
<xref rid="JR22080374-46" ref-type="bibr">46</xref>
It is believed that competition between erythroid and platelet precursors in the stem cell population is responsible for this phenomenon.
<xref rid="JR22080374-38" ref-type="bibr">38</xref>
Despite a similar decrease of peripheral platelet counts, we observed normal megakaryocyte counts in the bone marrow of mice injected with EPO for 2 weeks. We speculate that morphological changes in the bone marrow are long-term consequences of EPO administration. In peripheral blood, we observed a significant increase in the platelet large cell ratio in mice treated with EPO for 2 weeks. This is likely due to an elevated count of reticulated platelets, which has been previously observed in response to EPO treatment.
<xref rid="JR22080374-47" ref-type="bibr">47</xref>
The presence of high levels of reticulated platelets indicates a high synthetic potential of megakaryocytes. Indeed, megakaryocytes possess high-affinity binding sites for EPO resulting in an increase in size, ploidy, and number of megakaryocytes in vitro.
<xref rid="JR22080374-48" ref-type="bibr">48</xref>
<xref rid="JR22080374-49" ref-type="bibr">49</xref>
Young, reticulated platelets are known risk factors for thrombosis, which may counterbalance the overall low platelet count in terms of thrombogenicity.
<xref rid="JR22080374-50" ref-type="bibr">50</xref>
<xref rid="JR22080374-51" ref-type="bibr">51</xref>
<xref rid="JR22080374-52" ref-type="bibr">52</xref>
However, the significant increase in DVT observed in chronic EPO-overexpressing mice is likely attributed to qualitative changes in RBCs. There are several ways in which RBCs can interact with platelets and fibrin. The FAS-L-FAS-R interplay between RBCs and platelets has been shown to enhance DVT formation.
<xref rid="JR22080374-31" ref-type="bibr">31</xref>
Additionally, interactions such as ICAM-4&#x02013;&#x003b1;1b&#x003b2;3 integrin and adhesion between RBCs and platelets mediated by GPIb and CD36 have been described.
<xref rid="JR22080374-53" ref-type="bibr">53</xref>
<xref rid="JR22080374-54" ref-type="bibr">54</xref>
As demonstrated in this study, the pronounced prothrombotic effect of RBCs only manifests after several weeks to months of EPO overproduction. Thus, we propose that RBC aging plays a role in this phenomenon. This is supported by the finding that RBCs in our Tg(EPO) mouse model exhibit characteristics of accelerated aging including decreased CD47 expression, leading to a 70% reduction in lifespan.
<xref rid="JR22080374-15" ref-type="bibr">15</xref>
</p><p>
During the ageing process, RBCs not only display increasing amounts of procoagulant phosphatidylserine on their surface but also exhibit heightened osmotic and mechanical fragility, which is also observed in Tg(EPO) mice.
<xref rid="JR22080374-32" ref-type="bibr">32</xref>
<xref rid="JR22080374-55" ref-type="bibr">55</xref>
Fragile RBCs are prone to hemolysis, resulting in the release of ADP and free hemoglobin. Furthermore, hemoglobin directly or indirectly contributes to increased platelet activity, for instance, by forming complexes with nitric oxide (NO).
<xref rid="JR22080374-56" ref-type="bibr">56</xref>
<xref rid="JR22080374-57" ref-type="bibr">57</xref>
<xref rid="JR22080374-58" ref-type="bibr">58</xref>
NO is essential for the survival of Tg(EPO) mice but dispensable for WT mice.
<xref rid="JR22080374-14" ref-type="bibr">14</xref>
Consistent with this, patients with polycythemia vera exhibit platelet hypersensitivity despite normal platelet counts, while plasma haptoglobin concentration, a marker for hemolysis, is decreased.
<xref rid="JR22080374-59" ref-type="bibr">59</xref>
<xref rid="JR22080374-60" ref-type="bibr">60</xref>
<xref rid="JR22080374-61" ref-type="bibr">61</xref>
<xref rid="JR22080374-62" ref-type="bibr">62</xref>
<xref rid="JR22080374-63" ref-type="bibr">63</xref>
<xref rid="JR22080374-64" ref-type="bibr">64</xref>
<xref rid="JR22080374-65" ref-type="bibr">65</xref>
Similarly, chronic subcutaneous EPO administration in hemodialysis patients leads to a prothrombotic phenotype similar to that of polycythemia vera patients.
<xref rid="JR22080374-66" ref-type="bibr">66</xref>
<xref rid="JR22080374-67" ref-type="bibr">67</xref>
<xref rid="JR22080374-68" ref-type="bibr">68</xref>
<xref rid="JR22080374-69" ref-type="bibr">69</xref>
<xref rid="JR22080374-70" ref-type="bibr">70</xref>
Notably, concentrated RBC transfusions result in the rapid clearance of up to 30% of transfused erythrocytes within 24&#x02009;hours due to their age, thus increasing the risk of DVT formation.
<xref rid="JR22080374-5" ref-type="bibr">5</xref>
<xref rid="JR22080374-71" ref-type="bibr">71</xref>
</p><p>
Clearance of RBCs primarily occurs in the spleen, where tissue-resident macrophages screen for surface markers such as CD47.
<xref rid="JR22080374-72" ref-type="bibr">72</xref>
Subsequently, RBCs are phagocytosed before reaching day 120 of their lifespan.
<xref rid="JR22080374-73" ref-type="bibr">73</xref>
The spleen plays a crucial role in maintaining the shape and membrane resilience of RBCs, acting as a guardian in this regard.
<xref rid="JR22080374-74" ref-type="bibr">74</xref>
However, shortly after splenectomy, the loss of the organ significantly increases the risk of DVT formation.
<xref rid="JR22080374-20" ref-type="bibr">20</xref>
In the long-term basis, we observed no difference in DVT formation after splenectomy, neither in WT mice nor in chronic EPO-overexpressing mice, despite the dramatic increase in macrophage-mediated RBC clearance in these mice.
<xref rid="JR22080374-15" ref-type="bibr">15</xref>
Since RBC clearance occurs primarily in the spleen and liver in mice, we hypothesize that the liver is capable of adequately compensating for the absence of the spleen after removal.
<xref rid="JR22080374-15" ref-type="bibr">15</xref>
</p><p>
Besides their activating effect on platelets, RBCs also directly impact the coagulation system. Previous data demonstrate that following TF activation, RBCs contribute to thrombin generation to a similar extent to platelets.
<xref rid="JR22080374-75" ref-type="bibr">75</xref>
Furthermore, RBCs expose phosphatidylserine, which activates the contact pathway.
<xref rid="JR22080374-31" ref-type="bibr">31</xref>
Notably, the coagulation system in Tg(EPO) mice exhibits normal activity in whole blood adjusted to a physiological hematocrit.
<xref rid="JR22080374-32" ref-type="bibr">32</xref>
Additionally, RBCs express a receptor with properties similar to the &#x003b1;
<sub>IIb</sub>
&#x003b2;
<sub>3</sub>
integrin enabling their interaction with fibrin.
<xref rid="JR22080374-76" ref-type="bibr">76</xref>
This interaction contributes to the formation of a dense fibrin meshwork consisting of thin fibers.
<xref rid="JR22080374-77" ref-type="bibr">77</xref>
Such a structure hinders clot dissolution, leading to slower lysis.
<xref rid="JR22080374-77" ref-type="bibr">77</xref>
In our histological analysis of thrombi, we confirm morphological changes in the fibrin meshwork, resulting in a thinner appearance in both EPO-overexpressing mice and mice subjected to short-term EPO injection.
</p><p>In summary, our data suggest that chronic EPO overproduction, leading to elevated hematocrit levels, is associated with an increased incidence of venous thrombosis. This is likely attributed to qualitative changes in RBCs that promote a thrombogenic environment. On the other hand, short-term EPO administration does not pose an increased risk of venous thrombosis. Furthermore, splenic clearance of altered RBCs does not play a significant role in DVT formation. Therefore, conditions involving chronically elevated RBC production should be closely monitored due to the heightened risk of venous thrombosis.</p></sec></body><back><sec><boxed-text content-type="backinfo"><p>
<bold>What is known about this topic?</bold>
</p><list list-type="bullet"><list-item><p>Patients with high hematocrit and/or excessively increased erythropoietin (EPO) serum concentrations are particularly prone to deep vein thrombus (DVT) formation.</p></list-item><list-item><p>The spleen is an important organ in RBC and platelet clearance. Splenectomy leads to an increased risk of thromboembolic events.</p></list-item></list><p>
<bold>What does this paper add?</bold>
</p><list list-type="bullet"><list-item><p>Chronic but not short-term EPO administration/overproduction drives DVT formation in mice.</p></list-item><list-item><p>EPO-mediated DVT is mostly independent of conventional players of DVT (neutrophils, platelets) and splenic erythrocyte clearance.</p></list-item></list></boxed-text></sec><ack><title>Acknowledgment</title><p>Finally, we thank Philipp Lange for the technical support in performing ultrasound analysis of myocardial performance and on mice and Dominic van den Heuvel for the support in confocal microscopy as well as Analysis with Imaris.</p></ack><fn-group><fn fn-type="COI-statement" id="d35e179"><p><bold>Conflict of Interest</bold> None declared.</p></fn></fn-group><fn-group><title>Authors' Contribution</title><fn id="FN22080374-1"><p>K.S., S.M., and S.S. conceived and designed the experiments. S.S., I.S., A.-L.S., and S.C. planned and performed histological and immunohistochemical analysis. B.K., I.S., A.-L.S., F.W., and M.v.B. did surgery for IVC flow reduction in mice. F.W. injected EPO into C57Bl/6J mice. I.S. performed splenectomy on mice. I.S. determined platelet circulation time. B.K. performed platelet clearance essay in liver and spleen FACS experiments. I.O. provided Tg(EPO) mice. S.S. and K.S. wrote the manuscript. All the authors reviewed and edited the manuscript.</p></fn></fn-group><sec sec-type="supplementary-material"><title>Supplementary Material</title><supplementary-material id="SM22080374-1"><media xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="10-1055-s-0043-1775965-s22080374.pdf"><caption><p>Supplementary Material</p></caption><caption><p>Supplementary Material</p></caption></media></supplementary-material></sec><ref-list><title>References</title><ref id="JR22080374-1"><label>1</label><mixed-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Ramsey</surname><given-names>G</given-names></name><name><surname>Lindholm</surname><given-names>P F</given-names></name></person-group><article-title>Thrombosis risk in cancer patients receiving red blood cell transfusions</article-title><source>Semin Thromb Hemost</source><year>2019</year><volume>45</volume><issue>06</issue><fpage>648</fpage><lpage>656</lpage><pub-id pub-id-type="pmid">31430787</pub-id>
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<article xmlns:mml="http://www.w3.org/1998/Math/MathML" article-type="research-article" xml:lang="en" dtd-version="1.3"><?properties open_access?><processing-meta base-tagset="archiving" mathml-version="3.0" table-model="xhtml" tagset-family="jats"><restricted-by>pmc</restricted-by></processing-meta><front><journal-meta><journal-id journal-id-type="nlm-ta">Thromb Haemost</journal-id><journal-id journal-id-type="iso-abbrev">Thromb Haemost</journal-id><journal-id journal-id-type="doi">10.1055/s-00035024</journal-id><journal-title-group><journal-title>Thrombosis and Haemostasis</journal-title></journal-title-group><issn pub-type="ppub">0340-6245</issn><issn pub-type="epub">2567-689X</issn><publisher><publisher-name>Georg Thieme Verlag KG</publisher-name><publisher-loc>R&#x000fc;digerstra&#x000df;e 14, 70469 Stuttgart, Germany</publisher-loc></publisher></journal-meta>
<article-meta><article-id pub-id-type="pmid">38788766</article-id><article-id pub-id-type="pmc">11518616</article-id>
<article-id pub-id-type="doi">10.1055/s-0044-1786809</article-id><article-id pub-id-type="publisher-id">TH-23-12-0539</article-id><article-categories><subj-group><subject>Atherosclerosis and Ischaemic Disease</subject></subj-group></article-categories><title-group><article-title>Exploring Causal Relationships between Circulating Inflammatory Proteins and Thromboangiitis Obliterans: A Mendelian Randomization Study</article-title></title-group><contrib-group><contrib contrib-type="author"><contrib-id contrib-id-type="orcid">http://orcid.org/0000-0003-3693-6153</contrib-id><name><surname>Zhang</surname><given-names>Bihui</given-names></name><xref rid="AF23120539-1" ref-type="aff">1</xref><xref rid="FN23120539-1" ref-type="fn">*</xref><xref rid="CO23120539-2" ref-type="author-notes"/></contrib><contrib contrib-type="author"><name><surname>He</surname><given-names>Rui</given-names></name><xref rid="AF23120539-2" ref-type="aff">2</xref><xref rid="FN23120539-1" ref-type="fn">*</xref></contrib><contrib contrib-type="author"><name><surname>Yao</surname><given-names>Ziping</given-names></name><xref rid="AF23120539-1" ref-type="aff">1</xref><xref rid="FN23120539-1" ref-type="fn">*</xref></contrib><contrib contrib-type="author"><name><surname>Li</surname><given-names>Pengyu</given-names></name><xref rid="AF23120539-1" ref-type="aff">1</xref></contrib><contrib contrib-type="author"><name><surname>Niu</surname><given-names>Guochen</given-names></name><xref rid="AF23120539-1" ref-type="aff">1</xref></contrib><contrib contrib-type="author"><name><surname>Yan</surname><given-names>Ziguang</given-names></name><xref rid="AF23120539-1" ref-type="aff">1</xref></contrib><contrib contrib-type="author"><name><surname>Zou</surname><given-names>Yinghua</given-names></name><xref rid="AF23120539-1" ref-type="aff">1</xref></contrib><contrib contrib-type="author"><name><surname>Tong</surname><given-names>Xiaoqiang</given-names></name><xref rid="AF23120539-1" ref-type="aff">1</xref></contrib><contrib contrib-type="author"><name><surname>Yang</surname><given-names>Min</given-names></name><xref rid="AF23120539-1" ref-type="aff">1</xref><xref rid="CO23120539-1" ref-type="author-notes"/></contrib></contrib-group><aff id="AF23120539-1"><label>1</label><institution>Department of Interventional Radiology and Vascular Surgery, Peking University First Hospital, Beijing, China</institution></aff><aff id="AF23120539-2"><label>2</label><institution>Department of Plastic Surgery and Burn, Peking University First Hospital, Beijing, China</institution></aff><author-notes><corresp id="CO23120539-1"><bold>Address for correspondence </bold>Min Yang, MD <institution>Department of Interventional Radiology and Vascular Surgery, Peking University First Hospital</institution><addr-line>No. 8, Xishiku Street, Xicheng-Qu, Beijing, 100000</addr-line><country>China</country><email>dryangmin@gmail.com</email></corresp><corresp id="CO23120539-2">Bihui Zhang, MD <institution>Department of Interventional Radiology and Vascular Surgery, Peking University First Hospital</institution><addr-line>No. 8, Xishiku Street, Xicheng-Qu, Beijing, 100000</addr-line><country>China</country><email>dr_zhangbihui@163.com</email></corresp></author-notes><pub-date pub-type="epub"><day>24</day><month>5</month><year>2024</year></pub-date><pub-date pub-type="collection"><month>11</month><year>2024</year></pub-date><pub-date pub-type="pmc-release"><day>1</day><month>5</month><year>2024</year></pub-date><volume>124</volume><issue>11</issue><fpage>1075</fpage><lpage>1083</lpage><history><date date-type="received"><day>07</day><month>12</month><year>2023</year></date><date date-type="accepted"><day>05</day><month>4</month><year>2024</year></date></history><permissions><copyright-statement>
The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution-NonDerivative-NonCommercial License, permitting copying and reproduction so long as the original work is given appropriate credit. Contents may not be used for commercial purposes, or adapted, remixed, transformed or built upon. (
<uri xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="https://creativecommons.org/licenses/by-nc-nd/4.0/">https://creativecommons.org/licenses/by-nc-nd/4.0/</uri>
)
</copyright-statement><copyright-year>2024</copyright-year><copyright-holder>The Author(s).</copyright-holder><license><ali:license_ref xmlns:ali="http://www.niso.org/schemas/ali/1.0/" specific-use="textmining" content-type="ccbyncndlicense">https://creativecommons.org/licenses/by-nc-nd/4.0/</ali:license_ref><license-p>This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License, which permits unrestricted reproduction and distribution, for non-commercial purposes only; and use and reproduction, but not distribution, of adapted material for non-commercial purposes only, provided the original work is properly cited.</license-p></license></permissions><abstract abstract-type="graphical"><p><bold>Background</bold>
&#x02003;Thromboangiitis obliterans (TAO) is a vascular condition characterized by poor prognosis and an unclear etiology. This study employs Mendelian randomization (MR) to investigate the causal impact of circulating inflammatory proteins on TAO.
</p><p><bold>Methods</bold>
&#x02003;In this MR analysis, summary statistics from a genome-wide association study meta-analysis of 91 inflammation-related proteins were integrated with independently sourced TAO data from the FinnGen consortium's R10 release. Methods such as inverse variance weighting, MR&#x02013;Egger regression, weighted median approaches, MR-PRESSO, and multivariable MR (MVMR) analysis were utilized.
</p><p><bold>Results</bold>
&#x02003;The analysis indicated an association between higher levels of C&#x02013;C motif chemokine 4 and a reduced risk of TAO, with an odds ratio (OR) of 0.44 (95% confidence interval [CI]: 0.29&#x02013;0.67;
<italic>p</italic>
&#x02009;=&#x02009;1.4&#x02009;&#x000d7;&#x02009;10
<sup>&#x02212;4</sup>
; adjusted
<italic>p</italic>
&#x02009;=&#x02009;0.013). Similarly, glial cell line-derived neurotrophic factor exhibited a suggestively protective effect against TAO (OR: 0.43, 95% CI: 0.22&#x02013;0.81;
<italic>p</italic>
&#x02009;=&#x02009;0.010; adjusted
<italic>p</italic>
&#x02009;=&#x02009;0.218). Conversely, higher levels of C&#x02013;C motif chemokine 23 were suggestively linked to an increased risk of TAO (OR: 1.88, 95% CI: 1.21&#x02013;2.93;
<italic>p</italic>
&#x02009;=&#x02009;0.005; adjusted
<italic>p</italic>
&#x02009;=&#x02009;0.218). The sensitivity analysis and MVMR revealed no evidence of heterogeneity or pleiotropy.
</p><p><bold>Conclusion</bold>
&#x02003;This study identifies C&#x02013;C motif chemokine 4 and glial cell line-derived neurotrophic factor as potential protective biomarkers for TAO, whereas C&#x02013;C motif chemokine 23 emerges as a suggestive risk marker. These findings elucidate potential causal relationships and highlight the significance of these proteins in the pathogenesis and prospective therapeutic strategies for TAO.
</p><graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="10-1055-s-0044-1786809-i23120539-toc.jpg"/></abstract><kwd-group><title>Keywords</title><kwd>thromboangiitis obliterans</kwd><kwd>Mendelian randomization</kwd><kwd>inflammatory proteins</kwd><kwd>biomarkers</kwd><kwd>therapeutic target</kwd></kwd-group><funding-group><funding-statement><bold>Funding</bold>
This research was funded by National High Level Hospital Clinical Research Funding (Interdepartmental Research Project of Peking University First Hospital) 2023IR32, the National Natural Science Foundation of China (82200537), and the Interdisciplinary Clinical Research Project of Peking University First Hospital, grant No. 2018CR33. The APC was funded by Peking University First Hospital.
</funding-statement></funding-group></article-meta></front><body><sec><title>Introduction</title><p>
Thromboangiitis obliterans (TAO), commonly referred to as Buerger's disease, is a distinct nonatherosclerotic, segmental inflammatory disorder that predominantly affects small- and medium-sized arteries and veins in both the upper and lower extremities.
<xref rid="JR23120539-1" ref-type="bibr">1</xref>
TAO, with an annual incidence of 12.6 per 100,000 in the United States, is observed worldwide but is more prevalent in the Middle East and Far East.
<xref rid="JR23120539-1" ref-type="bibr">1</xref>
The disease typically presents in patients &#x0003c;45 years of age. Despite over a century of recognition, advancements in comprehending its etiology, pathophysiology, and optimal treatment strategies have been limited.
<xref rid="JR23120539-2" ref-type="bibr">2</xref>
<xref rid="JR23120539-3" ref-type="bibr">3</xref>
Vascular event-free survival and amputation-free survival rates at 5, 10, and 15 years are reported at 41 and 85%, 23 and 74%, and 19 and 66%, respectively.
<xref rid="JR23120539-4" ref-type="bibr">4</xref>
</p><p>
An immune-mediated response is implicated in TAO pathogenesis.
<xref rid="JR23120539-5" ref-type="bibr">5</xref>
Recent studies have identified a balanced presence of CD4+ and CD8+ T cells near the internal lamina. Additionally, macrophages and S100+ dendritic cells are present in thrombi and intimal layers.
<xref rid="JR23120539-5" ref-type="bibr">5</xref>
<xref rid="JR23120539-6" ref-type="bibr">6</xref>
Elevated levels of diverse cytokines in TAO patients highlight the critical importance of inflammatory and autoimmune mechanisms.
<xref rid="JR23120539-2" ref-type="bibr">2</xref>
<xref rid="JR23120539-7" ref-type="bibr">7</xref>
Nonetheless, the clinical significance of these cytokines is yet to be fully understood, due to the scarcity of comprehensive experimental and clinical studies. Investigating circulating inflammatory proteins could shed light on the biological underpinnings of TAO, offering new diagnostic and therapeutic avenues.
</p><p>
Mendelian randomization (MR) is an approach that leverages genetic variants associated with specific exposures to infer causal relationships between risk factors and disease outcomes.
<xref rid="JR23120539-8" ref-type="bibr">8</xref>
This method, which relies on the random distribution of genetic variants during meiosis, helps minimize confounding factors and biases inherent in environmental or behavioral influences.
<xref rid="JR23120539-9" ref-type="bibr">9</xref>
It is particularly useful in addressing limitations of conventional observational studies and randomized controlled trials, especially for rare diseases like TAO.
<xref rid="JR23120539-10" ref-type="bibr">10</xref>
For a robust MR analysis, three critical assumptions must be met: the genetic variants should be strongly associated with the risk factor, not linked to confounding variables, and affect the outcome solely through the risk factor, excluding any direct causal pathways.
<xref rid="JR23120539-10" ref-type="bibr">10</xref>
In the present study, a MR was employed to evaluate the impact of genetically proxied inflammatory protein levels on the risk of developing TAO.
</p></sec><sec><title>Materials and Methods</title><sec><title>Study Design</title><p>
The current research represents a MR analysis conducted in accordance with STROBE-MR guidelines.
<xref rid="JR23120539-11" ref-type="bibr">11</xref>
Genetic variants associated with circulating inflammatory proteins were identified from a comprehensive genome-wide meta-analysis, which analyzed 91 plasma proteins in a sample of 14,824 individuals of European descent, spanning 11 distinct cohorts.
<xref rid="JR23120539-12" ref-type="bibr">12</xref>
This study utilized the Olink Target-96 Inflammation immunoassay panel to focus on 92 inflammation-related proteins. However, due to assay issues, brain-derived neurotrophic factor was subsequently removed from the panel by Olink, resulting in the inclusion of 91 proteins in the analysis. Protein quantitative trait locus (pQTL) mapping was employed to determine genetic impacts on these inflammation-related proteins. The data on these 91 plasma inflammatory proteins, including the pQTL findings, are accessible in the EBI GWAS Catalog (accession numbers GCST90274758 to GCST90274848).
</p><p>
Flowchart of the study is shown in
<xref rid="FI23120539-1" ref-type="fig">Fig. 1</xref>
. Summary statistics for TAO in the genome-wide association study (GWAS) were derived from the FinnGen consortium R10 release (finngen_R10_I9_THROMBANG). Launched in 2017, the FinnGen study is a comprehensive nationwide effort combining genetic information from Finnish biobanks with digital health records from national registries.
<xref rid="JR23120539-13" ref-type="bibr">13</xref>
The GWAS included a substantial cohort of 412,181 Finnish participants, analyzing 21,311,942 variants, with TAO cases (114) and controls (381,977) identified according to International Classification of Diseases (ICD)-8 (44310), ICD-9 (4431A), and ICD-10 (I73.1) classifications.
</p><fig id="FI23120539-1"><label>Fig. 1</label><caption><p>
The flowchart of the study. The whole workflow of MR analysis. GWAS, genome-wide association study; TAO, thromboangiitis obliterans; SNP, single nucleotide polymorphism; MR, Mendelian randomization.
</p></caption><graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="10-1055-s-0044-1786809-i23120539-1"/></fig><p>All included studies had received approval from their respective institutional review boards and ethical committees.</p></sec><sec><title>Instrumental Variable Selection</title><p>
We employed comprehensive GWAS summary statistics for 91 inflammation-related proteins to select genetic instruments. The criteria for eligibility included: (1) single nucleotide polymorphisms (SNPs) must exhibit a genome-wide significant association with each protein (
<italic>p</italic>
&#x02009;&#x0003c;&#x02009;5.0&#x02009;&#x000d7;&#x02009;10
<sup>&#x02212;6</sup>
); (2) SNPs should be independently associated with the exposure, meaning they must not be in linkage disequilibrium (defined as
<italic>r</italic>
<sup>2</sup>
&#x02009;&#x0003c;&#x02009;0.01, distance&#x02009;&#x0003e;&#x02009;10,000&#x02009;kb) with other SNPs for the same exposure; (3) the chosen genetic instruments must account for at least 0.1% of the exposure variance, ensuring sufficient strength for the genetic instrumental variables (IVs) to assess a causal effect. For each exposure, we harmonized IVs to ensure compatibility and consistency between different data sources and variables. Since smoking is a well-accepted risk factor for TAO, SNPs that were associated with smoking or thrombo-associated events were deleted for MR due to the PhenoScanner V2 database (http://www.phenoscanner.medschl.cam.ac.uk/), details are shown in
<xref rid="SM23120539-1" ref-type="supplementary-material">Supplementary Table S1</xref>
(available in the online version).
<xref rid="JR23120539-14" ref-type="bibr">14</xref>
</p></sec><sec><title>Statistical Analysis</title><p>
The random-effects inverse variance weighted (IVW) method was used as the primary MR method to estimate the causal relationships between circulating inflammatory proteins and TAO. The IVW method offers a consistent estimate of the causal effect of exposure on the outcome, under the assumption that each genetic variant meets the IV criteria.
<xref rid="JR23120539-15" ref-type="bibr">15</xref>
<xref rid="JR23120539-16" ref-type="bibr">16</xref>
For sensitivity analysis, multiple methods, including MR&#x02013;Egger regression, MR pleiotropy Residual Sum and Outlier (MR-PRESSO), and weighted median approaches, were employed in this study to examine the robustness of results. An adaptation of MR&#x02013;Egger regression is capable of identifying certain violations of standard IV assumptions, providing an adjusted estimate that is unaffected by these issues. This method also measures the extent of directional pleiotropy and serves as a robustness check.
<xref rid="JR23120539-17" ref-type="bibr">17</xref>
The weighted median is consistent even when up to 50% of the information comes from invalid IVs.
<xref rid="JR23120539-18" ref-type="bibr">18</xref>
For SNPs numbering more than three, MRPRESSO was employed to identify and adjust for horizontal pleiotropy. This method can pinpoint horizontal pleiotropic outliers among SNPs and deliver results matching those from IVW when outliers are absent.
<xref rid="JR23120539-19" ref-type="bibr">19</xref>
Leave-one-out analysis was conducted to determine if significant findings were driven by a single SNP. To mitigate potential pleiotropic effects attributable to smoking, a multivariable MR (MVMR) analysis incorporating adjustments for genetically predicted smoking behaviors was conducted. The GWAS data pertaining to smoking were sourced from the EBI GWAS Catalog (GCST90029014), ensuring no sample overlap with the FinnGen database.
<xref rid="JR23120539-20" ref-type="bibr">20</xref>
</p><p>
Heterogeneity among individual SNP-based estimates was assessed using Cochran's Q value. In instances with only one SNP for the exposure, the Wald ratio method was applied, dividing the SNP&#x02013;outcome association estimate by the SNP&#x02013;exposure association estimate to determine the causal link. The F-statistic was estimated to evaluate the strength of each instrument, with an F-statistic greater than 10 indicating a sufficiently strong instrument.
<xref rid="JR23120539-21" ref-type="bibr">21</xref>
False discovery rate (FDR) correction was conducted by the Benjamini&#x02013;Hochberg method, with a FDR of adjusted
<italic>p</italic>
&#x02009;&#x0003c;&#x02009;0.1. A suggestive association was considered when
<italic>p</italic>
&#x02009;&#x0003c;&#x02009;0.05 but adjusted
<italic>p</italic>
&#x02265; 0.1. All analyses were two-sided and performed using the TwoSampleMR (version 0.5.8), MendelianRandomization (version 0.9.0), and MRPRESSO (version 1.0) packages in R software version 4.3.2.
</p></sec></sec><sec><title>Results</title><sec><title>Selection of Instrumental Variables</title><p>
The association between 91 circulating inflammatory proteins and TAO through the IVW method is detailed in
<xref rid="SM23120539-1" ref-type="supplementary-material">Supplementary Table S2</xref>
(available in the online version). After an extensive quality control review, 173 SNPs associated with six circulating inflammation-related proteins were identified as IVs for TAO. Notably, C&#x02013;C motif chemokine 23 (CCL23) levels were linked to 30 SNPs, C&#x02013;C motif chemokine 25 to 37 SNPs, C&#x02013;C motif chemokine 28 to 21 SNPs, C&#x02013;C motif chemokine 4 (CCL4) to 27 SNPs, glial cell line-derived neurotrophic factor (GDNF) to 22 SNPs, and stem cell factor to 36 SNPs.
</p></sec><sec><title>The Causal Role of Inflammation-Related Proteins in TAO</title><p>
Elevated genetically predicted CCL4 levels were linked to a decreased TAO risk, as shown in
<xref rid="FI23120539-2" ref-type="fig">Fig. 2</xref>
. Specifically, each unit increase in the genetically predicted level of CCL4 was associated with an odds ratio (OR) of 0.44 (95% confidence interval [CI]: 0.29&#x02013;0.67;
<italic>p</italic>
&#x02009;=&#x02009;1.4&#x02009;&#x000d7;&#x02009;10
<sup>&#x02212;4</sup>
; adjusted
<italic>p</italic>
&#x02009;=&#x02009;0.013) for TAO. Similarly, levels of C&#x02013;C motif chemokine 28 (OR: 0.33; 95% CI: 0.12&#x02013;0.91;
<italic>p</italic>
&#x02009;=&#x02009;0.034; adjusted
<italic>p</italic>
&#x02009;=&#x02009;0.579), GDNF (OR: 0.43, 95% CI: 0.22&#x02013;0.81;
<italic>p</italic>
&#x02009;=&#x02009;0.010; adjusted
<italic>p</italic>
&#x02009;=&#x02009;0.218), and stem cell factor (OR: 0.49, 95% CI: 0.29&#x02013;0.84;
<italic>p</italic>
&#x02009;=&#x02009;0.009; adjusted
<italic>p</italic>
&#x02009;=&#x02009;0.218) also showed a suggestive inverse association with TAO, as depicted in
<xref rid="FI23120539-2" ref-type="fig">Fig. 2</xref>
. Conversely, higher levels of genetically predicted CCL23 (OR: 1.88, 95% CI: 1.21&#x02013;2.93;
<italic>p</italic>
&#x02009;=&#x02009;0.005; adjusted
<italic>p</italic>
&#x02009;=&#x02009;0.218) and C&#x02013;C motif chemokine 25 (OR: 1.44, 95% CI: 1.01&#x02013;2.06;
<italic>p</italic>
&#x02009;=&#x02009;0.046; adjusted
<italic>p</italic>
&#x02009;=&#x02009;0.579) suggested an increased risk of TAO.
</p><fig id="FI23120539-2"><label>Fig. 2</label><caption><p>
Causal relationship between circulating inflammatory proteins and TAO. TAO, thromboangiitis obliterans.
</p></caption><graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="10-1055-s-0044-1786809-i23120539-2"/></fig></sec><sec><title>Sensitivity Analysis</title><p>
MR&#x02013;Egger regression intercepts were not significantly different from zero, suggesting no horizontal pleiotropy (all intercept
<italic>p</italic>
&#x02009;&#x0003e;&#x02009;0.05), as depicted in
<xref rid="FI23120539-2" ref-type="fig">Fig. 2</xref>
. The MR-PRESSO test also found no pleiotropic outliers among these SNPs (
<italic>p</italic>
&#x02009;&#x0003e;&#x02009;0.05), further corroborating the absence of pleiotropy. Consistency with these findings was confirmed by the weighted median approach. Scatter plots illustrating the genetic associations with circulating inflammatory proteins and TAO are presented in
<xref rid="FI23120539-3" ref-type="fig">Fig. 3</xref>
. Cochran's Q test detected no heterogeneity among the genetic IVs for the measured levels (all
<italic>p</italic>
&#x02009;&#x0003e;&#x02009;0.1). Additionally, funnel plots showed no significant asymmetry, suggesting negligible publication bias and directional horizontal pleiotropy (
<xref rid="FI23120539-4" ref-type="fig">Fig. 4</xref>
). The robustness of these causal estimates was further validated by a leave-one-out analysis, demonstrating that no single IV disproportionately influenced the observed causal relationships, as shown in
<xref rid="FI23120539-5" ref-type="fig">Fig. 5</xref>
.
</p><fig id="FI23120539-3"><label>Fig. 3</label><caption><p>
Scatter plots for the causal association between circulating inflammatory proteins and TAO. TAO, thromboangiitis obliterans.
</p></caption><graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="10-1055-s-0044-1786809-i23120539-3"/></fig><fig id="FI23120539-4"><label>Fig. 4</label><caption><p>
Funnel plots of circulating inflammatory proteins.
</p></caption><graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="10-1055-s-0044-1786809-i23120539-4"/></fig><fig id="FI23120539-5"><label>Fig. 5</label><caption><p>
Leave-one-out plots for the causal association between circulating inflammatory proteins and TAO. TAO, thromboangiitis obliterans.
</p></caption><graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="10-1055-s-0044-1786809-i23120539-5"/></fig></sec><sec><title>MVMR Analysis</title><p><xref rid="FI23120539-6" ref-type="fig">Fig. 6</xref>
reveals that, even after adjusting for genetically predicted smoking, the level of CCL4 still exerts a direct protective influence against TAO (IVW: OR= 0.54,
<italic>p</italic>
&#x02009;=&#x02009;0.009; MR&#x02013;Egger:
<italic>p</italic>
&#x02009;=&#x02009;0.013, intercept
<italic>p</italic>
&#x02009;=&#x02009;0.843). The level of CCL23 is suggestively associated with an increased risk of TAO (IVW: OR&#x02009;=&#x02009;2.24,
<italic>p</italic>
&#x02009;=&#x02009;0.011; MR&#x02013;Egger:
<italic>p</italic>
&#x02009;=&#x02009;0.019, intercept
<italic>p</italic>
&#x02009;=&#x02009;0.978). GDNF levels show a suggestively protective effect against TAO (IVW: OR&#x02009;=&#x02009;0.315,
<italic>p</italic>
&#x02009;=&#x02009;0.016; MR&#x02013;Egger:
<italic>p</italic>
&#x02009;=&#x02009;0.020, intercept
<italic>p</italic>
&#x02009;=&#x02009;0.634). However, no significant direct impacts were observed for the levels of C&#x02013;C motif chemokine 25 (
<italic>p</italic>
&#x02009;=&#x02009;0.079), C&#x02013;C motif chemokine 28 (
<italic>p</italic>
&#x02009;=&#x02009;0.179), or stem cell factor (
<italic>p</italic>
&#x02009;=&#x02009;0.159) on TAO.
</p><fig id="FI23120539-6"><label>Fig. 6</label><caption><p>
Results from multivariable Mendelian randomization analysis on the impact of circulating inflammatory proteins on TAO, after adjusting for genetically predicted smoking. IVW, inverse variance weighting; TAO, thromboangiitis obliterans.
</p></caption><graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="10-1055-s-0044-1786809-i23120539-6"/></fig></sec></sec><sec><title>Discussion</title><p>This study utilized a two-sample and MVMR approach to evaluate the causal relationships between specific circulating inflammation-related proteins and TAO. Utilizing summary statistics from GWAS meta-analyses for these proteins, alongside TAO data from the FinnGen consortium R10 release and GWAS information on smoking, our findings underscore a protective influence of CCL4 and GDNF on TAO. In contrast, elevated levels of CCL23 emerge as potential risk indicators for TAO. These insights position these proteins as potential biomarkers for TAO, offering new avenues for understanding its pathogenesis.</p><p>
C&#x02013;C motif chemokines, a subfamily of small, secreted proteins, engage with G protein-coupled chemokine receptors on the cell surface, which are distinguished by directly juxtaposed cysteines.
<xref rid="JR23120539-22" ref-type="bibr">22</xref>
Their renowned function is to orchestrate cell migration, particularly of leukocytes, playing crucial roles in both protective and destructive immune and inflammatory responses.
<xref rid="JR23120539-23" ref-type="bibr">23</xref>
CCL4, also known as the macrophage inflammatory protein, is a significant member of the CC chemokine family. This protein, encoded by the CCL4 gene in humans, interacts with CCR5 and is identified as a pivotal human immunodeficiency virus-suppressive factor secreted by CD8+ T-cells.
<xref rid="JR23120539-24" ref-type="bibr">24</xref>
Additionally, its involvement has been increasingly recognized in cardiovascular diseases.
<xref rid="JR23120539-23" ref-type="bibr">23</xref>
While CCL4 exhibits a protective effect in Type 1 diabetes mellitus patients, it is also found to be elevated in conditions such as atherosclerosis and myocardial infarction.
<xref rid="JR23120539-23" ref-type="bibr">23</xref>
CCL4's ability to activate PI3K and MAPK signaling pathways and inhibit the NF-&#x003ba;B pathway contributes to the enhanced proliferation of porcine uterine luminal epithelial cells.
<xref rid="JR23120539-25" ref-type="bibr">25</xref>
This mechanism may elucidate CCL4's protective role in TAO, as demonstrated in this study (OR: 0.44; 95% CI: 0.29&#x02013;0.67;
<italic>p</italic>
&#x02009;=&#x02009;1.4&#x02009;&#x000d7;&#x02009;10
<sup>&#x02212;4</sup>
; adjusted
<italic>p</italic>
&#x02009;=&#x02009;0.013), highlighting its potential as both a biomarker and a therapeutic target.
</p><p>
CCL23, also known as myeloid progenitor inhibitory factor-1, represents another key member of the CC chemokine subfamily. It plays a role in the inflammatory process, capable of inhibiting the release of polymorphonuclear leukocytes from the bone marrow.
<xref rid="JR23120539-26" ref-type="bibr">26</xref>
As a relatively novel chemokine, CCL23's biological significance remains partially unexplored.
<xref rid="JR23120539-27" ref-type="bibr">27</xref>
Circulating CCL23 exhibited a continuous increase from baseline to 24&#x02009;hours in ischemic stroke patients and could predict the clinical outcome after 3 months.
<xref rid="JR23120539-28" ref-type="bibr">28</xref>
Elevated blood levels of CCL23 have been linked with antineutrophil cytoplasmic antibody-associated vasculitis.
<xref rid="JR23120539-29" ref-type="bibr">29</xref>
Although its mechanisms are largely uncharted, CCL23 is known to facilitate the chemotaxis of human THP-1 monocytes, increase adhesion molecule CD11c expression, and stimulate MMP-2 release from THP-1 monocytes.
<xref rid="JR23120539-30" ref-type="bibr">30</xref>
Moreover, CCL23 can enhance leucocyte trafficking and direct the migration of monocytes, macrophages, dendritic cells, and T lymphocytes.
<xref rid="JR23120539-27" ref-type="bibr">27</xref>
This study posits CCL23 as a suggestive risk factor for TAO (OR: 1.88, 95% CI: 1.21&#x02013;2.93;
<italic>p</italic>
&#x02009;=&#x02009;0.005; adjusted
<italic>p</italic>
&#x02009;=&#x02009;0.218), warranting further investigation into its precise role.
</p><p>
GDNF was first discovered as a potent survival factor for midbrain dopaminergic neurons and has shown promise in preserving these neurons in animal models of Parkinson's disease.
<xref rid="JR23120539-31" ref-type="bibr">31</xref>
Recent studies have further elucidated GDNF's significance in neuronal safeguarding and cerebral recuperation.
<xref rid="JR23120539-32" ref-type="bibr">32</xref>
Additionally, GDNF has been implicated in inflammatory bowel disease (IBD), where it bolsters the integrity of the intestinal epithelial barrier and facilitates wound repair, while also exerting an immunomodulatory influence.
<xref rid="JR23120539-33" ref-type="bibr">33</xref>
<xref rid="JR23120539-34" ref-type="bibr">34</xref>
In our study, GDNF is identified as a potential protective agent against TAO, with an OR of 0.43 (95% CI: 0.22&#x02013;0.81;
<italic>p</italic>
&#x02009;=&#x02009;0.010; adjusted
<italic>p</italic>
&#x02009;=&#x02009;0.218). It is postulated that GDNF's protective mechanism in TAO may involve the inhibition of apoptosis through the activation of MAPK and AKT pathways, akin to its action in IBD.
<xref rid="JR23120539-34" ref-type="bibr">34</xref>
</p><p>This study utilized MR analysis to ascertain the causal relationship between circulating inflammation-related proteins and TAO. This approach was chosen to mitigate confounding factors and the potential reverse causation in causal inference. Genetic variations linked to these proteins were sourced from a recent GWAS meta-analysis, ensuring robust instrument strength in the MR analysis. MR-PRESSO and MR&#x02013;Egger regression intercept tests were employed to assess the level of pleiotropy. A two-sample MR design was adopted, using nonoverlapping summary data for exposure and outcomes to minimize bias. An MVMR was finally performed to adjust the possible cofounding of smoking.</p><p>Nonetheless, this study is subject to several limitations. First, the absence of additional GWAS cohorts encompassing TAO precluded replication analysis, thereby constraining the validation of the causal relationship and impacting the study's credibility. Second, while the case count in our study is constrained, potentially increasing the likelihood of Type II errors, robust IVs were carefully chosen, and both sensitivity and MVMR analyses were conducted. These measures were taken to mitigate risks, and the outcomes affirm the study's resilience. Third, given that the FinnGen study exclusively comprised Finnish participants and considering the lower prevalence of TAO in Northeastern European countries compared with other regions globally, the findings' applicability may be somewhat restricted.</p></sec><sec><title>Conclusion</title><p>This two-sample and MVMR analysis reveals a protective effect of CCL4 and GDNF on TAO, and suggests a potential causal relationship between CCL23 and TAO. These findings offer new perspectives on potential biomarkers and therapeutic targets for TAO.</p></sec></body><back><sec><boxed-text content-type="backinfo"><p>
<bold>What is known about this topic?</bold>
</p><list list-type="bullet"><list-item><p>Thromboangiitis obliterans (TAO), or Buerger's disease, is a distinct, nonatherosclerotic inflammatory condition.</p></list-item><list-item><p>It primarily impacts small- and medium-sized arteries and veins in the extremities, with uncertain etiology and prognosis.</p></list-item><list-item><p>The disease is thought to involve an immune response, but evidence supporting this is limited.</p></list-item></list><p>
<bold>What does this paper add?</bold>
</p><list list-type="bullet"><list-item><p>Utilizes a two-sample and multivariable Mendelian randomization approach, integrating GWAS data of 91 inflammation-related proteins with TAO data.</p></list-item><list-item><p>Identifies C&#x02013;C motif chemokine 4 and glial cell line-derived neurotrophic factor as potential protective biomarkers for TAO, offering new insights for diagnosis and treatment.</p></list-item><list-item><p>Suggests that C&#x02013;C motif chemokine 23 emerges as a suggestive risk marker in TAO, offering new insights for diagnosis and treatment.</p></list-item></list></boxed-text></sec><ack><title>Acknowledgment</title><p>This manuscript underwent editing and enhancement by ChatGPT-4. We want to acknowledge the participants and investigators of the FinnGen study and the GWAS research for their generous sharing of data.</p></ack><fn-group><fn fn-type="COI-statement" id="d35e141"><p><bold>Conflict of Interest</bold> None declared.</p></fn></fn-group><fn-group><title>Data Availability Statement</title><fn id="FN23120539-2"><p>
The datasets analyzed during the current study are available in the EBI GWAS Catalog (accession numbers GCST90274758 to GCST90274848 and GCST90029014),
<uri xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="https://www.ebi.ac.uk/gwas/">https://www.ebi.ac.uk/gwas/</uri>
, and the FinnGen repository (finngen_R10_I9_THROMBANG),
<uri xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="https://www.finngen.fi/en/access_results">https://www.finngen.fi/en/access_results</uri>
.
</p></fn></fn-group><fn-group><title>Ethical Approval Statement</title><fn id="FN23120539-3"><p>This research has been conducted using published studies and consortia providing publicly available summary statistics. All original studies have been approved by the corresponding ethical review board, and the participants have provided informed consent. In addition, no individual-level data were used in this study. Therefore, no new ethical review board approval was required</p></fn></fn-group><fn-group><title>Authors' Contribution</title><fn id="FN23120539-4"><p>Conception and design: M.Y. and B.Z. Administrative support: X.T. and Y.Z. Provision of study materials or patients: Z.Y. and G.N. Collection and assembly of data: B.Z., Z.Y., and R.H. Data analysis and interpretation: B.Z., R.H., and Z.Y. Manuscript writing: All authors. Final approval of manuscript: All authors.</p></fn></fn-group><fn-group><fn id="FN23120539-1"><label>*</label><p>
<italic>These authors equally contributed to this paper and thus shared the co-first authorship.</italic>
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tests/data/xml/1349-7235-63-2593.nxml
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tests/test_backend_html.py
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@ -42,7 +42,7 @@ def test_heading_levels():
def get_html_paths():
# Define the directory you want to search
directory = Path("./tests/data/html/")
directory = Path(os.path.dirname(__file__) + f"/data/html/")
# List all PDF files in the directory and its subdirectories
html_files = sorted(directory.rglob("*.html"))
@ -100,3 +100,11 @@ def test_e2e_html_conversions():
pred_json: str = json.dumps(doc.export_to_dict(), indent=2)
assert verify_export(pred_json, str(gt_path) + ".json"), "export to json"
def main():
test_e2e_html_conversions()
if __name__ == "__main__":
main()

6
tests/test_backend_xml.py → tests/test_backend_pubmed.py
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@ -8,17 +8,17 @@ from docling.datamodel.base_models import InputFormat
from docling.datamodel.document import ConversionResult
from docling.document_converter import DocumentConverter
GENERATE = False
GENERATE = True
def get_xml_paths():
directory = Path(os.path.dirname(__file__) + f"/data/xml/")
directory = Path(os.path.dirname(__file__) + f"/data/pubmed/")
xml_files = sorted(directory.rglob("*.nxml"))
return xml_files
def get_converter():
converter = DocumentConverter(allowed_formats=[InputFormat.XML])
converter = DocumentConverter(allowed_formats=[InputFormat.PUBMED])
return converter