对不同基因小鼠中枢神经系统自身免疫的分析揭示了独特的表型和机制。

IF 6.3 1区 医学 Q1 MEDICINE, RESEARCH & EXPERIMENTAL JCI insight Pub Date : 2024-11-08 DOI:10.1172/jci.insight.184138
Emily A Nelson, Anna L Tyler, Taylor Lakusta-Wong, Karolyn G Lahue, Katherine C Hankes, Cory Teuscher, Rachel M Lynch, Martin T Ferris, J Matthew Mahoney, Dimitry N Krementsov
{"title":"对不同基因小鼠中枢神经系统自身免疫的分析揭示了独特的表型和机制。","authors":"Emily A Nelson, Anna L Tyler, Taylor Lakusta-Wong, Karolyn G Lahue, Katherine C Hankes, Cory Teuscher, Rachel M Lynch, Martin T Ferris, J Matthew Mahoney, Dimitry N Krementsov","doi":"10.1172/jci.insight.184138","DOIUrl":null,"url":null,"abstract":"<p><p>Multiple sclerosis (MS) is a complex disease with significant heterogeneity in disease course and progression. Genetic studies have identified numerous loci associated with MS risk, but the genetic basis of disease progression remains elusive. To address this, we leveraged the Collaborative Cross (CC), a genetically diverse mouse strain panel, and experimental autoimmune encephalomyelitis (EAE). The 32 CC strains studied captured a wide spectrum of EAE severity, trajectory, and presentation, including severe-progressive, monophasic, relapsing remitting, and axial rotary-EAE (AR-EAE), accompanied by distinct immunopathology. Sex differences in EAE severity were observed in 6 strains. Quantitative trait locus analysis revealed distinct genetic linkage patterns for different EAE phenotypes, including EAE severity and incidence of AR-EAE. Machine learning-based approaches prioritized candidate genes for loci underlying EAE severity (Abcc4 and Gpc6) and AR-EAE (Yap1 and Dync2h1). This work expands the EAE phenotypic repertoire and identifies potentially novel loci controlling unique EAE phenotypes, supporting the hypothesis that heterogeneity in MS disease course is driven by genetic variation.</p>","PeriodicalId":14722,"journal":{"name":"JCI insight","volume":null,"pages":null},"PeriodicalIF":6.3000,"publicationDate":"2024-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Analysis of CNS autoimmunity in genetically diverse mice reveals unique phenotypes and mechanisms.\",\"authors\":\"Emily A Nelson, Anna L Tyler, Taylor Lakusta-Wong, Karolyn G Lahue, Katherine C Hankes, Cory Teuscher, Rachel M Lynch, Martin T Ferris, J Matthew Mahoney, Dimitry N Krementsov\",\"doi\":\"10.1172/jci.insight.184138\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Multiple sclerosis (MS) is a complex disease with significant heterogeneity in disease course and progression. Genetic studies have identified numerous loci associated with MS risk, but the genetic basis of disease progression remains elusive. To address this, we leveraged the Collaborative Cross (CC), a genetically diverse mouse strain panel, and experimental autoimmune encephalomyelitis (EAE). The 32 CC strains studied captured a wide spectrum of EAE severity, trajectory, and presentation, including severe-progressive, monophasic, relapsing remitting, and axial rotary-EAE (AR-EAE), accompanied by distinct immunopathology. Sex differences in EAE severity were observed in 6 strains. Quantitative trait locus analysis revealed distinct genetic linkage patterns for different EAE phenotypes, including EAE severity and incidence of AR-EAE. Machine learning-based approaches prioritized candidate genes for loci underlying EAE severity (Abcc4 and Gpc6) and AR-EAE (Yap1 and Dync2h1). This work expands the EAE phenotypic repertoire and identifies potentially novel loci controlling unique EAE phenotypes, supporting the hypothesis that heterogeneity in MS disease course is driven by genetic variation.</p>\",\"PeriodicalId\":14722,\"journal\":{\"name\":\"JCI insight\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":6.3000,\"publicationDate\":\"2024-11-08\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"JCI insight\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1172/jci.insight.184138\",\"RegionNum\":1,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"MEDICINE, RESEARCH & EXPERIMENTAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"JCI insight","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1172/jci.insight.184138","RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MEDICINE, RESEARCH & EXPERIMENTAL","Score":null,"Total":0}
引用次数: 0

摘要

多发性硬化症(MS)是一种复杂的疾病,其病程和进展具有显著的异质性。遗传学研究发现了许多与多发性硬化症风险相关的基因位点,但疾病进展的遗传学基础仍然难以捉摸。为了解决这个问题,我们利用了合作杂交(CC)这一基因多样化的小鼠品系面板和实验性自身免疫性脑脊髓炎(EAE)。所研究的 32 个 CC 株系涵盖了 EAE 严重程度、发展轨迹和表现形式的广泛范围,包括重度进展型、单相型、复发缓解型和轴旋转型 (AR)-EAE,并伴有不同的免疫病理。在六个品系中观察到了EAE严重程度的性别差异。定量性状位点分析揭示了不同EAE表型的不同遗传连锁模式,包括EAE严重程度和AR-EAE发病率。基于机器学习的方法优先选择了EAE严重程度(Abcc4和Gpc6)和AR-EAE(Yap1和Dync2h1)基因座的候选基因。这项工作扩大了 EAE 表型的范围,并确定了控制独特 EAE 表型的新基因座,支持了多发性硬化症病程的异质性由遗传变异驱动的假设。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Analysis of CNS autoimmunity in genetically diverse mice reveals unique phenotypes and mechanisms.

Multiple sclerosis (MS) is a complex disease with significant heterogeneity in disease course and progression. Genetic studies have identified numerous loci associated with MS risk, but the genetic basis of disease progression remains elusive. To address this, we leveraged the Collaborative Cross (CC), a genetically diverse mouse strain panel, and experimental autoimmune encephalomyelitis (EAE). The 32 CC strains studied captured a wide spectrum of EAE severity, trajectory, and presentation, including severe-progressive, monophasic, relapsing remitting, and axial rotary-EAE (AR-EAE), accompanied by distinct immunopathology. Sex differences in EAE severity were observed in 6 strains. Quantitative trait locus analysis revealed distinct genetic linkage patterns for different EAE phenotypes, including EAE severity and incidence of AR-EAE. Machine learning-based approaches prioritized candidate genes for loci underlying EAE severity (Abcc4 and Gpc6) and AR-EAE (Yap1 and Dync2h1). This work expands the EAE phenotypic repertoire and identifies potentially novel loci controlling unique EAE phenotypes, supporting the hypothesis that heterogeneity in MS disease course is driven by genetic variation.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
JCI insight
JCI insight Medicine-General Medicine
CiteScore
13.70
自引率
1.20%
发文量
543
审稿时长
6 weeks
期刊介绍: JCI Insight is a Gold Open Access journal with a 2022 Impact Factor of 8.0. It publishes high-quality studies in various biomedical specialties, such as autoimmunity, gastroenterology, immunology, metabolism, nephrology, neuroscience, oncology, pulmonology, and vascular biology. The journal focuses on clinically relevant basic and translational research that contributes to the understanding of disease biology and treatment. JCI Insight is self-published by the American Society for Clinical Investigation (ASCI), a nonprofit honor organization of physician-scientists founded in 1908, and it helps fulfill the ASCI's mission to advance medical science through the publication of clinically relevant research reports.
期刊最新文献
A therapeutic HBV vaccine containing a checkpoint modifier enhances CD8+ T cell and antiviral responses. Pivotal roles for cancer cell-intrinsic mPGES-1 and autocrine EP4 signaling in suppressing antitumor immunity. SLC4A11 mediates ammonia import and promotes cancer stemness in hepatocellular carcinoma. Targeting heterogeneous tumor microenvironments in pancreatic cancer mouse models of metastasis by TGF-β depletion. Deletion of Gba in neurons, but not microglia, causes neurodegeneration in a Gaucher mouse model.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1