A T-Cell-Derived 3-Gene Signature Distinguishes SARS-CoV-2 from Common Respiratory Viruses

Author:

Li Yang12ORCID,Tao Xinya2,Ye Sheng3,Tai Qianchen4,You Yu-Ang5,Huang Xinting2ORCID,Liang Mifang6,Wang Kai7ORCID,Wen Haiyan8,You Chong129,Zhang Yan10,Zhou Xiaohua124ORCID

Affiliation:

1. Beijing International Center for Mathematical Research, Peking University, Beijing 100871, China

2. Chongqing Research Institute of Big Data, Peking University, Chongqing 400041, China

3. Chongqing Center for Disease Control and Prevention, Chongqing 400707, China

4. Department of Probability and Statistics, School of Mathematical Sciences, Peking University, Beijing 100091, China

5. Institute of Pharmaceutical Science, King’s College London, London WC2R 2LS, UK

6. NHC Key Laboratory of Medical Virology and Viral Diseases, National Institute for Viral Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing 102206, China

7. Key Laboratory of Molecular Biology for Infectious Diseases (Ministry of Education), Department of Infectious Diseases, Institute for Viral Hepatitis, The Second Affiliated Hospital of Chongqing Medical University, Chongqing 400010, China

8. Chongqing International Travel Health Care Center, Chongqing 401120, China

9. Shanghai Institute for Mathematics and Interdisciplinary Sciences, Fudan University, Shanghai 200433, China

10. Sports & Medicine Integration Research Center (SMIRC), Capital University of Physical Education and Sports, Beijing 100088, China

Abstract

Research on the host responses to respiratory viruses could help develop effective interventions and therapies against the current and future pandemics from the host perspective. To explore the pathogenesis that distinguishes SARS-CoV-2 infections from other respiratory viruses, we performed a multi-cohort analysis with integrated bioinformatics and machine learning. We collected 3730 blood samples from both asymptomatic and symptomatic individuals infected with SARS-CoV-2, seasonal human coronavirus (sHCoVs), influenza virus (IFV), respiratory syncytial virus (RSV), or human rhinovirus (HRV) across 15 cohorts. First, we identified an enhanced cellular immune response but limited interferon activities in SARS-CoV-2 infection, especially in asymptomatic cases. Second, we identified a SARS-CoV-2-specific 3-gene signature (CLSPN, RBBP6, CCDC91) that was predominantly expressed by T cells, could distinguish SARS-CoV-2 infection, including Omicron, from other common respiratory viruses regardless of symptoms, and was predictive of SARS-CoV-2 infection before detectable viral RNA on RT-PCR testing in a longitude follow-up study. Thereafter, a user-friendly online tool, based on datasets collected here, was developed for querying a gene of interest across multiple viral infections. Our results not only identify a unique host response to the viral pathogenesis in SARS-CoV-2 but also provide insights into developing effective tools against viral pandemics from the host perspective.

Funder

National Natural Science Foundation of China

Chongqing Municipal Commission of Science and Technology

Research project of the General Administration of Customs

National Key Research and Development Program

Bill & Melinda Gates Foundation

Publisher

MDPI AG

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3