Evidence map›Paper›PMID 39066192›Full record

ArticleViruses2024

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

Yang Li, Xinya Tao, Sheng Ye, Qianchen Tai, Yu-Ang You, Xinting Huang, Mifang Liang, Kai Wang, Haiyan Wen, Chong You and 2 more

Abstract read
In one paragraph

Article in Viruses, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. Review
  2. Article
  3. Article
  4. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors.

Yang LiBeijing International Center for Mathematical Research, Peking University, Beijing 100871, China.ORCID 0000-0001-8771-7044
Xinya TaoChongqing Research Institute of Big Data, Peking University, Chongqing 400041, China.
Sheng YeChongqing Center for Disease Control and Prevention, Chongqing 400707, China.
Qianchen TaiDepartment of Probability and Statistics, School of Mathematical Sciences, Peking University, Beijing 100091, China.
Yu-Ang YouInstitute of Pharmaceutical Science, King's College London, London WC2R 2LS, UK.
Xinting HuangChongqing Research Institute of Big Data, Peking University, Chongqing 400041, China.ORCID 0009-0004-7518-5844
Mifang LiangNHC 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.
Kai WangKey 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.ORCID 0000-0002-0137-1247
Haiyan WenChongqing International Travel Health Care Center, Chongqing 401120, China.
Chong YouBeijing International Center for Mathematical Research, Peking University, Beijing 100871, China.
Yan ZhangSports & Medicine Integration Research Center (SMIRC), Capital University of Physical Education and Sports, Beijing 100088, China.
Xiaohua ZhouBeijing International Center for Mathematical Research, Peking University, Beijing 100871, China.ORCID 0000-0001-7935-1222

Funding

Chongqing Municipal Commission of Science and Technology CSTB2022TIAD-GPX0073Gates Foundation INV-016826National Key Research and Development Program 2023YFC3041500National Natural Science Foundation of China 82041023Research project of the General Administration of Customs 2022HK68
6 · The paper itself

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.

Indexed as

COVID-19SARS-CoV-2T-LymphocytesAdultCohort StudiesComputational BiologyFemaleHumansMaleMiddle AgedRespiratory Tract Infectionsbiomarkersdatabasediagnosticsgene expressionSARS-CoV-2

Identifiers

PMID39066192
PMCPMC11281602

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.