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Machine learning identifies T cell receptor repertoire signatures associated with COVID-19 severity.
Park, Jonathan J; Lee, Kyoung A V; Lam, Stanley Z; Moon, Katherine S; Fang, Zhenhao; Chen, Sidi.
  • Park JJ; Department of Genetics, Yale School of Medicine, New Haven, CT, USA.
  • Lee KAV; Systems Biology Institute, Yale University, West Haven, CT, USA.
  • Lam SZ; Center for Cancer Systems Biology, Yale University, West Haven, CT, USA.
  • Moon KS; MD-PhD Program, Yale University, New Haven, CT, USA.
  • Fang Z; Molecular Cell Biology, Genetics, and Development Program, Yale University, New Haven, CT, USA.
  • Chen S; Department of Genetics, Yale School of Medicine, New Haven, CT, USA.
Commun Biol ; 6(1): 76, 2023 01 20.
Article in English | MEDLINE | ID: covidwho-2212035
ABSTRACT
T cell receptor (TCR) repertoires are critical for antiviral immunity. Determining the TCR repertoire composition, diversity, and dynamics and how they change during viral infection can inform the molecular specificity of host responses to viruses such as SARS-CoV-2. To determine signatures associated with COVID-19 disease severity, here we perform a large-scale analysis of over 4.7 billion sequences across 2130 TCR repertoires from COVID-19 patients and healthy donors. TCR repertoire analyses from these data identify and characterize convergent COVID-19-associated CDR3 gene usages, specificity groups, and sequence patterns. Here we show that T cell clonal expansion is associated with the upregulation of T cell effector function, TCR signaling, NF-kB signaling, and interferon-gamma signaling pathways. We also demonstrate that machine learning approaches accurately predict COVID-19 infection based on TCR sequence features, with certain high-power models reaching near-perfect AUROC scores. These analyses provide a systems immunology view of T cell adaptive immune responses to COVID-19.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: COVID-19 Type of study: Experimental Studies / Prognostic study / Randomized controlled trials Limits: Humans Language: English Journal: Commun Biol Year: 2023 Document Type: Article Affiliation country: S42003-023-04447-4

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Full text: Available Collection: International databases Database: MEDLINE Main subject: COVID-19 Type of study: Experimental Studies / Prognostic study / Randomized controlled trials Limits: Humans Language: English Journal: Commun Biol Year: 2023 Document Type: Article Affiliation country: S42003-023-04447-4