Machine learning identifies T cell receptor repertoire signatures associated with COVID-19 severity

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. Signatures associated with COVID-19 disease severity are studied, primarily using machine learning models for classification on the basis of TCR repertoire analysis and combining such data/analysis with single cell transcriptomic data.

Automated summary

By analyzing a large-scale dataset of TCR repertoires, we identified signatures associated with COVID-19 disease severity and accurately predicted COVID-19 infection using machine learning models. These analyses provide a systems immunology view of T cell adaptive immune responses to COVID-19.