Deep learning identifies antigenic determinants of severe SARS-CoV-2 infection within T-cell repertoires

Abstract SARS-CoV-2 infection is characterized by a highly variable clinical course with patients experiencing asymptomatic infection all the way to requiring critical care support. This variation in clinical course has led physicians and scientists to study factors that may predispose certain indiv...

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Autores principales:	John-William Sidhom, Alexander S. Baras
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/ca8b01617b884c0dbc22dd42758dab8e
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spelling	oai:doaj.org-article:ca8b01617b884c0dbc22dd42758dab8e2021-12-02T16:08:06ZDeep learning identifies antigenic determinants of severe SARS-CoV-2 infection within T-cell repertoires10.1038/s41598-021-93608-82045-2322https://doaj.org/article/ca8b01617b884c0dbc22dd42758dab8e2021-07-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-93608-8https://doaj.org/toc/2045-2322Abstract SARS-CoV-2 infection is characterized by a highly variable clinical course with patients experiencing asymptomatic infection all the way to requiring critical care support. This variation in clinical course has led physicians and scientists to study factors that may predispose certain individuals to more severe clinical presentations in hopes of either identifying these individuals early in their illness or improving their medical management. We sought to understand immunogenomic differences that may result in varied clinical outcomes through analysis of T-cell receptor sequencing (TCR-Seq) data in the open access ImmuneCODE database. We identified two cohorts within the database that had clinical outcomes data reflecting severity of illness and utilized DeepTCR, a multiple-instance deep learning repertoire classifier, to predict patients with severe SARS-CoV-2 infection from their repertoire sequencing. We demonstrate that patients with severe infection have repertoires with higher T-cell responses associated with SARS-CoV-2 epitopes and identify the epitopes that result in these responses. Our results provide evidence that the highly variable clinical course seen in SARS-CoV-2 infection is associated to certain antigen-specific responses.John-William SidhomAlexander S. BarasNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-8 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q John-William Sidhom Alexander S. Baras Deep learning identifies antigenic determinants of severe SARS-CoV-2 infection within T-cell repertoires
description	Abstract SARS-CoV-2 infection is characterized by a highly variable clinical course with patients experiencing asymptomatic infection all the way to requiring critical care support. This variation in clinical course has led physicians and scientists to study factors that may predispose certain individuals to more severe clinical presentations in hopes of either identifying these individuals early in their illness or improving their medical management. We sought to understand immunogenomic differences that may result in varied clinical outcomes through analysis of T-cell receptor sequencing (TCR-Seq) data in the open access ImmuneCODE database. We identified two cohorts within the database that had clinical outcomes data reflecting severity of illness and utilized DeepTCR, a multiple-instance deep learning repertoire classifier, to predict patients with severe SARS-CoV-2 infection from their repertoire sequencing. We demonstrate that patients with severe infection have repertoires with higher T-cell responses associated with SARS-CoV-2 epitopes and identify the epitopes that result in these responses. Our results provide evidence that the highly variable clinical course seen in SARS-CoV-2 infection is associated to certain antigen-specific responses.
format	article
author	John-William Sidhom Alexander S. Baras
author_facet	John-William Sidhom Alexander S. Baras
author_sort	John-William Sidhom
title	Deep learning identifies antigenic determinants of severe SARS-CoV-2 infection within T-cell repertoires
title_short	Deep learning identifies antigenic determinants of severe SARS-CoV-2 infection within T-cell repertoires
title_full	Deep learning identifies antigenic determinants of severe SARS-CoV-2 infection within T-cell repertoires
title_fullStr	Deep learning identifies antigenic determinants of severe SARS-CoV-2 infection within T-cell repertoires
title_full_unstemmed	Deep learning identifies antigenic determinants of severe SARS-CoV-2 infection within T-cell repertoires
title_sort	deep learning identifies antigenic determinants of severe sars-cov-2 infection within t-cell repertoires
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/ca8b01617b884c0dbc22dd42758dab8e
work_keys_str_mv	AT johnwilliamsidhom deeplearningidentifiesantigenicdeterminantsofseveresarscov2infectionwithintcellrepertoires AT alexandersbaras deeplearningidentifiesantigenicdeterminantsofseveresarscov2infectionwithintcellrepertoires
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Deep learning identifies antigenic determinants of severe SARS-CoV-2 infection within T-cell repertoires

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