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Tracking SARS-CoV-2 T cells with epitope-T-cell receptor recognition models

View ORCID ProfilePieter Meysman, Anna Postovskaya, View ORCID ProfileNicolas De Neuter, View ORCID ProfileBenson Ogunjimi, View ORCID ProfileKris Laukens
doi: https://doi.org/10.1101/2020.09.09.289355
Pieter Meysman
1ADREM data lab, University of Antwerp
2Antwerp Unit for Data Analysis and Computation in Immunology and Sequencing (AUDACIS), Antwerp
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  • For correspondence: pieter.meysman@uantwerpen.be
Anna Postovskaya
1ADREM data lab, University of Antwerp
2Antwerp Unit for Data Analysis and Computation in Immunology and Sequencing (AUDACIS), Antwerp
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Nicolas De Neuter
1ADREM data lab, University of Antwerp
2Antwerp Unit for Data Analysis and Computation in Immunology and Sequencing (AUDACIS), Antwerp
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Benson Ogunjimi
2Antwerp Unit for Data Analysis and Computation in Immunology and Sequencing (AUDACIS), Antwerp
3Antwerp Center for Translational Immunology and Virology (ACTIV), Vaccine & Infectious Disease Institute (VAXINFECTIO), University of Antwerp
4Centre for Health Economics Research & Modeling Infectious Diseases (CHERMID), Vaccine & Infectious Disease Institute (VAXINFECTIO), University of Antwerp
5Department of Paediatrics, Antwerp University Hospital
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Kris Laukens
1ADREM data lab, University of Antwerp
2Antwerp Unit for Data Analysis and Computation in Immunology and Sequencing (AUDACIS), Antwerp
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Abstract

Much is still not understood about the human adaptive immune response to SARS-CoV-2, the causative agent of COVID-19. In this paper, we demonstrate the use of machine learning to classify SARS-CoV-2 epitope specific T-cell clonotypes in T-cell receptor (TCR) sequencing data. We apply these models to public TCR data and show how they can be used to study T-cell longitudinal profiles in COVID-19 patients to characterize how the adaptive immune system reacts to the SARS-CoV-2 virus. Our findings confirm prior knowledge that SARS-CoV-2 reactive T-cell diversity increases over the course of disease progression. However our results show a difference between those T cells that react to epitope unique to SARS-CoV-2, which show a more prominent increase, and those T cells that react to epitopes common to other coronaviruses, which begin at a higher baseline.

Competing Interest Statement

The authors have declared no competing interest.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC 4.0 International license.
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Posted September 09, 2020.
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Tracking SARS-CoV-2 T cells with epitope-T-cell receptor recognition models
Pieter Meysman, Anna Postovskaya, Nicolas De Neuter, Benson Ogunjimi, Kris Laukens
bioRxiv 2020.09.09.289355; doi: https://doi.org/10.1101/2020.09.09.289355
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Tracking SARS-CoV-2 T cells with epitope-T-cell receptor recognition models
Pieter Meysman, Anna Postovskaya, Nicolas De Neuter, Benson Ogunjimi, Kris Laukens
bioRxiv 2020.09.09.289355; doi: https://doi.org/10.1101/2020.09.09.289355

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