Frontiers in Immunology (May 2023)
Leveraging T-cell receptor – epitope recognition models to disentangle unique and cross-reactive T-cell response to SARS-CoV-2 during COVID-19 progression/resolution
- Anna Postovskaya,
- Anna Postovskaya,
- Anna Postovskaya,
- Anna Postovskaya,
- Alexandra Vujkovic,
- Alexandra Vujkovic,
- Alexandra Vujkovic,
- Tessa de Block,
- Lida van Petersen,
- Maartje van Frankenhuijsen,
- Isabel Brosius,
- Emmanuel Bottieau,
- Christophe Van Dijck,
- Christophe Van Dijck,
- Caroline Theunissen,
- Sabrina H. van Ierssel,
- Sabrina H. van Ierssel,
- Erika Vlieghe,
- Esther Bartholomeus,
- Esther Bartholomeus,
- Kerry Mullan,
- Kerry Mullan,
- Kerry Mullan,
- Wim Adriaensen,
- Guido Vanham,
- Benson Ogunjimi,
- Benson Ogunjimi,
- Benson Ogunjimi,
- Benson Ogunjimi,
- Kris Laukens,
- Kris Laukens,
- Kris Laukens,
- Koen Vercauteren,
- Pieter Meysman,
- Pieter Meysman,
- Pieter Meysman
Affiliations
- Anna Postovskaya
- Adrem Data Lab, Department of Computer Science, University of Antwerp, Antwerp, Belgium
- Anna Postovskaya
- Biomedical Informatics Research Network Antwerp (BIOMINA), University of Antwerp, Antwerp, Belgium
- Anna Postovskaya
- Antwerp Unit for Data Analysis and Computation in Immunology and Sequencing (AUDACIS), University of Antwerp, Antwerp, Belgium
- Anna Postovskaya
- Clinical Virology Unit, Department of Clinical Sciences, Institute of Tropical Medicine, Antwerp, Belgium
- Alexandra Vujkovic
- Adrem Data Lab, Department of Computer Science, University of Antwerp, Antwerp, Belgium
- Alexandra Vujkovic
- Antwerp Unit for Data Analysis and Computation in Immunology and Sequencing (AUDACIS), University of Antwerp, Antwerp, Belgium
- Alexandra Vujkovic
- Clinical Virology Unit, Department of Clinical Sciences, Institute of Tropical Medicine, Antwerp, Belgium
- Tessa de Block
- Clinical Virology Unit, Department of Clinical Sciences, Institute of Tropical Medicine, Antwerp, Belgium
- Lida van Petersen
- Department of Clinical Sciences, Institute of Tropical Medicine, Antwerp, Belgium
- Maartje van Frankenhuijsen
- Department of Clinical Sciences, Institute of Tropical Medicine, Antwerp, Belgium
- Isabel Brosius
- Department of Clinical Sciences, Institute of Tropical Medicine, Antwerp, Belgium
- Emmanuel Bottieau
- Department of Clinical Sciences, Institute of Tropical Medicine, Antwerp, Belgium
- Christophe Van Dijck
- Department of Clinical Sciences, Institute of Tropical Medicine, Antwerp, Belgium
- Christophe Van Dijck
- Centre for Health Economics Research & Modeling Infectious Diseases (CHERMID), Vaccine & Infectious Disease Institute (VAXINFECTIO), University of Antwerp, Antwerp, Belgium
- Caroline Theunissen
- Department of Clinical Sciences, Institute of Tropical Medicine, Antwerp, Belgium
- Sabrina H. van Ierssel
- Department of General Internal Medicine, Infectious Diseases and Tropical Medicine, Antwerp University Hospital, Edegem, Belgium
- Sabrina H. van Ierssel
- Global Health Institute, University of Antwerp, Antwerp, Belgium
- Erika Vlieghe
- Department of General Internal Medicine, Infectious Diseases and Tropical Medicine, Antwerp University Hospital, Edegem, Belgium
- Esther Bartholomeus
- Antwerp Unit for Data Analysis and Computation in Immunology and Sequencing (AUDACIS), University of Antwerp, Antwerp, Belgium
- Esther Bartholomeus
- Department of Medical Genetics, University of Antwerp, Antwerp, Belgium
- Kerry Mullan
- Adrem Data Lab, Department of Computer Science, University of Antwerp, Antwerp, Belgium
- Kerry Mullan
- Biomedical Informatics Research Network Antwerp (BIOMINA), University of Antwerp, Antwerp, Belgium
- Kerry Mullan
- Antwerp Unit for Data Analysis and Computation in Immunology and Sequencing (AUDACIS), University of Antwerp, Antwerp, Belgium
- Wim Adriaensen
- 0Clinical Immunology Unit, Department of Clinical Sciences, Institute of Tropical Medicine, Antwerp, Belgium
- Guido Vanham
- Global Health Institute, University of Antwerp, Antwerp, Belgium
- Benson Ogunjimi
- Antwerp Unit for Data Analysis and Computation in Immunology and Sequencing (AUDACIS), University of Antwerp, Antwerp, Belgium
- Benson Ogunjimi
- Centre for Health Economics Research & Modeling Infectious Diseases (CHERMID), Vaccine & Infectious Disease Institute (VAXINFECTIO), University of Antwerp, Antwerp, Belgium
- Benson Ogunjimi
- 1Antwerp Center for Translational Immunology and Virology (ACTIV), Vaccine and Infectious Disease Institute, University of Antwerp, Antwerp, Belgium
- Benson Ogunjimi
- 2Department of Paediatrics, Antwerp University Hospital, Antwerp, Belgium
- Kris Laukens
- Adrem Data Lab, Department of Computer Science, University of Antwerp, Antwerp, Belgium
- Kris Laukens
- Biomedical Informatics Research Network Antwerp (BIOMINA), University of Antwerp, Antwerp, Belgium
- Kris Laukens
- Antwerp Unit for Data Analysis and Computation in Immunology and Sequencing (AUDACIS), University of Antwerp, Antwerp, Belgium
- Koen Vercauteren
- Clinical Virology Unit, Department of Clinical Sciences, Institute of Tropical Medicine, Antwerp, Belgium
- Pieter Meysman
- Adrem Data Lab, Department of Computer Science, University of Antwerp, Antwerp, Belgium
- Pieter Meysman
- Biomedical Informatics Research Network Antwerp (BIOMINA), University of Antwerp, Antwerp, Belgium
- Pieter Meysman
- Antwerp Unit for Data Analysis and Computation in Immunology and Sequencing (AUDACIS), University of Antwerp, Antwerp, Belgium
- DOI
- https://doi.org/10.3389/fimmu.2023.1130876
- Journal volume & issue
-
Vol. 14
Abstract
Despite the general agreement on the significance of T cells during SARS-CoV-2 infection, the clinical impact of specific and cross-reactive T-cell responses remains uncertain. Understanding this aspect could provide insights for adjusting vaccines and maintaining robust long-term protection against continuously emerging variants. To characterize CD8+ T-cell response to SARS-CoV-2 epitopes unique to the virus (SC2-unique) or shared with other coronaviruses (CoV-common), we trained a large number of T-cell receptor (TCR) – epitope recognition models for MHC-I-presented SARS-CoV-2 epitopes from publicly available data. These models were then applied to longitudinal CD8+ TCR repertoires from critical and non-critical COVID-19 patients. In spite of comparable initial CoV-common TCR repertoire depth and CD8+ T-cell depletion, the temporal dynamics of SC2-unique TCRs differed depending on the disease severity. Specifically, while non-critical patients demonstrated a large and diverse SC2-unique TCR repertoire by the second week of the disease, critical patients did not. Furthermore, only non-critical patients exhibited redundancy in the CD8+ T-cell response to both groups of epitopes, SC2-unique and CoV-common. These findings indicate a valuable contribution of the SC2-unique CD8+ TCR repertoires. Therefore, a combination of specific and cross-reactive CD8+ T-cell responses may offer a stronger clinical advantage. Besides tracking the specific and cross-reactive SARS-CoV-2 CD8+ T cells in any TCR repertoire, our analytical framework can be expanded to more epitopes and assist in the assessment and monitoring of CD8+ T-cell response to other infections.
Keywords
- TCR repertoire analysis
- CD8+ T-cell response
- COVID-19
- SARS-CoV-2 epitopes
- immunoinformatics
- cross-reactive T-cell response