Evidence map›Paper›PMID 37325653›Full record

ArticleFrontiers in immunology2023

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, Alexandra Vujkovic, Tessa de Block, Lida van Petersen, Maartje van Frankenhuijsen, Isabel Brosius, Emmanuel Bottieau, Christophe Van Dijck, Caroline Theunissen, Sabrina H van Ierssel and 9 more

Open access · goldAbstract read
In one paragraph

Article in Frontiers in immunology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
0.6field-weighted citation impact, top 35% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed, 3 citations in OpenAlex.

  1. Review
  2. Article
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  4. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

19 authors at 4 institutions in 1 country.

Anna PostovskayaAdrem Data Lab, Department of Computer Science, University of Antwerp, Antwerp, Belgium.
Alexandra VujkovicAdrem Data Lab, Department of Computer Science, University of Antwerp, Antwerp, Belgium.
Tessa de BlockClinical Virology Unit, Department of Clinical Sciences, Institute of Tropical Medicine, Antwerp, Belgium.
Lida van PetersenDepartment of Clinical Sciences, Institute of Tropical Medicine, Antwerp, Belgium.
Maartje van FrankenhuijsenDepartment of Clinical Sciences, Institute of Tropical Medicine, Antwerp, Belgium.
Isabel BrosiusDepartment of Clinical Sciences, Institute of Tropical Medicine, Antwerp, Belgium.
Emmanuel BottieauDepartment of Clinical Sciences, Institute of Tropical Medicine, Antwerp, Belgium.
Christophe Van DijckDepartment of Clinical Sciences, Institute of Tropical Medicine, Antwerp, Belgium.
Caroline TheunissenDepartment of Clinical Sciences, Institute of Tropical Medicine, Antwerp, Belgium.
Sabrina H van IersselDepartment of General Internal Medicine, Infectious Diseases and Tropical Medicine, Antwerp University Hospital, Edegem, Belgium.
Erika VliegheDepartment of General Internal Medicine, Infectious Diseases and Tropical Medicine, Antwerp University Hospital, Edegem, Belgium.
Esther BartholomeusAntwerp Unit for Data Analysis and Computation in Immunology and Sequencing (AUDACIS), University of Antwerp, Antwerp, Belgium.
Kerry MullanAdrem Data Lab, Department of Computer Science, University of Antwerp, Antwerp, Belgium.
Wim AdriaensenClinical Immunology Unit, Department of Clinical Sciences, Institute of Tropical Medicine, Antwerp, Belgium.
Guido VanhamGlobal Health Institute, University of Antwerp, Antwerp, Belgium.
Benson OgunjimiAntwerp Unit for Data Analysis and Computation in Immunology and Sequencing (AUDACIS), University of Antwerp, Antwerp, Belgium.
Kris LaukensAdrem Data Lab, Department of Computer Science, University of Antwerp, Antwerp, Belgium.
Koen VercauterenClinical Virology Unit, Department of Clinical Sciences, Institute of Tropical Medicine, Antwerp, Belgium.
Pieter MeysmanAdrem Data Lab, Department of Computer Science, University of Antwerp, Antwerp, Belgium.
Instituut voor Tropische Geneeskunde · BEAntwerp University Hospital · BEBiomedical Informatics Research Center Antwerp · BEUniversity of Antwerp · BE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

COVID-19SARS-CoV-2CD8-Positive T-LymphocytesEpitopes, T-LymphocyteHumansReceptors, Antigen, T-CellEpitopes, T-LymphocyteReceptors, Antigen, T-CellCD8+ T-cell responseCOVID-19cross-reactive T-cell responseimmunoinformaticsmachine learning modelsSARS-CoV-2 epitopesTCR repertoire analysis

Identifiers

PMID37325653
PMCPMC10264683
OpenAlexW4378906594

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.