Machine learning analysis of the T cell receptor repertoire identifies sequence features of self-reactivity.

CD4 T cells CD5 CDR3 beta chain T cell receptor chronic infection heterogeneity machine learning retrogenic mice self-reactivity thymic development

Journal

Cell systems
ISSN: 2405-4720
Titre abrégé: Cell Syst
Pays: United States
ID NLM: 101656080

Informations de publication

Date de publication:
28 Nov 2023
Historique:
received: 27 05 2023
revised: 01 09 2023
accepted: 09 11 2023
medline: 8 12 2023
pubmed: 8 12 2023
entrez: 7 12 2023
Statut: aheadofprint

Résumé

The T cell receptor (TCR) determines specificity and affinity for both foreign and self-peptides presented by the major histocompatibility complex (MHC). Although the strength of TCR interactions with self-pMHC impacts T cell function, it has been challenging to identify TCR sequence features that predict T cell fate. To discern patterns distinguishing TCRs from naive CD4

Identifiants

DOI: 10.1016/j.cels.2023.11.004 PMID: 38061355
pubmed: 38061355
pii: S2405-4712(23)00327-7
doi: 10.1016/j.cels.2023.11.004
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Informations de copyright

Copyright © 2023 The Author(s). Published by Elsevier Inc. All rights reserved.

Déclaration de conflit d'intérêts

Declaration of interests The authors declare no competing interests.

Auteurs

Johannes Textor (J)

Data Science Group, Institute for Computing and Information Sciences, Radboud University, Nijmegen 6525 EC, the Netherlands; Medical BioSciences, Radboudumc, Nijmegen 6525 GA, the Netherlands. Electronic address: johannes.textor@ru.nl.

Franka Buytenhuijs (F)

Data Science Group, Institute for Computing and Information Sciences, Radboud University, Nijmegen 6525 EC, the Netherlands.

Dakota Rogers (D)

Department of Physiology, McGill University, Montreal, QC H3G 0B1, Canada; McGill Research Centre on Complex Traits, McGill University, Montreal, QC H3G 0B1, Canada.

Ève Mallet Gauthier (ÈM)

Immunology-Oncology Unit, Maisonneuve-Rosemont Hospital Research Center, Montreal, QC H1T 2M4, Canada; Department of Microbiology, Infectious Diseases, and Immunology, Université de Montréal, Montréal, QC H3T 1J4, Canada.

Shabaz Sultan (S)

Data Science Group, Institute for Computing and Information Sciences, Radboud University, Nijmegen 6525 EC, the Netherlands; Medical BioSciences, Radboudumc, Nijmegen 6525 GA, the Netherlands.

Inge M N Wortel (IMN)

Data Science Group, Institute for Computing and Information Sciences, Radboud University, Nijmegen 6525 EC, the Netherlands; Medical BioSciences, Radboudumc, Nijmegen 6525 GA, the Netherlands.

Kathrin Kalies (K)

Institut für Anatomie, Universität zu Lübeck, 23562 Lübeck, Germany.

Anke Fähnrich (A)

Institut für Anatomie, Universität zu Lübeck, 23562 Lübeck, Germany.

René Pagel (R)

Institut für Anatomie, Universität zu Lübeck, 23562 Lübeck, Germany.

Heather J Melichar (HJ)

Immunology-Oncology Unit, Maisonneuve-Rosemont Hospital Research Center, Montreal, QC H1T 2M4, Canada; Department of Medicine, Université de Montréal, Montréal, QC H1T 2M4, Canada; Department of Microbiology & Immunology, McGill University, Montreal, QC H3A 1A3, Canada; Rosalind and Morris Goodman Cancer Institute, McGill University, Montreal, QC H3A 1A3, Canada.

Jürgen Westermann (J)

Institut für Anatomie, Universität zu Lübeck, 23562 Lübeck, Germany.

Judith N Mandl (JN)

Department of Physiology, McGill University, Montreal, QC H3G 0B1, Canada; Department of Microbiology & Immunology, McGill University, Montreal, QC H3A 1A3, Canada; McGill Research Centre on Complex Traits, McGill University, Montreal, QC H3G 0B1, Canada. Electronic address: judith.mandl@mcgill.ca.

Classifications MeSH