A somatic hypermutation-based machine learning model stratifies individuals with Crohn's disease and controls.
Journal
Genome research
ISSN: 1549-5469
Titre abrégé: Genome Res
Pays: United States
ID NLM: 9518021
Informations de publication
Date de publication:
01 2023
01 2023
Historique:
received:
13
02
2022
accepted:
07
12
2022
pubmed:
17
12
2022
medline:
4
2
2023
entrez:
16
12
2022
Statut:
ppublish
Résumé
Crohn's disease (CD) is a chronic relapsing-remitting inflammatory disorder of the gastrointestinal tract that is characterized by altered innate and adaptive immune function. Although massively parallel sequencing studies of the T cell receptor repertoire identified oligoclonal expansion of unique clones, much less is known about the B cell receptor (BCR) repertoire in CD. Here, we present a novel BCR repertoire sequencing data set from ileal biopsies from pediatric patients with CD and controls, and identify CD-specific somatic hypermutation (SHM) patterns, revealed by a machine learning (ML) algorithm trained on BCR repertoire sequences. Moreover, ML classification of a different data set from blood samples of adults with CD versus controls identified that V gene usage, clusters, or mutation frequencies yielded excellent results in classifying the disease (F1 > 90%). In summary, we show that an ML algorithm enables the classification of CD based on unique BCR repertoire features with high accuracy.
Identifiants
pubmed: 36526432
pii: gr.276683.122
doi: 10.1101/gr.276683.122
pmc: PMC9977146
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
71-79Informations de copyright
© 2023 Safra et al.; Published by Cold Spring Harbor Laboratory Press.
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