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An interpretable classification model using gluten-specific TCR sequences shows diagnostic potential in coeliac disease

Abstract:
Coeliac disease (CeD) is a T-cell mediated enteropathy triggered by dietary gluten which remains substantially under-diagnosed around the world. The diagnostic gold-standard requires histological assessment of intestinal biopsies taken at endoscopy while consuming a gluten-containing diet. However, there is a lack of concordance between pathologists in histological assessment, and both endoscopy and gluten challenge are burdensome and unpleasant for patients. Identification of gluten-specific T-cell receptors (TCRs) in the TCR repertoire could provide a less subjective diagnostic test, and potentially remove the need to consume gluten. We review published gluten-specific TCR sequences, and develop an interpretable machine learning model to investigate their diagnostic potential. To investigate this, we sequenced the TCR repertoires of mucosal CD4$^{+}$ T cells from 20 patients with and without CeD. These data were used as a training dataset to develop the model, then an independently published dataset of 20 patients was used as the testing dataset. We determined that this model has a training accuracy of 100\% and testing accuracy of 80\% for the diagnosis of CeD, including in patients on a gluten-free diet (GFD). We identified 20 CD4$^{+}$ TCR sequences with the highest diagnostic potential for CeD. The sequences identified here have the potential to provide an objective diagnostic test for CeD, which does not require the consumption of gluten.
Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.3390/biom13121707

Authors

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Institution:
University of Oxford
Division:
MSD
Department:
NDM
Sub department:
NDM Experimental Medicine
Research group:
Translational Gastroenterology Unit
Oxford college:
Trinity College
Role:
Author
ORCID:
0000-0003-2365-2012


More from this funder
Funder identifier:
https://ror.org/029chgv08
Grant:
222426/Z/21/Z
More from this funder
Funder identifier:
https://ror.org/00c489v88
Grant:
SGL025\1066
SGL025\1066


Publisher:
MDPI
Journal:
Biomolecules More from this journal
Volume:
13
Issue:
12
Article number:
1707
Place of publication:
Switzerland
Publication date:
2023-11-25
Acceptance date:
2023-11-21
DOI:
EISSN:
2218-273X
Pmid:
38136579


Language:
English
Keywords:
Subtype:
Review
Pubs id:
1590577
Local pid:
pubs:1590577
Deposit date:
2025-03-27
ARK identifier:

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