Prioritization of infectious epitopes for translational investigation in type 1 diabetes etiology.

Bioinformatics Islet autoimmunity Molecular mimicry Sequence homology Translational science Type 1 diabetes mellitus

Journal

Journal of autoimmunity
ISSN: 1095-9157
Titre abrégé: J Autoimmun
Pays: England
ID NLM: 8812164

Informations de publication

Date de publication:
27 Sep 2023
Historique:
received: 01 06 2023
revised: 28 07 2023
accepted: 14 09 2023
medline: 30 9 2023
pubmed: 30 9 2023
entrez: 29 9 2023
Statut: aheadofprint

Résumé

Molecular mimicry is one mechanism by which infectious agents are thought to trigger islet autoimmunity in type 1 diabetes. With a growing number of reported infectious agents and islet antigens, strategies to prioritize the study of infectious agents are critically needed to expedite translational research into the etiology of type 1 diabetes. In this work, we developed an in-silico pipeline for assessing molecular mimicry in type 1 diabetes etiology based on sequence homology, empirical binding affinity to specific MHC molecules, and empirical potential for T-cell immunogenicity. We then assess whether potential molecular mimics were conserved across other pathogens known to infect humans. Overall, we identified 61 potentially high-impact molecular mimics showing sequence homology, strong empirical binding affinity, and empirical immunogenicity linked with specific MHC molecules. We further found that peptide sequences from 32 of these potential molecular mimics were conserved across several human pathogens. These findings facilitate translational evaluation of molecular mimicry in type 1 diabetes etiology by providing a curated and prioritized list of peptides from infectious agents for etiopathologic investigation. These results may also provide evidence for generation of infectious and HLA-specific preclinical models and inform future screening and preventative efforts in genetically susceptible populations.

Identifiants

pubmed: 37774556
pii: S0896-8411(23)00124-5
doi: 10.1016/j.jaut.2023.103115
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

103115

Subventions

Organisme : NCATS NIH HHS
ID : UL1 TR002538
Pays : United States
Organisme : NCATS NIH HHS
ID : UM1 TR004409
Pays : United States

Informations de copyright

Copyright © 2023. Published by Elsevier Ltd.

Auteurs

Sejal Mistry (S)

Department of Biomedical Informatics, University of Utah, Salt Lake City, USA; Center of Excellence for Exposure Health Informatics, University of Utah, Salt Lake City, USA.

Ramkiran Gouripeddi (R)

Department of Biomedical Informatics, University of Utah, Salt Lake City, USA; Center of Excellence for Exposure Health Informatics, University of Utah, Salt Lake City, USA; Clinical and Translational Science Institute, University of Utah, Salt Lake City, USA.

Julio C Facelli (JC)

Department of Biomedical Informatics, University of Utah, Salt Lake City, USA; Center of Excellence for Exposure Health Informatics, University of Utah, Salt Lake City, USA; Clinical and Translational Science Institute, University of Utah, Salt Lake City, USA. Electronic address: julio.facelli@utah.edu.

Classifications MeSH