Evidence map›Paper›PMID 37774556›Full record

ArticleJournal of autoimmunity2023

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

Sejal Mistry, Ramkiran Gouripeddi, Julio C Facelli

Open access · greenAbstract read
In one paragraph

Article in Journal of autoimmunity, 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
0.8field-weighted citation impact, top 27% 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, 5 citations in OpenAlex.

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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

3 authors at 1 institution in 1 country.

Sejal MistryDepartment 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 GouripeddiDepartment 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 FacelliDepartment 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.
University of Utah · US

Funding

Utah Center for Clinical and Translational ScienceUL1TR002538 · NCATS · UNIVERSITY OF UTAH · PI HESS, RACHEL, MAJERSIK, JENNIFER JUHL · 2018 to 2022
$26.0M
CTSA UM1 Program at University of UtahUM1TR004409 · NCATS · UTAH STATE HIGHER EDUCATION SYSTEM--UNIVERSITY OF UTAH · PI RACHEL HESS, Jennifer Juhl Majersik · 2023 to 2026
$21.9M
ChimeraX -- Next Generation Visualization and Analysis Software for Multiscale ModelingR01GM129325 · NIGMS · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI FERRIN, THOMAS E · 2018 to 2025
$5.2M
Identifying Temporal Sequences of Exposures in Type 1 Diabetes EtiologyF30DK134113 · NIDDK · UNIVERSITY OF UTAH · PI MISTRY, SEJAL · 2022 to 2024
$144k
NCATS NIH HHS UL1 TR002538NCATS NIH HHS UM1 TR004409NIDDK NIH HHS F30 DK134113NIGMS NIH HHS R01 GM129325
6 · The paper itself

Abstract

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.

Indexed as

Diabetes Mellitus, Type 1Molecular MimicryAutoimmunityEpitopesEpitopes, T-LymphocyteHLA AntigensHumansPeptidesT-LymphocytesTranslational Research, BiomedicalEpitopesEpitopes, T-LymphocyteHLA AntigensPeptidesBioinformaticsIslet autoimmunityMolecular mimicrySequence homologyTranslational scienceType 1 diabetes mellitus

Identifiers

PMID37774556
PMCPMC10965504
OpenAlexW4387113476

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

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.