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.
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.
J Autoimmun. 2023 Nov;140:103115. doi: 10.1016/j.jaut.2023.103115. Epub 2023 Sep 27.
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.
分子模拟是一种被认为可以引发 1 型糖尿病胰岛自身免疫的机制。随着越来越多的报道的感染因子和胰岛抗原，优先研究感染因子的策略对于加速 1 型糖尿病病因学的转化研究至关重要。在这项工作中，我们开发了一种基于序列同源性、与特定 MHC 分子的经验结合亲和力以及 T 细胞免疫原性的经验潜力来评估 1 型糖尿病病因学中分子模拟的计算管道。然后，我们评估了这些潜在的分子模拟物是否在已知感染人类的其他病原体中保守。总的来说，我们确定了 61 种具有潜在高影响的分子模拟物，它们具有序列同源性、强大的经验结合亲和力和与特定 MHC 分子相关的经验免疫原性。我们还发现，这些潜在的分子模拟物中有 32 种的肽序列在几种人类病原体中是保守的。这些发现通过提供从感染因子中提取的经过精心整理和优先排序的肽列表，促进了 1 型糖尿病病因学中分子模拟的转化评估，以便进行病因学研究。这些结果还可以为产生具有感染性和 HLA 特异性的临床前模型提供证据，并为易感人群的未来筛查和预防工作提供信息。
