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Nevarez-Mejia, J.

Publications and source records attributed to Nevarez-Mejia, J..

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COMBO-RATE: An experimentally validated bioinformatic tool to identify promiscuous HLA restrictions

Defining HLA restriction of T cell epitopes is essential for understanding immune responses in infectious disease, autoimmunity, and vaccine design. Current bioinformatic programs, including the IEDB RATE tool, enable inference of single-HLA restrictions from immune response data of HLA-typed donors. However, T cell epitopes are frequently presented by multiple HLA alleles, a phenomenon termed promiscuous restriction, limiting the utility of single-allele approaches. To address this limitation, we developed COMBO-RATE, an extension of RATE that systematically evaluates combinations of HLA alleles to identify multi-allelic restriction patterns. Analysis of three independent datasets spanning distinct antigen systems and different epitope discovery strategies revealed that promiscuous restriction is a near-universal feature of immunodominant epitopes. Focusing on the 43 immunodominant CD4 T cell epitopes identified in a B. pertussis genome-wide screen, COMBO-RATE outperformed conventional RATE, identifying restrictions for 35 of 43 epitopes, compared to 24 by RATE alone, and uncovered 64 additional allele restrictions, including 29 unique alleles. Experimental validation using single-HLA transfected cell lines and antigen presentation assays confirmed COMBO-RATE-inferred restrictions, demonstrating that a single epitope can be independently presented by distinct HLA alleles. Overall, COMBO-RATE provides a robust and scalable framework for defining complete HLA restriction profiles from existing population response data, with important implications for the design of vaccines requiring broad HLA coverage across genetically diverse populations. This pipeline is available as both a Python package and a user-friendly web application.

immunology↗

Spatial Multi-omics of Arterial Regions from Cardiac Allograft Vasculopathy Rejected Grafts

2. AbstractO_ST_ABSBackgroundC_ST_ABSCardiac allograft vasculopathy (CAV) is a major cause of late-graft failure and mortality following heart transplantation. The mechanisms underlying vascular remodeling are poorly understood. A major immune risk factor associated with the development of CAV is the presence of donor-specific antibodies (DSA) that induce chronic endothelial cell (EC) injury, leukocyte recruitment and inflammation resulting in thickening of arterial intima. Here, we molecularly characterized innate and adaptive immune cells present in the arteries of rejected cardiac allografts with DSA and identified protein and transcriptomic signatures distinguishing early and late CAV lesions. MethodsArterial areas of interest (AOIs) from CAV+DSA+ rejected cardiac allografts (N=3; 2 females, 1 male) were subjected to GeoMx digital spatial profiling (DSP). AOIs were scored on the level of CAV progression/neointimal thickening (22 AOIs total; 11 high and 11 low neointima) and were subjected to whole transcriptome and protein profiling. ResultsAOIs with low neointima significantly increased markers for activated inflammatory infiltrates, transcripts of EC activation and gene modules involved in activating metalloproteinases and TP53 regulation of caspases. Inflammatory and apoptotic protein markers significantly correlated with inflammatory modules in AOIs with low neointima. AOIs with high neointima increased TGF{beta}-regulated transcripts and modules enriched for platelet activation/aggregation. Proteins encoding SMCs and growth factors/survival correlated with modules enriched for proliferation/repair in AOIs with high neointima. Key transcripts in promoting proliferation, migration, and EndoMT were significantly associated with increasing neointima scores. ConclusionOur results reveal new protein and transcriptomic signatures associated with CAV progression. Lesions exhibiting inflammatory profiles appear to be early lesions that transition to later proliferative/pro-fibrotic phenotype CAV lesions. These findings should form the foundation for the identification of improved biomarkers to guide CAV treatment.

immunology↗