Zdinak, P. M. | Search bioRxiv

bioRxiv2026

Cell-based epitope screen identifies a novel B chain-derived Hybrid Insulin Peptide recognized by human and mouse autoreactive CD4 T cells

Type 1 diabetes (T1D) is mediated by autoreactive CD4 T cells that recognize pancreatic {beta}-cell antigens MHC class II molecules. The B-chain of insulin is thought to be a primary autoantigen although native peptide sequences from the B-chain bind weakly to disease-associated MHC risk alleles. Hybrid Insulin Peptides (HIPs), consist of proinsulin fragments fused with peptides from other {beta}-cell proteins, and may endow B-chain peptides with C-terminal acid residues that allow strong binding to MHC risk alleles and subsequent stimulation of autoreactive T cells. Here we applied a high-throughput cell-based screening platform with Signaling and Antigen-presenting Bifunctional Receptors (SABRs) to interrogate a theoretical library of insulin B-chain HIPs against clonally expanded CD4 T cell receptors from NOD mouse islets. We identified a previously undescribed HIP containing a fragment of insulin B-chain combined with a calreticulin-derived peptide. I-Ag7 tetramers loaded with the InsB/Calr HIP bind to T cell clones as well as islet-infiltrating CD4 T cells and CD4 T cells from a recent-onset HLA-DQ2 T1D donor reacted exclusively to the InsB/Calr HIP. Collectively, these results demonstrate that insulin B-chain-derived peptides can undergo post-translational modification through HIP formation to generate neoepitopes with increased antigenicity, and underscore the utility of cell-based screening for identifying disease-relevant neoepitopes in T1D.

immunology↗

bioRxiv2022

SLIDE: Significant Latent Factor Interaction Discovery and Exploration across biological domains

Modern multi-omic technologies can generate deep multi-scale profiles. However, differences in data modalities, multicollinearity of the data, and large numbers of irrelevant features make the analyses and integration of high-dimensional omic datasets challenging. Here, we present Significant Latent factor Interaction Discovery and Exploration (SLIDE), a first-in-class interpretable machine learning technique for identifying significant interacting latent factors underlying outcomes of interest from high-dimensional omic datasets. SLIDE makes no assumptions regarding data-generating mechanisms, comes with theoretical guarantees regarding identifiability of the latent factors/corresponding inference, outperforms/performs at least as well as state-of-the-art approaches in terms of prediction, and provides inference beyond prediction. Using SLIDE on scRNA-seq data from systemic sclerosis (SSc) patients, we first uncovered significant interacting latent factors underlying SSc pathogenesis. In addition to accurately predicting SSc severity and outperforming existing benchmarks, SLIDE uncovered significant factors that included well-elucidated altered transcriptomic states in myeloid cells and fibroblasts, an intriguing keratinocyte-centric signature validated by protein staining, and a novel mechanism involving altered HLA signaling in myeloid cells, that has support in genetic data. SLIDE also worked well on spatial transcriptomic data and was able to accurately identify significant interacting latent factors underlying immune cell partitioning by 3D location within lymph nodes. Finally, SLIDE leveraged paired scRNA-seq and TCR-seq data to elucidate latent factors underlying extents of clonal expansion of CD4 T cells in a nonobese diabetic model of T1D. The latent factors uncovered by SLIDE included well-known activation markers, inhibitory receptors and intracellular regulators of receptor signaling, but also honed in on several novel naive and memory states that standard analyses missed. Overall, SLIDE is a versatile engine for biological discovery from modern multi-omic datasets.

systems biology↗