Biology subjects

Joglekar, A. V.

Publications and source records attributed to Joglekar, A. V..

7 recordsLinked to original sources

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↗

bioRxiv2026

Coincident epithelial signals restrain commensal-specific CD8αβ+ T cells in the intestine

The intestinal epithelium harbors a large population of microbiota-dependent CD8{beta}+ T cells whose antigen specificity and regulation are ill-defined. By identifying MHCIa-restricted TCRs and generating tetramers against the gut commensal Segmented Filamentous Bacteria, we demonstrate that a single commensal species drives a clonally expanded, antigen-specific CD8{beta}+ T cell within the intraepithelial lymphocyte compartment. Mechanistically, the intestinal epithelium coordinates coincident signals governing this population: peptide:MHC-dependent TCR engagement drives pIEL accumulation, while v{beta}6-mediated TGF{beta} activation restraints effector cell differentiation. Perturbation of epithelial cell-mediated TGF{beta} activation diverts commensal-specific CD8+ T cells toward inflammatory differentiation states transcriptionally convergent with those observed in ulcerative colitis. The intestinal epithelium thus functions as a dual-signal organizer of commensal-specific CD8+ T cell responses, coupling differentiation to restraint through spatially coincident molecular cues.

immunology↗

bioRxiv2024

Sliding Window INteraction Grammar (SWING): a generalized interaction language model for peptide and protein interactions

The explosion of sequence data has allowed the rapid growth of protein language models (pLMs). pLMs have now been employed in many frameworks including variant-effect and peptide-specificity prediction. Traditionally, for protein-protein or peptide-protein interactions (PPIs), corresponding sequences are either co-embedded followed by post-hoc integration or the sequences are concatenated prior to embedding. Interestingly, no method utilizes a language representation of the interaction itself. We developed an interaction LM (iLM), which uses a novel language to represent interactions between protein/peptide sequences. Sliding Window Interaction Grammar (SWING) leverages differences in amino acid properties to generate an interaction vocabulary. This vocabulary is the input into a LM followed by a supervised prediction step where the LMs representations are used as features. SWING was first applied to predicting peptide:MHC (pMHC) interactions. SWING was not only successful at generating Class I and Class II models that have comparable prediction to state-of-the-art approaches, but the unique Mixed Class model was also successful at jointly predicting both classes. Further, the SWING model trained only on Class I alleles was predictive for Class II, a complex prediction task not attempted by any existing approach. For de novo data, using only Class I or Class II data, SWING also accurately predicted Class II pMHC interactions in murine models of SLE (MRL/lpr model) and T1D (NOD model), that were validated experimentally. To further evaluate SWINGs generalizability, we tested its ability to predict the disruption of specific protein-protein interactions by missense mutations. Although modern methods like AlphaMissense and ESM1b can predict interfaces and variant effects/pathogenicity per mutation, they are unable to predict interaction-specific disruptions. SWING was successful at accurately predicting the impact of both Mendelian mutations and population variants on PPIs. This is the first generalizable approach that can accurately predict interaction-specific disruptions by missense mutations with only sequence information. Overall, SWING is a first-in-class generalizable zero-shot iLM that learns the language of PPIs.

systems biology↗

bioRxiv2023

Temporal dynamics and genomic programming of plasma cell fates

Affinity-matured plasma cells (PCs) of varying lifespans are generated through a germinal center (GC) response. The developmental dynamics and genomic programs of antigen-specific PC precursors remain to be elucidated. Using a model antigen, we demonstrate biphasic generation of PC precursors, with those generating long-lived bone marrow PCs preferentially produced in the late phase of GC response. Clonal tracing using scRNA-seq+BCR-seq in spleen and bone marrow compartments, coupled with adoptive transfer experiments, reveal a novel PC transition state that gives rise to functionally competent PC precursors. The latter undergo clonal expansion, dependent on inducible expression of TIGIT. We propose a model for the proliferation and programming of precursors of long-lived PCs, based on extended antigen encounters followed by reduced antigen availability.

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↗

bioRxiv2022

De novo identification of CD4+ T cell epitopes

CD4+ T cells recognize peptide antigens presented on class II Major Histocompatibility Complex (MHC-II) molecules to carry out their function. The remarkable diversity of T cell receptor (TCR) sequences and lack of antigen discovery approaches for MHC-II make profiling the specificities of CD4+ T cells challenging. We have expanded our platform of Signaling and Antigen-presenting Bifunctional Receptors to encode MHC-II molecules presenting covalently linked peptides (SABR-IIs) for CD4+ cell antigen discovery. SABR-IIs can present epitopes to CD4+ T cells and induce signaling upon their recognition, allowing a readable output. Here, we demonstrate that SABR-IIs libraries presenting endogenous and post-translationally modified epitopes can be used for antigen discovery. Using SABR-II libraries in conjunction with single cell RNA sequencing, we de-convoluted multiple highly expanded TCRs from pancreatic islets of Non-Obese Diabetic (NOD) mice. We compounded antigen discovery by incorporating computational TCR similarity prediction metrics followed by experimental validation. Finally, we showed SABR-IIs presenting epitopes in class II HLA alleles can be used for antigen discovery for human CD4+ T cells. Taken together, we have developed a rapid, flexible, scalable, and versatile approach for the de novo identification of CD4+ T cell ligands from single cell RNA sequencing data using experimental and computational approaches.

immunology↗

bioRxiv2019

T Cell Receptor Immunotherapy Drives Human Immunodeficiency Virus Evolution in Humanized Mice

T cell receptor mediated immunotherapy using engineered Hematopoietic Stem/Progenitor Cells leads to durable partial suppression of HIV in humanized mice. Sustained viral suppression is accompanied by viral evolution under selection pressure. This study highlights the potential for TCR immunotherapy and the need to target multiple epitopes.\n\nAbstractEffective CD8+ T cell responses targeted to the KK10 epitope of HIV presented by HLA-B*27:05, a protective HLA allele, correlate with the ability to control infection without antiretroviral therapy (ART). Here, we report an immunotherapy approach using two B*27:05-KK10-specific T Cell Receptors (TCRs) isolated from HIV controllers. Immunocompromised mice engrafted with human Hematopoietic Stem/Progenitor Cells (HSPCs) encoding for the TCRs showed differentiation into functionally active engineered T cells. Following infection with HIV, both TCRs showed sustained, albeit modest, viral suppression over 32 weeks, accompanied by a concomitant increase in CD4+ T cells. Sequencing of viral quasi-species from the plasma of infected mice demonstrated clear evidence for viral evolution under selection pressure from the TCRs. The most commonly observed mutation in the KK10 epitope was L6M, which preserved viral fitness but showed attenuated recognition by the TCRs. These studies show that TCR-immunotherapy was able to suppress HIV infection long-term while driving HIV evolution in humanized mice.

immunology↗