Biology subjects

Raman, I.

Publications and source records attributed to Raman, I..

6 recordsLinked to original sources

bioRxiv2026

Distributed Clonal Deletion Prevents Autoimmune Disease Progression

Self-reactive B cells are generated during normal development and can acquire increased pathogenicity through activation-induced cytidine deaminase (AID)-mediated diversification following activation. Clonal deletion is thought to eliminate these cells, yet how deletion is distributed across developmental and activation stages to prevent autoimmune disease remains unclear. Here, we show that clonal deletion is enforced through temporally distinct mitochondrial apoptosis (MOMP) checkpoints that differentially regulate autoreactive B cell fate and disease progression. Using conditional Bcl-2 expression to inhibit MOMP either before or after B cell activation, we find that early inhibition permits the survival and maturation of autoreactive B cells after peripheral egress, expanding the pool of cells available for activation. These cells subsequently undergo AID-dependent diversification, producing class-switched IgG autoantibodies with expanded antigen breadth that target a wider range of self-antigens and drive lethal, female-biased autoimmune disease characterized by complement activation and kidney pathology. In contrast, inhibition of MOMP only after activation allows the accumulation of germinal center, switched memory, and plasma cells and promotes autoantibody production, but results in more restricted IgG autoreactivity, limited complement activation and limited tissue damage, and normal survival. Notably, early MOMP inhibition does not expand immature bone marrow B cells, indicating that a major clonal deletion checkpoint operates in the periphery rather than during initial B cell generation. Together, these findings support a Distributed Clonal Deletion Model in which early checkpoints restrict the entry of autoreactive B cells into diversification pathways, while later checkpoints limit the persistence of diversified autoreactive clones, thereby constraining autoimmune disease progression. One Sentence SummaryDistributed clonal deletion prevents autoimmune disease progression by restricting the breadth of autoreactive clones entering immune responses, with early MOMP checkpoints limiting diversification and later checkpoints constraining persistence.

immunology↗

bioRxiv2026

A microbiota-derived bile acid overcomes antibiotic-induced hyporesponsiveness to immune checkpoint therapy by enhancing CD8+ T cell antitumor immunity

Gut microbiota are critical determinants of effective immune checkpoint therapy (ICT), yet the microbial mediators and host mechanisms that enhance antitumor immunity remain poorly understood. Here, we identify the microbiota-derived bile acid taurodeoxycholic acid (TDCA) as a metabolite associated with immune checkpoint therapy (ICT) response. TDCA administration alone is sufficient to overcome antibiotic-induced ICT hyporesponsiveness across multiple murine tumor models. Mechanistically, TDCA directly enhances CD8 T cell-mediated antitumor immunity, increasing cytotoxicity. These effects required signaling through the bile acid receptor TGR5. Together, these findings reveal TDCA as a gut microbial metabolite that restores ICT efficacy after antibiotic disruption by directly augmenting CD8 T cell anti-tumor activity. This work supports metabolite replacement as a therapeutic strategy to mitigate antibiotic-associated loss of cancer immunotherapy response. SignificanceTDCA is a microbiota-derived metabolite that restores immune checkpoint therapy efficacy after antibiotic disruption by directly enhancing CD8 T-cell-mediated anti-tumor immunity through bile acid receptor TGR5 signaling. Our findings suggest that supplementation with defined microbial metabolites can mitigate antibiotic-associated loss of immunotherapy response without requiring broader microbiome reconstitution.

cancer biology↗

bioRxiv2025

Biomechanical Phenotyping Reveals Unique Mechanobiological Signatures of Early-Onset Colorectal Cancer

While both incidence and mortality of sporadic average-onset colorectal cancer (AO CRC, above 50 years of age) are in constant decline, sporadic early-onset colorectal cancer (EO CRC, under 50 years of age) is rising rapidly. Yet, the causes behind this rise remain poorly understood. Epidemiological studies indicate that lifestyle and environmental exposures may result in chronic inflammation, which is known to trigger tissue fibrosis. This study tests the hypothesis that fibrotic remodeling and biomechanical stiffening of colorectal tissues represent measurable hallmarks and potential drivers of EO CRC. Using primary human tissues, this work shows that EO CRC is associated with changes in collagen microstructure, increased stiffness and elevated viscosity of primary tumors. Spatial transcriptional profiling and immunostaining reveal pro-fibrotic signatures in stromal cells, alongside enhanced Yes-associated protein (YAP) mechanotransduction and proliferation in epithelial cells of EO CRC tissues. Mechanistically, increasing matrix stiffness in vitro promotes proliferation of epithelial cells in 2D and 3D colorectal cancer models. Together, these findings establish EO CRC as a disease marked by early and widespread biomechanical remodeling, suggesting that a fibrotic and stiffened tissue microenvironment may orchestrate EO CRC tumor initiation.

bioengineering↗

bioRxiv2024

Serum and Tear Autoantibodies from NOD and NOR Mice as Potential Diagnostic Indicators of Local and Systemic Inflammation in Sjögren's Disease

BackgroundSjogrens Disease (SjD) is an autoimmune disease characterized by lymphocytic infiltration of salivary and lacrimal glands (LG). The LG produces the protein-rich aqueous component of tears, and SjD-associated autoimmune dacryoadenitis (AD) may thus alter tear autoantibody composition. MethodsThe presence of tertiary lymphoid structures (TLS) in LG from two murine models of SjD-associated AD, male NOD and male NOR mice, were evaluated using immunofluorescence. IgG and IgA reactivity in serum and tears from these models were probed in three studies against a panel of 80-120 autoantigens using autoantibody microarrays relative to serum and tears from healthy male BALB/c mice. Data were analyzed by R package Limma. ResultsAnalysis of immunofluorescence in LG sections from both SjD models showed TLS. Only one autoantibody was significantly elevated in tears and serum in both SjD models across all studies. Three autoantibodies were significantly elevated in serum but not in tears in both SjD models across all studies. Conversely, six IgG and thirteen IgA autoantibodies (6 sharing the same autoantigen) were significantly elevated in tears but not serum in both SjD models. ConclusionNOD and NOR mice with SjD-associated AD have distinct autoantibody profiles in tears and serum. Tear IgA isotype autoantibodies showed a greater diversity than tear IgG autoantibodies. TLS observed in LG are a likely source of the tear autoantibodies.

immunology↗

bioRxiv2024

An Artificial Intelligence Model for Profiling the Landscape of Antigen-binding Affinities of Massive BCR Sequencing Data

The interaction between antigens and antibodies (B cell receptors, BCRs) is the key step underlying the function of the humoral immune system in cancers and other biological contexts. The capability to profile the landscape of antigen-binding affinities of a vast number of BCRs will provide a powerful tool to reveal novel insights at unprecedented levels and will yield powerful tools for translational development. However, current experimental approaches for detecting antibody-antigen interactions are costly and time-consuming, and can only achieve low-to-mid throughput. On the other hand, bioinformatics tools in the field of antibody informatics mostly focus on optimization of antibodies given known binding antigens, which is a very different research question and of limited scope. In this work, we developed an innovative Artificial Intelligence tool, Cmai, to address the prediction of binding between antibodies and antigens that can be scaled to high-throughput sequencing data. We devised a biomarker metric based on the output from Cmai applied to high-throughput BCR sequencing data to predict the landscape of the antigen-binding affinities of the BCRs. We found that extracellular antigens on malignant tumor cells are inducing B cell infiltrations, and the infiltrating B cells have a greater tendency to co-localize with tumor cells expressing these antigens. We further found that the abundance of tumor antigen-targeting antibodies is predictive of immune-checkpoint inhibitor (ICI) treatment response. On the other hand, we found that, during immune-related adverse events (irAEs) caused by ICI treatment, humoral immunity is preferentially responsive to intracellular antigens from the organs affected by the irAEs. Overall, we developed a powerful tool for elucidating the antigen binding landscape of BCR repertoires, and it shall also propell antibody-centric translational applications for cancers and other diseases.

immunology↗

bioRxiv2023

pan-MHC and cross-Species Prediction of T Cell Receptor-Antigen Binding

Profiling the binding of T cell receptors (TCRs) of T cells to antigenic peptides presented by MHC proteins is one of the most important unsolved problems in modern immunology. Experimental methods to probe TCR-antigen interactions are slow, labor-intensive, costly, and yield moderate throughput. To address this problem, we developed pMTnet-omni, an Artificial Intelligence (AI) system based on hybrid protein sequence and structure information, to predict the pairing of TCRs of {beta} T cells with peptide-MHC complexes (pMHCs). pMTnet-omni is capable of handling peptides presented by both class I and II pMHCs, and capable of handling both human and mouse TCR-pMHC pairs, through information sharing enabled this hybrid design. pMTnet-omni achieves a high overall Area Under the Curve of Receiver Operator Characteristics (AUROC) of 0.888, which surpasses competing tools by a large margin. We showed that pMTnet-omni can distinguish binding affinity of TCRs with similar sequences. Across a range of datasets from various biological contexts, pMTnet-omni characterized the longitudinal evolution and spatial heterogeneity of TCR-pMHC interactions and their functional impact. We successfully developed a biomarker based on pMTnet-omni for predicting immune-related adverse events of immune checkpoint inhibitor (ICI) treatment in a cohort of 57 ICI-treated patients. pMTnet-omni represents a major advance towards developing a clinically usable AI system for TCR-pMHC pairing prediction that can aid the design and implementation of TCR-based immunotherapeutics.

immunology↗