Biology subjects

Keshari, S.

Publications and source records attributed to Keshari, S..

3 recordsLinked to original sources

bioRxiv2025

Cell cycle-coupled transcriptional network orchestrates human B cell fate bifurcation

Bifurcation of activated human B cells into plasmablast (PB) and germinal center precursor (preGC) fates underlies protective and autoimmune antibody responses, yet gene regulatory networks (GRNs) governing the alternative trajectories remain poorly defined. Using temporal single-cell multiomics, we assembled state-specific human B cell GRNs spanning four scales: transcription factor (TF)-to-fate, TF-to-gene, cis-regulatory element (CRE)-to-gene and nucleotide-to-CRE. Applying the framework to in vitro differentiated and tonsil B cells revealed concordant regulatory architectures and impacts of in silico TF perturbations. CRISPR perturbations validated many TF-to-fate and TF-to-gene predictions and revealed a mutually repressive BATF-IRF4/PRDM1 network module. The GRNs were used to predict and interpret effects of autoimmune disease variants, uncovering partitioning of disease risk at distinct TF motifs and B cell states. Predicted functional variants were independently supported by chromatin and expression QTLs and reporter assays. A web application enables exploration of B cell regulatory determinants of vaccine responses and autoimmune diseases.

immunology↗

bioRxiv2025

Cracking the code of co-authorship networks geo-temporally using interpretable machine learning

An exponential growth in the scientific literature necessitates the development of highly scalable computational tools that can effectively analyze and distill insights from complex, interconnected research landscapes. We introduce Distributed, Interpretable, and Scalable computing for Co-authorship Networks (DISCo-Net), a robust and scalable tool engineered to curate and examine large-scale co-authorship networks by harnessing the power of distributed computing and advanced relational database queries. We use DISCo-Net to analyze co-authorship networks derived from millions of papers in the life sciences and physical sciences over more than two decades. Using a range of deep learning approaches, we surprisingly found that pre-trained zero-shot embeddings from a sentence transformer better captured global co-authorship relationships than a complex graphical attention transformer. Even more surprisingly, a simple interpretable Term Frequency-Inverse Document Frequency (TF-IDF) model performed as well as the Bidirectional Encoder Representations from Transformers (BERT) model. Through topic modeling on TF-IDF document descriptors, we identified nine major research areas prevalent globally over the past 24 years and captured topic-specific shifting trends in scientific output. Our study draws an innovative parallel between collaborative research networks and genomic regulatory structures, applying genomics data analysis methodologies to uncover patterns in global scientific collaboration. This approach reveals interpretable alignments between research interests and human developmental stages, while also identifying emerging influential players in the global research landscape. The findings highlight potential far-reaching consequences of current funding challenges, particularly in the U.S., and offer actionable insights for optimizing resource allocation and fostering innovation in an interconnected global scientific community.

scientific communication and education↗

bioRxiv2023

Overlapping and Distinct Mechanisms of Effective Neoantigen Cancer Vaccines and Immune Checkpoint Therapy

The goal of therapeutic cancer vaccines and immune checkpoint therapy (ICT) is to eliminate cancer by expanding and/or sustaining T cells with anti-tumor capabilities. However, whether cancer vaccines and ICT enhance anti-tumor immunity by distinct or overlapping mechanisms remains unclear. Here, we compared effective therapeutic tumor-specific mutant neoantigen (NeoAg) cancer vaccines with anti-CTLA-4 and/or anti-PD-1 ICT in preclinical models. Both NeoAg vaccines and ICT induce expansion of intratumoral NeoAg-specific CD8 T cells, though the degree of expansion and acquisition of effector activity was much more substantial following NeoAg vaccination. Further, we found that NeoAg vaccines are particularly adept at inducing proliferating and stem-like NeoAg-specific CD8 T cells. Single cell T cell receptor (TCR) sequencing revealed that TCR clonotype expansion and diversity of NeoAg-specific CD8 T cells relates to their phenotype and functional state associated with specific immunotherapies employed. Effective NeoAg vaccines and ICT required both CD8 and CD4 T cells. While NeoAg vaccines and anti-PD-1 affected the CD4 T cell compartment, it was to less of an extent than observed with anti-CTLA-4, which notably induced ICOS+Bhlhe40+ Th1-like CD4 T cells and, when combined with anti-PD-1, a small subset of Th2-like CD4 T cells. Although effective NeoAg vaccines or ICT expanded intratumoral M1-like iNOS+ macrophages, NeoAg vaccines expanded rather than suppressed (as observed with ICT) M2-like CX3CR1+CD206+ macrophages, associated with the vaccine adjuvant. Further, combining NeoAg vaccination with ICT induced superior efficacy compared to either therapy in isolation, highlighting the utility of combining these modalities to eliminate cancer. HighlightsO_LINeoAg cancer vaccines utilize distinct mechanisms from CTLA-4 or PD-1 ICT C_LIO_LINeoAg vaccines induce TCF1+ stem-like and proliferating NeoAg-specific CD8 T cells C_LIO_LICD8 TCR clonotype expansion relates to phenotype and functional state associated with immunotherapy C_LIO_LINeoAg vaccines induce partially distinct macrophage remodeling from ICT C_LIO_LINeoAg vaccines synergize with ICT, exceeding combination CTLA-4/PD-1 ICT efficacy C_LI

immunology↗