Biology subjects

Munshi, N. V.

Publications and source records attributed to Munshi, N. V..

7 recordsLinked to original sources

bioRxiv2026

Systematic CRISPRi perturbation of 1,408 autism risk genes maps multilevel transcriptional convergence in human cortical neurons

Genetic heterogeneity in autism spectrum disorder (ASD) complicates the identification of shared molecular pathways amenable to therapeutic intervention. Here, we perform a CRISPRi Perturb-seq screen targeting 1,408 ASD risk genes in human embryonic stem cell-derived immature cortical neurons and profile the resulting transcriptomic effects by single-cell RNA sequencing. We identify 215 ASD risk genes whose repression induces significant global transcriptomic dysregulation. Leveraging this functional atlas, we characterize candidate genes based on transcriptional similarity to established ASD hub genes. We identify gene programs recurrently dysregulated across perturbations, anchored by processes governing microtubule dynamics, neuron differentiation, cell migration, and transmembrane transport. We further identify CHAMP1 as a previously unrecognized regulator of Wnt signaling and define specific ASD risk genes that modulate the rate of cortical neuron differentiation. Analysis of differentially expressed genes reveals both convergent and perturbation-specific downstream transcriptional responses. Together, these findings provide a multilevel map of transcriptomic convergence in ASD and establish a framework for identifying both pathway-level and genotype-specific therapeutic strategies.

neuroscience↗

bioRxiv2026

Combinatorial transcription factor interactions drive modular gene regulatory networks

Transcription factors (TFs) cooperatively drive gene regulatory networks (GRNs) to establish transcriptional states. Forced induction of TFs in combination can reprogram cell state by supplanting existing GRNs. Thus, TFs and GRNs are the building blocks to engineering transcriptional state. However, one key challenge is that the relationship between TF combinations and GRNs remains largely uncharacterized and difficult to accurately predict. Here, we apply single-cell overexpression screens to map the combinatorial activities of [~]100 TFs to gene expression states. Our analysis identifies diverse TF combinations driving cell-type specific regulatory programs. Notably, different TF combinations induce shared gene sets with cell-type specific functions, suggesting a modular regulatory architecture of the transcriptome. Furthermore, we define pairwise TF interactions and show that cooperative interactions improve transcriptional reprogramming. Finally, we developed tools to predict combinatorial TF phenotypes. These findings improve our understanding of cell state and how to manipulate it for biomedical applications. HIGHLIGHTSO_LICombinatorial over-expression screens for [~]100 transcription factors (TFs). C_LIO_LIDiverse TF combinations drive cell-type specific regulatory programs. C_LIO_LITF regulatory networks reveal a modular regulatory architecture of the transcriptome. C_LIO_LITF-TF interactions and predictive models enhance reprogramming cocktails. C_LI

genomics↗

bioRxiv2026

PEPR-GNN: Perturbation-Enhancer-Promoter-RNA Graph Neural Networks for Multiome Perturb-Seq modeling of regulomes

Cellular reprogramming is a complex interplay between perturbations and regulatory elements, culminating in gene expression changes. Current computational approaches do not explicitly model these regulatory interactions. Here, we performed combinatorial reprogramming with cardiac transcription factors, followed by Multiome Perturb-Seq to measure perturbations, open chromatin, and gene expression in individual cells. We then developed PEPR-GNN (Perturbation-Enhancer-Promoter-RNA Graph Neural Network), a theoretical and computational framework to model regulome responses during complex genetic perturbations. By statistically associating gene regulatory relationships, PEPR-GNN organizes genes into regulomes with shared gene regulatory responses to reprogramming, including easy-to-reprogram cardiac genes, difficult-to-reprogram fibroblast genes, and context-specific genes where the impact of a reprogramming factor depends on the presence of others. Finally, we use PEPR-GNN for in silico modeling of how genetic modifications of enhancers can be used to tune gene responses to reprogramming. Overall, through the use of causal perturbation information and an enhancer-aware regulome model of gene regulation, PEPR-GNN can effectively model complex cellular responses to perturbation. HighlightsO_LIMultiome Perturb-Seq of GHMT reprogramming in MEFs with RNA/ATAC-Seq readout. C_LIO_LIPEPR-GNN: a computational framework to model perturbation-induced regulomes. C_LIO_LIPEPR-GNN aids the interpretation of regulomes by diverse reprogramming responses. C_LIO_LIPEPR-GNN enables in silico perturbation to tune gene responses to reprogramming. C_LI

genomics↗

bioRxiv2026

Epithelial sensing of vitamin A shapes intestinal antimicrobial defense

Vitamin A is a central regulator of intestinal adaptive immunity, but its role in innate immunity is less defined. Antimicrobial proteins form a chemical barrier that protects the intestinal epithelium from microbial invasion. Among these, REG3 family lectins are induced by the microbiota, yet how nutritional cues intersect with microbial signals to control their expression remains unclear. Here, we show that dietary vitamin A promotes expression of REG3 antimicrobial lectins, including REG3G, in intestinal epithelial cells from both mice and humans. This induction is mediated by retinoic acid and requires retinoic acid receptor (RAR) signaling. Mechanistically, RARs bind directly to the Reg3g promoter adjacent to a STAT3 binding site. As STAT3 mediates microbiota-induced IL-22 signaling in epithelial cells, this arrangement provides a molecular framework for integrating nutritional and microbial inputs at the level of REG3G transcription. Extending these findings, we demonstrate that vitamin A-retinoic acid signaling similarly promotes expression of -defensin antimicrobial proteins. Together, these findings define a transcriptional mechanism by which vitamin A enhances epithelial antimicrobial defenses and strengthens mucosal innate immunity. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=187 SRC="FIGDIR/small/710399v1_ufig1.gif" ALT="Figure 1"> View larger version (53K): org.highwire.dtl.DTLVardef@18dd8aforg.highwire.dtl.DTLVardef@18cf2fdorg.highwire.dtl.DTLVardef@a4dc89org.highwire.dtl.DTLVardef@18aa9c7_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsO_LIVitamin A promotes epithelial expression of REG3 antimicrobial proteins in the intestine C_LIO_LIRetinoic acid receptors (RARs) directly activate mouse Reg3g and human REG3G transcription C_LIO_LIRARs bind the Reg3g promoter adjacent to STAT3, integrating nutritional and microbial signals C_LIO_LIVitamin A-RAR signaling broadly regulates epithelial antimicrobial programs, including -defensins C_LI

immunology↗

bioRxiv2025

Comprehensive perturbation of transcription factors in human cardiomyocytes reveals the regulatory architecture of congenital heart disease

Transcription factors (TFs), including DNA binding proteins and epigenetic co-regulators, control the timing of gene programs during lineage commitment1. However, systematically defining the dynamic activities of the human genomes nearly 2000 TFs during development remains challenging2,3. Here, we use Perturb-Seq to map the transcriptional impact of nearly all human TFs and a subset of enhancers during cardiomyocyte differentiation. Our results suggest that developmental regulatory networks are distributed across highly connected hub TFs4-6 that are dynamic across lineage and time, rather than organizing into top-down hierarchies directed by "master regulators". We also identify distinct TF ensembles that regulate sequential decision points during cardiac cell-fate specification by coordinating lineage-specific activation with alternate lineage repression. Separately, analysis of TF-TF cooperativity uncovers a dynamic interaction between MEF2 family TFs7 and members of the Polycomb Repressive complex 18-10 to execute alternative fate repression. Building on these regulatory interactions, we construct a deep learning transformer model to accurately predict perturbed TFs driving altered regulatory networks in patient-derived transcriptomes. Together, our results define the regulatory network architecture of human lineage specification and provide a platform for predicting TF function and interpreting disease mechanisms.

genomics↗

bioRxiv2025

Benchmarking and optimizing Perturb-seq in differentiating human pluripotent stem cells

Perturb-seq is a powerful approach to systematically assess how genes and enhancers impact the molecular and cellular pathways of development and disease. However, technical challenges have limited its application in stem cell-based systems. Here, we benchmarked Perturb-seq across multiple CRISPRi modalities, on diverse genomic targets, in multiple human pluripotent stem cells, during directed differentiation to multiple lineages, and across multiple sgRNA delivery systems. To ensure cost-effective production of large-scale Perturb-seq datasets as part of the Impact of Genomic Variants on Function (IGVF) consortium, our optimized protocol dynamically assesses experiment quality across the weeks-long procedure. Our analysis of 1,996,260 sequenced cells across benchmarking datasets reveals shared regulatory networks linking disease-associated enhancers and genes with downstream targets during cardiomyocyte differentiation. This study establishes open tools and resources for interrogating genome function during stem cell differentiation.

genomics↗

bioRxiv2022

CHD-associated enhancers shape human cardiomyocyte lineage commitment

Enhancers orchestrate gene expression programs that drive multicellular development and lineage commitment. Thus, genetic variants at enhancers are thought to contribute to developmental diseases by altering cell fate commitment. However, while many variant-containing enhancers have been identified, studies to endogenously test the impact of these enhancers on lineage commitment have been lacking. We perform a single-cell CRISPRi screen to assess the endogenous roles of 25 enhancers and putative cardiac target genes implicated in genetic studies of congenital heart defects (CHD). We identify 16 enhancers whose repression leads to deficient differentiation of human cardiomyocytes (CMs). A focused CRISPRi validation screen shows that repression of TBX5 enhancers delays the transcriptional switch from mid- to late-stage CM states. Endogenous genetic deletions of two TBX5 enhancers phenocopy epigenetic perturbations. Together, these results identify critical enhancers of cardiac development and suggest that misregulation of these enhancers could contribute to cardiac defects in human patients. HIGHLIGHTSO_LISingle-cell enhancer perturbation screens during human cardiomyocyte differentiation. C_LIO_LIPerturbation of CHD-linked enhancers/genes causes deficient CM differentiation. C_LIO_LIRepression or knockout of TBX5 enhancers delays transition from mid to late CM states. C_LIO_LIDeficient differentiation coincides with reduced expression of known cardiac genes. C_LI

genomics↗