Biology subjects

Sahni, N.

Publications and source records attributed to Sahni, N..

At least 19 recordsLinked to original sources

bioRxiv2026

eSIG-Net: Accurate prediction of single-mutation induced perturbations on protein interactions using a language model

Most proteins exert their functions in complex with other interactors. Single mutations can exhibit a profound impact on perturbing protein interactions, leading to human disease. However, predicting the effect of single mutations on protein interactions remains a major computational challenge. Deep learning, particularly protein language models or transformers, has become an effective tool in bioinformatics for protein structure prediction. However, the functional divergence of mutations makes it difficult to predict their interaction perturbation profiles. To address this fundamental challenge, we present eSIG-Net (edgetic mutation Sequence-based Interaction Grammar Network), a novel sequence-based "Interaction Language Model" for predicting protein interaction alterations caused by single mutations. eSIG-Net combines various protein sequence embeddings, introduces a mutation-encoding module with syntax and evolutionary insights, and employs contrastive learning to evaluate mutation-induced interaction changes. eSIG-Net significantly outperforms current state-of-the-art sequence-based and structure-based prediction methods at predicting mutational impact on protein interactions. We highlight examples where eSIG-Net nominates causal variants with high confidence and elucidates their functional role under relevant biological contexts. Together, eSIG-Net is a first-in-kind "interaction language model" that can accurately predict interaction-specific rewiring by single mutations with only sequence information, and exhibits generalizability across biological contexts.

bioinformatics↗

bioRxiv2026

Deep Plasma Proteomics Coupled with Functional Genomics Reveals Drivers of Parkinson's Disease Progression and Levodopa Response

Parkinsons disease (PD) is a progressive neurodegenerative disorder lacking disease-modifying therapies, and its clinical management is limited by the absence of accessible biomarkers for tracking disease progression and treatment response. To map these complex disease trajectories, we implemented an ultra-deep plasma proteomics workflow integrating Mag-Net extracellular vesicle enrichment with Orbitrap Astral mass spectrometry to profile longitudinal samples from PD patients. This approach quantified 6,481 plasma proteins at unprecedented depth in PD studies, revealing distinct signatures directly associated with disease duration and dopaminergic therapy exposure. Candidate biomarkers were subsequently validated in an independent cohort using ELISA, demonstrating robust predictive utility in AI-driven prediction models. To uncover the mechanistic drivers underlying these systemic changes, we intersected our proteomic data with novel proteome-wide gene overexpression perturbation screens designed to identify regulators of alpha-synuclein pre-formed fibril (PFF) uptake and PFF-induced neuronal toxicity. Finally, an integrative network analysis combining three independent proteome-wide assays revealed that key pathological hubs, such as CD14, IFNG, and PLAT, are targets of currently approved pharmacological agents. Collectively, these findings provide a comprehensive, systems-level map for PD biomarker discovery and highlight druggable pathways to advance precision medicine strategies.

neuroscience↗

bioRxiv2025

Viral transcriptional regulators extensively rewire host pathways through diverse mechanisms

Viral transcriptional regulators (vTRs) reprogram host gene regulatory networks to promote replication, persistence, and immune evasion. Despite the identification of hundreds of vTRs in human viruses, how they rewire host pathways remains unclear. Here, we systematically profiled 95 vTRs from diverse human viruses across multiple functional assays. vTRs perturb immune, cell proliferation/death, and signaling pathways through various mechanisms; some bind DNA directly, others cooperate or antagonize human transcription factors (hTFs), and some remodel chromatin. vTRs can act as activators or repressors and recruit similar but not identical repertoires of proteins as hTFs. These findings reveal vTRs as versatile transcriptional modulators that converge on conserved host "pressure points" while diversifying across pathways to promote viral replication and persistence. Notably, many vTR dysregulate genes within autoimmune, neurological, and cardiovascular risk loci, revealing mechanistic links to disease. Together, we provide a comprehensive resource for understanding and targeting viral control of human transcription.

genomics↗

bioRxiv2025

EGFR suppression and drug-induced potentiation are widespread features of oncogenic RTK fusions

Regulation of cancer cells by their environment contributes to tumorigenesis and drug response, though the extent to which the oncogenic state can alter a cells perception of its environment is not clear. Prior studies found that EML4-ALK, a receptor tyrosine kinase (RTK) fusion oncoprotein, suppresses transmembrane receptor signaling through EGFR. Moreover, suppression was reversed with targeted ALK inhibition, thereby promoting survival and drug tolerance. Here we tested whether such modulation of EGFR was common among other RTK fusions, which collectively are found in [~]5% of all cancers. Using live- and fixed-cell microscopy in isogenic and patient-derived cell lines, we found that a wide variety of RTK fusions suppress transmembrane EGFR and sequester essential adaptor proteins in the cytoplasm, as evidenced by the localization of endogenous Grb2. Targeted therapies rapidly released Grb2 from sequestration and potentiated EGFR. Synthetic optogenetic analogs of RTK fusions confirmed that cytoplasmic sequestration of Grb2 was sufficient to suppress perception of extracellular EGF and could do so without driving signaling from the synthetic fusion itself, demonstrating that fusion signaling and suppression of EGFR could be functionally decoupled. Our study uncovers that a large number of RTK fusions simultaneously act as both activators and suppressors of signaling, the mechanisms of which could be exploited for new biomimetic therapies that enhance cell killing and suppress drug tolerance.

cancer biology↗

bioRxiv2025

Expanding the DNA Motif Lexicon of the Transcriptional Regulatory Code

Transcriptional regulatory sequences in metazoans contain intricate combinations of transcription factor (TF) motifs. Stereospecific arrangements of simple motifs constitute composite elements (CEs) that enhance DNA-protein interaction specificity and enable combinatorial regulatory logic. Despite their importance, CEs remain underexplored. We advance CE discovery and functional characterization by developing an integrated framework that combines computational prediction, experimental testing and deep learning. The extended TF motif catalog comprises both synergistic and counteracting CEs, which are supported by evidence of TF binding in vivo and in vitro. A deep learning model GRACE trained on customized massively parallel reporter assays learns the lexicon of CEs at single-nucleotide resolution. Comparative analysis with a neural network model trained on chromatin accessibility demonstrates striking convergence and distinctions within the expanded regulatory lexicon, enabling joint predictions of motif contributions and the impact of variants on chromatin structure and transcriptional activity in diverse cellular contexts.

genomics↗

bioRxiv2025

Nuclear Phase Separation Drives NPM1-mutant Acute Myeloid Leukemia

During cancer development, mutations promote gene expression changes that cause transformation. Leukemia is frequently associated with aberrant HOXA expression driven by translocations in nucleoporin genes or KMT2A, and mutations in NPM1. How disparate mutations converge on this regulatory pathway is not understood. Here we demonstrate that mutant NPM1 (NPM1c) forms nuclear condensates in multiple human cell lines, mouse models, and primary patient samples. We show NPM1c phase separation is necessary and sufficient to coordinate the recruitment of NUP98 and KMT2A to condensates. Through extensive mutagenesis and pharmacological destabilization of phase separation, we find that NPM1c condensates are necessary for regulating gene expression, promoting in vivo expansion, and maintaining the undifferentiated leukemic state. Finally, we show that nucleoporin and KMT2A fusion proteins form condensates that are biophysically indistinguishable from NPM1c condensates. Together, these data define a new condensate underlying leukemias that we term coordinating bodies (C-bodies), and propose C-bodies as a therapeutic vulnerability.

biophysics↗

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

Succinate dehydrogenase activity supports de novo purine synthesis

The de novo purine synthesis pathway is fundamental for nucleic acid production and cellular energetics, yet the role of mitochondrial metabolism in modulating this process remains underexplored. In many cancers, metabolic reprogramming supports rapid proliferation and survival, but the specific contributions of the tricarboxylic acid (TCA) cycle enzymes to nucleotide biosynthesis are not fully understood. Here, we demonstrate that the TCA cycle enzyme succinate dehydrogenase (SDH) is essential for maintaining optimal de novo purine synthesis in normal and cancer cells. Genetic or pharmacological inhibition of SDH markedly attenuates purine synthesis, leading to a significant reduction in cell proliferation. Mechanistically, SDH inhibition causes an accumulation of succinate, which directly impairs the purine biosynthetic pathway. In response, cancer cells compensate by upregulating the purine salvage pathway, a metabolic adaptation that represents a potential therapeutic vulnerability. Notably, co-inhibition of SDH and the purine salvage pathway induces pronounced antiproliferative and antitumoral effects in preclinical models. These findings not only reveal a signaling role for mitochondrial succinate in regulating nucleotide metabolism but also provide a promising therapeutic strategy for targeting metabolic dependencies in cancer.

biochemistry↗

bioRxiv2025

Therapeutic Potential of PRMT1 as a Critical Survival Dependency Target in Multiple Myeloma

Multiple myeloma (MM) is a neoplasm of antibody-producing plasma cells and is the second most prevalent hematological malignancy worldwide. Development of drug resistance and disease relapse significantly impede the success of MM treatment, highlighting the critical need to discover novel therapeutic targets. In a custom CRISPR/Cas9 screen targeting 197 DNA damage response-related genes, Protein Arginine N-Methyltransferase 1 (PRMT1) emerged as a top hit, revealing it as a potential therapeutic vulnerability and survival dependency in MM cells. PRMT1, a major Type I PRMT enzyme, catalyzes the asymmetric transfer of methyl groups to arginine residues, influencing gene transcription and protein function through post-translational modification. Dysregulation or overexpression of PRMT1 has been observed in various malignancies including MM and is linked to chemoresistance. Treatment with the Type I PRMT inhibitor GSK3368715 resulted in a dose-dependent reduction in cell survival across a panel of MM cell lines. This was accompanied by reduced levels of asymmetric dimethylation of arginine (ADMA) and increased arginine monomethylation (MMA) in MM cells. Cell cycle analysis revealed an accumulation of cells in the G0/G1 phase and a reduction in the S phase upon GSK3368715 treatment. Additionally, PRMT1 inhibition led to a significant downregulation of genes involved in cell proliferation, DNA replication, and DNA damage response (DDR), likely inducing genomic instability and impairing tumor growth. This was supported by Reverse Phase Protein Array (RPPA) analyses, which revealed a significant reduction in levels of proteins associated with cell cycle regulation and DDR pathways. Overall, our findings indicate that MM cells critically depend on PRMT1 for survival, highlighting the therapeutic potential of PRMT1 inhibition in treating MM.

cancer biology↗

bioRxiv2024

CCGM: a Compound Coarse Grain Model representation for enhanced chemotype exploration, annotation and screening

Structurally similar compounds often exhibit similar bioactivity, making similarity estimation an essential step in many cheminformatics workflows. Traditionally, compound similarity has been evaluated using diverse molecular representations, such as molecular fingerprints, compound 3D structural features, and physicochemical properties. These methods have proven effective, particularly during the early stages of drug discovery, where the primary goal is to identify initial hits from large compound libraries. However, these representation and methods often fall short during the hit-to-lead development phase, where modifications to the core scaffold or chemotype are performed and evaluated. To address this limitation, we developed the Compound-Coarse-Grain-Model (CCGM), a framework that represents structural features of a compound as nodes and edges within a simplified graph. This approach augments the pharmacophore and chemotype features of the compound within the graph, enabling the identification of compounds with similar chemotype and pharmacophore features more effectively than conventional methods. CCGM is particularly useful for when screening large libraries to identify compounds with similar chemotypes and for filtering generative designs to retain designs with similar pharmacophore features.

bioinformatics↗

bioRxiv2024

Widespread variation in molecular interactions and regulatory properties among transcription factor isoforms

Most human Transcription factors (TFs) genes encode multiple protein isoforms differing in DNA binding domains, effector domains, or other protein regions. The global extent to which this results in functional differences between isoforms remains unknown. Here, we systematically compared 693 isoforms of 246 TF genes, assessing DNA binding, protein binding, transcriptional activation, subcellular localization, and condensate formation. Relative to reference isoforms, two-thirds of alternative TF isoforms exhibit differences in one or more molecular activities, which often could not be predicted from sequence. We observed two primary categories of alternative TF isoforms: "rewirers" and "negative regulators", both of which were associated with differentiation and cancer. Our results support a model wherein the relative expression levels of, and interactions involving, TF isoforms add an understudied layer of complexity to gene regulatory networks, demonstrating the importance of isoform-aware characterization of TF functions and providing a rich resource for further studies.

systems biology↗

bioRxiv2023

Pervasive mislocalization of pathogenic coding variants underlying human disorders

Widespread sequencing has yielded thousands of missense variants predicted or confirmed as disease-causing. This creates a new bottleneck: determining the functional impact of each variant - largely a painstaking, customized process undertaken one or a few genes or variants at a time. Here, we established a high-throughput imaging platform to assay the impact of coding variation on protein localization, evaluating 3,547 missense variants of over 1,000 genes and phenotypes. We discovered that mislocalization is a common consequence of coding variation, affecting about one-sixth of all pathogenic missense variants, all cellular compartments, and recessive and dominant disorders alike. Mislocalization is primarily driven by effects on protein stability and membrane insertion rather than disruptions of trafficking signals or specific interactions. Furthermore, mislocalization patterns help explain pleiotropy and disease severity and provide insights on variants of unknown significance. Our publicly available resource will likely accelerate the understanding of coding variation in human diseases.

cell biology↗

bioRxiv2023

Population-level comparisons of gene regulatory networks modeled on high-throughput single-cell transcriptomics data.

Single-cell technologies enable high-resolution studies of phenotype-defining molecular mechanisms. However, data sparsity and cellular heterogeneity make modeling biological variability across single-cell samples difficult. We present SCORPION, a tool that uses a message-passing algorithm to reconstruct comparable gene regulatory networks from single cell/nuclei RNA-seq data that are suitable for population-level comparisons by leveraging the same baseline priors. Using synthetic data, we found that SCORPION outperforms 12 other gene regulatory network reconstruction techniques. Using supervised experiments, we show that SCORPION can accurately identify differences in regulatory networks between wild-type and transcription factor-perturbed cells. We demonstrate SCORPIONs scalability to population-level analyses using a single-cell RNA-seq atlas containing 200,436 cells from colorectal cancer and adjacent healthy tissues. The differences detected by SCORPION between tumor regions are consistent across population cohorts, as well as with our understanding of disease progression and elucidate phenotypic regulators that may impact patient survival.

bioinformatics↗

bioRxiv2022

Rapid iPSC inclusionopathy models shed light on formation, consequence and molecular subtype of alpha-synuclein inclusions

Intracellular inclusions accompanying neurodegeneration are histopathologically and ultrastructurally heterogeneous but the significance of this heterogeneity is unclear. iPSC models, while promising for disease modeling, do not form inclusions in a reasonable timeframe and suffer from limited tractability. Here, we developed an iPSC toolbox utilizing piggyBac-based or targeted transgenes to rapidly induce CNS cells with concomitant expression of aggregation-prone proteins. This system is amenable to screening and longitudinal tracking at single-cell and single-inclusion resolution. For proof-of-principle, cortical neuron -synuclein "inclusionopathy" models were engineered to form inclusions through exogenous seeding or -synuclein mutation. These models recapitulated known fibril- and lipid-rich inclusion subtypes, uncovering dynamic interactions between them, and refined the classification of inclusions in postmortem brain. Genetic-modifier and protein-interaction screens pinpointed proteins like RhoA whose sequestration into specific inclusion subtypes is likely to be toxic. This iPSC platform should enhance our understanding of proteinaceous pathologies in neurodegeneration and facilitate therapeutics development.

neuroscience↗

bioRxiv2022

Beyond BRCA: Discovery of novel causes and consequences of homologous recombination deficiencies

Since the discovery of BRCA1 and BRCA2 mutations as cancer risk factors, we have gained substantial insight into their role in maintaining genomic stability through homologous recombination (HR) DNA repair. However, upon pan-cancer analysis of tumors from The Cancer Genome Atlas (TCGA), we found that mutations in BRCA1/2 and other classical HR genes only identified 10-20% of tumors that display genomic evidence of HR deficiency (HRD), suggesting that the cause of the vast majority of HR defects in tumors is unknown. As HRD both predisposes individuals to cancer development and leads to therapeutic vulnerabilities, it is critical to define the spectrum of genetic events that drive HRD. Here, we employed a network-based approach leveraging the abundance of molecular characterization data from TCGA to identify novel drivers of HRD. We discovered that over half of putative genes driving HRD originated outside of canonical DNA damage response genes, with a particular enrichment for RNA binding protein (RBP)-encoding genes. These novel drivers of HRD were cross-validated using an independent ICGC cohort, and were enriched in GWAS loci associated with cancer risk. Experimental approaches validated over 90% of our predictions in a panel of 50 genes tested by siRNA and 31 additional engineered mutations identified from TCGA patient tumors. Moreover, genetic suppression of identified RBPs or pharmacological inhibition of RBPs induced PARP inhibition. Further mechanistic studies indicate that some RBPs are recruited to sites of DNA damage to facilitate repair, whereas others control the expression of canonical HR genes. Overall, this study greatly expands the repertoire of known drivers of HRD and their contributions to DNA damage repair, which has implications for not only future mechanistic studies, but also for genetic screening and therapy stratification. HIGHLIGHTSO_LIThe majority of HR deficiencies detected cannot be directly attributed to aberrations in canonical HR genes. C_LIO_LIIntegrated network analysis identifies RNA binding proteins (RBPs) as a novel driver of HR deficiency in patient tumors. C_LIO_LIRBP dysfunction can produce HR deficiencies through both dysregulation of canonical HR genes and action at sites of DNA damage. C_LI

systems biology↗

bioRxiv2022

Drug combination prioritization for cancer treatment using single-cell RNA-seq based transfer learning

Precision oncology seeks to match patients to the optimal pharmacological regimen; yet, due to tumor heterogeneity, this is challenging. Numerous studies have been conducted to produce clinically relevant pharmacological response forecasts by integrating modern machine learning algorithms and several data types. Insufficient patient numbers and lack of knowledge of the molecular targets for each drug under study limit their use. As a proof of concept, we use single-cell RNA-seq based transfer learning to contextualize patients tumor cells in terms of their more similar cell lines with known susceptibility to drug combinations. Our objective is to maximize the translational potential of in-vitro assays for identifying synergistic drug combinations and prioritizing them for clinical use. Consistent findings in a cohort of breast cancer patients corroborated our understanding of the diseases molecular subtypes. To aid in creating personalized treatments and data-driven clinical trials, we identified the most prevalent cell lines and prioritized synergistic combinations based on tumor compositions at various resolution levels.

bioinformatics↗

bioRxiv2022

Deep learning based on multi-omics integration identifies potential therapeutic targets in breast cancer

Effective and precise classification of breast cancer patients for their disease risks is critical to improve early diagnosis and patient survival. In the recent past, a significant amount of multi-omics data derived from cancer patients has emerged. However, a robust framework for integrating multi-omics data to subgroup cancer patients and predict survival prognosis is still lacking. In addition, effective therapeutic targets for treating breast cancer patients with poor prognoses are in dire need. To begin to resolve this difficulty, we developed and optimized a sophisticated deep learning-based model in breast cancer that can accurately stratify patients based on their prognosis. We built a survival-associated predictive framework integrating transcription profile, miRNA expression, somatic mutations, copy number variation, DNA methylation and protein expression. This framework achieved promising performance in distinguishing high-risk breast cancer patients from those with good prognoses. Furthermore, we constructed multiple fully connected neural networks that are trained on prioritized multi-omics signatures or even only potential single-omics signatures, based on our customized scoring system. Together, the landmark multi-omics signatures we identified may serve as potential therapeutic targets in breast cancer.

bioinformatics↗

bioRxiv2020

e-MutPath: Computational modelling reveals the functional landscape of genetic mutations rewiring interactome networks

Understanding the functional impact of cancer somatic mutations represents a critical knowledge gap for implementing precision oncology. It has been increasingly appreciated that the edgotype of a genomic mutation provides a fundamental link between genotype and phenotype. However, specific effects on biological signaling networks for the majority of mutations are largely unknown by experimental approaches. To resolve this challenge, we developed e-MutPath, a network-based computational method to identify candidate edgetic mutations that perturb functional pathways. e-MutPath identifies informative paths that could be used to distinguish disease risk factors from neutral elements and to stratify disease subtypes with clinical relevance. The predicted targets are enriched in cancer vulnerability genes, known drug targets but depleted for proteins associated with side effects, demonstrating the power of network-based strategies to investigate the functional impact and perturbation profiles of genomic mutations. Together, e-MutPath represents a robust computational tool to systematically assign functions to genetic mutations, especially in the context of their specific pathway perturbation effect. The code for e-MutPath is available as a user-friendly R package at the GitHub website (https://github.com/lyshaerbin/eMutPath).

systems biology↗