Nidhi Sahni

Mapping Parkinson's Disease Progression through Deep Plasma Proteomics and Functional Genomics

This study identifies plasma protein biomarkers and mechanistic pathways in Parkinson's disease using deep proteomics and functional genomics.

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

eSIG-Net predicts how single mutations affect protein interactions using a sequence-based interaction language model, outperforming existing methods.

Viral transcriptional regulators extensively rewire host pathways through diverse mechanisms

This study systematically profiles 95 viral transcriptional regulators (vTRs) across multiple assays, revealing their diverse mechanisms of rewiring host pathways to promote viral replication and disease links.

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

The paper shows that RTK fusions suppress EGFR and sequester Grb2, and targeted therapies reverse this suppression, enhancing EGFR signaling and drug response.

Nuclear Phase Separation Drives NPM1-mutant Acute Myeloid Leukemia

This study shows that mutant NPM1 forms nuclear condensates called C-bodies, which are essential for leukemic gene expression and cell survival, and identifies them as potential therapeutic targets.

Succinate dehydrogenase activity supports de novo purine synthesis

This study shows that succinate dehydrogenase supports de novo purine synthesis in cancer cells, and its inhibition reduces proliferation by impairing purine biosynthesis and inducing a compensatory salvage pathway.

Expanding the DNA Motif Lexicon of the Transcriptional Regulatory Code

The paper expands the DNA motif lexicon of transcriptional regulatory code by identifying composite elements and using a deep learning model to predict their activity and impact of variants.

Rewriting regulatory DNA to dissect and reprogram gene expression

The paper introduces Variant-FlowFISH to measure the effects of DNA sequence variants on gene expression, identifies regulatory mechanisms, and benchmarks predictive models.

Multi-scale gene regulatory networks for resolving transcription factor and autoimmune variant impacts on human B cell fate dynamics

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

The paper identifies two categories of transcription factor isoforms with distinct molecular functions and shows they contribute to gene regulatory complexity.

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

This study identifies PRMT1 as a critical survival dependency in multiple myeloma cells, showing that its inhibition reduces cell survival and impairs tumor growth.

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

This paper develops an iPSC platform to rapidly generate alpha-synuclein inclusions and investigates their formation, subtypes, and molecular interactions.

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

This paper introduces SCORPION, a tool for reconstructing gene regulatory networks from single-cell RNA-seq data, showing it outperforms existing methods and detects consistent regulatory differences in colorectal cancer.

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

This study identifies RNA binding proteins as novel drivers of homologous recombination deficiency in tumors, expanding the known genetic causes beyond BRCA1/2 mutations.

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

This study uses single-cell RNA-seq based transfer learning to prioritize synergistic drug combinations for cancer treatment by aligning patient tumor cells with similar cell lines.

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

The paper develops a deep learning model integrating multi-omics data to stratify breast cancer patients by prognosis and identify potential therapeutic targets.

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

e-MutPath identifies edgetic mutations that perturb functional pathways, revealing cancer vulnerability genes and drug targets through network-based analysis.