Biology subjects

Bommineni, V.

Publications and source records attributed to Bommineni, V..

3 recordsLinked to original sources

bioRxiv2026

Genetic dependency of atrial fibrillation-associated risk genes across tissue types: Discovering novel therapeutic targets

BackgroundAddressing the underlying causes of atrial fibrillation (AFib) is critically important. While potential AFib-related genes have been recognized, the impact of modifying these genes in humans remains poorly understood. ObjectiveWe assessed the cellular dependencies of 309 genes previously associated with AFib through genome-wide association studies using data from the Cancer Dependency Map project, aiming to prioritize potential therapeutic targets with minimal off-target effects. MethodsWe analyzed CRISPR-Cas9 knockout (CHRONOS scores) and RNA interference (RNAi) knockdown (DEMETER2 scores) screening data from 1,927 human cell lines across 24 tissue types, focusing on tissues associated with AFib initiation, presentation, and progression: autonomic ganglia, central nervous system (CNS), and soft tissue. We examined the expression and dependency scores of the AFib-associated genes, identifying significant correlations between gene expression and cellular dependency within specific tissues using Pearson correlation coefficients and controlling the false discovery rate (FDR) at 5%. ResultsOut of the 309 AFib-associated genes, 206 genes (66.7%) had CHRONOS dependency scores and 229 (74.1%) had DEMETER2 dependency scores available. Several genes showed significant negative dependency scores (CHRONOS < -0.5) across multiple tissues, indicating potential off-target effects if inhibited. In contrast, we identified 12 genes with significant expression-driven dependencies within AFib-associated tissues. In CNS cell lines, HAND2 (R = -0.456, FDR = 0.002) and VGLL2 (R = -0.434, FDR = 0.005) showed significant negative correlations between gene expression and cellular dependency. In soft tissue cell lines, BEST3 (R = -0.679, FDR = 0.001) and PITX2 (R = -0.679, FDR = 0.001) also demonstrated strong negative correlations. Additionally, ERBB4 in CNS lines showed a significant negative correlation (R = -0.361, FDR = 0.048). These findings suggest that inhibiting these genes may selectively affect high-expressing cells in AFib-associated tissues while minimizing effects on other tissues. ConclusionOur analysis identified HAND2, VGLL2, BEST3, and ERBB4 as potential therapeutic targets for AFib, demonstrating significant expression-driven dependencies in AFib-associated tissues with no pan-tissue essentiality. These results provide a quantitative basis for developing targeted therapies with reduced off-target effects. CONDENSED ABSTRACTAtrial fibrillation (AFIB) is one of the most common cardiac arrhythmias with numerous known risk factors. Although many AFIB-associated genes have been identified, the impact of screening or the effects of modifying these genes in humans remain poorly understood. We examined CRISPR knockout and RNAi knockdown screen data from nearly 2,000 human cell lines to assess the cellular dependencies of 309 genes associated with AFIB, previously identified through genome-wide association studies. Some genes demonstrate broad cell dependencies across various tissue types, indicating potential off-target effects if inhibited. Conversely, HAND2, VGLL2, BEST3, and ERBB4 were identified as genes of interest because their genetic knockouts specifically impacted high-expressing cells from tissue lineages pertinent to AFIB and/or were not pan-dependent. Overall, analyses of genetic screen data identified AFIB-associated genes whose knockout or knockdown selectively affected cell lines of relevant tissue lineages, prioritizing targets for potential AFIB treatments.

genomics↗

bioRxiv2026

Hepatic Transcriptomic Landscape of Chicken Reveals Host Responses to Spotty Liver Disease

Spotty Liver Disease (SLD) is an acute bacterial infection of layer chickens in production, caused by Campylobacter hepaticus, and occurs most frequently in barn-housed and free-range systems. The disease is characterized by a sharp decline in egg production and increased mortality. The hallmark pathological feature is 1-2 mm white to grey necrotic foci distributed across the liver surface. Despite its growing economic impact on commercial poultry, the molecular mechanisms underlying host responses to C. hepaticus infection remain poorly understood. To address this gap, we performed a comprehensive transcriptome analysis of liver tissue from chickens naturally infected with SLD compared to uninfected controls. High-throughput transcriptome sequencing, yielding 9,277 differentially expressed genes (DEG), of which 3,063 were upregulated and 6,214 were downregulated. Functional pathway enrichment analysis revealed significant alterations in immune and metabolic processes associated with SLD pathophysiology. Infected chickens exhibited significant activation of immune response pathways, particularly cytokine-cytokine receptor interactions involving interleukins IL-22, IL-21, and IL-6, along with enhanced cell signaling, and cell adhesion. Among the individual genes, C1QTNF1 and the adhesion molecule gene ADGRD1 were notably overexpressed, indicating enhanced inflammatory activity. In contrast, core hepatic metabolic functions were profoundly reduced, as evidenced by downregulation of oxidative phosphorylation, fatty acid metabolism, iron ion binding, and heme binding pathways. A marked increase in serum amyloid A gene (SAA) expression further confirmed robust acute-phase responses and compromised liver function during infection. Together, these findings demonstrate a complex interplay between inflammatory activation and metabolic dysregulation during SLD. The strong upregulation of acute-phase proteins and pro-inflammatory cytokines demonstrates the hosts vigorous attempt to combat bacterial infection, whereas the concurrent suppression of essential metabolic pathways reflects the pathological consequences of SLD. This study provides a transcriptomic characterization of host responses to C. hepaticus infection, offering insights into SLD pathogenesis and potential avenues for targeted intervention.

microbiology↗

bioRxiv2022

The Radiogenomic and Spatiogenomic Landscapes of Glioblastoma, and Their Relationship to Oncogenic Drivers

Glioblastoma (GBM) is well-known for its molecular and spatial heterogeneity, which poses a challenge for precision therapies and clinical trial stratification. Here, in a comprehensive radiogenomics study of 358 GBMs, we investigated the associations between the imaging and spatial characteristics of the tumors with their cancer gene mutation status, as well as with the cross-sectionally inferred likely order of mutational events. We show that cross-validated machine learning analysis of multi-parametric MRI scans results in distinctive in vivo imaging signatures of several mutations, which are relatively more distinctive in homogeneous tumors which harbor only one of these mutations. These imaging signatures offer mechanistic insights into how various mutations influence the phenotype of the tumor and its surrounding infiltrated brain tissue via neovascularization and vascular leakage, increased cell density, invasion and migration, and other characteristics captured by respective imaging features. Furthermore, we found that spatial location and tumor distribution vary, depending on the GBMs molecular characteristics. Finally, distinct imaging and spatial characteristics were associated with cross-sectionally estimated evolutionary trajectories of the tumors. Collectively, our study establishes a panel of in vivo and clinically accessible imaging-AI biomarkers of GBM that reflect their molecular composition and oncogenic drivers.

bioengineering↗