Biology subjects

Gural, B.

Publications and source records attributed to Gural, B..

4 recordsLinked to original sources

bioRxiv2025

A systems genetics approach to uncover mitochondrial drivers of heart failure reveals mitochondria-nuclear cross talk in genetically diverse mouse strains

The contribution of mitochondrial genetics to heart failure is thought to be reciprocal. Its complex traits are influenced by genetic and environmental factors. Genetically diverse mouse strains form a vital repository to uncover the interaction between the mitochondrial and nuclear genomes underlying heart failure due to their traceable genetic origins, maternal lineages and controlled genetic variation. Using systems genetics, we studied this cross-talk in the Collaborative Cross (CC) mice challenged with heart failure (HF). We used 63 strains of CC-mice that grouped into 8 mitochondrial haplotypes and subjected them to HF using isoproterenol (Iso), a beta-adrenergic stimulant that mimics progressive stress induced HF in humans. The Alzet osmotic pumps delivered a consistent dose of the drug for 21 days following which the mice were euthanized for organ collection. A group of mice with saline loaded pumps acted as control (Ctrl). Baseline and end of study echocardiography were recorded for these mice. Bulk RNA sequencing was carried out on the left ventricles and data analyzed on R-studio. The CC-strains showed differences across the haplotypes for organ weights and heart function. Noticeable treatment specific gene expression differences were observed for 34 nuclear encoded genes from MitoCarta3.0 unlike mt-DNA encoded genes that were insignificant after correcting for sex and haplotypes. These genes were associated with multiple metabolic pathways in the cell. Trait-GWAS associations with markers from the mitochondrial genome were observed only for mice from Iso group with cell surface area (p = 6.19e-06) and change in ejection fraction (p = 9.22e-05) as top hits under FWER threshold of 0.01. Cyfip2 was the top candidate gene (p = 2.66e-07) among the 831 hits (47-MitoCarta & 784 other nuclear) based on our trans-eQTL analysis. The eQTL genes influenced critical pathways of OXPHOS, myogenesis, apoptosis etc. Our approach uncovered 24 gene candidates associated with mt-DNA and HF that overlapped with our previous mi-eQTL reports. CC-mice revealed ancestry dependent effects underlying HF for studying mito-nuclear interactions. Despite establishing differences, modeling these interaction needs development of complex cybrid systems to evaluate the bidirectional impact on HF. Author SummaryThe Collaborative Cross (CC) mouse is a genetically diverse population that has been used to study complex diseases. Recently, our group has comprehensively characterized the heart from 63 strains of the CC and reported genetic associations with heart failure (HF). Traditionally, genetic abnormalities underlying a disease are related to the nuclear genome but there exists an alternate genome within the cells, the mitochondrial DNA (mt-DNA), whose contribution is understudied. The mt-DNA of CC mice is maternally derived from the 8 founder strains, and our sequencing data holds this information, allowing us the ability to use them to study the contribution of mitochondrial ancestry (haplotypes) to HF. We found haplotype differences in terms of cardiac function and gene expression (both nuclear and mitochondrial) that were associated with HF. It revealed stress induced changes in the heart (trait and gene expression) linked with regions in the mt-DNA that encode genes involved in oxidative phosphorylation and metabolism. We uncovered 24 high-confidence nuclear gene candidates that agreed with our previous analyses and were associated with regions on the mt-DNA. These findings open up new opportunities to study the CC and advance the understanding of Mito-nuclear interactions in HF pathophysiology.

genetics↗

bioRxiv2025

LocusPackRat: a Semi-Automated Framework for Prioritizing Candidate Genes from Large GWAS Intervals

Genome-wide association studies (GWAS) routinely implicate broad loci that span tens of megabases and contain dozens of genes, making the leap from locus to causal gene challenging, especially in model organism cohorts with reduced mapping resolution. We developed LocusPackRat, a semi-automated, easily extendible package that assembles standardized packets of evidence to accelerate candidate gene prioritization. Each packet merges study-specific information for each gene in a locus such as differential expression between conditions or presence of cis-eQTLs with functional/disease annotations pulled from InterMine and Open Targets. Packets are identically structured and easily disseminated to support side-by-side comparison and team review. We demonstrate LocusPackRats efficacy on a recent GWAS study of cardiac hypertrophy and failure in the Collaborative Cross. LocusPackRat shortens the path from statistical association to mechanistic hypotheses and improves the likelihood of successful experimental validation and is easily adaptable to other genetic reference populations or even human cohorts.

bioinformatics↗

bioRxiv2025

Genetic Determinants of Heart Failure Susceptibility and Response in the Collaborative Cross Mouse Population

Genetic variation and lived experiences shape how our hearts respond to chronic stress. The specific genetic mechanisms which underly cardiac remodeling, however, are still unclear, due in part to the challenge of accounting for environmental effects in human population studies. To overcome this challenge, we used the Collaborative Cross (CC) mouse population to investigate heritable susceptibility to cardiovascular stress by chronic {beta}-adrenergic receptor stimulation. Across 8 founder and 63 CC lines, we measured cardiac structure and function, organ weights, cell and tissue morphology, and left ventricular gene expression. Genome-wide scans detected 49 genome-wide significant loci, collapsing to 20 unique intervals (nine significant for multiple traits and eleven trait-specific), averaging 12.83 Mb in size. To identify high-confidence candidate genes from these loci, we augmented our trait mapping with associations between loci and gene expression, isoproterenol-dependent transcriptional changes, coding variants drawn from sequencing data, tractability in our in vitro rat cardiomyocyte model, and previously reported protein functions and mouse or human phenotypes. This approach recovered both known regulators, such as Hey2, and new candidates. Functional tests in in vitro models highlight three candidate genes that modulate hypertrophic growth: Abcb10, Mrps5 and Lmod3. Abcb10 knockdown increased cell size at baseline and further with isoproterenol, consistent with loss of a mitochondrial stress-buffering role. Mrps5 knockdown blunted stress-induced hypertrophy. Paradoxical upregulation of Lmod3 after siRNA transfection (validated at the protein level) also attenuated hypertrophy, consistent with reinforcement of actin-assembly control under catecholamine stress. Together, these results reveal heritable pathways of {beta}-adrenergic remodeling in mice and provide an interpretable, translational, and stepwise framework to prioritize candidate genes within broad loci for mechanistic studies of heart failure.

genomics↗

bioRxiv2024

Novel Insights into Post-Myocardial Infarction Cardiac Remodeling through Algorithmic Detection of Cell-Type Composition Shifts

BackgroundRecent advances in single cell sequencing have led to an increased focus on the role of cell-type composition in phenotypic presentation and disease progression. Cell-type composition research in the heart is challenging due to large, frequently multinucleated cardiomyocytes that preclude most single cell approaches from obtaining accurate measurements of cell composition. Our in silico studies reveal that ignoring cell type composition when calculating differentially expressed genes (DEGs) can have significant consequences. For example, a relatively small change in cell abundance of only 10% can result in over 25% of DEGs being false positives. MethodsWe have implemented an algorithmic approach that uses snRNAseq datasets as a reference to accurately calculate cell type compositions from bulk RNAseq datasets through robust data cleaning, gene selection, and multi-sample cross-subject and cross-cell-type deconvolution. We applied our approach to cardiomyocyte-specific 1A adrenergic receptor (CM-1A-AR) knockout mice. 8-12 week-old mice (either WT or CM-1A-KO) were subjected to permanent left coronary artery (LCA) ligation or sham surgery (n=4 per group). Transcriptomes from the infarct border zones were collected 3 days later and analyzed using our algorithm to determine cell-type abundances, corrected differential expression calculations using DESeq2, and validated these findings using RNAscope. ResultsUncorrected DEGs for the CM-1A-KO X LCA interaction term featured many cell-type specific genes such as Timp4 (fibroblasts) and Aplnr (cardiomyocytes) and overall GO enrichment for terms pertaining to cardiomyocyte differentiation (P=3.1E-4). Using our algorithm, we observe a striking loss of cardiomyocytes and gain in fibroblasts in the 1A-KO + LCA mice that was not recapitulated in WT + LCA animals, although we did observe a similar increase in macrophage abundance in both conditions. This recapitulates prior results that showed a much more severe heart failure phenotype in CM-1A-KO + LCA mice. Following correction for cell-type, our DEGs now highlight a novel set of genes enriched for GO terms such as cardiac contraction (P=3.7E-5) and actin filament organization (P=6.3E-5). ConclusionsOur algorithm identifies and corrects for cell-type abundance in bulk RNAseq datasets opening new avenues for research on novel genes and pathways as well as an improved understanding of the role of cardiac cell types in cardiovascular disease.

bioinformatics↗