Hockett, A. | Search bioRxiv

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↗