Biology subjects

Rau, C. D.

Publications and source records attributed to Rau, C. D..

8 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↗

bioRxiv2025

Optimizing Strain Selection for Association Studies Under Hard Cost Constraints

Quantitative genetics methods that link genotype to phenotype can be especially powerful tools in model organisms and non-human populations, where researchers can apply well-controlled perturbations to commercially-available collections of strains. However, purchasing and phenotyping large collections can be cost-prohibitive. We evaluate several approaches to select subsets of a panel of strains with the aim of maximizing power under fixed budgetary constraints. Some approaches focus solely on costs, others on genetic diversity, and some on both simultaneously. To assess how these results generalize, we simulate data across two biological and statistical settings: phylogenetic regression across bacterial isolates, and linear-mixed model regression across mouse strains. Surprisingly, we find that simply selecting the cheapest strains until the budget is exhausted (MinCost) is usually among the top-performing methods, and that algorithms that consider genetic diversity without weighting by cost tend to show much lower power overall. Methods that consider both cost and diversity are typically, though not universally, the top performers. A comparison of diversity-maximizing objectives reveals that the most commonly studied objective (MaxMin) tends to be the among the least effective at retaining power. Instead, two alternative objectives that have not been previously studied in this context -- maximizing the total minor allele frequency (MaxMAF) and maximizing the total sum of pairwise genetic distances (MaxSum) -- typically perform best. Finally, in addition to fully simulated data, we also test these approaches by subsampling real data on cardiovascular phenotypes from the Hybrid Mouse Diversity Panel (HMDP). The three best methods on these data are cost-aware (p-Median, MaxSum, and MinCost). Based on these results, we recommend investigators consider approaches that maximize total MAF or pairwise genetic distance weighted by strain cost, but we also conclude that a surprisingly robust approach is to simply pick the most strains one can afford.

genomics↗

bioRxiv2024

Mapping DNA Methylation to Cardiac Pathologies Induced by Beta-Adrenergic Stimulation in a Large Panel of Mice

BackgroundHeart failure (HF) is a leading cause of morbidity and mortality worldwide, with over 18 million deaths annually. Despite extensive research, genetic and environmental factors contributing to HF remain complex and poorly understood. Recent studies suggest that epigenetic modifications, such as DNA methylation, may play a crucial role in regulating HF-associated phenotypes. In this study, we leverage the Hybrid Mouse Diversity Panel (HMDP), a cohort of over 100 inbred mouse strains, to investigate the role of DNA methylation in HF progression. ObjectiveWe aim to identify epigenetic modifications associated with HF by integrating DNA methylation data with gene expression and phenotypic traits. Using isoproterenol (ISO)-induced cardiac hypertrophy and failure in HMDP mice, we explore the relationship between methylation patterns and HF susceptibility. MethodsWe performed reduced representational bisulfite sequencing (RRBS) to capture DNA methylation at single-nucleotide resolution in the left ventricles of 90 HMDP mouse strains under both control and ISO-treated conditions. We identified differentially methylated regions (DMRs) and performed an epigenome-wide association study (EWAS) using the MACAU algorithm. We identified likely candidate genes within each locus through integration of our results with previously reported sequence variation, gene expression, and HF-related phenotypes. In vitro approaches were employed to validate key findings, including gene knockdown experiments in neonatal rat ventricular myocytes (NRVMs). We also examined the effects of preventing DNA methyltransferase activity on HF progression. ResultsOur EWAS identified 56 CpG loci significantly associated with HF phenotypes, including 18 loci where baseline DNA methylation predicted post-ISO HF progression. Key candidate genes, such as Prkag2, Anks1, and Mospd3, were identified based on their epigenetic regulation and association with HF traits. In vitro follow-up on a number of genes confirmed that knockdown of Anks1 and Mospd3 in NRVMs resulted in significant alterations in cell size and blunting of ISO-induced hypertrophy, demonstrating their functional relevance in HF pathology. Furthermore, treatment with the DNA methyltransferase inhibitor RG108 in ISO-treated BTBRT mice significantly reduced cardiac hypertrophy and preserved ejection fraction compared to mice only treated with ISO, highlighting the therapeutic potential of targeting DNA methylation in HF. Differential expression analysis revealed that RG108 treatment restored the expression of several methylation-sensitive genes, further supporting the role of epigenetic regulation in HF. ConclusionOur study demonstrates a clear interplay between DNA methylation, gene expression, and HF-associated phenotypes. We identified several novel epigenetic loci and candidate genes that contribute to HF progression, offering new insights into the molecular mechanisms of HF. These findings underscore the importance of epigenetic regulation in cardiac disease and suggest potential therapeutic strategies for modifying HF outcomes through targeting DNA methylation.

genomics↗

bioRxiv2022

Histone H1.0 Couples Cellular Mechanical Behaviors to Chromatin Structure

Tuning of genome structure and function is accomplished by chromatin binding proteins, which determine the transcriptome and phenotype of the cell. We sought to investigate how communication between extracellular stress and chromatin structure may regulate cellular mechanical behaviors. We demonstrate that the linker histone H1.0, which compacts nucleosomes into higher order chromatin fibers, controls genome organization and cellular stress response. Histone H1.0 has privileged expression in fibroblasts across tissue types in mice and humans, and modulation of its expression is necessary and sufficient to mount a myofibroblast phenotype in these cells. Depletion of histone H1.0 prevents transforming growth factor beta (TGF-{beta})-induced fibroblast contraction, proliferation and migration in a histone H1 isoform-specific manner via inhibition of a transcriptome comprised of extracellular matrix, cytoskeletal and contractile genes. Histone H1.0 is associated with local regulation of gene expression via mechanisms involving chromatin fiber compaction and reprogramming of histone acetylation, rendering the cell stiffer in response to cytokine stimulation. Knockdown of histone H1.0 prevented locus-specific histone H3 lysine 27 acetylation by TGF-{beta} and decreased levels of both HDAC1 and the chromatin reader BRD4, thereby preventing transcription of a fibrotic gene program. Transient depletion of histone H1.0 in vivo decompacts chromatin and prevents fibrosis in cardiac muscle, thereby linking chromatin structure with fibroblast phenotype in response to extracellular stress. Our work identifies an unexpected role of linker histones to orchestrate cellular mechanical behaviors, directly coupling cellular force generation, nuclear organization and gene transcription. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=166 SRC="FIGDIR/small/518399v1_ufig1.gif" ALT="Figure 1"> View larger version (103K): org.highwire.dtl.DTLVardef@11a38e1org.highwire.dtl.DTLVardef@8c4ab4org.highwire.dtl.DTLVardef@7c822aorg.highwire.dtl.DTLVardef@45d02b_HPS_FORMAT_FIGEXP M_FIG C_FIG

cell biology↗

bioRxiv2019

The Effects of Mutations are Modified by Genetic Background in Mice

BackgroundThe majority of quantitative genetic models used to map complex traits assume that alleles have similar effects across all individuals. Significant evidence suggests, however, that epistatic interactions modulate the impact of many alleles. Nevertheless, identifying epistatic interactions remains computationally and statistically challenging. In this work, we address some of these challenges by developing a statistical test for polygenic epistasis that determines whether the effect of an allele is altered by the global genetic ancestry proportion from distinct progenitors. ResultsWe applied our method to data from mice and yeast. For the mice, we observed 49 significant genotype-by-ancestry interaction associations across 14 phenotypes as well as over 1,400 Bonferroni-corrected genotype-by-ancestry interaction associations for mouse gene expression data. For the yeast, we observed 92 significant genotype-by-ancestry interactions across 38 phenotypes. Given this evidence of epistasis, we test for and observe evidence of rapid selection pressure on ancestry specific polymorphisms within one of the cohorts, consistent with epistatic selection. ConclusionsUnlike our prior work in human populations, we observe widespread evidence of ancestry-modified SNP effects, perhaps reflecting the greater divergence present in crosses using mice and yeast. Author SummaryMany statistical tests which link genetic markers in the genome to differences in traits rely on the assumption that the same polymorphism will have identical effects in different individuals. However, there is substantial evidence indicating that this is not the case. Epistasis is the phenomenon in which multiple polymorphisms interact with one another to amplify or negate each others effects on a trait. We hypothesized that individual SNP effects could be changed in a polygenic manner, such that the proportion of as genetic ancestry, rather than specific markers, might be used to capture epistatic interactions. Motivated by this possibility, we develop a new statistical test that allowed us to examine the genome to identify polymorphisms which have different effects depending on the ancestral makeup of each individual. We use our test in two different populations of inbred mice and a yeast panel and demonstrate that these sorts of variable effect polymorphisms exist in 14 different physical traits in mice and 38 phenotypes in yeast as well as in murine gene expression. We use the term "polygenic epistasis" to distinguish these interactions from the more conventional two- or multi-locus interactions.

genomics↗

bioRxiv2017

A personalized, multi-omics approach identifies genes involved in cardiac hypertrophy and heart failure

Identifying genes underlying complex diseases remains a major challenge. Biomarkers are typically identified by comparing average levels of gene expression in populations of healthy and diseased individuals. However, genetic diversities may undermine the effort to uncover genes with significant but individual contribution to the spectrum of disease phenotypes within a population. Here we leverage the Hybrid Mouse Diversity Panel (HMDP), a model system of 100+ genetically diverse strains of mice exhibiting different complex disease traits, to develop a personalized differential gene expression analysis that is able to identify disease-associated genes missed by traditional population-wide methods. The population-level and personalized approaches are compared for isoproterenol(ISO)-induced cardiac hypertrophy and heart failure using pre- and post-ISO gene expression and phenotypic data. The personalized approach identifies 36 Fold-Change (FC) genes predictive of the severity of cardiac hypertrophy, and enriched in genes previously associated with cardiac diseases in human. Strikingly, these genes are either up- or down-regulated at the individual strain level, and are therefore missed when averaging at the population level. Using insights from the gene regulatory network and protein-protein interactome, we identify Hes1 as a strong candidate FC gene. We validate its role by showing that even a mild knockdown of 20-40% of Hes1 can induce a dramatic reduction of hypertrophy by 80-90% in rat neonatal cardiac cells. These findings emphasize the importance of a personalized approach to identify causal genes underlying complex diseases as well as to develop personalized therapies.\n\nSignificanceA traditional approach to investigate the genetic basis of complex diseases is to look for genes with a global change in expression between diseased and healthy individuals. Here, we investigate individual changes of gene expression by inducing heart failure in 100 strains of genetically distinct mice. We find that genes associated to the severity of the disease are either up- or down-regulated across individuals and are therefore missed by a traditional population level approach. However, they are enriched in human cardiac disease genes and form a coregulated module strongly interacting with a cardiac hypertrophic signaling network in the human interactome. Our analysis demonstrates that individualized approaches are crucial to reveal all genes involved in the development of complex diseases.

systems biology↗