A systems approach deconvolutes genes specific to and enriched in endothelium from whole-organ transcriptome data, with applications to other cell types and tissues.

In this chapter we address the recent explosion in large multilevel population studies such as the METSIM study in humans as well as large panels of animal models such as the Hybrid Mouse Diversity Panel or the BXD set of recombinant inbred strains. These studies have harnessed the increasing affordability of large-scale high-throughput profiling to gather massive quantities of data. These datasets, spread across different -omics levels (genome, transcriptome, etc.), different tissues (e.g. heart, plasma, bone) and different environmental factors (e.g. diet, drugs) each individually have led to a number of novel findings relevant to a variety of complex diseases and other phenotypes. The analysis of these results, however, is often limited to individuals with a comprehensive understanding of database languages such as SQL. In this chapter, we describe the development of a GUI-based database analysis suite, using the Hybrid Mouse Diversity Panel as an example to lay out a series of methods for visualization and integration of large systems genetics datasets. The database is based on the Shiny suite of tools in R, and is transferrable to other SQL-based datasets.

Purpose of review

In contrast to many other human diseases, the use of genome-wide association studies (GWAS) to identify genes for heart failure (HF) has had limited success. We will discuss the underlying challenges as well as potential new approaches to understanding the genetics of common forms of HF.

Recent findings

Recent research using intermediate phenotypes, more detailed and quantitative stratification of HF symptoms, founder populations and novel animal models has begun to allow researchers to make headway toward explaining the genetics underlying HF using GWAS techniques.

Summary

By expanding analyses of HF to improved clinical traits, additional HF classifications and innovative model systems, the intractability of human HF GWAS should be ameliorated significantly.

Cardiovascular diseases are the leading cause of death worldwide. Complex diseases with highly heterogenous disease progression among patient populations, cardiovascular diseases feature multifactorial contributions from both genetic and environmental stressors. Despite significant effort utilizing multiple approaches from molecular biology to genome-wide association studies, the genetic landscape of cardiovascular diseases, particularly for the nonfamilial forms of heart failure, is still poorly understood. In the past decade, systems-level approaches based on omics technologies have become an important approach for the study of complex traits in large populations. These advances create opportunities to integrate genetic variation with other biological layers to identify and prioritize candidate genes, understand pathogenic pathways, and elucidate gene-gene and gene-environment interactions. In this review, we will highlight some of the recent progress made using systems genetics approaches to uncover novel mechanisms and molecular bases of cardiovascular pathophysiological manifestations. The key technology and data analysis platforms necessary to implement systems genetics will be described, and the current major challenges and future directions will also be discussed. For complex cardiovascular diseases, such as heart failure, systems genetics represents a powerful strategy to obtain mechanistic insights and to develop individualized diagnostic and therapeutic regiments, paving the way for precision cardiovascular medicine.

Human genome-wide association studies have identified thousands of loci associated with disease phenotypes. Genome-wide association studies also have become feasible using rodent models and these have some important advantages over human studies, including controlled environment, access to tissues for molecular profiling, reproducible genotypes, and a wide array of techniques for experimental validation. Association mapping with common mouse inbred strains generally requires 100 or more strains to achieve sufficient power and mapping resolution; in contrast, sample sizes for human studies typically are one or more orders of magnitude greater than this. To enable well-powered studies in mice, we have generated high-density genotypes for ∼175 inbred strains of mice using the Mouse Diversity Array. These new data increase marker density by 1.9-fold, have reduced missing data rates, and provide more accurate identification of heterozygous regions compared with previous genotype data. We report the discovery of new loci from previously reported association mapping studies using the new genotype data. The data are freely available for download, and Web-based tools provide easy access for association mapping and viewing of the underlying intensity data for individual loci.

Background

Network construction and analysis algorithms provide scientists with the ability to sift through high-throughput biological outputs, such as transcription microarrays, for small groups of genes (modules) that are relevant for further research. Most of these algorithms ignore the important role of non-linear interactions in the data, and the ability for genes to operate in multiple functional groups at once, despite clear evidence for both of these phenomena in observed biological systems.

Results

We have created a novel co-expression network analysis algorithm that incorporates both of these principles by combining the information-theoretic association measure of the maximal information coefficient (MIC) with an Interaction Component Model. We evaluate the performance of this approach on two datasets collected from a large panel of mice, one from macrophages and the other from liver by comparing the two measures based on a measure of module entropy, Gene Ontology (GO) enrichment, and scale-free topology (SFT) fit. Our algorithm outperforms a widely used co-expression analysis method, weighted gene co-expression network analysis (WGCNA), in the macrophage data, while returning comparable results in the liver dataset when using these criteria. We demonstrate that the macrophage data has more non-linear interactions than the liver dataset, which may explain the increased performance of our method, termed Maximal Information Component Analysis (MICA) in that case.

Conclusions

In making our network algorithm more accurately reflect known biological principles, we are able to generate modules with improved relevance, particularly in networks with confounding factors such as gene by environment interactions.

Background

Chronic stress-induced cardiac pathology exhibits both a wide range in severity and a high degree of heterogeneity in clinical manifestation in human patients. This variability is contributed to by complex genetic and environmental etiologies within the human population. Genetic approaches to elucidate the genetics underlying the acquired forms of cardiomyopathies, including genome-wide association studies, have been largely unsuccessful, resulting in limited knowledge as to the contribution of genetic variations for this important disease.

Methods and results

Using the β-adrenergic agonist isoproterenol as a specific pathological stressor to circumvent the problem of etiologic heterogeneity, we performed a genome-wide association study for genes influencing cardiac hypertrophy and fibrosis in a large panel of inbred mice. Our analyses revealed 7 significant loci and 17 suggestive loci, containing an average of 14 genes, affecting cardiac hypertrophy, fibrosis, and surrogate traits relevant to heart failure. Several loci contained candidate genes which are known to contribute to Mendelian cardiomyopathies in humans or have established roles in cardiac pathology based on molecular or genetic studies in mouse models. In particular, we identify Abcc6 as a novel gene underlying a fibrosis locus by validating that an allele with a splice mutation of Abcc6 dramatically and rapidly promotes isoproterenol-induced cardiac fibrosis.

Conclusions

Genetic variants significantly contribute to the phenotypic heterogeneity of stress-induced cardiomyopathy. Systems genetics is an effective approach to identify genes and pathways underlying the specific pathological features of cardiomyopathies. Abcc6 is a previously unrecognized player in the development of stress-induced cardiac fibrosis.

Right ventricular dysfunction is highly prevalent across cardiopulmonary diseases and independently predicts death in both heart failure (HF) and pulmonary hypertension (PH). Progression towards right ventricular failure (RVF) can occur in spite of optimal medical treatment of HF or PH, highlighting current insufficient understanding of RVF molecular pathophysiology. To identify molecular mechanisms that may distinctly underlie RVF, we investigated the cardiac ventricular transcriptome of advanced HF patients, with and without RVF. Using an integrated systems genomic and functional biology approach, we identified an RVF-specific gene module, for which WIPI1 served as a hub and HSPB6 and MAP4 as drivers, and confirmed the ventricular specificity of Wipi1, Hspb6, and Map4 transcriptional changes in adult murine models of pressure overload induced RV- versus LV- failure. We uncovered a shift towards non-canonical autophagy in the failing RV that correlated with RV-specific Wipi1 upregulation. In vitro siRNA silencing of Wipi1 in neonatal rat ventricular myocytes limited non-canonical autophagy and blunted aldosterone-induced mitochondrial superoxide levels. Our findings suggest that Wipi1 regulates mitochondrial oxidative signaling and non-canonical autophagy in cardiac myocytes. Together with our human transcriptomic analysis and corroborating studies in an RVF mouse model, these data render Wipi1 a potential target for RV-directed HF therapy.

Cardiac failure has been widely associated with an increase in glucose utilization. The aim of our study was to identify factors that mechanistically bridge this link between hyperglycemia and heart failure. Here, we screened the Hybrid Mouse Diversity Panel (HMDP) for substrate-specific cardiomyocyte candidates based on heart transcriptional profile and circulating nutrients. Next, we utilized an in vitro model of rat cardiomyocytes to demonstrate that the gene expression changes were in direct response to substrate abundance. After overlaying candidates of interest with a separate HMDP study evaluating isoproterenol-induced heart failure, we chose to focus on the gene Trp53inp2 as a cardiomyocyte glucose utilization-specific factor. Trp53inp2 gene knockdown in rat cardiomyocytes reduced expression and protein abundance of key glycolytic enzymes. This resulted in reduction of both glucose uptake and glycogen content in cardiomyocytes stimulated with isoproterenol. Furthermore, this reduction effectively blunted the capacity of glucose and isoprotereonol to synergistically induce hypertrophic gene expression and cell size expansion. We conclude that Trp53inp2 serves as regulator of cardiomyocyte glycolytic activity and can consequently regulate hypertrophic response in the context of elevated glucose content.NEW & NOTEWORTHY Here, we apply a novel method for screening transcripts based on a substrate-specific expression pattern to identify Trp53inp2 as an induced cardiomyocyte glucose utilization factor. We further show that reducing expression of the gene could effectively blunt hypertrophic response in the context of elevated glucose content.

We previously reported a genetic analysis of heart failure traits in a population of inbred mouse strains treated with isoproterenol to mimic catecholamine-driven cardiac hypertrophy. Here, we apply a co-expression network algorithm, wMICA, to perform a systems-level analysis of left ventricular transcriptomes from these mice. We describe the features of the overall network but focus on a module identified in treated hearts that is strongly related to cardiac hypertrophy and pathological remodeling. Using the causal modeling algorithm NEO, we identified the gene Adamts2 as a putative regulator of this module and validated the predictive value of NEO using small interfering RNA-mediated knockdown in neonatal rat ventricular myocytes. Adamts2 silencing regulated the expression of the genes residing within the module and impaired isoproterenol-induced cellular hypertrophy. Our results provide a view of higher order interactions in heart failure with potential for diagnostic and therapeutic insights.