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BackgroundNetwork 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.ResultsWe 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.ConclusionsIn 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.

The Hybrid Mouse Diversity Panel (HMDP) is a collection of approximately 100 well-characterized inbred strains of mice that can be used to analyze the genetic and environmental factors underlying complex traits. While not nearly as powerful for mapping genetic loci contributing to the traits as human genome-wide association studies, it has some important advantages. First, environmental factors can be controlled. Second, relevant tissues are accessible for global molecular phenotyping. Finally, because inbred strains are renewable, results from separate studies can be integrated. Thus far, the HMDP has been studied for traits relevant to obesity, diabetes, atherosclerosis, osteoporosis, heart failure, immune regulation, fatty liver disease, and host-gut microbiota interactions. High-throughput technologies have been used to examine the genomes, epigenomes, transcriptomes, proteomes, metabolomes, and microbiomes of the mice under various environmental conditions. All of the published data are available and can be readily used to formulate hypotheses about genes, pathways and interactions.

RNA splicing is a major contributor to total transcriptome complexity; however, the functional role and regulation of splicing in heart failure remain poorly understood. Here, we used a total transcriptome profiling and bioinformatic analysis approach and identified a muscle-specific isoform of an RNA splicing regulator, RBFox1 (also known as A2BP1), as a prominent regulator of alternative RNA splicing during heart failure. Evaluation of developing murine and zebrafish hearts revealed that RBFox1 is induced during postnatal cardiac maturation. However, we found that RBFox1 is markedly diminished in failing human and mouse hearts. In a mouse model, RBFox1 deficiency in the heart promoted pressure overload-induced heart failure. We determined that RBFox1 is a potent regulator of RNA splicing and is required for a conserved splicing process of transcription factor MEF2 family members that yields different MEF2 isoforms with differential effects on cardiac hypertrophic gene expression. Finally, induction of RBFox1 expression in murine pressure overload models substantially attenuated cardiac hypertrophy and pathological manifestations. Together, this study identifies regulation of RNA splicing by RBFox1 as an important player in transcriptome reprogramming during heart failure that influence pathogenesis of the disease.

We aimed to understand the genetic control of cardiac remodeling using an isoproterenol-induced heart failure model in mice, which allowed control of confounding factors in an experimental setting. We characterized the changes in cardiac structure and function in response to chronic isoproterenol infusion using echocardiography in a panel of 104 inbred mouse strains. We showed that cardiac structure and function, whether under normal or stress conditions, has a strong genetic component, with heritability estimates of left ventricular mass between 61% and 81%. Association analyses of cardiac remodeling traits, corrected for population structure, body size and heart rate, revealed 17 genome-wide significant loci, including several loci containing previously implicated genes. Cardiac tissue gene expression profiling, expression quantitative trait loci, expression-phenotype correlation, and coding sequence variation analyses were performed to prioritize candidate genes and to generate hypotheses for downstream mechanistic studies. Using this approach, we have validated a novel gene, Myh14, as a negative regulator of ISO-induced left ventricular mass hypertrophy in an in vivo mouse model and demonstrated the up-regulation of immediate early gene Myc, fetal gene Nppb, and fibrosis gene Lgals3 in ISO-treated Myh14 deficient hearts compared to controls.

Transcriptome remodeling in heart disease occurs through the coordinated actions of transcription factors, histone modifications, and other chromatin features at pathology-associated genes. The extent to which genome-wide chromatin reorganization also contributes to the resultant changes in gene expression remains unknown. We examined the roles of two chromatin structural proteins, Ctcf (CCCTC-binding factor) and Hmgb2 (high mobility group protein B2), in regulating pathologic transcription and chromatin remodeling. Our data demonstrate a reciprocal relationship between Hmgb2 and Ctcf in controlling aspects of chromatin structure and gene expression. Both proteins regulate each others' expression as well as transcription in cardiac myocytes; however, only Hmgb2 does so in a manner that involves global reprogramming of chromatin accessibility. We demonstrate that the actions of Hmgb2 on local chromatin accessibility are conserved across genomic loci, whereas the effects on transcription are loci-dependent and emerge in concert with histone modification and other chromatin features. Finally, although both proteins share gene targets, Hmgb2 and Ctcf, neither binds these genes simultaneously nor do they physically colocalize in myocyte nuclei. Our study uncovers a previously unknown relationship between these two ubiquitous chromatin proteins and provides a mechanistic explanation for how Hmgb2 regulates gene expression and cellular phenotype. Furthermore, we provide direct evidence for structural remodeling of chromatin on a genome-wide scale in the setting of cardiac disease.

RationaleOnly a small portion of the known heritability of cardiovascular diseases, such as heart failure, can be explained based on single-gene mutations. Chromatin structure and regulation provide a substrate through which genetic differences in noncoding regions may affect cellular function and response to disease, but the mechanisms are unknown.ObjectiveWe conducted genome-wide measurements of DNA methylation in different strains of mice that are susceptible and resistant to isoproterenol-induced dysfunction to test the hypothesis that this epigenetic mark may play a causal role in the development of heart failure.Methods and resultsBALB/cJ and BUB/BnJ mice, determined to be susceptible and resistant to isoproterenol-induced heart failure, respectively, were administered the drug for 3 weeks via osmotic minipump. Reduced representational bisulfite sequencing was then used to compare the differences between the cardiac DNA methylomes in the basal state between strains and then after isoproterenol treatment. Single-base resolution DNA methylation measurements were obtained and revealed a bimodal distribution of methylation in the heart, enriched in lone intergenic CpGs and depleted from CpG islands around genes. Isoproterenol induced global decreases in methylation in both strains; however, the basal methylation pattern between strains shows striking differences that may be predictive of disease progression before environmental stress. The global correlation between promoter methylation and gene expression (as measured by microarray) was modest and revealed itself only with focused analyses of transcription start site and gene body regions (in contrast to when gene methylation was examined in toto). Modules of comethylated genes displayed correlation with other protein-based epigenetic marks, supporting the hypothesis that chromatin modifications act in a combinatorial manner to specify transcriptional phenotypes in the heart.ConclusionsThis study provides the fir

Purpose Edge illumination (EI) X-ray phase-contrast imaging (XPCI) has been under development at University College London in recent years, and has shown great potential for both laboratory and synchrotron applications. In this work, we propose a new acquisition and processing scheme. Contrary to existing retrieval methods for EI, which require as input two images acquired in different setup configurations, the proposed approach can retrieve an approximate map of the X-ray phase from a single image, thus significantly simplifying the acquisition procedure and reducing data collection times. Methods The retrieval method is analytically derived, based on the assumption of a quasi-homogeneous object, i.e. an object featuring a constant ratio between refractive index and absorption coefficient. The noise properties of the input and retrieved images are also theoretically analyzed under the developed formalism. The method is applied to experimental synchrotron images of a biological object. Results The experimental results show that the method can provide high-quality images, where the “edge” signal typical of XPCI images is transformed to an “area” contrast that enables an easier interpretation of the sample geometry. Moreover, the retrieved images confirm that the method is highly stable against noise. Conclusions We anticipate that the developed approach will become the method of choice for a variety of applications of EI XPCI, thanks to its ability to simplify the acquisition procedure and reduce acquisitions time and dose to the sample. Future work will focus on the adaptation of the method to computed tomography and to polychromatic radiation from X-ray tubes.