Spatial Principles of Chromatin Architecture Associated With Organ-Specific Gene Regulation

Packaging of the genome in the nucleus is a non-random process that is thought to directly contribute to cell type-specific transcriptomes, although this hypothesis remains untested. Epigenome architecture, as assayed by chromatin conformation capture techniques, such as Hi-C, has recently been described in the mammalian cardiac myocyte and found to be remodeled in the setting of heart failure. In the present study, we sought to determine whether the structural features of the epigenome are conserved between different cell types by investigating Hi-C and RNA-seq data from heart and liver. Investigation of genes with enriched expression in heart or liver revealed nuanced interaction paradigms between organs: first, the log2 ratios of heart:liver (or liver:heart) intrachromosomal interactions are higher in organ-specific gene sets (p = 0.009), suggesting that organ-specific genes have specialized chromatin structural features. Despite similar number of total interactions between cell types, intrachromosomal interaction profiles in heart but not liver demonstrate that genes forming promoter-to-transcription-end-site loops in the cardiac nucleus tend to be involved in cardiac-related pathways. The same analysis revealed an analogous organ-specific interaction profile for liver-specific loop genes. Investigation of A/B compartmentalization (marker of chromatin accessibility) revealed that in the heart, 66.7% of cardiac-specific genes are in compartment A, while 66.1% of liver-specific genes are found in compartment B, suggesting that there exists a cardiac chromatin topology that allows for expression of cardiac genes. Analyses of interchromosomal interactions revealed a relationship between interchromosomal interaction count and organ-specific gene localization (p = 2.2 × 10−16) and that, for both organs, regions of active or inactive chromatin tend to segregate in 3D space (i.e., active with active, inactive with inactive). 3D models of topologically associating domains (TADs) suggest that TADs tend to interact with regions of similar compartmentalization across chromosomes, revealing trans structural interactions contributing to genomic compartmentalization at distinct structural scales. These models reveal discordant nuclear compaction strategies, with heart packaging compartment A genes preferentially toward the center of the nucleus and liver exhibiting preferential arrangement toward the periphery. Taken together, our data suggest that intra- and interchromosomal chromatin architecture plays a role in orchestrating tissue-specific gene expression

genomeSidekick: A user-friendly epigenomics data analysis tool

Recent advances in epigenomics measurements have resulted in a preponderance of genomic sequencing datasets that require focused analyses to discover mechanisms governing biological processes. In addition, multiple epigenomics experiments are typically performed within the same study, thereby increasing the complexity and difficulty of making meaningful inferences from large datasets. One gap in the sequencing data analysis pipeline is the availability of tools to efficiently browse genomic data for scientists that do not have bioinformatics training. To bridge this gap, we developed genomeSidekick, a graphical user interface written in R that allows researchers to perform bespoke analyses on their transcriptomic and chromatin accessibility or chromatin immunoprecipitation data without the need for command line tools. Importantly, genomeSidekick outputs lists of up- and downregulated genes or chromatin features with differential accessibility or occupancy; visualizes omics data using interactive volcano plots; performs Gene Ontology analyses locally; and queries PubMed for selected gene candidates for further evaluation. Outputs can be saved using the user interface and the code underlying genomeSidekick can be edited for custom analyses. In summary, genomeSidekick brings wet lab scientists and bioinformaticians into a shared fluency with the end goal of driving mechanistic discovery

Unwind to the beat: chromatin and cardiac conduction

How chromatin accessibility and structure endow highly specialized cells with their unique phenotypes is an area of intense investigation. In the mammalian heart, an exclusive subset of cardiac cells comprise the conduction system. Many molecular components of this system are well studied and genetic variation in some of the components induces abnormal cardiac conduction. However, genetic risk for cardiac arrhythmias in human populations also occurs in noncoding regions. A study by Bhattacharyya, Kollipara, et al. in this issue of the JCI examines how chromatin accessibility and structure may explain the mechanisms by which noncoding variants increase susceptibility to cardiac arrhythmias. We discuss the implications of these findings for cell type-specific gene regulation and highlight potential therapeutic strategies to engineer locus-specific epigenomic remodeling in vivo

Taking Data Science to Heart: Next Scale of Gene Regulation

Purpose of reviewTechnical advances have facilitated high-throughput measurements of the genome in the context of cardiovascular biology. These techniques bring a deluge of gargantuan datasets, which in turn present two fundamentally new opportunities for innovation-data processing and knowledge integration-toward the goal of meaningful basic and translational discoveries.Recent findingsBig data, integrative analyses, and machine learning have brought cardiac investigations to the cutting edge of chromatin biology, not only to reveal basic principles of gene regulation in the heart, but also to aid in the design of targeted epigenetic therapies.SummaryCardiac studies using big data are only beginning to integrate the millions of recorded data points and the tools of machine learning are aiding this process. Future experimental design should take into consideration insights from existing genomic datasets, thereby focusing on heretofore unexplored epigenomic contributions to disease pathology

Epigenomes in Cardiovascular Disease.

If unifying principles could be revealed for how the same genome encodes different eukaryotic cells and for how genetic variability and environmental input are integrated to impact cardiovascular health, grand challenges in basic cell biology and translational medicine may succumb to experimental dissection. A rich body of work in model systems has implicated chromatin-modifying enzymes, DNA methylation, noncoding RNAs, and other transcriptome-shaping factors in adult health and in the development, progression, and mitigation of cardiovascular disease. Meanwhile, deployment of epigenomic tools, powered by next-generation sequencing technologies in cardiovascular models and human populations, has enabled description of epigenomic landscapes underpinning cellular function in the cardiovascular system. This essay aims to unpack the conceptual framework in which epigenomes are studied and to stimulate discussion on how principles of chromatin function may inform investigations of cardiovascular disease and the development of new therapies

genomeSidekick: A user-friendly epigenomics data analysis tool

Recent advances in epigenomics measurements have resulted in a preponderance of genomic sequencing datasets that require focused analyses to discover mechanisms governing biological processes. In addition, multiple epigenomics experiments are typically performed within the same study, thereby increasing the complexity and difficulty of making meaningful inferences from large datasets. One gap in the sequencing data analysis pipeline is the availability of tools to efficiently browse genomic data for scientists that do not have bioinformatics training. To bridge this gap, we developed genomeSidekick, a graphical user interface written in R that allows researchers to perform bespoke analyses on their transcriptomic and chromatin accessibility or chromatin immunoprecipitation data without the need for command line tools. Importantly, genomeSidekick outputs lists of up- and downregulated genes or chromatin features with differential accessibility or occupancy; visualizes omics data using interactive volcano plots; performs Gene Ontology analyses locally; and queries PubMed for selected gene candidates for further evaluation. Outputs can be saved using the user interface and the code underlying genomeSidekick can be edited for custom analyses. In summary, genomeSidekick brings wet lab scientists and bioinformaticians into a shared fluency with the end goal of driving mechanistic discovery