Targeting long non-coding RNA NUDT6 enhances smooth muscle cell survival and limits vascular disease progression.

Long non-coding RNAs (lncRNAs) orchestrate various biological processes and regulate the development of cardiovascular diseases. Their potential therapeutic benefit to tackle disease progression has recently been extensively explored. Our study investigates the role of lncRNA Nudix Hydrolase 6 (NUDT6) and its antisense target fibroblast growth factor 2 (FGF2) in two vascular pathologies: abdominal aortic aneurysms (AAA) and carotid artery disease. Using tissue samples from both diseases, we detected a substantial increase of NUDT6, whereas FGF2 was downregulated. Targeting Nudt6 in vivo with antisense oligonucleotides in three murine and one porcine animal model of carotid artery disease and AAA limited disease progression. Restoration of FGF2 upon Nudt6 knockdown improved vessel wall morphology and fibrous cap stability. Overexpression of NUDT6 in vitro impaired smooth muscle cell (SMC) migration, while limiting their proliferation and augmenting apoptosis. By employing RNA pulldown followed by mass spectrometry as well as RNA immunoprecipitation, we identified Cysteine and Glycine Rich Protein 1 (CSRP1) as another direct NUDT6 interaction partner, regulating cell motility and SMC differentiation. Overall, the present study identifies NUDT6 as a well-conserved antisense transcript of FGF2. NUDT6 silencing triggers SMC survival and migration and could serve as a novel RNA-based therapeutic strategy in vascular diseases.

Epidermal growth factor receptor inhibition prevents vascular calcifying extracellular vesicle biogenesis.

Chronic kidney disease (CKD) increases the risk of cardiovascular disease, including vascular calcification, leading to higher mortality. The release of calcifying extracellular vesicles (EVs) by vascular smooth muscle cells (VSMCs) promotes ectopic mineralization of vessel walls. Caveolin-1 (CAV1), a structural protein in the plasma membrane, plays a major role in calcifying EV biogenesis in VSMCs. Epidermal growth factor receptor (EGFR) colocalizes with and influences the intracellular trafficking of CAV1. Using a diet-induced mouse model of CKD followed by a high-phosphate diet to promote vascular calcification, we assessed the potential of EGFR inhibition to prevent vascular calcification. Furthermore, we computationally analyzed 7,651 individuals in the Multi-Ethnic Study of Atherosclerosis (MESA) and Framingham cohorts to assess potential correlations between coronary artery calcium and single-nucleotide polymorphisms (SNPs) associated with elevated serum levels of EGFR. Mice with CKD developed widespread vascular calcification, associated with increased serum levels of EGFR. In both the CKD mice and human VSMC culture, EGFR inhibition significantly reduced vascular calcification by mitigating the release of CAV1-positive calcifying EVs. EGFR inhibition also increased bone mineral density in CKD mice. Individuals in the MESA and Framingham cohorts with SNPs associated with increased serum EGFR exhibit elevated coronary artery calcium. Given that EGFR inhibitors exhibit clinical safety and efficacy in other pathologies, the current data suggest that EGFR may represent an ideal target to prevent pathological vascular calcification in CKD.NEW & NOTEWORTHY Here, we investigate the potential of epidermal growth factor receptor (EGFR) inhibition to prevent vascular calcification, a leading indicator of and contributor to cardiovascular morbidity and mortality. EGFR interacts and affects the trafficking of the plasma membrane scaffolding protein caveolin-1. Previous studies reported a key role for caveolin-1 in the development of specialized extracellular vesicles that mediate vascular calcification; however, no role of EGFR has been reported. We demonstrated that EGFR inhibition modulates caveolin-1 trafficking and hinders calcifying extracellular vesicle formation, which prevents vascular calcification. Given that EGFR inhibitors are clinically approved for other indications, this may represent a novel therapeutic strategy for vascular calcification.

Cardioinformatics: the nexus of bioinformatics and precision cardiology.

Cardiovascular disease (CVD) is the leading cause of death worldwide, causing over 17 million deaths per year, which outpaces global cancer mortality rates. Despite these sobering statistics, most bioinformatics and computational biology research and funding to date has been concentrated predominantly on cancer research, with a relatively modest footprint in CVD. In this paper, we review the existing literary landscape and critically assess the unmet need to further develop an emerging field at the multidisciplinary interface of bioinformatics and precision cardiovascular medicine, which we refer to as 'cardioinformatics'.

Dysregulation of the histone demethylase KDM6B in alcohol dependence is associated with epigenetic regulation of inflammatory signaling pathways.

Epigenetic enzymes oversee long-term changes in gene expression by integrating genetic and environmental cues. While there are hundreds of enzymes that control histone and DNA modifications, their potential roles in substance abuse and alcohol dependence remain underexplored. A few recent studies have suggested that epigenetic processes could underlie transcriptomic and behavioral hallmarks of alcohol addiction. In the present study, we sought to identify epigenetic enzymes in the brain that are dysregulated during protracted abstinence as a consequence of chronic and intermittent alcohol exposure. Through quantitative mRNA expression analysis of over 100 epigenetic enzymes, we identified 11 that are significantly altered in alcohol-dependent rats compared with controls. Follow-up studies of one of these enzymes, the histone demethylase KDM6B, showed that this enzyme exhibits region-specific dysregulation in the prefrontal cortex and nucleus accumbens of alcohol-dependent rats. KDM6B was also upregulated in the human alcoholic brain. Upregulation of KDM6B protein in alcohol-dependent rats was accompanied by a decrease of trimethylation levels at histone H3, lysine 27 (H3K27me3), consistent with the known demethylase specificity of KDM6B. Subsequent epigenetic (chromatin immunoprecipitation [ChIP]-sequencing) analysis showed that alcohol-induced changes in H3K27me3 were significantly enriched at genes in the IL-6 signaling pathway, consistent with the well-characterized role of KDM6B in modulation of inflammatory responses. Knockdown of KDM6B in cultured microglial cells diminished IL-6 induction in response to an inflammatory stimulus. Our findings implicate a novel KDM6B-mediated epigenetic signaling pathway integrated with inflammatory signaling pathways that are known to underlie the development of alcohol addiction.

How the strengths of Lisp-family languages facilitate building complex and flexible bioinformatics applications.

We present a rationale for expanding the presence of the Lisp family of programming languages in bioinformatics and computational biology research. Put simply, Lisp-family languages enable programmers to more quickly write programs that run faster than in other languages. Languages such as Common Lisp, Scheme and Clojure facilitate the creation of powerful and flexible software that is required for complex and rapidly evolving domains like biology. We will point out several important key features that distinguish languages of the Lisp family from other programming languages, and we will explain how these features can aid researchers in becoming more productive and creating better code. We will also show how these features make these languages ideal tools for artificial intelligence and machine learning applications. We will specifically stress the advantages of domain-specific languages (DSLs): languages that are specialized to a particular area, and thus not only facilitate easier research problem formulation, but also aid in the establishment of standards and best programming practices as applied to the specific research field at hand. DSLs are particularly easy to build in Common Lisp, the most comprehensive Lisp dialect, which is commonly referred to as the 'programmable programming language'. We are convinced that Lisp grants programmers unprecedented power to build increasingly sophisticated artificial intelligence systems that may ultimately transform machine learning and artificial intelligence research in bioinformatics and computational biology.

MicroScope: ChIP-seq and RNA-seq software analysis suite for gene expression heatmaps.

BACKGROUND: Heatmaps are an indispensible visualization tool for examining large-scale snapshots of genomic activity across various types of next-generation sequencing datasets. However, traditional heatmap software do not typically offer multi-scale insight across multiple layers of genomic analysis (e.g., differential expression analysis, principal component analysis, gene ontology analysis, and network analysis) or multiple types of next-generation sequencing datasets (e.g., ChIP-seq and RNA-seq). As such, it is natural to want to interact with a heatmap's contents using an extensive set of integrated analysis tools applicable to a broad array of genomic data types. RESULTS: We propose a user-friendly ChIP-seq and RNA-seq software suite for the interactive visualization and analysis of genomic data, including integrated features to support differential expression analysis, interactive heatmap production, principal component analysis, gene ontology analysis, and dynamic network analysis. CONCLUSIONS: MicroScope is hosted online as an R Shiny web application based on the D3 JavaScript library: http://microscopebioinformatics.org/ . The methods are implemented in R, and are available as part of the MicroScope project at: https://github.com/Bohdan-Khomtchouk/Microscope .

CETP and SGLT2 inhibitor combination therapy increases glycemic control: a 2x2 factorial Mendelian randomization analysis.

Introduction: Cholesteryl ester transfer protein (CETP) inhibitors, initially developed for treating hyperlipidemia, have shown promise in reducing the risk of new-onset diabetes during clinical trials. This positions CETP inhibitors as potential candidates for repurposing in metabolic disease treatment. Given their oral administration, they could complement existing oral medications like sodium-glucose cotransporter 2 (SGLT2) inhibitors, potentially delaying the need for injectable therapies such as insulin. Methods: We conducted a 2x2 factorial Mendelian Randomization analysis involving 233,765 participants from the UK Biobank. This study aimed to evaluate whether simultaneous genetic inhibition of CETP and SGLT2 enhances glycemic control compared to inhibiting each separately. Results: Our findings indicate that dual genetic inhibition of CETP and SGLT2 significantly reduces glycated hemoglobin levels compared to controls and single-agent inhibition. Additionally, the combined inhibition is linked to a lower incidence of diabetes compared to both the control group and SGLT2 inhibition alone. Discussion: These results suggest that combining CETP and SGLT2 inhibitor therapies may offer superior glycemic control over SGLT2 inhibitors alone. Future clinical trials should investigate the potential of repurposing CETP inhibitors for metabolic disease treatment, providing an oral therapeutic option that could benefit high-risk patients before they require injectable therapies like insulin or glucagon-like peptide-1 (GLP-1) receptor agonists.

Machine learning classifiers predict key genomic and evolutionary traits across the kingdoms of life.

In this study, we investigate how an organism's codon usage bias can serve as a predictor and classifier of various genomic and evolutionary traits across the domains of life. We perform secondary analysis of existing genetic datasets to build several AI/machine learning models. When trained on codon usage patterns of nearly 13,000 organisms, our models accurately predict the organelle of origin and taxonomic identity of nucleotide samples. We extend our analysis to identify the most influential codons for phylogenetic prediction with a custom feature ranking ensemble. Our results suggest that the genetic code can be utilized to train accurate classifiers of taxonomic and phylogenetic features. We then apply this classification framework to open reading frame (ORF) detection. Our statistical model assesses all possible ORFs in a nucleotide sample and rejects or deems them plausible based on the codon usage distribution. Our dataset and analyses are made publicly available on GitHub and the UCI ML Repository to facilitate open-source reproducibility and community engagement.

Computational integration and meta-analysis of abandoned cardio-(vascular/renal/metabolic) therapeutics discontinued during clinical trials from 2011 to 2022.

Cardiovascular/renal/metabolic (CVRM) diseases collectively comprise the leading cause of death worldwide and disproportionally affect older demographics and historically underrepresented minority populations. Despite these critical unmet needs, pharmaceutical research and development (R&D) efforts have historically struggled with high drug failure rates, low approval rates, and other challenges. Drug repurposing is one approach to recovering R&D costs and meeting unmet demands in therapeutic markets. While there are multiple approaches to conducting drug repurposing, we recognize the importance of bringing together and consolidating discontinued drug information to help identify prospective repurposing candidates. In this study, we have harmonized and integrated information on all relevant CVRM drug assets from U.S. Securities and Exchange Commission (SEC) filings, clinical trial records, PharmGKB, Open Targets, and other platforms. A list of existing therapeutics discontinued or shelved by pharmaceutical/biotechnology companies in 2011-2022 were manually curated and interpreted for insights using information on each drug's genetic target, mechanism of action (MOA), clinical indication, and R&D information including highest phase of clinical development, year of discontinuation, previous repurposing attempts (if any), and other actionable metadata. This study also summarizes the profiles of CVRM drugs discontinued within the past decade and identifies the limitations of publicly available information on discontinued drug assets. The constructed database could serve as a tool for identifying candidates for drug repurposing and developing query methods for collecting R&D information.

CETP and SGLT2 inhibitor combination therapy improves glycemic control.

Importance: Cholesteryl ester transfer protein (CETP) inhibition has been associated with decreased risk of new-onset diabetes in past clinical trials exploring their efficacy in cardiovascular disease and can potentially be repurposed to treat metabolic disease. Notably, as an oral drug it can potentially be used to supplement existing oral drugs such as sodium-glucose cotransporter 2 (SGLT2) inhibitors before patients are required to take injectable drugs such as insulin. Objective: To identify whether CETP inhibitors could be used as an oral add-on to SGLT2 inhibition to improve glycemic control. Design Setting and Participants: 2×2 factorial Mendelian Randomization (MR) is performed on the general population of UK Biobank participants with European ancestry. Exposures: Previously constructed genetic scores for CETP and SGLT2 function are combined in a 2×2 factorial framework to characterize the associations between joint CETP and SGLT2 inhibition compared to either alone. Main Outcomes and Measures: Glycated hemoglobin and type-2 diabetes incidence. Results: Data on 233,765 UK Biobank participants suggests that individuals with genetic inhibition of both CETP and SGLT2 have significantly lower glycated hemoglobin levels (mmol/mol) than control (Effect size: -0.136; 95% CI: -0.190 to -0.081; p-value: 1.09E-06), SGLT2 inhibition alone (Effect size: -0.082; 95% CI: -0.140 to -0.024; p-value: 0.00558), and CETP inhibition alone (Effect size: -0.08479; 95% CI: -0.136 to -0.033; p-value: 0.00118). Furthermore, joint CETP and SGLT2 inhibition is associated with decreased incidence of diabetes (log-odds ratio) compared to control (Effect size: -0.068; 95% CI: -0.115 to -0.021; p-value: 4.44E-03) and SGLT2 inhibition alone (Effect size: -0.062; 95% CI: -0.112 to -0.012; p-value: 0.0149). Conclusions and Relevance: Our results suggest that CETP and SGLT2 inhibitor therapy may improve glycemic control over SGLT2 inhibitors alone. Future clinical trials can explore whether CETP inhibitors can be repurposed to treat metabolic disease and provide an oral therapeutic option to benefit high-risk patients before escalation to injectable drugs such as insulin or glucagon-like peptide 1 (GLP1) receptor agonists.

Altered Caveolin-1 Dynamics Result in Divergent Mineralization Responses in Bone and Vascular Calcification.

Introduction: Though vascular smooth muscle cells adopt an osteogenic phenotype during pathological vascular calcification, clinical studies note an inverse correlation between bone mineral density and arterial mineral-also known as the calcification paradox. Both processes are mediated by extracellular vesicles (EVs) that sequester calcium and phosphate. Calcifying EV formation in the vasculature requires caveolin-1 (CAV1), a membrane scaffolding protein that resides in membrane invaginations (caveolae). Of note, caveolin-1-deficient mice, however, have increased bone mineral density. We hypothesized that caveolin-1 may play divergent roles in calcifying EV formation from vascular smooth muscle cells (VSMCs) and osteoblasts (HOBs). Methods: Primary human coronary artery VSMCs and osteoblasts were cultured for up to 28 days in an osteogenic media. CAV1 expression was knocked down using siRNA. Methyl ß-cyclodextrin (MßCD) and a calpain inhibitor were used, respectively, to disrupt and stabilize the caveolar domains in VSMCs and HOBs. Results: CAV1 genetic variation demonstrates significant inverse relationships between bone-mineral density (BMD) and coronary artery calcification (CAC) across two independent epidemiological cohorts. Culture in osteogenic (OS) media increased calcification in HOBs and VSMCs. siRNA knockdown of CAV1 abrogated VSMC calcification with no effect on osteoblast mineralization. MßCD-mediated caveolae disruption led to a 3-fold increase of calcification in VSMCs treated with osteogenic media (p < 0.05) but hindered osteoblast mineralization (p < 0.01). Conversely, stabilizing caveolae by calpain inhibition prevented VSMC calcification (p < 0.05) without affecting osteoblast mineralization. There was no significant difference in CAV1 content between lipid domains from HOBs cultured in OS and control media. Conclusion: Our data indicate fundamental cellular-level differences in physiological and pathophysiological mineralization mediated by CAV1 dynamics. This is the first study to suggest that divergent mechanisms in calcifying EV formation may play a role in the calcification paradox. Supplementary Information: The online version contains supplementary material available at 10.1007/s12195-023-00779-7.

Targeting the cytoskeleton and extracellular matrix in cardiovascular disease drug discovery.

INTRODUCTION: Currently, cardiovascular disease (CVD) drug discovery has focused primarily on addressing the inflammation and immunopathology aspects inherent to various CVD phenotypes such as cardiac fibrosis and coronary artery disease. However, recent findings suggest new biological pathways for cytoskeletal and extracellular matrix (ECM) regulation across diverse CVDs, such as the roles of matricellular proteins (e.g. tenascin-C) in regulating the cellular microenvironment. The success of anti-inflammatory drugs like colchicine, which targets microtubule polymerization, further suggests that the cardiac cytoskeleton and ECM provide prospective therapeutic opportunities. AREAS COVERED: Potential therapeutic targets include proteins such as gelsolin and calponin 2, which play pivotal roles in plaque development. This review focuses on the dynamic role that the cytoskeleton and ECM play in CVD pathophysiology, highlighting how novel target discovery in cytoskeletal and ECM-related genes may enable therapeutics development to alter the regulation of cellular architecture in plaque formation and rupture, cardiac contractility, and other molecular mechanisms. EXPERT OPINION: Further research into the cardiac cytoskeleton and its associated ECM proteins is an area ripe for novel target discovery. Furthermore, the structural connection between the cytoskeleton and the ECM provides an opportunity to evaluate both entities as sources of potential therapeutic targets for CVDs.

Enhanced single-cell RNA-seq workflow reveals coronary artery disease cellular cross-talk and candidate drug targets.

BACKGROUND AND AIMS: The atherosclerotic plaque microenvironment is highly complex, and selective agents that modulate plaque stability are not yet available. We sought to develop a scRNA-seq analysis workflow to investigate this environment and uncover potential therapeutic approaches. We designed a user-friendly, reproducible workflow that will be applicable to other disease-specific scRNA-seq datasets. METHODS: Here we incorporated automated cell labeling, pseudotemporal ordering, ligand-receptor evaluation, and drug-gene interaction analysis into a ready-to-deploy workflow. We applied this pipeline to further investigate a previously published human coronary single-cell dataset by Wirka et al. Notably, we developed an interactive web application to enable further exploration and analysis of this and other cardiovascular single-cell datasets. RESULTS: We revealed distinct derivations of fibroblast-like cells from smooth muscle cells (SMCs), and showed the key changes in gene expression along their de-differentiation path. We highlighted several key ligand-receptor interactions within the atherosclerotic environment through functional expression profiling and revealed several avenues for future pharmacological development for precision medicine. Further, our interactive web application, PlaqView (www.plaqview.com), allows lay scientists to explore this and other datasets and compare scRNA-seq tools without prior coding knowledge. CONCLUSIONS: This publicly available workflow and application will allow for more systematic and user-friendly analysis of scRNA datasets in other disease and developmental systems. Our analysis pipeline provides many hypothesis-generating tools to unravel the etiology of coronary artery disease. We also highlight potential mechanisms for several drugs in the atherosclerotic cellular environment. Future releases of PlaqView will feature more scRNA-seq and scATAC-seq atherosclerosis-related datasets to provide a critical resource for the field, and to promote data harmonization and biological interpretation.

NERO: a biomedical named-entity (recognition) ontology with a large, annotated corpus reveals meaningful associations through text embedding.

Machine reading (MR) is essential for unlocking valuable knowledge contained in millions of existing biomedical documents. Over the last two decades1,2, the most dramatic advances in MR have followed in the wake of critical corpus development3. Large, well-annotated corpora have been associated with punctuated advances in MR methodology and automated knowledge extraction systems in the same way that ImageNet4 was fundamental for developing machine vision techniques. This study contributes six components to an advanced, named entity analysis tool for biomedicine: (a) a new, Named Entity Recognition Ontology (NERO) developed specifically for describing textual entities in biomedical texts, which accounts for diverse levels of ambiguity, bridging the scientific sublanguages of molecular biology, genetics, biochemistry, and medicine; (b) detailed guidelines for human experts annotating hundreds of named entity classes; (c) pictographs for all named entities, to simplify the burden of annotation for curators; (d) an original, annotated corpus comprising 35,865 sentences, which encapsulate 190,679 named entities and 43,438 events connecting two or more entities; (e) validated, off-the-shelf, named entity recognition (NER) automated extraction, and; (f) embedding models that demonstrate the promise of biomedical associations embedded within this corpus.

HeartBioPortal2.0: new developments and updates for genetic ancestry and cardiometabolic quantitative traits in diverse human populations.

Cardiovascular disease (CVD) is the leading cause of death worldwide for all genders and across most racial and ethnic groups. However, different races and ethnicities exhibit different rates of CVD and its related cardiorenal and metabolic comorbidities, suggesting differences in genetic predisposition and risk of onset, as well as socioeconomic and lifestyle factors (diet, exercise, etc.) that act upon an individual's unique underlying genetic background. Here, we present HeartBioPortal2.0, a major update to HeartBioPortal, the world's largest CVD genetics data precision medicine platform for harmonized CVD-relevant genetic variants, which now enables search and analysis of human genetic information related to heart disease across ethnically diverse populations and cardiovascular/renal/metabolic quantitative traits pertinent to CVD pathophysiology. HeartBioPortal2.0 is structured as a cloud-based computing platform and knowledge portal that consolidates a multitude of CVD-relevant genomic data modalities into a single powerful query and browsing interface between data and user via a user-friendly web application publicly available to the scientific research community. Since its initial release, HeartBioPortal2.0 has added new cardiovascular/renal/metabolic disease-relevant gene expression data as well as genetic association data from numerous large-scale genome-wide association study consortiums such as CARDIoGRAMplusC4D, TOPMed, FinnGen, AFGen, MESA, MEGASTROKE, UK Biobank, CHARGE, Biobank Japan and MyCode, among other studies. In addition, HeartBioPortal2.0 now includes support for quantitative traits and ethnically diverse populations, allowing users to investigate the shared genetic architecture of any gene or its variants across the continuous cardiometabolic spectrum from health (e.g. blood pressure traits) to disease (e.g. hypertension), facilitating the understanding of CVD trait genetics that inform health-to-disease transitions and endophenotypes. Custom visualizations in the new and improved user interface, including performance enhancements and new security features such as user authentication, collectively re-imagine HeartBioPortal's user experience and provide a data commons that co-locates data, storage and computing infrastructure in the context of studying the genetic basis behind the leading cause of global mortality. Database URL: https://www.heartbioportal.com/.

Gaussian-Distributed Codon Frequencies of Genomes.

DNA encodes protein primary structure using 64 different codons to specify 20 different amino acids and a stop signal. Frequencies of codon occurrence when ordered in descending sequence provide a global characterization of a genome's preference (bias) for using the different codons of the redundant genetic code. Whereas frequency/rank relations have been described by empirical expressions, here we propose a statistical model in which two different forms of codon usage co-exist in a genome. We investigate whether such a model can account for the range of codon usages observed in a large set of genomes from different taxa. The differences in frequency/rank relations across these genomes can be expressed in a single parameter, the proportion of the two codon compartments. One compartment uses different codons with weak bias according to a Gaussian distribution of frequency, the other uses different codons with strong bias. In prokaryotic genomes both compartments appear to be present in a wide range of proportions, whereas in eukaryotic genomes the compartment with Gaussian distribution tends to dominate. Codon frequencies that are Gaussian-distributed suggest that many evolutionary conditions are involved in shaping weakly-biased codon usage, whereas strong bias in codon usage suggests dominance of few evolutionary conditions.

shinyheatmap: Ultra fast low memory heatmap web interface for big data genomics.

BACKGROUND: Transcriptomics, metabolomics, metagenomics, and other various next-generation sequencing (-omics) fields are known for their production of large datasets, especially across single-cell sequencing studies. Visualizing such big data has posed technical challenges in biology, both in terms of available computational resources as well as programming acumen. Since heatmaps are used to depict high-dimensional numerical data as a colored grid of cells, efficiency and speed have often proven to be critical considerations in the process of successfully converting data into graphics. For example, rendering interactive heatmaps from large input datasets (e.g., 100k+ rows) has been computationally infeasible on both desktop computers and web browsers. In addition to memory requirements, programming skills and knowledge have frequently been barriers-to-entry for creating highly customizable heatmaps. RESULTS: We propose shinyheatmap: an advanced user-friendly heatmap software suite capable of efficiently creating highly customizable static and interactive biological heatmaps in a web browser. shinyheatmap is a low memory footprint program, making it particularly well-suited for the interactive visualization of extremely large datasets that cannot typically be computed in-memory due to size restrictions. Also, shinyheatmap features a built-in high performance web plug-in, fastheatmap, for rapidly plotting interactive heatmaps of datasets as large as 105-107 rows within seconds, effectively shattering previous performance benchmarks of heatmap rendering speed. CONCLUSIONS: shinyheatmap is hosted online as a freely available web server with an intuitive graphical user interface: http://shinyheatmap.com. The methods are implemented in R, and are available as part of the shinyheatmap project at: https://github.com/Bohdan-Khomtchouk/shinyheatmap. Users can access fastheatmap directly from within the shinyheatmap web interface, and all source code has been made publicly available on Github: https://github.com/Bohdan-Khomtchouk/fastheatmap.

Ischemic Preconditioning Confers Epigenetic Repression of Mtor and Induction of Autophagy Through G9a-Dependent H3K9 Dimethylation.

BACKGROUND: Ischemic preconditioning (IPC) protects the heart from prolonged ischemic insult and reperfusion injury through a poorly understood mechanism. Post-translational modifications of histone residues can confer rapid and drastic switches in gene expression in response to various stimuli, including ischemia. The aim of this study was to investigate the effect of histone methylation in the response to cardiac ischemic preconditioning. METHODS AND RESULTS: We used cardiac biopsies from mice subjected to IPC to quantify global levels of 3 of the most well-studied histone methylation marks (H3K9me2, H3K27me3, and H3K4me3) with Western blot and found that H3K9me2 levels were significantly increased in the area at risk compared to remote myocardium. In order to assess which genes were affected by the increase in H3K9me2 levels, we performed ChIP-Seq and transcriptome profiling using microarray. Two hundred thirty-seven genes were both transcriptionally repressed and enriched in H3K9me2 in the area at risk of IPC mice. Of these, Mtor (Mechanistic target of rapamycin) was chosen for mechanistic studies. Knockdown of the major H3K9 methyltransferase G9a resulted in a significant decrease in H3K9me2 levels across Mtor, increased Mtor expression, as well as decreased autophagic activity in response to rapamycin and serum starvation. CONCLUSIONS: IPC confers an increase of H3K9me2 levels throughout the Mtor gene-a master regulator of cellular metabolism and a key player in the cardioprotective effect of IPC-leading to transcriptional repression via the methyltransferase G9a. The results of this study indicate that G9a has an important role in regulating cardiac autophagy and the cardioprotective effect of IPC.