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DNA methylation patterns from peripheral blood separate coronary artery disease patients with and without heart failure.

ABSTRACT: Coronary artery disease (CAD) and ischaemic cardiomyopathy are leading causes of mortality and morbidity worldwide. Currently there are only two well established biomarkers used for heart failure diagnosis and prognosis. Therefore, more research into the genetic and epigenetic mechanisms of heart failure (HF) will allow identification of biomarkers that can be used for different stages of HF. DNA methylation is an epigenetic mark that is increasingly recognised to play a critical role in cardiovascular disease. However, there are no methylation studies that specifically focus on ischaemic cardiomyopathy. Here we performed a small scale methylome wide association study to identify epigenetic markers in peripheral blood in a cohort of patients with and without HF who otherwise share a similar coronary ischaemic burden, age, sex, and ethnicity. We identified 68 significantly differentially methylated regions in our data. Of these regions, 48 occurred within gene bodies and 25 were located near enhancer elements, some within coding genes and some in non-coding genes. Our gene set enrichment analyses identified 103 significantly enriched gene sets in HF. We have demonstrated the utility of methyl-binding domain capture sequencing to evaluate DNA methylation markers in a perioperative cohort of cardiac surgical patients with ischaemic cardiomyopathy and HF. The peripheral methylation status of specific coding genes, such as HDAC9, are candidates for larger longitudinal studies. We have further demonstrated the value and feasibility of examining DNA methylation patterns from peripheral blood to highlight cellular pathways and processes that may contribute to the development of HF.

ORGANISM(S): Homo sapiens

PROVIDER: GSE134766 | GEO | 2019/07/25

REPOSITORIES: GEO

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Project description:Background: Heart failure (HF) has been recognized as global pandemic with a high rate of associated hospitalization, morbidity and mortality. Although numerous advances have been made, its representative molecular signatures remain largely unknown, especially the role of genes in HF progression. The aim of the present prospective follow-up study was to reveal potential biomarkers associated with the progression of heart failure. Methods: We collected left ventricular heart tissue from 21 HF patients and 9 healthy donors as study cohort and generated multi-level transcriptomic data. By using Masson staining to calculate the fibrosis percentage for each sample, we applied lasso regression model to identify the genes associated with fibrosis as well as progression. The genes were further validated by immunohistochemistry (IHC) staining in the same cohort and qRT-PCR using another independent cohort (20 HF and 9 healthy donors). Enzyme linked immunosorbent assay (ELISA) was used to measure the plasma level in validation cohort (87 HF patients) for predicting HF progression. Results: Based on the multi-level transcriptomic data, we examined differentially expressed genes [mRNAs, microRNAs, and long non-coding RNAs (lncRNAs)] in study cohort. The follow-up functional annotations and regulatory network reveal their potential roles in regulating extracellular matrix. We further identified several genes that were associated with fibrosis. By using the survival time before transplantation, COL1A1 was identified as a potential biomarker for HF progression and its up-regulation was confirmed by both IHC and qRT-PCR. Furthermore, COL1A1 content >281.7 ng/ml in plasma was found to be associated with poor survival within one year of heart transplantation from heart failure [Hazard Ratio (HR): 27.52, 95% Confidence Interval (CI): 7.474 to 101.3, Log-rank p-value<1.0×10-4]. Conclusions: Our results suggested that COL1A1 is a plasma biomarker of HF and associated with HF progression, especially to predict the 1-year survival from HF onset to transplantation.

Project description:The goal of this experiment was to identify gene expression changes that are common in different heart failure (HF) types or specific to an etiology of HF. HF groups studied include doxorubicin induced cardiomyopathy, familial dilated cardiomyopathy, hypertrophic cardiomyopathy, idiopathic dilated cardiomyopathy, ischemic heart disease, peripartum cardiomyopathy, and viral induced cardiomyopathy. Non-diseased left ventricles (LV) were obtained from donor hearts not used for transplantation (these were considered to be unsuitable for transplantation for a variety of reasons including the lack of a tissue-compatible recipient). Failing LV were obtained from patients undergoing heart transplantation. Transmural sections of LV anterior free wall were trimmed of fat, dissected into 1-1.5 gm pieces, and immediately frozen in the operating theater. 100-200 mg of frozen transmural LV was ground to a fine powder in liquid nitrogen using a mortar and pestle. Then total RNA was isolated by a method described by Wei and Khan (A Molecular Cloning Manual). The purity of the RNA extract was assessed by measuring the absorbance at 260 nm and 280 nm. Its quantity and integrity was then examined using RNA Nano Chips on a 2100 Bioanalyzer (Agilent Technologies, Palo Alto, CA, USA). 10 ?g of RNA extracts and a universal human reference RNA (Stratagene, La Jolla, CA, USA) were reverse transcribed with Cy3-dUTP and Cy5-dUTP, respectively, to produce labeled cDNA probes using Thermoscript reverse transcriptase according Hwang JJ et. al. (Physiological Genomics 10: 31-44, 2002). Raw scanned images were processed using ScanAlyze version 2.50 (Michael Eisen, Stanford University, CA, USA). Cy3 and Cy5 scans were superimposed, local backgrounds were subtracted, and the fluorescence intensities of each spot were quantified. All further analyses of CardioChips were done using GeneSpring version 6.1 (Silicon Genetics, Redwood City, CA, USA). The data from each array was normalized first to the reference RNA and then a LOWESS curve was fit to the log-intensity versus log-ratio plot for each array and for each gene across all experiments. 40.0% of the data was used to calculate the LOWESS fit at each point. Differentially expressed genes in each HF group compared to donor hearts were identified by a one-way ANOVA test (P <0.05) with the benjamini and hochberg multiple testing corrections and we selected those genes with an average difference greater than 1.5 fold. A hierarchical clustering analysis was performed using pearson correlation with the separation ratio of 1.0 and minimum distance of 0.001 as similarity measure. Using a cardiovascular-specific gene array (CardioChip) of 42 heart failure (HF) patients from a wide range of etiology groups and 8 non-failing donors, we have identified down-regulation of LIM domain protein which may be an important pathway for the clinical progression of HF. We identified six genes, encoding LIM domain and Homer proteins, that are down-regulated in terminally failing hearts. The LIM and cysteine rich domain 1 (LMCD1) gene, in particular, was significantly down-regulated in all HF samples. This novel finding suggests that the LMCD 1 is a universal biomarker for end-stage HF. Other LIM domain genes were also down-regulated but only in non-familial dilated forms of cardiomyopathy. This is probably due to the fact that down-regulation of LIM protein expression may disrupt the cytoskeletal architecture, leading to dilated cardiomyopathy. In addition to identifying the LIM domain genes as possible regulatory genes involved in HF, we also demonstrated that the gene expression profile was able to classify multiple HF patients groups. This paper is the first to examine viral-induced cardiomyopathy, doxorubicin toxicity cardiomyopathy and perimartum cardiomyopathy and to perform the clustering analysis of those HF types providing new insights into human HF.

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DNA methylation patterns from peripheral blood separate coronary artery disease patients with and without heart failure.

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