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Systems analysis of dilated cardiomyopathy in the next generation sequencing era

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Dilated cardiomyopathy (DCM) is a form of severe failure of cardiac muscle caused by a long list of etiologies ranging from myocardial infarction, DNA mutations in cardiac genes, to toxics. Systems analysis integrating next‐generation sequencing (NGS)‐based omics approaches, such as the sequencing of DNA, RNA, and chromatin, provide valuable insights into DCM mechanisms. The outcome and interpretation of NGS methods can be affected by the localization of cardiac biopsy, level of tissue degradation, and variable ratios of different cell populations, especially in the presence of fibrosis. Heart tissue composition may even differ between sexes, or siblings carrying the same disease causing mutation. Therefore, before planning any experiments, it is important to fully appreciate the complexities of DCM, and the selection of samples suitable for given research question should be an interdisciplinary effort involving clinicians and biologists. The list of NGS omics datasets in DCM to date is short. More studies have to be performed to contribute to public data repositories and facilitate systems analysis. In addition, proper data integration is a difficult task requiring complex computational approaches. Despite these complications, there are multiple promising implications of systems analysis in DCM. By combining various types of datasets, for example, RNA‐seq, ChIP‐seq, or 4C, deep insights into cardiac biology, and possible biomarkers and treatment targets, can be gained. Systems analysis can also facilitate the annotation of noncoding mutations in cardiac‐specific DNA regulatory regions that play a substantial role in maintaining the tissue‐ and cell‐specific transcriptional programs in the heart. This article is categorized under: Physiology > Mammalian Physiology in Health and Disease Laboratory Methods and Technologies > Genetic/Genomic Methods Laboratory Methods and Technologies > RNA Methods
Possible sources of heterogeneity in dilated cardiomyopathy (DCM) next‐generation sequencing (NGS)‐omics analyses impairing the quality of systems data analysis. (a) Degradation present in cardiac material. Example shows Bioanalyzer results of the RNA integrity of 22 RNA samples divided into two RNA‐seq runs, each containing 11 samples. It can immediately be appreciated that Run 2 contains RNA samples with variable levels of degradation. In case all samples from Run 1 belong to one tested group and all samples from Run 2 to the other, most of the observed differentially expressed gene signals might be due to this batch effect. (b) Various fibrosis patterns between different DCM conditions. Histological findings in control heart without evident fibrosis and hearts with a pathogenic genetic mutation: desminopathy (DES), phospholamban (PLN), and desmosomal (PKP2) showing subepicardial fibrosis pattern and lamin A/C (LMNA) and sarcomeric (TTN) subtypes showing subendocardial fibrosis pattern. (c) Different cell compositions throughout the heart. Transversal heart slice from a patient with DCM due to PLN mutation showing various representations of cardiomyocytes and fibrofatty tissue replacement among the right and left ventricles and the interventricular septum. (b) and (c) The slides were stained with Masson’s trichrome (red: cardiomyocytes, blue: fibrosis, and white: fat). Source: Sepehrkhouy et al. ()
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Systems analysis modules suitable for dilated cardiomyopathy (DCM) research. Whole genome sequencing or genome‐wide association study (GWAS) data related to DCM could benefit from approach of systems analysis modules. Based on possibly functional coding and noncoding part of human genome, genome‐wide amount of data can be narrowed‐down. Based on general knowledge, these suggested modules contain the expected amount of DNA structures: (a) exonic sequences of ca. 200 diagnostic cardiomyopathy genes; (b) exonic sequences more than 15,000 protein‐coding genes expressed in heart (based on RNA‐seq); (c) exonic sequences of ca. 2500 noncoding RNA genes (RNA‐seq); (d) more than 20,000 active promoters and enhancers in cardiac tissue (ChIP‐seq); (e) more than 200 transcription factors, which binding motifs are enriched in promoters and enhancers in cardiac tissue (ChIP‐seq); (f) 16 kb of the whole mitochondrial DNA sequence
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Example of utilization of next‐generation sequencing (NGS)‐based “omics” in dilated cardiomyopathy (DCM) using systems analysis approach. Schematic overview of the process of annotating variants in functional DNA regions in cardiac tissue. Peaks represent sequencing coverage distribution. ChIP‐seq and RNA‐seq need to be performed on cardiac tissue to obtain relevant information about the location of DNA regulatory variants (promoters and enhancers) and expressed genes. Subsequently, whole genome sequencing performed on DNA isolated from blood of a larger DCM population can be performed to detect variants overlapping with functional regions and genes detected in cardiac tissue. Three examples of types of possibly functional genetic variants are shown, including noncoding mutation impairing a transcription factor‐binding site in a cardiac enhancer, classical protein‐coding mutation based on a substitution, and mutation in RNA gene. All three types of mutations have the potential to cause of modify DCM phenotype
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Various gene annotations and pathway enrichment software give different results. A single set of 40 cardiac genes as part of Gene Ontology (GO) Biological Process GO:0045214—sarcomere organization—were used to test two widely used gene annotation and pathway enrichment methods: (a) String (https://stringdb.org/) and (b) GeneMANIA (http://genemania.org/). Both methods provide different types of very complex information, which is difficult to quickly process by naked eye. Interestingly, pathway/network enrichment analysis using GO Biological Process shows completely different results among the top five processes in both methods, including different false discovery rate (FDR) values. GeneMANIA added additional 20 genes to build connections between the submitted 40 genes
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Preferred sample distribution to avoid batch effects: (a) Good practice when planning a next‐generation sequencing (NGS)‐based “omics” experiment regardless of the selected method (whole genome sequencing, Methyl‐seq, RNA‐seq, ChIP‐seq, DNAse‐seq, etc.). Groups should be equally distributed over batches during DNA, RNA, or chromatin isolation, NGS library preparation, and sequencing. (b) Using this principle, the resulting clustering based on principal component analysis (PCA) is more likely to be based on real biological signal, rather than technical batch effect. (c) A schematic example of the intensity of signal (any NGS method) showing interindividual variability and differences between tested groups
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Laboratory Methods and Technologies > RNA Methods
Laboratory Methods and Technologies > Genetic/Genomic Methods
Physiology > Mammalian Physiology in Health and Disease

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