Epigenetic Regulation of Alternative mRNA Splicing in Dilated Cardiomyopathy

Posttranscriptional Regulation by Proteins and Noncoding RNAs

Posttranscriptional regulation comprises those mechanisms occurring after the initial copy of the DNA sequence is transcribed into an intermediate RNA molecule (i.e., messenger RNA) until such a molecule is used as a template to generate a protein. A subset of these posttranscriptional regulatory mechanisms essentially are destined to process the immature mRNA toward its mature form, conferring the adequate mRNA stability, providing the means for pertinent introns excision, and controlling mRNA turnover rate and quality control check. An additional layer of complexity is added in certain cases, since discrete nucleotide modifications in the mature RNA molecule are added by RNA editing, a process that provides large mature mRNA diversity. Moreover, a number of posttranscriptional regulatory mechanisms occur in a cell- and tissue-specific manner, such as alternative splicing and noncoding RNA-mediated regulation. In this chapter, we will briefly summarize current state-of-the-art knowledge of general posttranscriptional mechanisms, while major emphases will be devoted to those tissue-specific posttranscriptional modifications that impact on cardiac development and congenital heart disease.

Indirect epigenetic testing identifies a diagnostic signature of cardiomyocyte DNA methylation in heart failure

Precision-based molecular phenotyping of heart failure must overcome limited access to cardiac tissue. Although epigenetic alterations have been found to underlie pathological cardiac gene dysregulation, the clinical utility of myocardial epigenomics remains narrow owing to limited clinical access to tissue. Therefore, the current study determined whether patient plasma confers indirect phenotypic, transcriptional, and/or epigenetic alterations to ex vivo cardiomyocytes to mirror the failing human myocardium. Neonatal rat ventricular myocytes (NRVMs) and single-origin human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) and were treated with blood plasma samples from patients with dilated cardiomyopathy (DCM) and donor subjects lacking history of cardiovascular disease. Following plasma treatments, NRVMs and hiPSC-CMs underwent significant hypertrophy relative to non-failing controls, as determined via automated high-content screening. Array-based DNA methylation analysis of plasma-treated hiPSC-CMs and cardiac biopsies uncovered robust, and conserved, alterations in cardiac DNA methylation, from which 100 sites were validated using an independent cohort. Among the CpG sites identified, hypo-methylation of the ATG promoter was identified as a diagnostic marker of HF, wherein cg03800765 methylation (AUC = 0.986, P < 0.0001) was found to out-perform circulating NT-proBNP levels in differentiating heart failure. Taken together, these findings support a novel approach of indirect epigenetic testing in human HF.

Multi-omics assessment of dilated cardiomyopathy using non-negative matrix factorization

Dilated cardiomyopathy (DCM), a myocardial disease, is heterogeneous and often results in heart failure and sudden cardiac death. Unavailability of cardiac tissue has hindered the comprehensive exploration of gene regulatory networks and nodal players in DCM. In this study, we carried out integrated analysis of transcriptome and methylome data using non-negative matrix factorization from a cohort of DCM patients to uncover underlying latent factors and covarying features between whole-transcriptome and epigenome omics datasets from tissue biopsies of living patients. DNA methylation data from Infinium HM450 and mRNA Illumina sequencing of n = 33 DCM and n = 24 control probands were filtered, analyzed and used as input for matrix factorization using R NMF package. Mann-Whitney U test showed 4 out of 5 latent factors are significantly different between DCM and control probands (P<0.05). Characterization of top 10% features driving each latent factor showed a significant enrichment of biological processes known to be involved in DCM pathogenesis, including immune response (P = 3.97E-21), nucleic acid binding (P = 1.42E-18), extracellular matrix (P = 9.23E-14) and myofibrillar structure (P = 8.46E-12). Correlation network analysis revealed interaction of important sarcomeric genes like Nebulin, Tropomyosin alpha-3 and ERC-protein 2 with CpG methylation of ATPase Phospholipid Transporting 11A0, Solute Carrier Family 12 Member 7 and Leucine Rich Repeat Containing 14B, all with significant P values associated with correlation coefficients >0.7. Using matrix factorization, multi-omics data derived from human tissue samples can be integrated and novel interactions can be identified. Hypothesis generating nature of such analysis could help to better understand the pathophysiology of complex traits such as DCM.

Fine-tuned KDM1A alternative splicing regulates human cardiomyogenesis through an enzymatic-independent mechanism

The histone demethylase KDM1A is a multi-faceted regulator of vital developmental processes, including mesodermal and cardiac tube formation during gastrulation. However, it is unknown whether the fine-tuning of KDM1A splicing isoforms, already shown to regulate neuronal maturation, is crucial for the specification and maintenance of cell identity during cardiogenesis. Here, we discovered a temporal modulation of ubKDM1A and KDM1A+2a during human and mice fetal cardiac development and evaluated their impact on the regulation of cardiac differentiation. We revealed a severely impaired cardiac differentiation in KDM1A^−/− hESCs that can be rescued by re-expressing ubKDM1A or catalytically impaired ubKDM1A-K661A, but not by KDM1A+2a or KDM1A+2a-K661A. Conversely, KDM1A+2a^−/− hESCs give rise to functional cardiac cells, displaying increased beating amplitude and frequency and enhanced expression of critical cardiogenic markers. Our findings prove the existence of a divergent scaffolding role of KDM1A splice variants, independent of their enzymatic activity, during hESC differentiation into cardiac cells.

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ubKDM1A and KDM1A+2a isoforms are fine-tuned during fetal cardiac development

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Depletion of KDM1A isoforms impairs hESC differentiation into cardiac cells

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KDM1A+2a ablation enhances the expression of key cardiac markers

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KDM1A isoforms exhibit enzymatic-independent divergent roles during cardiogenesis

Using Machine Learning to Predict Obesity Based on Genome-Wide and Epigenome-Wide Gene-Gene and Gene-Diet Interactions

Obesity is associated with many chronic diseases that impair healthy aging and is governed by genetic, epigenetic, and environmental factors and their complex interactions. This study aimed to develop a model that predicts an individual's risk of obesity by better characterizing these complex relations and interactions focusing on dietary factors. For this purpose, we conducted a combined genome-wide and epigenome-wide scan for body mass index (BMI) and up to three-way interactions among 402,793 single nucleotide polymorphisms (SNPs), 415,202 DNA methylation sites (DMSs), and 397 dietary and lifestyle factors using the generalized multifactor dimensionality reduction (GMDR) method. The training set consisted of 1,573 participants in exam 8 of the Framingham Offspring Study (FOS) cohort. After identifying genetic, epigenetic, and dietary factors that passed statistical significance, we applied machine learning (ML) algorithms to predict participants' obesity status in the test set, taken as a subset of independent samples (n = 394) from the same cohort. The quality and accuracy of prediction models were evaluated using the area under the receiver operating characteristic curve (ROC-AUC). GMDR identified 213 SNPs, 530 DMSs, and 49 dietary and lifestyle factors as significant predictors of obesity. Comparing several ML algorithms, we found that the stochastic gradient boosting model provided the best prediction accuracy for obesity with an overall accuracy of 70%, with ROC-AUC of 0.72 in test set samples. Top predictors of the best-fit model were 21 SNPs, 230 DMSs in genes such as CPT1A, ABCG1, SLC7A11, RNF145, and SREBF1, and 26 dietary factors, including processed meat, diet soda, French fries, high-fat dairy, artificial sweeteners, alcohol intake, and specific nutrients and food components, such as calcium and flavonols. In conclusion, we developed an integrated approach with ML to predict obesity using omics and dietary data. This extends our knowledge of the drivers of obesity, which can inform precision nutrition strategies for the prevention and treatment of obesity. Clinical Trial Registration: [www.ClinicalTrials.gov], the Framingham Heart Study (FHS), [NCT00005121].

Electrical Ventricular Remodeling in Dilated Cardiomyopathy

Ventricular arrhythmias contribute significantly to morbidity and mortality in patients with heart failure (HF). Pathomechanisms underlying arrhythmogenicity in patients with structural heart disease and impaired cardiac function include myocardial fibrosis and the remodeling of ion channels, affecting electrophysiologic properties of ventricular cardiomyocytes. The dysregulation of ion channel expression has been associated with cardiomyopathy and with the development of arrhythmias. However, the underlying molecular signaling pathways are increasingly recognized. This review summarizes clinical and cellular electrophysiologic characteristics observed in dilated cardiomyopathy (DCM) with ionic and structural alterations at the ventricular level. Furthermore, potential translational strategies and therapeutic options are highlighted.

Dilated cardiomyopathy: a new insight into the rare but common cause of heart failure

Heart failure is a global health burden responsible for high morbidity and mortality with a prevalence of greater than 60 million individuals worldwide. One of the major causes of heart failure is dilated cardiomyopathy (DCM), characterized by associated systolic dysfunction. During the last few decades, there have been remarkable advances in our understanding about the genetics of dilated cardiomyopathy. The genetic causes were initially thought to be associated with mutations in genes encoding proteins that are localized to cytoskeleton and sarcomere only; however, with the advancement in mechanistic understanding, the roles of ion channels, Z-disc, mitochondria, nuclear proteins, cardiac transcription factors (e.g., NKX-2.5, TBX20, GATA4), and the factors involved in calcium homeostasis have also been identified and found to be implicated in both familial and sporadic DCM cases. During past few years, next-generation sequencing (NGS) has been established as a diagnostic tool for genetic analysis and it has added significantly to the existing candidate gene list for DCM. The animal models have also provided novel insights to develop a better treatment strategy based on phenotype-genotype correlation, epigenetic and phenomic profiling. Most of the DCM biomarkers that are used in routine genetic and clinical testing are structural proteins, but during the last few years, the role of mi-RNA has also emerged as a biomarker due to their accessibility through noninvasive methods. Our increasing genetic knowledge can improve the clinical management of DCM by bringing clinicians and geneticists on one platform, thereby influencing the individualized clinical decision making and leading to precision medicine.