The discovery of rare genetic variants is accelerating, and clear guidelines for distinguishing disease-causing sequence variants from the many potentially functional variants present in any human genome are urgently needed. Without rigorous standards we risk an acceleration of false-positive reports of causality, which would impede the translation of genomic research findings into the clinical diagnostic setting and hinder biological understanding of disease. Here we discuss the key challenges of assessing sequence variants in human disease, integrating both gene-level and variant-level support for causality. We propose guidelines for summarizing confidence in variant pathogenicity and highlight several areas that require further resource development.

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Summary Familial hypertrophic cardiomyopathy (HCM) is a prevalent hereditary cardiac disorder linked to arrhythmia and sudden cardiac death. While the causes of HCM have been identified as genetic mutations in the cardiac sarcomere, the pathways by which sarcomeric mutations engender myocyte hypertrophy and electrophysiological abnormalities are not understood. To elucidate the mechanisms underlying HCM development, we generated patient-specific induced pluripotent stem cell cardiomyocytes (iPSC-CMs) from a ten-member family cohort carrying a hereditary HCM missense mutation (Arg663His) in the MYH7 gene. Diseased iPSC-CMs recapitulated numerous aspects of the HCM phenotype including cellular enlargement and contractile arrhythmia at the single-cell level. Calcium (Ca2+) imaging indicated dysregulation of Ca2+ cycling and elevation in intracellular Ca2+ ([Ca2+]i) are central mechanisms for disease pathogenesis. Pharmacological restoration of Ca2+ homeostasis prevented development of hypertrophy and electrophysiological irregularities. We anticipate that these findings will help elucidate the mechanisms underlying HCM development and identify novel therapies for the disease.

SummaryThe role of long noncoding RNA (lncRNA) in adult hearts is unknown; also unclear is how lncRNA modulates nucleosome remodeling. An estimated 70% of mouse genes undergo antisense transcription1, including myosin heavy chain 7 (Myh7) that encodes molecular motor proteins for heart contraction2. Here, we identify a cluster of lncRNA transcripts from Myh7 loci and show a new lncRNA–chromatin mechanism for heart failure. In mice, these transcripts, which we named Myosin Heavy Chain Associated RNA Transcripts (MyHEART or Mhrt), are cardiac-specific and abundant in adult hearts. Pathological stress activates the Brg1-Hdac-Parp chromatin repressor complex3 to inhibit Mhrt transcription in the heart. Such stress-induced Mhrt repression is essential for cardiomyopathy to develop: restoring Mhrt to the pre-stress level protects the heart from hypertrophy and failure. Mhrt antagonizes the function of Brg1, a chromatin-remodeling factor that is activated by stress to trigger aberrant gene expression and cardiac myopathy3. Mhrt prevents Brg1 from recognizing its genomic DNA targets, thus inhibiting chromatin targeting and gene regulation by Brg1. Mhrt binds to the helicase domain of Brg1, and this domain is crucial for tethering Brg1 to chromatinized DNA targets. Brg1 helicase has dual nucleic acid-binding specificities: it is capable of binding lncRNA (Mhrt) and chromatinized—but not naked—DNA. This dual-binding feature of helicase enables a competitive inhibition mechanism by which Mhrt sequesters Brg1 from its genomic DNA targets to prevent chromatin remodeling. A Mhrt-Brg1 feedback circuit is thus crucial for heart function. Human MHRT also originates from MYH7 loci and is repressed in various types of myopathic hearts, suggesting a conserved lncRNA mechanism in human cardiomyopathy. Our studies identify the first cardioprotective lncRNA, define a new targeting mechanism for ATP-dependent chromatin-remodeling factors, and establish a new paradigm for lncRNA–chromatin interaction.

Dilated cardiomyopathy (DCM) is the most common cardiomyopathy, characterized by ventricular dilatation, systolic dysfunction, and progressive heart failure. DCM is the most common diagnosis leading to heart transplantation and places a significant burden on healthcare worldwide. The advent of induced pluripotent stem cells (iPSCs) offers an exceptional opportunity for creating disease-specific models, investigating underlying mechanisms, and optimizing therapy. Here we generated cardiomyocytes (CMs) from iPSCs derived from patients of a DCM family carrying a point mutation (R173W) in the gene encoding sarcomeric protein cardiac troponin T. Compared to the control healthy individuals in the same family cohort, DCM iPSC-CMs exhibited altered Ca2+ handling, decreased contractility, and abnormal sarcomeric α-actinin distribution. When stimulated with β-adrenergic agonist, DCM iPSC-CMs showed characteristics of failure such as reduced beating rates, compromised contraction, and significantly more cells with abnormal sarcomeric α-actinin distribution. β-adrenergic blocker treatment and over-expression of sarcoplasmic reticulum Ca2+ ATPase (Serca2a) improved DCM iPSC-CMs function. Our study demonstrated that human DCM iPSC-CMs recapitulated to some extent the disease phenotypes morphologically and functionally, and thus can serve as a useful platform for exploring molecular and cellular mechanisms and optimizing treatment of this particular disease.

SUMMARYCardiac hypertrophy and failure are characterized by transcriptional reprogramming of gene expression. Adult cardiomyocytes in mice express primarily α-myosin heavy chain (α-MHC), whereas embryonic cardiomyocytes express β-MHC. Cardiac stress triggers adult hearts to undergo hypertrophy and a shift from α-MHC to fetal β-MHC expression. Here we show that Brg1, a chromatin-remodeling protein, plays critical roles in regulating cardiac growth, differentiation and gene expression. In embryos, Brg1 promotes myocyte proliferation by maintaining BMP10 and suppressing p57kip2 expression. It preserves fetal cardiac differentiation by interacting with HDAC and PARP to repress α-MHC and activate β-MHC. In adults, Brg1 is turned off in cardiomyocytes. It is reactivated by cardiac stresses and complexes with its embryonic partners, HDAC and PARP, to induce a pathological α- to β-MHC shift. Preventing Brg1 re-expression decreases hypertrophy and reverses such MHC switch. Brg1 is activated in certain patients with hypertrophic cardiomyopathy, its level correlating with disease severity and MHC changes. Our studies show that Brg1 maintains cardiomyocytes in an embryonic state, and demonstrate an epigenetic mechanism by which three classes of chromatin-modifying factors, Brg1, HDAC and PARP, cooperate to control developmental and pathological gene expression.

There is great potential for genome sequencing to enhance patient care through improved diagnostic sensitivity and more precise therapeutic targeting. To maximize this potential, genomics strategies that have been developed for genetic discovery - including DNA-sequencing technologies and analysis algorithms - need to be adapted to fit clinical needs. This will require the optimization of alignment algorithms, attention to quality-coverage metrics, tailored solutions for paralogous or low-complexity areas of the genome, and the adoption of consensus standards for variant calling and interpretation. Global sharing of this more accurate genotypic and phenotypic data will accelerate the determination of causality for novel genes or variants. Thus, a deeper understanding of disease will be realized that will allow its targeting with much greater therapeutic precision.

Artificial intelligence and machine learning are poised to influence nearly every aspect of the human condition, and cardiology is not an exception to this trend. This paper provides a guide for clinicians on relevant aspects of artificial intelligence and machine learning, reviews selected applications of these methods in cardiology to date, and identifies how cardiovascular medicine could incorporate artificial intelligence in the future. In particular, the paper first reviews predictive modeling concepts relevant to cardiology such as feature selection and frequent pitfalls such as improper dichotomization. Second, it discusses common algorithms used in supervised learning and reviews selected applications in cardiology and related disciplines. Third, it describes the advent of deep learning and related methods collectively called unsupervised learning, provides contextual examples both in general medicine and in cardiovascular medicine, and then explains how these methods could be applied to enable precision cardiology and improve patient outcomes.