The mechanical output of contracting cardiomyocytes, the muscle cells of the heart, relates to healthy and disease states of the heart. Culturing cardiomyocytes on arrays of elastomeric microposts can enable inexpensive and high-throughput studies of heart disease at the single-cell level. However, cardiomyocytes weakly adhere to these microposts, which limits the possibility of using biomechanical assays of single cardiomyocytes to study heart disease. We hypothesized that a stable covalent attachment of laminin to the surface of microposts improves cardiomyocyte contractility. We cultured cells on polydimethylsiloxane microposts with laminin covalently bonded with the organosilanes 3-glycidoxypropyltrimethoxysilane and 3-aminopropyltriethoxysilane with glutaraldehyde. We measured displacement of microposts induced by the contractility of mouse neonatal cardiomyocytes, which attach better than mature cardiomyocytes to substrates. We observed time-dependent changes in contractile parameters such as micropost deformation, contractility rates, contraction and relaxation speeds, and the times of contractions. These parameters were affected by the density of laminin on microposts and by the stability of laminin binding to micropost surfaces. Organosilane-mediated binding resulted in higher laminin surface density and laminin binding stability. 3-glycidoxypropyltrimethoxysilane provided the highest laminin density but did not provide stable protein binding with time. Higher surface protein binding stability and strength were observed with 3-aminopropyltriethoxysilane with glutaraldehyde. In cultured cardiomyocytes, contractility rate, contraction speeds, and contraction time increased with higher laminin stability. Given these variations in contractile function, we conclude that binding of laminin to microposts via 3-aminopropyltriethoxysilane with glutaraldehyde improves contractility observed by an increase in beating rate and contraction speed as it occurs during the postnatal maturation of cardiomyocytes. This approach is promising for future studies to mimic in vivo tissue environments.

Detecting epistatic drivers of human phenotypes is a considerable challenge. Traditional approaches use regression to sequentially test multiplicative interaction terms involving pairs of genetic variants. For higher-order interactions and genome-wide large-scale data, this strategy is computationally intractable. Moreover, multiplicative terms used in regression modeling may not capture the form of biological interactions. Building on the Predictability, Computability, Stability (PCS) framework, we introduce the epiTree pipeline to extract higher-order interactions from genomic data using tree-based models. The epiTree pipeline first selects a set of variants derived from tissue-specific estimates of gene expression. Next, it uses iterative random forests (iRF) to search training data for candidate Boolean interactions (pairwise and higher-order). We derive significance tests for interactions, based on a stabilized likelihood ratio test, by simulating Boolean tree-structured null (no epistasis) and alternative (epistasis) distributions on hold-out test data. Finally, our pipeline computes PCS epistasis p-values that probabilisticly quantify improvement in prediction accuracy via bootstrap sampling on the test set. We validate the epiTree pipeline in two case studies using data from the UK Biobank: predicting red hair and multiple sclerosis (MS). In the case of predicting red hair, epiTree recovers known epistatic interactions surrounding MC1R and novel interactions, representing non-linearities not captured by logistic regression models. In the case of predicting MS, a more complex phenotype than red hair, epiTree rankings prioritize novel interactions surrounding HLA-DRB1, a variant previously associated with MS in several populations. Taken together, these results highlight the potential for epiTree rankings to help reduce the design space for follow up experiments.

As genomic technologies rapidly develop, polygenic scores (PGS) are entering into a growing conversation on how to improve precision in public health and prevent chronic disease. While the integration of PGS into public health and clinical services raises potential benefits, it also introduces potential harms. In particular, there is a high level of uncertainty about how to incorporate PGS into clinical settings in a manner that is equitable, just, and aligned with the long-term goals of many healthcare systems to support person-centered and value-based care. This paper argues that any conversation about whether and how to design and implement PGS clinical services requires dynamic engagement with local communities, patients, and families. These parties often face the consequences, both positive and negative, of such uncertainties and should therefore drive clinical translation. As a collaborative effort between hospital stakeholders, community partners, and researchers, this paper describes a community-empowered co-design process for addressing uncertainty and making programmatic decisions about the implementation of PGS into clinical services. We provide a framework for others interested in designing clinical programs that are responsive to, and inclusive and respectful of, local communities.

Recent rise in the number and therapeutic potential of genome engineering technologies has generated excitement for their application in cardiovascular therapeutics. One significant barrier to their implementation is costly and time-consuming reagent development for novel variants. We have previously shown that disease-associated variants cluster in functional protein domains where variants are found in the general population at lower frequency.1,2 These findings may provide an opportunity to pre-emptively target multiple pathogenic variant clusters (“pathogenic hotspots”) that address a majority of pathogenic and likely pathogenic (P/LP) variants for a given disease with a small number of pre-designed reagents. Prime editing, a search-and-replace editing technology, can overwrite genomic sequences with a reverse transcriptase guided by a Cas9 nickase and prime editing guide (peg)RNA.3 We hypothesized that most cardiovascular disease-relevant genes in ClinVar would display regional variant clustering, and that multiple variants within a regional hotspot could be targeted with a limited number of prime editing reagents.

Apelin is a peptide ligand for an orphan G-protein coupled receptor (APJ receptor) and serves as a critical gradient for migration of mesodermal cells fated to contribute to the myocardial lineage. The present study was designed to establish a robust cardiac differentiation protocol, specifically, to evaluate the effect of apelin on directed differentiation of mouse and human embryonic stem cells (mESCs and hESCs) into cardiac lineage. Different concentrations of apelin (50, 100, 500 nM) were evaluated to determine its differentiation potential. The optimized dose of apelin was then combined with mesodermal differentiation factors, including BMP-4, activin-A, and bFGF, in a developmentally specific temporal sequence to examine the synergistic effects on cardiac differentiation. Cellular, molecular, and physiologic characteristics of the apelin-induced contractile embryoid bodies (EBs) were analyzed. It was found that 100 nM apelin resulted in highest percentage of contractile EB for mESCs while 500 nM had the highest effects on hESCs. Functionally, the contractile frequency of mESCs-derived EBs (mEBs) responded appropriately to increasing concentration of isoprenaline and diltiazem. Positive phenotype of cardiac specific markers was confirmed in the apelin-treated groups. The protocol, consisting of apelin and mesodermal differentiation factors, induced contractility in significantly higher percentage of hESC-derived EBs (hEBs), up-regulated cardiac-specific genes and cell surface markers, and increased the contractile force. In conclusion, we have demonstrated that the treatment of apelin enhanced cardiac differentiation of mouse and human ESCs and exhibited synergistic effects with mesodermal differentiation factors.

Background

Long QT syndrome (LQTS) is a hereditary disease that predisposes patients to life-threatening cardiac arrhythmias and sudden cardiac death. Our previous study of the human ether-à-go-go related gene (hERG)-encoded K⁺ channel (K_v11.1) supports an association between hERG and RING finger protein 207 (RNF207) variants in aggravating the onset and severity of LQTS, specifically T613M hERG (hERG_T613M) and RNF207 frameshift (RNF207_G603fs) mutations. However, the underlying mechanistic underpinning remains unknown.

Objective

The purpose of the present study was to test the role of RNF207 in the function of hERG-encoded K⁺ channel subunits.

Methods

Whole-cell patch-clamp experiments were performed in human embryonic kidney (HEK 293) cells and human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) together with immunofluorescent confocal and high resolution microscopy, auto-ubiquitinylation assays, and co-immunoprecipitation experiments to test the functional interactions between hERG and RNF207.

Results

Here, we demonstrated that RNF207 serves as an E3 ubiquitin ligase and targets misfolded hERG_T613M proteins for degradation. RNF207_G603fs exhibits decreased activity and hinders the normal degradation pathway; this increases the levels of hERG_T613M subunits and their dominant-negative effect on the wild-type subunits, ultimately resulting in decreased current density. Similar findings are shown for hERG_A614V, a known dominant-negative mutant subunit. Finally, the presence of RNF207_G603fs with hERG_T613M results in significantly prolonged action potential durations and reduced hERG current in human-induced pluripotent stem cell-derived cardiomyocytes.

Conclusion

Our study establishes RNF207 as an interacting protein serving as a ubiquitin ligase for hERG-encoded K⁺ channel subunits. Normal function of RNF207 is critical for the quality control of hERG subunits and consequently cardiac repolarization. Moreover, our study provides evidence for protein quality control as a new paradigm in life-threatening cardiac arrhythmias in patients with LQTS.

Allele-specific RNA silencing has been shown to be an effective therapeutic treatment in a number of diseases, including neurodegenerative disorders. Studies of allele-specific silencing in hypertrophic cardiomyopathy (HCM) to date have focused on mouse models of disease. We here examine allele-specific silencing in a human-cell model of HCM. We investigate two methods of silencing, short hairpin RNA (shRNA) and antisense oligonucleotide (ASO) silencing, using a human induced pluripotent stem cell-derived cardiomyocyte (hiPSC-CM) model. We used cellular micropatterning devices with traction force microscopy and automated video analysis to examine each strategy's effects on contractile defects underlying disease. We find that shRNA silencing ameliorates contractile phenotypes of disease, reducing disease-associated increases in cardiomyocyte velocity, force, and power. We find that ASO silencing, while better able to target and knockdown a specific disease-associated allele, showed more modest improvements in contractile phenotypes. These findings are the first exploration of allele-specific silencing in a human HCM model and provide a foundation for further exploration of silencing as a therapeutic treatment for MYH7-mutation-associated cardiomyopathy.

The electrocardiogram (ECG) is the most frequently performed cardiovascular diagnostic test, but it is unclear how much information resting ECGs contain about long term cardiovascular risk. Here we report that a deep convolutional neural network can accurately predict the long-term risk of cardiovascular mortality and disease based on a resting ECG alone. Using a large dataset of resting 12-lead ECGs collected at Stanford University Medical Center, we developed SEER, the Stanford Estimator of Electrocardiogram Risk. SEER predicts 5-year cardiovascular mortality with an area under the receiver operator characteristic curve (AUC) of 0.83 in a held-out test set at Stanford, and with AUCs of 0.78 and 0.83 respectively when independently evaluated at Cedars-Sinai Medical Center and Columbia University Irving Medical Center. SEER predicts 5-year atherosclerotic disease (ASCVD) with an AUC of 0.67, similar to the Pooled Cohort Equations for ASCVD Risk, while being only modestly correlated. When used in conjunction with the Pooled Cohort Equations, SEER accurately reclassified 16% of patients from low to moderate risk, uncovering a group with an actual average 9.9% 10-year ASCVD risk who would not have otherwise been indicated for statin therapy. SEER can also predict several other cardiovascular conditions such as heart failure and atrial fibrillation. Using only lead I of the ECG it predicts 5-year cardiovascular mortality with an AUC of 0.80. SEER, used alongside the Pooled Cohort Equations and other risk tools, can substantially improve cardiovascular risk stratification and aid in medical decision making.

The field of genetics and genomics has advanced considerably with the achievement of recent milestones encompassing the identification of many loci for cardiovascular disease and variable drug responses. Despite this achievement, a gap exists in the understanding and advancement to meaningful translation that directly affects disease prevention and clinical care. The purpose of this scientific statement is to address the gap between genetic discoveries and their practical application to cardiovascular clinical care. In brief, this scientific statement assesses the current timeline for effective translation of basic discoveries to clinical advances, highlighting past successes. Current discoveries in the area of genetics and genomics are covered next, followed by future expectations, tools, and competencies for achieving the goal of improving clinical care.

Cardiac hypertrophy is initiated as an adaptive response to sustained overload but progresses pathologically as heart failure ensues. Here we report that genetic loss of APJ, a G-protein-coupled receptor, confers resistance to chronic pressure overload by markedly reducing myocardial hypertrophy and heart failure. In contrast, mice lacking apelin (the endogenous APJ ligand) remain sensitive, suggesting an apelin-independent function of APJ. Freshly isolated APJ-null cardiomyocytes exhibit an attenuated response to stretch, indicating that APJ is a mechanosensor. Activation of APJ by stretch increases cardiomyocyte cell size and induces molecular markers of hypertrophy. Whereas apelin stimulates APJ to activate Gαi and elicits a protective response, stretch signals in an APJ-dependent, G-protein-independent fashion to induce hypertrophy. Stretch-mediated hypertrophy is prevented by knockdown of β-arrestins or by pharmacological doses of apelin acting through Gαi. Taken together, our data indicate that APJ is a bifunctional receptor for both mechanical stretch and the endogenous peptide apelin. By sensing the balance between these stimuli, APJ occupies a pivotal point linking sustained overload to cardiomyocyte hypertrophy.