Using induced pluripotent stem cells for drug discovery in arrhythmias.

INTRODUCTION: Arrhythmias are disturbances in the normal rhythm of the heart and account for significant cardiovascular morbidity and mortality worldwide. Historically, preclinical research has been anchored in animal models, though physiological differences between these models and humans have limited their clinical translation. The discovery of human induced pluripotent stem cells (iPSC) and subsequent differentiation into cardiomyocyte has led to the development of new in vitro models of arrhythmias with the hope of a new pathway for both exploration of pathogenic variants and novel therapeutic discovery. AREAS COVERED: The authors describe the latest two-dimensional in vitro models of arrhythmias, several examples of the use of these models in drug development, and the role of gene editing when modeling diseases. They conclude by discussing the use of three-dimensional models in the study of arrythmias and the integration of computational technologies and machine learning with experimental technologies. EXPERT OPINION: Human iPSC-derived cardiomyocytes models have significant potential to augment disease modeling, drug discovery, and toxicity studies in preclinical development. While there is initial success with modeling arrhythmias, the field is still in its nascency and requires advances in maturation, cellular diversity, and readouts to emulate arrhythmias more accurately.

BeatProfiler: Multimodal In Vitro Analysis of Cardiac Function Enables Machine Learning Classification of Diseases and Drugs.

Goal: Contractile response and calcium handling are central to understanding cardiac function and physiology, yet existing methods of analysis to quantify these metrics are often time-consuming, prone to mistakes, or require specialized equipment/license. We developed BeatProfiler, a suite of cardiac analysis tools designed to quantify contractile function, calcium handling, and force generation for multiple in vitro cardiac models and apply downstream machine learning methods for deep phenotyping and classification. Methods: We first validate BeatProfiler's accuracy, robustness, and speed by benchmarking against existing tools with a fixed dataset. We further confirm its ability to robustly characterize disease and dose-dependent drug response. We then demonstrate that the data acquired by our automatic acquisition pipeline can be further harnessed for machine learning (ML) analysis to phenotype a disease model of restrictive cardiomyopathy and profile cardioactive drug functional response. To accurately classify between these biological signals, we apply feature-based ML and deep learning models (temporal convolutional-bidirectional long short-term memory model or TCN-BiLSTM). Results: Benchmarking against existing tools revealed that BeatProfiler detected and analyzed contraction and calcium signals better than existing tools through improved sensitivity in low signal data, reduction in false positives, and analysis speed increase by 7 to 50-fold. Of signals accurately detected by published methods (PMs), BeatProfiler's extracted features showed high correlations to PMs, confirming that it is reliable and consistent with PMs. The features extracted by BeatProfiler classified restrictive cardiomyopathy cardiomyocytes from isogenic healthy controls with 98% accuracy and identified relax90 as a top distinguishing feature in congruence with previous findings. We also show that our TCN-BiLSTM model was able to classify drug-free control and 4 cardiac drugs with different mechanisms of action at 96% accuracy. We further apply Grad-CAM on our convolution-based models to identify signature regions of perturbations by these drugs in calcium signals. Conclusions: We anticipate that the capabilities of BeatProfiler will help advance in vitro studies in cardiac biology through rapid phenotyping, revealing mechanisms underlying cardiac health and disease, and enabling objective classification of cardiac disease and responses to drugs.

The evolving use of biomarkers in heart transplantation: Consensus of an expert panel.

In heart transplantation, the use of biomarkers to detect the risk of rejection has been evolving. In this setting, it is becoming less clear as to what is the most reliable test or combination of tests to detect rejection and assess the state of the alloimmune response. Therefore, a virtual expert panel was organized in heart and kidney transplantation to evaluate emerging diagnostics and how they may be best utilized to monitor and manage transplant patients. This manuscript covers the heart content of the conference and is a work product of the American Society of Transplantation's Thoracic and Critical Care Community of Practice. This paper reviews currently available and emerging diagnostic assays and defines the unmet needs for biomarkers in heart transplantation. Highlights of the in-depth discussions among conference participants that led to development of consensus statements are included. This conference should serve as a platform to further build consensus within the heart transplant community regarding the optimal framework to implement biomarkers into management protocols and to improve biomarker development, validation and clinical utility. Ultimately, these biomarkers and novel diagnostics should improve outcomes and optimize quality of life for our transplant patients.

Engineered cardiac tissue model of restrictive cardiomyopathy for drug discovery.

Restrictive cardiomyopathy (RCM) is defined as increased myocardial stiffness and impaired diastolic relaxation leading to elevated ventricular filling pressures. Human variants in filamin C (FLNC) are linked to a variety of cardiomyopathies, and in this study, we investigate an in-frame deletion (c.7416_7418delGAA, p.Glu2472_Asn2473delinAsp) in a patient with RCM. Induced pluripotent stem cell-derived cardiomyocytes (iPSC-CMs) with this variant display impaired relaxation and reduced calcium kinetics in 2D culture when compared with a CRISPR-Cas9-corrected isogenic control line. Similarly, mutant engineered cardiac tissues (ECTs) demonstrate increased passive tension and impaired relaxation velocity compared with isogenic controls. High-throughput small-molecule screening identifies phosphodiesterase 3 (PDE3) inhibition by trequinsin as a potential therapy to improve cardiomyocyte relaxation in this genotype. Together, these data demonstrate an engineered cardiac tissue model of RCM and establish the translational potential of this precision medicine approach to identify therapeutics targeting myocardial relaxation.

STK25 inhibits PKA signaling by phosphorylating PRKAR1A.

In the heart, protein kinase A (PKA) is critical for activating calcium handling and sarcomeric proteins in response to beta-adrenergic stimulation leading to increased myocardial contractility and performance. The catalytic activity of PKA is tightly regulated by regulatory subunits that inhibit the catalytic subunit until released by cAMP binding. Phosphorylation of type II regulatory subunits promotes PKA activation; however, the role of phosphorylation in type I regulatory subunits remain uncertain. Here, we utilize human induced pluripotent stem cell cardiomyocytes (iPSC-CMs) to identify STK25 as a kinase of the type Iα regulatory subunit PRKAR1A. Phosphorylation of PRKAR1A leads to inhibition of PKA kinase activity and increased binding to the catalytic subunit in the presence of cAMP. Stk25 knockout in mice diminishes Prkar1a phosphorylation, increases Pka activity, and augments contractile response to beta-adrenergic stimulation. Together, these data support STK25 as a negative regulator of PKA signaling through phosphorylation of PRKAR1A.

Alterations in the kallikrein-kinin system predict death after heart transplant.

Heart transplantation remains the definitive treatment for end stage heart failure. Because availability is limited, risk stratification of candidates is crucial for optimizing both organ allocations and transplant outcomes. Here we utilize proteomics prior to transplant to identify new biomarkers that predict post-transplant survival in a multi-institutional cohort. Microvesicles were isolated from serum samples and underwent proteomic analysis using mass spectrometry. Monte Carlo cross-validation (MCCV) was used to predict survival after transplant incorporating select recipient pre-transplant clinical characteristics and serum microvesicle proteomic data. We identified six protein markers with prediction performance above AUROC of 0.6, including Prothrombin (F2), anti-plasmin (SERPINF2), Factor IX, carboxypeptidase 2 (CPB2), HGF activator (HGFAC) and low molecular weight kininogen (LK). No clinical characteristics demonstrated an AUROC > 0.6. Putative biological functions and pathways were assessed using gene set enrichment analysis (GSEA). Differential expression analysis identified enriched pathways prior to transplant that were associated with post-transplant survival including activation of platelets and the coagulation pathway prior to transplant. Specifically, upregulation of coagulation cascade components of the kallikrein-kinin system (KKS) and downregulation of kininogen prior to transplant were associated with survival after transplant. Further prospective studies are warranted to determine if alterations in the KKS contributes to overall post-transplant survival.

Changes in extracellular matrix in failing human non-ischemic and ischemic hearts with mechanical unloading.

Ischemic and non-ischemic cardiomyopathies have distinct etiologies and underlying disease mechanisms, which require in-depth investigation for improved therapeutic interventions. The goal of this study was to use clinically obtained myocardium from healthy and heart failure patients, and characterize the changes in extracellular matrix (ECM) in ischemic and non-ischemic failing hearts, with and without mechanical unloading. Using tissue engineering methodologies, we also investigated how diseased human ECM, in the absence of systemic factors, can influence cardiomyocyte function. Heart tissues from heart failure patients with ischemic and non-ischemic cardiomyopathy were compared to explore differential disease phenotypes and reverse remodeling potential of left ventricular assisted device (LVAD) support at transcriptomic, proteomic and structural levels. The collected data demonstrated that the differential ECM compositions recapitulated the disease microenvironment and induced cardiomyocytes to undergo disease-like functional alterations. In addition, our study also revealed molecular profiles of non-ischemic and ischemic heart failure patients and explored the underlying mechanisms of etiology-specific impact on clinical outcome of LVAD support and tendency towards reverse remodeling.

Plasma kallikrein predicts primary graft dysfunction after heart transplant.

BACKGROUND: Primary graft dysfunction (PGD) is the leading cause of early mortality after heart transplant. Pre-transplant predictors of PGD remain elusive and its etiology remains unclear. METHODS: Microvesicles were isolated from 88 pre-transplant serum samples and underwent proteomic evaluation using TMT mass spectrometry. Monte Carlo cross validation (MCCV) was used to predict the occurrence of severe PGD after transplant using recipient pre-transplant clinical characteristics and serum microvesicle proteomic data. Putative biological functions and pathways were assessed using gene set enrichment analysis (GSEA) within the MCCV prediction methodology. RESULTS: Using our MCCV prediction methodology, decreased levels of plasma kallikrein (KLKB1), a critical regulator of the kinin-kallikrein system, was the most predictive factor identified for PGD (AUROC 0.6444 [0.6293, 0.6655]; odds 0.1959 [0.0592, 0.3663]. Furthermore, a predictive panel combining KLKB1 with inotrope therapy achieved peak performance (AUROC 0.7181 [0.7020, 0.7372]) across and within (AUROCs of 0.66-0.78) each cohort. A classifier utilizing KLKB1 and inotrope therapy outperforms existing composite scores by more than 50 percent. The diagnostic utility of the classifier was validated on 65 consecutive transplant patients, resulting in an AUROC of 0.71 and a negative predictive value of 0.92-0.96. Differential expression analysis revealed a enrichment in inflammatory and immune pathways prior to PGD. CONCLUSIONS: Pre-transplant level of KLKB1 is a robust predictor of post-transplant PGD. The combination with pre-transplant inotrope therapy enhances the prediction of PGD compared to pre-transplant KLKB1 levels alone and the resulting classifier equation validates within a prospective validation cohort. Inflammation and immune pathway enrichment characterize the pre-transplant proteomic signature predictive of PGD.

Heart regeneration in mouse and human: A bioengineering perspective.

In this short review, we draw parallels and stress differences between heart regeneration in mice and human, from a bioengineering perspective. As the prevailing dogma that the adult heart is completely post-mitotic is starting to change, there are multiple opportunities for augmenting the limited but definitive turnover of cardiomyocytes, to the extent necessary developing clinically relevant modalities for enhancing heart repair. We discuss some of the most promising among these new directions: mobilization of paracrine signaling by therapeutic cells, cell-free therapy of the heart using extracellular vesicles, and direct reprograming of endogenous cells. These new directions share the cell-free, mechanistic approach to heart repair that could be translated into the clinic faster and safer than the traditional cell therapies.

Management of primary graft failure after heart transplantation: Preoperative risks, perioperative events, and postoperative decisions.

Primary graft failure (PGF) after heart transplantation (HT) is a devastating and unexpected event characterized by failure of the graft to adequately support recipient circulation necessitating high doses of vasopressors and inotropes and/or temporary mechanical circulatory support. Although it represents an increasingly common event in the current era, there remains a high degree of variability in prevalence, reported risk factors, and approach to this clinical entity. The purpose of the current review is to highlight preoperative considerations including known incidence and risk factors, perioperative issues involving the identification and management of PGF, and postoperative decisions related to weaning of mechanical circulatory support and titration of immunosuppressive therapy. Lastly, we highlight future directions in PGF research, involving basic and translational research, that have the potential to uncover novel strategies of risk stratification and treatment. CASE: Our patient is a 53-year-old man with end-stage non-ischemic dilated cardiomyopathy complicated by ventricular tachycardia (VT), post-capillary pulmonary hypertension, and renal insufficiency. After progressing to NYHA Class IV symptoms, he underwent implantation of a durable left ventricular assist device (LVAD) as bridge to transplant (BTT). On device support, he developed recurrent VT resulting in multiple defibrillator discharges and hospital admission for intravenous anti-arrhythmic therapy. He is subsequently upgraded to a higher status on the waiting list. A suitable donor is identified, with an appropriate predicted heart mass and an anticipated ischemic time of <4 hours. He is taken to the operating room, where at the time of anesthesia induction he develops vasodilatory shock, requiring high-dose vasopressors, and cardiopulmonary bypass (CPB) support for dissection. After surgical anastomosis, cross clamp removal and reperfusion, graft function is extremely poor, there is significant bradycardia requiring pacing, and the patient is unable to be weaned successfully from CPB. Venoarterial extracorporeal membrane oxygenation (VA-ECMO) is initiated, and the patient is transferred to the intensive care unit. Retrospective flow crossmatch is negative. This patient is suffering from severe primary graft failure.