EGFR/IGF1R Signaling Modulates Relaxation in Hypertrophic Cardiomyopathy

BACKGROUND: Diastolic dysfunction is central to diseases such as heart failure with preserved ejection fraction and hypertrophic cardiomyopathy (HCM). However, therapies that improve cardiac relaxation are scarce, partly due to a limited understanding of modulators of cardiomyocyte relaxation. We hypothesized that cardiac relaxation is regulated by multiple unidentified proteins and that dysregulation of kinases contributes to impaired relaxation in patients with HCM. METHODS: We optimized and increased the throughput of unloaded shortening measurements and screened a kinase inhibitor library in isolated adult cardiomyocytes from wild-type mice. One hundred fifty-seven kinase inhibitors were screened. To assess which kinases are dysregulated in patients with HCM and could contribute to impaired relaxation, we performed a tyrosine and global phosphoproteomics screen and integrative inferred kinase activity analysis using HCM patient myocardium. Identified hits from these 2 data sets were validated in cardiomyocytes from a homozygous MYBPC3c.2373insG HCM mouse model. RESULTS: Screening of 157 kinase inhibitors in wild-type (N=33) cardiomyocytes (n=24 563) resulted in the identification of 17 positive inotropes and 21 positive lusitropes, almost all of them novel. The positive lusitropes formed 3 clusters: cell cycle, EGFR (epidermal growth factor receptor)/IGF1R (insulin-like growth factor 1 receptor), and a small Akt (α-serine/threonine protein kinase) signaling cluster. By performing phosphoproteomic profiling of HCM patient myocardium (N=24 HCM and N=8 donors), we demonstrated increased activation of 6 of 8 proteins from the EGFR/IGFR1 cluster in HCM. We validated compounds from this cluster in mouse HCM (N=12) cardiomyocytes (n=2023). Three compounds from this cluster were able to improve relaxation in HCM cardiomyocytes. CONCLUSIONS: We showed the feasibility of screening for functional modulators of cardiomyocyte relaxation and contraction, parameters that we observed to be modulated by kinases involved in EGFR/IGF1R, Akt, cell cycle signaling, and FoxO (forkhead box class O) signaling, respectively. Integrating the screening data with phosphoproteomics analysis in HCM patient tissue indicated that inhibition of EGFR/IGF1R signaling is a promising target for treating impaired relaxation in HCM.</p

High heart rate associated early repolarization causes J-waves in both zebra finch and mouse

High heart rates are a feature of small endothermic—or warm-blooded—mammals and birds. In small mammals, the QT interval is short, and local ventricular recordings reveal early repolarization that coincides with the J-wave on the ECG, a positive deflection following the QRS complex. Early repolarization contributes to short QT-intervals thereby enabling brief cardiac cycles and high heart rates. We therefore hypothesized high hearts rates associate with early repolarization and J-waves on the ECG of endothermic birds. We tested this hypothesis by comparing isolated hearts of zebra finches and mice and recorded pseudo-ECGs and optical action potentials (zebra finch, n = 8; mouse, n = 8). In both species, heart rate exceeded 300 beats per min, and total ventricular activation was fast (QRS < 10 ms). Ventricular activation progressed from the left to the right ventricle in zebra finch, whereas it progressed from apex-to-base in mouse. In both species, the early repolarization front followed the activation front, causing a positive J-wave in the pseudo-ECG. Inhibition of early repolarization by 4-aminopyridine reduced J-wave amplitude in both species. Action potential duration was similar between ventricles in zebra finch, whereas in mouse the left ventricular action potential was longer. Accordingly, late repolarization had opposite directions in zebra finch (left-right) and mouse (right-left). This caused a similar direction for the zebra finch J-wave and T-wave, whereas in the mouse they were discordant. Our findings demonstrate that early repolarization and the associated J-wave may have evolved by convergence in association with high heart rates

Blood-based biomarkers for the prediction of hypertrophic cardiomyopathy prognosis: a systematic review and meta-analysis

Aims: Hypertrophic cardiomyopathy (HCM) is the most prevalent monogenic heart disease. HCM is an important cause of sudden cardiac death and may also lead to outflow tract obstruction and heart failure. Disease severity is highly variable and risk stratification remains limited. Therefore, we aimed to review current knowledge of prognostic blood-based biomarkers in HCM. Methods and results: A systematic literature search was performed on PubMed, Embase, and the Cochrane library to identify studies assessing plasma or serum biomarkers for outcomes involving malignant ventricular arrhythmia, outflow tract obstruction, and heart failure. Risk of bias was assessed using the QUIPS tool. Meta-analyses were performed using the random effects method. A total of 26 unique cohort studies assessing 42 biomarkers were identified. Overall risk of bias was moderate. Thirty-two biomarkers were significantly associated to an HCM outcome in at least one study (nine biomarkers in at least two studies). In pooled analyses, cardiovascular mortality was predicted by N-terminal prohormone of brain natriuretic peptide (hazard ratio [HR] 5.38 per log[pg/mL], 95% confidence interval [CI] 2.07-14.03, P < 0.001, I2 = 0%) and high-sensitivity C-reactive protein (HR 1.30 per μg/mL, 95% CI 1.00-1.68, P = 0.05, I2 = 78%), all-cause mortality by low-density lipoprotein cholesterol (HR 0.63 per μmol/mL, 95% CI 0.49-0.80, P < 0.001, I2 = 0%), and a combined congestive heart failure, malignant ventricular arrhythmia, and stroke outcome by high-sensitivity cardiac troponin T (pooled HR 4.19 for ≥0.014 ng/mL, 95% CI 2.22-7.88, P < 0.001, I2 = 0%). Quality of evidence was low-moderate. Conclusions: Several blood-based biomarkers were identified as predictors of HCM outcomes. Additional studies are required to validate their prognostic utility within current risk stratification models

Blood-based biomarkers for the prediction of hypertrophic cardiomyopathy prognosis: a systematic review and meta-analysis

Aims: Hypertrophic cardiomyopathy (HCM) is the most prevalent monogenic heart disease. HCM is an important cause of sudden cardiac death and may also lead to outflow tract obstruction and heart failure. Disease severity is highly variable and risk stratification remains limited. Therefore, we aimed to review current knowledge of prognostic blood-based biomarkers in HCM. Methods and results: A systematic literature search was performed on PubMed, Embase, and the Cochrane library to identify studies assessing plasma or serum biomarkers for outcomes involving malignant ventricular arrhythmia, outflow tract obstruction, and heart failure. Risk of bias was assessed using the QUIPS tool. Meta-analyses were performed using the random effects method. A total of 26 unique cohort studies assessing 42 biomarkers were identified. Overall risk of bias was moderate. Thirty-two biomarkers were significantly associated to an HCM outcome in at least one study (nine biomarkers in at least two studies). In pooled analyses, cardiovascular mortality was predicted by N-terminal prohormone of brain natriuretic peptide (hazard ratio [HR] 5.38 per log[pg/mL], 95% confidence interval [CI] 2.07–14.03, P < 0.001, I 2 = 0%) and high-sensitivity C-reactive protein (HR 1.30 per μg/mL, 95% CI 1.00–1.68, P = 0.05, I 2 = 78%), all-cause mortality by low-density lipoprotein cholesterol (HR 0.63 per μmol/mL, 95% CI 0.49–0.80, P < 0.001, I 2 = 0%), and a combined congestive heart failure, malignant ventricular arrhythmia, and stroke outcome by high-sensitivity cardiac troponin T (pooled HR 4.19 for ≥0.014 ng/mL, 95% CI 2.22–7.88, P < 0.001, I 2 = 0%). Quality of evidence was low–moderate. Conclusions: Several blood-based biomarkers were identified as predictors of HCM outcomes. Additional studies are required to validate their prognostic utility within current risk stratification models

Metabolomics in severe aortic stenosis reveals intermediates of nitric oxide synthesis as most distinctive markers

Background: Calcific aortic valve disease (CAVD) is a rapidly growing global health problem with an estimated 12.6 million cases globally in 2017 and a 112% increase of deaths since 1990 due to aging and population growth. CAVD may develop into aortic stenosis (AS) by progressive nar-rowing of the aortic valve. AS is underdiagnosed, and if treatment by aortic valve replacement (AVR) is delayed, this leads to poor recovery of cardiac function, absence of symptomatic improvement and marked increase of mortality. Considering the current limitations to define the stage of AS-induced cardiac remodeling, there is need for a novel method to aid in the diagnosis of AS and timing of intervention, which may be found in metabolomics profiling of patients. Methods: Serum samples of nine healthy controls and 10 AS patients before and after AVR were analyzed by untargeted mass spectrometry. Multivariate modeling was performed to determine a metabolic profile of 30 serum metabolites which distinguishes AS patients from controls. Human cardiac microvascular endothelial cells (CMECs) were incubated with serum of the AS patients and then stained for ICAM-1 with Western Blot to analyze the effect of AS patient serum on endothelial cell activation. Results: The top 30 metabolic profile strongly distinguishes AS patients from healthy controls and includes 17 metabolites related to nitric oxide metabolism and 12 metabolites related to inflammation, in line with the known pathomechanism for calcific aortic valve disease. Nine metabolites correlate strongly with left ventricular mass, of which three show reversal back to control values after AVR. Western blot analysis of CMECs incubated with AS patient sera shows a significant reduction (14%) in ICAM-1 in AS samples taken after AVR compared to AS patient sera before AVR. Conclusion: Our study defined a top 30 metabolic profile with biological and clinical relevance, which may be used as blood biomarker to identify AS patients in need of cardiac surgery. Future studies are warranted in patients with mild-to-moderate AS to determine if these metabolites reflect disease severity and can be used to identify AS patients in need of cardiac surgery

Distinct metabolomic signatures in preclinical and obstructive hypertrophic cardiomyopathy

Hypertrophic Cardiomyopathy (HCM) is a common inherited heart disease with poor risk prediction due to incomplete penetrance and a lack of clear genotype–phenotype correlations. Advanced imaging techniques have shown altered myocardial energetics already in preclinical gene variant carriers. To determine whether disturbed myocardial energetics with the potential to serve as biomarkers are also reflected in the serum metabolome, we analyzed the serum metabolome of asymptomatic carriers in comparison to healthy controls and obstructive HCM patients (HOCM). We performed non-quantitative direct-infusion high-resolution mass spectrometry-based untargeted metabolomics on serum from fasted asymptomatic gene variant carriers, symptomatic HOCM patients and healthy controls (n = 31, 14 and 9, respectively). Biomarker panels that discriminated the groups were identified by performing multivariate modeling with gradient-boosting classifiers. For all three group-wise comparisons we identified a panel of 30 serum metabolites that best discriminated the groups. These metabolite panels performed equally well as advanced cardiac imaging modalities in distinguishing the groups. Seven metabolites were found to be predictive in two different comparisons and may play an important role in defining the disease stage. This study reveals unique metabolic signatures in serum of preclinical carriers and HOCM patients that may potentially be used for HCM risk stratification and precision therapeutics

Distinct Metabolomic Signatures in Preclinical and Obstructive Hypertrophic Cardiomyopathy

Hypertrophic Cardiomyopathy (HCM) is a common inherited heart disease with poor risk prediction due to incomplete penetrance and a lack of clear genotype-phenotype correlations. Advanced imaging techniques have shown altered myocardial energetics already in preclinical gene variant carriers. To determine whether disturbed myocardial energetics with the potential to serve as biomarkers are also reflected in the serum metabolome, we analyzed the serum metabolome of asymptomatic carriers in comparison to healthy controls and obstructive HCM patients (HOCM). We performed non-quantitative direct-infusion high-resolution mass spectrometry-based untargeted metabolomics on serum from fasted asymptomatic gene variant carriers, symptomatic HOCM patients and healthy controls (n = 31, 14 and 9, respectively). Biomarker panels that discriminated the groups were identified by performing multivariate modeling with gradient-boosting classifiers. For all three group-wise comparisons we identified a panel of 30 serum metabolites that best discriminated the groups. These metabolite panels performed equally well as advanced cardiac imaging modalities in distinguishing the groups. Seven metabolites were found to be predictive in two different comparisons and may play an important role in defining the disease stage. This study reveals unique metabolic signatures in serum of preclinical carriers and HOCM patients that may potentially be used for HCM risk stratification and precision therapeutics