Echocardiography is a reliable and reproducible method to assess non-invasively cardiac function in clinical and experimental research. Significant progress in the development of echocardiographic equipment and transducers has led to the successful translation of this methodology from humans to rodents, allowing for the scoring of disease severity and progression, testing of new drugs, and monitoring cardiac function in genetically modified or pharmacologically treated animals. However, as yet, there is no standardization in the procedure to acquire echocardiographic measurements in small animals. This position paper focuses on the appropriate acquisition and analysis of echocardiographic parameters in adult mice and rats, and provides reference values, representative images, and videos for the accurate and reproducible quantification of left ventricular function in healthy and pathological conditions.

Diverse genetic backgrounds often lead to phenotypic heterogeneity in cardiomyopathies (CMPs). Previous genotype-phenotype studies have primarily focused on the analysis of a single phenotype, and the diagnostic and prognostic features of the CMP genotype across different phenotypic expressions remain poorly understood. We sought to define differences in outcome prediction when stratifying patients based on phenotype at presentation compared with genotype in a large cohort of patients with CMPs and positive genetic testing. Dilated cardiomyopathy (DCM), arrhythmogenic right ventricular cardiomyopathy, left-dominant arrhythmogenic cardiomyopathy, and biventricular arrhythmogenic cardiomyopathy were examined in this study. A total of 281 patients (80% DCM) with pathogenic or likely pathogenic variants were included. The primary and secondary outcomes were: 1) all-cause mortality (D)/heart transplant (HT); 2) sudden cardiac death/major ventricular arrhythmias (SCD/MVA); and 3) heart failure-related death (DHF)/HT/left ventricular assist device implantation (LVAD). Survival analysis revealed that SCD/MVA events occurred more frequently in patients without a DCM phenotype and in carriers of DSP, PKP2, LMNA, and FLNC variants. However, after adjustment for age and sex, genotype-based classification, but not phenotype-based classification, was predictive of SCD/MVA. LMNA showed the worst trends in terms of D/HT and DHF/HT/LVAD. Genotypes were associated with significant phenotypic heterogeneity in genetic cardiomyopathies. Nevertheless, in our study, genotypic-based classification showed higher precision in predicting the outcome of patients with CMP than phenotype-based classification. These findings add to our current understanding of inherited CMPs and contribute to the risk stratification of patients with positive genetic testing.

To study the impact of genotype on the performance of the 2019 risk model for arrhythmogenic right ventricular cardiomyopathy (ARVC). The study cohort comprised 554 patients with a definite diagnosis of ARVC and no history of sustained ventricular arrhythmia (VA). During a median follow-up of 6.0 (3.1,12.5) years, 100 patients (18%) experienced the primary VA outcome (sustained ventricular tachycardia, appropriate implantable cardioverter defibrillator intervention, aborted sudden cardiac arrest, or sudden cardiac death) corresponding to an annual event rate of 2.6% [95% confidence interval (CI) 1.9–3.3]. Risk estimates for VA using the 2019 ARVC risk model showed reasonable discriminative ability but with overestimation of risk. The ARVC risk model was compared in four gene groups: PKP2 (n = 118, 21%); desmoplakin (DSP) (n = 79, 14%); other desmosomal (n = 59, 11%); and gene elusive (n = 160, 29%). Discrimination and calibration were highest for PKP2 and lowest for the gene-elusive group. Univariable analyses revealed the variable performance of individual clinical risk markers in the different gene groups, e.g. right ventricular dimensions and systolic function are significant risk markers in PKP2 but not in DSP patients and the opposite is true for left ventricular systolic function. The 2019 ARVC risk model performs reasonably well in gene-positive ARVC (particularly for PKP2) but is more limited in gene-elusive patients. Genotype should be included in future risk models for ARVC. ARVC, arrhythmogenic right ventricular cardiomyopathy; AUC, area under the curve; ECHO, echocardiography; CMR, cardiac magnetic resonance; HR, hazard ratio; LGE, late gadolinium enhancement; LV, left ventricular; LVEDD, left ventricular enddiastolic diameter; LVEF, left ventricular ejection fraction; PLAX, parasternal long-axis; PVC, premature ventricular complex; RV, right ventricular; RVEDV, right ventricular end-diastolic volume; RVEF, right ventricular ejection fraction; RVOT, right ventricular outflow tract; TWI, T-wave inversion; VT, ventricular tachycardia.

Therapies for patients with myocardial infarction and heart failure are urgently needed, in light of the breadth of these conditions and lack of curative treatments. To systematically identify previously unidentified cardioactive biologicals in an unbiased manner in vivo, we developed cardiac FunSel, a method for the systematic, functional selection of effective factors using a library of 1198 barcoded adeno-associated virus (AAV) vectors encoding for the mouse secretome. By pooled vector injection into the heart, this library was screened to functionally select for factors that confer cardioprotection against myocardial infarction. After two rounds of iterative selection in mice, cardiac FunSel identified three proteins [chordin-like 1 (Chrdl1), family with sequence similarity 3 member C (Fam3c), and Fam3b] that preserve cardiomyocyte viability, sustain cardiac function, and prevent pathological remodeling. In particular, Chrdl1 exerted its protective activity by binding and inhibiting extracellular bone morphogenetic protein 4 (BMP4), which resulted in protection against cardiomyocyte death and induction of autophagy in cardiomyocytes after myocardial infarction. Chrdl1 also inhibited fibrosis and maladaptive cardiac remodeling by binding transforming growth factor–β (TGF-β) and preventing cardiac fibroblast differentiation into myofibroblasts. Production of secreted and circulating Chrdl1, Fam3c, and Fam3b from the liver also protected the heart from myocardial infarction, thus supporting the use of the three proteins as recombinant factors. Together, these findings disclose a powerful method for the in vivo, unbiased selection of tissue-protective factors and describe potential cardiac therapeutics.

Objective: Despite growing evidence about myocardial injury in hospitalized COronaVIrus Disease 2019 (COVID-19) patients, the mechanism behind this injury is only poorly understood and little is known about its association with SARS-CoV-2-mediated myocarditis. Furthermore, definite evidence of the presence and role of SARS-CoV-2 in cardiomyocytes in the clinical scenario is still lacking. Methods: We histologically characterized myocardial tissue of 40 patients deceased with severe SARS-CoV-2 infection during the first wave of the pandemic. Clinical data were also recorded and analyzed. In case of findings supportive of myocardial inflammation, histological analysis was complemented by RT-PCR and immunohistochemistry for SARS-CoV-2 viral antigens and in situ RNA hybridization for the detection of viral genomes. Results: Both chronic and acute myocardial damage was invariably present, correlating with the age and comorbidities of our population. Myocarditis of overt entity was found in one case (2.5%). SARS-CoV-2 genome was not found in the cardiomyocytes of the patient with myocarditis, while it was focally and negligibly present in cardiomyocytes of patients with known viral persistence in the lungs and no signs of myocardial inflammation. The presence of myocardial injury was not associated with myocardial inflammatory infiltrates. Conclusions: In this autopsy cohort of COVID-19 patients, myocarditis is rarely found and not associated with SARS-CoV-2 presence in cardiomyocytes. Chronic and acute forms of myocardial damage are constantly found and correlate with the severity of COVID-19 disease and pre-existing comorbidities. Graphic