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Global microRNA profiling of the mouse ventricles during development of severe hypertrophic cardiomyopathy and heart failure.

ABSTRACT: MicroRNAs (miRNAs) regulate post-transcriptional gene expression during development and disease. We have determined the miRNA expression levels of early- and end-stage hypertrophic cardiomyopathy (HCM) in a severe, transgenic mouse model of the disease. Five miRNAs were differentially expressed at an early stage of HCM development. Time-course analysis revealed that decreased expression of miR-1 and miR-133a commences at a pre-disease stage, and precedes upregulation of target genes causal of cardiac hypertrophy and extracellular matrix remodelling, suggesting a role for miR-1 and miR-133a in early disease development. At end-stage HCM, 16 miRNA are dysregulated to form an expression profile resembling that of other forms of cardiac hypertrophy, suggesting common responses. Analysis of the mRNA transcriptome revealed that miRNAs potentially target 15.7% upregulated and 4.8% downregulated mRNAs at end-stage HCM, and regulate mRNAs associated with cardiac hypertrophy and electrophysiology, calcium signalling, fibrosis, and the TGF-? signalling pathway. Collectively, these results define the miRNA expression signatures during development and progression of severe HCM and highlight critical miRNA regulated gene networks that are involved in disease pathogenesis.

SUBMITTER: Bagnall RD

PROVIDER: S-EPMC3443088 | biostudies-literature | 2012

REPOSITORIES: biostudies-literature

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Global microRNA profiling of the mouse ventricles during development of severe hypertrophic cardiomyopathy and heart failure.

Bagnall Richard D RD Tsoutsman Tatiana T Shephard Rhian E RE Ritchie William W Semsarian Christopher C

PloS one 20120914 9

MicroRNAs (miRNAs) regulate post-transcriptional gene expression during development and disease. We have determined the miRNA expression levels of early- and end-stage hypertrophic cardiomyopathy (HCM) in a severe, transgenic mouse model of the disease. Five miRNAs were differentially expressed at an early stage of HCM development. Time-course analysis revealed that decreased expression of miR-1 and miR-133a commences at a pre-disease stage, and precedes upregulation of target genes causal of ca ...[more]

PMID: 23024758

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Project description:Background: Development of advanced heart failure (HF) symptoms is the most common adverse pathway in hypertrophic cardiomyopathy (HCM) patients. Currently, there is a limited ability to identify HCM patients at risk of HF. Objectives: In this study, we present a machine learning (ML)-based model to identify individual HCM patients who are at high risk of developing advanced HF symptoms. Methods: From a consecutive cohort of HCM patients evaluated at the Tufts HCM Institute from 2001 to 2018, we extracted a set of 64 potential risk factors measured at baseline. Only patients with New York Heart Association (NYHA) functional class I/II and LV ejection fraction (LVEF) by echocardiography >35% were included. The study cohort (n = 1,427 patients) was split into three disjoint subsets: development (50%), model selection (10%), and independent validation (40%). The least absolute shrinkage and selection operator was used to select the most influential clinical variables. An ensemble of ML classifiers, including logistic regression, was used to identify patients with high risk of developing a HF outcome. Study outcomes were defined as progression to NYHA class III/IV, drop in LVEF below 35%, septal reduction procedure, and/or heart transplantation. Results: During a mean follow-up of 4.7 ± 3.7 years, advanced HF occurred in 283 (20% out of 1,427) patients. The model features included patients' sex, NYHA class (I or II), HCM type (i.e., obstructive or not), LV wall thickness, LVEF, presence of HF symptoms (e.g., dyspnea, presyncope), comorbidities (atrial fibrillation, hypertension, mitral regurgitation, and systolic anterior motion), and type of cardiac medications. The developed risk stratification model showed strong differentiation power to identify patients at advanced HF risk in the testing dataset (c-statistics = 0.81; 95% confidence interval [CI]: 0.76, 0.86). The model allowed correct identification of high-risk patients with accuracy 74% (CI: 0.70, 0.78), sensitivity 80% (CI: 0.77, 0.83), and specificity 72% (CI: 0.68, 0.76). The model performance was comparable among different sex and age groups. Conclusions: A 5-year risk prediction of progressive HF in HCM patients can be accurately estimated using ML analysis of patients' clinical and imaging parameters. A set of 17 clinical and imaging variables were identified as the most important predictors of progressive HF in HCM.

Project description:BACKGROUND:Left-sided congestive heart failure (CHF) is characterized by increased filling pressures and related Doppler echocardiographic (DE) filling patterns. HYPOTHESIS:Doppler echocardiographic variables of left ventricular filling derived from transmitral flow, pulmonary vein flow, and tissue Doppler can be used to detect CHF in cats with hypertrophic cardiomyopathy (HCM). ANIMALS:Forty-seven client-owned cats. METHODS:Prospective clinical cohort study. Cats underwent physical examination, thoracic radiography, analysis of N-terminal pro-brain natriuretic peptide (NT-proBNP), and transthoracic echocardiography and were divided into 3 age-matched groups: Group 1 (apparently healthy control), Group 2 (preclinical HCM), and Group 3 (HCM and CHF). Measured and calculated variables included respiratory rate, DE estimates, serum NT-proBNP concentration, and radiographic CHF score. Groups were compared using ANOVA, and receiver operating characteristic (ROC) curve and multivariate analyses were used to identify diagnostic cutoffs for the detection of CHF. RESULTS:Fifteen cats were in Group 1, 17 in Group 2, and 15 in Group 3. The ROC analysis indicated that the ratio of peak velocity of early diastolic transmitral flow to peak velocity of late diastolic transmitral flow (area under the curve [AUC], 1.0; diagnostic cutoff, 1.77; P =?.001), ratio of left atrial size to aortic annular dimension (AUC, 0.91; diagnostic cutoff, 1.96; P =?.003), left atrial diameter (AUC, 0.89; cutoff, 18.5?mm; P =?.004), diastolic functional class (AUC, 0.89; cutoff, class 2; P =?.005), respiratory (AUC, 0.79; cutoff, 36 breaths per minute [brpm]; P =?.02), and the ratio of the peak velocity of fused early and late transmitral flow velocities to the peak velocity of the fused early and late diastolic tissue Doppler waveforms (AUC, 0.74; cutoff, 15.1; P =?.05) performed best for detecting CHF. CONCLUSIONS AND CLINICAL IMPORTANCE:Various DE variables can be used to detect CHF in cats with HCM. Determination of the clinical benefit of such variables in initiating treatments and assessing treatment success needs further study.

Dataset Information

Global microRNA profiling of the mouse ventricles during development of severe hypertrophic cardiomyopathy and heart failure.

Publications

Global microRNA profiling of the mouse ventricles during development of severe hypertrophic cardiomyopathy and heart failure.

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