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Histone methyltransferase Smyd1 regulates mitochondrial energetics in the heart
Smyd1, a muscle-specific histone methyltransferase, has established roles in skeletal and cardiac muscle development, but its role in the adult heart remains poorly understood. Our prior work demonstrated that cardiac-specific deletion of Smyd1 in adult mice (Smyd1-KO) leads to hypertrophy and heart failure. Here we show that down-regulation of mitochondrial energetics is an early event in these Smyd1-KO mice preceding the onset of structural abnormalities. This early impairment of mitochondrial energetics in Smyd1-KO mice is associated with a significant reduction in gene and protein expression of PGC-1α, PPARα, and RXRα, the master regulators of cardiac energetics. The effect of Smyd1 on PGC-1α was recapitulated in primary cultured rat ventricular myocytes, in which acute siRNA-mediated silencing of Smyd1 resulted in a greater than twofold decrease in PGC-1α expression without affecting that of PPARα or RXRα. In addition, enrichment of histone H3 lysine 4 trimethylation (a mark of gene activation) at the PGC-1α locus was markedly reduced in Smyd1-KO mice, and Smyd1-induced transcriptional activation of PGC-1α was confirmed by luciferase reporter assays. Functional confirmation of Smyd1's involvement showed an increase in mitochondrial respiration capacity induced by overexpression of Smyd1, which was abolished by siRNA-mediated PGC-1α knockdown. Conversely, overexpression of PGC-1α rescued transcript expression and mitochondrial respiration caused by silencing Smyd1 in cardiomyocytes. These findings provide functional evidence for a role of Smyd1, or any member of the Smyd family, in regulating cardiac energetics in the adult heart, which is mediated, at least in part, via modulating PGC-1α
Developing and Testing a Model to Predict Outcomes of Organizational Change
OBJECTIVE: To test the effectiveness of a Bayesian model employing subjective probability estimates for predicting success and failure of health care improvement projects. DATA SOURCES: Experts' subjective assessment data for model development and independent retrospective data on 221 healthcare improvement projects in the United States, Canada, and the Netherlands collected between 1996 and 2000 for validation. METHODS: A panel of theoretical and practical experts and literature in organizational change were used to identify factors predicting the outcome of improvement efforts. A Bayesian model was developed to estimate probability of successful change using subjective estimates of likelihood ratios and prior odds elicited from the panel of experts. A subsequent retrospective empirical analysis of change efforts in 198 health care organizations was performed to validate the model. Logistic regression and ROC analysis were used to evaluate the model's performance using three alternative definitions of success. DATA COLLECTION: For the model development, experts' subjective assessments were elicited using an integrative group process. For the validation study, a staff person intimately involved in each improvement project responded to a written survey asking questions about model factors and project outcomes. RESULTS: Logistic regression chi-square statistics and areas under the ROC curve demonstrated a high level of model performance in predicting success. Chi-square statistics were significant at the 0.001 level and areas under the ROC curve were greater than 0.84. CONCLUSIONS: A subjective Bayesian model was effective in predicting the outcome of actual improvement projects. Additional prospective evaluations as well as testing the impact of this model as an intervention are warranted