The pathogenesis of nonalcoholic steatohepatitis is multifactorial, involving steatosis, lipotoxicity, hepatic inflammation, and fibrosis. The present studies show that acetyl-CoA carboxylase inhibition produces direct improvements in hepatic steatosis, inflammation, and fibrosis in both primary human cell systems and rodent nonalcoholic fatty liver disease/nonalcoholic steatohepatitis models.

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The Ca2+-responsive phosphatase calcineurin/ protein phosphatase 2B dephosphorylates the transcription factor NFATc3. In the myocardium activation of NFATc3 down-regulates the expression of voltage-gated K+ (Kv) channels after myocardial infarction (MI). This prolongs action potential duration and increases the probability of arrhythmias. Although recent studies infer that calcineurin is activated by local and transient Ca2+ signals the molecular mechanism that underlies the process is unclear in ventricular myocytes. Here we test the hypothesis that sequestering of calcineurin to the sarcolemma of ventricular myocytes by the anchoring protein AKAP150 is required for acute activation of NFATc3 and the concomitant down-regulation of Kv channels following MI. Biochemical and cell based measurements resolve that approximately 0.2% of the total calcineurin activity in cardiomyocytes is associated with AKAP150. Electrophysiological analyses establish that formation of this AKAP150-calcineurin signaling dyad is essential for the activation of the phosphatase and the subsequent down-regulation of Kv channel currents following MI. Thus AKAP150-mediated targeting of calcineurin to sarcolemmal micro-domains in ventricular myocytes contributes to the local and acute gene remodeling events that lead to the down-regulation of Kv currents.

Echocardiography (echo) is a translationally relevant ultrasound imaging modality widely used to assess cardiac structure and function in preclinical models of heart failure (HF) during research and drug development. Though echo is a very valuable tool, the image analysis is a time consuming, resource demanding process, and is susceptible to inter-reader variability. Recent advancements in deep learning have enabled researchers to automate image processing and reduce analysis time and inter-reader variability in the field of medical imaging. In the present study, we developed a fully automated tool - Mouse Echo Neural Net (MENN) - for the analysis of both long axis brightness (B)-mode and short axis motion (M)-mode images of the left ventricle. MENN is a series of fully convolutional neural networks that were trained and validated using manually segmented B-mode and M-mode echo images of the left ventricle. The segmented images were then used to compute cardiac structural and functional metrics. The performance of MENN was further validated in two preclinical models of HF. MENN achieved excellent correlations (Pearson's r = 0.85 to 0.99) and good to excellent agreement between automated and manual analyses. Further inter-reader variability analysis showed that MENN has better agreements with an expert analyst than both a trained analyst and a novice. Notably, the use of MENN reduced manual analysis time by >92%. In conclusion, we developed an automated echocardiography analysis tool that allows for fast and accurate analysis of B-mode and M-mode mouse echo data and mitigates the issue of inter-reader variability in manual analysis.