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The phosphoinositide 3-kinase (PI3K) pathway regulates multiple steps in glucose metabolism and also cytoskeletal functions, such as cell movement and attachment. Here, we show that PI3K directly coordinates glycolysis with cytoskeletal dynamics in an AKT-independent manner. Growth factors or insulin stimulate the PI3K-dependent activation of Rac, leading to disruption of the actin cytoskeleton, release of filamentous actin-bound aldolase A, and an increase in aldolase activity. Consistently, PI3K inhibitors, but not AKT, SGK, or mTOR inhibitors, cause a significant decrease in glycolysis at the step catalyzed by aldolase, while activating PIK3CA mutations have the opposite effect. These results point toward a master regulatory function of PI3K that integrates an epithelial cell's metabolism and its form, shape, and function, coordinating glycolysis with the energy-intensive dynamics of actin remodeling.

Heart Failure (HF) continues to be a complex public health issue with increasing world population prevalence. Although overall mortality has decreased for HF and hypertrophic cardiomyopathy (HCM), a precursor for HF, their prevalence continues to increase annually. Because the etiology of HF and HCM is heterogeneous, it has been difficult to identify novel therapies to combat these diseases. Isoproterenol (ISP), a non-selective β-adrenoreceptor agonist, is commonly used to induce cardiotoxicity and cause acute and chronic HCM and HF in mice. However, the variability in dose and duration of ISP treatment used in studies has made it difficult to determine the optimal combination of ISP dose and delivery method to develop a reliable ISP-induced mouse model for disease. Here we examined cardiac effects induced by ISP via subcutaneous (SQ) and SQ-minipump (SMP) infusions across 3 doses (2, 4, and 10mg/kg/day) over 2 weeks to determine whether SQ and SMP ISP delivery induced comparable disease severity in C57BL/6J mice. To assess disease, we measured body and heart weight, surface electrocardiogram (ECG), and echocardiography recordings. We found all 3 ISP doses comparably increase heart weight, but these increases are more pronounced when ISP was administered via SMP. We also found that the combination of ISP treatment and delivery method induces contrasting heart rate, RR interval, and R and S amplitudes that may place SMP treated mice at higher risk for sustained disease burden. Mice treated via SMP also had increased heart wall thickness and LV Mass, but mice treated via SQ showed greater increase in gene markers for hypertrophy and fibrosis. Overall, these data suggest that at 2 weeks, mice treated with 2, 4, or 10mg/kg/day ISP via SQ and SMP routes cause similar pathological heart phenotypes but highlight the importance of drug delivery method to induce differing disease pathways. [ABSTRACT FROM AUTHOR]

Friedreich's ataxia (FA) is an autosomal recessive disorder caused by a deficiency in frataxin (FXN), a mitochondrial protein that plays a critical role in the synthesis of iron-sulfur clusters (Fe-S), vital inorganic cofactors necessary for numerous cellular processes. FA is characterized by progressive ataxia and hypertrophic cardiomyopathy, with cardiac dysfunction as the most common cause of mortality in patients. Commonly used cardiac-specific mouse models of FA use the muscle creatine kinase (MCK) promoter to express Cre recombinase in cardiomyocytes and striated muscle cells in mice with one conditional Fxn allele and one floxed-out/null allele. These mice quickly develop cardiomyopathy that becomes fatal by 9-11 wk of age. Here, we generated a cardiac-specific model with floxed Fxn allele homozygosity (MCK-Fxnflox/flox). MCK-Fxnflox/flox mice were phenotypically normal at 9 wk of age, despite no detectable FXN protein expression. Between 13 and 15 wk of age, these mice began to display progressive cardiomyopathy, including decreased ejection fraction and fractional shortening and increased left ventricular mass. MCK-Fxnflox/flox mice began to lose weight around 16 wk of age, characteristically associated with heart failure in other cardiacspecific FA models. By 18 wk of age, MCK-Fxnflox/flox mice displayed elevated markers of Fe-S deficiency, cardiac stress and injury, and cardiac fibrosis. This modified model reproduced important pathophysiological and biochemical features of FA over a longer timescale than previous cardiac-specific mouse models, offering a larger window for studying potential therapeutics. [ABSTRACT FROM AUTHOR]

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. Although echo is a very valuable tool, the image analysis is a time-consuming, resource-demanding process, and is susceptible to interreader variability. Recent advancements in deep learning have enabled researchers to automate image processing and reduce analysis time and interreader variability in the field of medical imaging. In the present study, we developed a fully automated tool, mouse-echocardiography neural net (MENN), for the analysis of both long-axis brightness (B)-mode and short-axis motion (M)-mode images of left ventricle. MENN is a series of fully convolutional neural networks that were trained and validated using manually segmented Bmode 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 = 0.85-0.99) and good-to-excellent agreement between automated and manual analyses. Further interreader 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 interreader variability in manual analysis. [ABSTRACT FROM AUTHOR]

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. Although echo is a very valuable tool, the image analysis is a time-consuming, resource-demanding process, and is susceptible to interreader variability. Recent advancements in deep learning have enabled researchers to automate image processing and reduce analysis time and interreader variability in the field of medical imaging. In the present study, we developed a fully automated tool, mouse-echocardiography neural net (MENN), for the analysis of both long-axis brightness (B)-mode and short-axis motion (M)-mode images of 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 = 0.85-0.99) and good-to-excellent agreement between automated and manual analyses. Further interreader 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 interreader variability in manual analysis. NEW & NOTEWORTHY Echocardiography is commonly used in preclinical research to evaluate cardiac structure and function. Despite the broad applications across therapeutic areas, the analysis of echo data is laborious and susceptible to interreader variability. In this study, we developed a fully automated mouse-echocardiography neural net (MENN). Cardiac measurements from MENN showed excellent correlations with manual analysis. Furthermore, the use of MENN leads to >92% reduction in analysis time and potentially eliminates the interobserver variability issue.