Activin A directly impairs human cardiomyocyte contractile function indicating a potential role in heart failure development.

Activin A has been linked to cardiac dysfunction in aging and disease, with elevated circulating levels found in patients with hypertension, atherosclerosis, and heart failure. Here, we investigated whether Activin A directly impairs cardiomyocyte (CM) contractile function and kinetics utilizing cell, tissue, and animal models. Hydrodynamic gene delivery-mediated overexpression of Activin A in wild-type mice was sufficient to impair cardiac function, and resulted in increased cardiac stress markers (N-terminal pro-atrial natriuretic peptide) and cardiac atrophy. In human-induced pluripotent stem cell-derived (hiPSC) CMs, Activin A caused increased phosphorylation of SMAD2/3 and significantly upregulated SERPINE1 and FSTL3 (markers of SMAD2/3 activation and activin signaling, respectively). Activin A signaling in hiPSC-CMs resulted in impaired contractility, prolonged relaxation kinetics, and spontaneous beating in a dose-dependent manner. To identify the cardiac cellular source of Activin A, inflammatory cytokines were applied to human cardiac fibroblasts. Interleukin -1ß induced a strong upregulation of Activin A. Mechanistically, we observed that Activin A-treated hiPSC-CMs exhibited impaired diastolic calcium handling with reduced expression of calcium regulatory genes (SERCA2, RYR2, CACNB2). Importantly, when Activin A was inhibited with an anti-Activin A antibody, maladaptive calcium handling and CM contractile dysfunction were abrogated. Therefore, inflammatory cytokines may play a key role by acting on cardiac fibroblasts, causing local upregulation of Activin A that directly acts on CMs to impair contractility. These findings demonstrate that Activin A acts directly on CMs, which may contribute to the cardiac dysfunction seen in aging populations and in patients with heart failure.

Development of a semi-automated segmentation tool for high frequency ultrasound image analysis of mouse echocardiograms.

Echocardiography is a widely used and clinically translatable imaging modality for the evaluation of cardiac structure and function in preclinical drug discovery and development. Echocardiograms are among the first in vivo diagnostic tools utilized to evaluate the heart due to its relatively low cost, high throughput acquisition, and non-invasive nature; however lengthy manual image analysis, intra- and inter-operator variability, and subjective image analysis presents a challenge for reproducible data generation in preclinical research. To combat the image-processing bottleneck and address both variability and reproducibly challenges, we developed a semi-automated analysis algorithm workflow to analyze long- and short-axis murine left ventricle (LV) ultrasound images. The long-axis B-mode algorithm executes a script protocol that is trained using a reference library of 322 manually segmented LV ultrasound images. The short-axis script was engineered to analyze M-mode ultrasound images in a semi-automated fashion using a pixel intensity evaluation approach, allowing analysts to place two seed-points to triangulate the local maxima of LV wall boundary annotations. Blinded operator evaluation of the semi-automated analysis tool was performed and compared to the current manual segmentation methodology for testing inter- and intra-operator reproducibility at baseline and after a pharmacologic challenge. Comparisons between manual and semi-automatic derivation of LV ejection fraction resulted in a relative difference of 1% for long-axis (B-mode) images and 2.7% for short-axis (M-mode) images. Our semi-automatic workflow approach reduces image analysis time and subjective bias, as well as decreases inter- and intra-operator variability, thereby enhancing throughput and improving data quality for pre-clinical in vivo studies that incorporate cardiac structure and function endpoints.

Large-scale phenome analysis defines a behavioral signature for Huntington's disease genotype in mice.

Rapid technological advances for the frequent monitoring of health parameters have raised the intriguing possibility that an individual's genotype could be predicted from phenotypic data alone. Here we used a machine learning approach to analyze the phenotypic effects of polymorphic mutations in a mouse model of Huntington's disease that determine disease presentation and age of onset. The resulting model correlated variation across 3,086 behavioral traits with seven different CAG-repeat lengths in the huntingtin gene (Htt). We selected behavioral signatures for age and CAG-repeat length that most robustly distinguished between mouse lines and validated the model by correctly predicting the repeat length of a blinded mouse line. Sufficient discriminatory power to accurately predict genotype required combined analysis of >200 phenotypic features. Our results suggest that autosomal dominant disease-causing mutations could be predicted through the use of subtle behavioral signatures that emerge in large-scale, combinatorial analyses. Our work provides an open data platform that we now share with the research community to aid efforts focused on understanding the pathways that link behavioral consequences to genetic variation in Huntington's disease.

Genetic deletion of transglutaminase 2 does not rescue the phenotypic deficits observed in R6/2 and zQ175 mouse models of Huntington's disease.

Huntington's disease (HD) is an autosomal dominant, progressive neurodegenerative disorder caused by expansion of CAG repeats in the huntingtin gene. Tissue transglutaminase 2 (TG2), a multi-functional enzyme, was found to be increased both in HD patients and in mouse models of the disease. Furthermore, beneficial effects have been reported from the genetic ablation of TG2 in R6/2 and R6/1 mouse lines. To further evaluate the validity of this target for the treatment of HD, we examined the effects of TG2 deletion in two genetic mouse models of HD: R6/2 CAG 240 and zQ175 knock in (KI). Contrary to previous reports, under rigorous experimental conditions we found that TG2 ablation had no effect on either motor or cognitive deficits, or on the weight loss. In addition, under optimal husbandry conditions, TG2 ablation did not extend R6/2 lifespan. Moreover, TG2 deletion did not change the huntingtin aggregate load in cortex or striatum and did not decrease the brain atrophy observed in either mouse line. Finally, no amelioration of the dysregulation of striatal and cortical gene markers was detected. We conclude that TG2 is not a valid therapeutic target for the treatment of HD.

Increased Body Weight of the BAC HD Transgenic Mouse Model of Huntington's Disease Accounts for Some but Not All of the Observed HD-like Motor Deficits.

The genome of the Bacterial Artificial Chromosome (BAC) transgenic mouse model of Huntington's Disease (BAC HD) contains the 170 kb human HTT locus modified by the addition of exon 1 with 97 mixed CAA-CAG repeats. BAC HD mice present robust behavioral deficits in both the open field and the accelerating rotarod tests, two standard behavioral assays of motor function. BAC HD mice, however, also typically present significantly increased body weights relative to wildtype littermate controls (WT) which potentially confounds the interpretation of any motor deficits associated directly with the effects of mutant huntingtin. In order to evaluate this possible confound of body weight, we directly compared the performance of BAC HD and WT female mice under food restricted versus free feeding conditions in both the open field and rotarod tasks to test the hypothesis that some of the motor deficits observed in this HTT-transgenic mouse line results solely from increased body weight. Our results suggest that the rotarod deficit exhibited by BAC HD mice is modulated by both body weight and non-body weight factors resulting from overexpression of full length mutant Htt. When body weights of WT and BAC HD transgenic mice were normalized using restricted feeding, the deficits exhibited by BAC HD mice on the rotarod task were less marked, but were still significant. Since the rotarod deficit between WT and BAC HD mice is attenuated when body weight is normalized by food restriction, utilization of this task in BAC HD mice during pre-clinical evaluation must be powered accordingly and results carefully considered as therapeutic benefit can result from decreased overall body weight and or motoric improvement that may not be related to body mass. Furthermore, after controlling for body weight differences, the hypoactive phenotype displayed by ad libitum fed BAC HD mice in the open field assay was not observed in the BAC HD mice undergoing food restriction. These findings suggest that assessment of spontaneous locomotor activity, as measured in the open field test, may not be the appropriate behavioral endpoint to evaluate the BAC HD mouse during preclinical evaluation since it appears that the apparent hypoactive phenotype in this model is driven primarily by body weight differences.