The Role of SIRT1 in Skeletal Muscle Function and Repair of Older Mice

Background Sirtuin 1 (SIRT1) is a NAD+ sensitive deacetylase that has been linked to longevity and has been suggested to confer beneficial effects that counter aging-associated deterioration. Muscle repair is dependent upon satellite cell function, which is reported to be reduced with aging; however, it is not known if this is linked to an aging-suppression of SIRT1. This study tested the hypothesis that Sirtuin 1 (SIRT1) overexpression would increase the extent of muscle repair and muscle function in older mice. Methods We examined satellite cell dependent repair in tibialis anterior, gastrocnemius, and soleus muscles of 13 young wild-type mice (20–30 weeks) and 49 older (80+ weeks) mice that were controls (n = 13), overexpressed SIRT1 in skeletal muscle (n = 14), and had a skeletal muscle SIRT1 knockout (n = 12) or a satellite cell SIRT1 knockout (n = 10). Acute muscle injury was induced by injection of cardiotoxin (CTX), and phosphate-buffered saline was used as a vector control. Plantarflexor muscle force and fatigue were evaluated before or 21 days after CTX injection. Satellite cell proliferation and mitochondrial function were also evaluated in undamaged muscles. Results Maximal muscle force was significantly lower in control muscles of older satellite cell knockout SIRT1 mice compared to young adult wild-type (YWT) mice (P \u3c 0.001). Mean contraction force at 40 Hz stimulation was significantly greater after recovery from CTX injury in older mice that overexpressed muscle SIRT1 than age-matched SIRT1 knockout mice (P \u3c 0.05). SIRT1 muscle knockout models (P \u3c 0.05) had greater levels of p53 (P \u3c 0.05 MKO, P \u3c 0.001 OE) in CTX-damaged tissues as compared to YWT CTX mice. SIRT1 overexpression with co-expression of p53 was associated with increased fatigue resistance and increased force potentiation during repeated contractions as compared to wild-type or SIRT1 knockout models (P \u3c 0.001). Muscle structure and mitochondrial function were not different between the groups, but proliferation of satellite cells was significantly greater in older mice with SIRT1 muscle knockout (P \u3c 0.05), but not older SIRT1 satellite cell knockout models, in vitro, although this effect was attenuated in vivo after 21 days of recovery. Conclusions The data suggest skeletal muscle structure, function, and recovery after CTX-induced injury are not significantly influenced by gain or loss of SIRT1 abundance alone in skeletal muscle; however, muscle function is impaired by ablation of SIRT1 in satellite cells. SIRT1 appears to interact with p53 to improve muscle fatigue resistance after repair from muscle injury

ROS Promote Epigenetic Remodeling and Cardiac Dysfunction in Offspring Following Maternal Engineered Nanomaterial (ENM) Exposure

Background: Nano-titanium dioxide (nano-TiO2) is amongst the most widely utilized engineered nanomaterials (ENMs). However, little is known regarding the consequences maternal ENM inhalation exposure has on growing progeny during gestation. ENM inhalation exposure has been reported to decrease mitochondrial bioenergetics and cardiac function, though the mechanisms responsible are poorly understood. Reactive oxygen species (ROS) are increased as a result of ENM inhalation exposure, but it is unclear whether they impact fetal reprogramming. The purpose of this study was to determine whether maternal ENM inhalation exposure influences progeny cardiac development and epigenomic remodeling. Results: Pregnant FVB dams were exposed to nano-TiO2 aerosols with a mass concentration of 12.09 ± 0.26 mg/m3 starting at gestational day five (GD 5), for 6 h over 6 non-consecutive days. Aerosol size distribution measurements indicated an aerodynamic count median diameter (CMD) of 156 nm with a geometric standard deviation (GSD) of 1.70. Echocardiographic imaging was used to assess cardiac function in maternal, fetal (GD 15), and young adult (11 weeks) animals. Electron transport chain (ETC) complex activities, mitochondrial size, complexity, and respiration were evaluated, along with 5-methylcytosine, Dnmt1 protein expression, and Hif1α activity. Cardiac functional analyses revealed a 43% increase in left ventricular mass and 25% decrease in cardiac output (fetal), with an 18% decrease in fractional shortening (young adult). In fetal pups, hydrogen peroxide (H2O2) levels were significantly increased (~ 10 fold) with a subsequent decrease in expression of the antioxidant enzyme, phospholipid hydroperoxide glutathione peroxidase (GPx4). ETC complex activity IV was decreased by 68 and 46% in fetal and young adult cardiac mitochondria, respectively. DNA methylation was significantly increased in fetal pups following exposure, along with increased Hif1α activity and Dnmt1 protein expression. Mitochondrial ultrastructure, including increased size, was observed at both fetal and young adult stages following maternal exposure. Conclusions: Maternal inhalation exposure to nano-TiO2 results in adverse effects on cardiac function that are associated with increased H2O2 levels and dysregulation of the Hif1α/Dnmt1 regulatory axis in fetal offspring. Our findings suggest a distinct interplay between ROS and epigenetic remodeling that leads to sustained cardiac contractile dysfunction in growing and young adult offspring following maternal ENM inhalation exposure

Machine-learning to Stratify Diabetic Patients Using Novel Cardiac Biomarkers and Integrative Genomics

Background: Diabetes mellitus is a chronic disease that impacts an increasing percentage of people each year. Among its comorbidities, diabetics are two to four times more likely to develop cardiovascular diseases. While HbA1c remains the primary diagnostic for diabetics, its ability to predict long-term, health outcomes across diverse demographics, ethnic groups, and at a personalized level are limited. The purpose of this study was to provide a model for precision medicine through the implementation of machine-learning algorithms using multiple cardiac biomarkers as a means for predicting diabetes mellitus development. Methods: Right atrial appendages from 50 patients, 30 non-diabetic and 20 type 2 diabetic, were procured from the WVU Ruby Memorial Hospital. Machine-learning was applied to physiological, biochemical, and sequencing data for each patient. Supervised learning implementing SHapley Additive exPlanations (SHAP) allowed binary (no diabetes or type 2 diabetes) and multiple classifcation (no diabetes, prediabetes, and type 2 diabetes) of the patient cohort with and without the inclusion of HbA1c levels. Findings were validated through Logistic Regression (LR), Linear Discriminant Analysis (LDA), Gaussian Naïve Bayes (NB), Support Vector Machine (SVM), and Classifcation and Regression Tree (CART) models with tenfold cross validation. Results: Total nuclear methylation and hydroxymethylation were highly correlated to diabetic status, with nuclear methylation and mitochondrial electron transport chain (ETC) activities achieving superior testing accuracies in the predictive model (~84% testing, binary). Mitochondrial DNA SNPs found in the D-Loop region (SNP-73G, -16126C, and -16362C) were highly associated with diabetes mellitus. The CpG island of transcription factor A, mitochondrial (TFAM) revealed CpG24 (chr10:58385262, P=0.003) and CpG29 (chr10:58385324, P=0.001) as markers correlating with diabetic progression. When combining the most predictive factors from each set, total nuclear methylation and CpG24 methylation were the best diagnostic measures in both binary and multiple classifcation sets. Conclusions: Using machine-learning, we were able to identify novel as well as the most relevant biomarkers associated with type 2 diabetes mellitus by integrating physiological, biochemical, and sequencing datasets. Ultimately, this approach may be used as a guideline for future investigations into disease pathogenesis and novel biomarker discover

Machine learning for spatial stratification of progressive cardiovascular dysfunction in a murine model of type 2 diabetes mellitus.

Speckle tracking echocardiography (STE) has been utilized to evaluate independent spatial alterations in the diabetic heart, but the progressive manifestation of regional and segmental cardiac dysfunction in the type 2 diabetic (T2DM) heart remains understudied. Therefore, the objective of this study was to elucidate if machine learning could be utilized to reliably describe patterns of the progressive regional and segmental dysfunction that are associated with the development of cardiac contractile dysfunction in the T2DM heart. Non-invasive conventional echocardiography and STE datasets were utilized to segregate mice into two pre-determined groups, wild-type and Db/Db, at 5, 12, 20, and 25 weeks. A support vector machine model, which classifies data using a single line, or hyperplane, that best separates each class, and a ReliefF algorithm, which ranks features by how well each feature lends to the classification of data, were used to identify and rank cardiac regions, segments, and features by their ability to identify cardiac dysfunction. STE features more accurately segregated animals as diabetic or non-diabetic when compared with conventional echocardiography, and the ReliefF algorithm efficiently ranked STE features by their ability to identify cardiac dysfunction. The Septal region, and the AntSeptum segment, best identified cardiac dysfunction at 5, 20, and 25 weeks, with the AntSeptum also containing the greatest number of features which differed between diabetic and non-diabetic mice. Cardiac dysfunction manifests in a spatial and temporal fashion, and is defined by patterns of regional and segmental dysfunction in the T2DM heart which are identifiable using machine learning methodologies. Further, machine learning identified the Septal region and AntSeptum segment as locales of interest for therapeutic interventions aimed at ameliorating cardiac dysfunction in T2DM, suggesting that machine learning may provide a more thorough approach to managing contractile data with the intention of identifying experimental and therapeutic targets

Mitochondrial proteome disruption in the diabetic heart through targeted epigenetic regulation at the mitochondrial heat shock protein 70 (mtHsp70) nuclear locus

Greater than 99% of the mitochondrial proteome is nuclear-encoded. The mitochondrion relies on a coordinated multi-complex process for nuclear genome-encoded mitochondrial protein import. Mitochondrial heat shock protein 70 (mtHsp70) is a key component of this process and a central constituent of the protein import motor. Type 2 diabetes mellitus (T2DM) disrupts mitochondrial proteomic signature which is associated with decreased protein import efficiency. The goal of this study was to manipulate the mitochondrial protein import process through targeted restoration of mtHsp70, in an effort to restore proteomic signature and mitochondrial function in the T2DM heart. A novel line of cardiac-specific mtHsp70 transgenic mice on the db/db background were generated and cardiac mitochondrial subpopulations were isolated with proteomic evaluation and mitochondrial function assessed. MicroRNA and epigenetic regulation of the mtHsp70 gene during T2DM were also evaluated. MtHsp70 overexpression restored cardiac function and nuclear-encoded mitochondrial protein import, contributing to a beneficial impact on proteome signature and enhanced mitochondrial function during T2DM. Further, transcriptional repression at the mtHSP70 genomic locus through increased localization of H3K27me3 during T2DM insult was observed. Our results suggest that restoration of a key protein import constituent, mtHsp70, provides therapeutic benefit through attenuation of mitochondrial and contractile dysfunction in T2DM

Reactive oxygen species damage drives cardiac and mitochondrial dysfunction following acute nano-titanium dioxide inhalation exposure

<p>Nanotechnology offers innovation in products from cosmetics to drug delivery, leading to increased engineered nanomaterial (ENM) exposure. Unfortunately, health impacts of ENM are not fully realized. Titanium dioxide (TiO₂) is among the most widely produced ENM due to its use in numerous applications. Extrapulmonary effects following pulmonary exposure have been identified and may involve reactive oxygen species (ROS). The goal of this study was to determine the extent of ROS involvement on cardiac function and the mitochondrion following nano-TiO₂ exposure. To address this question, we utilized a transgenic mouse model with overexpression of a novel mitochondrially-targeted antioxidant enzyme (phospholipid hydroperoxide glutathione peroxidase; mPHGPx) which provides protection against oxidative stress to lipid membranes. MPHGPx mice and littermate controls were exposed to nano-TiO₂ aerosols (Evonik, P25) to provide a calculated pulmonary deposition of 11 µg/mouse. Twenty-four hours following exposure, we observed diastolic dysfunction as evidenced by E/A ratios greater than 2 and increased radial strain during diastole in wild-type mice (<i>p</i> < 0.05 for both), indicative of restrictive filling. Overexpression of mPHGPx mitigated the contractile deficits resulting from nano-TiO₂ exposure. To investigate the cellular mechanisms associated with the observed cardiac dysfunction, we focused our attention on the mitochondrion. We observed a significant increase in ROS production (<i>p</i> < 0.05) and decreased mitochondrial respiratory function (<i>p</i> < 0.05) following nano-TiO₂ exposure which were attenuated in mPHGPx transgenic mice. In summary, nano-TiO₂ inhalation exposure is associated with cardiac diastolic dysfunction and mitochondrial functional alterations, which can be mitigated by the overexpression of mPHGPx, suggesting ROS contribution in the development of contractile and bioenergetic dysfunction.</p