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

Crystal structure of the mitochondrial protein mitoNEET bound to a benze-sulfonide ligand

MitoNEET (gene cisd1) is a mitochondrial outer membrane [2Fe-2S] protein and is a potential drug target in several metabolic diseases. Previous studies have demonstrated that mitoNEET functions as a redox-active and pH-sensing protein that regulates mitochondrial metabolism, although the structural basis of the potential drug binding site(s) remains elusive. Here we report the crystal structure of the soluble domain of human mitoNEET with a sulfonamide ligand, furosemide. Exploration of the high-resolution crystal structure is used to design mitoNEET binding molecules in a pilot study of molecular probes for use in future development of mitochondrial targeted therapies for a wide variety of metabolic diseases, including obesity, diabetes and neurodegenerative diseases such as Alzheimer’s and Parkinson’s disease

Crystal Structure of the Mitochondrial Protein mitoNEET Bound to a Benze-sulfonide Ligand

MitoNEET (gene cisd1) is a mitochondrial outer membrane [2Fe-2S] protein and is a potential drug target in several metabolic diseases. Previous studies have demonstrated that mitoNEET functions as a redox-active and pH-sensing protein that regulates mitochondrial metabolism, although the structural basis of the potential drug binding site(s) remains elusive. Here we report the crystal structure of the soluble domain of human mitoNEET with a sulfonamide ligand, furosemide. Exploration of the high-resolution crystal structure is used to design mitoNEET binding molecules in a pilot study of molecular probes for use in future development of mitochondrial targeted therapies for a wide variety of metabolic diseases, including obesity, diabetes and neurodegenerative diseases such as Alzheimer’s and Parkinson’s disease

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