A novel multi-task machine learning classifier for rare disease patterning using cardiac strain imaging data

To provide accurate predictions, current machine learning-based solutions require large, manually labeled training datasets. We implement persistent homology (PH), a topological tool for studying the pattern of data, to analyze echocardiography-based strain data and differentiate between rare diseases like constrictive pericarditis (CP) and restrictive cardiomyopathy (RCM). Patient population (retrospectively registered) included those presenting with heart failure due to CP (n = 51), RCM (n = 47), and patients without heart failure symptoms (n = 53). Longitudinal, radial, and circumferential strains/strain rates for left ventricular segments were processed into topological feature vectors using Machine learning PH workflow. In differentiating CP and RCM, the PH workflow model had a ROC AUC of 0.94 (Sensitivity = 92%, Specificity = 81%), compared with the GLS model AUC of 0.69 (Sensitivity = 65%, Specificity = 66%). In differentiating between all three conditions, the PH workflow model had an AUC of 0.83 (Sensitivity = 68%, Specificity = 84%), compared with the GLS model AUC of 0.68 (Sensitivity = 52% and Specificity = 76%). By employing persistent homology to differentiate the "pattern" of cardiac deformations, our machine-learning approach provides reasonable accuracy when evaluating small datasets and aids in understanding and visualizing patterns of cardiac imaging data in clinically challenging disease states.

Advancing Precision Medicine: Algebraic Topology and Differential Geometry in Radiology and Computational Pathology

Precision medicine aims to provide personalized care based on individual patient characteristics, rather than guideline-directed therapies for groups of diseases or patient demographics. Images-both radiology- and pathology-derived-are a major source of information on presence, type, and status of disease. Exploring the mathematical relationship of pixels in medical imaging ("radiomics") and cellular-scale structures in digital pathology slides ("pathomics") offers powerful tools for extracting both qualitative and, increasingly, quantitative data. These analytical approaches, however, may be significantly enhanced by applying additional methods arising from fields of mathematics such as differential geometry and algebraic topology that remain underexplored in this context. Geometry's strength lies in its ability to provide precise local measurements, such as curvature, that can be crucial for identifying abnormalities at multiple spatial levels. These measurements can augment the quantitative features extracted in conventional radiomics, leading to more nuanced diagnostics. By contrast, topology serves as a robust shape descriptor, capturing essential features such as connected components and holes. The field of topological data analysis was initially founded to explore the shape of data, with functional network connectivity in the brain being a prominent example. Increasingly, its tools are now being used to explore organizational patterns of physical structures in medical images and digitized pathology slides. By leveraging tools from both differential geometry and algebraic topology, researchers and clinicians may be able to obtain a more comprehensive, multi-layered understanding of medical images and contribute to precision medicine's armamentarium.

Prediction of heart failure and all-cause mortality using cardiac ultrasomics in patients with breast cancer

Breast cancer chemotherapy/immunotherapy can be associated with treatment-limiting cardiotoxicity. Radiomics techniques applied to ultrasound, known as ultrasomics, can be used in cardio-oncology to leverage echocardiography for added prognostic value. To utilize ultrasomics features collected prior to antineoplastic therapy to enhance prediction of mortality and heart failure (HF) in patients with breast cancer. Patients were retrospectively recruited in a study at the West Virginia University Cancer Institute. The final inclusion criteria were met by a total of 134 patients identified for the study. Patients were imaged using echocardiography in the parasternal long axis prior to receiving chemotherapy. All-cause mortality and HF, developed during treatment, were the primary outcomes. 269 features were assessed, grouped into four major classes: demographics (n = 21), heart function (n = 7), antineoplastic medication (n = 17), and ultrasomics (n = 224). Data was split into an internal training (60%, n = 81) and testing (40%, n = 53) set. Ultrasomics features augmented classification of mortality (area under the curve (AUC) 0.89 vs. 0.65, P = 0.003), when compared to demographic variables. When developing a risk prediction score for each feature category, ultrasomics features were significantly associated with both mortality (P = 0.031, log-rank test) and HF (P = 0.002, log-rank test). Further, only ultrasomics features provided significant improvement over demographic variables when predicting mortality (C-Index: 0.78 vs. 0.65, P = 0.044) and HF (C-Index: 0.77 vs. 0.60, P = 0.017), respectively. With further investigation, a clinical decision support tool could be developed utilizing routinely obtained patient data alongside ultrasomics variables to augment treatment regimens.

N6-methyladenosine (M6A) in fetal offspring modifies mitochondrial gene expression following gestational nano-TiO2 inhalation exposure

N6-methyladenosine (m6A) is the most prominent epitranscriptomic modification to RNA in eukaryotes, but it's role in adaptive changes within the gestational environment are poorly understood. We propose that gestational exposure to nano titanium dioxide (TiO2) contributes to cardiac m6A methylation in fetal offspring and influences mitochondrial gene expression. 10-week-old pregnant female FVB/NJ wild-type mice underwent 6 nonconsecutive days of whole-body inhalation exposure beginning on gestational day (GD) 5. Mice were exposed to filtered room air or nano-TiO2 with a target aerosol mass concentration of 12 mg/m3. At GD 15 mice were humanely killed and cardiac RNA and mitochondrial proteins extracted. Immunoprecipitation with m6A antibodies was performed followed by sequencing of immunoprecipitant (m6A) and input (mRNA) on the Illumina NextSeq 2000. Protein extraction, preparation, and LC-MS/MS were used for mitochondrial protein quantification. There were no differences in maternal or fetal pup weights, number of pups, or pup heart weights between exposure and control groups. Transcriptomic sequencing revealed 3648 differentially expressed mRNA in nano-TiO2 exposed mice (Padj ≤ 0.05). Transcripts involved in mitochondrial bioenergetics were significantly downregulated (83 of 85 genes). 921 transcripts revealed significant m6A methylation sites (Padj ≤ 0.10). 311 of the 921 mRNA were identified to have both 1) significantly altered expression and 2) differentially methylated sites. Mitochondrial proteomics revealed decreased expression of ATP Synthase subunits in the exposed group (P ≤ 0.05). The lack of m6A modifications to mitochondrial transcripts suggests a mechanism for decreased transcript stability and reduced protein expression due to gestational nano-TiO2 inhalation exposure.

CT-derived pectoralis composition and incident pneumonia hospitalization using fully automated deep-learning algorithm: multi-ethnic study of atherosclerosis

BACKGROUND

Pneumonia-related hospitalization may be associated with advanced skeletal muscle loss due to aging (i.e., sarcopenia) or chronic illnesses (i.e., cachexia). Early detection of muscle loss may now be feasible using deep-learning algorithms applied on conventional chest CT.

OBJECTIVES

To implement a fully automated deep-learning algorithm for pectoralis muscle measures from conventional chest CT and investigate longitudinal associations between these measures and incident pneumonia hospitalization according to Chronic Obstructive Pulmonary Disease (COPD) status.

MATERIALS AND METHODS

This analysis from the Multi-Ethnic Study of Atherosclerosis included participants with available chest CT examinations between 2010 and 2012. We implemented pectoralis muscle composition measures from a fully automated deep-learning algorithm (Mask R-CNN, built on the Faster Region Proposal Network (R-) Convolutional Neural Network (CNN) with an extension for mask identification) for two-dimensional segmentation. Associations between CT-derived measures and incident pneumonia hospitalizations were evaluated using Cox proportional hazards models adjusted for multiple confounders which include but are not limited to age, sex, race, smoking, BMI, physical activity, and forced-expiratory-volume-at-1 s-to-functional-vital-capacity ratio. Stratification analyses were conducted based on baseline COPD status.

RESULTS

This study included 2595 participants (51% female; median age: 68 (IQR: 61, 76)) CT examinations for whom we implemented deep learning-derived measures for longitudinal analyses. Eighty-six incident pneumonia hospitalizations occurred during a median 6.67-year follow-up. Overall, pectoralis muscle composition measures did not predict incident pneumonia. However, in fully-adjusted models, only among participants with COPD (N = 507), CT measures like extramyocellular fat index (hazard ratio: 1.98, 95% CI: 1.22, 3.21, p value: 0.02), were independently associated with incident pneumonia.

CONCLUSION

Reliable deep learning-derived pectoralis muscle measures could predict incident pneumonia hospitalization only among participants with known COPD.

CLINICAL RELEVANCE STATEMENT

Pectoralis muscle measures obtainable at zero additional cost or radiation exposure from any chest CT may have independent predictive value for clinical outcomes in chronic obstructive pulmonary disease patients.

KEY POINTS

•Identification of independent and modifiable risk factors of pneumonia can have important clinical impact on patients with chronic obstructive pulmonary disease. •Opportunistic CT measures of adipose tissue within pectoralis muscles using deep-learning algorithms can be quickly obtainable at zero additional cost or radiation exposure. •Deep learning-derived pectoralis muscle measurements of intermuscular fat and its subcomponents are independently associated with subsequent incident pneumonia hospitalization.

Lung-gut axis of microbiome alterations following co-exposure to ultrafine carbon black and ozone

BACKGROUND

Microbial dysbiosis is a potential mediator of air pollution-induced adverse outcomes. However, a systemic comparison of the lung and gut microbiome alterations and lung-gut axis following air pollution exposure is scant. In this study, we exposed male C57BL/6J mice to inhaled air, CB (10 mg/m3), O3 (2 ppm) or CB + O3 mixture for 3 h/day for either one day or four consecutive days and were euthanized 24 h post last exposure. The lung and gut microbiome were quantified by 16 s sequencing.

RESULTS

Multiple CB + O3 exposures induced an increase in the lung inflammatory cells (neutrophils, eosinophils and B lymphocytes), reduced absolute bacterial load in the lungs and increased load in the gut. CB + O3 exposure was more potent as it decreased lung microbiome alpha diversity just after a single exposure. CB + O3 co-exposure uniquely increased Clostridiaceae and Prevotellaceae in the lungs. Serum short chain fatty acids (SCFA) (acetate and propionate) were increased significantly only after CB + O3 co-exposure. A significant increase in SCFA producing bacterial families (Ruminococcaceae, Lachnospiraceae, and Eubacterium) were also observed in the gut after multiple exposures. Co-exposure induced significant alterations in the gut derived metabolite receptors/mediator (Gcg, Glp-1r, Cck) mRNA expression. Oxidative stress related mRNA expression in lungs, and oxidant levels in the BALF, serum and gut significantly increased after CB + O3 exposures.

CONCLUSION

Our study confirms distinct gut and lung microbiome alterations after CB + O3 inhalation co-exposure and indicate a potential homeostatic shift in the gut microbiome to counter deleterious impacts of environmental exposures on metabolic system.

Topological data analysis in medical imaging: current state of the art

Machine learning, and especially deep learning, is rapidly gaining acceptance and clinical usage in a wide range of image analysis applications and is regarded as providing high performance in detecting anatomical structures and identification and classification of patterns of disease in medical images. However, there are many roadblocks to the widespread implementation of machine learning in clinical image analysis, including differences in data capture leading to different measurements, high dimensionality of imaging and other medical data, and the black-box nature of machine learning, with a lack of insight into relevant features. Techniques such as radiomics have been used in traditional machine learning approaches to model the mathematical relationships between adjacent pixels in an image and provide an explainable framework for clinicians and researchers. Newer paradigms, such as topological data analysis (TDA), have recently been adopted to design and develop innovative image analysis schemes that go beyond the abilities of pixel-to-pixel comparisons. TDA can automatically construct filtrations of topological shapes of image texture through a technique known as persistent homology (PH); these features can then be fed into machine learning models that provide explainable outputs and can distinguish different image classes in a computationally more efficient way, when compared to other currently used methods. The aim of this review is to introduce PH and its variants and to review TDA's recent successes in medical imaging studies.

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.

Eosinophilic myocarditis: Case report and brief review of the literature

Eosinophilic myocarditis (EM) is a cardiac manifestation of hypereosinophilic syndrome with a high mortality rate. EM shares imaging features similar to other restrictive cardiopathies, and include patchy intramural late gadolinium enhancement on cardiac magnetic resonance with or without presence of biventricular thrombus. Diagnosis is confirmed on histopathology, and is the current gold standard. Here we report clinical presentation and imaging findings of EM in a 70-year-old woman who presented with fever and chills.

NUDT7 regulates total hepatic CoA levels and the composition of the intestinal bile acid pool in male mice fed a Western diet

Nudix hydrolase 7 (NUDT7) is an enzyme that hydrolyzes CoA species, is highly expressed in the liver, and resides in the peroxisomes. Peroxisomes are organelles where the preferential oxidation of dicarboxylic fatty acids occurs and where the hepatic synthesis of the primary bile acids cholic acid and chenodeoxycholic acid is completed. We previously showed that liver-specific overexpression of NUDT7 affects peroxisomal lipid metabolism but does not prevent the increase in total liver CoA levels that occurs during fasting. We generated Nudt7^-/- mice to further characterize the role that peroxisomal (acyl-)CoA degradation plays in the modulation of the size and composition of the acyl-CoA pool and in the regulation of hepatic lipid metabolism. Here, we show that deletion of Nudt7 alters the composition of the hepatic acyl-CoA pool in mice fed a low-fat diet, but only in males fed a Western diet does the lack of NUDT7 activity increase total liver CoA levels. This effect is driven by the male-specific accumulation of medium-chain dicarboxylic acyl-CoAs, which are produced from the β-oxidation of dicarboxylic fatty acids. We also show that, under conditions of elevated synthesis of chenodeoxycholic acid derivatives, Nudt7 deletion promotes the production of tauromuricholic acid, decreasing the hydrophobicity index of the intestinal bile acid pool and increasing fecal cholesterol excretion in male mice. These findings reveal that NUDT7-mediated hydrolysis of acyl-CoA pathway intermediates in liver peroxisomes contributes to the regulation of dicarboxylic fatty acid metabolism and the composition of the bile acid pool.

Traumatic right ventricular rupture: Case report and brief review of the literature

Rupture of the right ventricular (RV) myocardium is associated with serious morbidity and mortality. Under very rare conditions, a tear in the ventricular wall can lead to the formation of a pseudoaneurysm: an external outpouching of the ventricle that is stabilized by the pericardium, thrombus formation, and/or adhesions. Here, we present a 75-year-old man with RV free wall rupture with pseudoaneurysm following a motor vehicle collision. With concerns for blunt cardiac trauma, initial CTA chest revealed focal outpouching and extension of contrast outside of the confines of the RV chamber, compatible with pseudoaneurysm formation. In this case, conservative management of the pseudoaneurysm was preferred over surgical management, due to the thin RV free wall and present comorbid conditions. We highlight how CTA chest offers a reliable tool for tracking the stability of pseudoaneurysms in the RV and can guide clinical management through directing treatment strategies and appropriate follow-up intervals.

Left Ventricular Segmental Strain Identifies Unique Myocardial Deformation Patterns After Intrinsic and Extrinsic Stressors in Mice

We used segmental strain analysis to evaluate whether intrinsic (diet-induced obesity [DIO]) and extrinsic (unpredictable chronic mild stress [UCMS]) stressors can alter deformational patterns of the left ventricle. Six-week-old male C57BL/6J mice were randomized into the lean or obese group (n = 24/group). Mice underwent 12 wk of DIO with a high-fat diet (HFD). At 18 wk, lean and obese mice were further randomized into UCMS and non-UCMS groups (UCMS, 7 h/d, 5 d/wk, for 8 wk). Echocardiography was performed at baseline (6 wk), post-HFD (18 wk) and post-UCMS (26 wk). Machine learning was applied to the DIO and UCMS groups. There was robust predictive accuracy (area under the receiver operating characteristic curve [AUC] = 0.921) when comparing obese with lean mice, with radial strain changes in the lateral (-64%, p ≤ 0.001) and anterior free (-53%, p < 0.001) walls being most informative. The ability to predict mice that underwent UCMS, irrespective of diet, was assessed (AUC = 0.886), revealing longitudinal strain rate of the anterior midwall and radial strain of the posterior septal wall as the top features. The wall segments indicate a predilection for changes in deformation patterns to the free wall (DIO) and septal wall (UCMS), indicating disease-specific alterations to the myocardium.

Genome-wide expression reveals potential biomarkers in breast cancer bone metastasis

Breast cancer metastases are most commonly found in bone, an indication of poor prognosis. Pathway-based biomarkers identification may help elucidate the cellular signature of breast cancer metastasis in bone, further characterizing the etiology and promoting new therapeutic approaches. We extracted gene expression profiles from mouse macrophages from the GEO dataset, GSE152795 using the GEO2R webtool. The differentially expressed genes (DEGs) were filtered by log2 fold-change with threshold 1.5 (FDR < 0.05). STRING database and Enrichr were used for GO-term analysis, miRNA and TF analysis associated with DEGs. Autodock Vienna was exploited to investigate interaction of anti-cancer drugs, Actinomycin-D and Adriamycin. Sensitivity and specificity of DEGs was assessed using receiver operating characteristic (ROC) analyses. A total of 61 DEGs, included 27 down-regulated and 34 up-regulated, were found to be significant in breast cancer bone metastasis. Major DEGs were associated with lipid metabolism and immunological response of tumor tissue. Crucial DEGs, Bcl3, ADGRG7, FABP4, VCAN, and IRF4 were regulated by miRNAs, miR-497, miR-574, miR-138 and TFs, CCDN1, STAT6, IRF8. Docking analysis showed that these genes possessed strong binding with the drugs. ROC analysis demonstrated Bcl3 is specific to metastasis. DEGs Bcl3, ADGRG7, FABP4, IRF4, their regulating miRNAs and TFs have strong impact on proliferation and metastasis of breast cancer in bone tissues. In conclusion, present study revealed that DEGs are directly involved in of breast tumor metastasis in bone tissues. Identified genes, miRNAs, and TFs can be possible drug targets that may be used for the therapeutics. However, further experimental validation is necessary.

Manipulation of the miR-378a/mt-ATP6 regulatory axis rescues ATP synthase in the diabetic heart and offers a novel role for lncRNA Kcnq1ot1

Diabetes mellitus has been linked to an increase in mitochondrial microRNA-378a (miR-378a) content. Enhanced miR-378a content has been associated with a reduction in mitochondrial genome-encoded mt-ATP6 abundance, supporting the hypothesis that miR-378a inhibition may be a therapeutic option for maintaining ATP synthase functionality during diabetes mellitus. Evidence also suggests that long noncoding RNAs (lncRNAs), including lncRNA potassium voltage-gated channel subfamily Q member 1 overlapping transcript 1 (Kcnq1ot1), participate in regulatory axes with microRNAs (miRs). Prediction analyses indicate that Kcnq1ot1 has the potential to bind miR-378a. This study aimed to determine if loss of miR-378a in a genetic mouse model could ameliorate cardiac dysfunction in type 2 diabetes mellitus (T2DM) and to ascertain whether Kcnq1ot1 interacts with miR-378a to impact ATP synthase functionality by preserving mt-ATP6 levels. MiR-378a was significantly higher in patients with T2DM and 25-wk-old Db/Db mouse mitochondria, whereas mt-ATP6 and Kcnq1ot1 levels were significantly reduced when compared with controls. Twenty-five-week-old miR-378a knockout Db/Db mice displayed preserved mt-ATP6 and ATP synthase protein content, ATP synthase activity, and preserved cardiac function, implicating miR-378a as a potential therapeutic target in T2DM. Assessments following overexpression of the 500-bp Kcnq1ot1 fragment in established mouse cardiomyocyte cell line (HL-1) cardiomyocytes overexpressing miR-378a revealed that Kcnq1ot1 may bind and significantly reduce miR-378a levels, and rescue mt-ATP6 and ATP synthase protein content. Together, these data suggest that Kcnq1ot1 and miR-378a may act as constituents in an axis that regulates mt-ATP6 content, and that manipulation of this axis may provide benefit to ATP synthase functionality in type 2 diabetic heart.

Transcriptomics of single dose and repeated carbon black and ozone inhalation co-exposure highlight progressive pulmonary mitochondrial dysfunction

BACKGROUND

Air pollution is a complex mixture of particles and gases, yet current regulations are based on single toxicant levels failing to consider potential interactive outcomes of co-exposures. We examined transcriptomic changes after inhalation co-exposure to a particulate and a gaseous component of air pollution and hypothesized that co-exposure would induce significantly greater impairments to mitochondrial bioenergetics. A whole-body inhalation exposure to ultrafine carbon black (CB), and ozone (O3) was performed, and the impact of single and multiple exposures was studied at relevant deposition levels. C57BL/6 mice were exposed to CB (10 mg/m3) and/or O3 (2 ppm) for 3 h (either a single exposure or four independent exposures). RNA was isolated from lungs and mRNA sequencing performed using the Illumina HiSeq. Lung pathology was evaluated by histology and immunohistochemistry. Electron transport chain (ETC) activities, electron flow, hydrogen peroxide production, and ATP content were assessed.

RESULTS

Compared to individual exposure groups, co-exposure induced significantly greater neutrophils and protein levels in broncho-alveolar lavage fluid as well as a significant increase in mRNA expression of oxidative stress and inflammation related genes. Similarly, a significant increase in hydrogen peroxide production was observed after co-exposure. After single and four exposures, co-exposure revealed a greater number of differentially expressed genes (2251 and 4072, respectively). Of these genes, 1188 (single exposure) and 2061 (four exposures) were uniquely differentially expressed, with 35 mitochondrial ETC mRNA transcripts significantly impacted after four exposures. Both O3 and co-exposure treatment significantly reduced ETC maximal activity for complexes I (- 39.3% and - 36.2%, respectively) and IV (- 55.1% and - 57.1%, respectively). Only co-exposure reduced ATP Synthase activity (- 35.7%) and total ATP content (30%). Further, the ability for ATP Synthase to function is limited by reduced electron flow (- 25%) and translation of subunits, such as ATP5F1, following co-exposure.

CONCLUSIONS

CB and O3 co-exposure cause unique transcriptomic changes in the lungs that are characterized by functional deficits to mitochondrial bioenergetics. Alterations to ATP Synthase function and mitochondrial electron flow underly a pathological adaptation to lung injury induced by co-exposure.

Deep neural survival networks for cardiovascular risk prediction: The Multi-Ethnic Study of Atherosclerosis (MESA)

BACKGROUND

There is growing interest in utilizing machine learning techniques for routine atherosclerotic cardiovascular disease (ASCVD) risk prediction. We investigated whether novel deep learning survival models can augment ASCVD risk prediction over existing statistical and machine learning approaches.

METHODS

6814 participants from the Multi-Ethnic Study of Atherosclerosis (MESA) were followed over 16 years to assess incidence of all-cause mortality (mortality) or a composite of major adverse events (MAE). Features were evaluated within the categories of traditional risk factors, inflammatory biomarkers, and imaging markers. Data was split into an internal training/testing (four centers) and external validation (two centers). Both machine learning (COXPH, RSF, and lSVM) and deep learning (nMTLR and DeepSurv) models were evaluated.

RESULTS

In comparison to the COXPH model, DeepSurv significantly improved ASCVD risk prediction for MAE (AUC: 0.82 vs. 0.80, P ≤ 0.001) and mortality (AUC: 0.87 vs. 0.84, P ≤ 0.001) with traditional risk factors alone. Implementing non-categorical NRI, we noted a >40% increase in correct reclassification compared to the COXPH model for both MAE and mortality (P ≤ 0.05). Assessing the relative risk of participants, DeepSurv was the only learning algorithm to develop a significantly improved risk score criteria, which outcompeted COXPH for both MAE (4.22 vs. 3.61, P = 0.043) and mortality (6.81 vs. 5.52, P = 0.044). The addition of inflammatory or imaging biomarkers to traditional risk factors showed minimal/no significant improvement in model prediction.

CONCLUSION

DeepSurv can leverage simple office-based clinical features alone to accurately predict ASCVD risk and cardiovascular outcomes, without the need for additional features, such as inflammatory and imaging biomarkers.

A vital sign-based prediction algorithm for differentiating COVID-19 versus seasonal influenza in hospitalized patients

Patients with influenza and SARS-CoV2/Coronavirus disease 2019 (COVID-19) infections have a different clinical course and outcomes. We developed and validated a supervised machine learning pipeline to distinguish the two viral infections using the available vital signs and demographic dataset from the first hospital/emergency room encounters of 3883 patients who had confirmed diagnoses of influenza A/B, COVID-19 or negative laboratory test results. The models were able to achieve an area under the receiver operating characteristic curve (ROC AUC) of at least 97% using our multiclass classifier. The predictive models were externally validated on 15,697 encounters in 3125 patients available on TrinetX database that contains patient-level data from different healthcare organizations. The influenza vs COVID-19-positive model had an AUC of 98.8%, and 92.8% on the internal and external test sets, respectively. Our study illustrates the potentials of machine-learning models for accurately distinguishing the two viral infections. The code is made available at https://github.com/ynaveena/COVID-19-vs-Influenza and may have utility as a frontline diagnostic tool to aid healthcare workers in triaging patients once the two viral infections start cocirculating in the communities.

Enhanced antioxidant capacity prevents epitranscriptomic and cardiac alterations in adult offspring gestationally-exposed to ENM

Maternal engineered nanomaterial (ENM) exposure during gestation has been associated with negative long-term effects on cardiovascular health in progeny. Here, we evaluate an epitranscriptomic mechanism that contributes to these chronic ramifications and whether overexpression of mitochondrial phospholipid hydroperoxide glutathione peroxidase (mPHGPx) can preserve cardiovascular function and bioenergetics in offspring following gestational nano-titanium dioxide (TiO₂) inhalation exposure. Wild-type (WT) and mPHGPx (Tg) dams were exposed to nano-TiO₂ aerosols with a mass concentration of 12.01 ± 0.50 mg/m³ starting from gestational day (GD) 5 for 360 mins/day for 6 nonconsecutive days over 8 days. Echocardiography was performed in pregnant dams, adult (11-week old) and fetal (GD 14) progeny. Mitochondrial function and global N⁶-methyladenosine (m⁶A) content were assessed in adult progeny. MPHGPx enzymatic function was further evaluated in adult progeny and m⁶A-RNA immunoprecipitation (RIP) was combined with RT-qPCR to evaluate m⁶A content in the 3'-UTR. Following gestational ENM exposure, global longitudinal strain (GLS) was 32% lower in WT adult offspring of WT dams, with preservation in WT offspring of Tg dams. MPHGPx activity was significantly reduced in WT offspring (29%) of WT ENM-exposed dams, but preserved in the progeny of Tg dams. M⁶A-RIP-qPCR for the SEC insertion sequence region of mPHGPx revealed hypermethylation in WT offspring from ENM-exposed WT dams, which was thwarted in the presence of the maternal transgene. Our findings implicate that m⁶A hypermethylation of mPHGPx may be culpable for diminished antioxidant capacity and resultant mitochondrial and cardiac deficits that persist into adulthood following gestational ENM inhalation exposure.

A Vital Sign-based Prediction Algorithm for Differentiating COVID-19 Versus Seasonal Influenza in Hospitalized Patients

Patients with influenza and SARS-CoV2/Coronavirus disease 2019 (COVID-19) infections have different clinical course and outcomes. We developed and validated a supervised machine learning pipeline to distinguish the two viral infections using the available vital signs and demographic dataset from the first hospital/emergency room encounters of 3,883 patients who had confirmed diagnoses of influenza A/B, COVID-19 or negative laboratory test results. The models were able to achieve an area under the receiver operating characteristic curve (ROC AUC) of at least 97% using our multiclass classifier. The predictive models were externally validated on 15,697 encounters in 3,125 patients available on TrinetX database that contains patient-level data from different healthcare organizations. The influenza vs. COVID-19-positive model had an AUC of 98%, and 92% on the internal and external test sets, respectively. Our study illustrates the potentials of machine-learning models for accurately distinguishing the two viral infections. The code is made available at https://github.com/ynaveena/COVID-19-vs-Influenza and may be have utility as a frontline diagnostic tool to aid healthcare workers in triaging patients once the two viral infections start cocirculating in the communities.

Cardiovascular adaptations to particle inhalation exposure: molecular mechanisms of the toxicology

Ambient air, occupational settings, and the use and distribution of consumer products all serve as conduits for toxicant exposure through inhalation. While the pulmonary system remains a primary target following inhalation exposure, cardiovascular implications are exceptionally culpable for increased morbidity and mortality. The epidemiological evidence for cardiovascular dysfunction resulting from acute or chronic inhalation exposure to particulate matter has been well documented, but the mechanisms driving the resulting disturbances remain elusive. In the current review, we aim to summarize the cellular and molecular mechanisms that are directly linked to cardiovascular health following exposure to a variety of inhaled toxicants. The purpose of this review is to provide a comprehensive overview of the biochemical changes in the cardiovascular system following particle inhalation exposure and to highlight potential biomarkers that exist across multiple exposure paradigms. We attempt to integrate these molecular signatures in an effort to provide direction for future investigations. This review also characterizes how molecular responses are modified in at-risk populations, specifically the impact of environmental exposure during critical windows of development. Maternal exposure to particulate matter during gestation can lead to fetal epigenetic reprogramming, resulting in long-term deficits to the cardiovascular system. In both direct and indirect (gestational) exposures, connecting the biochemical mechanisms with functional deficits outlines pathways that can be targeted for future therapeutic intervention. Ultimately, future investigations integrating "omics"-based approaches will better elucidate the mechanisms that are altered by xenobiotic inhalation exposure, identify biomarkers, and guide in clinical decision making.

Pyrvinium Pamoate Use in a B cell Acute Lymphoblastic Leukemia Model of the Bone Tumor Microenvironment

PURPOSE

Pyrvinium pamoate (PP) is an anthelmintic drug that has been found to have anti-cancer activity in several cancer types. In the present study, we evaluated PP for potential anti-leukemic activity in B cell acute lymphoblastic leukemia (ALL) cell lines, in an effort to evaluate the repurposing potential of this drug in leukemia.

METHODS

ALL cells were treated with PP at various concentrations to determine its effect on cell proliferation. Metabolic function was tested by evaluating Extracellular Acidification Rate (ECAR) and Oxygen Consumption Rate (OCR). Lastly, 3D spheroids were grown, and PP was reformulated into nanoparticles to evaluate distribution effectiveness.

RESULTS

PP was found to inhibit ALL proliferation, with varied selectivity to different ALL cell subtypes. We also found that PP's cell death activity was specific for leukemic cells, as primary normal immune cells were resistant to PP-mediated cell death. Metabolic studies indicated that PP, in part, inhibits mitochondrial oxidative phosphorylation. To increase the targeting of PP to a hypoxic bone tumor microenvironment (BTME) niche, we successfully encapsulated PP in a nanoparticle drug delivery system and demonstrated that it retained its anti-leukemic activity in a hemosphere assay.

CONCLUSION

We have demonstrated that PP is a novel therapeutic lead compound that counteracts the respiratory reprogramming found in refractory ALL cells and can be effectively formulated into a nanoparticle delivery system to target the BTME.

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 classification (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 Classification 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 classification 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 discovery.

Mitochondrial dysfunction in type 2 diabetes mellitus: an organ-based analysis

Type 2 diabetes mellitus (T2DM) is a systemic disease characterized by hyperglycemia, hyperlipidemia, and organismic insulin resistance. This pathological shift in both circulating fuel levels and energy substrate utilization by central and peripheral tissues contributes to mitochondrial dysfunction across organ systems. The mitochondrion lies at the intersection of critical cellular pathways such as energy substrate metabolism, reactive oxygen species (ROS) generation, and apoptosis. It is the disequilibrium of these processes in T2DM that results in downstream deficits in vital functions, including hepatocyte metabolism, cardiac output, skeletal muscle contraction, β-cell insulin production, and neuronal health. Although mitochondria are known to be susceptible to a variety of genetic and environmental insults, the accumulation of mitochondrial DNA (mtDNA) mutations and mtDNA copy number depletion is helping to explain the prevalence of mitochondrial-related diseases such as T2DM. Recent work has uncovered novel mitochondrial biology implicated in disease progressions such as mtDNA heteroplasmy, noncoding RNA (ncRNA), epigenetic modification of the mitochondrial genome, and epitranscriptomic regulation of the mtDNA-encoded mitochondrial transcriptome. The goal of this review is to highlight mitochondrial dysfunction observed throughout major organ systems in the context of T2DM and to present new ideas for future research directions based on novel experimental and technological innovations in mitochondrial biology. Finally, the field of mitochondria-targeted therapeutics is discussed, with an emphasis on novel therapeutic strategies to restore mitochondrial homeostasis in the setting of T2DM.

miRNA-378a as a key regulator of cardiovascular health following engineered nanomaterial inhalation exposure

Nano-titanium dioxide (nano-TiO₂), though one of the most utilized and produced engineered nanomaterials (ENMs), diminishes cardiovascular function through dysregulation of metabolism and mitochondrial bioenergetics following inhalation exposure. The molecular mechanisms governing this cardiac dysfunction remain largely unknown. The purpose of this study was to elucidate molecular mediators that connect nano-TiO₂ exposure with impaired cardiac function. Specifically, we were interested in the role of microRNA (miRNA) expression in the resulting dysfunction. Not only are miRNA global regulators of gene expression, but also miRNA-based therapeutics provide a realistic treatment modality. Wild type and MiRNA-378a knockout mice were exposed to nano-TiO₂ with an aerodynamic diameter of 182 ± 1.70 nm and a mass concentration of 11.09 mg/m³ for 4 h. Cardiac function, utilizing the Vevo 2100 Imaging System, electron transport chain complex activities, and mitochondrial respiration assessed cardiac and mitochondrial function. Immunoblotting and qPCR examined molecular targets of miRNA-378a. MiRNA-378a-3p expression was increased 48 h post inhalation exposure to nano-TiO₂. Knockout of miRNA-378a preserved cardiac function following exposure as revealed by preserved E/A ratio and E/SR ratio. In knockout animals, complex I, III, and IV activities (∼2- to 6-fold) and fatty acid respiration (∼5-fold) were significantly increased. MiRNA-378a regulated proteins involved in mitochondrial fusion, transcription, and fatty acid metabolism. MiRNA-378a-3p acts as a negative regulator of mitochondrial metabolic and biogenesis pathways. MiRNA-378a knockout animals provide a protective effect against nano-TiO₂ inhalation exposure by altering mitochondrial structure and function. This is the first study to manipulate a miRNA to attenuate the effects of ENM exposure.

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

>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.

Maternal engineered nanomaterial inhalation during gestation alters the fetal transcriptome

Background

The integration of engineered nanomaterials (ENM) is well-established and widespread in clinical, commercial, and domestic applications. Cardiovascular dysfunctions have been reported in adult populations after exposure to a variety of ENM. As the diversity of these exposures continues to increase, the fetal ramifications of maternal exposures have yet to be determined. We, and others, have explored the consequences of ENM inhalation during gestation and identified many cardiovascular and metabolic outcomes in the F1 generation. The purpose of these studies was to identify genetic alterations in the F1 generation of Sprague-Dawley rats that result from maternal ENM inhalation during gestation. Pregnant dams were exposed to nano-titanium dioxide (nano-TiO₂) aerosols (10 ± 0.5 mg/m³) for 7-8 days (calculated, cumulative lung deposition = 217 ± 1 μg) and on GD (gestational day) 20 fetal hearts were isolated. DNA was extracted and immunoprecipitated with modified chromatin marks histone 3 lysine 4 tri-methylation (H3K4me3) and histone 3 lysine 27 tri-methylation (H3K27me3). Following chromatin immunoprecipitation (ChIP), DNA fragments were sequenced. RNA from fetal hearts was purified and prepared for RNA sequencing and transcriptomic analysis. Ingenuity Pathway Analysis (IPA) was then used to identify pathways most modified by gestational ENM exposure.

Results

The results of the sequencing experiments provide initial evidence that significant epigenetic and transcriptomic changes occur in the cardiac tissue of maternal nano-TiO₂ exposed progeny. The most notable alterations in major biologic systems included immune adaptation and organismal growth. Changes in normal physiology were linked with other tissues, including liver and kidneys.

Conclusions

These results are the first evidence that maternal ENM inhalation impacts the fetal epigenome.

Electronic supplementary material

The online version of this article (10.1186/s12989-017-0239-8) contains supplementary material, which is available to authorized users.

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

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 (p < 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 (p < 0.05) and decreased mitochondrial respiratory function (p < 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.

Regulating microRNA expression: at the heart of diabetes mellitus and the mitochondrion

Type 2 diabetes mellitus is a major risk factor for cardiovascular disease and mortality. Uncontrolled type 2 diabetes mellitus results in a systemic milieu of increased circulating glucose and fatty acids. The development of insulin resistance in cardiac tissue decreases cellular glucose import and enhances mitochondrial fatty acid uptake. While triacylglycerol and cytotoxic lipid species begin to accumulate in the cardiomyocyte, the energy substrate utilization ratio of free fatty acids to glucose changes to almost entirely free fatty acids. Accumulating evidence suggests a role of miRNA in mediating this metabolic transition. Energy substrate metabolism, apoptosis, and the production and response to excess reactive oxygen species are regulated by miRNA expression. The current momentum for understanding the dynamics of miRNA expression is limited by a lack of understanding of how miRNA expression is controlled. While miRNAs are important regulators in both normal and pathological states, an additional layer of complexity is added when regulation of miRNA regulators is considered. miRNA expression is known to be regulated through a number of mechanisms, which include, but are not limited to, epigenetics, exosomal transport, processing, and posttranscriptional sequestration. The purpose of this review is to outline how mitochondrial processes are regulated by miRNAs in the diabetic heart. Furthermore, we will highlight the regulatory mechanisms, such as epigenetics, exosomal transport, miRNA processing, and posttranslational sequestration, that participate as regulators of miRNA expression. Additionally, current and future treatment strategies targeting dysfunctional mitochondrial processes in the diseased myocardium, as well as emerging miRNA-based therapies, will be summarized.

Exploring the mitochondrial microRNA import pathway through Polynucleotide Phosphorylase (PNPase)

Cardiovascular disease is the primary cause of mortality for individuals with type 2 diabetes mellitus. During the diabetic condition, cardiovascular dysfunction can be partially attributed to molecular changes in the tissue, including alterations in microRNA (miRNA) interactions. MiRNAs have been reported in the mitochondrion and their presence may influence cellular bioenergetics, creating decrements in functional capacity. In this study, we examined the roles of Argonaute 2 (Ago2), a protein associated with cytosolic and mitochondrial miRNAs, and Polynucleotide Phosphorylase (PNPase), a protein found in the inner membrane space of the mitochondrion, to determine their role in mitochondrial miRNA import. In cardiac tissue from human and mouse models of type 2 diabetes mellitus, Ago2 protein levels were unchanged while PNPase protein expression levels were increased; also, there was an increase in the association between both proteins in the diabetic state. MiRNA-378 was found to be significantly increased in db/db mice, leading to decrements in ATP6 levels and ATP synthase activity, which was also exhibited when overexpressing PNPase in HL-1 cardiomyocytes and in HL-1 cells with stable miRNA-378 overexpression (HL-1-378). To assess potential therapeutic interventions, flow cytometry evaluated the capacity for targeting miRNA-378 species in mitochondria through antimiR treatment, revealing miRNA-378 level-dependent inhibition. Our study establishes PNPase as a contributor to mitochondrial miRNA import through the transport of miRNA-378, which may regulate bioenergetics during type 2 diabetes mellitus. Further, our data provide evidence that manipulation of PNPase levels may enhance the delivery of antimiR therapeutics to mitochondria in physiological and pathological conditions.

Maternal-engineered nanomaterial exposure disrupts progeny cardiac function and bioenergetics

Nanomaterial production is expanding as new industrial and consumer applications are introduced. Nevertheless, the impacts of exposure to these compounds are not fully realized. The present study was designed to determine whether gestational nano-sized titanium dioxide exposure impacts cardiac and metabolic function of developing progeny. Pregnant Sprague-Dawley rats were exposed to nano-aerosols (~10 mg/m³, 130- to 150-nm count median aerodynamic diameter) for 7-8 nonconsecutive days, beginning at gestational day 5-6 Physiological and bioenergetic effects on heart function and cardiomyocytes across three time points, fetal (gestational day 20), neonatal (4-10 days), and young adult (6-12 wk), were evaluated. Functional analysis utilizing echocardiography, speckle-tracking based strain, and cardiomyocyte contractility, coupled with mitochondrial energetics, revealed effects of nano-exposure. Maternal exposed progeny demonstrated a decrease in E- and A-wave velocities, with a 15% higher E-to-A ratio than controls. Myocytes isolated from exposed animals exhibited ~30% decrease in total contractility, departure velocity, and area of contraction. Bioenergetic analysis revealed a significant increase in proton leak across all ages, accompanied by decreases in metabolic function, including basal respiration, maximal respiration, and spare capacity. Finally, electron transport chain complex I and IV activities were negatively impacted in the exposed group, which may be linked to a metabolic shift. Molecular data suggest that an increase in fatty acid metabolism, uncoupling, and cellular stress proteins may be associated with functional deficits of the heart. In conclusion, gestational nano-exposure significantly impairs the functional capabilities of the heart through cardiomyocyte impairment, which is associated with mitochondrial dysfunction.NEW & NOTEWORTHY Cardiac function is evaluated, for the first time, in progeny following maternal nanomaterial inhalation. The findings indicate that exposure to nano-sized titanium dioxide (nano-TiO₂) during gestation negatively impacts cardiac function and mitochondrial respiration and bioenergetics. We conclude that maternal nano-TiO₂ inhalation contributes to adverse cardiovascular health effects, lasting into adulthood.

Role of microRNA in metabolic shift during heart failure

Heart failure (HF) is an end point resulting from a number of disease states. The prognosis for HF patients is poor with survival rates precipitously low. Energy metabolism is centrally linked to the development of HF, and it involves the proteomic remodeling of numerous pathways, many of which are targeted to the mitochondrion. microRNAs (miRNA) are noncoding RNAs that influence posttranscriptional gene regulation. miRNA have garnered considerable attention for their ability to orchestrate changes to the transcriptome, and ultimately the proteome, during HF. Recently, interest in the role played by miRNA in the regulation of energy metabolism at the mitochondrion has emerged. Cardiac proteome remodeling during HF includes axes impacting hypertrophy, oxidative stress, calcium homeostasis, and metabolic fuel transition. Although it is established that the pathological environment of hypoxia and hemodynamic stress significantly contribute to the HF phenotype, it remains unclear as to the mechanistic underpinnings driving proteome remodeling. The aim of this review is to present evidence highlighting the role played by miRNA in these processes as a means for linking pathological stimuli with proteomic alteration. The differential expression of proteins of substrate transport, glycolysis, β-oxidation, ketone metabolism, the citric acid cycle (CAC), and the electron transport chain (ETC) are paralleled by the differential expression of miRNA species that modulate these processes. Identification of miRNAs that translocate to cardiomyocyte mitochondria (miR-181c, miR-378) influencing the expression of the mitochondrial genome-encoded transcripts as well as suggested import modulators are discussed. Current insights, applications, and challenges of miRNA-based therapeutics are also described.