The complex pathophysiology underlying cardiovascular diseases (CVDs) remains poorly characterized. Aberrant gene expression modulation may result from alterations in enzymatic activities and/or non-coding RNA modulations, which result in altering the epigenome, epigenetic /histone modifications, epitranscriptome and then modulating gene expression. In recent years, many human epigenetic mechanisms have been reported to be involved in atherosclerosis, coronary heart disease, ischemic stroke, peripheral artery disease, cardiomyopathy and heart failure. The translational and clinical studies in this front would lead to the development of novel therapeutics for those diseases.

Lewis Katz School of Medicine, Temple University

University of Kentucky

Temple University

Boston University

Sun Yat-sen Memorial Hospital

Stanford University

Frontiers in Cardiovascular Medicine

Cardiovascular Therapeutics

Novel Epigenetic Medicine and Cardiovascular Diseases

Unveiling the secrets of genome’s flexibility does not only foster new research in the field, but also gives rise to the exploration and development of novel epigenetic-based therapies as an approach to alleviate disease phenotypes. A better understanding of chromatin biology (DNA/histone complexes) and non-coding RNAs (ncRNAs) has enabled the development of epigenetic drugs able to modulate transcriptional programs implicated in cardiovascular diseases. This particularly applies to heart failure, where epigenetic networks have shown to underpin several pathological features, such as left ventricular hypertrophy, fibrosis, cardiomyocyte apoptosis and microvascular dysfunction. Targeting epigenetic signals might represent a promising approach, especially in patients with heart failure with preserved ejection fraction (HFpEF), where prognosis remains poor and breakthrough therapies have yet to be approved. In this setting, epigenetics can be employed for the development of customized therapeutic approaches thus paving the way for personalized medicine. Even though the beneficial effects of epi-drugs are gaining attention, the number of epigenetic compounds used in the clinical practice remains low suggesting that more selective epi-drugs are needed. From DNA-methylation changes to non-coding RNAs, we can establish brand-new regulations for drug targets with the aim of restoring healthy epigenomes and transcriptional programs in the failing heart. In the present review, we bring the timeline of epi-drug discovery and development, thus highlighting the emerging role of epigenetic therapies in heart failure.

Unveiling the secrets of genome&#x2019;s flexibility does not only foster new research in the field, but also gives rise to the exploration and development of novel epigenetic-based therapies as an approach to alleviate disease phenotypes. A better understanding of chromatin biology (DNA/histone complexes) and non-coding RNAs (ncRNAs) has enabled the development of epigenetic drugs able to modulate transcriptional programs implicated in cardiovascular diseases. This particularly applies to heart failure, where epigenetic networks have shown to underpin several pathological features, such as left ventricular hypertrophy, fibrosis, cardiomyocyte apoptosis and microvascular dysfunction. Targeting epigenetic signals might represent a promising approach, especially in patients with heart failure with preserved ejection fraction (HFpEF), where prognosis remains poor and breakthrough therapies have yet to be approved. In this setting, epigenetics can be employed for the development of customized therapeutic approaches thus paving the way for personalized medicine. Even though the beneficial effects of epi-drugs are gaining attention, the number of epigenetic compounds used in the clinical practice remains low suggesting that more selective epi-drugs are needed. From DNA-methylation changes to non-coding RNAs, we can establish brand-new regulations for drug targets with the aim of restoring healthy epigenomes and transcriptional programs in the failing heart. In the present review, we bring the timeline of epi-drug discovery and development, thus highlighting the emerging role of epigenetic therapies in heart failure.

Epi-Drugs in Heart Failure

BackgroundStroke is a major global health burden, and risk prediction is essential for the primary prevention of stroke. However, uncertainty remains about the optimal prediction model for analyzing stroke risk. In this study, we aim to determine the most effective stroke prediction method in a Chinese hypertensive population using machine learning and establish a general methodological pipeline for future analysis.</sec>MethodsThe training set included 70% of data (n = 14,491) from the China Stroke Primary Prevention Trial (CSPPT). Internal validation was processed with the rest 30% of CSPPT data (n = 6,211), and external validation was conducted using a nested case–control (NCC) dataset (n = 2,568). The primary outcome was the first stroke. Four received analysis methods were processed and compared: logistic regression (LR), stepwise logistic regression (SLR), extreme gradient boosting (XGBoost), and random forest (RF). Population characteristic data with inclusion and exclusion of laboratory variables were separately analyzed. Accuracy, sensitivity, specificity, kappa, and area under receiver operating characteristic curves (AUCs) were used to make model assessments with AUCs the top concern. Data balancing techniques, including random under-sampling (RUS) and synthetic minority over-sampling technique (SMOTE), were applied to process this unbalanced training set.</sec>ResultsThe best model performance was observed in RUS-applied RF model with laboratory variables. Compared with null models (sensitivity = 0, specificity = 100, and mean AUCs = 0.643), data balancing techniques improved overall performance with RUS, demonstrating a more satisfactory effect in the current study (RUS: sensitivity = 63.9; specificity = 53.7; and mean AUCs = 0.624. Adding laboratory variables improved the performance of analysis methods. All results were reconfirmed in validation sets. The top 10 important variables were determined by the analysis method with the best performance.</sec>ConclusionAmong the tested methods, the most effective stroke prediction model in targeted population is RUS-applied RF. From the insights, the current study revealed, we provided general frameworks for building machine learning-based prediction models.</sec>

Background: Stroke is a major global health burden, and risk prediction is essential for the primary prevention of stroke. However, uncertainty remains about the optimal prediction model for analyzing stroke risk. In this study, we aim to determine the most effective stroke prediction method in a Chinese hypertensive population using machine learning and establish a general methodological pipeline for future analysis.Methods: The training set included 70% of data (n = 14,491) from the China Stroke Primary Prevention Trial (CSPPT). Internal validation was processed with the rest 30% of CSPPT data (n = 6,211), and external validation was conducted using a nested case–control (NCC) dataset (n = 2,568). The primary outcome was the first stroke. Four received analysis methods were processed and compared: logistic regression (LR), stepwise logistic regression (SLR), extreme gradient boosting (XGBoost), and random forest (RF). Population characteristic data with inclusion and exclusion of laboratory variables were separately analyzed. Accuracy, sensitivity, specificity, kappa, and area under receiver operating characteristic curves (AUCs) were used to make model assessments with AUCs the top concern. Data balancing techniques, including random under-sampling (RUS) and synthetic minority over-sampling technique (SMOTE), were applied to process this unbalanced training set.Results: The best model performance was observed in RUS-applied RF model with laboratory variables. Compared with null models (sensitivity = 0, specificity = 100, and mean AUCs = 0.643), data balancing techniques improved overall performance with RUS, demonstrating a more satisfactory effect in the current study (RUS: sensitivity = 63.9; specificity = 53.7; and mean AUCs = 0.624. Adding laboratory variables improved the performance of analysis methods. All results were reconfirmed in validation sets. The top 10 important variables were determined by the analysis method with the best performance.Conclusion: Among the tested methods, the most effective stroke prediction model in targeted population is RUS-applied RF. From the insights, the current study revealed, we provided general frameworks for building machine learning-based prediction models.

Novel Insights on Establishing Machine Learning-Based Stroke Prediction Models Among Hypertensive Adults

BackgroundMaclpil is a proinflammatory long non-coding RNA highly expressed on monocyte-derived macrophages in the ischemic brain. This study investigated the impact and the mechanisms of systemically delivering nanoparticle Maclpil short interfering RNA (siRNA) on experimental ischemic stroke in a mouse model.</sec>MethodsIschemic stroke (focal cerebral ischemia) was induced in male C57BL/6 mice through the middle cerebral artery occlusion. Three hours thereafter, mice were intravenously injected with Maclpil siRNA or scramble siRNA nanoparticles. Bone marrow cell-derived macrophages were transfected with Maclpil or scramble siRNA and subjected to oxygen glucose deprivation culture. The influence of silencing Maclpil on stroke outcomes, neuroinflammation, and macrophage fates was assessed via histology, flow cytometry, Western blotting, and quantitative PCR analysis.</sec>ResultsThree days following stroke induction, siRNA silencing Maclpil substantially reduced ischemic infarction size and improved neurological behaviors. Silencing Maclpil also markedly attenuated the accumulation of monocyte-derived macrophages, CD4+ T cells, and CD8+ T cells in the ischemic hemisphere without affecting microglia cellularity. Reciprocally, myeloid cells and both subsets of T cells were elevated in mouse peripheral blood following Maclpil siRNA treatment. Under oxygen glucose deprivation conditions that mimicked hypoxia and hypoglycemia in vitro, Maclpil siRNA silencing augmented macrophage apoptosis in conjunction with upregulation of proapoptotic Bax and caspase 3 expressions. siRNA knocking down Maclpil skewed macrophages from proinflammatory classical toward anti-inflammatory alternative activation as evidenced by increased arginase 1, Ym1, and Fizz1 and reduced inducible nitric oxide synthase, IL-1β, and TNF-α mRNA levels. Consistent with macrophage phenotype switching, silencing Maclpil by siRNA enhanced fatty acid oxidation as indicated by increased mRNA levels of 3 key metabolic enzymes (ACADM, ACADVL, and HADHA).</sec>ConclusionSystemically silencing Maclpil by siRNA nanoparticles attenuated experimental ischemic stroke by promoting macrophage apoptosis and anti-inflammatory alternative activation. Identifying and targeting Maclpil human homolog(s) may help develop a novel therapy for stroke clinical management.</sec>

Background: Maclpil is a proinflammatory long non-coding RNA highly expressed on monocyte-derived macrophages in the ischemic brain. This study investigated the impact and the mechanisms of systemically delivering nanoparticle Maclpil short interfering RNA (siRNA) on experimental ischemic stroke in a mouse model.Methods: Ischemic stroke (focal cerebral ischemia) was induced in male C57BL/6 mice through the middle cerebral artery occlusion. Three hours thereafter, mice were intravenously injected with Maclpil siRNA or scramble siRNA nanoparticles. Bone marrow cell-derived macrophages were transfected with Maclpil or scramble siRNA and subjected to oxygen glucose deprivation culture. The influence of silencing Maclpil on stroke outcomes, neuroinflammation, and macrophage fates was assessed via histology, flow cytometry, Western blotting, and quantitative PCR analysis.Results: Three days following stroke induction, siRNA silencing Maclpil substantially reduced ischemic infarction size and improved neurological behaviors. Silencing Maclpil also markedly attenuated the accumulation of monocyte-derived macrophages, CD4+ T cells, and CD8+ T cells in the ischemic hemisphere without affecting microglia cellularity. Reciprocally, myeloid cells and both subsets of T cells were elevated in mouse peripheral blood following Maclpil siRNA treatment. Under oxygen glucose deprivation conditions that mimicked hypoxia and hypoglycemia in vitro, Maclpil siRNA silencing augmented macrophage apoptosis in conjunction with upregulation of proapoptotic Bax and caspase 3 expressions. siRNA knocking down Maclpil skewed macrophages from proinflammatory classical toward anti-inflammatory alternative activation as evidenced by increased arginase 1, Ym1, and Fizz1 and reduced inducible nitric oxide synthase, IL-1β, and TNF-α mRNA levels. Consistent with macrophage phenotype switching, silencing Maclpil by siRNA enhanced fatty acid oxidation as indicated by increased mRNA levels of 3 key metabolic enzymes (ACADM, ACADVL, and HADHA).Conclusion: Systemically silencing Maclpil by siRNA nanoparticles attenuated experimental ischemic stroke by promoting macrophage apoptosis and anti-inflammatory alternative activation. Identifying and targeting Maclpil human homolog(s) may help develop a novel therapy for stroke clinical management.

Systemically Silencing Long Non-coding RNAs Maclpil With Short Interfering RNA Nanoparticles Alleviates Experimental Ischemic Stroke by Promoting Macrophage Apoptosis and Anti-inflammatory Activation

Environment, diseases, lack of exercise, and aged tendency of population have becoming crucial factors that induce vascular aging. Vascular aging is unmodifiable risk factor for diseases like diabetes, hypertension, atherosclerosis, and hyperlipidemia. Effective interventions to combat this vascular function decline is becoming increasingly urgent as the rising hospitalization rate caused by vascular aging-related diseases. Fortunately, recent transformative omics approaches have enabled us to examine vascular aging mechanisms at unprecedented levels and precision, which make our understanding of slowing down or reversing vascular aging become possible. Epigenetic viz. DNA methylation, histone modifications, and non-coding RNA-based mechanisms, is a hallmark of vascular aging, its deregulation leads to aberrant transcription changes in tissues. Epigenetics mechanisms by mediating covalent modifications to DNA and histone proteins, consequently, influence the sensitivity and activities of signaling pathways in cells and tissues. A growing body of evidence supports correlations between epigenetic changes and vascular aging. In this article, we will provide a comprehensive overview of epigenetic changes associated with vascular aging based on the recent findings with a focus on molecular mechanisms of action, strategies to reverse epigenetic changes, and future perspectives.

Environment, diseases, lack of exercise, and aged tendency of population have becoming crucial factors that induce vascular aging. Vascular aging is unmodifiable risk factor for diseases like diabetes, hypertension, atherosclerosis, and hyperlipidemia. Effective interventions to combat this vascular function decline is becoming increasingly urgent as the rising hospitalization rate caused by vascular aging-related diseases. Fortunately, recent transformative omics approaches have enabled us to examine vascular aging mechanisms at unprecedented levels and precision, which make our understanding of slowing down or reversing vascular aging become possible. Epigenetic viz. DNA methylation, histone modifications, and non-coding RNA-based mechanisms, is a hallmark of vascular aging, its deregulation leads to aberrant transcription changes in tissues. Epigenetics mechanisms by mediating covalent modifications to DNA and histone proteins, consequently, influence the sensitivity and activities of signaling pathways in cells and tissues. A growing body of evidence supports correlations between epigenetic changes and vascular aging. In this article, we will provide a comprehensive overview of epigenetic changes associated with vascular aging based on the recent findings with a focus on molecular mechanisms of action, strategies to reverse epigenetic changes, and future perspectives.