Clinical epigenetics and restoring of metabolic health in severely obese patients undergoing batriatric and metabolic surgery

Epigenetic-sensitive mechanisms, mainly DNA methylation, mirror the relationship between environmental and genetic risk factors able to affect the sensitiveness to development of obesity and its comorbidities. Bariatric and metabolic surgery may reduce obesity-related cardiovascular risk through tissue-specific DNA methylation changes. Among the most robust results, differential promoter methylation of ACACA, CETP, CTGF, S100A8, and S100A9 genes correlated significantly with the levels of mRNA before and after gastric bypass surgery (RYGB) in obese women. Additionally, promoter hypermethylation of NFKB1 gene was significantly associated with reduced blood pressure in obese patients after RYGB suggesting useful non-invasive biomarkers. Of note, sperm-related DNA methylation signatures of genes regulating the central control of appetite, such as MC4R, BDNF, NPY, and CR1, and other genes including FTO, CHST8, and SH2B1 were different in obese patients as compared to non-obese subjects and patients who lost weight after RYGB surgery. Importantly, transgenerational studies provided relevant evidence of the potential effect of bariatric and metabolic surgery on DNA methylation. For example, peripheral blood biospecimens isolated from siblings born from obese mothers before bariatric surgery showed different methylation signatures in the insulin receptor and leptin signaling axis as compared to siblings born from post-obese mothers who underwent surgery. This evidence suggests that bariatric and metabolic surgery of mothers may affect the epigenetic profiles of the offspring with potential implication for primary prevention of severe obesity. We update on tissue-specific epigenetic signatures as potential mechanisms underlying the restoration of metabolic health after surgery suggesting useful predictive biomarkers

Autoptic findings of sudden cardiac death (SCD) in patients with arrhythmogenic ventricular cardiomiopathy (AVC) from left ventricle and biventricular involvement.

Objectives: To evaluate autoptic histopathological findings of arrhythmogenic ventricular cardiomyopathy (AVC) as major cause of sudden cardiac death (SCD) in young adults. Background: According to Heart Rhythm Society (HRS)'s international consensus, histological criteria for AVC diagnosis include a progressive myocardial atrophy of the right ventricle characterized by a transmural fatty or fibrofatty replacement in a segmental or diffuse pattern (residual myocytes <60 % vs 60–75 % by morphometric analysis) explaining the electrical instability with increased risk of SCD. However, there is increasing evidence for atypical patterns of localizations and percentage of fibrofatty replacement suggesting the need to update histopathological features of AVC. Methods: Histology examination of ventricles, atria, and septum was performed on 10 autopsy of SCD due to AVC. Staining with hematoxylin-eosin and PicroSirius Red/Fast Green were performed on the heart samples to identify specific fibrofatty patterns. Results: Our analysis showed that: 1) myocardial replacement by a diffuse segmental fatty or fibro-fatty tissue characterized right and left ventricles as well as atrial walls; 2) the degree of fibrofatty tissue replacement was less than 40 % both in left ventricle (n = 4, 40 %) and biventricular (n = 6, 60 %) localization; 3) perivascular fibrosis, inflammatory infiltrate, areas of hypertrophy and/or areas of coagulative necrosis as signs of hypoxic damage in the first stage. Conclusions: We confirmed prior evidence for fibrofatty replacement both in biventricular and septal localizations. Importantly, we observed a less degree (<40 %) of fibrofatty replacement as compared to current guidelines. This supports the need to further explore the histological patterns of fibrofatty infiltration in a larger study population to improve the histological diagnostic criteria of AVC

Association Between Circulating CD4+ T Cell Methylation Signatures of Network-Oriented SOCS3 Gene and Hemodynamics in Patients Suffering Pulmonary Arterial Hypertension

Pathogenic DNA methylation changes may be involved in pulmonary arterial hypertension (PAH) onset and its progression, but there is no data on potential associations with patient-derived hemodynamic parameters. The reduced representation bisulfite sequencing (RRBS) platform identified N= 631 differentially methylated CpG sites which annotated to N= 408 genes (DMGs) in circulating CD4(+) T cells isolated from PAH patients vs. healthy controls (CTRLs). A promoter-restricted network analysis established the PAH subnetwork that included 5 hub DMGs (SOCS3, GNAS, ITGAL, NCOR2, NFIC) and 5 non-hub DMGs (NR4A2, GRM2, PGK1, STMN1, LIMS2). The functional analysis revealed that the SOCS3 gene was the most recurrent among the top ten significant pathways enriching the PAH subnetwork, including the growth hormone receptor and the interleukin-6 signaling. Correlation analysis showed that the promoter methylation levels of each network-oriented DMG were associated individually with hemodynamic parameters. In particular, SOCS3 hypomethylation was negatively associated with right atrial pressure (RAP) and positively associated with cardiac index (CI) (vertical bar r vertical bar &gt;= 0.6). A significant upregulation of the SOCS3, ITGAL, NFIC, NCOR2, and PGK1 mRNA levels (qRT-PCR) in peripheral blood mononuclear cells from PAH patients vs. CTRLs was found (P &lt;= 0.05). By immunoblotting, a significant upregulation of the SOCS3 protein was confirmed in PAH patients vs. CTRLs (P &lt; 0.01). This is the first network-oriented study which integrates circulating CD4(+) T cell DNA methylation signatures, hemodynamic parameters, and validation experiments in PAH patients at first diagnosis or early follow-up. Our data suggests that SOCS3 gene might be involved in PAH pathogenesis and serve as potential prognostic biomarker

Clinical epigenetics settings for cancer and cardiovascular diseases: real-life applications of network medicine at the bedside

Despite impressive efforts invested in epigenetic research in the last 50 years, clinical applications are still lacking. Only a few university hospital centers currently use epigenetic biomarkers at the bedside. Moreover, the overall concept of precision medicine is not widely recognized in routine medical practice and the reductionist approach remains predominant in treating patients affected by major diseases such as cancer and cardiovascular diseases. By its' very nature, epigenetics is integrative of genetic networks. The study of epigenetic biomarkers has led to the identification of numerous drugs with an increasingly significant role in clinical therapy especially of cancer patients. Here, we provide an overview of clinical epigenetics within the context of network analysis. We illustrate achievements to date and discuss how we can move from traditional medicine into the era of network medicine (NM), where pathway-informed molecular diagnostics will allow treatment selection following the paradigm of precision medicine

Precision Medicine in Patients with Differential Diabetic Phenotypes: Novel Opportunities from Network Medicine

Introduction: Diabetes mellitus (DM) comprises differential clinical phenotypes ranging from rare monogenic to common polygenic forms, such as type 1 (T1DM), type 2 (T2DM), and gestational diabetes, which are associated with cardiovascular complications. Also, the high-risk prediabetic state is rising worldwide, suggesting the urgent need for early personalized strategies to prevent and treat a hyperglycemic state.Objective: We aim to discuss the advantages and challenges of Network Medicine approaches in clarifying disease-specific molecular pathways, which may open novel ways for repurposing approved drugs to reach diabetes precision medicine and personalized therapy.Conclusion: The interactome or protein-protein interactions (PPIs) is a useful tool to identify subtle molecular differences between precise diabetic phenotypes and predict putative novel drugs. Despite being previously unappreciated as T2DM determinants, the growth factor receptor-bound protein 14 (GRB14), calmodulin 2 (CALM2), and protein kinase C-alpha (PRKCA) might have a relevant role in disease pathogenesis. Besides, in silico platforms have suggested that diflunisal, nabumetone, niflumic acid, and valdecoxib may be suitable for the treatment of T1DM; phenoxybenzamine and idazoxan for the treatment of T2DM by improving insulin secretion; and hydroxychloroquine reduce the risk of coronary heart disease (CHD) by counteracting inflammation. Network medicine has the potential to improve precision medicine in diabetes care and enhance personalized therapy. However, only randomized clinical trials will confirm the clinical utility of network-oriented biomarkers and drugs in the management of DM

Cardiovascular risk factors and molecular routes underlying endothelial dysfunction: Novel opportunities for primary prevention

One of the major challenges of cardiovascular primary prevention approach is the absence of early biomarkers of endothelial dysfunction which may be useful for identifying at-risk subjects. Endothelial dysfunction is a systemic disorder in which traditional cardiovascular risk factors, such as aging, gender, hypertension, smoking, hyperglycemia, and dyslipidemia, as well as emerging risk determinants, such as fetal factors, gut microbiome alteration, clonal hematopoiesis, air pollution, and sleep disorders act synergistically to tip the endothelial balance in favor of vasoconstrictive, pro-inflammatory, and pro-thrombotic phenotypes. Endothelial dysfunction can start already in fetal life and may be regained once detrimental stimuli are removed. The hallmark of endothelial dysfunction is a marked reduction of nitric oxide (NO) bioavailability owing to epigenetic-sensitive dysregulation of the endothelial nitric oxide synthase (eNOS) gene and upregulation of reactive oxygen species (ROS) in endothelial cells (ECs). Advance in liquid-based assays and molecular biology tools are providing novel potential EC-specific biomarkers for prediction and diagnosis of endothelial dysfunction. Significant associations between clinically useful indexes of endothelial dysfunction, mainly brachial artery flow-mediated dilation (FMD), and increased number of endothelial microparticles (EMPs), increased levels of endoglin and endocan, as well as reduced levels of irisin were observed in subjects with one or more traditional risk factors. However, none entered in clinical practice yet. Smoking cessation, weight loss, physical exercise, and diet control are the milestones of cardiovascular primary prevention, and they may restore endothelial function via epigenetic-sensitive pathways able to reduce inflammation and oxidative stress and increase NO production . We briefly summarize well-known and novel molecular routes driving early endothelial dysfunction mainly in human ECs and related potential biomarkers which may add predictive or diagnostic value to the traditional non-invasive techniques. Also, we focus on clinical trials investigating lifestyle modifications and their impact on molecular routes involved in restoring endothelial function

"Transplantomics" for predicting allograft rejection: real-life applications and new strategies from Network Medicine

Although decades of the reductionist approach achieved great milestones in optimizing the immunosuppres-sion therapy, traditional clinical parameters still fail in predicting both acute and chronic (mainly) rejection events leading to higher rates across all solid organ transplants. To clarify the underlying immune-related cel-lular and molecular mechanisms, current biomedical research is increasingly focusing on "transplantomics" which relies on a huge quantity of big data deriving from genomics, transcriptomics, epigenomics, proteomics, and metabolomics platforms. The AlloMap (gene expression) and the AlloSure (donor-derived cell-free DNA) tests represent two successful examples of how omics and liquid biopsy can really improve the precision med-icine of heart and kidney transplantation. One of the major challenges in translating big data in clinically useful biomarkers is the integration and interpretation of the different layers of omics datasets. Network Medicine offers advanced bioinformatic-molecular strategies which were widely used to integrate large omics datasets and clinical information in end-stage patients to prioritize potential biomarkers and drug targets. The applica-tion of network-oriented approaches to clarify the complex nature of graft rejection is still in its infancy. Here, we briefly discuss the real-life clinical applications derived from omics datasets as well as novel opportunities for establishing predictive tests in solid organ transplantation. Also, we provide an original "graft rejection interactome" and propose network-oriented strategies which can be useful to improve precision medicine of solid organ transplantation

Pursuing functional biomarkers in complex disease: Focus on pulmonary arterial hypertension

A major gap in diagnosis, classification, risk stratification, and prediction of therapeutic response exists in pulmonary arterial hypertension (PAH), driven in part by a lack of functional biomarkers that are also disease-specific. In this regard, leveraging big data-omics analyses using innovative approaches that integrate network medicine and machine learning correlated with clinically useful indices or risk stratification scores is an approach well-positioned to advance PAH precision medicine. For example, machine learning applied to a panel of 48 cytokines, chemokines, and growth factors could prognosticate PAH patients with immune-dominant subphenotypes at elevated or low-risk for mortality. Here, we discuss strengths and weaknesses of the most current studies evaluating omics-derived biomarkers in PAH. Progress in this field is offset by studies with small sample size, pervasive limitations in bioinformatics, and lack of standardized methods for data processing and interpretation. Future success in this field, in turn, is likely to hinge on mechanistic validation of data outputs in order to couple functional biomarker data with target-specific therapeutics in clinical practice