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

The function and therapeutic potential of transfer RNA-derived small RNAs in cardiovascular diseases: A review

Transfer RNA-derived small RNAs (tsRNAs) are a class of small non-coding RNA (sncRNA) molecules derived from tRNA, including tRNA derived fragments (tRFs) and tRNA halfs (tiRNAs). tsRNAs can affect cell functions by participating in gene expression regulation, translation regulation, intercellular signal transduction, and immune response. They have been shown to play an important role in various human diseases, including cardiovascular diseases (CVDs). Targeted regulation of tsRNAs expression can affect the progression of CVDs. The tsRNAs induced by pathological conditions can be detected when released into the extracellular, giving them enormous potential as disease biomarkers. Here, we review the biogenesis, degradation process and related functional mechanisms of tsRNAs, and discuss the research progress and application prospects of tsRNAs in different CVDs, to provide a new perspective on the treatment of CVDs.

Epidrugs in the clinical management of atherosclerosis: Mechanisms, challenges and promises

Atherosclerosis is a complex and multigenic pathology associated with significant epigenetic reprogramming. Traditional factors (age, sex, obesity, hyperglycaemia, dyslipidaemia, hypertension) and non-traditional factors (foetal indices, microbiome alteration, clonal hematopoiesis, air pollution, sleep disorders) induce endothelial dysfunction, resulting in reduced vascular tone and increased vascular permeability, inflammation and shear stress. These factors induce paracrine and autocrine interactions between several cell types, including vascular smooth muscle cells, endothelial cells, monocytes/macrophages, dendritic cells and T cells. Such cellular interactions lead to tissue-specific epigenetic reprogramming regulated by DNA methylation, histone modifications and microRNAs, which manifests in atherosclerosis. Our review outlines epigenetic signatures during atherosclerosis, which are viewed as potential clinical biomarkers that may be adopted as new therapeutic targets. Additionally, we emphasize epigenetic modifiers referred to as 'epidrugs' as potential therapeutic molecules to correct gene expression patterns and restore vascular homeostasis during atherosclerosis. Further, we suggest nanomedicine-based strategies involving the use of epidrugs, which may selectively target cells in the atherosclerotic microenvironment and reduce off-target effects.

Network-based artificial intelligence approaches for advancing personalized psychiatry

Psychiatric disorders have a complex biological underpinning likely involving an interplay of genetic and environmental risk contributions. Substantial efforts are being made to use artificial intelligence approaches to integrate features within and across data types to increase our etiological understanding and advance personalized psychiatry. Network science offers a conceptual framework for exploring the often complex relationships across different levels of biological organization, from cellular mechanistic to brain-functional and phenotypic networks. Utilizing such network information effectively as part of artificial intelligence approaches is a promising route toward a more in-depth understanding of illness biology, the deciphering of patient heterogeneity, and the identification of signatures that may be sufficiently predictive to be clinically useful. Here, we present examples of how network information has been used as part of artificial intelligence within psychiatry and beyond and outline future perspectives on how personalized psychiatry approaches may profit from a closer integration of psychiatric research, artificial intelligence development, and network science.

Transgenerational Epigenetic Inheritance of Cardiovascular Diseases: A Network Medicine Perspective

INTRODUCTION

The ability to identify early epigenetic signatures underlying the inheritance of cardiovascular risk, including trans- and intergenerational effects, may help to stratify people before cardiac symptoms occur.

METHODS

Prospective and retrospective cohorts and case-control studies focusing on DNA methylation and maternal/paternal effects were searched in Pubmed from 1997 to 2023 by using the following keywords: DNA methylation, genomic imprinting, and network analysis in combination with transgenerational/intergenerational effects.

RESULTS

Maternal and paternal exposures to traditional cardiovascular risk factors during critical temporal windows, including the preconceptional period or early pregnancy, may perturb the plasticity of the epigenome (mainly DNA methylation) of the developing fetus especially at imprinted loci, such as the insulin-like growth factor type 2 (IGF2) gene. Thus, the epigenome is akin to a "molecular archive" able to memorize parental environmental insults and predispose an individual to cardiovascular diseases onset in later life. Direct evidence for human transgenerational epigenetic inheritance (at least three generations) of cardiovascular risk is lacking but it is supported by epidemiological studies. Several blood-based association studies showed potential intergenerational epigenetic effects (single-generation studies) which may mediate the transmittance of cardiovascular risk from parents to offspring.

DISCUSSION

In this narrative review, we discuss some relevant examples of trans- and intergenerational epigenetic associations with cardiovascular risk. In our perspective, we propose three network-oriented approaches which may help to clarify the unsolved issues regarding transgenerational epigenetic inheritance of cardiovascular risk and provide potential early biomarkers for primary prevention.

Bioinformatics analysis of the microRNA genes associated with type 2 cardiorenal syndrome

BACKGROUND

MicroRNAs (miRNAs) are important regulatory factors in the normal developmental stages of the heart and kidney. However, it is currently unclear how miRNA is expressed in type 2 cardiorenal syndrome (CRS). This study aimed to detect the differential expression of miRNAs and to clarify the main enrichment pathways of differentially expressed miRNA target genes in type 2 CRS.

METHODS

Five cases of healthy control (Group 1), eight of chronic heart failure (CHF, Group 2) and seven of type 2 CRS (Group 3) were enrolled, respectively. Total RNA was extracted from the peripheral blood of each group. To predict the miRNA target genes and biological signalling pathways closely related to type 2 CRS, the Agilent miRNA microarray platform was used for miRNA profiling and bioinformatics analysis of the isolated total RNA samples.

RESULTS

After the microarray analysis was done to screen for differentially expressed circulating miRNAs among the three different groups of samples, the target genes and bioinformatic pathways of the differential miRNAs were predicted. A total of 38 differential miRNAs (15 up- and 23 down-regulated) were found in Group 3 compared with Group 1, and a total of 42 differential miRNAs (11 up- and 31 down-regulated) were found in Group 3 compared to Group 2. According to the Gene Ontology (GO) function and Kyoto Encyclopaedia of Genes and Genomes (KEGG) pathway analysis, the top 10 lists of molecular functions, cellular composition and biological processes, and the top 30 signalling pathways of predicted gene targets of the differentially expressed miRNAs were discriminated among the three groups.

CONCLUSION

Between the patients with CHF and type 2 CRS, miRNAs were differentially expressed. Prediction of target genes of differentially expressed miRNAs and the use of GO function and KEGG pathway analysis may reveal the molecular mechanisms of CRS. Circulating miRNAs may contribute to the diagnosis of CRS, and further and larger studies are needed to enhance the robustness of our findings.

Epigenome editing for targeted DNA (de)methylation: a new perspective in modulating gene expression

Traditionally, it has been believed that inheritance is driven as phenotypic variations resulting from changes in DNA sequence. However, this paradigm has been challenged and redefined in the contemporary era of epigenetics. The changes in DNA methylation, histone modification, non-coding RNA biogenesis, and chromatin remodeling play crucial roles in genomic functions and regulation of gene expression. More importantly, some of these changes are inherited to the next generations as a part of epigenetic memory and play significant roles in gene expression. The sum total of all changes in DNA bases, histone proteins, and ncRNA biogenesis constitutes the epigenome. Continuous progress in deciphering epigenetic regulations and the existence of heritable epigenetic/epiallelic variations associated with trait of interest enables to deploy epigenome editing tools to modulate gene expression. DNA methylation marks can be utilized in epigenome editing for the manipulation of gene expression. Initially, genome/epigenome editing technologies relied on zinc-finger protein or transcriptional activator-like effector protein. However, the discovery of clustered regulatory interspaced short palindromic repeats CRISPR)/deadCRISPR-associated protein 9 (dCas9) enabled epigenome editing to be more specific/efficient for targeted DNA (de)methylation. One of the major concerns has been the off-target effects, wherein epigenome editing may unintentionally modify gene/regulatory element which may cause unintended change/harmful effects. Moreover, epigenome editing of germline cell raises several ethical/safety issues. This review focuses on the recent developments in epigenome editing tools/techniques, technological limitations, and future perspectives of this emerging technology in therapeutics for human diseases as well as plant improvement to achieve sustainable developmental goals.

Traditional Therapeutics and Potential Epidrugs for CVD: Why Not Both?

Cardiovascular disease (CVD) is the leading cause of death worldwide. In addition to the high mortality rate, people suffering from CVD often endure difficulties with physical activities and productivity that significantly affect their quality of life. The high prevalence of debilitating risk factors such as obesity, type 2 diabetes mellitus, smoking, hypertension, and hyperlipidemia only predicts a bleak future. Current traditional CVD interventions offer temporary respite; however, they compound the severe economic strain of health-related expenditures. Furthermore, these therapeutics can be prescribed indefinitely. Recent advances in the field of epigenetics have generated new treatment options by confronting CVD at an epigenetic level. This involves modulating gene expression by altering the organization of our genome rather than altering the DNA sequence itself. Epigenetic changes are heritable, reversible, and influenced by environmental factors such as medications. As CVD is physiologically and pathologically diverse in nature, epigenetic interventions can offer a ray of hope to replace or be combined with traditional therapeutics to provide the prospect of addressing more than just the symptoms of CVD. This review discusses various risk factors contributing to CVD, perspectives of current traditional medications in practice, and a focus on potential epigenetic therapeutics to be used as alternatives.

Bioinformatic platforms for clinical stratification of natural history of atherosclerotic cardiovascular diseases

Although bioinformatic methods gained a lot of attention in the latest years, their use in real-world studies for primary and secondary prevention of atherosclerotic cardiovascular diseases (ASCVD) is still lacking. Bioinformatic resources have been applied to thousands of individuals from the Framingham Heart Study as well as health care-associated biobanks such as the UK Biobank, the Million Veteran Program, and the CARDIoGRAMplusC4D Consortium and randomized controlled trials (i.e. ODYSSEY, FOURIER, ASPREE, and PREDIMED). These studies contributed to the development of polygenic risk scores (PRS), which emerged as novel potent genetic-oriented tools, able to calculate the individual risk of ASCVD and to predict the individual response to therapies such as statins and proprotein convertase subtilisin/kexin type 9 inhibitor. ASCVD are the first cause of death around the world including coronary heart disease (CHD), peripheral artery disease, and stroke. To achieve the goal of precision medicine and personalized therapy, advanced bioinformatic platforms are set to link clinically useful indices to heterogeneous molecular data, mainly epigenomics, transcriptomics, metabolomics, and proteomics. The DIANA study found that differential methylation of ABCA1, TCF7, PDGFA, and PRKCZ significantly discriminated patients with acute coronary syndrome from healthy subjects and their expression levels positively associated with CK-MB serum concentrations. The ARIC Study revealed several plasma proteins, acting or not in lipid metabolism, with a potential role in determining the different pleiotropic effects of statins in each subject. The implementation of molecular high-throughput studies and bioinformatic techniques into traditional cardiovascular risk prediction scores is emerging as a more accurate practice to stratify patients earlier in life and to favour timely and tailored risk reduction strategies. Of note, radiogenomics aims to combine imaging features extracted for instance by coronary computed tomography angiography and molecular biomarkers to create CHD diagnostic algorithms useful to characterize atherosclerotic lesions and myocardial abnormalities. The current view is that such platforms could be of clinical value for prevention, risk stratification, and treatment of ASCVD.

Integration of RNA-Seq and Machine Learning Identifies Hub Genes for Empagliflozin Benefitable Heart Failure with Reduced Ejection Fraction

Purpose

This study aimed to analyze the hub genes of heart failure with reduced ejection fraction (HFrEF) treated with Empagliflozin using RNA sequencing (RNA-seq) and bioinformatics methods, including machine learning.

Methods

From February 2021 to February 2023, nine patients with HFrEF were enrolled from our hospital's cardiovascular department. In addition to routine drug treatment, these patients received 10 mg of Empagliflozin once daily for two months. Efficacy was assessed and RNA-seq was performed on peripheral blood before and after treatment with empagliflozin. HFrEF-related hub genes were identified through bioinformatics analyses including differential gene expression analysis, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses, immune infiltration analysis, machine learning, immune cell correlation analysis and clinical indicator correlation analysis.

Results

The nine patients included in this study completed a two-month treatment regimen, with an average age of 62.11 ± 6.36 years. By performing bioinformatics analysis on the transcriptome from the treatment groups, 42 differentially expressed genes were identified, with six being up-regulated and 36 being down-regulated (|log2FC|>1 and adj.pvalue<0.05). Immune infiltration analysis of these genes demonstrated a significant difference in the proportion of plasma between the pre-treatment and post-treatment groups (p<0.05). Two hub genes, GTF2IP14 and MTLN, were finally identified through machine learning. Further analysis of the correlation between the hub genes and immune cells suggested a negative correlation between GTF2IP14 and naive B cells, and a positive correlation between MTLN and regulatory T cells and resting memory CD4+ T cells (p<0.05).

Conclusion

Through RNA-seq and bioinformatics analysis, this study identified GTF2IP14 and MTLN as the hub genes of HFrEF, and their mechanisms may be related to immune inflammatory responses and various immune cells.

An evidence-based debate on epigenetics and immunosenescence in COVID-19

Immunosenescence contributes to the decline of immune function leading to a reduced ability to respond to severe coronavirus disease 2019 (COVID-19) in elderly patients. Clinical course of COVID-19 is widely heterogeneous and guided by the possible interplay between genetic background and epigenetic-sensitive mechanisms underlying the immunosenescence which could explain, at least in part, the higher percentage of disease severity in elderly individuals. The most convincing evidence regards the hypomethylation of the angiotensin-converting enzyme 2 (ACE2) promoter gene in lungs as well as the citrullination of histone H3 in neutrophils which have been associated with worsening of COVID-19 outcome in elderly patients. In contrast, centenarians who have showed milder symptoms have been associated to a younger "epigenetic age" based on DNA methylation profiles at specific genomic sites (epigenetic clock). Some large prospective studies showed that the acceleration of epigenetic aging as well as the shortening of telomeres were significantly associated with lymphopenia and poor outcome suggesting prognostic biomarkers in elderly COVID-19 patients. Furthermore, randomized clinical trials showed that statins, L-arginine, and resveratrol could mediate anti-inflammatory effects via indirect epigenetic interference and might improve COVID-19 outcome. Here, we discuss the epigenetic-sensitive events which might contribute to increase the risk of severity and mortality in older subjects and possible targeted therapies to counteract immunosenescence.

Artificial intelligence, big data and heart transplantation: Actualities

BACKGROUND

As diagnostic and prognostic models developed by traditional statistics perform poorly in real-world, artificial intelligence (AI) and Big Data (BD) may improve the supply chain of heart transplantation (HTx), allocation opportunities, correct treatments, and finally optimize HTx outcome. We explored available studies, and discussed opportunities and limits of medical application of AI to the field of HTx.

METHOD

A systematic overview of studies published up to December 31st, 2022, in English on peer-revied journals, have been identified through PUBMED-MEDLINE-WEB of Science, referring to HTx, AI, BD. Studies were grouped in 4 domains based on main studies' objectives and results: etiology, diagnosis, prognosis, treatment. A systematic attempt was made to evaluate studies by the Prediction model Risk Of Bias ASsessment Tool (PROBAST) and the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD).

RESULTS

Among the 27 publications selected, none used AI applied to BD. Of the selected studies, 4 fell in the domain of etiology, 6 in the domain of diagnosis, 3 in the domain of treatment, and 17 in that of prognosis, as AI was most frequently used for algorithmic prediction and discrimination of survival, but in retrospective cohorts and registries. AI-based algorithms appeared superior to probabilistic functions to predict patterns, but external validation was rarely employed. Indeed, based on PROBAST, selected studies showed, to some extent, significant risk of bias (especially in the domain of predictors and analysis). In addition, as example of applicability in the real-world, a free-use prediction algorithm developed through AI failed to predict 1-year mortality post-HTx in cases from our center.

CONCLUSIONS

While AI-based prognostic and diagnostic functions performed better than those developed by traditional statistics, risk of bias, lack of external validation, and relatively poor applicability, may affect AI-based tools. More unbiased research with high quality BD meant for AI, transparency and external validations, are needed to have medical AI as a systematic aid to clinical decision making in HTx.

Basic Pathogenic Mechanisms and Epigenetic Players Promoted by Extracellular Vesicles in Vascular Damage

Both progression from the early pathogenic events to clinically manifest cardiovascular diseases (CVD) and cancer impact the integrity of the vascular system. Pathological vascular modifications are affected by interplay between endothelial cells and their microenvironment. Soluble factors, extracellular matrix molecules and extracellular vesicles (EVs) are emerging determinants of this network that trigger specific signals in target cells. EVs have gained attention as package of molecules with epigenetic reversible activity causing functional vascular changes, but their mechanisms are not well understood. Valuable insights have been provided by recent clinical studies, including the investigation of EVs as potential biomarkers of these diseases. In this paper, we review the role and the mechanism of exosomal epigenetic molecules during the vascular remodeling in coronary heart disease as well as in cancer-associated neoangiogenesis.

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.

Multiomics Network Medicine Approaches to Precision Medicine and Therapeutics in Cardiovascular Diseases

Cardiovascular diseases (CVD) are the leading cause of death worldwide and display complex phenotypic heterogeneity caused by many convergent processes, including interactions between genetic variation and environmental factors. Despite the identification of a large number of associated genes and genetic loci, the precise mechanisms by which these genes systematically influence the phenotypic heterogeneity of CVD are not well understood. In addition to DNA sequence, understanding the molecular mechanisms of CVD requires data from other omics levels, including the epigenome, the transcriptome, the proteome, as well as the metabolome. Recent advances in multiomics technologies have opened new precision medicine opportunities beyond genomics that can guide precise diagnosis and personalized treatment. At the same time, network medicine has emerged as an interdisciplinary field that integrates systems biology and network science to focus on the interactions among biological components in health and disease, providing an unbiased framework through which to integrate systematically these multiomics data. In this review, we briefly present such multiomics technologies, including bulk omics and single-cell omics technologies, and discuss how they can contribute to precision medicine. We then highlight network medicine-based integration of multiomics data for precision medicine and therapeutics in CVD. We also include a discussion of current challenges, potential limitations, and future directions in the study of CVD using multiomics network medicine approaches.

"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 immunosuppression 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 cellular 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 medicine 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 application 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.

Targeted genetic analysis unveils novel associations between ACE I/D and APO T158C polymorphisms with D-dimer levels in severe COVID-19 patients with pulmonary embolism

Only a percentage of COVID-19 patients develop thrombotic complications. We hypothesized that genetic profiles may explain part of the inter-individual differences. Our goal was to evaluate the genotypic distribution of targeted DNA polymorphisms in COVID-19 patients complicated (PE+) or not (PE-) by pulmonary embolism. We designed a retrospective observational study enrolling N = 94 consecutive patients suffering severe COVID-19 with pulmonary embolism (PE+, N = 47) or not (PE-, N = 47) during hospitalization. A panel of N = 13 prothrombotic DNA polymorphisms (FV R506Q and H1299R, FII G20210A, MTHFR C677T and A1298C, CBS 844ins68, PAI-1 4G/5G, GPIIIa HPA-1 a/b, ACE I/D, AGT T9543C, ATR-1 A1166C, FGB - 455G > A, FXIII103G > T) and N = 2 lipid metabolism-related DNA polymorphisms (APOE T 112C and T158C) were investigated using Reverse Dot Blot technique. Then, we investigated possible associations between genotypic subclasses and demographic, clinical, and laboratory parameters including age, obesity, smoking, pro-inflammatory cytokines, drug therapy, and biomarkers of thrombotic risk such as D-dimer (DD). We found that 58.7% of PE+ had homozygous mutant D/D genotype at ACE I/D locus vs. PE- (40.4%) and 87% of PE+ had homozygous mutant C/C genotype at APOE T158C locus vs. PE- (68.1%). In PE+ group, DD levels were significantly higher in D/D and I/D genotypes at ACE I/D locus (P = 0.00066 and P = 0.00023, respectively) and in C/C and T/C genotypes at APOE T158C locus (P = 1.6e-06 and P = 0.0012, respectively) than PE- group. For the first time, we showed significant associations between higher DD levels and ACE I/D and APOE T158C polymorphisms in PE+ vs. PE- patients suggesting potential useful biomarkers of poor clinical outcome.

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) (|r|≥ 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 ≤ 0.05). By immunoblotting, a significant upregulation of the SOCS3 protein was confirmed in PAH patients vs. CTRLs (P < 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.

The roles of histone modifications in tumorigenesis and associated inhibitors in cancer therapy

Histone modifications are key factors in chromatin packaging, and are responsible for gene regulation during cell fate determination and development. Abnormal alterations in histone modifications potentially affect the stability of the genome and disrupt gene expression patterns, leading to many diseases, including cancer. In recent years, mounting evidence has shown that various histone modifications altered by aberrantly expressed modifier enzymes contribute to tumor development and metastasis through the induction of epigenetic, transcriptional, and phenotypic changes. In this review, we will discuss the existing histone modifications, both well-studied and rare ones, and their roles in solid tumors and hematopoietic cancers, to identify the molecular pathways involved and investigate targeted therapeutic drugs to reorganize the chromatin and enhance cancer treatment efficiency. Finally, clinical inhibitors of histone modifications are summarized to better understand the developmental stage of cancer therapy in using these drugs to inhibit the histone modification enzymes.

DIAMIN: a software library for the distributed analysis of large-scale molecular interaction networks

Background

Huge amounts of molecular interaction data are continuously produced and stored in public databases. Although many bioinformatics tools have been proposed in the literature for their analysis, based on their modeling through different types of biological networks, several problems still remain unsolved when the problem turns on a large scale.

Results

We propose , that is, a high-level software library to facilitate the development of applications for the efficient analysis of large-scale molecular interaction networks. relies on distributed computing, and it is implemented in Java upon the framework Apache Spark. It delivers a set of functionalities implementing different tasks on an abstract representation of very large graphs, providing a built-in support for methods and algorithms commonly used to analyze these networks. has been tested on data retrieved from two of the most used molecular interactions databases, resulting to be highly efficient and scalable. As shown by different provided examples, can be exploited by users without any distributed programming experience, in order to perform various types of data analysis, and to implement new algorithms based on its primitives.

Conclusions

The proposed has been proved to be successful in allowing users to solve specific biological problems that can be modeled relying on biological networks, by using its functionalities. The software is freely available and this will hopefully allow its rapid diffusion through the scientific community, to solve both specific data analysis and more complex tasks.

Big Data in cardiac surgery: real world and perspectives

Big Data, and the derived analysis techniques, such as artificial intelligence and machine learning, have been considered a revolution in the modern practice of medicine. Big Data comes from multiple sources, encompassing electronic health records, clinical studies, imaging data, registries, administrative databases, patient-reported outcomes and OMICS profiles. The main objective of such analyses is to unveil hidden associations and patterns. In cardiac surgery, the main targets for the use of Big Data are the construction of predictive models to recognize patterns or associations better representing the individual risk or prognosis compared to classical surgical risk scores. The results of these studies contributed to kindle the interest for personalized medicine and contributed to recognize the limitations of randomized controlled trials in representing the real world. However, the main sources of evidence for guidelines and recommendations remain RCTs and meta-analysis. The extent of the revolution of Big Data and new analytical models in cardiac surgery is yet to be determined.

Increased Risk of Hereditary Prostate Cancer in Italian Families with Hereditary Breast and Ovarian Cancer Syndrome Harboring Mutations in BRCA and in Other Susceptibility Genes

Hereditary prostate cancer (HPCa) has the highest heritability of any cancer in men. Interestingly, it occurs in several hereditary syndromes, including breast and ovarian cancer (HBOC) and Lynch syndrome (LS). Several gene mutations related to these syndromes have been identified as biomarkers in HPCa. The goal of this study was to screen for germline mutations in susceptibility genes by using a multigene panel, and to subsequently correlate the results with clinical and laboratory parameters. This was undertaken in 180 HBOC families, which included 217 males with prostate cancer (PCa). Mutational analysis was further extended to 104 family members of mutated patients. Screening of HBOC families revealed that 30.5% harbored germline mutations in susceptibility genes, with 21.6% harboring pathogenic variants (PVs) and 8.9% having variants of uncertain significance (VUS). We found PVs at similar frequency in BRCA1 and BRCA2 genes (8.8% and 9.4%, respectively), while 0.56% of PVs were present in well-established susceptibility genes PALB2, TP53 and RAD51C. Moreover, 0.56% of monoallelic PVs were present in MUTYH, a gene whose function in tumorigenesis in the context of PCa is still unclear. Finally, we reported double heterozygosity (DH) in BRCA1/2 genes in a single family, and found double mutation (DM) present in BRCA2 in a separate family. There was no significant difference between the mean age of onset of PCa in HBOC families with or without germline mutations in susceptibility genes, while the mean survival was highest in mutated patients compared to wild type. Furthermore, PCa is the second most recurrent cancer in our cohort, resulting in 18% of cases in both mutated and non-mutated families. Our investigation shows that PVs were located mostly in the 3′ of BRCA1 and BRCA2 genes, and in BRCA2, most PVs fell in exon 11, suggesting a mutation cluster region relating to risk of HPCa. A total of 65 family members inherited the proband’s mutation; of these, 24 developed cancer, with 41 remaining unaffected.

An Optimized Comparative Proteomic Approach as a Tool in Neurodegenerative Disease Research

Recent advances in proteomic technologies now allow unparalleled assessment of the molecular composition of a wide range of sample types. However, the application of such technologies and techniques should not be undertaken lightly. Here, we describe why the design of a proteomics experiment itself is only the first step in yielding high-quality, translatable results. Indeed, the effectiveness and/or impact of the majority of contemporary proteomics screens are hindered not by commonly considered technical limitations such as low proteome coverage but rather by insufficient analyses. Proteomic experimentation requires a careful methodological selection to account for variables from sample collection, through to database searches for peptide identification to standardised post-mass spectrometry options directed analysis workflow, which should be adjusted for each study, from determining when and how to filter proteomic data to choosing holistic versus trend-wise analyses for biologically relevant patterns. Finally, we highlight and discuss the difficulties inherent in the modelling and study of the majority of progressive neurodegenerative conditions. We provide evidence (in the context of neurodegenerative research) for the benefit of undertaking a comparative approach through the application of the above considerations in the alignment of publicly available pre-existing data sets to identify potential novel regulators of neuronal stability.

Molecular basis of epigenetic regulation in cancer diagnosis and treatment

The global cancer cases and mortality rates are increasing and demand efficient biomarkers for accurate screening, detection, diagnosis, and prognosis. Recent studies have demonstrated that variations in epigenetic mechanisms like aberrant promoter methylation, altered histone modification and mutations in ATP-dependent chromatin remodelling complexes play an important role in the development of carcinogenic events. However, the influence of other epigenetic alterations in various cancers was confirmed with evolving research and the emergence of high throughput technologies. Therefore, alterations in epigenetic marks may have clinical utility as potential biomarkers for early cancer detection and diagnosis. In this review, an outline of the key epigenetic mechanism(s), and their deregulation in cancer etiology have been discussed to decipher the future prospects in cancer therapeutics including precision medicine. Also, this review attempts to highlight the gaps in epigenetic drug development with emphasis on integrative analysis of epigenetic biomarkers to establish minimally non-invasive biomarkers with clinical applications.

De novo DNA methylation induced by circulating extracellular vesicles from acute coronary syndrome patients

BACKGROUND AND AIMS

DNA methylation is associated with gene silencing, but its clinical role in cardiovascular diseases (CVDs) remains to be elucidated. We hypothesized that extracellular vesicles (EVs) may carry epigenetic changes, showing themselves as a potentially valuable non-invasive diagnostic liquid biopsy. We isolated and characterized circulating EVs of acute coronary syndrome (ACS) patients and assessed their role on DNA methylation in epigenetic modifications.

METHODS

EVs were recovered from plasma of 19 ACS patients and 50 healthy subjects (HS). Flow cytometry, qRT-PCR, and Western blot (WB) were performed to evaluate both intra-vesicular and intra-cellular signals. ShinyGO, PANTHER, and STRING tools were used to perform GO and PPI network analyses.

RESULTS

ACS-derived EVs showed increased levels of DNA methyltransferases (DNMTs) (p<0.001) and Ten-eleven translocation (TET) genes reduction. Specifically, de novo methylation transcripts, as DNMT3A and DNMT3B, were significantly increased in plasma ACS-EVs. DNA methylation analysis on PBMCs from healthy donors treated with HS- and ACS-derived EVs showed an important role of DNMTs carried by EVs. PPI network analysis evidenced that ACS-EVs induced changes in PBMC methylome. In the most enriched subnetwork, the hub gene SRC was connected to NOTCH1, FOXO3, CDC42, IKBKG, RXRA, DGKG, BAIAP2 genes that were showed to have many molecular effects on various cell types into onset of several CVDs. Modulation in gene expression after ACS-EVs treatment was confirmed for SRC, NOTCH1, FOXO3, RXRA, DGKG and BAIAP2 (p<0.05).

CONCLUSIONS

Our data showed an important role for ACS-derived EVs in gene expression modulation through de novo DNA methylation signals, and modulating signalling pathways in target cells.

Digital methylation assessments of alcohol and cigarette consumption account for common variance in accelerated epigenetic ageing

Smoking and Heavy Alcohol Consumption (HAC) are established risk factors for myriad complex disorders of ageing. Yet many prior studies of Epigenetic Ageing (EA) have shown only modest effects of smoking and drinking on accelerated ageing. One potential reason for this conundrum might be the reliance of some prior EA studies on self-reported substance use, which may be unreliable in many samples. To test whether novel, non-self-reported indices would show a stronger association of smoking and HAC to EA, we used methylation sensitive digital PCR (MSdPCR) and data from 437 African American subjects from Wave 7 of the Family and Community Health Study Offspring Cohort to examine the effects of subjective and objective measures of smoking and HAC on 7 indices of EA. Because of limited overall correlations between the various EA indices, we examined patterns of association separately for each index. Consistent with expectations, MSdPCR assessments of smoking and HAC, but not self-reported alcohol consumption, were strongly correlated with accelerated EA. MSdPCR assessments of smoking and HAC accounted for 57% of GrimAge acceleration and the shared variance in GrimAge and DunedinPOAM accelerated EA. We conclude that MSdPCR assessments of smoking and HAC are valuable tools for understanding EA, represent directly targetable conditions for the prevention of premature ageing, and substantially improve upon self-reported assessment of smoking and HAC.

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.

Effects of novel SGLT2 inhibitors on cancer incidence in hyperglycemic patients: a meta-analysis of randomized clinical trials

Epidemiological evidence shows that diabetic patients have an increased cancer risk and a higher mortality rate. Glucose could play a central role in metabolism and growth of many tumor types, and this possible mechanism is supported by the high rate of glucose demand and uptake in cancer. Thus, growing evidence suggests that hyperglycemia contributes to cancer progression but also to its onset. Many mechanisms underlying this association have been hypothesized, such as insulin resistance, hyperinsulinemia, and increased inflammatory processes. Inflammation is a common pathophysiological feature in both diabetic and oncological patients, and inflammation linked to high glucose levels sensitizes microenvironment to tumorigenesis, promoting the development of malignant lesions by altering and sustaining a pathological condition in tissues. Glycemic control is the first goal of antidiabetic therapy, and glucose level reduction has also been associated with favorable outcomes in cancer. Here, we describe key events in carcinogenesis focusing on hyperglycemia as supporter in tumor progression and in particular, related to the role of a specific hypoglycemic drug class, sodium-glucose linked transporters (SGLTs). We also discuss the use of SGLT2 inhibitors as a novel potential cancer therapy. Our meta-analysis showed that SGLT-2 inhibitors were significantly associated with an overall reduced risk of cancer as compared to placebo (RR = 0.35, CI 0.33-0.37, P = 0. 00) with a particular effectiveness for dapaglifozin and ertuglifozin (RR = 0. 06, CI 0. 06-0. 07 and RR = 0. 22, CI 0. 18-0. 26, respectively). Network Medicine approaches may advance the possible repurposing of these drugs in patients with concomitant diabetes and cancer.

DNA methylation and breast cancer: A way forward (Review)

The current management of breast cancer (BC) lacks specific non‑invasive biomarkers able to provide an early diagnosis of the disease. Epigenetic‑sensitive signatures are influenced by environmental exposures and are mediated by direct molecular mechanisms, mainly guided by DNA methylation, which regulate the interplay between genetic and non‑genetic risk factors during cancerogenesis. The inactivation of tumor suppressor genes due to promoter hypermethylation is an early event in carcinogenesis. Of note, targeted tumor suppressor genes are frequently hypermethylated in patient‑derived BC tissues and peripheral blood biospecimens. In addition, epigenetic alterations in triple‑negative BC, as the most aggressive subtype, have been identified. Thus, detecting both targeted and genome‑wide DNA methylation changes through liquid‑based assays appears to be a useful clinical strategy for early detection, more accurate risk stratification and a personalized prediction of therapeutic response in patients with BC. Of note, the DNA methylation profile may be mapped by isolating the circulating tumor DNA from the plasma as a more accessible biospecimen. Furthermore, the sensitivity to treatment with chemotherapy, hormones and immunotherapy may be altered by gene‑specific DNA methylation, suggesting novel potential drug targets. Recently, the use of epigenetic drugs administered alone and/or with anticancer therapies has led to remarkable results, particularly in patients with BC resistant to anticancer treatment. The aim of the present review was to provide an update on DNA methylation changes that are potentially involved in BC development and their putative clinical utility in the fields of diagnosis, prognosis and therapy.

Severe Cardiac Toxicity Induced by Cancer Therapies Requiring Intensive Care Unit Admission

A steadying increase of cancer survivors has been observed as a consequence of more effective therapies. However, chemotherapy regimens are often associated with significant toxicity, and cardiac damage emerges as a prominent clinical issue. Many mechanisms sustain chemotherapy-induced cardiac toxicity: direct myocyte damage, arrhythmia induction, coronary vasospasm, and accelerated atherosclerosis. Anthracyclines are the most studied cardiotoxic drugs and represent a clinical model for cardiac damage induced by chemotherapy. In patients suffering from advanced heart failure (HF) because of chemotherapy-related cardiomyopathy, when refractory to optimal medical therapy, mechanical circulatory support or heart transplantation represents an effective treatment. Here, the main mechanisms of cardiac toxicity induced by cancer therapies are analyzed, with a focus on patients requiring intensive care unit (ICU) admission during the course of the disease because of acute cardiac toxicity, takotsubo syndrome, and acute-on-chronic HF in patients suffering from chemotherapy-induced cardiomyopathy. In a subset of patients, cardiac toxicity can be acute and life-threatening, leading to overt cardiogenic shock. The management of critically ill cancer patients poses a unique challenge and requires a multidisciplinary approach. Moreover, no etiologic therapy is available, and only supportive measures can be implemented.

Epigenetic Therapies for Heart Failure: Current Insights and Future Potential

Despite the current reductionist approach providing an optimal indication for diagnosis and treatment of patients with heart failure with reduced ejection fraction (HFrEF), there are no standard pharmacological therapies for heart failure with preserved ejection fraction (HFpEF). Although in its infancy in cardiovascular diseases, the epigenetic-based therapy ("epidrugs") is capturing the interest of physician community. In fact, an increasing number of controlled clinical trials is evaluating the putative beneficial effects of: 1) direct epigenetic-oriented drugs, eg, apabetalone, and 2) repurposed drugs with a possible indirect epigenetic interference, eg, metformin, statins, sodium glucose transporter inhibitors 2 (SGLT2i), and omega 3 polyunsaturated fatty acids (PUFAs) in both HFrEF and HFpEF, separately. Apabetalone is the first and unique direct epidrug tested in cardiovascular patients to date, and the BETonMACE trial has reported a reduction in first HF hospitalization (any EF value) and cardiovascular death in patients with type 2 diabetes and recent acute coronary syndrome, suggesting a possible role in secondary prevention. Patients with HFpEF seem to benefit from supplementation to the standard therapy with statins, metformin, and SGLT2i owing to their ability in reducing mortality. In contrast, the vasodilator hydralazine, with or without isosorbide dinitrate, did not provide beneficial effects. In HFrEF, metformin and SGLT2i could reduce the risk of incident HF and mortality in affected patients whereas clinical trials based on statins provided mixed results. Furthermore, PUFAs diet supplementation was significantly associated with reduced cardiovascular risk in both HFpEF and HFrEF. Future large trials will reveal whether direct and indirect epitherapy will remain a work in progress or become a useful way to customize the therapy in the real-world management of HFpEF and HFrEF. Our goal is to discuss the recent advancement in the epitherapy as a possible way to improve personalized therapy of HF.

Epigenetics of addiction

Substance use disorders are complex biopsychosocial disorders that have substantial negative neurocognitive impact in various patient populations. These diseases involve the compulsive use of licit or illicit substances despite adverse medicolegal consequences and appear to be secondary to long-lasting epigenetic and transcriptional adaptations in brain reward and non-reward circuits. The accumulated evidence supports the notion that repeated drug use causes changes in post-translational histone modifications and in DNA methylation/hydroxymethylation processes in several brain regions. This review provides an overview of epigenetic changes reported in models of cocaine, methamphetamine, and opioid use disorders. The accumulated data suggest that future therapeutic interventions should focus on the development of epigenetic drugs against addictive diseases.

Epigenetic-based therapy in allogenic hematopoietic stem cell transplantation: Novel opportunities for personalized treatment

Current management of patients undergoing allogeneic hematopoietic stem cell transplantation (allo-HSCT) lacks immunosuppressant drugs able to block the host immune response toward the graft antigens. Novel treatments may include epigenetic compounds (epidrugs) some of which have been yet approved by the Food and Drugs Administration for the treatment of specific blood malignancies. The most investigated in clinical trials for allo-HSCT are DNA demethylating agents (DNMTi), such as azacitidine (Vidaza) and decitabine (Dacogen) as well as histone deacetylases inhibitors (HDACi), such as vorinostat (Zolinza) and panobinostat (Farydak). Indeed, azacitidine monotherapy before allo-HSCT may reduce the conventional chemotherapy-related complications, whereas it may reduce relapse risk and death after allo-HSCT. Besides, a decitabine-containing conditioning regimen could protect against graft versus host disease (GVHD) and respiratory infections after allo-HSCT. Regarding HDACi, the addition of vorinostat and panobinostat to the conditioning regimen after allo-HSCT seems to reduce the incidence of acute GVHD. Furthermore, panobinostat alone or in combination with low-dose decitabine may reduce the relapse rate in high-risk patients with acute myeloid leukemia patients after allo-HSCT. We discuss the phase 1 and 2 clinical trials evaluating the possible beneficial effects of repurposing specific epidrugs which may guide personalized therapy in the setting of allo-HSCT.