Making Sense of the Epigenome Using Data Integration Approaches

Spatially resolved epigenome sequencing via Tn5 transposition and deterministic DNA barcoding in tissue

Spatial epigenetic mapping of tissues enables the study of gene regulation programs and cellular functions with the dependency on their local tissue environment. Here we outline a complete procedure for two spatial epigenomic profiling methods: spatially resolved genome-wide profiling of histone modifications using in situ cleavage under targets and tagmentation (CUT&Tag) chemistry (spatial-CUT&Tag) and transposase-accessible chromatin sequencing (spatial-ATAC-sequencing) for chromatin accessibility. Both assays utilize in-tissue Tn5 transposition to recognize genomic DNA loci followed by microfluidic deterministic barcoding to incorporate spatial address codes. Furthermore, these two methods do not necessitate prior knowledge of the transcription or epigenetic markers for a given tissue or cell type but permit genome-wide unbiased profiling pixel-by-pixel at the 10 μm pixel size level and single-base resolution. To support the widespread adaptation of these methods, details are provided in five general steps: (1) sample preparation; (2) Tn5 transposition in spatial-ATAC-sequencing or antibody-controlled pA-Tn5 tagmentation in CUT&Tag; (3) library preparation; (4) next-generation sequencing; and (5) data analysis using our customed pipelines available at: https://github.com/dyxmvp/Spatial_ATAC-seq and https://github.com/dyxmvp/spatial-CUT-Tag . The whole procedure can be completed on four samples in 2-3 days. Familiarity with basic molecular biology and bioinformatics skills with access to a high-performance computing environment are required. A rudimentary understanding of pathology and specimen sectioning, as well as deterministic barcoding in tissue-specific skills (e.g., design of a multiparameter barcode panel and creation of microfluidic devices), are also advantageous. In this protocol, we mainly focus on spatial profiling of tissue region-specific epigenetic landscapes in mouse embryos and mouse brains using spatial-ATAC-sequencing and spatial-CUT&Tag, but these methods can be used for other species with no need for species-specific probe design.

Web-based multi-omics integration using the Analyst software suite

The growing number of multi-omics studies demands clear conceptual workflows coupled with easy-to-use software tools to facilitate data analysis and interpretation. This protocol covers three key components involved in multi-omics analysis, including single-omics data analysis, knowledge-driven integration using biological networks and data-driven integration through joint dimensionality reduction. Using the dataset from a recent multi-omics study of human pancreatic islet tissue and plasma samples, the first section introduces how to perform transcriptomics/proteomics data analysis using ExpressAnalyst and lipidomics data analysis using MetaboAnalyst. On the basis of significant features detected in these workflows, the second section demonstrates how to perform knowledge-driven integration using OmicsNet. The last section illustrates how to perform data-driven integration from the normalized omics data and metadata using OmicsAnalyst. The complete protocol can be executed in ~2 h. Compared with other available options for multi-omics integration, the Analyst software suite described in this protocol enables researchers to perform a wide range of omics data analysis tasks via a user-friendly web interface.

Wakefield M. The role of aberrant DNA methylation in cancer initiation and clinical impacts

Epigenetic alterations, including aberrant DNA methylation, are now recognized as bone fide hallmarks of cancer, which can contribute to cancer initiation, progression, therapy responses and therapy resistance. Methylation of gene promoters can have a range of impacts on cancer risk, clinical stratification and therapeutic outcomes. We provide several important examples of genes, which can be silenced or activated by promoter methylation and highlight their clinical implications. These include the mismatch DNA repair genes MLH1 and MSH2, homologous recombination DNA repair genes BRCA1 and RAD51C, the TERT oncogene and genes within the P15/P16/RB1/E2F tumour suppressor axis. We also discuss how these methylation changes might occur in the first place - whether in the context of the CpG island methylator phenotype or constitutional DNA methylation. The choice of assay used to measure methylation can have a significant impact on interpretation of methylation states, and some examples where this can influence clinical decision-making are presented. Aberrant DNA methylation patterns in circulating tumour DNA (ctDNA) are also showing great promise in the context of non-invasive cancer detection and monitoring using liquid biopsies; however, caution must be taken in interpreting these results in cases where constitutional methylation may be present. Thus, this review aims to provide researchers and clinicians with a comprehensive summary of this broad, but important subject, illustrating the potentials and pitfalls of assessing aberrant DNA methylation in cancer.

DNA methylation of hematopoietic stem/progenitor cells from donor peripheral blood to patient bone marrow: implications for allogeneic hematopoietic stem cell transplantation

Allogeneic hematopoietic stem cell transplantation (AHSCT) is a life-saving treatment for selected hematological malignancies. So far, it remains unclear whether transplanted hematopoietic stem/progenitor cells (HSPCs) undergo epigenetic changes upon engraftment in recipient bone marrow (BM) after AHSCT and whether these changes might be useful in the transplant diagnostics. The purpose of this study was to characterize the whole genome methylation profile of HSPCs following AHSCT. Moreover, the relationship between the observed methylation signature and patient outcome was analyzed. Mobilized peripheral blood (mPB)-HSPCs from seven donors and BM-HSPCs longitudinally collected from transplanted patients with hematological malignancies up to one year from AHSCT (a total of twenty-eight samples) were analyzed using DNA methylation based-arrays. The obtained data showed that DNA methylation of mPB-HSPCs differs between young and adult donors and changes following HSPC engraftment in the BM of recipient patients. Looking at methylation in promoter regions, at 30 days post-AHSCT, BM-HSPCs showed a higher number of differentially methylated genes (DMGs) compared to those of mPB-HSPCs, with a prevalent hyper-methylation. These changes were maintained during all the analyzed time points, and methylation became like the donors after one year from transplant. Functional analysis of these DMGs showed an enrichment in cell adhesion, differentiation and cytokine (interleukin-2, -5 and -7) production and signaling pathways. Of note, DNA methylation analysis allowed to identify a potential "cancer/graft methylation signature" of transplant failure. It was evident in the latest available post-transplant BM-HSPC sample (at 160 days) and surprisingly already in early phase (at 30 days) in patients whose transplant was doomed to fail. Overall, the analysis of HSPC methylation profile could offer useful prognostic information to potentially assess engraftment success and predict graft failure in AHSCT.

Epidrugs in the Therapy of Central Nervous System Disorders: A Way to Drive on?

The polygenic nature of neurological and psychiatric syndromes and the significant impact of environmental factors on the underlying developmental, homeostatic, and neuroplastic mechanisms suggest that an efficient therapy for these disorders should be a complex one. Pharmacological interventions with drugs selectively influencing the epigenetic landscape (epidrugs) allow one to hit multiple targets, therefore, assumably addressing a wide spectrum of genetic and environmental mechanisms of central nervous system (CNS) disorders. The aim of this review is to understand what fundamental pathological mechanisms would be optimal to target with epidrugs in the treatment of neurological or psychiatric complications. To date, the use of histone deacetylases and DNA methyltransferase inhibitors (HDACis and DNMTis) in the clinic is focused on the treatment of neoplasms (mainly of a glial origin) and is based on the cytostatic and cytotoxic actions of these compounds. Preclinical data show that besides this activity, inhibitors of histone deacetylases, DNA methyltransferases, bromodomains, and ten-eleven translocation (TET) proteins impact the expression of neuroimmune inflammation mediators (cytokines and pro-apoptotic factors), neurotrophins (brain-derived neurotropic factor (BDNF) and nerve growth factor (NGF)), ion channels, ionotropic receptors, as well as pathoproteins (β-amyloid, tau protein, and α-synuclein). Based on this profile of activities, epidrugs may be favorable as a treatment for neurodegenerative diseases. For the treatment of neurodevelopmental disorders, drug addiction, as well as anxiety disorders, depression, schizophrenia, and epilepsy, contemporary epidrugs still require further development concerning a tuning of pharmacological effects, reduction in toxicity, and development of efficient treatment protocols. A promising strategy to further clarify the potential targets of epidrugs as therapeutic means to cure neurological and psychiatric syndromes is the profiling of the epigenetic mechanisms, which have evolved upon actions of complex physiological lifestyle factors, such as diet and physical exercise, and which are effective in the management of neurodegenerative diseases and dementia.

EpiCompare: R package for the comparison and quality control of epigenomic peak files

Summary

EpiCompare combines a variety of downstream analysis tools to compare, quality control and benchmark different epigenomic datasets. The package requires minimal input from users, can be run with just one line of code and provides all results of the analysis in a single interactive HTML report. EpiCompare thus enables downstream analysis of multiple epigenomic datasets in a simple, effective and user-friendly manner.

Availability and implementation

EpiCompare is available on Bioconductor (≥ v3.15): https://bioconductor.org/packages/release/bioc/html/EpiCompare.html; all source code is publicly available via GitHub: https://github.com/neurogenomics/EpiCompare; documentation website https://neurogenomics.github.io/EpiCompare; and EpiCompare DockerHub repository: https://hub.docker.com/repository/docker/neurogenomicslab/epicompare.

Promises and challenges in pharmacoepigenetics

Pharmacogenetics, the study of how interindividual genetic differences affect drug response, does not explain all observed heritable variance in drug response. Epigenetic mechanisms, such as DNA methylation, and histone acetylation may account for some of the unexplained variances. Epigenetic mechanisms modulate gene expression and can be suitable drug targets and can impact the action of nonepigenetic drugs. Pharmacoepigenetics is the field that studies the relationship between epigenetic variability and drug response. Much of this research focuses on compounds targeting epigenetic mechanisms, called epigenetic drugs, which are used to treat cancers, immune disorders, and other diseases. Several studies also suggest an epigenetic role in classical drug response; however, we know little about this area. The amount of information correlating epigenetic biomarkers to molecular datasets has recently expanded due to technological advances, and novel computational approaches have emerged to better identify and predict epigenetic interactions. We propose that the relationship between epigenetics and classical drug response may be examined using data already available by (1) finding regions of epigenetic variance, (2) pinpointing key epigenetic biomarkers within these regions, and (3) mapping these biomarkers to a drug-response phenotype. This approach expands on existing knowledge to generate putative pharmacoepigenetic relationships, which can be tested experimentally. Epigenetic modifications are involved in disease and drug response. Therefore, understanding how epigenetic drivers impact the response to classical drugs is important for improving drug design and administration to better treat disease.

Morphology and gene expression profiling provide complementary information for mapping cell state

Morphological and gene expression profiling can cost-effectively capture thousands of features in thousands of samples across perturbations by disease, mutation, or drug treatments, but it is unclear to what extent the two modalities capture overlapping versus complementary information. Here, using both the L1000 and Cell Painting assays to profile gene expression and cell morphology, respectively, we perturb human A549 lung cancer cells with 1,327 small molecules from the Drug Repurposing Hub across six doses, providing a data resource including dose-response data from both assays. The two assays capture both shared and complementary information for mapping cell state. Cell Painting profiles from compound perturbations are more reproducible and show more diversity but measure fewer distinct groups of features. Applying unsupervised and supervised methods to predict compound mechanisms of action (MOAs) and gene targets, we find that the two assays not only provide a partially shared but also a complementary view of drug mechanisms. Given the numerous applications of profiling in biology, our analyses provide guidance for planning experiments that profile cells for detecting distinct cell types, disease phenotypes, and response to chemical or genetic perturbations.

The potential of DNA methylation as a biomarker for obesity and smoking

DNA methylation is an epigenetic modification that has consistently been shown to be linked with a variety of human traits and diseases. Because DNA methylation is dynamic and potentially reversible in nature and can reflect environmental exposures and predict the onset of diseases, it has piqued interest as a potential disease biomarker. DNA methylation patterns are more stable than transcriptomic or proteomic patterns, and they are relatively easy to measure to track exposure to different environments and risk factors. Importantly, technologies for DNA methylation quantification have become increasingly cost effective-accelerating new research in the field-and have enabled the development of novel DNA methylation biomarkers. Quite a few DNA methylation-based predictors for a number of traits and diseases already exist. Such predictors show potential for being more accurate than self-reported or measured phenotypes (such as smoking behavior and body mass index) and may even hold potential for applications in clinics. In this review, we will first discuss the advantages and challenges of DNA methylation biomarkers in general. We will then review the current state and future potential of DNA methylation biomarkers in two human traits that show rather consistent alterations in methylome-obesity and smoking. Lastly, we will briefly speculate about the future prospects of DNA methylation biomarkers, and possible ways to achieve them.

DNA methylation as a mediator of genetic and environmental influences on Parkinson's disease susceptibility: Impacts of alpha-Synuclein, physical activity, and pesticide exposure on the epigenome

Parkinson's disease (PD) is a neurodegenerative disorder with a complex etiology and increasing prevalence worldwide. As PD is influenced by a combination of genetic and environment/lifestyle factors in approximately 90% of cases, there is increasing interest in identification of the interindividual mechanisms underlying the development of PD as well as actionable lifestyle factors that can influence risk. This narrative review presents an outline of the genetic and environmental factors contributing to PD risk and explores the possible roles of cytosine methylation and hydroxymethylation in the etiology and/or as early-stage biomarkers of PD, with an emphasis on epigenome-wide association studies (EWAS) of PD conducted over the past decade. Specifically, we focused on variants in the SNCA gene, exposure to pesticides, and physical activity as key contributors to PD risk. Current research indicates that these factors individually impact the epigenome, particularly at the level of CpG methylation. There is also emerging evidence for interaction effects between genetic and environmental contributions to PD risk, possibly acting across multiple omics layers. We speculated that this may be one reason for the poor replicability of the results of EWAS for PD reported to date. Our goal is to provide direction for future epigenetics studies of PD to build upon existing foundations and leverage large datasets, new technologies, and relevant statistical approaches to further elucidate the etiology of this disease.

A review of deep learning applications in human genomics using next-generation sequencing data

Genomics is advancing towards data-driven science. Through the advent of high-throughput data generating technologies in human genomics, we are overwhelmed with the heap of genomic data. To extract knowledge and pattern out of this genomic data, artificial intelligence especially deep learning methods has been instrumental. In the current review, we address development and application of deep learning methods/models in different subarea of human genomics. We assessed over- and under-charted area of genomics by deep learning techniques. Deep learning algorithms underlying the genomic tools have been discussed briefly in later part of this review. Finally, we discussed briefly about the late application of deep learning tools in genomic. Conclusively, this review is timely for biotechnology or genomic scientists in order to guide them why, when and how to use deep learning methods to analyse human genomic data.

Chile Pepper (Capsicum) Breeding and Improvement in the "Multi-Omics" Era

Chile pepper (Capsicum spp.) is a major culinary, medicinal, and economic crop in most areas of the world. For more than hundreds of years, chile peppers have "defined" the state of New Mexico, USA. The official state question, "Red or Green?" refers to the preference for either red or the green stage of chile pepper, respectively, reflects the value of these important commodities. The presence of major diseases, low yields, decreased acreages, and costs associated with manual labor limit production in all growing regions of the world. The New Mexico State University (NMSU) Chile Pepper Breeding Program continues to serve as a key player in the development of improved chile pepper varieties for growers and in discoveries that assist plant breeders worldwide. Among the traits of interest for genetic improvement include yield, disease resistance, flavor, and mechanical harvestability. While progress has been made, the use of conventional breeding approaches has yet to fully address producer and consumer demand for these traits in available cultivars. Recent developments in "multi-omics," that is, the simultaneous application of multiple omics approaches to study biological systems, have allowed the genetic dissection of important phenotypes. Given the current needs and production constraints, and the availability of multi-omics tools, it would be relevant to examine the application of these approaches in chile pepper breeding and improvement. In this review, we summarize the major developments in chile pepper breeding and present novel tools that can be implemented to facilitate genetic improvement. In the future, chile pepper improvement is anticipated to be more data and multi-omics driven as more advanced genetics, breeding, and phenotyping tools are developed.

Methods for Stratification and Validation Cohorts: A Scoping Review

Personalized medicine requires large cohorts for patient stratification and validation of patient clustering. However, standards and harmonized practices on the methods and tools to be used for the design and management of cohorts in personalized medicine remain to be defined. This study aims to describe the current state-of-the-art in this area. A scoping review was conducted searching in PubMed, EMBASE, Web of Science, Psycinfo and Cochrane Library for reviews about tools and methods related to cohorts used in personalized medicine. The search focused on cancer, stroke and Alzheimer’s disease and was limited to reports in English, French, German, Italian and Spanish published from 2005 to April 2020. The screening process was reported through a PRISMA flowchart. Fifty reviews were included, mostly including information about how data were generated (25/50) and about tools used for data management and analysis (24/50). No direct information was found about the quality of data and the requirements to monitor associated clinical data. A scarcity of information and standards was found in specific areas such as sample size calculation. With this information, comprehensive guidelines could be developed in the future to improve the reproducibility and robustness in the design and management of cohorts in personalized medicine studies.

Functional annotation of breast cancer risk loci: current progress and future directions

Genome-wide association studies coupled with large-scale replication and fine-scale mapping studies have identified more than 150 genomic regions that are associated with breast cancer risk. Here, we review efforts to translate these findings into a greater understanding of disease mechanism. Our review comes in the context of a recently published fine-scale mapping analysis of these regions, which reported 352 independent signals and a total of 13,367 credible causal variants. The vast majority of credible causal variants map to noncoding DNA, implicating regulation of gene expression as the mechanism by which functional variants influence risk. Accordingly, we review methods for defining candidate-regulatory sequences, methods for identifying putative target genes and methods for linking candidate-regulatory sequences to putative target genes. We provide a summary of available data resources and identify gaps in these resources. We conclude that while much work has been done, there is still much to do. There are, however, grounds for optimism; combining statistical data from fine-scale mapping with functional data that are more representative of the normal "at risk" breast, generated using new technologies, should lead to a greater understanding of the mechanisms that influence an individual woman's risk of breast cancer.

Ethical implications of epigenetics in the era of personalized medicine

Given the increasing research activity on epigenetics to monitor human diseases and its connection with lifestyle and environmental expositions, the field of epigenetics has attracted a great deal of interest also at the ethical and societal level. In this review, we will identify and discuss current ethical, legal and social issues of epigenetics research in the context of personalized medicine. The review covers ethical aspects such as how epigenetic information should impact patient autonomy and the ability to generate an intentional and voluntary decision, the measures of data protection related to privacy and confidentiality derived from epigenome studies (e.g., risk of discrimination, patient re-identification and unexpected findings) or the debate in the distribution of responsibilities for health (i.e., personal versus public responsibilities). We pay special attention to the risk of social discrimination and stigmatization as a consequence of inferring information related to lifestyle and environmental exposures potentially contained in epigenetic data. Furthermore, as exposures to the environment and individual habits do not affect all populations equally, the violation of the principle of distributive justice in the access to the benefits of clinical epigenetics is discussed. In this regard, epigenetics represents a great opportunity for the integration of public policy measures aimed to create healthier living environments. Whether these public policies will coexist or, in contrast, compete with strategies reinforcing the personalized medicine interventions needs to be considered. The review ends with a reflection on the main challenges in epigenetic research, some of them in a technical dimension (e.g., assessing causality or establishing reference epigenomes) but also in the ethical and social sphere (e.g., risk to add an epigenetic determinism on top of the current genetic one). In sum, integration into life science investigation of social experiences such as exposure to risk, nutritional habits, prejudice and stigma, is imperative to understand epigenetic variation in disease. This pragmatic approach is required to locate clinical epigenetics out of the experimental laboratories and facilitate its implementation into society.

Epigenomics as a paradigm to understand the nuances of phenotypes

Quantifying the relative importance of genomic and epigenomic modulators of phenotype is a focal challenge in comparative physiology, but progress is constrained by availability of data and analytic methods. Previous studies have linked physiological features to coding DNA sequence, regulatory DNA sequence, and epigenetic state, but few have disentangled their relative contributions or unambiguously distinguished causative effects ('drivers') from correlations. Progress has been limited by several factors, including the classical approach of treating continuous and fluid phenotypes as discrete and static across time and environment, and difficulty in considering the full diversity of mechanisms that can modulate phenotype, such as gene accessibility, transcription, mRNA processing and translation. We argue that attention to phenotype nuance, progressing to association with epigenetic marks and then causal analyses of the epigenetic mechanism, will enable clearer evaluation of the evolutionary path. This would underlie an essential paradigm shift, and power the search for links between genomic and epigenomic features and physiology. Here, we review the growing knowledge base of gene-regulatory mechanisms and describe their links to phenotype, proposing strategies to address widely recognized challenges.

Biophysics is reshaping our perception of the epigenome: from DNA-level to high-throughput studies

Epigenetic research holds great promise to advance our understanding of biomarkers and regulatory processes in health and disease. An increasing number of new approaches, ranging from molecular to biophysical analyses, enable identifying epigenetic changes on the level of a single gene or the whole epigenome. The aim of this review is to highlight how the field is shifting from completely molecular-biology-driven solutions to multidisciplinary strategies including more reliance on biophysical analysis tools. Biophysics not only offers technical advancements in imaging or structure analysis but also helps to explore regulatory interactions. New computational methods are also being developed to meet the demand of growing data volumes and their processing. Therefore, it is important to capture these new directions in epigenetics from a biophysical perspective and discuss current challenges as well as multiple applications of biophysical methods and tools. Specifically, we gradually introduce different biophysical research methods by first considering the DNA-level information and eventually higher-order chromatin structures. Moreover, we aim to highlight that the incorporation of bioinformatics, machine learning, and artificial intelligence into biophysical analysis allows gaining new insights into complex epigenetic processes. The gained understanding has already proven useful in translational and clinical research providing better patient stratification options or new therapeutic insights. Together, this offers a better readiness to transform bench-top experiments into industrial high-throughput applications with a possibility to employ developed methods in clinical practice and diagnostics.

Toward a Framework for Assessing Privacy Risks in Multi-Omic Research and Databases

While the accumulation and increased circulation of genomic data have captured much attention over the past decade, privacy risks raised by the diversification and integration of omics have been largely overlooked. In this paper, we propose the outline of a framework for assessing privacy risks in multi-omic research and databases. Following a comparison of privacy risks associated with genomic and epigenomic data, we dissect ten privacy risk-impacting omic data properties that affect either the risk of re-identification of research participants, or the sensitivity of the information potentially conveyed by biological data. We then propose a three-step approach for the assessment of privacy risks in the multi-omic era. Thus, we lay grounds for a data property-based, 'pan-omic' approach that moves away from genetic exceptionalism. We conclude by inviting our peers to refine these theoretical foundations, put them to the test in their respective fields, and translate our approach into practical guidance.

Targeting neurotransmitter-mediated inflammatory mechanisms of psychiatric drugs to mitigate the double burden of multimorbidity and polypharmacy

The increased incidence of multimorbidities and polypharmacy is a major concern, particularly in the growing aging population. While polypharmacy can be beneficial, in many cases it can be more harmful than no treatment, especially in individuals suffering from psychiatric disorders, who have elevated risks of multimorbidity and polypharmacy. Age-related chronic inflammation and immunopathologies might contribute to these increased risks in this population, but the optimal clinical management of drug-drug interactions and the neuro-immune mechanisms that are involved warrants further investigation. Given that neurotransmitter systems, which psychiatric medications predominantly act on, can influence the development of inflammation and the regulation of immune function, it is important to better understand these interactions to develop more successful strategies to manage these comorbidities and complicated polypharmacy. I propose that expanding upon research in translationally relevant human in vitro models, in tandem with other preclinical models, is critical to defining the neurotransmitter-mediated mechanisms by which psychiatric drugs alter immune function. This will define more precisely the interactions of psychiatric drugs and other immunomodulatory drugs, used in combination, enabling identification of novel targets to be translated into more efficacious diagnostic, preventive, and therapeutic interventions. This interdisciplinary approach will aid in better precision polypharmacy for combating adverse events associated with multimorbidity and polypharmacy in the future.

Machine Learning in Epigenomics: Insights into Cancer Biology and Medicine

The recent deluge of genome-wide technologies for the mapping of the epigenome and resulting data in cancer samples has provided the opportunity for gaining insights into and understanding the roles of epigenetic processes in cancer. However, the complexity, high-dimensionality, sparsity, and noise associated with these data pose challenges for extensive integrative analyses. Machine Learning (ML) algorithms are particularly suited for epigenomic data analyses due to their flexibility and ability to learn underlying hidden structures. We will discuss four overlapping but distinct major categories under ML: dimensionality reduction, unsupervised methods, supervised methods, and deep learning (DL). We review the preferred use cases of these algorithms in analyses of cancer epigenomics data with the hope to provide an overview of how ML approaches can be used to explore fundamental questions on the roles of epigenome in cancer biology and medicine.

Epigenetic Regulation of Endothelial Dysfunction and Inflammation in Pulmonary Arterial Hypertension

Once perceived as a disorder treated by vasodilation, pulmonary artery hypertension (PAH) has emerged as a pulmonary vascular disease with severe endothelial cell dysfunction. In the absence of a cure, many studies seek to understand the detailed mechanisms of EC regulation to potentially create more therapeutic options for PAH. Endothelial dysfunction is characterized by complex phenotypic changes including unchecked proliferation, apoptosis-resistance, enhanced inflammatory signaling and metabolic reprogramming. Recent studies have highlighted the role of epigenetic modifications leading to pro-inflammatory response pathways, endothelial dysfunction, and the progression of PAH. This review summarizes the existing literature on epigenetic mechanisms such as DNA methylation, histone modifications, and non-coding RNAs, which can lead to aberrant endothelial function. Our goal is to develop a conceptual framework for immune dysregulation and epigenetic changes in endothelial cells in the context of PAH. These studies as well as others may lead to advances in therapeutics to treat this devastating disease.

Histone post-translational modifications as potential therapeutic targets for pain management

Effective pharmacological management of pain associated with tissue pathology is an unmet medical need. Transcriptional modifications in nociceptive pathways are pivotal for the development and the maintenance of pain associated with tissue damage. Accumulating evidence has shown the importance of the epigenetic control of transcription in nociceptive pathways via histone post-translational modifications (PTMs). Hence, histone PTMs could be targets for novel effective analgesics. Here, we discuss the current understanding of histone PTMs in the modulation of gene expression affecting nociception and pain phenotypes following tissue injury. We also provide a critical view of the translational implications of preclinical models and discuss opportunities and challenges of targeting histone PTMs to relieve pain in clinically relevant tissue injuries.

Privacy considerations for sharing genomics data

An increasing amount of attention has been geared towards understanding the privacy risks that arise from sharing genomic data of human origin. Most of these efforts have focused on issues in the context of genomic sequence data, but the popularity of techniques for collecting other types of genome-related data has prompted researchers to investigate privacy concerns in a broader genomic context. In this review, we give an overview of different types of genome-associated data, their individual ways of revealing sensitive information, the motivation to share them as well as established and upcoming methods to minimize information leakage. We further discuss the concise threats that are being posed, who is at risk, and how the risk level compares to potential benefits, all while addressing the topic in the context of modern technology, methodology, and information sharing culture. Additionally, we will discuss the current legal situation regarding the sharing of genomic data in a selection of countries, evaluating the scope of their applicability as well as their limitations. We will finalize this review by evaluating the development that is required in the scientific field in the near future in order to improve and develop privacy-preserving data sharing techniques for the genomic context.

Exploiting Single-Cell Tools in Gene and Cell Therapy

Single-cell molecular tools have been developed at an incredible pace over the last five years as sequencing costs continue to drop and numerous molecular assays have been coupled to sequencing readouts. This rapid period of technological development has facilitated the delineation of individual molecular characteristics including the genome, transcriptome, epigenome, and proteome of individual cells, leading to an unprecedented resolution of the molecular networks governing complex biological systems. The immense power of single-cell molecular screens has been particularly highlighted through work in systems where cellular heterogeneity is a key feature, such as stem cell biology, immunology, and tumor cell biology. Single-cell-omics technologies have already contributed to the identification of novel disease biomarkers, cellular subsets, therapeutic targets and diagnostics, many of which would have been undetectable by bulk sequencing approaches. More recently, efforts to integrate single-cell multi-omics with single cell functional output and/or physical location have been challenging but have led to substantial advances. Perhaps most excitingly, there are emerging opportunities to reach beyond the description of static cellular states with recent advances in modulation of cells through CRISPR technology, in particular with the development of base editors which greatly raises the prospect of cell and gene therapies. In this review, we provide a brief overview of emerging single-cell technologies and discuss current developments in integrating single-cell molecular screens and performing single-cell multi-omics for clinical applications. We also discuss how single-cell molecular assays can be usefully combined with functional data to unpick the mechanism of cellular decision-making. Finally, we reflect upon the introduction of spatial transcriptomics and proteomics, its complementary role with single-cell RNA sequencing (scRNA-seq) and potential application in cellular and gene therapy.

Epigenetic Remodeling in Obesity-Related Vascular Disease

Significance: The prevalence of obesity and cardiometabolic phenotypes is alarmingly increasing across the globe and is associated with atherosclerotic vascular complications and high mortality. In spite of multifactorial interventions, vascular residual risk remains high in this patient population, suggesting the need for breakthrough therapies. The mechanisms underpinning obesity-related vascular disease remain elusive and represent an intense area of investigation. Recent Advances: Epigenetic modifications-defined as environmentally induced chemical changes of DNA and histones that do not affect DNA sequence-are emerging as a potent modulator of gene transcription in the vasculature and might significantly contribute to the development of obesity-induced endothelial dysfunction. DNA methylation and histone post-translational modifications cooperate to build complex epigenetic signals, altering transcriptional networks that are implicated in redox homeostasis, mitochondrial function, vascular inflammation, and perivascular fat homeostasis in patients with cardiometabolic disturbances. Critical Issues: Deciphering the epigenetic landscape in the vasculature is extremely challenging due to the complexity of epigenetic signals and their function in regulating transcription. An overview of the most important epigenetic pathways is required to identify potential molecular targets to treat or prevent obesity-related endothelial dysfunction and atherosclerotic disease. This would enable the employment of precision medicine approaches in this setting. Future Directions: Current and future research efforts in this field entail a better definition of the vascular epigenome in obese patients as well as the unveiling of novel, cell-specific chromatin-modifying drugs that are able to erase specific epigenetic signals that are responsible for maladaptive transcriptional alterations and vascular dysfunction in obese patients. Antioxid. Redox Signal. 34, 1165-1199.

The role of epigenetic modifications for the pathogenesis of Crohn's disease

Epigenetics has become a promising field for finding new biomarkers and improving diagnosis, prognosis, and drug response in inflammatory bowel disease. The number of people suffering from inflammatory bowel diseases, especially Crohn's disease, has increased remarkably. Crohn's disease is assumed to be the result of a complex interplay between genetic susceptibility, environmental factors, and altered intestinal microbiota, leading to dysregulation of the innate and adaptive immune response. While many genetic variants have been identified to be associated with Crohn's disease, less is known about the influence of epigenetics in the pathogenesis of this disease. In this review, we provide an overview of current epigenetic studies in Crohn's disease. In particular, we enable a deeper insight into applied bioanalytical and computational tools, as well as a comprehensive update toward the cell-specific evaluation of DNA methylation and histone modifications.

Machine learning for deciphering cell heterogeneity and gene regulation

Epigenetics studies inheritable and reversible modifications of DNA that allow cells to control gene expression throughout their development and in response to environmental conditions. In computational epigenomics, machine learning is applied to study various epigenetic mechanisms genome wide. Its aim is to expand our understanding of cell differentiation, that is their specialization, in health and disease. Thus far, most efforts focus on understanding the functional encoding of the genome and on unraveling cell-type heterogeneity. Here, we provide an overview of state-of-the-art computational methods and their underlying statistical concepts, which range from matrix factorization and regularized linear regression to deep learning methods. We further show how the rise of single-cell technology leads to new computational challenges and creates opportunities to further our understanding of epigenetic regulation.

Multi-Omics Data Analysis of Gene Expressions and Alterations, Cancer-Associated Fibroblast and Immune Infiltrations, Reveals the Onco-Immune Prognostic Relevance of STAT3/CDK2/4/6 in Human Malignancies

Simple Summary

Signal transducer and activator of transcription 3 (STAT3)/Cyclin-dependent kinases are multifunctional proteins that play instrumental roles in carcinogenesis. However, the genetic alterations of the STAT3/CDK2/4/6 signaling axis and its role in predicting immune infiltration and immunotherapeutic response remain unclear. Here, we used in silico analyses of multi-Omics data to map out the role of epigenetic and genetic alterations of STAT3/CDK2/4/6 in tumor immune infiltrations, immunotherapy response, and prognosis of cancer patients. Our study collectively suggested that STAT3/CDK2/4/6 are important onco-immune signatures that contribute to tumor immune invasion, poor prognoses, and immune therapy failure. Our finding may be clinically useful in designing therapeutic strategies, prognosis assessment, and follow-up management in patients receiving immunotherapy in multiple cancers.

Abstract

Signal transducer and activator of transcription 3 (STAT3)/Cyclin-dependent kinases are multifunctional proteins that play an important implicative role in cancer initiations, progression, drug resistance, and metastasis, and has been extensively explored in cancer therapy. However, the genetic alterations of STAT3/CDK2/4/6 and its role in predicting immune infiltration and immunotherapeutic response are yet to be well exploited. In this study, we use in silico methods to analyze differential expression, prognostic value, genetic and epigenetic alterations, association with tumor-infiltrating immune cells, and cancer-associated fibroblast (CAF) infiltrations of STAT3/CDK2/4/6 in multiple cancer types. Our results revealed that the expression of STAT3/CDK2/4/6 was altered in various cancers and is associated with poor overall and disease-free survival of the cohorts. Moreover, genetic alterations in STAT3/CDK2/4/6 co-occurred with a number of other genetic alterations and are associated with poorer prognoses of the cohorts. The protein-protein interaction (PPI) network analysis suggests CDK2/4/6/STAT3 may directly interact with factors that promote tumorigenesis and immune response. We found that STAT3/CDK2/4/6 expressions were associated with infiltrations of CAF and the various immune cells in multiple cancers and it’s associated with poor response to immunotherapy. Collectively, our study suggested that STAT3/CDK2/4/6 are important onco-immune signatures that play central roles in tumor immune invasion, poor prognoses and, immune therapy response. Findings from the present study may therefore be clinically useful in prognosis assessment and follow-up management of immunotherapy.

Differential methylation of imprinting genes and MHC locus in 22q11.2 deletion syndrome-related schizophrenia spectrum disorders

OBJECTIVES

22q11.2 deletion syndrome (DS) is the strongest known genetic risk for schizophrenia. Methylome screening was conducted to elucidate possible involvement of epigenetic alterations in the emergence of schizophrenia spectrum disorders (SZ-SD) in 22q11.2DS.

METHODS

Sixteen adult men with/without SZ-SD were recruited from a 22q11.2DS cohort and underwent genome-wide DNA methylation profile analysis. Differentially methylated probes (DMPs) and regions (DMRs) were analysed using the ChAMP software.

RESULTS

The DMPs (p-value <10^-6) and DMRs (p-valueArea <0.01) were enriched in two gene sets, 'imprinting genes' and 'chr6p21', a region overlapping the MHC locus. Most of the identified imprinting genes are involved in neurodevelopment and located in clusters under imprinting control region (ICR) regulation, including PEG10, SGCE (7q21.3), GNAS, GNAS-AS1 (20q13.32) and SNHG14, SNURF-SNRPN, SNORD115 (15q11.2). The differentially methylated genes from the MHC locus included immune HLA-genes and non-immune genes, RNF39, PPP1R18 and NOTCH4, implicated in neurodevelopment and synaptic plasticity. The most significant DMR is located in MHC locus and covered the transcription regulator ZFP57 that is required for control and maintenance of gene imprinting at multiple ICRs.

CONCLUSIONS

The differential methylation in imprinting genes and in chr6p21-22 indicate the neurodevelopmental nature of 22q11.2DS-related SZ and the major role of MHC locus in the risk to develop SZ.

Help, there are ‘omics’ in my comparative physiology!

‘Omics’ methods, such as transcriptomics, proteomics, lipidomics or metabolomics, yield simultaneous measurements of many related molecules in a sample. These approaches have opened new opportunities to generate and test hypotheses about the mechanisms underlying biochemical and physiological phenotypes. In this Commentary, we discuss general approaches and considerations for successfully integrating omics into comparative physiology. The choice of omics approach will be guided by the availability of existing resources and the time scale of the process being studied. We discuss the use of whole-organism extracts (common in omics experiments on small invertebrates) because such an approach may mask underlying physiological mechanisms, and we consider the advantages and disadvantages of pooling samples within biological replicates. These methods can bring analytical challenges, so we describe the most easily analyzed omics experimental designs. We address the propensity of omics studies to digress into ‘fishing expeditions’ and show how omics can be used within the hypothetico-deductive framework. With this Commentary, we hope to provide a roadmap that will help newcomers approach omics in comparative physiology while avoiding some of the potential pitfalls, which include ambiguous experiments, long lists of candidate molecules and vague conclusions.

Ketotherapy as an epigenetic modifier in cancer

Epigenetic alterations in cancer play a variety of roles. Aberrant DNA methylation, as one of the epigenetic mechanisms, has been widely studied in both tumor and liquid biopsies and provide a useful bench mark for treatment response in cancer. Recently, several studies have reported an association between the type of diet and epigenetic modifications. Whereby there is a growing interest in finding the "anti-cancer diet formula", if such a thing exists. In this sense, ketogenic diets (KD) have reported potentially beneficial effects, which were able to prevent malignancies and decrease tumor growth. Some studies have even shown increased survival in cancer patients, reduced side effects of cytotoxic treatments, and intensified efficacy of cancer therapies. Although the biological mechanisms of KD are not well understood, it has been reported that KD may affect DNA methylation by modulating the expression of crucial genes involved in tumor survival and proliferation. However, there are many considerations to take into account to use ketotherapy in cancer, such as epigenetic mark, type of cancer, immunological and metabolic state or microbiota profile. In this review, we argue about ketotherapy as a potential strategy to consider as coadjuvant of cancer therapy. We will focus on mainly epigenetic mechanisms and dietary approach that could be included in the current clinical practice guidelines.

Omics Biomarkers in Obesity: Novel Etiological Insights and Targets for Precision Prevention

PURPOSE OF REVIEW

Omics-based technologies were suggested to provide an advanced understanding of obesity etiology and its metabolic consequences. This review highlights the recent developments in "omics"-based research aimed to identify obesity-related biomarkers.

RECENT FINDINGS

Recent advances in obesity and metabolism research increasingly rely on new technologies to identify mechanisms in the development of obesity using various "omics" platforms. Genetic and epigenetic biomarkers that translate into changes in transcriptome, proteome, and metabolome could serve as targets for obesity prevention. Despite a number of promising candidate biomarkers, there is an increased demand for larger prospective cohort studies to validate findings and determine biomarker reproducibility before they can find applications in primary care and public health. "Omics" biomarkers have advanced our knowledge on the etiology of obesity and its links with chronic diseases. They bring substantial promise in identifying effective public health strategies that pave the way towards patient stratification and precision prevention.

Epigenomic technologies for precision oncology

Epigenetic patterns in a cell control the expression of genes and consequently determine the phenotype of a cell. Cancer cells possess altered epigenomes which include aberrant patterns of DNA methylation, histone tail modifications, nucleosome positioning and of the three-dimensional chromatin organization within a nucleus. These altered epigenetic patterns are potential useful biomarkers to detect cancer cells and to classify tumor types. In addition, the cancer epigenome dictates the response of a cancer cell to therapeutic intervention and, therefore its knowledge, will allow to predict response to different therapeutic approaches. Here we review the current state-of-the-art technologies that have been developed to decipher epigenetic patterns on the genomic level and discuss how these methods are potentially useful for precision oncology.

From genome sequencing to the discovery of potential biomarkers in liver disease

Chronic liver disease progresses through several stages, fatty liver, steatohepatitis, cirrhosis, and eventually, it leads to hepatocellular carcinoma (HCC) over a long period of time. Since a large proportion of patients with HCC are accompanied by cirrhosis, it is considered to be an important factor in the diagnosis of liver cancer. This is because cirrhosis leads to an irreversible harmful effect, but the early stages of chronic liver disease could be reversed to a healthy state. Therefore, the discovery of biomarkers that could identify the early stages of chronic liver disease is important to prevent serious liver damage. Biomarker discovery at liver cancer and cirrhosis has enhanced the development of sequencing technology. Next generation sequencing (NGS) is one of the representative technical innovations in the biological field in the recent decades and it is the most important thing to design for research on what type of sequencing methods are suitable and how to handle the analysis steps for data integration. In this review, we comprehensively summarized NGS techniques for identifying genome, transcriptome, DNA methylome and 3D/4D chromatin structure, and introduced framework of processing data set and integrating multi-omics data for uncovering biomarkers.

Reaching the End-Game for GWAS: Machine Learning Approaches for the Prioritization of Complex Disease Loci

Genome-wide association studies (GWAS) have revealed thousands of genetic loci that underpin the complex biology of many human traits. However, the strength of GWAS - the ability to detect genetic association by linkage disequilibrium (LD) - is also its limitation. Whilst the ever-increasing study size and improved design have augmented the power of GWAS to detect effects, differentiation of causal variants or genes from other highly correlated genes associated by LD remains the real challenge. This has severely hindered the biological insights and clinical translation of GWAS findings. Although thousands of disease susceptibility loci have been reported, causal genes at these loci remain elusive. Machine learning (ML) techniques offer an opportunity to dissect the heterogeneity of variant and gene signals in the post-GWAS analysis phase. ML models for GWAS prioritization vary greatly in their complexity, ranging from relatively simple logistic regression approaches to more complex ensemble models such as random forests and gradient boosting, as well as deep learning models, i.e., neural networks. Paired with functional validation, these methods show important promise for clinical translation, providing a strong evidence-based approach to direct post-GWAS research. However, as ML approaches continue to evolve to meet the challenge of causal gene identification, a critical assessment of the underlying methodologies and their applicability to the GWAS prioritization problem is needed. This review investigates the landscape of ML applications in three parts: selected models, input features, and output model performance, with a focus on prioritizations of complex disease associated loci. Overall, we explore the contributions ML has made towards reaching the GWAS end-game with consequent wide-ranging translational impact.

epiCOLOC: Integrating Large-Scale and Context-Dependent Epigenomics Features for Comprehensive Colocalization Analysis

High-throughput genome-wide epigenomic assays, such as ChIP-seq, DNase-seq and ATAC-seq, have profiled a huge number of functional elements across numerous human tissues/cell types, which provide an unprecedented opportunity to interpret human genome and disease in context-dependent manner. Colocalization analysis determines whether genomic features are functionally related to a given search and will facilitate identifying the underlying biological functions characterizing intricate relationships with queries for genomic regions. Existing colocalization methods leveraged diverse assumptions and background models to assess the significance of enrichment, however, they only provided limited and predefined sets of epigenomic features. Here, we comprehensively collected and integrated over 44,385 bulk or single-cell epigenomic assays across 53 human tissues/cell types, such as transcription factor binding, histone modification, open chromatin and transcriptional event. By classifying these profiles into hierarchy of tissue/cell type, we developed a web portal, epiCOLOC (http://mulinlab.org/epicoloc or http://mulinlab.tmu.edu.cn/epicoloc), for users to perform context-dependent colocalization analysis in a convenient way.

The Promises and Challenges of Toxico-Epigenomics: Environmental Chemicals and Their Impacts on the Epigenome

BACKGROUND

It has been estimated that a substantial portion of chronic and noncommunicable diseases can be caused or exacerbated by exposure to environmental chemicals. Multiple lines of evidence indicate that early life exposure to environmental chemicals at relatively low concentrations could have lasting effects on individual and population health. Although the potential adverse effects of environmental chemicals are known to the scientific community, regulatory agencies, and the public, little is known about the mechanistic basis by which these chemicals can induce long-term or transgenerational effects. To address this question, epigenetic mechanisms have emerged as the potential link between genetic and environmental factors of health and disease.

OBJECTIVES

We present an overview of epigenetic regulation and a summary of reported evidence of environmental toxicants as epigenetic disruptors. We also discuss the advantages and challenges of using epigenetic biomarkers as an indicator of toxicant exposure, using measures that can be taken to improve risk assessment, and our perspectives on the future role of epigenetics in toxicology.

DISCUSSION

Until recently, efforts to apply epigenomic data in toxicology and risk assessment were restricted by an incomplete understanding of epigenomic variability across tissue types and populations. This is poised to change with the development of new tools and concerted efforts by researchers across disciplines that have led to a better understanding of epigenetic mechanisms and comprehensive maps of epigenomic variation. With the foundations now in place, we foresee that unprecedented advancements will take place in the field in the coming years. https://doi.org/10.1289/EHP6104.

Epigenetics: Linking Early Postnatal Nutrition to Obesity Programming?

Despite constant research and public policy efforts, the obesity epidemic continues to be a major public health threat, and new approaches are urgently needed. It has been shown that nutrient imbalance in early life, from conception to infancy, influences later obesity risk, suggesting that obesity could result from "developmental programming". In this review, we evaluate the possibility that early postnatal nutrition programs obesity risk via epigenetic mechanisms, especially DNA methylation, focusing on four main topics: (1) the dynamics of epigenetic processes in key metabolic organs during the early postnatal period; (2) the epigenetic effects of alterations in early postnatal nutrition in animal models or breastfeeding in humans; (3) current limitations and remaining outstanding questions in the field of epigenetic programming; (4) candidate pathways by which early postnatal nutrition could epigenetically program adult body weight set point. A particular focus will be given to the potential roles of breast milk fatty acids, neonatal metabolic and hormonal milieu, and gut microbiota. Understanding the mechanisms by which early postnatal nutrition can promote lifelong metabolic modifications is essential to design adequate recommendations and interventions to "de-program" the obesity epidemic.