DNA Methylation Datasets Research Articles

Integrative analysis and machine learning application to TCGA DNA methylation, RNA-sequencing, and variant dataset in endometrial cancer for predicting recurrence

Integrative analysis and machine learning application to TCGA DNA methylation, RNA-sequencing, and variant dataset in endometrial cancer for predicting recurrence

Biological aging analysis based on DNA methylation status for social anxiety disorder.

Social anxiety disorder (SAD) is a common disorder characterized by excessive fear of scrutiny and embarrassment, leading to severe distress and avoidance behaviors or dysfunctions. SAD and other relevant diseases have been reported to be associated with a higher risk of aging-related diseases, such as Alzheimer's disease, Parkinson's disease, and diabetes mellitus. Recently, epigenetic clock analysis, which measures biological aging based on comprehensive DNA methylation (DNAm) status, has been widely conducted. We conducted epigenetic clock analyses in patients with SAD and controls, examining various epigenetic age acceleration and DNAm-based predictive values of aging-related proteins (GrimAge components and GrimAge2 components), including leptin level. We used the publicly available DNAm dataset, GSE164056, which consists of 66 patients with SAD and 77 controls of Caucasian descent aged between 18 and 50 years. We conducted regression analyses investigating the association between SAD and various indices of epigenetic aging, using age and sex as covariates. None of the epigenetic clocks showed significant differences in age acceleration. Of the DNAm-based predictive values of aging-related proteins, leptin level in GrimAge components (q = 0.0123) and GrimAge2 components (q = 0.0123) were significantly lower in patients with SAD than in controls. The results of this study suggested that leptin may be involved in SAD pathogenesis as an aging-related protein. Therefore, further studies with different designs are required.

The role of DNA methylation in chondrogenesis of human iPSCs as a stable marker of cartilage quality

BackgroundLack of insight into factors that determine purity and quality of human iPSC (hiPSC)-derived neo-cartilage precludes applications of this powerful technology toward regenerative solutions in the clinical setting. Here, we set out to generate methylome-wide landscapes of hiPSC-derived neo-cartilages from different tissues-of-origin and integrated transcriptome-wide data to identify dissimilarities in set points of methylation with associated transcription and the respective pathways in which these genes act.MethodsWe applied in vitro chondrogenesis using hiPSCs generated from two different tissue sources: skin fibroblasts and articular cartilage. Upon differentiation toward chondrocytes, these are referred to as hFiCs and hCiC, respectively. Genome-wide DNA methylation and RNA sequencing datasets were generated of the hiPSC-derived neo-cartilages, and the epigenetically regulated transcriptome was compared to that of neo-cartilage deposited by human primary articular cartilage (hPAC).ResultsMethylome-wide landscapes of neo-cartilages of hiPSCs reprogrammed from two different somatic tissues were 85% similar to that of hPACs. By integration of transcriptome-wide data, differences in transcriptionally active CpGs between hCiC relative to hPAC were prioritized. Among the CpG-gene pairs lower expressed in hCiCs relative to hPACs, we identified genes such as MGP, GDF5, and CHAD enriched in closely related pathways and involved in cartilage development that likely mark phenotypic differences in chondrocyte states. Vice versa, among the CpG-gene pairs higher expressed, we identified genes such as KIF1A or NKX2-2 enriched in neurogenic pathways and likely reflecting off target differentiation.ConclusionsWe did not find significant variation between the neo-cartilages derived from hiPSCs of different tissue sources, suggesting that application of a robust differentiation protocol such as we applied here is more important as compared to the epigenetic memory of the cells of origin. Results of our study could be further exploited to improve quality, purity, and maturity of hiPSC-derived neo-cartilage matrix, ultimately to realize introduction of sustainable, hiPSC-derived neo-cartilage implantation into clinical practice.

Glucose deprivation-induced disulfidptosis in human nucleus pulposus cells: a novel pathological mechanism of intervertebral disc degeneration

BackgroundLimited supply of certain nutrients and deregulation of nucleus pulposus (NP) plays a key role in the pathogenesis of intervertebral disc degeneration (IVDD). However, whether nutrient deprivation-induced cell death, particularly disulfidptosis, contributes to the depletion of NP cells and the development of IVDD, is unknown.MethodsRNA-seq, single-cell RNA-seq, and Genome-wide DNA methylation datasets of nucleus pulposus tissue were collected for bioinformatic analysis. Predictive models of disulfidptosis related genes in IVDD were constructed by machine learning and their differential expression was analyzed. In addition, we performed cell subsets identification analysis, cell-cell communications analysis, and functional enrichment analysis of key genes in the core subset based on single-cell RNA-seq data of NP tissues isolated from one normal sample and one IVDD sample. Finally, glucose deprivation-induced disulfidptosis in human NP cells (HNPCs) was verified by various cell death inhibitors and disulfidptosis-related molecular markers.ResultsWe found the disulfidptosis signal was significantly activated in the IVDD group. Using single-cell RNA-seq analysis, we focused on the chondrocytes and found that disulfidptosis-related genes significantly highly expressed in the IVDD C4 chondrocyte subset, which was identified as a new disulfidptosis-associated cell subset. Correlation analysis revealed the negative correlation between SLC7A11 (driving gene of disulfidptosis) and the glucose transporter GLUTs (SLC2A1-4) family genes (suppressing genes of disulfidptosis) in the IVDD group. We also found obvious cell death in HNPC upon glucose starvation, while employment of various cell death inhibitors could not inhibit glucose starvation-induced death in HNPCs. Moreover, the accumulation of disulfide bonds in cytoskeletal proteins was indicated by slowed migration in non-reducible protein blotting experiments. 2-DG, a key disulfidptosis inhibitor, significantly rescued cell death caused by glucose starvation through lowering the NADP+/NADPH ratio.ConclusionsWe validated the occurrence of disulfidptosis in HPNCs and identified a novel disulfidptosis-associated cell subset, followed by experimental verification of disulfidptosis in a glucose-limited context to mimic a fall in nutrient supply during the development disc degeneration. These findings provided new insights into the pathological mechanisms of IVDD and encourage us to explore potential therapeutic targets involved in the regulation of disulfidptosis for the prevention of intervertebral disc degeneration.Graphical

Uncertainty quantification in epigenetic clocks via conformalized quantile regression.

DNA methylation (DNAm) is a chemical modification of DNA that can be influenced by various factors, including age, environment, and lifestyle. An epigenetic clock is a predictive tool that measures biological age based on DNAm levels. It can provide insights into an individual's biological age, which may differ from their chronological age. This difference, known as the epigenetic age acceleration, may indicate the state of one's health and risk for age-related diseases. Moreover, epigenetic clocks are used in studies of aging to assess the effectiveness of anti-aging interventions and to understand the underlying mechanisms of aging and disease. Various epigenetic clocks have been developed using samples from different populations, tissues, and cell types, typically by training high-dimensional linear regression models with an elastic net penalty. While these models can predict mean biological age with high precision, there is a lack of uncertainty quantification which is important for interpreting the precision of age estimations and for clinical decision-making. To understand the distribution of a biological age clock beyond its mean, we propose a general pipeline for training epigenetic clocks, based on an integration of high-dimensional quantile regression and conformal prediction, to effectively reveal population heterogeneity and construct prediction intervals. Our approach produces adaptive prediction intervals not only achieving nominal coverage but also accounting for the inherent variability across individuals. By using the data collected from 728 blood samples in 11 DNAm datasets from children, we find that our quantile regression-based prediction intervals are narrower than those derived from conventional mean regression-based epigenetic clocks. This observation demonstrates an improved statistical efficiency over the existing pipeline for training epigenetic clocks. In addition, the resulting intervals have a synchronized varying pattern to age acceleration, effectively revealing cellular evolutionary heterogeneity in age patterns in different developmental stages during individual childhoods and adolescent cohort. Our findings suggest that conformalized high-dimensional quantile regression can produce valid prediction intervals and uncover underlying population heterogeneity. Although our methodology focuses on the distribution of aging in children, it is applicable to a broader range of populations to improve understanding of epigenetic age beyond the mean. This inference-based toolbox could provide valuable insights for future applications of epigenetic interventions for age-related diseases.

OS03-10 Early biomarkers of cancer susceptibility in benzene-exposed workers: integrated analysis of gene expression, microRNA alterations, and DNA methylation datasets

OS03-10 Early biomarkers of cancer susceptibility in benzene-exposed workers: integrated analysis of gene expression, microRNA alterations, and DNA methylation datasets

ScEpiAge: an age predictor highlighting single-cell ageing heterogeneity in mouse blood

Ageing is the accumulation of changes and decline of function of organisms over time. The concept and biomarkers of biological age have been established, notably DNA methylation-based clocks. The emergence of single-cell DNA methylation profiling methods opens the possibility of studying the biological age of individual cells. Here, we generate a large single-cell DNA methylation and transcriptome dataset from mouse peripheral blood samples, spanning a broad range of ages. The number of genes expressed increases with age, but gene-specific changes are small. We next develop scEpiAge, a single-cell DNA methylation age predictor, which can accurately predict age in (very sparse) publicly available datasets, and also in single cells. DNA methylation age distribution is wider than technically expected, indicating epigenetic age heterogeneity and functional differences. Our work provides a foundation for single-cell and sparse data epigenetic age predictors, validates their functionality and highlights epigenetic heterogeneity during ageing.

The CALERIE™ Genomic Data Resource.

Caloric restriction (CR) slows biological aging and prolongs healthy lifespan in model organisms. Findings from CALERIE-2™ - the first ever randomized, controlled trial of long-term CR in healthy, non-obese humans - broadly supports a similar pattern of effects in humans. To expand our understanding of the molecular pathways and biological processes underpinning CR effects in humans, we generated a series of genomic datasets from stored biospecimens collected from n=218 participants during the trial. These data constitute the first publicly-accessible genomic data resource for a randomized controlled trial of an intervention targeting the biology of aging. Datasets include whole-genome SNP genotypes, and three-timepoint-longitudinal DNA methylation, mRNA, and small RNA datasets generated from blood, skeletal muscle, and adipose tissue samples (total sample n=2327). The CALERIE Genomic Data Resource described in this article is available from the Aging Research Biobank. This mult-itissue, multi-omic, longitudinal data resource has great potential to advance translational geroscience.

Molecular precision medicine: Multi-omics-based stratification model for acute myeloid leukemia

Acute myeloid leukemia (AML), as the most common malignancy of the hematopoietic system, poses challenges in treatment efficacy, relapse, and drug resistance. In this study, we have utilized 151 RNA sequencing datasets, 194 DNA methylation datasets, and 200 somatic mutation datasets from the AML cohort in the TCGA database to develop a multi-omics stratification model. This model enables comparison of prognosis, clinical features, gene mutations, immune microenvironment and drug sensitivity across subgroups. External validation datasets have been sourced from the GEO database, which includes 562 mRNA datasets and 136 miRNA datasets from 984 adult AML patients. Through multi-omics-based stratification model, we classified 126 AML patients into 4 clusters (CS). CS4 had the best prognosis, with the youngest age, highest M3 subtype proportion, fewest copy number alterations, and common mutations in WT1, FLT3, and KIT genes. It showed sensitivity to HDAC inhibitors and BCL-2 inhibitors. Both the M3 subtype and CS4 were identified as independent protective factors for survival. Conversely, CS3 had the worst prognosis due to older age, high copy number alterations, and frequent mutations in RUNX1, DNMT3A, and TP53 genes. Additionally, it showed higher proportions of cytotoxic cells and Tregs, suggesting potential sensitivity to mTOR inhibitors. CS1 had a better prognosis than CS2, with more copy number alterations, while CS2 had higher monocyte proportions. CS1 showed good sensitivity to cytarabine, while CS2 was sensitive to RXR agonists. Both CS1 and CS2, which predominantly featured mutations in FLT3, NPM1, and DNMT3A genes, benefited from FLT3 inhibitors. Using the Kappa test, our stratification model underwent robust validation in the miRNA and mRNA external validation datasets. With advancements in sequencing technology and machine learning algorithms, AML is poised to transition towards multi-omics precision medicine in the future. We aspire for our study to offer new perspectives on multi-drug combination clinical trials and multi-targeted precision medicine for AML.

In Silico Analysis of Alzheimer's Disease Mechanism Through DNA Methylation and Gene Expression Data

Alzheimer's Disease (AD) is a debilitating disease impairing memory and thought process with currently no cure. In the current study, GEO2R, an integrated tool in the GEO database, was used to analyze DNA methylation and gene expression datasets associated with AD. Data from the BioGRID Database were used to create a PPI network of differentially methylation and expressed AD genes (DEMEGs). Cytoscape was used to image and analyze the PPI network topologically. The DAVID bioinformatics program was utilized to do enrichment analysis in order to uncover disease associations and signaling pathways. Furthermore, small molecules were predicted using Connectivity Map 2 (Cmap 2) as potential therapeutic agents that might be exploited as pharmacological targets for the study's DEMEGs. As a result, 502 mutual DEMEGs and several hubs that may be researched further as new biomarker candidates for AD such as SMURF1 and UBE2D2 were identified. The link between AD and the MAPK signaling pathway, as well as addiction and brain diseases such as ADHD and epilepsy has been established. Additionally, candidate small molecules that can be used as therapeutics such as flucloxacillin, butamben, and acetohexamide were proposed. This study integrated DNA methylation and gene expression data to further our knowledge of the AD disease mechanism.

Directional integration and pathway enrichment analysis for multi-omics data

Omics techniques generate comprehensive profiles of biomolecules in cells and tissues. However, a holistic understanding of underlying systems requires joint analyses of multiple data modalities. We present DPM, a data fusion method for integrating omics datasets using directionality and significance estimates of genes, transcripts, or proteins. DPM allows users to define how the input datasets are expected to interact directionally given the experimental design or biological relationships between the datasets. DPM prioritises genes and pathways that change consistently across the datasets and penalises those with inconsistent directionality. To demonstrate our approach, we characterise gene and pathway regulation in IDH-mutant gliomas by jointly analysing transcriptomic, proteomic, and DNA methylation datasets. Directional integration of survival information in ovarian cancer reveals candidate biomarkers with consistent prognostic signals in transcript and protein expression. DPM is a general and adaptable framework for gene prioritisation and pathway analysis in multi-omics datasets.

Image-based deep learning model using DNA methylation data predicts the origin of cancer of unknown primary

Cancer of unknown primary (CUP) is a rare type of metastatic cancer in which the origin of the tumor is unknown. Since the treatment strategy for patients with metastatic tumors depends on knowing the primary site, accurate identification of the origin site is important. Here, we developed an image-based deep-learning model that utilizes a vision transformer algorithm for predicting the origin of CUP. Using DNA methylation dataset of 8,233 primary tumors from The Cancer Genome Atlas (TCGA), we categorized 29 cancer types into 18 organ classes and extracted 2,312 differentially methylated CpG sites (DMCs) from non-squamous cancer group and 420 DMCs from squamous cell cancer group. Using these DMCs, we created organ-specific DNA methylation images and used them for model training and testing. Model performance was evaluated using 394 metastatic cancer samples from TCGA (TCGA-meta) and 995 samples (693 primary and 302 metastatic cancers) obtained from 20 independent external studies. We identified that the DNA methylation image reveals a distinct pattern based on the origin of cancer. Our model achieved an overall accuracy of 96.95 % in the TCGA-meta dataset. In the external validation datasets, our classifier achieved overall accuracies of 96.39 % and 94.37 % in primary and metastatic tumors, respectively. Especially, the overall accuracies for both primary and metastatic samples of non-squamous cell cancer were exceptionally high, with 96.79 % and 96.85 %, respectively.

MDB-19. THE 3D CHROMATIN LANDSCAPE OF MEDULLOBLASTOMA

Abstract BACKGROUND Medulloblastoma (MB) is a highly malignant childhood cerebellar tumor comprised of four molecularly and clinically distinct subgroups. Sporadic yet subgroup-specific genetic and epigenetic landscapes suggest that higher-order chromatin topology may contribute to distinct subgroup identities and disease biology. Herein, we investigated the 3D chromatin landscape of MB by performing genome-wide chromosome conformation capture (Hi-C) across molecular subgroups using a cohort of n=24 patient-derived xenografts and primary tumor specimens, aiming to elucidate regulatory mechanisms facilitated by genome organization. METHODS Integrative analyses were adapted to incorporate a wide variety of sample-paired datasets. Chromatin contact matrices generated from Hi-C allowed for the mapping of active/inactive compartments (A/B compartments), topologically associated domains (TADs), and distal pairwise chromatin contacts (loops). Topological insights of chromatin architecture were complemented with whole-genome sequencing, RNA-seq, ATAC-seq, DNA methylation, CTCF and histone modification (H3K27ac/H3K27me3) ChIP-seq datasets to dissect mechanisms of gene regulation. RESULTS Unsupervised analysis revealed that chromatin compartmentalization is subgroup stratified. Gene loci contained in subgroup-unique active compartments correspondingly exhibited subgroup-specific chromatin contact patterns, adopted open chromatin conformations, and remained transcriptionally active. As an example, LIN28B resides within a Group 3-specific ‘A’ compartment where its local topology displays higher complexity compared to that of other subgroups. This consequentially facilitates a Group 3-unique loop connecting the LIN28B promoter with a distal enhancer, resulting in subgroup-specific transcriptional upregulation. Targeted displacement of CTCF that anchors and stabilizes chromatin looping at the LIN28B locus was sufficient to alter its transcriptional activity. Using similar lines of investigation, mechanistic insights into the topological determinants of known enhancer-hijacking events, including GFI1, GFI1B and PRDM6, were resolved. CONCLUSIONS Pioneering this integrative approach centered on comprehensive Hi-C maps, our study provides a high-resolution blueprint of the 3D chromatin landscape defining MB subgroups, informing the mechanistic basis of recurrent driver gene activation, and disclosing putative subgroup-specific vulnerabilities.

MDB-59. INTEGRATIVE PROTEO-GENOMICS TO DECIPHER MOLECULAR MECHANISMS UNDERLYING SUBGROUP-SPECIFIC HETEROGENEITY IN MEDULLOBLASTOMA

Abstract BACKGROUND Medulloblastoma (MB), an aggressive pediatric brain cancer, poses significant treatment challenges due to its complex disease biology, comprising a collection of discrete molecular subtypes that require different treatment. Although the genomics, transcriptomics, and epigenetic landscapes of biologically distinct MB subgroups are well-annotated and have contributed to enhanced diagnostics and risk stratification, the intersection of these molecular layers with the MB proteome remains poorly defined METHODS To overcome this knowledge gap, we sought to create a deep proteomics and phospho-proteomics cohort from a large MB clinical trial cohort (SJMB12), comprising 166 primary patient tumors, complemented with matched DNA methylation, whole-exome sequencing (WES), and RNA sequencing (RNA-seq) datasets. Proteomics data were interrogated using unsupervised computational techniques and evaluated for pathway enrichment and kinase activity. To complement these bulk tumor datasets, we investigated the tumor microenvironment focusing on immune and glial cell populations, using single-nuclei RNA and ATAC sequencing (snRNA-seq and snATAC-seq) for validation RESULTS Distinct proteomics subgroups were identified, correlating with established MB clinical subgroups and unveiling previously unrecognized proteomics subgroups. The integration of proteomics with mutational data revealed downstream effects of genetic alterations not evident in the transcriptome, implicating novel molecular mechanisms contributing to disease pathogenesis. Protein-protein interaction networks discovered novel signaling hubs and pathways, highlighting the power of proteomics to detect subgroup-specific drivers of disease that may represent attractive therapeutic vulnerabilities CONCLUSIONS Our multi-omics approach applied to a large series of primary MB patient tumors revealed novel mechanisms underlying MB subgroup biology that were previously hidden at the genetic, epigenetic, and transcriptional level. These findings offer a comprehensive, systems biology view of MB pathogenesis, fostering provocative insights that serve as a foundation for future discovery and the development of more effective personalized treatment strategies.

A novel blood-based epigenetic biosignature in first-episode schizophrenia patients through automated machine learning

Schizophrenia (SCZ) is a chronic, severe, and complex psychiatric disorder that affects all aspects of personal functioning. While SCZ has a very strong biological component, there are still no objective diagnostic tests. Lately, special attention has been given to epigenetic biomarkers in SCZ. In this study, we introduce a three-step, automated machine learning (AutoML)-based, data-driven, biomarker discovery pipeline approach, using genome-wide DNA methylation datasets and laboratory validation, to deliver a highly performing, blood-based epigenetic biosignature of diagnostic clinical value in SCZ. Publicly available blood methylomes from SCZ patients and healthy individuals were analyzed via AutoML, to identify SCZ-specific biomarkers. The methylation of the identified genes was then analyzed by targeted qMSP assays in blood gDNA of 30 first-episode drug-naïve SCZ patients and 30 healthy controls (CTRL). Finally, AutoML was used to produce an optimized disease-specific biosignature based on patient methylation data combined with demographics. AutoML identified a SCZ-specific set of novel gene methylation biomarkers including IGF2BP1, CENPI, and PSME4. Functional analysis investigated correlations with SCZ pathology. Methylation levels of IGF2BP1 and PSME4, but not CENPI were found to differ, IGF2BP1 being higher and PSME4 lower in the SCZ group as compared to the CTRL group. Additional AutoML classification analysis of our experimental patient data led to a five-feature biosignature including all three genes, as well as age and sex, that discriminated SCZ patients from healthy individuals [AUC 0.755 (0.636, 0.862) and average precision 0.758 (0.690, 0.825)]. In conclusion, this three-step pipeline enabled the discovery of three novel genes and an epigenetic biosignature bearing potential value as promising SCZ blood-based diagnostics.

DNA methylation signatures provide novel diagnostic biomarkers and predict responses of immune therapy for breast cancer.

Breast cancer (BRCA) is one of the most common malignant tumors affecting women worldwide. DNA methylation modifications can influence oncogenic pathways and provide potential diagnostic and therapeutic targets for precision oncology. In this study, we used non-parametric permutation tests to identify differentially methylated positions (DMPs) between paired tumor and normal BRCA tissue samples from the Cancer Genome Atlas (TCGA) database. Then, we applied non-negative matrix factorization (NMF) to the DMPs to derive eight distinct DNA methylation signatures. Among them, signatures Hyper-S3 and Hypo-S4 signatures were associated with later tumor stages, while Hyper-S1 and Hypo-S3 exhibited higher methylation levels in earlier stages. Signature Hyper-S3 displayed an effect on overall survival. We further validated the four stage-associated signatures using an independent BRCA DNA methylation dataset from peripheral blood samples. Results demonstrated that 24 commonly hypomethylated sites in Hypo-S4 showed lower methylation in BRCA patients compared to healthy individuals, suggesting its potential as an early diagnostic biomarker. Furthermore, we found that methylation of 23 probes from four stage-related signatures exhibited predictive power for immune therapy response. Notably, methylation levels of all three probes from the Hypo-S4 and activity of the Hypo-S4 demonstrated highly positive relevance to PD-L1 gene expression, implying their significant predictive values for immunotherapy outcomes. GO and KEGG pathway enrichment analysis revealed that genes with these 23 immunotherapy-related methylation probes are mainly involved in glycan degradation or protein deglycosylation. These methylation signatures and probes may serve as novel epigenetic biomarkers for predicting tumor immunotherapy response. Our findings provide new insights into precision oncology approaches for BRCA.

Epigenetic Insights Into Necrotizing Enterocolitis: Unraveling Methylation-Regulated Biomarkers.

Necrotizing enterocolitis (NEC) is a multifactorial gastrointestinal disease with high morbidity and mortality among premature infants. This study aimed to identify novel methylation-regulated biomarkers in NEC intestinal tissue through multiomics analysis. We analyzed DNA methylation and transcriptome datasets from ileum and colon tissues of patients with NEC. We identify methylation-related differential genes (MrDEGs) based on the rule that the degree of methylation in the promoter region is inversely proportional to RNA transcription. These MrDEGs included ADAP1, GUCA2A, BCL2L14, FUT3, MISP, USH1C, ITGA3, UNC93A and IL22RA1. Single-cell data revealed that MrDEGs were mainly located in the intestinal epithelial part of intestinal tissue. These MrDEGs were verified through Target gene bisulfite sequencing and RT-qPCR. We successfully identified and verified the ADAP1, GUCA2A, IL22RA1 and MISP, primarily expressed in intestinal epithelial villus cells through single-cell data. Through single-gene gene set enrichment analysis, we found that these genes participate mainly in the pathological process of T-cell differentiation and the suppression of intestinal inflammation in NEC. This study enhances our understanding of the pathogenesis of NEC and may promote the development of new precision medicine methods for NEC prediction and diagnosis.

Genome-wide DNA methylation and transcriptomic analysis of liver tissues subjected to early ischemia/reperfusion injury upon human liver transplantation

Introduction and ObjectivesEpigenetic changes represent a mechanism connecting external stresses with long-term modifications of gene expression programs. In solid organ transplantation, ischemia-reperfusion injury (IRI) appears to induce epigenomic changes in the graft, although the currently available data are extremely limited. The present study aimed to characterize variations in DNA methylation and their effects on the transcriptome in liver transplantation from brain-dead donors. Patients and Methods12 liver grafts were evaluated through serial biopsies at different timings in the procurement-transplantation process: T0 (warm procurement, in donor), T1 (bench surgery), and T2 (after reperfusion, in recipient). DNA methylation (DNAm) and transcriptome profiles of biopsies were analyzed using microarrays and RNAseq. ResultsSignificant variations in DNAm were identified, particularly between T2 and T0. Functional enrichment of the best 1000 ranked differentially methylated promoters demonstrated that 387 hypermethylated and 613 hypomethylated promoters were involved in spliceosomal assembly and response to biotic stimuli, and inflammatory immune responses, respectively. At the transcriptome level, T2 vs. T0 showed an upregulation of 337 and downregulation of 61 genes, collectively involved in TNF-α, NFKB, and interleukin signaling. Cell enrichment analysis individuates macrophages, monocytes, and neutrophils as the most significant tissue-cell type in the response. ConclusionsIn the process of liver graft procurement-transplantation, IRI induces significant epigenetic changes that primarily act on the signaling pathways of inflammatory responses dependent on TNF-α, NFKB, and interleukins. Our DNAm datasets are the early IRI methylome literature and will serve as a launch point for studying the impact of epigenetic modification in IRI.

Molecular adaptations in response to exercise training are associated with tissue-specific transcriptomic and epigenomic signatures

Regular exercise has many physical and brain health benefits, yet the molecular mechanisms mediating exercise effects across tissues remain poorly understood. Here we analyzed 400 high-quality DNA methylation, ATAC-seq, and RNA-seq datasets from eight tissues from control and endurance exercise-trained (EET) rats. Integration of baseline datasets mapped the gene location dependence of epigenetic control features and identified differing regulatory landscapes in each tissue. The transcriptional responses to 8weeks of EET showed little overlap across tissues and predominantly comprised tissue-type enriched genes. We identified sex differences in the transcriptomic and epigenomic changes induced by EET. However, the sex-biased gene responses were linked to shared signaling pathways. We found that many G protein-coupled receptor-encoding genes are regulated by EET, suggesting a role for these receptors in mediating the molecular adaptations to training across tissues. Our findings provide new insights into the mechanisms underlying EET-induced health benefits across organs.

Histone proteoform analysis reveals epigenetic changes in adult mouse brown adipose tissue in response to cold stress

BackgroundRegulation of the thermogenic response by brown adipose tissue (BAT) is an important component of energy homeostasis with implications for the treatment of obesity and diabetes. Our preliminary analyses of RNA-Seq data uncovered many nodes representing epigenetic modifiers that are altered in BAT in response to chronic thermogenic activation. Thus, we hypothesized that chronic thermogenic activation broadly alters epigenetic modifications of DNA and histones in BAT.ResultsMotivated to understand how BAT function is regulated epigenetically, we developed a novel method for the first-ever unbiased top-down proteomic quantitation of histone modifications in BAT and validated our results with a multi-omic approach. To test our hypothesis, wildtype male C57BL/6J mice were housed under chronic conditions of thermoneutral temperature (TN, 28°C), mild cold/room temperature (RT, 22°C), or severe cold (SC, 8°C) and BAT was analyzed for DNA methylation and histone modifications. Methylation of promoters and intragenic regions in genomic DNA decrease in response to chronic cold exposure. Integration of DNA methylation and RNA expression datasets suggest a role for epigenetic modification of DNA in regulation of gene expression in response to cold. In response to cold housing, we observe increased bulk acetylation of histones H3.2 and H4, increased histone H3.2 proteoforms with di- and trimethylation of lysine 9 (K9me2 and K9me3), and increased histone H4 proteoforms with acetylation of lysine 16 (K16ac) in BAT.ConclusionsOur results reveal global epigenetically-regulated transcriptional “on” and “off” signals in murine BAT in response to varying degrees of chronic cold stimuli and establish a novel methodology to quantitatively study histones in BAT, allowing for direct comparisons to decipher mechanistic changes during the thermogenic response. Additionally, we make histone PTM and proteoform quantitation, RNA splicing, RRBS, and transcriptional footprint datasets available as a resource for future research.