Diagnostic classification based on DNA methylation profiles using sequential machine learning approaches.

Aberrant methylation patterns in human DNA have great potential for the discovery of novel diagnostic and disease progression biomarkers. In this paper we used machine learning algorithms to identify promising methylation sites for diagnosing cancerous tissue and to classify patients based on methylation values at these sites. We used genome-wide DNA methylation patterns from both cancerous and normal tissue samples, obtained from the Genomic Data Commons consortium and trialled our methods on three types of urological cancer. A decision tree was used to identify the methylation sites most useful for diagnosis. The identified locations were then used to train a neural network to classify samples as either cancerous or non-cancerous. Using this two-step approach we found strong indicative biomarker panels for each of the three cancer types. These methods could likely be translated to other cancers and improved by using non-invasive liquid methods such as blood instead of biopsy tissue.

A comprehensive map of the aging blood methylome in humans.

BACKGROUND: During aging, the human methylome undergoes both differential and variable shifts, accompanied by increased entropy. The distinction between variably methylated positions (VMPs) and differentially methylated positions (DMPs), their contribution to epigenetic age, and the role of cell type heterogeneity remain unclear. RESULTS: We conduct a comprehensive analysis of > 32,000 human blood methylomes from 56 datasets (age range = 6-101 years). We find a significant proportion of the blood methylome that is differentially methylated with age (48% DMPs; FDR < 0.005) and variably methylated with age (37% VMPs; FDR < 0.005), with considerable overlap between the two groups (59% of DMPs are VMPs). Bivalent and Polycomb regions become increasingly methylated and divergent between individuals, while quiescent regions lose methylation more uniformly. Both chronological and biological clocks, but not pace-of-aging clocks, show a strong enrichment for CpGs undergoing both mean and variance changes during aging. The accumulation of DMPs shifting towards a methylation fraction of 50% drives the increase in entropy, smoothening the epigenetic landscape. However, approximately a quarter of DMPs exhibit anti-entropic effects, opposing this direction of change. While changes in cell type composition minimally affect DMPs, VMPs and entropy measurements are moderately sensitive to such alterations. CONCLUSION: This study represents the largest investigation to date of genome-wide DNA methylation changes and aging in a single tissue, providing valuable insights into primary molecular changes relevant to chronological and biological aging.

Prostate cancer subtyping and differential methylation analysis based on the ETS family of transcription factors fusion genes.

Prostate cancer (PCa) is a highly heterogeneous disease, encompassing various molecular and clinical pathological subtypes. Fusion genes play a facilitating role in the occurrence and progression of PCa. We categorized PCa samples into the ETS family of transcription factors fusion positive and fusion negative subtypes based on fusion genes. This subtyping method is closely related to the epigenomic DNA methylation profiles of PCa, with each sample cluster including more than 85% of the patients. We conducted an analysis of the distribution of the ETS family fusion genes on chromosomes, fusion modes within reading frames, and predictions of structural domains. Among these, the highest frequency of the ETS family related fusion genes occurred on chromosome 21. Compared to the parental genes, fusion genes exhibited new structural domains, such as IG_like, and the most common fusion mode was out-of-frame fusion. The correlation between the methylation levels of hypermethylated CpG sites and the expression levels of their corresponding mRNAs indicates that CD8A and B3GNT5 (with correlations of - 0.388 and - 0.253, respectively) could serve as potential prognostic markers for PCa.

Discovery and validation of colorectal cancer tissue-specific methylation markers: a dual-center retrospective cohort study.

BACKGROUND AND PURPOSE: Early detection, diagnosis, and treatment of colorectal cancer and its precancerous lesions can significantly improve patients' survival rates. The purpose of this research is to identify methylation markers specific to colorectal cancer tissues and validate their diagnostic capability in colorectal cancer and precancerous changes by measuring the level of DNA methylation in stool samples. METHOD: We analyzed samples from six cancer tissues and adjacent normal tissues and fecal samples from 758 participants, including 62 patients with interfering diseases. Bioinformatics databases were used to screen for candidate biomarkers for CRC, and quantitative methylation-specific PCR methods were applied for identification. The methylation levels of the candidate biomarkers in fecal and tissue samples were measured. Logistic regression and random forest models were built and validated using fecal sample data from one of the centers, and the independent or combined diagnostic value of the candidate biomarkers in fecal samples for CRC and precancerous lesions was analyzed. Finally, the diagnostic capability and stability of the model were validated at another medical center. RESULTS: This study identified two colorectal cancer CpG sites with tissue specificity. These two biomarkers have certain diagnostic power when used individually, but their diagnostic value for colorectal cancer and colorectal adenoma is more significant when they are used in combination. CONCLUSION: The results indicate that a DNA methylation biomarker combined diagnostic model based on two CpG sites, cg13096260 and cg12587766, has the potential for screening and diagnosing precancerous lesions and colorectal cancer. Additionally, compared to traditional diagnostic models, machine learning algorithms perform better but may yield more false-positive results, necessitating further investigation.

Immune microenvironmental heterogeneity according to tumor DNA methylation phenotypes in microsatellite instability-high colorectal cancers.

The detailed association between tumor DNA methylation, including CpG island methylation, and tumor immunity is poorly understood. CpG island methylator phenotype (CIMP) is observed typically in sporadic colorectal cancers (CRCs) with microsatellite instability-high (MSI-H). Here, we investigated the differential features of the tumor immune microenvironment according to CIMP status in MSI-H CRCs. CIMP-high (CIMP-H) or CIMP-low/negative (CIMP-L/0) status was determined using MethyLight assay in 133 MSI-H CRCs. All MSI-H CRCs were subjected to digital pathology-based quantification of CD3 + /CD8 + /CD4 + /FoxP3 + /CD68 + /CD204 + /CD177 + tumor-infiltrating immune cells using whole-slide immunohistochemistry. Programmed death-ligand 1 (PD-L1) immunohistochemistry was evaluated using the tumor proportion score (TPS) and combined positive score (CPS). Representative cases were analyzed using whole-exome and RNA-sequencing. In 133 MSI-H CRCs, significantly higher densities of CD8 + tumor-infiltrating lymphocytes (TILs) were observed in CIMP-H tumors compared with CIMP-L/0 tumors. PD-L1 TPS and CPS in CIMP-H tumors were higher than in CIMP-L/0 tumors. Next-generation sequencing revealed that, compared with CIMP-L/0 tumors, CIMP-H tumors had higher fractions of CD8 + T cells/cytotoxic lymphocytes, higher cytolytic activity scores, and activated immune-mediated cell killing pathways. In contrast to CIMP-L/0 tumors, most CIMP-H tumors were identified as consensus molecular subtype 1, an immunogenic transcriptomic subtype of CRC. However, there were no differences in tumor mutational burden (TMB) between CIMP-H and CIMP-L/0 tumors in MSI-H CRCs. In conclusion, CIMP-H is associated with abundant cytotoxic CD8 + TILs and PD-L1 overexpression independent of TMB in MSI-H CRCs, suggesting that CIMP-H tumors represent a typical immune-hot subtype and are optimal candidates for immunotherapy in MSI-H tumors.

Prediction of Multiple Degenerative Diseases Based on DNA Methylation in a Co-Physiology Mechanisms Perspective.

Degenerative diseases oftentimes occur within the continuous process of aging, and the corresponding clinical manifestations may be neurodegeneration, neoplastic diseases, or various human complex diseases. DNA methylation provides the opportunity to explore aging and degenerative diseases as epigenetic traits. It has already been applied to age prediction and disease diagnosis. It has been shown that various degenerative diseases share co-physiology mechanisms with each other, clues of which may be gained from studying the aging process. Here, we endeavor to predict the risk of degenerative diseases in an aging-relevant comorbid mechanism perspective. Firstly, an epigenetic clock method was implemented based on a multi-scale convolutional neural network, and a Shapley feature attribution analysis was applied to discover the aging-related CpG sites. Then, these sites were further screened to a smaller subset composed of 196 sites by using biomics analysis according to their biological functions and mechanisms. Finally, we constructed a multilayer perceptron (MLP)-based degenerative disease risk prediction model, Mlp-DDR, which was well trained and tested to accurately classify nine degenerative diseases. Recent studies also suggest that DNA methylation plays a significant role in conditions like osteoporosis and osteoarthritis, broadening the potential applications of our model. This approach significantly advances the ability to understand degenerative diseases and represents a substantial shift from traditional diagnostic methods. Despite the promising results, limitations regarding model complexity and dataset diversity suggest directions for future research, including the development of tissue-specific epigenetic clocks and the inclusion of a wider range of diseases.

Universal penalized regression (Elastic-net) model with differentially methylated promoters for oral cancer prediction.

BACKGROUND: DNA methylation showed notable potential to act as a diagnostic marker in many cancers. Many studies proposed DNA methylation biomarker in OSCC detection, while most of these studies are limited to specific cohorts or geographical location. However, the generalizability of DNA methylation as a diagnostic marker in oral cancer across different geographical locations is yet to be investigated. METHODS: We used genome-wide methylation data from 384 oral cavity cancer and normal tissues from TCGA HNSCC and eastern India. The common differentially methylated CpGs in these two cohorts were used to develop an Elastic-net model that can be used for the diagnosis of OSCC. The model was validated using 812 HNSCC and normal samples from different anatomical sites of oral cavity from seven countries. Droplet Digital PCR of methyl-sensitive restriction enzyme digested DNA (ddMSRE) was used for quantification of methylation and validation of the model with 22 OSCC and 22 contralateral normal samples. Additionally, pyrosequencing was used to validate the model using 46 OSCC and 25 adjacent normal and 21 contralateral normal tissue samples. RESULTS: With ddMSRE, our model showed 91% sensitivity, 100% specificity, and 95% accuracy in classifying OSCC from the contralateral normal tissues. Validation of the model with pyrosequencing also showed 96% sensitivity, 91% specificity, and 93% accuracy for classifying the OSCC from contralateral normal samples, while in case of adjacent normal samples we found similar sensitivity but with 20% specificity, suggesting the presence of early disease methylation signature at the adjacent normal samples. Methylation array data of HNSCC and normal tissues from different geographical locations and different anatomical sites showed comparable sensitivity, specificity, and accuracy in detecting oral cavity cancer with across. Similar results were also observed for different stages of oral cavity cancer. CONCLUSIONS: Our model identified crucial genomic regions affected by DNA methylation in OSCC and showed similar accuracy in detecting oral cancer across different geographical locations. The high specificity of this model in classifying contralateral normal samples from the oral cancer compared to the adjacent normal samples suggested applicability of the model in early detection.

DMAP2: A Pipeline for Analysis of Whole-Genome-Scale DNA Methylation Sequencing Data.

DNA methylation is well-established as a major epigenetic mechanism that can control gene expression and is involved in both normal development and disease. Analysis of high-throughput-sequencing-based DNA methylation data is a step toward understanding the relationship between disease and phenotype. Analysis of CpG methylation at single-base resolution is routinely done by bisulfite sequencing, in which methylated Cs remain as C while unmethylated Cs are converted to U, subsequently seen as T nucleotides. Sequence reads are aligned to the reference genome using mapping tools that accept the C-T ambiguity. Then, various statistical packages are used to identify differences in methylation between (groups of) samples. We have previously developed the Differential Methylation Analysis Pipeline (DMAP) as an efficient, fast, and flexible tool for this work, both for whole-genome bisulfite sequencing (WGBS) and reduced-representation bisulfite sequencing (RRBS). The protocol described here includes a series of scripts that simplify the use of DMAP tools and that can accommodate the wider range of input formats now in use to perform analysis of whole-genome-scale DNA methylation sequencing data in various biological and clinical contexts. © 2024 The Author(s). Current Protocols published by Wiley Periodicals LLC. Basic Protocol: DMAP2 workflow for whole-genome bisulfite sequencing (WGBS) and reduced-representation bisulfite sequencing (RRBS).

Beyond the base pairs: comparative genome-wide DNA methylation profiling across sequencing technologies.

Deoxyribonucleic acid (DNA) methylation plays a key role in gene regulation and is critical for development and human disease. Techniques such as whole-genome bisulfite sequencing (WGBS) and reduced representation bisulfite sequencing (RRBS) allow DNA methylation analysis at the genome scale, with Illumina NovaSeq 6000 and MGI Tech DNBSEQ-T7 being popular due to their efficiency and affordability. However, detailed comparative studies of their performance are not available. In this study, we constructed 60 WGBS and RRBS libraries for two platforms using different types of clinical samples and generated approximately 2.8 terabases of sequencing data. We systematically compared quality control metrics, genomic coverage, CpG methylation levels, intra- and interplatform correlations, and performance in detecting differentially methylated positions. Our results revealed that the DNBSEQ platform exhibited better raw read quality, although base quality recalibration indicated potential overestimation of base quality. The DNBSEQ platform also showed lower sequencing depth and less coverage uniformity in GC-rich regions than did the NovaSeq platform and tended to enrich methylated regions. Overall, both platforms demonstrated robust intra- and interplatform reproducibility for RRBS and WGBS, with NovaSeq performing better for WGBS, highlighting the importance of considering these factors when selecting a platform for bisulfite sequencing.

Personality vulnerability to depression, resilience, and depressive symptoms: epigenetic markers among perinatal women.

Background: We examined differences in DNA methylation patterns in the NR3C1 and FKBP5 genes in relation to personality vulnerability to depression, resilience, and perinatal depressive symptoms, whilst also considering possible moderating effects of childhood traumatic events. Methods: N = 160 perinatal women were assessed at late pregnancy and 1 year postpartum for personality vulnerability to depression, resilience, depressive symptoms, and childhood traumatic events with self-reported questionnaires. NR3C1 and FKBP5 methylation markers were analyzed via sodium bisulfite sequencing. Associations of methylation markers with the above mentioned variables were tested using multivariable regressions. Results: NR3C1 methylation at CpGs 1, 4 and average methylation sites were negatively associated with resilience; NR3C1 methylation at CpG 2 was positively associated with postpartum depressive symptoms; methylation at CpG 4 was positively associated with prenatal depressive symptoms. The interaction between current distress due to interpersonal traumatic events and NR3C1 CpG sites in relation to personality vulnerability was significant on CpG sites 3 and 4, whereas the interaction between current distress due to total traumatic events and NR3C1 in relation to personality vulnerability was significant on CpG site 2. FKBP5 showed no significant associations with the outcomes. Conclusions: This study identified associations between NR3C1 methylation and resilience as well as perinatal depressive symptoms. Interestingly, an interaction between early trauma and personality vulnerability was noted. Our findings on these specific DNA methylation markers may, if replicated and integrated into risk prediction models, contribute to early diagnosis of mothers at risk, targeted health promotion, and early interventions.

DNA methylation variations of DNA damage response correlate survival and local immune status in melanomas.

AIM: We aimed to explore the impact of DNA methylation alterations on the DNA damage response (DDR) in melanoma prognosis and immunity. MATERIAL & METHODS: Different melanoma cohorts with molecular and clinical data were included. RESULTS: Hierarchical clustering utilizing different combinations of DDR-relevant CpGs yielded distinct melanoma subtypes, which were characteristic of different prognoses, transcriptional function profiles of DDR, and immunity and immunotherapy responses but were associated with similar tumor mutation burdens. We then constructed and validated a clinically applicable 4-CpG risk-score signature for predicting survival and immunotherapy response. CONCLUSION: Our study describes the close interrelationship among DNA methylation, DDR machinery, local tumor immune status, melanoma prognosis, and immunotherapy response.

The association between cumulative exposure to PM2.5 and DNA methylation measured using methyl-capture sequencing among COPD patients.

BACKGROUND: Particulate matter with a diameter of < 2.5 µm (PM2.5) influences gene regulation via DNA methylation; however, its precise mechanism of action remains unclear. Thus, this study aimed to examine the connection between personal PM2.5 exposure and DNA methylation in CpG islands as well as explore the associated gene pathways. METHODS: A total of 95 male patients with chronic obstructive pulmonary disease (COPD) were enrolled in this study. PM2.5 concentrations were measured for 12 months, with individual exposure recorded for 24 h every 3 months. Mean indoor and estimated individual PM2.5 exposure levels were calculated for short-term (7 days), mid-term (35 days), and long-term (90 days). DNA methylation analysis was performed on the blood samples, which, after PCR amplification and hybridization, were finally sequenced using an Illumina NovaSeq 6000 system. Correlation between PM2.5 exposure and CpG methylation sites was confirmed via a mixed-effects model. Functional enrichment analysis was performed on unique CpG methylation sites associated with PM2.5 exposure to identify the relevant biological functions or pathways. RESULTS: The number of CpG sites showing differential methylation was 36, 381, and 182 for the short-, mid-, and long-term indoor models, respectively, and 3, 98, and 28 for the short-, mid-, and long-term estimated exposure models, respectively. The representative genes were TMTC2 (p = 1.63 × 10-3, R2 = 0.656), GLRX3 (p = 1.46 × 10-3, R2 = 0.623), DCAF15 (p = 2.43 × 10-4, R2 = 0.623), CNOT6L (p = 1.46 × 10-4, R2 = 0.609), BSN (p = 2.21 × 10-5, R2 = 0.606), and SENP6 (p = 1.59 × 10-4, R2 = 0.604). Functional enrichment analysis demonstrated that the related genes were mostly associated with pathways related to synaptic transmission in neurodegenerative diseases and cancer. CONCLUSION: A significant association was observed between PM2.5 exposure and DNA methylation upon short-term exposure, and the extent of DNA methylation was the highest upon mid-term exposure. Additionally, various pathways related to neurodegenerative diseases and cancer were associated with patients with COPD. GOV IDENTIFIER: NCT04878367.

Identification and functional characterisation of DNA methylation differences between East- and West-originating Finns.

Eastern and Western Finns show a striking difference in coronary heart disease-related mortality; genetics is a known contributor for this discrepancy. Here, we discuss the potential role of DNA methylation in mediating the discrepancy in cardiometabolic disease-risk phenotypes between the sub-populations. We used data from the Young Finns Study (n = 969) to compare the genome-wide DNA methylation levels of East- and West-originating Finns. We identified 21 differentially methylated loci (FDR < 0.05; Δß >2.5%) and 7 regions (smoothed FDR < 0.05; CpGs ≥ 5). Methylation at all loci and regions associates with genetic variants (p < 5 × 10-8). Independently of genetics, methylation at 11 loci and 4 regions associates with transcript expression, including genes encoding zinc finger proteins. Similarly, methylation at 5 loci and 4 regions associates with cardiometabolic disease-risk phenotypes including triglycerides, glucose, cholesterol, as well as insulin treatment. This analysis was also performed in LURIC (n = 2371), a German cardiovascular patient cohort, and results replicated for the association of methylation at cg26740318 and DMR_11p15 with diabetes-related phenotypes and methylation at DMR_22q13 with triglyceride levels. Our results indicate that DNA methylation differences between East and West Finns may have a functional role in mediating the cardiometabolic disease discrepancy between the sub-populations.

G-quadruplex forming regions in GCK and TM6SF2 are targets for differential DNA methylation in metabolic disease and hepatocellular carcinoma patients.

The alarming increase in global rates of metabolic diseases (MetDs) and their association with cancer risk renders them a considerable burden on our society. The interplay of environmental and genetic factors in causing MetDs may be reflected in DNA methylation patterns, particularly at non-canonical (non-B) DNA structures, such as G-quadruplexes (G4s) or R-loops. To gain insight into the mechanisms of MetD progression, we focused on DNA methylation and functional analyses on intragenic regions of two MetD risk genes, the glucokinase (GCK) exon 7 and the transmembrane 6 superfamily 2 (TM6SF2) intron 2-exon 3 boundary, which harbor non-B DNA motifs for G4s and R-loops.Pyrosequencing of 148 blood samples from a nested cohort study revealed significant differential methylation in GCK and TM6SF2 in MetD patients versus healthy controls. Furthermore, these regions harbor hypervariable and differentially methylated CpGs also in hepatocellular carcinoma versus normal tissue samples from The Cancer Genome Atlas (TCGA). Permanganate/S1 nuclease footprinting with direct adapter ligation (PDAL-Seq), native polyacrylamide DNA gel electrophoresis and circular dichroism (CD) spectroscopy revealed the formation of G4 structures in these regions and demonstrated that their topology and stability is affected by DNA methylation. Detailed analyses including histone marks, chromatin conformation capture data, and luciferase reporter assays, highlighted the cell-type specific regulatory function of the target regions. Based on our analyses, we hypothesize that changes in DNA methylation lead to topological changes, especially in GCK exon 7, and cause the activation of alternative regulatory elements or potentially play a role in alternative splicing.Our analyses provide a new view on the mechanisms underlying the progression of MetDs and their link to hepatocellular carcinomas, unveiling non-B DNA structures as important key players already in early disease stages.

Cancer-associated DNA hypermethylation of Polycomb targets requires DNMT3A dual recognition of histone H2AK119 ubiquitination and the nucleosome acidic patch.

During tumor development, promoter CpG islands that are normally silenced by Polycomb repressive complexes (PRCs) become DNA-hypermethylated. The molecular mechanism by which de novo DNA methyltransferase(s) [DNMT(s)] catalyze CpG methylation at PRC-regulated regions remains unclear. Here, we report a cryo-electron microscopy structure of the DNMT3A long isoform (DNMT3A1) amino-terminal region in complex with a nucleosome carrying PRC1-mediated histone H2A lysine-119 monoubiquitination (H2AK119Ub). We identify regions within the DNMT3A1 amino terminus that bind H2AK119Ub and the nucleosome acidic patch. This bidentate interaction is required for effective DNMT3A1 engagement with H2AK119Ub-modified chromatin in cells. Further, aberrant redistribution of DNMT3A1 to Polycomb target genes recapitulates the cancer-associated DNA hypermethylation signature and inhibits their transcriptional activation during cell differentiation. This effect is rescued by disruption of the DNMT3A1-acidic patch interaction. Together, our analyses reveal a binding interface critical for mediating promoter CpG island DNA hypermethylation, a major molecular hallmark of cancer.

Discovery and Validation of Methylation Signatures in Circulating Cell-Free DNA for the Detection of Colorectal Cancer.

This study was conducted with the primary objective of assessing the performance of cfDNA methylation in the detection of colorectal cancer (CRC). Five tumor tissue, 20 peripheral blood leucocyte, and 169 cfDNA samples were collected for whole-genome bisulfite sequencing (WGBS) analysis. Bioinformatic analysis was conducted to identify differentially methylated regions (DMRs) and their functional characteristics. Quantitative methylation-specific PCR (qMSP) was used to validate the methylation levels of DMRs in the tissues and leucocytes. cfDNA samples from CRC patients and healthy controls were used to evaluate the performance of the DMR analysis. WGBS analysis revealed a decrease in DNA methylation levels in the CpG context in CRC tumor tissues compared with adjacent normal tissues. A total of 132 DMRs in cfDNA were identified as potential markers for diagnosing CRC. In a cohort of 95 CRC patients and 74 healthy controls, a combination of the three DMRs (DAB1, PPP2R5C, and FAM19A5) yielded an AUC of 0.763, achieving 64.21% sensitivity and 78.38% specificity in discriminating CRC patients from healthy controls. This study provides insights into DNA methylation patterns in CRC and identifies a set of DMRs in cfDNA with potential diagnostic value for CRC. These DMRs hold promise as biomarkers for CRC detection, offering promise for non-invasive CRC diagnosis. Further research is warranted to validate these findings in larger cohorts.

The ICF syndrome protein CDCA7 harbors a unique DNA binding domain that recognizes a CpG dyad in the context of a non-B DNA.

CDCA7, encoding a protein with a carboxyl-terminal cysteine-rich domain (CRD), is mutated in immunodeficiency, centromeric instability, and facial anomalies (ICF) syndrome, a disease related to hypomethylation of juxtacentromeric satellite DNA. How CDCA7 directs DNA methylation to juxtacentromeric regions is unknown. Here, we show that the CDCA7 CRD adopts a unique zinc-binding structure that recognizes a CpG dyad in a non-B DNA formed by two sequence motifs. CDCA7, but not ICF mutants, preferentially binds the non-B DNA with strand-specific CpG hemi-methylation. The unmethylated sequence motif is highly enriched at centromeres of human chromosomes, whereas the methylated motif is distributed throughout the genome. At S phase, CDCA7, but not ICF mutants, is concentrated in constitutive heterochromatin foci, and the formation of such foci can be inhibited by exogenous hemi-methylated non-B DNA bound by the CRD. Binding of the non-B DNA formed in juxtacentromeric regions during DNA replication provides a mechanism by which CDCA7 controls the specificity of DNA methylation.

Paternal high-fat diet affects weight and DNA methylation of their offspring.

Obesity poses a public health threat, reaching epidemic proportions. Our hypothesis suggests that some of this epidemic stems from its transmission across generations via paternal epigenetic mechanisms. To investigate this possibility, we focused on examining the paternal transmission of CpG methylation. First-generation male Wistar rats were fed either a high-fat diet (HF) or chow and were mated with females fed chow. We collected sperm from these males. The resulting offspring were raised on a chow diet until day 35, after which they underwent a dietary challenge. Diet-induced obese (DIO) male rats passed on the obesogenic trait to both male and female offspring. We observed significant hypermethylation of the Pomc promoter in the sperm of HF-treated males and in the hypothalamic arcuate nucleus (Arc) of their offspring at weaning. However, these differences in Arc methylation decreased later in life. This hypermethylation is correlated with increased expression of DNMT3B. Further investigating genes in the Arc that might be involved in obesogenic transgenerational transmission, using reduced representation bisulfite sequencing (RRBS) we identified 77 differentially methylated regions (DMRs), highlighting pathways associated with neuronal development. These findings support paternal CpG methylation as a mechanism for transmitting obesogenic traits across generations.

Intergenerational association of DNA methylation between parents and offspring.

Early patterning of DNA methylation (DNAm) may play an important role in later disease development. To better understand intergenerational epigenetic inheritance, we investigated the correlation between DNAm in blood in mother-newborn and in father-newborn pairs in the Isle of Wight (IoW) birth cohort. For parent-newborn pairs (n = 48), offspring DNAm was measured in cord blood and the parent's DNAm in whole blood. Mothers' DNAm was analyzed at birth (Guthrie card), age 18, early and late pregnancy respectively, and fathers' DNAm was measured during the mother's pregnancy. Linear regressions were applied to assess the intergenerational correlation of parental DNAm with that of offspring. Among various pairs of mother-newborn and father-newborn DNAm, the pairs where the mothers' DNAm was measured at age 18 years exhibited the highest number of CpGs with significant intergenerational correlation in DNAm, with 1829 CpGs (0.54%) of the 338,526 CpGs studied (FDR < 0.05). Amongst these 1829 CpGs, 986 (54%) are known quantitative trait loci (QTL) for CpG methylation (methQTL). When the mother's DNAm was assessed at early pregnancy, the number of CpGs showing intergenerational correlation was the smallest (384 CpGs, 0.11%). The second smallest number of such CpGs (559 CpGs, 0.17%) was found when investigating DNAm in offspring cord blood and father pairs. The low proportions of intergenerationally correlated CpGs suggest that epigenetic inheritance is limited.

Detection of differentially methylated CpGs between tumour and adjacent benign cells in diagnostic prostate cancer samples.

Differentially methylated CpG sites (dmCpGs) that distinguish prostate tumour from adjacent benign tissue could aid in the diagnosis and prognosis of prostate cancer. Previously, the identification of such dmCpGs has only been undertaken in radical prostatectomy (RP) samples and not primary diagnostic tumour samples (needle biopsy or transurethral resection of the prostate). We interrogated an Australian dataset comprising 125 tumour and 43 adjacent histologically benign diagnostic tissue samples, including 41 paired samples, using the Infinium Human Methylation450 BeadChip. Regression analyses of paired tumour and adjacent benign samples identified 2,386 significant dmCpGs (Bonferroni p < 0.01; delta-ß ≥ 40%), with LASSO regression selecting 16 dmCpGs that distinguished tumour samples in the full Australian diagnostic dataset (AUC = 0.99). Results were validated in independent North American (npaired = 19; AUC = 0.87) and The Cancer Genome Atlas (TCGA; npaired = 50; AUC = 0.94) RP datasets. Two of the 16 dmCpGs were in genes that were significantly down-regulated in Australian tumour samples (Bonferroni p < 0.01; GSTM2 and PRKCB). Ten additional dmCpGs distinguished low (n = 34) and high Gleason (n = 88) score tumours in the diagnostic Australian dataset (AUC = 0.95), but these performed poorly when applied to the RP datasets (North American: AUC = 0.66; TCGA: AUC = 0.62). The DNA methylation marks identified here could augment and improve current diagnostic tests and/or form the basis of future prognostic tests.