Selected CpG Sites Research Articles

DNA methylation changes in the genome of patients with hypogonadotropic hypogonadism

Although some Mendelian neurodevelopmental disorders have been shown to entail specific DNA methylation changes designated as epi-signatures, it remains unknown whether epi-signatures are consistent features of other genetic disorders. Here, we analyzed DNA methylation profiles of patients with hypogonadotropic hypogonadism (HH), a rare neuroendocrine disorder typically caused by monogenic or oligogenic mutations. First, we performed microarray-based genome-wide methylation analyses of nine patients with HH due to ANOS1, SOX2, or SOX10 variants and 12 control individuals. The results showed that 1118 probes were differentially methylated in one or more patients. The differentially methylated probes were highly variable among patients. No significant methylation changes were observed in genes functionally associated with ANOS1, SOX2, or SOX10. Then, we performed pyrosequencing of six selected CpG sites in the nine patients and 35 additional HH patients. The results of the patients were compared with those of 48 fertile men. There were no common methylation changes among these patients, with the exception of hypermethylation of two CpG sites in the ZNF245 promoter of three patients. Hypermethylation of the promoter has previously been reported as a very rare epigenetic polymorphism in the general population. These results indicate that genomes of HH patients have considerable DNA methylation changes; however, these changes are more likely to be physiological epigenetic variations than disease-specific epi-signatures. Our data suggest a possible association between hypermethylation of the ZNF254 promoter and HH, which needs to be examined in future studies.

DNA methylation of bone morphogenetic protein 7 in leukocytes as a possible biomarker for hand osteoarthritis: A pilot study.

Hand osteoarthritis (HOA), characterized by an earlier onset age and reduced susceptibility to mechanical stress compared with knee and hip osteoarthritis, is considered a suitable disease for identifying predictive biomarkers of osteoarthritis. In particular, DNA methylation variants, expected to contribute to HOA susceptibility, hold potential as osteoarthritis biomarkers. In this study, leukocyte DNA methylation patterns were analyzed in blood samples from patients with HOA, aiming to identify disease-specific biomarkers for osteoarthritis. Using DNA methylation microarrays, we analyzed samples from three subjects with HOA and three age- and gender-matched healthy individuals. For validation, pyrosequencing analysis was conducted using samples from 16 to 9 subjects with and without HOA, respectively. From 735,026 probes in the DNA methylation array, the Top 100 CpG sites associated with HOA, based on low adjusted P-values, including those targeting bone morphogenetic protein 7 (BMP7), SBF2-AS1, PLOD2, ICOS, and CSF1R were identified. Validation analysis revealed significantly higher methylation levels in the BMP7-related site in the HOA group compared with the control group, even after adjusting for age, gender, and body mass index (p = 0.037). In contrast, no significant difference was observed in the other selected CpG sites between the HOA and control groups. This study highlights the significantly increased frequency of methylation at the specific BMP7 site in leukocytes of patients with HOA, suggesting its potential as a biomarker for HOA. Measurement of methylation levels at the CpG sites identified in this study offers a potential approach to prevent future osteoarthritis progression, providing valuable insights into disease management.

A semen-specific deoxyribonucleic acid methylation model for epigenetic age estimation and its robustness under environmental challenges.

In forensic investigations, semen samples are a common form of biological evidence, especially in cases involving sexual assault. Therefore, accurately estimating the age of an individual is crucial in criminal cases. This study presents a novel age estimation model based on semen-specific CpG methylation patterns. A multiplex panel was developed, consisting of 12 CpG sites (PARP14, C5orf25, cg23488376, MXRA5, PFKFB3, DLL1, NOX4, cg12837463, TTC7B, KCNA7, NKX2-1, and SYNE4), which exhibit strong correlations with age. Additionally, this study investigates the resilience of these methylation markers under simulated environmental challenges. We collected ejaculate samples from a diverse cohort of 115 male individuals, aged 20-71 years, who underwent deoxyribonucleic acid extraction and bisulfite conversion. Methylation levels of the selected CpG sites were assessed using a SNaPshot assay, which revealed significant correlations with chronological age. We developed and validated two robust age estimation models through stepwise and enter regression analyses, achieving reliable accuracy with mean absolute errors ranging from 3.81 to 4.1 years. Additionally, the study also investigated the robustness of semen stains under diverse environmental conditions, including fabric type, washing, hematin exposure, and UV-C light. The selected methylation markers demonstrated remarkable resilience despite the challenges posed by washing procedures and environmental exposure, confirming their potential for age estimation in forensic genetics. This research presents successful age estimation models, emphasizing the strong correlations between methylation levels and chronological age. The proposed methodology's accuracy is affirmed through model validation on an independent test set, while also highlighting the resilience of semen stains on fabrics under varying storage and washing conditions.

Maximizing Insights from Longitudinal Epigenetic Age Data: Simulations, Applications, and Practical Guidance.

Epigenetic Age (EA) is an age estimate, developed using DNA methylation (DNAm) states of selected CpG sites across the genome. Although EA and chronological age are highly correlated, EA may not increase uniformly with time. Departures, known as epigenetic age acceleration (EAA), are common and have been linked to various traits and future disease risk. Limited by available data, most studies investigating these relationships have been cross-sectional - using a single EA measurement. However, the recent growth in longitudinal DNAm studies has led to analyses of associations with EA over time. These studies differ in (i) their choice of model; (ii) the primary outcome (EA vs. EAA); and (iii) in their use of chronological age or age-independent time variables to account for the temporal dynamic. We evaluated the robustness of each approach using simulations and tested our results in two real-world examples, using biological sex and birthweight as predictors of longitudinal EA. Our simulations showed most accurate effect sizes in a linear mixed model or generalized estimating equation, using chronological age as the time variable. The use of EA versus EAA as an outcome did not strongly impact estimates. Applying the optimal model in real-world data uncovered an accelerated EA rate in males and an advanced EA that decelerates over time in children with higher birthweight. Our results can serve as a guide for forthcoming longitudinal EA studies, aiding in methodological decisions that may determine whether an association is accurately estimated, overestimated, or potentially overlooked.

Blood-based DNA methylation markers for lung cancer prediction

ObjectiveScreening high-risk individuals with low-dose CT reduces mortality from lung cancer, but many lung cancers occur in individuals who are not eligible for screening. Risk biomarkers may be useful to...

Prediction of disease-free survival for precision medicine using cooperative learning on multi-omic data.

In precision medicine, both predicting the disease susceptibility of an individual and forecasting its disease-free survival are areas of key research. Besides the classical epidemiological predictor variables, data from multiple (omic) platforms are increasingly available. To integrate this wealth of information, we propose new methodology to combine both cooperative learning, a recent approach to leverage the predictive power of several datasets, and polygenic hazard score models. Polygenic hazard score models provide a practitioner with a more differentiated view of the predicted disease-free survival than the one given by merely a point estimate, for instance computed with a polygenic risk score. Our aim is to leverage the advantages of cooperative learning for the computation of polygenic hazard score models via Cox's proportional hazard model, thereby improving the prediction of the disease-free survival. In our experimental study, we apply our methodology to forecast the disease-free survival for Alzheimer's disease (AD) using three layers of data. One layer contains epidemiological variables such as sex, APOE (apolipoprotein E, a genetic risk factor for AD) status and 10 leading principal components. Another layer contains selected genomic loci, and the last layer contains methylation data for selected CpG sites. We demonstrate that the survival curves computed via cooperative learning yield an AUC of around $0.7$, above the state-of-the-art performance of its competitors. Importantly, the proposed methodology returns (1) a linear score that can be easily interpreted (in contrast to machine learning approaches), and (2) a weighting of the predictive power of the involved data layers, allowing for an assessment of the importance of each omic (or other) platform. Similarly to polygenic hazard score models, our methodology also allows one to compute individual survival curves for each patient.

Machine learning unveils an immune-related DNA methylation profile in germline DNA from breast cancer patients

BackgroundThere is an unmet need for precise biomarkers for early non-invasive breast cancer detection. Here, we aimed to identify blood-based DNA methylation biomarkers that are associated with breast cancer.MethodsDNA methylation profiling was performed for 524 Asian Chinese individuals, comprising 256 breast cancer patients and 268 age-matched healthy controls, using the Infinium MethylationEPIC array. Feature selection was applied to 649,688 CpG sites in the training set. Predictive models were built by training three machine learning models, with performance evaluated on an independent test set. Enrichment analysis to identify transcription factors binding to regions associated with the selected CpG sites and pathway analysis for genes located nearby were conducted.ResultsA methylation profile comprising 51 CpGs was identified that effectively distinguishes breast cancer patients from healthy controls achieving an AUC of 0.823 on an independent test set. Notably, it outperformed all four previously reported breast cancer-associated methylation profiles. Enrichment analysis revealed enrichment of genomic loci associated with the binding of immune modulating AP-1 transcription factors, while pathway analysis of nearby genes showed an overrepresentation of immune-related pathways.ConclusionThis study has identified a breast cancer-associated methylation profile that is immune-related to potential for early cancer detection.

A cost-effective blood DNA methylation-based age estimation method in domestic cats, Tsushima leopard cats (Prionailurus bengalensis euptilurus) and Panthera species, using targeted bisulphite sequencing and machine learning models.

Individual age can be used to design more efficient and suitable management plans in both insitu and ex situ conservation programmes for targeted wildlife species. DNA methylation is a promising marker of epigenetic ageing that can accurately estimate age from small amounts of biological material, which can be collected in a minimally invasive manner. In this study, we sequenced five targeted genetic regions and used 8-23 selected CpG sites to build age estimation models using machine learning methods at only about $3-7 per sample. Blood samples of seven Felidae species were used, ranging from small to big, and domestic to endangered species: domestic cats (Felis catus, 139 samples), Tsushima leopard cats (Prionailurus bengalensis euptilurus, 84 samples) and five Panthera species (96 samples). The models achieved satisfactory accuracy, with the mean absolute error of the most accurate models recorded at 1.966, 1.348 and 1.552 years in domestic cats, Tsushima leopard cats and Panthera spp. respectively. We developed the models in domestic cats and Tsushima leopard cats, which were applicable to individuals regardless of health conditions; therefore, these models are applicable to samples collected from individuals with diverse characteristics, which is often the case in conservation. We also showed the possibility of developing universal age estimation models for the five Panthera spp. using only two of the five genetic regions. We do not recommend building a common age estimation model for all the target species using our markers, because of the degraded performance of models that included all species.

MICKEY: Memory-efficient deep learning for personalized biomarker discovery and cancer origin prediction from DNA methylation data.

109 Background: Carcinoma of unknown primary (CUP) accounts for up to 5% of all cancer cases and presents challenges in identifying primary cancer sites and successful treatment. DNA methylation abnormalities on the 5'-cytosine-phosphate-guanine-3' (CpG) motif across the genome are associated with carcinogenesis, enabling their use as cancer biomarkers for early diagnosis and tissue origin prediction. This study proposes a Combined Approach for 1) CpG site selection, 2) predicting cancer origin and identifying biomarkers, and 3) developing an open-source application for user-friendly data analysis. Methods: To emulate the state-like progression of cancer and accommodate missing values, discretized methylation beta values and missing values were tokenized. Feature selection using L1 regularization of lambda 0.0003 resulted in 152 CpG sites out of an initial set of 312,792. Three independent sets were selected for each of the 3 self-attention type: vanilla scaled dot-product, dense synthesizer, and factorized dense synthesizer. Self-attention enhances the biomarker discovery approach toward personalization as it generates a unique attention map for each sample input that highlights essential features for primary site prediction. A permutation test further evaluated contribution by the selected CpG sites, confirming biomarkers identification for each primary site. An open-source application was developed to predict cancer origin using methylation beta values, providing a user-friendly interface displaying predicted primary sites and corresponding percentages. Results: Independent sets of selected CpG performs comparably to the initial set with robustness (most well above 97% in precision, recall, and F1) across different clinical (sample type: normal, primary, metastatic, recurrent; and AJCC pathologic stage) and demographic groups (age, gender, race). The model identifies important biomarkers previously shown in literatures i.e. GATA4 and HOXD. Conclusions: This study offers valuable insights into feature selection, primary site prediction, and biomarker discovery using DNA methylation data, with potential practical applications for healthcare facilities and in personalized cancer treatment.

Placental epigenetic marks related to gestational weight gain reveal potential genes associated with offspring obesity parameters.

Offspring exposed to gestational obesity have an increased risk for chronic diseases. Increasing evidence suggests that epigenetics may play a mechanistic role in metabolic programming. This study aimed to identify placental DNA methylation marks associated with gestational weight gain (GWG) and to study their association with offspring obesity parameters at school age. A global methylation array was performed in 24 placentas from mothers with different degrees of GWG (screening sample). The methylation percentage of four cytosine-guanine (CpG) sites and the relative expression of the respective annotated genes were studied in 90 additional placentas (validation sample). Associations of these epigenetic marks with clinical parameters in the offspring at 6 years of age were examined. The screening analysis identified 104 CpG sites (97 genes) associated with GWG. The validation analysis of four selected CpG sites (annotating for FRAT1, SNX5, and KCNK3 genes) showed that the upregulation of SNX5 methylation, the downregulation of FRAT1 methylation, and KCNK3 underexpression associated with an adverse metabolic phenotype in childrenof women with increased GWG. These results suggest that placental regulation of FRAT1, SNX5, and KCNK3 relates to obesity parameters in offspring exposed to excessive GWG and thereby could condition the risk for future metabolic disorders.

Maternal-infant interaction quality is associated with child NR3C1 CpG site methylation at 7 years of age.

Infancy is both a critical window for hypothalamic-pituitary-adrenal (HPA) axis development, and a sensitive period for social-emotional influences. We hypothesized that the social-emotional quality of maternal-infant interactions are associated with methylation of HPA-axis gene NR3C1 later in childhood. Using a subsample of 114 mother-infant pairs from the Avon Longitudinal Study of Parents and Children (ALSPAC), linear regression models were created to predict variance in methylation of seven selected CpG sites from NR3C1 in whole blood at age 7 years, including the main predictor variable of the first principal component score of observed maternal-infant interaction quality (derived from the Thorpe Interaction Measure at 12 months of age) and covariates of cell-type proportion, maternal financial difficulties and marital status at 8 months postnatal, child birthweight, and sex. CpG site cg27122725 methylation was negatively associated with warmer, more positive maternal interaction with her infant (β=0.19, p=.02, q=0.13). In sensitivity analyses, the second highest quartile of maternal behavior (neutral, hesitant behavior) was positively associated with cg12466613 methylation. The other five CpG sites were not significantly associated with maternal-infant interaction quality. Narrow individual variation of maternal interaction with her infant is associated with childhood methylation of two CpG sites on NR3C1 that may be particularly sensitive to environmental influences. Infancy may be a sensitive period for even small influences from the social-emotional environment on the epigenetic determinants of HPA-axis function.

DNA methylation levels of RELN promoter region in ultra-high risk, first episode and chronic schizophrenia cohorts of schizophrenia

The essential role of the Reelin gene (RELN) during brain development makes it a prominent candidate in human epigenetic studies of Schizophrenia. Previous literature has reported differing levels of DNA methylation (DNAm) in patients with psychosis. Therefore, this study aimed to (1) examine and compare RELN DNAm levels in subjects at different stages of psychosis cross-sectionally, (2) analyse the effect of antipsychotics (AP) on DNAm, and (3) evaluate the effectiveness and applicability of RELN promoter DNAm as a possible biological-based marker for symptom severity in psychosis.. The study cohort consisted of 56 healthy controls, 87 ultra-high risk (UHR) individuals, 26 first-episode (FE) psychosis individuals and 30 chronic schizophrenia (CS) individuals. The Positive and Negative Syndrome Scale (PANSS) was used to assess Schizophrenia severity. After pyrosequencing selected CpG sites of peripheral blood, the Average mean DNAm levels were compared amongst the 4 subgroups. Our results showed differing levels of DNAm, with UHR having the lowest (7.72 ± 0.19) while the CS had the highest levels (HC: 8.78 ± 0.35; FE: 7.75 ± 0.37; CS: 8.82 ± 0.48). Significantly higher Average mean DNAm levels were found in CS subjects on AP (9.12 ± 0.61) compared to UHR without medication (UHR(−)) (7.39 ± 0.18). A significant association was also observed between the Average mean DNAm of FE and PANSS Negative symptom factor (R2 = 0.237, ß = −0.401, *p = 0.033). In conclusion, our findings suggested different levels of DNAm for subjects at different stages of psychosis. Those subjects that took AP have different DNAm levels. There were significant associations between FE DNAm and Negative PANSS scores. With more future experiments and on larger cohorts, there may be potential use of DNAm of the RELN gene as one of the genes for the biological-based marker for symptom severity in psychosis.

Hypermethylation in the promoter region of the ADRA1A gene is associated with opioid use disorder in Han Chinese

Opioid use disorder is a chronic brain disease influenced by genetic and epigenetic factors, accounting for approximately 50% of the liability. Adrenergic signaling is involved in opioid use disorder. To demonstrate the associations between methylation alterations in the alpha-1-adrenergic receptor (ADRA1A) gene and opioid use disorder, in the present study, we first examined and compared the methylation levels of 97 CpG sites in the promoter region of the ADRA1A gene in the peripheral blood in 120 patients with heroin use disorder and 111 healthy controls. Correlations between methylation levels and duration of heroin/methadone use were then analyzed. Finally, the predicted binding transcription factors (TFs) and their target sequences in the promoter region of the ADRA1A gene, which include the selected CpG sites, were screened in the JASPAR database. Our results demonstrated that hypermethylation in the promoter region of the ADRA1A gene in the blood was associated with opioid use disorder. Correlations between methylation levels of several CpG sites and duration of heroin/methadone use were observed. TFs TFAP2A and RUNX1 were predicted to bind to the target sequences, which include the CpG sites selected in the current study, in the promoter region of the ADRA1A gene. Our findings further extend the associations between methylation alterations in the ADRA1A gene and opioid use disorder potentially through mechanisms of gene expression regulations in the ADRA1A gene.

Accurate age estimation from blood samples of Han Chinese individuals using eight high-performance age-related CpG sites

Age-related CpG sites (AR-CpGs) are currently the most promising biomarkers for forensic age estimation. In our previous studies, we first validated the age correlation of seven reported AR-CpGs in blood samples of Chinese Han population. Subsequently, we screened some good age predictors from blood samples of Chinese Han population, and built pyrosequencing-based age prediction models. However, it is still important to select a set of high-performance AR-CpGs in a specific racial group and establish a simple and efficient method for accurate age estimation for forensic purpose. In this study, eight AR-CpGs, namely chr6: 11,044,628 (ELOVL2), cg06639320 (FHL2), chr1: 207,823,723 (C1orf132), cg19283806 (CCDC102B), cg14361627 (KLF14), cg17740900 (SYNE2), cg07553761 (TRIM59), and cg26947034, were selected based on our previous studies, and a multiplex methylation SNaPshot assay was developed to investigate DNA methylation levels at these AR-CpGs in 529 blood samples (aged 2-82years) from Han Chinese population. All selected CpG sites showed strong age correlation with the correlation coefficient (r) from 0.8363 to 0.9251. Multiple linear regression (MLR) and support vector regression (SVR) age prediction models were simultaneously established to fit change characteristics of DNA methylation levels of eight AR-CpGs with the age in 374 donors' blood samples. The MLR model enabled age prediction with R2 = 0.923, mean absolute error (MAE) = 3.52, while the SVR model enabled age prediction with R2 = 0.935, MAE = 2.88. One hundred fifty-five independent samples were used as a validation set to test the two models' performance, and the prediction MAE for the validation set was 3.71 and 3.34 for the MLR and SVR models, respectively. For the MLR and SVR models, the correct prediction rate at ± 5years reached a high level of 79.35% and 83.23%, respectively. In general, these statistical parameters indicated that the SVR model outperformed the MLR model in age prediction of the Han Chinese population. In addition, our method provides sufficient sensitivity in forensic applications and allows for 100% efficiency when examining bloodstains kept in room conditions for up to 43days. These results indicate that our multiplex methylation SNaPshot assay is a reliable, effective, and accurate method for age prediction in blood samples from the Chinese Han population.

Male-specific coordinated changes in expression of miRNA genes, but not other genes within the DLK1-DIO3 locus in multiple sclerosis

The role of miRNAs, small non-coding regulatory RNAs, in the molecular mechanisms of multiple sclerosis (MS) development has been intensively studied. MiRNAs tend to be clustered within imprinted regions, and the largest number of miRNA genes is observed in the DLK1-DIO3 locus. Earlier using RNA-seq we identified sex-specific upregulation of the set of miRNA genes from this locus in peripheral blood mononuclear cells (PBMC) of treatment-naive relapsing-remitting MS (RRMS) patients. In the present study we set up to independently investigate the expression of a vast array of genes present in the DLK1-DIO3 imprinted locus. First, we analyzed the expression of miRNA genes, which levels in RRMS were mostly inconsistent based on RNA-seq data and not previously explored using qPCR. We identified that all selected miRNAs – miR-337-3p and -665 from 14q32.2 cluster and miR-370c, -380, -494, -654-3p, -300, -539, -668, and -323b-5p – were upregulated in MS men, but not women when compared to controls, regardless of conflicting RNA-seq data. The expression of miRNAs from the DLK1-DIO3 locus was highly correlated, indicating the existence of a common regulatory mechanism(s) that controls miRNA expression, regardless of the position of their genes within this region. Second, we performed the expression analysis of non-miRNA genes within the locus. The genes encoding proteins (DLK1, DIO3, RTL1), long non-coding RNAs (MEG3, MEG8, and MEG9) and small nucleolar RNAs (SNORD112, SNORD113-5, SNORD113-7, SNORD114-3, SNORD114-8, SNORD114-19) were not dysregulated in RRMS both in men and women. DNA methylation analysis of selected CpG sites within the differentially methylated regions IG-DMR, MEG3-DMR, and MEG8-DMR of the DLK1-DIO3 imprinted locus pointed out that they were not involved in the regulation of miRNA gene expression in RRMS, at least in PBMC population. The question of whether the observed changes in expression of miRNA genes (given that there is a constant expression of other non-miRNA genes of the DLK1-DIO3 locus) are involved in the development of RRMS or are they a consequence of the disease progress, remains open and needs further investigation.

Investigation of BRCA1 methylation and FAT3 mutation as a potential biomarker in ovarian cancer samples

Ovarian cancer is one of the most dangerous and leading gynecological cancer with significant cancer related mortality among women. As the cancer detection is the key to increased survival, we aimed to test molecular markers in Ovarian Cancer (OC) Figo stage I, II and III tumor samples in order to identify genetic/epigenetic events having biomarker potential. Seven genes (BRCA1, IGFBP-3, DAPK1, RASSF1, CDKN2A, MLH1, and ANO5) were studied for their methylation status and three genes (FAT1, FAT3 and NF1) were tested for their mutation status on 24 ovarian cancer tumor samples and 10 normal ovarian tissue samples. The methylation frequency for BRCA1 and DAPK1 was found to be 83.3% and 8.3% respectively while no hypermethylation was observed for IGFBP-3, RASSF1, CDKN2A, ANO5 and MLH1 genes for selected CpG sites in all samples. BRCA1 hypermethylation was frequently found in serous (88.7%) than mucinous (66.6%) histotype (p < 0.035). Statistically, BRCA1 methylation was found commonly in older females (>45 year) (88%, n = 18), however, data was not significant (p < 0.25). BRCA1 methylation was also found to be associated with Figo stage III (78.5%) patients (p < 0.04), suggesting that this could be a late event in progression of ovarian cancers. Out of 24 OC samples, FAT1 (4.16%), FAT3(16.6%), and NF1 (8.3%) were found to be mutated for selected alleles. FAT3 mutation was recurrent in mucinous histology but did not display any significant correlation with clinic-pathological characteristics of tumor samples. Our study reports, frequent BRCA1 promoter methylation in serous and mucinous OC samples and therefore, it may serve as potential biomarker in therapeutic settings in future. FAT3 is an emerging diagnostic biomarker however, its clinical utility is required to be validated in a large cohort of ovarian cancer samples.

Naturalistic Stress Hormone Levels Drive Cumulative Epigenomic Changes along the Cellular Lifespan.

Environmental stress is ubiquitous in modern societies and can exert a profound and cumulative impact on cell function and health phenotypes. This impact is thought to be in large part mediated by the action of glucocorticoid stress hormones, primarily cortisol in humans. While the underlying molecular mechanisms are unclear, epigenetics—the chemical changes that regulate genomic function without altering the genetic code—has emerged as a key link between environmental exposures and phenotypic outcomes. The present study assessed genome-wide DNA (CpG) methylation, one of the key epigenetic mechanisms, at three timepoints during prolonged (51-day) exposure of cultured human fibroblasts to naturalistic cortisol levels, which can be reached in human tissues during in vivo stress. The findings support a spatiotemporal model of profound and widespread stress hormone-driven methylomic changes that emerge at selected CpG sites, are more likely to spread to nearby located CpGs, and quantitatively accrue at open sea, glucocorticoid receptor binding, and chromatin-accessible sites. Taken together, these findings provide novel insights into how prolonged stress may impact the epigenome, with potentially important implications for stress-related phenotypes.

Age Prediction of Human Based on DNA Methylation by Blood Tissues.

In recent years, scientists have found a close correlation between DNA methylation and aging in epigenetics. With the in-depth research in the field of DNA methylation, researchers have established a quantitative statistical relationship to predict the individual ages. This work used human blood tissue samples to study the association between age and DNA methylation. We built two predictors based on healthy and disease data, respectively. For the health data, we retrieved a total of 1191 samples from four previous reports. By calculating the Pearson correlation coefficient between age and DNA methylation values, 111 age-related CpG sites were selected. Gradient boosting regression was utilized to build the predictive model and obtained the R2 value of 0.86 and MAD of 3.90 years on testing dataset, which were better than other four regression methods as well as Horvath’s results. For the disease data, 354 rheumatoid arthritis samples were retrieved from a previous study. Then, 45 CpG sites were selected to build the predictor and the corresponded MAD and R2 were 3.11 years and 0.89 on the testing dataset respectively, which showed the robustness of our predictor. Our results were better than the ones from other four regression methods. Finally, we also analyzed the twenty-four common CpG sites in both healthy and disease datasets which illustrated the functional relevance of the selected CpG sites.

Identification of Diagnostic CpG Signatures in Patients with Gestational Diabetes Mellitus via Epigenome-Wide Association Study Integrated with Machine Learning.

Background Gestational diabetes mellitus (GDM) is the most prevalent metabolic disease during pregnancy, but the diagnosis is controversial and lagging partly due to the lack of useful biomarkers. CpG methylation is involved in the development of GDM. However, the specific CpG methylation sites serving as diagnostic biomarkers of GDM remain unclear. Here, we aimed to explore CpG signatures and establish the predicting model for the GDM diagnosis. Methods DNA methylation data of GSE88929 and GSE102177 were obtained from the GEO database, followed by the epigenome-wide association study (EWAS). GO and KEGG pathway analyses were performed by using the clusterProfiler package of R. The PPI network was constructed in the STRING database and Cytoscape software. The SVM model was established, in which the β-values of selected CpG sites were the predictor variable and the occurrence of GDM was the outcome variable. Results We identified 62 significant CpG methylation sites in the GDM samples compared with the control samples. GO and KEGG analyses based on the 62 CpG sites demonstrated that several essential cellular processes and signaling pathways were enriched in the system. A total of 12 hub genes related to the identified CpG sites were found in the PPI network. The SVM model based on the selected CpGs within the promoter region, including cg00922748, cg05216211, cg05376185, cg06617468, cg17097119, and cg22385669, was established, and the AUC values of the training set and testing set in the model were 0.8138 and 0.7576. The AUC value of the independent validation set of GSE102177 was 0.6667. Conclusion We identified potential diagnostic CpG signatures by EWAS integrated with the SVM model. The SVM model based on the identified 6 CpG sites reliably predicted the GDM occurrence, contributing to the diagnosis of GDM. Our finding provides new insights into the cross-application of EWAS and machine learning in GDM investigation.

CpG islands in MyD88 and ASC/PYCARD/TMS1 promoter regions are differentially methylated in head and neck squamous cell carcinoma and primary lung squamous cell carcinoma

BackgroundPatients with head and neck squamous cell carcinoma (HNSCC) can develop lung squamous cell carcinoma (LuSCC), which could be the second primary tumor or HNSCC metastasis. Morphologically it is difficult to distinguish metastatic HNSCC from a second primary tumor which presents a significant diagnostic challenge. Differentiation of those two malignancies is important because the recommended treatments for metastatic HNSCC and primary LuSCC differ significantly. We investigated if the quantification of the promotor methylation status in HNSCC and LuSCC differs.MethodsPrimary HNSCC (N = 36) and LuSCC (N = 17) were included in this study. Methylation status in the ASC/TMS1/PYCARD (apoptosis-associated speck-like protein containing a caspase recruitment domain; 8 CpG sites) and MyD88 (Myeloid differentiation primary response protein 88; 10 CpG sites) promoters was analyzed. Bisulfite converted DNA, isolated from tumor tissue was quantified using pyrosequencing. Results of pyrosequencing analysis were expressed as a percentage for each tested CpG site. Receiver-operating characteristic (ROC) curve analysis was used for the evaluation of the diagnostic properties of selected biomarkers.ResultsCpG sites located in the promoters of ASC/TMS1/PYCARD_CpG8 (− 65 upstream) and MyD88_CpG4 (− 278 upstream) are significantly hypermethylated in the HNSCC when compared with LuSCC (p ≤ 0.0001). By performing ROC curve analysis we showed that corresponding areas under the curve (AUC) were 85–95%, indicating that selected CpG sites are useful for a distinction between primary LuSCC and primary HNSCC.ConclusionsResults of the present study indicate that there is a significant difference in the methylation status of tested genes between primary HNSCC and LuSCC. However, to prove this approach as a useful tool for distinguishing second primary LuSCC from HNSCC metastasis, it would be necessary to include a larger number of samples, and most importantly, metastatic samples.