Crohn's disease (CD) and ulcerative colitis (UC) are chronic relapsing inflammatory bowel disorders (IBD), the pathogenesis of which is uncertain but includes genetic susceptibility factors, immune-mediated tissue injury and environmental influences, most of which appear to act via the gut microbiome. We hypothesized that host-microbe alterations could be used to prognostically stratify patients experiencing relapses up to four years after endoscopy. We therefore examined multiple omics data, including published and new datasets, generated from paired inflamed and non-inflamed mucosal biopsies from 142 patients with IBD (54 CD; 88 UC) and from 34 control (non-diseased) biopsies. The relapse-predictive potential of 16S rRNA gene and transcript amplicons (standing and active microbiota) were investigated along with host transcriptomics, epigenomics and genetics. While standard single-omics analysis could not distinguish between patients who relapsed and those that remained in remission within four years of colonoscopy, we did find an association between the number of flares and a patient's succinotype. Our multi-omics machine learning approach was also able to predict relapse when combining features from the microbiome and human host. Therefore multi-omics, rather than single omics, better predicts relapse within 4 years of colonoscopy, while a patient's succinotype is associated with a higher frequency of relapses.

Vasospasm is a potentially preventable cause of poor prognosis in patients with aneurysmal subarachnoid hemorrhage (aSAH). Epigenetics might provide insight on its molecular mechanisms. We aimed to analyze the association between differential DNA methylation (DNAm) and development of vasospasm. We conducted an epigenome-wide association study in 282 patients with aSAH admitted to our hospital. DNAm was assessed with the EPIC Illumina chip (> 850 K CpG sites) in whole-blood samples collected at hospital admission. We identified differentially methylated positions (DMPs) at the CpG level using Cox regression models adjusted for potential confounders, and then we used the DMP results to find differentially methylated regions (DMRs) and enriched biological pathways. A total of 145 patients (51%) experienced vasospasm. In the DMP analysis, we identified 31 CpGs associated with vasospasm at p-value < 10-5. One of them (cg26189827) was significant at the genome-wide level (p-value < 10-8), being hypermethylated in patients with vasospasm and annotated to SUGCT gene, mainly expressed in arteries. Region analysis revealed 13 DMRs, some of them annotated to interesting genes such as POU5F1, HLA-DPA1, RUFY1, and CYP1A1. Functional enrichment analysis showed the involvement of biological processes related to immunity, inflammatory response, oxidative stress, endothelial nitric oxide, and apoptosis. Our findings show, for the first time, a distinctive epigenetic signature of vasospasm in aSAH, establishing novel links with essential biological pathways, including inflammation, immune responses, and oxidative stress. Although further validation is required, our results provide a foundation for future research into the complex pathophysiology of vasospasm.

DNA methylation (DNAm) is a chemical modification of DNA that can be influenced by various factors, including age, the 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 reflect health status and the risk for age-related diseases. Moreover, epigenetic clocks are used in studies of aging to assess the effectiveness of antiaging 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 based on DNAm 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 data sets 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 measures of biological aging in children, it is applicable to a broader range of age groups 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.

Green space exposure during pregnancy has been associated with lower risk of adverse birth outcomes, but the biological mechanisms remain unclear. Epigenetic changes, such as DNA methylation (DNAm), may contribute to this association. The placenta, crucial for foetal development, has been understudied in relation to prenatal green space exposure and DNAm on a genome-wide scale. Here, we aimed to investigate the association between green space exposure during pregnancy and epigenome-wide placental DNAm in 550 mother-child pairs from the Barcelona Life Study Cohort (BiSC) in Spain. Green space exposure was assessed as (i) residential surrounding greenness (satellite-based Normalized Difference Vegetation Index (NDVI) in buffers of 100 m, 300 m and 500 m), (ii) residential distance to the nearest major green space (meters), (iii) use of green space (hours/week), and (iv) visual access to greenery through the home window (≥half of the view). Placental DNAm was measured with the EPIC array. Differentially methylated positions (DMPs) were identified using robust linear regression models adjusted for covariates, while differentially methylated regions (DMRs) were identified using the dmrff method. After Bonferroni correction, cg14852540, annotated to SLC25A10 gene, showed an inverse association with residential greenness within 500 m buffer. Additionally, 101 DMPs were suggestively significant (p-values <1 × 10-5) and annotated to genes involved in glucocorticoid-related pathways, inflammatory response, oxidative stress response, and oocyte maturation. No DMRs were identified. Overall, we identified an association between residential greenness and DNAm levels at one CpG in the SLC25A10 gene. Larger studies are needed to validate these findings and understand the biological pathways.

A number of studies have shown that methylomes of blood cells of COVID-19 patients display infection-related methylation changes. Those methylation abnormalities were associated with clinical outcomes of the infection and, thus, can potentially be utilized as biomarkers in clinical COVID-19 management. However, a number of parameters need to be assessed, before a clinical use of a biomarker candidate is proposed, with the most important one being reproducible detection in independent target populations.

We benchmarked data reported in publications investigating genome-wide methylation changes occurring in blood cells during SARS-CoV-2 virus infection to assess potential applicability of these changes as biomarkers in clinical management of COVID-19 patients.

We identified nine studies, in which infection-related genome-wide methylation changes in blood cells of COVID-19 patients were reported and associations of those changes with outcomes of the infection was assessed. Our benchmarking analysis showed that each of those studies included patients with different clinical characteristics and each study utilized different symptoms assessment criteria. Most importantly, no uniform data analysis methodologies to identify virus-related methylation changes were used in the analyzed studies. Consequently, analyzed studies, except for one, which was based on uniform data analysis workflow applied across independent patient cohorts, reported sufficiently uniform results that would allow to propose the use of the identified infection-related methylation changes as biomarkers for clinical management of COVID-19 patients.

Due to lack of coherence between studies reporting SARS-CoV-2 associated methylation changes in blood, the potential use of already reported methylation changes as biomarkers for clinical management of COVID-19 patients needs to be further assessed in rigorously controlled studies. At the same time unified data analysis framework appears to be the most critical in development of biomarkers associated with the severity of SARS-CoV-2 infection.

One-carbon metabolism (OCM), a biochemical pathway dependent on micronutrients including B vitamins, plays an essential role in aging-related physiological processes. DNA methylation-based aging biomarkers may be influenced by OCM.

This study investigated associations of OCM-related biomarkers with epigenetic aging biomarkers in the cross-sectional National Health and Nutrition Examination Survey (1999-2002).

Blood DNA methylation was measured in adults aged ≥50 y. The following epigenetic aging biomarkers were included: Horvath1, Horvath2, Hannum, PhenoAge, GrimAge2, Dunedin Pace-of-Aging (DunedinPoAm), and DNA methylation telomere length (DNAmTL). We tested for associations of serum folate, red blood cell folate, vitamin B12, homocysteine (Hcy), and methylmalonic acid (MMA) concentrations with epigenetic age deviation (EAD) among 2346 participants with epigenetic and nutritional status biomarkers using adjusted survey-weighted general linear regression models.

A doubling of serum folate concentration was associated with -0.82 y (95% confidence interval: -1.40, -0.23) lower GrimAge EAD, -0.13 SDs (-0.22, -0.03) lower DunedinPoAm, and 0.02 kb (0.00, 0.04) greater DNAmTL EAD. Conversely, a doubling in Hcy concentration was associated with 1.05 y (0.06, 2.04) greater PhenoAge EAD, 1.93 y (1.16, 2.71) greater GrimAge2 EAD, and 0.26 SDs (0.10, 0.41) greater DunedinPoAm. Associations of serum folate with EAD were attenuated after adjusting for smoking status, alcohol intake, and estimated glomerular filtration rate. Furthermore, smoking modified the associations of Hcy with GrimAge2 EAD. Chronic kidney disease modified associations of B12 and MMA with Horvath1 and GrimAge2 EAD, respectively.

In a nationally representative sample of United States adults, higher concentration of folate, a carbon donor, was associated with lower EAD, and higher concentration of Hcys, an indicator of OCM deficiencies, was associated with greater EAD; however, some associations were influenced by smoking and renal function. Future research should focus on high-risk populations. Long-term randomized controlled trials are also needed to establish causality and investigate the clinical relevance of changes in EAD.

The results of studies assessing impact of arsenic exposure on methylome are to large extent inconsistent. To contribute to understanding of effect of arsenic exposure on methylome of the exposed cells, we assess the impact of low-level arsenic exposure on methylome of blood cells in three cohorts of exposed individuals.

The Infinium MethylationEPIC array (Illumina Inc.) was used for genome-wide methylation profiling and robust linear regression to identify arsenic-related methylation changes in blood cells from healthy individuals with a 12-year cancer-free follow-up and breast cancer patients, sampled on average 4.29 years before diagnosis, as well as methylomics data from cord blood samples of Biomarkers of Exposure to Arsenic cohort.

Our analysis identified a 2,453 arsenic-associated methylation changes in blood from healthy individuals, 9,662 in breast cancer patients and 6,745 in cord blood samples. Similarly to previous studies methylation changes that we identified in each cohort, overlapped only to some extent. However, molecular processes linked to identified methylation changes were very similar in each of the cohorts. And included pathways that could be clearly associated with the adverse effects of arsenic exposure and specifically cancer in the cohort of cancer patients. Moreover, the genomic regions harboring identified in each cohort methylation changes were similar and predominantly included regions participating in regulation of gene transcription.

Overall, our findings show that specificity of arsenic related methylation changes is low but the impact of these changes on cell physiology is very similar across three cohorts we studded.

Boys' pubertal overweight associates with future offspring's asthma and low lung function. To identify how paternal overweight is associated with offspring's DNA methylation (DNAm), we conducted an epigenome-wide association study of father's body silhouette (FBS) at three timepoints (age 8, voice break and 30) and change in FBS between these times, with offspring DNAm, in the RHINESSA cohort (N = 339). We identified 2005 differentially methylated cytosine-phosphate-guanine (dmCpG) sites (FDR < 0.05), including dmCpGs associated with offspring asthma (119), lung function (178) and BMI (291). Voice break FBS associated with dmCpGs in loci including KCNJ10, FERMT1, NCK2 and WWP1. Change in FBS across sexual maturation associated with DNAm at loci including NOP10, TRRAP, EFHD1, MRPL17 and NORD59A;ATP5B and showed strong correlation in reduced gene expression in loci NAP1L5, ATP5B, ZNF695, ZNF600, VTRNA2-1, SOAT2 and AGPAT2. We identified 24 imprinted genes including: VTRNA2-1, BLCAP, WT1, NAP1L5 and PTPRN2. Identified pathways relate to lipid and glucose metabolism and adipogenesis. Father's overweight at puberty and during reproductive maturation was strongly associated with offspring DNA, suggesting a key role for epigenetic mechanisms in intergenerational transfer from father to offspring in humans. The results support an important vulnerability window in male puberty for future offspring health.

Prenatal Exposure to Arsenic (As) is associated with child neurodevelopment disorders. However, the specific mechanisms involved remain unclear. This work intends to investigate the associations between prenatal As exposure and epigenome-wide Deoxyribo Nucleic Acid methylation (DNAm) and evaluate the role of DNAm in moderating the association between prenatal As exposure and child neurodevelopment. An As-related epigenome-wide DNAm association analysis was performed using robust linear models, and mediation analysis was further applied to explore potential DNAm mediators. Robust linear models were applied to perform an epigenome-wide association study (EWAS) for DNAm related to As exposure. Mediation analysis was subsequently conducted to explore potential DNAm mediators. The mental development index (MDI) score was found to be inversely associated with urinary As levels during the third trimester [β = -3.52, 95 % CI: -6.34, -0.71]. A total of 48 differential DNAm locations and 4 differentially methylated regions were found to be associated with urinary As concentration. Three cytidylyl phosphate guanosine positions (annotated to ARMC5, KIAA1217, and intergenic region, mediated proportion is around 30 %) mediated the association between urinary As and a reduction of MDI score (P < 0.05). Our findings indicated adverse effects of prenatal As exposure on child neurodevelopment, and specific DNAm played the role of partial mediator.

Ovarian cancer has the highest mortality rate among gynecological cancers, making early detection crucial, as the five-year survival rate drops from 92% with early-stage diagnosis compared to 31% with late-stage diagnosis. Current diagnostic methods such as histopathological examination and detection of cancer antigen 125 and human epididymis protein 4 biomarkers are either invasive or lack specificity and sensitivity. However, the Papanicolaou (Pap) test, which is widely used for cervical cancer screening, shows the potential for detecting ovarian cancer by identifying tumor DNA in cervical scrapings. Since aberrant DNA methylation patterns are linked to cancer progression, DNA methylation offers a promising avenue for early diagnosis. Therefore, this study aimed to develop a methylation-based machine-learning model to stratify patients with ovarian cancer from the cervical scraping samples collected via Pap test.

Cervical scrapings were collected by gynecologists using conventional Pap smears. In total, 160 samples were collected: 95 normal, 37 benign, and 28 malignant. Methylation data were generated using the Illumina Infinium MethylationEPIC BeadChip array, which contains approximately 850,000 CpG loci. Methylation data were initially divided into training and testing sets in a 3:1 ratio comprising 120 and 40 samples, respectively. A two-step methylation-based model was trained using the training data for classification: a principal component analysis (PCA) model, consisting of 30 features, to classify samples as normal or tumor; then a gradient boosting model, containing 16 features, to further stratify tumor samples as benign or malignant. The two-step model achieved an accuracy of 0.88 and an F1-score of 0.86 on the testing data. Furthermore, an over-representation analysis was conducted to explore the functions associated with genes mapped from differentially methylated positions (DMPs) in comparisons between normal and tumor samples, as well as between benign and malignant samples. These results suggest that DMPs may be associated with olfactory transduction when comparing normal versus tumor samples, and immune regulation when comparing benign and malignant samples.

Our two-step model shows promise for predicting ovarian cancer and suggests that cervical scrapings may be a viable alternative for sample collection during screening.

Alveolar rhabdomyosarcoma (ARMS) primarily affects children in the first decade of life, but it can also occur during adolescence, typically with a more favorable prognosis. This study aimed to explore differences in DNA methylation (DNAm) and gene expression profiles that may account for the worse prognosis in younger patients; and to investigate possible new therapeutic targets.

We conducted whole-genome DNAm and transcriptome analyses on 10 parameningeal head and neck ARMS patients, including 4 patients under 1 year old and 6 over 10 years old. Among the differentially expressed genes, we focused on actionable therapeutic targets and confirmed their protein expression levels by immunohistochemistry. We validated the biological relevance of molecules of interest through functional experiments on rhabdomyosarcoma cell lines.

DNAm profiles did not significantly differ across age groups, while gene expression was the primary driver of observed differences. Several enriched pathways characterized younger patients with respect to older ones, including FAS, Integrin, PI3 kinase, and p53 by glucose deprivation. Among actionable molecules, cyclin dependent kinase 9 (CDK9) emerged as a promising therapy target, highly expressed in younger patients. Of note, CDK9 inhibitors specifically inhibit cell growth in bi- and three-dimensional ARMS cellular models, both as a monotherapy and in combination with BRD4 inhibitors.

Despite the small sample size, these findings suggest potential age-related molecular mechanisms and highlight candidate genes for further investigation as novel therapeutic targets. Notably, we identified CDK9 as a promising target, warranting further exploration in the context of ARMS treatment.

Epigenetic aging estimators commonly track chronological and biological aging, quantifying its accumulation (i.e., epigenetic age acceleration) or speed (i.e., epigenetic aging pace). Their scores reflect a combination of inherent biological programming and the impact of environmental factors, which are suggested to vary at different life stages. The transition from adolescence to adulthood is an important period in this regard, marked by an increasing and, then, stabilizing epigenetic aging variance. Whether this pattern arises from environmental influences or genetic factors is still uncertain. This study delves into understanding the genetic and environmental contributions to variance in epigenetic aging across these developmental stages. Using twin modeling, we analyzed four estimators of epigenetic aging, namely Horvath Acceleration, PedBE Acceleration, GrimAge Acceleration, and DunedinPACE, based on saliva samples collected at two timepoints approximately 2.5 years apart from 976 twins of four birth cohorts (aged about 9.5, 15.5, 21.5, and 27.5 years at first and 12, 18, 24, and 30 years at second measurement occasion).

Half to two-thirds (50-68%) of the differences in epigenetic aging were due to unique environmental factors, indicating the role of life experiences and epigenetic drift, besides measurement error. The remaining variance was explained by genetic (Horvath Acceleration: 24%; GrimAge Acceleration: 32%; DunedinPACE: 47%) and shared environmental factors (Horvath Acceleration: 26%; PedBE Acceleration: 47%). The genetic and shared environmental factors represented the primary sources of stable differences in corresponding epigenetic aging estimators over 2.5 years. Age moderation analyses revealed that the variance due to individually unique environmental sources was smaller in younger than in older cohorts in epigenetic aging estimators trained on chronological age (Horvath Acceleration: 47-49%; PedBE Acceleration: 33-68%). The variance due to genetic contributions, in turn, potentially increased across age groups for epigenetic aging estimators trained in adult samples (Horvath Acceleration: 18-39%; GrimAge Acceleration: 24-43%; DunedinPACE: 42-57%).

Transition to adulthood is a period of the increasing variance in epigenetic aging. Both environmental and genetic factors contribute to this trend. The degree of environmental and genetic contributions can be partially explained by the design of epigenetic aging estimators.

Estimating individual age from DNA methylation at age associated CpG sites may provide key information facilitating forensic investigations. Systematic marker screening and feature selection play a critical role in ensuring the performance of the final prediction model. In the discovery stage, we screened for 811876 CpGs from whole blood of 2664 Chinese individuals ranging from 18 to 83 years of age based on a stepwise conditional epigenome-wide association study (SCEWAS). The SCEWAS identified 28 CpGs showing genome-wide significant and independent effects. Further restricting this panel to 10 most informative CpGs showed a tolerable loss of information. A linear model consisting of these 10 CpGs could explain 93% of the age variance (R2 = 0.93) in the training set (n = 2664). In an independent test set of Chinese individuals (n = 648), this model also provided highly accurate predictions (R2 = 0.85, mean absolute deviation, MAD = 3.20 years). The model was additionally validated in a public dataset of multiple ancestral origins (86 Europeans, 14 Asians, and 273 Africans) and the prediction accuracy reduced significantly (R2 = 0.85, MAD = 6.21 years), as might be expected due to different genomic backgrounds, sample sizes, and age ranges. Our 10 CpG model also outperformed the recently proposed 9-CpG model constructed in 390 Chinese males (R2 = 0.79 in test set). We also demonstrated that our SCEWAS approach outperformed the traditional EWAS and the elastic net approach in obtaining a small set of most age informative CpGs. Overall, our systematic genome-wide feature selection identified a small panel of 10 CpGs for accurate age estimation with high potential in forensic applications.

DNA-methylation profile scores (MPSs) index biology relevant for lifelong physical and cognitive health, but information on their longitudinal stability in childhood is lacking. Using two waves of data collected from 2014 to 2022 (Mlag between waves = 2.41 years) from N = 407 participants (Mage = 12.05 years, 51% female, 60% White), test-retest correlations were estimated for four salivary MPSs related to aging (PhenoAgeAccel, GrimAgeAccel, DunedinPACE), and cognitive function (Epigenetic-g). MPSs varied in longitudinal stability (test-retest rs = 0.38 to 0.76). MPSs did not differ in children exposed to the COVID-19 pandemic, but race-ethnic and sex differences were apparent. Further research is necessary to understand which environmental perturbations impact DNA-methylation trajectories and when children are most sensitive to those impacts.

Maternal infections during pregnancy can increase the risk to offspring of developing a neurodevelopmental disorder. Given the global prevalence and severity of infection with Severe Acute Respiratory Syndrome related Coronavirus 2 (SARS-CoV-2), the objective of this study was to determine if in utero exposure to severe maternal SARS-CoV-2 infection alters infant neurodevelopmental outcomes at 12 months and to identify potential biological markers of adverse infant outcomes. Mother-infant dyads exposed to severe SARS-CoV-2 infection (requiring hospitalization) during pregnancy and age and sociodemographic matched control dyads were recruited from Monash Medical Centre, Australia in 2021/22 and prospectively assessed over 12 months. Maternal serum cytokine levels and Edinburgh Postnatal Depression Scale (EPDS) scores were assessed at birth. DNA methylation was assessed from infant buccal swabs at birth (Illumina EPIC BeadChip). Infant neurodevelopmental outcomes at 12 months were assessed using the Ages and Stages Questionnaire (ASQ-3). Mothers exposed to severe SARS-CoV-2 exhibited elevated serum IL-6 and IL-17A and higher EPDS scores than controls at birth. Infants exposed to severe SARS-CoV-2 in utero demonstrated over 3000 significant differentially methylated sites within their genomes compared to non-exposed (adjusted p-value < 0.05), including genes highly relevant to ASD and synaptic pathways. At 12 months, severe SARS-CoV-2 exposed infants scored lower on the ASQ-3 than non-exposed infants, and communication and problem-solving scores negatively correlated with maternal IL-6 levels at birth. DNA methylation changes therefore unveil potential mechanisms linking infection exposure to delayed neurodevelopment and maternal serum IL-6 levels may be a potential biomarker of child developmental delay. Mothers exposed to severe SARS-CoV-2 infections show elevated pro-inflammatory cytokines. Infants exposed in utero to severe SARS-CoV-2 infection show altered DNA methylation at birth and delayed development at 12 months of age. Created in Biorender.com.

Due to the ever-increasing wirelessly transmitted data, the development of new transmission standards and higher frequencies in the 5G band is required. Despite basic biophysical considerations that argue against health effects, there is public concern about this technology. Because the skin penetration depth at these frequencies is only 1 mm or less, we exposed fibroblasts and keratinocytes to electromagnetic fields up to ten times the permissible limits, for 2 and 48 h in a fully blinded experimental design. Sham-exposed cells served as negative, and UV-exposed cells as positive controls. Differences in gene expression and methylation due to exposure were small and not higher than expected by chance. These data strongly support the assessment that there is no evidence for exposure-induced damage to human skin cells.

Maintenance of the mitochondrial inner membrane potential (ΔΨm) is critical for many aspects of mitochondrial function. While ΔΨm loss and its consequences are well studied, little is known about the effects of mitochondrial hyperpolarization. In this study, we used cells deleted of ATP5IF1 (IF1), a natural inhibitor of the hydrolytic activity of the ATP synthase, as a genetic model of increased resting ΔΨm. We found that the nuclear DNA hypermethylates when the ΔΨm is chronically high, regulating the transcription of mitochondrial, carbohydrate and lipid genes. These effects can be reversed by decreasing the ΔΨm and recapitulated in wild-type (WT) cells exposed to environmental chemicals that cause hyperpolarization. Surprisingly, phospholipid changes, but not redox or metabolic alterations, linked the ΔΨm to the epigenome. Sorted hyperpolarized WT and ovarian cancer cells naturally depleted of IF1 also showed phospholipid remodeling, indicating this as an adaptation to mitochondrial hyperpolarization. These data provide a new framework for how mitochondria can impact epigenetics and cellular biology to influence health outcomes, including through chemical exposures and in disease states.

Alzheimer's disease (AD) is the most common neurodegenerative disease affecting the elderly, with its diagnosis at early stages crucial for effective intervention. Recent evidence increasingly supports the role of epigenetic alterations in AD pathogenesis, highlighting the need for innovative biomarkers that reflect these changes. This study aimed to characterize the genome-wide DNA methylation profiles of cell-free DNA in peripheral blood for potential biomarkers associated with AD.

The Illumina Infinium array was utilized to detect the methylation patterns of circulating cell-free DNA from AD patients and healthy controls. The R Bioconductor Linear Models for Microarray Data (LIMMA) package was employed to identify methylation variable positions (MVPs), and Probe Lasso was used to pinpoint differentially methylated regions (DMRs) linked to AD. Bioinformatics enrichment analysis of the annotated genes was performed using EnrichR. A second cohort was recruited to validate the methylation changes at the C-terminal binding protein1 (CTBP1) promoter cytosine-phosphate-guanine (CpG) sites via pyrosequencing. Additionally, microarray data from the Gene Expression Omnibus (GEO) database were analyzed to further validate gene expression and immune infiltration.

A unique DNA methylation landscape in peripheral blood was characterized for AD patients and 4335 MVPs showed significant differential methylation (p < 0.01). Functional annotation and pathway enrichment analysis underscored processes and pathways inherent in the nervous system. Probe Lasso identified 68 DMRs annotated to 10 genes, with hypermethylation of CpG islands in the CTBP1 TSS1500 promoter showing significant differences when AD and controls were compared (p < 0.01), with an area under the receiver operating characteristic (ROC) curve (AUC) of 0.779. Analysis of immune cell infiltration revealed CTBP1 expression is significantly correlated with altered distribution of immune cells (p < 0.001), underscoring its potential role in modulating immune responses in AD. Moreover, CTBP1 expression levels significantly varied across multiple GEO datasets.

AD displays distinct DNA methylation patterns in peripheral blood and CTBP1 promoter hypermethylation represents a promising potential biomarker for AD diagnosis.

Epigenetic algorithms of aging, health, and cognition, based on DNA-methylation (DNAm) patterns, are prominent tools for measuring biological age and have been linked to age-related diseases, cognitive decline, and mortality. While most of these methylation profile scores (MPSs) are developed in blood tissue, there is growing interest in using less invasive tissues like saliva. The aim of the current study is to probe the cross-tissue intraclass correlation coefficients (ICCs) of MPSs developed in blood applied to saliva DNAm from the same people. While our primary focus is on MPSs that were previously found to be robustly correlated with social determinants of health, including second- and third-generation clocks and MPSs of physiology and cognition, we also report ICC values for first-generation clocks to enable comparison across metrics. We pooled three publicly available datasets that had both saliva and blood DNAm from the same individuals (total n = 107, aged 5-74 years), corrected MPSs for cell composition within each tissue, and computed the cross-tissue ICCs.

We found that after correcting for cell composition, saliva-blood cross-tissue ICCs were moderate for second- and third-generation indices of aging and MPSs of physiology and cognition. Specifically, PCGrimAge had the highest ICC (0.76), followed by PCPhenoAge (0.72), a measure of cognitive performance (Epigenetic-g, 0.69), DunedinPACE (0.68), PCGrimAge Acceleration (0.67), PCPhenoAge Acceleration (0.66), an MPS of hs-CRP (0.58), and BMI (0.54). These ICCs appear lower than previous reports on within-tissue ICCs (saliva ICCs range from 0.67 to 0.85, blood ICCs range from 0.73 to 0.93). Cross-tissue ICCs values for first-generation biological age acceleration measures were poor, ranging from 0.19 to 0.25.

Our findings suggest that applying second- and third-generation MPSs of biological age acceleration and related phenotypes developed in blood to saliva DNAm results in moderate cross-tissue similarity and the precise cross-tissue correspondence differs by measure. While the degree of cross-tissue similarity of several MPSs may suffice for some research settings, it may not be suitable in clinical or commercial applications. Collection of both blood and saliva DNAm samples is necessary to validate existing algorithms and to customize MPSs in saliva DNAm.

This study aims to evaluate differences between Infinium MethylationEPIC (EPICv1) and Infinium MethylationEPICv2 (EPICv2) arrays in estimating DNAm age with eleven DNAm clocks using buffy coat, peripheral blood mononuclear cell (PBMC), and saliva from 16 healthy middle-aged individuals. DNAm ages were estimated using six principal component-based (PC) clocks (PCHorvath1, PCHorvath2, PCHannum, PCPhenoAge, PCGrimAge, and PCDNAmTL) and five non-PC clocks (DunedinPACE, DNAmFit, YingCausAge, YingAdaptAge, and YingDamAge) across all biological samples. Agreement between arrays was assessed using Spearman correlation, Bland-Altman plots, and Wilcoxon Signed-Rank test. The 16 individuals with median age of 48 [43.5;53.8] years, were predominantly female, Chinese and non-smokers. High correlations (ρ > 0.8) were observed between EPICv1 and EPICv2 except for DunedinPACE, YingDamAge and YingAdaptAge. PC-based clocks showed lower systematic bias (MAPE:0.118-8.98%) compared to non-PC-based clocks (MAPE:5.31-21.2%). Saliva samples demonstrated greatest variability between arrays. EPICv2 introduces systematic biases especially in non-PC-based clocks and between different biological samples.

This study investigated the genetic and epigenetic mechanisms underlying the comorbidity of five substance dependence diagnoses (SDs; alcohol, AD; cannabis, CaD; cocaine, CoD; opioid, OD; tobacco, TD). A latent class analysis (LCA) was performed on 22,668 individuals from six cohorts to identify comorbid DSM-IV SD patterns. In subsets of this sample, we tested SD-latent classes with respect to polygenic overlap of psychiatric and psychosocial traits in 7659 individuals of European descent and epigenome-wide changes in 886 individuals of African, European, and Admixed-American descents. The LCA identified four latent classes related to SD comorbidities: AD + TD, CoD + TD, AD + CoD + OD + TD (i.e., polysubstance addiction, PSU), and TD. In the epigenome-wide association analysis, SPATA4 cg02833127 was associated with CoD + TD, AD + TD, and PSU latent classes. AD + TD latent class was also associated with CpG sites located on ARID1B, NOTCH1, SERTAD4, and SIN3B, while additional epigenome-wide significant associations with CoD + TD latent class were observed in ANO6 and MOV10 genes. PSU-latent class was also associated with a differentially methylated region in LDB1. We also observed shared polygenic score (PGS) associations for PSU, AD + TD, and CoD + TD latent classes (i.e., attention-deficit hyperactivity disorder, anxiety, educational attainment, and schizophrenia PGS). In contrast, TD-latent class was exclusively associated with posttraumatic stress disorder-PGS. Other specific associations were observed for PSU-latent class (subjective wellbeing-PGS and neuroticism-PGS) and AD + TD-latent class (bipolar disorder-PGS). In conclusion, we identified shared and unique genetic and epigenetic mechanisms underlying SD comorbidity patterns. These findings highlight the importance of modeling the co-occurrence of SD diagnoses when investigating the molecular basis of addiction-related traits.

This study aimed to identify biomarkers for colorectal cancer (CRC) with representative gene functions and high classification accuracy in tissue and blood samples.

We integrated CRC DNA methylation profiles from The Cancer Genome Atlas and comorbidity patterns of CRC to select biomarker candidates. We clustered these candidates near the promoter regions into multiple functional groups based on their functional annotations. To validate the selected biomarkers, we applied 3 machine learning techniques to construct models and compare their prediction performances.

The 10 screened genes showed significant methylation differences in both tissue and blood samples. Our test results showed that 3-gene combinations achieved outstanding classification performance. Selecting 3 representative biomarkers from different genetic functional clusters, the combination of ADHFE1, ADAMTS5, and MIR129-2 exhibited the best performance across the 3 prediction models, achieving a Matthews correlation coefficient > .85 and an F1-score of .9.

Using integrated DNA methylation analysis, we identified 3 CRC-related biomarkers with remarkable classification performance. These biomarkers can be used to design a practical clinical toolkit for CRC diagnosis assistance and may also serve as candidate biomarkers for further clinical experiments through liquid biopsies.

The use of cannabis has been associated with both therapeutic and harmful effects. As with cigarette smoking, cannabis smoking may affect the epigenetic regulation (e.g., DNA methylation) of gene expression which could result in long term health effects. The study of DNA methylation in cannabis smoking has to date been restricted to young adults and there remains yet no evaluation of whether cannabis smoking cessation can reverse epigenetic disturbances. Here, we aimed to investigate the relationship between genome-wide DNA methylation and cannabis smoking.

We used peripheral blood from a subset of older adults within the Canadian Cohort of Obstructive Lung Disease (CanCOLD) cohort (n = 93) to conduct an epigenome-wide DNA methylation analysis that identified differential methylated positions (DMPs) associated with cannabis smoking at a false discovery rate < 0.05. Using these DMPs, we then identified differentially methylated genes (DMGs) that enriched pathways associated with both former and current cannabis smoking status.

We found DMPs corresponding to 12,115 DMGs and 10,806 DMGs that distinguished the current and former cannabis smoking groups, respectively, from the never cannabis smoking group. 5,915 of these DMGs were shared between the current and former cannabis smoking groups. 50 enriched pathways were also shared between the current and former cannabis smoking groups, which were heavily represented by multiple aging- and cancer-related pathways.

Our findings indicate that in older adults, cannabis smoking is linked with epigenome-wide disruptions, many of which persist despite cannabis smoking cessation. Epigenetic modulation of genes associated with aging and cancer that remains even after quitting cannabis should serve as a caution that there may be long-lasting epigenetic injury with cannabis smoking.

NCT00920348.

Recent studies show that patients with Alzheimer's disease (AD) harbor specific methylation marks in the brain that, if accessible, could be used as epigenetic biomarkers. Liquid biopsy enables the study of circulating cell-free DNA (cfDNA) fragments originated from dead cells, including neurons affected by neurodegenerative processes. Here, we isolated and epigenetically characterized plasma cfDNA from 35 patients with AD and 35 cognitively healthy controls by using the Infinium® MethylationEPIC BeadChip array. Bioinformatics analysis was performed to identify differential methylation positions (DMPs) and regions (DMRs), including APOE ε4 genotype stratified analysis. Plasma pTau181 (Simoa) and cerebrospinal fluid (CSF) core biomarkers (Fujirebio) were also measured and correlated with differential methylation marks. Validation was performed with bisulfite pyrosequencing and bisulfite cloning sequencing. Epigenome-wide cfDNA analysis identified 102 DMPs associated with AD status. Most DMPs correlated with clinical cognitive and functional tests including 60% for Mini-Mental State Examination (MMSE) and 80% for Global Deterioration Scale (GDS), and with AD blood and CSF biomarkers. In silico functional analysis connected 30 DMPs to neurological processes, identifying key regulators such as SPTBN4 and APOE genes. Several DMRs were annotated to genes previously reported to harbor epigenetic brain changes in AD (HKR1, ZNF154, HOXA5, TRIM40, ATG16L2, ADAMST2) and were linked to APOE ε4 genotypes. Notably, a DMR in the HKR1 gene, previously shown to be hypermethylated in the AD hippocampus, was validated in cfDNA from an orthogonal perspective. These results support the feasibility of studying cfDNA to identify potential epigenetic biomarkers in AD. Thus, liquid biopsy could improve non-invasive AD diagnosis and aid personalized medicine by detecting epigenetic brain markers in blood.

Most molecular classifications of cancer are based on bulk-tissue profiles that measure an average over many distinct cell types. As such, cancer subtypes inferred from transcriptomic or epigenetic data are strongly influenced by cell-type composition and do not necessarily reflect subtypes defined by cell-type-specific cancer-associated alterations, which could lead to suboptimal cancer classifications.

To address this problem, we here propose the novel concept of cell-type-specific combinatorial clustering (CELTYC), which aims to group cancer samples by the molecular alterations they display in specific cell types. We illustrate this concept in the context of DNA methylation data of liver and kidney cancer, deriving in each case novel cancer subtypes and assessing their prognostic relevance against current state-of-the-art prognostic models.

In both liver and kidney cancer, we reveal improved cell-type-specific prognostic models, not discoverable using standard methods. In the case of kidney cancer, we show how combinatorial indexing of epithelial and immune-cell clusters define improved prognostic models driven by synergy of high mitotic age and altered cytokine signaling. We validate the improved prognostic models in independent datasets and identify underlying cytokine-immune-cell signatures driving poor outcome.

In summary, cell-type-specific combinatorial clustering is a valuable strategy to help dissect and improve current prognostic classifications of cancer in terms of the underlying cell-type-specific epigenetic and transcriptomic alterations.

Circulating lipid concentrations are clinically associated with cardiometabolic diseases. The phenotypic variance explained by identified genetic variants remains limited, highlighting the importance of searching for additional factors beyond genetic sequence variants. DNA methylation has been linked to lipid concentrations in previous studies, although most of the studies harbored moderate sample sizes and exhibited underrepresentation of non-European ancestry populations. In addition, knowledge of nongenetic factors on lipid profiles is extremely limited. In the Population Architecture Using Genomics and Epidemiology (PAGE) Study, we performed methylome-wide association analysis on 9,561 participants from diverse race and ethnicity backgrounds for HDL-c, LDL-c, TC, and TG levels, and also tested interactions between smoking or alcohol intake and methylation in their association with lipid levels. We identified novel CpG sites at 16 loci (P < 1.18E-7) with successful replication on 3,215 participants. One additional novel locus was identified in the self-reported White participants (P = 4.66E-8). Although no additional CpG sites were identified in the genome-wide interaction analysis, 13 reported CpG sites showed significant heterogeneous association across smoking or alcohol intake strata. By mapping novel and reported CpG sites to genes, we identified enriched pathways directly linked to lipid metabolism as well as ones spanning various biological functions. These findings provide new insights into the regulation of lipid concentrations.

Emerging evidence suggests that abnormal DNA methylation patterns may play a role in the progression of adolescent idiopathic scoliosis (AIS). However, the mechanisms underlying the influence of DNA methylation on curve severity remain largely unknown.

To characterize the DNA methylation profiles associated with the curve severity of AIS.

Retrospective study with prospectively collected clinical data and blood samples.

A total of 7 AIS monozygotic twin pairs discordant for curve severity were included. Genome-wide methylation profile from blood samples were quantified by Illumina Infinium MethylationEPIC BeadChip (850K chip). Cell type composition of the samples was estimated by RefbaseEWAS method. Differentially methylated CpG sites were identified through comparison between patients with low and high Cobb angle. We also performed a gene-based collapsing analysis using mCSEA by aggregating the CpG sites based on promoter region. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis were performed using clusterProfiler package.

Genome-wide DNA methylation analysis identified multiple differentially methylated positions across the genome. Gene-based collapsing analysis identified 212 differentially methylated genes (FDR adjusted p<.05), most of which (186/212) were hypermethylated in the group with high Cobb angle. Some of the identified genes were well-documented in AIS literature, such as TBX1, PAX3 and LBX1. Functional enrichment analysis revealed that the differentially methylated genes (DMGs) were involved in pattern specification process, skeletal development, muscle function, neurotransmission and several signaling pathways (cAMP, Wnt and prolactin).

The study represents the largest systematic epigenomic analyses of monozygotic twins discordant for curve severity and supports the important role of altered DNA methylation in AIS.

The identified CpG sites provide insight into novel biomarkers predicting curve progression of AIS. Furthermore, the differentially methylated genes and enriched pathways may serve as interventional therapy target for AIS patients.

DNA methylation variants have been widely used as biomarkers of ageing and several mathematical models have been developed to estimate the biological age. More recently, DNA technology has triggered efforts toward the simplification of the array-based epigenetic clocks and targeted approaches, based on the assessment of a small number of CpG sites have been developed. Among the markers included in these clocks, ELOVL2, FHL2, KLF14, C1orf132/MIR29B2C, and TRIM59 resulted to be the most strongly validated markers. We tested the reproducibility and validation of a previously developed targeted epigenetic clock purposely optimized for the measurement of chronological age in blood samples. The clock includes DNAm biomarkers strongly correlated with chronological age whose DNA methylation levels were measured by using a multiplex methylation SNaPshot assay. We found that epigenetic age, calculated using the developed clock, was highly correlated with age (r = 0.97) in a total of 201 blood samples covering a full spectrum of human ages. For 74 of these, methylation profiles of the whole genome were obtained through the Infinium Methylation EPIC v2.0 Kit which also allowed to estimate the most frequently used clocks of Horvath. These results show the potential of our efficient and affordable test for simultaneously measuring DNA methylation levels at multiple target CpG sites to assess chronological age. We observed a strong correlation between the prediction models for the analyzed CpG sites measured using the SNaPshot method and those obtained with the Illumina EPIC array, especially with the Horvath2 clock, which was specifically developed for DNA from skin and blood cells.

Post-operative delirium (POD) is a common complication after surgery especially in elderly patients, characterized by acute disturbances in consciousness and cognition, which negatively impacts long-term outcomes. Effective treatments remain elusive due to the unclear pathophysiology of POD. To address the knowledge gap, we investigated DNA methylation profiles and gene expression changes in brain cells from POD and non-POD patients who underwent brain resection surgery for medication refractory epilepsy. DNA methylation analysis revealed alteration in epigenetic status of immune and inflammation-related genes. Single-nucleus RNA sequencing (snRNAseq) identified POD-specific glial cell alterations, particularly in microglia, where neuroinflammation was strongly enhanced, consistent with epigenetic findings. Astrocytes exhibited changes in synapse-related functions and migration. Furthermore, downstream analysis indicated similarities between POD-associated glial cell states and pathologies such as encephalitis and dementia. Overall, this study-the first multi-omics analysis of brain tissue from POD patients-provides direct evidence of glial cell contributions to POD pathogenesis, and highlights potential therapeutic targets.

Increasing evidence supports the role of the placenta in neurodevelopment and in the onset of neuropsychiatric disorders. Recently, mQTL and iQTL maps have proven useful in understanding relationships between SNPs and GWAS that are not captured by eQTL. In this context, we propose that part of the genetic predisposition to complex neuropsychiatric disorders acts through placental DNA methylation. We construct a public placental cis-mQTL database including 214,830 CpG sites calculated in 368 fetal placenta DNA samples from the INMA project, and run cell type-, gestational age- and sex-imQTL models. We combine these data with summary statistics of GWAS on ten neuropsychiatric disorders using summary-based Mendelian randomization and colocalization. We also evaluate the influence of identified DNA methylation sites on placental gene expression in the RICHS cohort. We find that placental cis-mQTLs are enriched in placenta-specific active chromatin regions, and establish that part of the genetic burden for schizophrenia, bipolar disorder, and major depressive disorder confers risk through placental DNA methylation. The potential causality of several of the observed associations is reinforced by secondary association signals identified in conditional analyses, the involvement of cell type-imQTLs, and the correlation of identified DNA methylation sites with the expression levels of relevant genes in the placenta.