A beta-mixture quantile normalization method for correcting probe design bias in Illumina Infinium 450 k DNA methylation data

Multiomics analysis of umbilical cord hematopoietic stem cells from a multiethnic cohort of Hawaii reveals the intergenerational effect of maternal prepregnancy obesity and risks for cancers

BACKGROUND

Maternal obesity is a health concern that may predispose newborns to a high risk of medical problems later in life. To understand the intergenerational effect of maternal obesity, we hypothesized that the maternal obesity effect is mediated by epigenetic changes in the CD34+/CD38-/Lin- hematopoietic stem cells (uHSCs) in the offspring. To investigate this, we conducted a DNA methylation centric multiomics study. We measured DNA methylation and gene expression of the CD34+/CD38-/Lin- uHSCs and metabolomics of the cord blood, all from a multiethnic cohort from Kapiolani Medical Center for Women and Children in Honolulu, Hawaii (n=72, collected between 2016 and 2018).

RESULTS

Differential methylation analysis unveiled a global hypermethylation pattern in the maternal prepregnancy obese group (BH adjusted P < 0.05), after adjusting for major clinical confounders. KEGG pathway enrichment, WGCNA, and PPI analyses revealed that hypermethylated CpG sites were involved in critical biological processes, including cell cycle, protein synthesis, immune signaling, and lipid metabolism. Utilizing Shannon entropy on uHSCs methylation, we discerned notably higher quiescence of uHSCs impacted by maternal obesity. Additionally, the integration of multiomics data-including methylation, gene expression, and metabolomics-provided further evidence of dysfunctions in adipogenesis, erythropoietin production, cell differentiation, and DNA repair, aligning with the findings at the epigenetic level. Furthermore, we trained a random forest classifier using the CpG sites in the genes of the top pathways associated with maternal obesity, and applied it to predict cancer versus adjacent normal sample labels in 14 Cancer Genome Atlas (TCGA) cancer types. Five of 14 cancers showed balanced accuracy of 0.6 or higher: LUSC (0.87), PAAD (0.83), KIRC (0.71), KIRP (0.63) and BRCA (0.60).

CONCLUSIONS

This study revealed the significant correlation between prepregnancy maternal obesity and multiomics-level molecular changes in the uHSCs of offspring, particularly at the DNA methylation level. These maternal-obesity-associated epigenetic markers in uHSCs may contribute to increased risks in certain cancers of the offspring. Larger and multicenter cohort validation studies are warranted to follow up the current single-site study.

regionalpcs improve discovery of DNA methylation associations with complex traits

We have developed the regionalpcs method, an approach for summarizing gene-level methylation. regionalpcs addresses the challenge of deciphering complex epigenetic mechanisms in diseases like Alzheimer's disease. In contrast to averaging, regionalpcs uses principal components analysis to capture complex methylation patterns across gene regions. Our method demonstrates a 54% improvement in sensitivity over averaging in simulations, providing a robust framework for identifying subtle epigenetic variations. Applying regionalpcs to Alzheimer's disease brain methylation data, combined with cell type deconvolution, we uncover 838 differentially methylated genes associated with neuritic plaque burden-significantly outperforming conventional methods. Integrating methylation quantitative trait loci with genome-wide association studies identified 17 genes with potential causal roles in Alzheimer's disease risk, including MS4A4A and PICALM. Available in the Bioconductor package regionalpcs, our approach facilitates a deeper understanding of the epigenetic landscape in Alzheimer's disease and opens avenues for research into complex diseases.

Characterization of DNA methylation clock algorithms applied to diverse tissue types

BACKGROUND

DNA methylation (DNAm) data from human samples has been leveraged to develop "epigenetic clock" algorithms that predict age and other aging-related phenotypes. Some DNAm clocks were trained using DNAm obtained from blood cells, while other clocks were trained using data from diverse tissue/cell types. To assess how DNAm clocks perform across non-blood tissue types, we applied DNAm algorithms to DNAm data generated from 9 different human tissue types.

METHODS

We generated array-based DNAm measurements for 973 samples from deceased tissue donors from the GTEx (Genotype Tissue Expression) project representing nine distinct tissue types: lung, colon, prostate, ovary, breast, kidney, testis, skeletal muscle, and whole blood. For all samples, we generated DNAm clock estimates for 8 epigenetic clocks and characterized these tissue-specific clock estimates in terms of their distributions, correlations with chronological age, correlations of clock estimates between tissue types, and association with participant characteristics.

RESULTS

For each clock, the mean DNAm age estimate varied substantially across tissue types, and the mean values for the different clocks varied substantially within tissue types. For most clocks, the correlation with chronological age varied across tissue types, with blood often showing the strongest correlation. Each clock showed strong correlation across tissues, with some evidence of some residual correlation after adjusting for chronological age. In lung tissue, smoking generally had a positive association with epigenetic age.

CONCLUSIONS

This work demonstrates how differences in epigenetic aging among tissue types leads to clear differences in DNAm clock characteristics across tissue types. Tissue or cell-type specific epigenetic clocks are needed to optimize predictive performance of DNAm clocks in non-blood tissues and cell types.

Integration of multi-omics layers empowers precision diagnosis through unveiling pathogenic mechanisms on maple syrup urine disease

Maple syrup urine disease (MSUD) is a rare inherited metabolic disorder characterized by deficient activity of the branched-chain alpha-ketoacid dehydrogenase (BCKDH) complex, required to metabolize the amino acids leucine, isoleucine, and valine. Despite its profound metabolic implications, the molecular alterations underlying this metabolic impairment had not yet been completely elucidated. We performed a comprehensive multi-omics integration analysis, including genomic, epigenomic, and transcriptomic data from fibroblasts derived from a cohort of MSUD patients and unaffected controls to genetically characterize an MSUD case and to unravel the MSUD pathophysiology. MSUD patients exhibit a defined episignature that reshapes the global DNA methylation landscape, resulting in the stimulation of HOX cluster genes and the restriction of cell cycle gene-related signatures. Subsequent data integration revealed the impact of AP1-related and CEBPB transcription factors on the observed molecular reorganization, with MEIS1 emerging as a potential downstream candidate affected by robust epigenetic repression in MSUD patients. Furthermore, the integration of multi-omics layers facilitated the identification of a strong epigenetic repression in the DBT promoter in a patient wherein no BCKDH pathogenic variants had been detected. A Circular Chromatin Conformation Capture assay indicated a disturbance of the interactions of DBT promoter, thereby unveiling alternative modes of disease inheritance. Integration of multi-omics data unveiled underlying molecular networks rewired in MSUD patients and represents a powerful approach with diagnostic potential for rare genetic disorders with unknown genetic bases.

DNA methylation in peripheral blood leukocytes in late onset Alzheimer's disease

Background

Chronic systemic inflammation is implicated in Alzheimer's disease (AD) pathogenesis and has measurable effects on blood cells. There is increasing interest in non-invasive diagnostic tools that use blood-based biomarkers for AD, such as DNA methylation. Notably, DNA methylation changes in blood are also linked to systemic inflammation. The evaluation of DNA methylation profiles in peripheral blood leukocytes as potential biomarkers for AD is promising.

Objective

To determine DNA methylation patterns in blood for AD, and to explore specific blood CpG sites that act as surrogates for brain-tissue methylation.

Methods

DNA methylation data from peripheral blood leukocytes of AD patients and controls were obtained from the Gene Expression Omnibus (GSE59685 and GSE53740). Differential methylation analysis was performed for individual CpGs Differentially methylated positions (DMPs) and regions with multiple probes (DMRs) and the intersection analysis of DMPs and DMRs was conducted. Functional enrichment analysis highlights relevant biological processes. Furthermore, previously validated specific CpGs used as surrogate of brain tissue were explored.

Results

DNA methylation patterns included BTBD3, PGPEP1L, DUSP29, and MIB2 top genes ordered by statistical significance were found in the intersection of DMP and DMR. Differential methylation analyses revealed differentially methylated genes including HOXA-AS3, HOXA6, CACNA1A, KMT5A, MIDEAS, FAM234A, and KATNBL1P6. Gene enrichment analysis showed immune processes and intracellular signaling disruptions. Surrogate genes from brain found differentially methylated were PCDHGB1-3 and PCDHGA1-6.

Conclusions

This study identified DNA methylation patterns in peripheral blood leukocytes as potential biomarkers for AD. These findings offer insights into epigenetic mechanisms associated with systemic peripheral inflammation in AD.

Cell-type specific epigenetic clocks to quantify biological age at cell-type resolution

The ability to accurately quantify biological age could help monitor and control healthy aging. Epigenetic clocks have emerged as promising tools for estimating biological age, yet they have been developed from heterogeneous bulk tissues, and are thus composites of two aging processes, one reflecting the change of cell-type composition with age and another reflecting the aging of individual cell-types. There is thus a need to dissect and quantify these two components of epigenetic clocks, and to develop epigenetic clocks that can yield biological age estimates at cell-type resolution. Here we demonstrate that in blood and brain, approximately 39% and 12% of an epigenetic clock's accuracy is driven by underlying shifts in lymphocyte and neuronal subsets, respectively. Using brain and liver tissue as prototypes, we build and validate neuron and hepatocyte specific DNA methylation clocks, and demonstrate that these cell-type specific clocks yield improved estimates of chronological age in the corresponding cell and tissue-types. We find that neuron and glia specific clocks display biological age acceleration in Alzheimer's Disease with the effect being strongest for glia in the temporal lobe. Moreover, CpGs from these clocks display a small but significant overlap with the causal DamAge-clock, mapping to key genes implicated in neurodegeneration. The hepatocyte clock is found accelerated in liver under various pathological conditions. In contrast, non-cell-type specific clocks do not display biological age-acceleration, or only do so marginally. In summary, this work highlights the importance of dissecting epigenetic clocks and quantifying biological age at cell-type resolution.

Multi-omics Analysis of Umbilical Cord Hematopoietic Stem Cells from a Multi-ethnic Cohort of Hawaii Reveals the Intergenerational Effect of Maternal Pre-Pregnancy Obesity and Risk Prediction for Cancers

Background

Maternal obesity is a health concern that may predispose newborns to a high risk of medical problems later in life. To understand the intergenerational effect of maternal obesity, we hypothesized that the maternal obesity effect is mediated by epigenetic changes in the CD34+/CD38-/Lin- hematopoietic stem cells (uHSCs) in the offspring. Towards this, we conducted a DNA methylation centric multi-omics study. We measured the DNA methylation and gene expression in the CD34+/CD38-/Lin- uHSCs and metabolomics of the cord blood, all from a multi-ethnic cohort (n=72) from Kapiolani Medical Center for Women and Children in Honolulu, Hawaii (collected between 2016 and 2018).

Results

Differential methylation (DM) analysis unveiled a global hypermethylation pattern in the maternal pre-pregnancy obese group (BH adjusted p<0.05), after adjusting for major clinical confounders. KEGG pathway enrichment, WGCNA, and PPI analyses revealed hypermethylated CpG sites were involved in critical biological processes, including cell cycle, protein synthesis, immune signaling, and lipid metabolism. . Utilizing Shannon entropy on uHSCs methylation, we discerned notably higher quiescence of uHSCs impacted by maternal obesity. Additionally, the integration of multi-omics data-including methylation, gene expression, and metabolomics-provided further evidence of dysfunctions in adipogenesis, erythropoietin production, cell differentiation, and DNA repair, aligning with the findings at the epigenetic level. Furthermore, we trained a random forest classifier using the CpG sites in the genes of the top pathways associated with maternal obesity, and applied it to predict cancer vs. adjacent normal labels from samples in 14 Cancer Genome Atlas (TCGA) cancer types. Five of 14 cancers showed balanced accuracy of 0.6 or higher: LUSC (0.87), PAAD (0.83), KIRC (0.71), KIRP (0.63) and BRCA (0.60).

Conclusions

This study revealed the significant correlation between pre-pregnancy maternal obesity and multi-omics level molecular changes in the uHSCs of offspring, particularly in DNA methylation. Moreover, these maternal obesity epigenetic markers in uHSCs may predispose offspring to higher risks in certain cancers.

Attenuated sex-related DNA methylation differences in cancer highlight the magnitude bias mediating existing disparities

BACKGROUND

DNA methylation (DNAm) influences both sex differences and cancer development, yet the mechanisms connecting these factors remain unclear.

METHODS

Utilizing data from The Cancer Genome Atlas, we conducted a comprehensive analysis of sex-related DNAm effects in nine non-reproductive cancers, compared to paired normal adjacent tissues (NATs), and validated the results using independent datasets. First, we assessed the extent of sex differential DNAm between cancers and NATs to explore how sex-related DNAm differences change in cancerous tissues. Next, we employed a multivariate adaptive shrinkage approach to model the covariance of cancer-related DNAm effects between sexes, aiming to elucidate how sex impacts aberrant DNAm patterns in cancers. Finally, we investigated correlations between the methylome and transcriptome to identify key signals driving sex-biased DNAm regulation in cancers.

RESULTS

Our analysis revealed a significant attenuation of sex differences in DNAm within cancerous tissues compared to baseline differences in normal tissues. We identified 3,452 CpGs (Pbonf < 0.05) associated with this reduction, with 72% of the linked genes involved in X chromosome inactivation. Through covariance analysis, we demonstrated that sex differences in cancer are predominantly driven by variations in the magnitude of shared DNAm signals, referred to as "amplification." Based on these patterns, we classified cancers into female- and male-biased groups and identified key CpGs exhibiting sex-specific amplification. These CpGs were enriched in binding sites of critical transcription factors, including P53, SOX2, and CTCF. Integrative multi-omics analyses uncovered 48 CpG-gene-cancer trios for females and 380 for males, showing similar magnitude differences in DNAm and gene expression, pointing to a sex-specific regulatory role of DNAm in cancer risk. Notably, several genes regulated by these trios were previously identified as drug targets for cancers, highlighting their potential as sex-specific therapeutic targets.

CONCLUSIONS

These findings advance our understanding of how sex, DNAm, and gene expression interact in cancer, offering insights into the development of sex-specific biomarkers and precision medicine.

A novel family of beta mixture models for the differential analysis of DNA methylation data: An application to prostate cancer

Identifying differentially methylated cytosine-guanine dinucleotide (CpG) sites between benign and tumour samples can assist in understanding disease. However, differential analysis of bounded DNA methylation data often requires data transformation, reducing biological interpretability. To address this, a family of beta mixture models (BMMs) is proposed that (i) objectively infers methylation state thresholds and (ii) identifies differentially methylated CpG sites (DMCs) given untransformed, beta-valued methylation data. The BMMs achieve this through model-based clustering of CpG sites and by employing parameter constraints, facilitating application to different study settings. Inference proceeds via an expectation-maximisation algorithm, with an approximate maximization step providing tractability and computational feasibility. Performance of the BMMs is assessed through thorough simulation studies, and the BMMs are used for differential analyses of DNA methylation data from a prostate cancer study. Intuitive and biologically interpretable methylation state thresholds are inferred and DMCs are identified, including those related to genes such as GSTP1, RASSF1 and RARB, known for their role in prostate cancer development. Gene ontology analysis of the DMCs revealed significant enrichment in cancer-related pathways, demonstrating the utility of BMMs to reveal biologically relevant insights. An R package betaclust facilitates widespread use of BMMs.

DNA-methylation age and accelerated epigenetic aging in blood as a tumor marker for predicting breast cancer susceptibility

BACKGROUND

DNA methylation (DNAm)-based marker of aging, referred to as 'epigenetic age' or 'DNAm age' is a highly accurate multi-tissue biomarker for aging, associated with age-related disease risk, including cancer. Breast cancer (BC), an age-associated disease, is associated with older DNAm age and epigenetic age acceleration (age accel) at tissue levels. But this raises a question on the predictability of DNAm age/age accel in BC development, emphasizing the importance of studying DNAm age in pre-diagnostic peripheral blood (PB) in BC etiology and prevention.

METHODS

We included postmenopausal women from the largest study cohort and prospectively investigated BC development with their pre-diagnostic DNAm in PB leukocytes (PBLs). We estimated Horvath's pan-tissue DNAm age and investigated whether DNAm age/age accel highly correlates with risk for developing subtype-specific BC and to what degree the risk is modified by hormones and lifestyle factors.

RESULTS

DNAm age in PBLs was tightly correlated with age in this age range, and older DNAm age and epigenetic age accel were significantly associated with risk for developing overall BC and luminal subtypes. Of note, in women with bilateral oophorectomy before natural menopause experiencing shorter lifetime estrogen exposure than those with natural menopause, epigenetic age accel substantially influenced BC development, independent of obesity status and exogeneous estrogen use.

CONCLUSIONS

Our findings contribute to better understanding of biologic aging processes that mediate BC carcinogenesis, detecting a non-invasive epigenetic aging marker that better reflects BC development, and ultimately identifying the elderly with high risk who can benefit from epigenetically targeted preventive interventions.

A landscape of X-inactivation during human T cell development

Females exhibit a more robust immune response to both self-antigens and non-self-antigens than males, resulting in a higher prevalence of autoimmune diseases but more effective responses against infection. Increased expression of X-linked immune genes in female T cells is thought to underlie this enhanced response. Here we isolate thymocytes from pediatric thymi of healthy males (46, XY), females (46, XX), a female with completely skewed X-chromosome inactivation (46, XX, cXCI) and a female with Turner syndrome (45, X0). Using whole exome sequencing, RNA sequencing and DNA methylation data, we present a sex-aware expression profile of T cell development and generate a high-resolution map of escape from X-chromosome inactivation (XCI). Unexpectedly, XCI is transcriptionally and epigenetically stable throughout T cell development, and is independent of expression of XIST, the lncRNA responsible for XCI initiation during early embryonic development. In thymocytes, several genes known to escape XCI are expressed from only one X-chromosome. Additionally, we further reveal that a second X-chromosome is dispensable for T cell development. Our study thus provides a high-resolution map of XCI during human development and suggests a re-evaluation of XCI in sex differences in T cell function.

Using parenclitic networks on phaeochromocytoma and paraganglioma tumours provides novel insights on global DNA methylation

Despite the prevalence of sequencing data in biomedical research, the methylome remains underrepresented. Given the importance of DNA methylation in gene regulation and disease, it is crucial to address the need for reliable differential methylation methods. This work presents a novel, transferable approach for extracting information from DNA methylation data. Our agnostic, graph-based pipeline overcomes the limitations of commonly used differential methylation techniques and addresses the "small n, big k" problem. Pheochromocytoma and Paraganglioma (PPGL) tumours with known genetic aetiologies experience extreme hypermethylation genome wide. To highlight the effectiveness of our method in candidate discovery, we present the first phenotypic classifier of PPGLs based on DNA methylation achieving 0.7 ROC-AUC. Each sample is represented by an optimised parenclitic network, a graph representing the deviation of the sample's DNA methylation from the expected non-aggressive patterns. By extracting meaningful topological features, the dimensionality and, hence, the risk of overfitting is reduced, and the samples can be classified effectively. By using an explainable classification method, in this case logistic regression, the key CG loci influencing the decision can be identified. Our work provides insights into the molecular signature of aggressive PPGLs and we propose candidates for further research. Our optimised parenclitic network implementation improves the potential utility of DNA methylation data and offers an effective and complete pipeline for studying such datasets.

Graph Convolutional Network for AD and MCI Diagnosis Utilizing Peripheral DNA Methylation: Réseau de neurones en graphes pour le diagnostic de la MA et du TCL à l'aide de la méthylation de l'ADN périphérique

OBJECTIVE

Blood DNA methylation (DNAm) alterations have been widely reported in the onset and progression of mild cognitive impairment (MCI) and Alzheimer's disease (AD); however, DNAm is underutilized as a diagnostic biomarker for these diseases. We aimed to evaluate the diagnostic performance of DNAm for MCI and AD, both individually and in combination with well-established AD biosignatures.

METHODS

A total of 1,891 blood samples from Alzheimer's Disease Neuroimaging Initiative (ADNI) studies were used to identify potential candidate DNAm biomarkers. Multimodal clinical data from 635 samples (normal control (NC), n = 193; MCI, n = 352; AD, n = 90) in the TADPOLE dataset were utilized to construct eight different classification models using a graph convolutional network, a machine learning framework.

RESULTS

After feature selection, 17 DNAm sites were selected for subsequent analysis. Remarkable differences in DNAm levels were observed at the screened DNAm loci in all three cohorts. Adopting DNAm features into multimodal models significantly improved the classification performance for three dichotomous subtasks (NC vs. non-NC, MCI vs. non-MCI, and AD vs. non-AD), especially when combined with cerebrospinal fluid (CSF) features for NC (area under the curve (AUC): 0.8534) and MCI classification (AUC: 0.7675). A weak correlation between DNAm and both magnetic resonance imaging and CSF features in the NC and MCI cohorts suggests good complementarity between modalities (correlation coefficient ≤0.2).

CONCLUSIONS

Our study offers new insights into peripheral DNAm in MCI and AD and suggests promising diagnostic performance of models integrating epigenomics, imaging, or CSF biomarkers.

PLAIN LANGUAGE SUMMARY TITLE

Using Machine Learning and Blood-Based Genetic Markers to Help Diagnose Mild Cognitive Impairment and Alzheimer's Disease.

Methods in DNA methylation array dataset analysis: A review

Understanding the intricate relationships between gene expression levels and epigenetic modifications in a genome is crucial to comprehending the pathogenic mechanisms of many diseases. With the advancement of DNA Methylome Profiling techniques, the emphasis on identifying Differentially Methylated Regions (DMRs/DMGs) has become crucial for biomarker discovery, offering new insights into the etiology of illnesses. This review surveys the current state of computational tools/algorithms for the analysis of microarray-based DNA methylation profiling datasets, focusing on key concepts underlying the diagnostic/prognostic CpG site extraction. It addresses methodological frameworks, algorithms, and pipelines employed by various authors, serving as a roadmap to address challenges and understand changing trends in the methodologies for analyzing array-based DNA methylation profiling datasets derived from diseased genomes. Additionally, it highlights the importance of integrating gene expression and methylation datasets for accurate biomarker identification, explores prognostic prediction models, and discusses molecular subtyping for disease classification. The review also emphasizes the contributions of machine learning, neural networks, and data mining to enhance diagnostic workflow development, thereby improving accuracy, precision, and robustness.

Chemotherapy-induced acceleration of DNA methylation-based biological age in breast cancer

Breast cancer is the most common cancer diagnosed in women and is often treated with chemotherapy. Although previous studies have demonstrated increasing biological age in patients who receive chemotherapy, evaluation of this association with DNA methylation-based markers of biological ageing may provide novel insight into the role of chemotherapy on the ageing process. We therefore sought to investigate the association between chemotherapy and markers of biological ageing as estimated from DNA methylation in women with breast cancer. DNA methylation profiling was performed on peripheral blood collected from 18 patients before and after the first cycle of chemotherapy using the Infinium HumanMethylation450 BeadChip. Six markers of biological age acceleration were estimated from DNA methylation levels. Multiple linear regression analyses were performed to evaluate the association between each metric of biological age acceleration and chemotherapy. After adjusting for chronological age and race, intrinsic epigenetic age acceleration (p = 0.041), extrinsic epigenetic age acceleration (p = 0.050), PhenoAge acceleration (p = 0.001), GrimAge acceleration (p < 0.001), and DunedinPACE (p = 0.006) were significantly higher and telomere length (p = 0.027) was significantly lower following the first cycle of chemotherapy compared to before treatment initiation. These results demonstrate greater biological ageing as estimated from DNA methylation following chemotherapy in women with breast cancer. Our findings illustrate that cytotoxic therapies may modulate the ageing process among breast cancer patients and may also have implications for age-related health conditions in cancer survivors.

Trauma context exerts intergenerational effects on child mental health via DNA methylation

Many people experience traumatic or negative events, but few develop mental health issues as a result. This study investigated whether newborn DNA methylation (DNAm) previously associated with maternal childhood physical abuse by her father affected the child's mental health and physical growth, as well as whether it mediated or moderated developmental outcomes.

METHODS

Study sample (N = 903) and data came from Bristol University's Avon Longitudinal Study of Parents and Children. DNAm was measured in cord blood at birth. DNAm data was preprocessed, normalized, and quality controlled before subsetting to 60 CpG sites of interest from previous research. Linear regression analysis examined newborn DNAm and child development outcome associations. Sobel test examined the mediating relationship between mother's history of childhood abuse by father, newborn targeted gene DNAm of significant CpG sites, and child's mental health and physical growth. Moderation analyses examined the interaction effects between the significant CpG sites and mothers' physical abuse by their fathers on child's mental health and physical growth.

RESULTS

Full cohort analyses showed that newborn DNAm of several different CpG sites associates with separation anxiety, fear, and unhappy/tearful presentations in children aged 6-7 y. Sex-specific associations emerged with boys showing associations with anxiety and fear, and girls showing associations with fear and unhappiness. In boys only, cord blood DNAm mediates the effect of maternal childhood trauma on offspring mental health. No moderation effects emerged.

CONCLUSION

Intergenerational effects of mother's relationship to her abuser present in newborn DNAm associate with 7-year-old child's mental health, show sex-specific effects, and newborn DNAm does mediate maternal childhood trauma effects on offspring mental health in early-life.

Navigating Illumina DNA methylation data: biology versus technical artefacts

Illumina-based BeadChip arrays have revolutionized genome-wide DNA methylation profiling, pushing it into diagnostics. However, comprehensive quality assessment remains challenging within a wide range of available tissue materials and sample preparation methods. This study tackles two critical issues: differentiating between biological effects and technical artefacts in suboptimal quality samples and the impact of the first sample on the Illumina-like normalization algorithm. We introduce three quality control scores based on global DNA methylation distribution (DB-Score), bin distance from copy number variation analysis (BIN-Score) and consistently methylated CpGs (CM-Score) that rely on biological features rather than internal array controls. These scores, designed to be adjustable for different analysis tools and sample cohort characteristics, were explored and benchmarked across independent cohorts. Additionally, we reveal deviations in beta values caused by different sample rankings with the Illumina-like normalization algorithm, verified these with whole-genome methylation sequencing data and showed effects on differential DNA methylation analysis. Our findings underscore the necessity of consistently utilizing a pre-defined normalization sample within the ranking process to boost reproducibility of the Illumina-like normalization algorithm. Overall, our study delivers valuable insights, practical recommendations and R functions designed to enhance reproducibility and quality assurance of DNA methylation analysis, particularly for challenging sample types.

Placental and immune cell DNA methylation reference panel for bulk tissue cell composition estimation in epidemiological studies

To distinguish DNA methylation (DNAm) from cell proportion changes in whole placental villous tissue research, we developed a robust cell type-specific DNAm reference to estimate cell composition. We collated new and existing cell type DNAm profiles quantified via Illumina EPIC or 450k microarrays. To estimate cell composition, we deconvoluted whole placental samples (n = 36) with robust partial correlation based on the top 30 hyper- and hypomethylated sites identified per cell type. To test deconvolution performance, we evaluated root mean square error in predicting principal components of DNAm variation in 204 external placental samples. We analyzed DNAm profiles (n = 368,435 sites) from 12 cell types: cytotrophoblasts (n = 18), endothelial cells (n = 19), Hofbauer cells (n = 26), stromal cells (n = 21), syncytiotrophoblasts (n = 4), six lymphocyte types (n = 36), and nucleated red blood cells (n = 11). Median cell composition was consistent with placental biology: 60.9% syncytiotrophoblast, 17.3% stromal, 8.8% endothelial, 3.7% cytotrophoblast, 3.7% Hofbauer, 1.7% nucleated red blood cells, and 1.2% neutrophils. Our expanded reference outperformed an existing reference in predicting DNAm variation (PC1, 15.4% variance explained, IQR = 21.61) with cell composition estimates (mean square error of prediction: 8.62 vs. 10.79, p-value < 0.001). This cell type reference can robustly estimate cell composition from whole placental DNAm data to detect important cell types, reveal biological mechanisms, and improve causal inference.

DNA methylation analysis of peripheral blood mononuclear cells in diagnosing breast cancer from benign breast lesions

BACKGROUND

With the increasing incidence of breast lesions, the differential diagnosis between benign lesions and breast cancer (BCa) has become a big challenge. Host peripheral blood mononuclear cells (PBMCs) could undergo changes in DNA methylation upon disease progression. However, the clinical value of DNA methylation of PBMCs in differentiating benign lesions and BCa is still unclear.

METHODS

DNA of PBMCs was isolated and the methylation status of PBMCs in patients with BCa and benign breast nodules was detected by using Illumina Infinium methylation EPIC array. The specific methylation targets were validated by pyrosequencing, Targeted Bisulfite Sequencing Assay, and Multiplex Methylation PCR Assay(MMPA).

RESULTS

cg26977936, cg23351954, and cg27209741 were validated as differentially methylated and showed the potential diagnostic values (sensitivity and specificity were 90.0%/53.3%, 43.3%/90.0%, 90.0%/43.3%, respectively). Moreover, a diagnostic model was established using these 3 CGs through logistic regression analysis, and the AUC reached 0.837. Next, this diagnostic model was validated in another, independent cohort with Targeted Bisulfite Sequencing Assay, and the clinical value in distinguishing benign and malignant breast disease was also confirmed (AUC = 0.827, P < 0.05). Finally, to better meet the need for the clinical test, we further validated the differential diagnostic efficacy of the 2 hypermethylated DMPs by establishing a Multiplex Methylation PCR Assay by coupling the 5'-flap endonuclease activity of Taq DNA polymerase and molecular beacon reporters.

CONCLUSIONS

DNA methylation changes in PBMCs showed great potential in discriminating BCa patients from breast benign lesion patients and may serve as a novel predictor in clinical applications.

Placenta Thickness Mediates the Association Between AKIP1 Methylation in Maternal Peripheral Blood and Full-Term Small for Gestational Age Neonates

BACKGROUND/OBJECTIVES

A-kinase-interacting protein 1 (AKIP1) has been discovered to be a pivotal signaling adaptor in the regulation of human labor and associated with preterm birth, but its effect on fetal growth was still unclear. Meanwhile, the regulation role of DNA methylation (DNAm) on placental and fetal development has been demonstrated. Therefore, we aimed to investigate the association of AKIP1 DNAm in maternal peripheral blood with placental development and full-term small for gestational age (FT-SGA) neonates, and to explore whether placenta mediate the association between AKIP1 DNAm and FT-SGA; Methods: This study was a case-control study including 84 FT-SGAs and 84 FT-AGAs derived from the Shenzhen Birth Cohort Study. The DNA methylation analysis of CpG in the target region of the AKIP1 gene was measured by the Sequenom MassARRAY EpiTYPER approach. Multiple-variable logistic and linear regression analyses were used to estimate the association between the DNAm of three validated CpG sites in the AKIP1 gene, placental thickness, and FT-SGA. Mediation analysis was used to examine the mediation effect of placental development on the association between the DNAm of AKIP1 and FT-SGA.

RESULTS

For every increment in standard deviation in the DNAm of CpG4 (cg00061907) at AKIP1, the risk of FT-SGA elevated by 2.01-fold (aOR = 2.01, 95%CI = 1.39~3.01), and the thickness of the placenta significantly decreased by a 0.19 standard deviation (β = -0.19, 95%CI = -0.32~ -0.06). Placental thickness mediated the 22.96% of the effect of the DNAm of CpG4 at AKIP1 on the risk of FT-SGA with statistical significance.

CONCLUSIONS

The findings in the present study suggested the mediating effect of placental thickness on the association of the DNAm of AKIP1 in maternal peripheral blood and the risk of FT-SGA, providing new evidence for the mechanism of maternal epigenetics in placental and fetal development.

Epigenetic age and long-term cancer risk following a stroke

BACKGROUND

The association between increased cancer risk following a cerebrovascular event (CVE) has been previously reported. We hypothesize that biological age (B-age) acceleration is involved in this association. Our study aims to examine B-age as a novel contributing factor to cancer development post-CVE.

METHODS

From our prospective stroke registry (BasicMar), we selected 940 cases with epigenetic data. For this study, we specifically analyzed 648 of these patients who had available data, no prior history of cancer, and a minimum follow-up of 3 months. The primary outcome was cancer incidence. B-age was estimated using DNA methylation data derived from whole blood samples obtained within 24 h of stroke onset, employing various epigenetic clocks (including Hannum, Horvath, PhenoAge, ZhangBLUP, ZhangEN, and the mitotic epiTOC). Extrinsic epigenetic age acceleration (EEAA) was calculated as the residuals from the regression of B-age against chronological age (C-age). For epiTOC, the age-adjusted values were obtained by regressing out the effect of age from the raw epiTOC measurements. Estimated white cell counts were derived from DNA methylation data, and these cell fractions were used to compute the intrinsic epigenetic age acceleration (IEAA). Subsequently, we evaluated the independent association between EEAA, IEAA, and cancer incidence while controlling for potential confounding variables.

RESULTS

Among 648 patients with a median follow-up of 8.15 years, 83 (12.8%) developed cancer. Cox multivariable analyses indicated significant associations between Hannum, Zhang, and epiTOC EEAA and the risk of cancer after CVE. After adjusting for multiple testing and competing risks, EEAA measured by Hannum clock maintained an independent association with cancer risk. Specifically, for each year increase in Hannum's EEAA, we observed a 6.0% increased incidence of cancer (HR 1.06 [1.02-1.10], p value = 0.002).

CONCLUSIONS

Our findings suggest that epigenetic accelerated aging, as indicated by Hannum's EEAA, may play a significant role in the increased cancer risk observed in CVE survivors.

Associations of epigenetic age acceleration at birth and age 12 years with adolescent cardiometabolic risk: the HOME study

BACKGROUND

Cardiometabolic risk factors among youth are rising. Epigenetic age acceleration, a biomarker for aging and disease-risk, has been associated with adiposity in children, but its association with other cardiometabolic risk markers remains understudied. We employed data from the Health Outcomes and Measures of the Environment (HOME) study, a prospective pregnancy and birth cohort in the greater Cincinnati metropolitan area, to examine whether accelerated epigenetic age at birth as well as accelerated epigenetic age and faster pace of biological aging at age 12 years were associated with higher cardiometabolic risk in adolescents.

RESULTS

After adjusting for potential confounders, including estimated cell type proportions, epigenetic gestational age acceleration at birth, derived from the Bohlin, Knight, and Haftorn clocks using cord blood DNA methylation data, was not associated with cardiometabolic risk z-scores or individual cardiometabolic risk score components (visceral fat, leptin to adiponectin ratio, HOMA-IR, triglycerides to HDL-C ratio, HbA1c, or systolic blood pressure) at age 12 years. We also did not observe any associations of epigenetic age acceleration, calculated with Horvath's skin and blood, Hannum's, and Wu's epigenetic clocks using peripheral blood at age 12 years, with these same cardiometabolic risk markers. In contrast, faster pace of biological aging was associated with higher cardiometabolic risk [βs (95% CIs)] cardiometabolic risk score 0.25 (0.07, 0.42); visceral fat 0.21 (0.05, 0.38); and hemoglobin A1c 0.23 (0.05, 0.41) per standard deviation increase in pace of biological aging. Faster pace of biological aging was also positively associated with systolic blood pressure, triglycerides to HDL-C ratio, HOMA-IR, and leptin to adiponectin ratio, although these associations were not statistically significant.

CONCLUSIONS

Our findings provide evidence that faster pace of biological aging was associated with higher cardiometabolic risk score, visceral fat, and HbA1c at age 12 years. Further research is needed to determine whether these associations persist from adolescence through adulthood.

Pre-diagnosis blood DNA methylation profiling of twin pairs discordant for breast cancer points to the importance of environmental risk factors

BACKGROUND

Assessment of breast cancer (BC) risk generally relies on mammography, family history, reproductive history, and genotyping of major mutations. However, assessing the impact of environmental factors, such as lifestyle, health-related behavior, or external exposures, is still challenging. DNA methylation (DNAm), capturing both genetic and environmental effects, presents a promising opportunity. Previous studies have identified associations and predicted the risk of BC using DNAm in blood; however, these studies did not distinguish between genetic and environmental contributions to these DNAm sites. In this study, associations between DNAm and BC are assessed using paired twin models, which control for shared genetic and environmental effects, allowing testing for associations between DNAm and non-shared environmental exposures and behavior.

RESULTS

Pre-diagnosis blood samples of 32 monozygotic (MZ) and 76 dizygotic (DZ) female twin pairs discordant for BC were collected at the mean age of 56.0 years, with the mean age at diagnosis 66.8 years and censoring 75.2 years. We identified 212 CpGs (p < 6.4*10-8) and 15 DMRs associated with BC risk across all pairs using paired Cox proportional hazard models. All but one of the BC risks associated with CpGs were hypomethylated, and 198/212 CpGs had their DNAm associated with BC risk independent of genetic effects. According to previous literature, at least five of the top CpGs were related to estrogen signaling. Following a comprehensive two-sample Mendelian randomization analysis, we found evidence supporting a dual causal impact of DNAm at cg20145695 (gene body of NXN, rs480351) with increased risk for estrogen receptor positive BC and decreased risk for estrogen receptor negative BC.

CONCLUSION

While causal effects of DNAm on BC risk are rare, most of the identified CpGs associated with the risk of BC appear to be independent of genetic effects. This suggests that DNAm could serve as a valuable biomarker for environmental risk factors for BC, and may offer potential benefits as a complementary tool to current risk assessment procedures.

MethylCallR : a comprehensive analysis framework for Illumina Methylation Beadchip

DNA methylation is a molecular process that mediates gene-environment interactions. Epigenome-wide association studies (EWAS) using the Illumina Human Methylation BeadChip are powerful tools for quantifying the relationship between DNA methylation and phenotypes. Recently, the Illumina Methylation EPICv2 BeadChip (EPICv2) was released, which includes new features, such as duplicated probes and changed probe names. Several published algorithms have been updated to address these features in EPICv2. However, appropriate EPICv2 preprocessing and integration with previous microarray versions remain complex. Therefore, MethylCallR, an open-source R package designed to provide standard procedures for performing EWAS using Illumina methylation microarrays including EPICv2, was developed. MethylCallR can be used to control duplicated probes in EPICv2, by using pre-set data implemented in MethylCallR or new customized data. MethylCallR includes a straightforward conversion function between different types of Illumina Human Methylation BeadChips. Using MethylCallR, potential outlier sample detection and statistical power estimation were conducted and used to select meaningful probes. Publicly available data was analyzed using MethylCallR and the findings were compared to that of a previous study.

Epigenetic Study of Cohort of Monozygotic Twins With Hypertrophic Cardiomyopathy Due to MYBPC3 (Cardiac Myosin-Binding Protein C)

BACKGROUND

Hypertrophic cardiomyopathy is an autosomal dominant cardiac disease. The mechanisms that determine its variable expressivity are poorly understood. Epigenetics could play a crucial role in bridging the gap between genotype and phenotype by orchestrating the interplay between the environment and the genome regulation. In this study we aimed to establish a possible correlation between the peripheral blood DNA methylation patterns and left ventricular hypertrophy severity in patients with hypertrophic cardiomyopathy, evaluating the potential impact of lifestyle variables and providing a biological context to the observed changes.

METHODS AND RESULTS

Methylation data were obtained from peripheral blood samples (Infinium MethylationEPIC BeadChip arrays). We employed multiple pair-matched models to extract genomic positions whose methylation correlates with the degree of left ventricular hypertrophy in 3 monozygotic twin pairs carrying the same founder pathogenic variant (MYBPC3 p.Gly263Ter). This model enables the isolation of the environmental influence, beyond age, on DNA methylation changes by removing the genetic background. Our results revealed a more anxious personality among more severely affected individuals. We identified 56 differentially methylated positions that exhibited moderate, proportional changes in methylation associated with left ventricular hypertrophy. These differentially methylated positions were enriched in regions regulated by repressor histone marks and tended to cluster at genes involved in left ventricular hypertrophy development, such as HOXA5, TRPC3, UCN3, or PLSCR2, suggesting that changes in peripheral blood may reflect myocardial alterations.

CONCLUSIONS

We present a unique pair-matched model, based on 3 monozygotic twin pairs carrying the same founder pathogenic variant and different phenotypes. This study provides further evidence of the pivotal role of epigenetics in hypertrophic cardiomyopathy variable expressivity.

Evaluating the connection between diet quality, EpiNutrient intake and epigenetic age: an observational study

BACKGROUND

DNA methylation (DNAm) has unique properties which makes it a potential biomarker for lifestyle-related exposures. Epigenetic clocks, particularly DNAm-based biological age predictors [epigenetic age (EA)], represent an exciting new area of clinical research and deviations of EA from chronological age [epigenetic age acceleration (EAA)] have been linked to overall health, age-related diseases, and environmental exposures.

OBJECTIVES

This observational study investigates the relationships between biological aging and various dietary factors within the LifeLines-DEEP Cohort. These factors include diet quality, processed food consumption, dietary glycemic load, and intake of vitamins involved in maintaining the epigenetic homeostasis (vitamins B-9, B-12, B-6, B-2, and C).

METHODS

Dietary records collected using food-frequency questionnaires were used to estimate diet quality [LifeLines Diet Score (LLDS)], measure the intake of unprocessed/ultraprocessed food according to the NOVA food classification system, and the adequacy of the dietary intake of vitamins B-9, B-12, B-2, B-6, and C. EA using Horvath, Hannum, Levine, and Horvath2 epigenetic clock models and DNAm-predicted telomere length (DNAm-TL) were calculated from DNAm data in 760 subjects. Associations between dietary factors and EAA were tested, adjusting for sex, energy intake, and body composition.

RESULTS

LLDS was associated with EAA (EAA_Horvath: β: -0.148; P = 1 × 10-4; EAA_Hannum: β: -0.148; P = 9 × 10-5; EAA_Levine: β: -0.174; P = 1 × 10-5; and EAA_Horvath2: β: -0.176; P = 4 × 10-6) and DNAm-TL (β: 0.116; P = 0.003). Particularly, EAA was associated with dietary glycemic load (EAA_Horvath: β: 0.476; P = 9 × 10-10; EAA_Hannum: β: 0.565; P = 1 × 10-13; EAA_Levine: β: 0.469; P = 5 × 10-9; EAA_Horvath2: β: 0.569; P = 1 × 10-13; and DNAmTL adjusted for age: β: -0.340; P = 2 × 10-5) and different measures of food processing (NOVA classes 1 and 4). Positive EAA was also associated with inadequate intake of vitamin B-12 (EAA_Horvath: β: -0.167; P = 0.002; EAA_Hannum: β: -0.144; P = 0.007; and EAA_Horvath2: β: -0.126; P = 0.019) and C (EAA_Hannum: β: -0.136; P = 0.010 and EAA_Horvath2: β: -0.151; P = 0.005).

CONCLUSIONS

Our findings corroborate the hypothesis that nutrition plays a pivotal role in influencing epigenetic homeostasis, especially DNAm, thereby contributing to individual health trajectories and the pace of aging.

Different genome-wide DNA methylation patterns in CD4+ T lymphocytes and CD14+ monocytes characterize relapse and remission of multiple sclerosis: Focus on GNAS

BACKGROUND

Relapsing-remitting multiple sclerosis (RRMS) is a most common form of multiple sclerosis in which periods of neurological worsening are followed by periods of clinical remission. RRMS relapses are caused by an acute autoimmune inflammatory process, which can occur in any area of the central nervous system. Although development of exacerbation cannot yet be accurately predicted, various external factors are known to affect its risk. These factors may trigger the pathological process through epigenetic mechanisms of gene expression regulation, first of all, through changes in DNA methylation.

METHODS

In the present work, we for the first time analyzed genome-wide DNA methylation patterns in CD4+ T lymphocytes and CD14+ monocytes of the same RRMS patients in relapse and remission. The effects of the differential methylation on gene expression were studied using qPCR.

RESULTS

We found 743 differentially methylated CpG positions (DMPs) in CD4+ cells and only 113 DMPs in CD14+ cells. They were mostly hypermethylated in RRMS relapse in both cell populations. However, the proportion of hypermethylated DMPs (as well as DMPs located within or in close proximity to CpG islands) was significantly higher in CD4+ T lymphocytes. In CD4+ and CD14+ cells we identified 469 and 67 DMP-containing genes, respectively; 25 of them were common for two cell populations. When we conducted a search for differentially methylated genomic regions (DMRs), we found a CD4+ specific DMR hypermethylated in RRMS relapse (adj. p = 0.03) within the imprinted GNAS locus. Total level of the protein-coding GNAS transcripts in CD4+ T cells decreased significantly in the row from healthy control to RRMS remission and then to RRMS relapse (adj. p = 3.1 × 10-7 and 0.011, respectively).

CONCLUSION

Our findings suggest that the epigenetic mechanism of DNA methylation in immune cells contributes to the development of RRMS relapse. Further studies are now required to validate these results and shed light on the molecular mechanisms underlying the observed GNAS methylation and expression changes.

A new insight of blood vs. buccal DNA methylation in the forensic identification of monozygotic triplets

The case of the monozygotic (MZ) twin as a suspect demonstrates a practical problem in forensic casework. As the MZ twins are genetically identical, they share the same short tandem repeat (STR) profile. Many studies showed that older MZ twins have significant differences in overall content and genomic distribution of methylation between them. However, studies addressing the investigation of epigenetic MZ triplet differentiation in various forensic reference materials are lacking. Here, one triplet set of Egyptian MZ twins was used as an analog to a forensic case. The genome-wide methylation analysis was performed via the new Human Methylation EPIC BeadChip array. Following normalization methods, potential differentially methylated positions (DMPs) were discovered. This resulted in the detection of 24 potential DMPs in reference-type blood DNA and 11 potential DMPs in reference-type buccal DNA. Then, the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) analyses were performed to show the associated biological functions. Our findings revealed that the 35 potential DMPs were enriched in 283 significant GO terms. These terms are mainly enriched in the immune system. Overall, this study demonstrates the general feasibility of epigenetic MZ triplet differentiation in the forensic context and highlights that some potential DMPs identified in blood DNA were not informative in buccal DNA. This is due to various reasons, including the tissue specificity of DNA methylation.

Diet Quality and Epigenetic Aging in the Women's Health Initiative

BACKGROUND

Higher diet quality scores are associated with a lower risk for many chronic diseases and all-cause mortality; however, it is unclear if diet quality is associated with aging biology.

OBJECTIVE

This study aimed to examine the association between diet quality and a measure of biological aging known as epigenetic aging.

DESIGN

A cross-sectional data analysis was used to examine the association between three diet quality scores based on self-reported food frequency questionnaire data and five measures of epigenetic aging based on DNA methylation (DNAm) data from peripheral blood.

PARTICIPANTS/SETTING

This study included 4,500 postmenopausal women recruited from multiple sites across the United States (1993-98), aged 50 to 79 years, with food frequency questionnaire and DNAm data available from the Women's Health Initiative baseline visit.

MAIN OUTCOME MEASURES

Five established epigenetic aging measures were generated from HumanMethylation450 Beadchip DNAm data, including AgeAccelHannum, AgeAccelHorvath, AgeAccelPheno, AgeAccelGrim, and DunedinPACE.

STATISTICAL ANALYSES PERFORMED

Linear mixed models were used to test for associations between three diet quality scores (Healthy Eating Index, Dietary Approaches to Stop Hypertension, and alternate Mediterranean diet scores) and epigenetic aging measures, adjusted for age, race and ethnicity, education, tobacco smoking, physical activity, Women's Health Initiative substudy from which DNAm data were obtained, and DNAm-based estimates of leukocyte proportions.

RESULTS

Healthy Eating Index, Dietary Approaches to Stop Hypertension, and alternate Mediterranean diet scores were all inversely associated with AgeAccelPheno, AgeAccelGrim, and DunedinPACE (P < 0.05), with the largest effects with DunedinPACE. A one standard deviation increment in diet quality scores was associated with a decrement (β ± SE) in DunedinPACE z score of -0.097 ± 0.014 (P = 9.70 x 10-13) for Healthy Eating Index, -0.107 ± 0.014 (P = 1.53 x 10-14) for Dietary Approaches to Stop Hypertension, and -0.068 ± 0.013 (P = 2.31 x 10-07) for the alternate Mediterranean diet.

CONCLUSIONS

In postmenopausal women, diet quality scores were inversely associated with DNAm-based measures of biological aging, particularly DunedinPACE.

Glioma immune microenvironment composition calculator (GIMiCC): a method of estimating the proportions of eighteen cell types from DNA methylation microarray data

A scalable platform for cell typing in the glioma microenvironment can improve tumor subtyping and immune landscape detection as successful immunotherapy strategies continue to be sought and evaluated. DNA methylation (DNAm) biomarkers for molecular classification of tumor subtypes have been developed for clinical use. However, tools that predict the cellular landscape of the tumor are not well-defined or readily available. We developed the Glioma Immune Microenvironment Composition Calculator (GIMiCC), an approach for deconvolution of cell types in gliomas using DNAm data. Using data from 17 isolated cell types, we describe the derivation of the deconvolution libraries in the biological context of selected genomic regions and validate deconvolution results using independent datasets. We utilize GIMiCC to illustrate that DNAm-based estimates of immune composition are clinically relevant and scalable for potential clinical implementation. In addition, we utilize GIMiCC to identify composition-independent DNAm alterations that are associated with high immune infiltration. Our future work aims to optimize GIMiCC and advance the clinical evaluation of glioma.

Association between maternal perinatal stress and depression and infant DNA methylation in the first year of life

Maternal stress and depression during pregnancy and the first year of the infant's life affect a large percentage of mothers. Maternal stress and depression have been associated with adverse fetal and childhood outcomes as well as differential child DNA methylation (DNAm). However, the biological mechanisms connecting maternal stress and depression to poor health outcomes in children are still largely unknown. Here we aim to determine whether prenatal stress and depression are associated with differences in cord blood mononuclear cell DNAm (CBMC-DNAm) in newborns (n = 119) and whether postnatal stress and depression are associated with differences in peripheral blood mononuclear cell DNAm (PBMC-DNAm) in children of 12 months of age (n = 113) from the Canadian Healthy Infant Longitudinal Development (CHILD) cohort. Stress was measured using the 10-item Perceived Stress Scale (PSS) and depression was measured using the 20-item Center for Epidemiologic Studies Depression Questionnaire (CESD). Both stress and depression were measured longitudinally at 18 weeks and 36 weeks of pregnancy and six months and 12 months postpartum. We conducted epigenome-wide association studies (EWAS) using robust linear regression followed by a sensitivity analysis in which we bias-adjusted for inflation and unmeasured confounding using the bacon and cate methods. To quantify the cumulative effect of maternal stress and depression, we created composite prenatal and postnatal adversity scores. We identified a significant association between prenatal stress and differential CBMC-DNAm at 8 CpG sites and between prenatal depression and differential CBMC-DNAm at 2 CpG sites. Additionally, we identified a significant association between postnatal stress and differential PBMC-DNAm at 8 CpG sites and between postnatal depression and differential PBMC-DNAm at 11 CpG sites. Using our composite scores, we further identified 2 CpG sites significantly associated with prenatal adversity and 7 CpG sites significantly associated with postnatal adversity. Several of the associated genes, including PLAGL1, HYMAI, BRD2, and ERC2 have been implicated in adverse fetal outcomes and neuropsychiatric disorders. These data further support the finding that differential DNAm may play a role in the relationship between maternal mental health and child health.

Genome-wide DNA Methylome and Transcriptome Profiling Reveals Key Genes Involved in the Dysregulation of Adipose Stem Cells in Crohn's Disease

BACKGROUND AND AIMS

Crohn's disease [CD] is characterised by the expansion of mesenteric adipose tissue [MAT], named creeping fat [CF], which seems to be directly related to disease activity. Adipose-stem cells [ASCs] isolated from the CF of patients with CD are extremely pro-inflammatory, which persists during disease remission. We hypothesised that the dysfunctional ASCs in CD accumulate epigenetic modifications triggered by the inflammatory environment, that could serve as molecular markers.

METHODS

Genome-wide DNA methylome and transcriptome profiling were performed in ASCs isolated from MAT biopsies of patients with active and inactive disease and from non-Crohn's disease patients [non-CD]. A validation cohort was used to test the main candidate genes via quantitative polymerase chain reaction in other fat depots and immune cells.

RESULTS

We found differences in DNA methylation and gene expression between ASCs isolated from patients with CD and from non-CD subjects, but we found no differences related to disease activity. Pathway enrichment analysis revealed that oxidative stress and immune response were significantly enriched in active CD, and integration analysis identified MAB21L2, a cell fate-determining gene, as the most affected gene in CD. Validation analysis confirmed the elevated gene expression of MAB21L2 in MAT and in adipose tissue macrophages in active CD. We also found a strong association between expression of the calcium channel subunit gene CACNA1H and disease remission, as CACNA1H expression was higher in ASCs and MAT from patients with inactive CD, and correlates negatively with C-reactive protein in peripheral blood mononuclear cells.

CONCLUSION

We identified a potential gene signature of CD in ASCs obtained from MAT. Integration analysis highlighted two novel genes demonstrating a negative correlation between promoter DNA methylation and transcription: one linked to ASCs in CD [MAB21L2] and the other [CACNA1H] related to disease remission.

Genome-wide methylation profiling reveals extracellular vesicle DNA as an ex vivo surrogate of cancer cell-derived DNA

Extracellular vesicle-derived DNA (evDNA) encapsulates the complete genome and mutational status of cells; however, whether cancer cell-derived evDNA mirrors the epigenetic features of parental genomic DNA remains uncertain. This study aimed to assess and compare the DNA methylation patterns of evDNA from cancer cell lines and primary cancer tissues with those of the nuclear genomic DNA. We isolated evDNA secreted by two cancer cell lines (HCT116 and MDA-MB-231) from various subcellular compartments, including the nucleus and cytoplasm. Additionally, we obtained evDNA and nuclear DNA (nDNA) from the primary cancer tissues of colon cancer patients. We conducted a comprehensive genome-wide DNA methylation analysis using the Infinium Methylation EPIC BeadChip, examining > 850,000 CpG sites. Remarkable similarities were observed between evDNA and nDNA methylation patterns in cancer cell lines and patients. This concordance extended to clinical cancer tissue samples, showcasing the potential utility of evDNA methylation patterns in deducing cellular origin within heterogeneous populations through methylation-based deconvolution. The observed concordance underscores the potential of evDNA as a noninvasive surrogate marker for discerning tissue origin, particularly in cancer tissues, offering a promising future for cancer diagnostics. This finding enhances our understanding of cellular origins and would help develop innovative diagnostic and therapeutic strategies for cancer.

Quantitative trait locus mapping in placenta: A comparative study of chorionic villus and birth placenta

The placenta, a pivotal player in the prenatal environment, holds crucial insights into early developmental pathways and future health outcomes. In this study, we explored genetic molecular regulation in chorionic villus samples (CVS) from the first trimester and placenta tissue at birth. We assessed quantitative trait locus (QTL) mapping on DNA methylation and gene expression data in a Finnish cohort of 574 individuals. We found more QTLs in birth placenta than in first-trimester placenta. Nevertheless, a substantial amount of associations overlapped in their effects and showed consistent direction in both tissues, with increasing molecular genetic effects from early pregnancy to birth placenta. The identified QTLs in birth placenta were most enriched in genes with placenta-specific expression. Conducting a phenome-wide-association study (PheWAS) on the associated SNPs, we observed numerous overlaps with genome-wide association study (GWAS) hits (spanning 57 distinct traits and 23 SNPs), with notable enrichments for immunological, skeletal, and respiratory traits. The QTL-SNP rs1737028 (chr6:29737993) presented with the highest number of GWAS hits. This SNP was related to HLA-G expression via DNA methylation and was associated with various immune, respiratory, and psychiatric traits. Our findings implicate increasing genetic molecular regulation during the course of pregnancy and support the involvement of placenta gene regulation, particularly in immunological traits. This study presents a framework for understanding placenta-specific gene regulation during pregnancy and its connection to health-related traits.

Methylation patterns at the adjacent CpG sites within enhancers are a part of cell identity

BACKGROUND

It is generally accepted that methylation status of CpG sites spaced up to 50 bp apart is correlated, and accumulation of locally disordered methylation at adjacent CpG sites is involved in neoplastic transformation, acting in similar way as stochastic accumulation of mutations.

RESULTS

We used EPIC microarray data from 596 samples, representing 12 healthy tissue and cell types, as well as 572 blood cancer specimens to analyze methylation status of adjacent CpG sites across human genome, and subsequently validated our findings with NGS and Sanger sequencing. Our analysis showed that there is a subset of the adjacent CpG sites in human genome, with cytosine at one CpG site methylated and the other devoid of methyl group. These loci map to enhancers that are targeted by families of transcription factors involved in cell differentiation. Moreover, our results suggest that the methylation at these loci differ between alleles within a cell, what allows for remarkable level of heterogeneity of methylation patterns. However, different types of specialized cells acquire only one specific and stable pattern of methylation at each of these loci and that pattern is to a large extent lost during neoplastic transformation.

CONCLUSIONS

We identified a substantial number of adjacent CpG loci in human genome that display remarkably stable and cell type specific methylation pattern. The methylation pattern at these loci appears to reflect different methylation of alleles in cells. Furthermore, we showed that changes of methylation status at those loci are likely to be involved in regulation of the activity of enhancers and contribute to neoplastic transformation.

Epigenetic and Genetic Profiling of Comorbidity Patterns among Substance Dependence Diagnoses

Objective

This study investigated the genetic and epigenetic mechanisms underlying the comorbidity patterns of five substance dependence diagnoses (SDs; alcohol, AD; cannabis, CaD; cocaine, CoD; opioid, OD; tobacco, TD).

Methods

A latent class analysis (LCA) was performed on 31,197 individuals (average age 42±11 years; 49% females) from six cohorts to identify comorbid DSM-IV SD patterns. In subsets of this sample, we tested SD-latent classes with respect to polygenic burden of psychiatric and behavioral traits and epigenome-wide changes in three population groups.

Results

An LCA identified four latent classes related to SD comorbidities: AD+TD, CoD+TD, AD+CoD+OD+TD (i.e., polysubstance use, 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).

Conclusions

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.

DNA methylation near MAD1L1, KDM2B, and SOCS3 mediates the effect of socioeconomic status on elevated body mass index in African American adults

Obesity and poverty disproportionally affect African American persons. Epigenetic mechanisms could partially explain the association between socioeconomic disadvantage and body mass index (BMI). We examined the extent to which epigenetic mechanisms mediate the effect of socioeconomic status (SES) on BMI. Using data from African American adults from the Atherosclerosis Risk in Communities (ARIC) Study (n = 2664, mean age = 57 years), education, income, and occupation were used to create a composite SES score at visit 1 (1987-1989). We conducted two methylation-wide association analyses to identify associations between SES (visit 1), BMI and cytosine-phosphate-guanine (CpG) sites measured at a subsequent visit (1990-1995). We then utilized structural equation modeling (SEM) to test whether identified sites mediated the association between earlier SES and BMI in sex-stratified models adjusted for demographic and risk factor covariates. Independent replication and meta-analyses were conducted using the Jackson Heart Study (JHS, n = 874, mean age 51 years, 2000-2004). Three CpG sites near MAD1L1, KDM2B, and SOCS3 (cg05095590, cg1370865, and cg18181703) were suggestively associated (P-value < 1.3×10-5) in ARIC and at array-wide significance (P-value < 1.3×10-7) in a combined meta-analysis of ARIC with JHS. SEM of these three sites revealed significant indirect effects in females (P-value < 5.8×10-3), each mediating 7%-20% of the total effect of SES on BMI. Nominally significant indirect effects were observed for two sites near MAD1L1 and KDM2B in males (P-value < 3.4×10-2), mediating -17 and -22% of the SES-BMI effect. These results provide further evidence that epigenetic modifications may be a potential pathway through which SES may "get under the skin" and contribute to downstream health disparities.

Sex differences in DNA methylation variations according to ART conception-evidence from the Norwegian mother, father, and child cohort study

Previous studies have shown cord-blood DNA methylation differences in newborns conceived using assisted reproductive technologies (ART) compared to those conceived naturally. However, whether these ART-related DNA methylation differences vary with children's sex is unknown. We hypothesize that the DNA methylation differences in cord blood between ART-conceived and naturally conceived newborns also varies by the sex of the child, with distinct patterns of differential methylation present in males and females. We investigated sex differences in cord-blood DNA methylation variation according to conception by ART using the Illumina MethylationEPIC platform, comparing 456 ART-conceived versus 507 naturally-conceived girls, and 503 ART-conceived and 473 naturally-conceived boys. We identified 37 differentially methylated CpGs according to ART-conception among girls, and 70 differentially methylated CpGs according to ART-conception among boys, when we used a 1% false discovery rate to account for multiple testing. Ten CpGs were differentially methylated according to conception by ART in both sexes. Among the genes that were associated with these CpGs, we found the BRCA1; NBR2 gene (two CpGs) was hypermethylated in girls while the APC2 (two CpGs) and NECAB3;ACTL10, (four CpGs) related to cellular signaling were hypomethylated in boys. These findings confirm the presence of sex-specific epigenetic differences, illustrating the nuanced impact of ART on the fetal epigenome. There is a need for further explorations into the implications for sex-specific developmental trajectories and health outcomes in ART-conceived children.

Blood Leukocyte DNA Methylation Markers of Periodontal Disease and Risk of Lung Cancer in a Case-Control Study Nested in the CLUE II Cohort

BACKGROUND

Periodontal disease and DNA methylation markers have separately been associated with lung cancer risk. Examining methylation levels at genomic regions previously linked to periodontal disease may provide insights on the link between periodontal disease and lung cancer.

METHODS

In a nested case-control study drawn from the CLUE II cohort, we measured DNA methylation levels in 208 lung cancer cases and 208 controls. We examined the association between 37 DNA-methylated 5'-C-phosphate-G-3' (CpG) sites at three genomic regions, homeobox 4 (HOXA4), zinc finger protein (ZFP57), and a long noncoding RNA gene located in Chr10 (ENSG00000231601), and lung cancer risk.

RESULTS

Statistically significant associations with lung cancer risk were observed for all 14 CpG sites from HOXA4 (OR ranging 1.41-1.62 for 1 SD increase in the DNA methylation level, especially within 15 years) and 1 CpG site on gene ENSG00000231601 (OR = 1.34 for 1 SD increase in the DNA methylation level). Although CpG sites on gene ZFP57 were not associated with lung cancer risk overall, statistically significant inverse associations were noted for six CpG sites when restricting follow-up to 15 years (OR = 0.73-0.77 for 1 SD increase in the DNA methylation level).

CONCLUSIONS

Key methylation levels associated with periodontal disease are also associated with lung cancer risk. For both HOXA4 and ZFP57, the associations were stronger within 15 years of follow-up, which suggest that, if causal, the impact of methylation is acting late in the natural history of lung cancer.

IMPACT

Identifying biological pathways that link periodontal disease and lung cancer could provide new opportunities for lung cancer detection and prevention.

Differences in DNA Methylation in Genes Involved in Vitamin D Metabolism Are Related to Insulin Requirement in Pregnant Women with Gestational Diabetes Mellitus

In a previous study performed by our group, pregnant women with Gestational Diabetes (GDM) showed higher vitamin D (VitD) levels in the last trimester, particularly in those requiring insulin. This phenomenon was not linked to factors like season or supplementation. This study aimed to investigate if insulin treatment in GDM affects DNA methylation in VitD metabolism genes. Thirty-two pregnant women were selected, half of whom had GDM, and were divided into insulin-treated and lifestyle groups. The DNA methylation levels in CpGs from 47 VitD metabolism-related genes were analyzed at the diagnostic visit (24-28 weeks) and before delivery. At week 36-38 of pregnancy, twenty-six CpG sites were differentially methylated (DMPs) in the insulin-treated group compared with the control group and the lifestyle group. Twenty-two of these DMPs were not different at the diagnostic visit. Six CpGs (cg18276810 (CTNNB1), cg03919554 (FGFR3), cg03984919 (NCOA1), cg19218509 (ASIP), cg09922639 (SMAD3), and cg25356935 (PDZD3)) showed significant correlations with VitD levels, not only before childbirth, but also in the postpartum period and at one year later. This suggests that insulin treatment in GDM could influence DNA methylation in genes involved in vitamin D metabolism, affecting VitD levels during and after pregnancy. Further research is warranted to elucidate these findings' clinical implications.

Single-nucleus transcriptomic profiling of human orbitofrontal cortex reveals convergent effects of aging and psychiatric disease

Aging is a complex biological process and represents the largest risk factor for neurodegenerative disorders. The risk for neurodegenerative disorders is also increased in individuals with psychiatric disorders. Here, we characterized age-related transcriptomic changes in the brain by profiling ~800,000 nuclei from the orbitofrontal cortex from 87 individuals with and without psychiatric diagnoses and replicated findings in an independent cohort with 32 individuals. Aging affects all cell types, with LAMP5+LHX6+ interneurons, a cell-type abundant in primates, by far the most affected. Disrupted synaptic transmission emerged as a convergently affected pathway in aged tissue. Age-related transcriptomic changes overlapped with changes observed in Alzheimer's disease across multiple cell types. We find evidence for accelerated transcriptomic aging in individuals with psychiatric disorders and demonstrate a converging signature of aging and psychopathology across multiple cell types. Our findings shed light on cell-type-specific effects and biological pathways underlying age-related changes and their convergence with effects driven by psychiatric diagnosis.

Multiomics and eXplainable artificial intelligence for decision support in insulin resistance early diagnosis: A pediatric population-based longitudinal study

Pediatric obesity can drastically heighten the risk of cardiometabolic alterations later in life, with insulin resistance standing as the cornerstone linking adiposity to the increased cardiovascular risk. Puberty has been pointed out as a critical stage after which obesity-associated insulin resistance is more difficult to revert. Timely prediction of insulin resistance in pediatric obesity is therefore vital for mitigating the risk of its associated comorbidities. The construction of effective and robust predictive systems for a complex health outcome like insulin resistance during the early stages of life demands the adoption of longitudinal designs for more causal inferences, and the integration of factors of varying nature involved in its onset. In this work, we propose an eXplainable Artificial Intelligence-based decision support pipeline for early diagnosis of insulin resistance in a longitudinal cohort of 90 children. For that, we leverage multi-omics (genomics and epigenomics) and clinical data from the pre-pubertal stage. Different data layers combinations, pre-processing techniques (missing values, feature selection, class imbalance, etc.), algorithms, training procedures were considered following good practices for Machine Learning. SHapley Additive exPlanations were provided for specialists to understand both the decision-making mechanisms of the system and the impact of the features on each automatic decision, an essential issue in high-risk areas such as this one where system decisions may affect people's lives. The system showed a relevant predictive ability (AUC and G-mean of 0.92). A deep exploration, both at the global and the local level, revealed promising biomarkers of insulin resistance in our population, highlighting classical markers, such as Body Mass Index z-score or leptin/adiponectin ratio, and novel ones such as methylation patterns of relevant genes, such as HDAC4, PTPRN2, MATN2, RASGRF1 and EBF1. Our findings highlight the importance of integrating multi-omics data and following eXplainable Artificial Intelligence trends when building decision support systems.

Discrepancies in readouts between Infinium MethylationEPIC v2.0 and v1.0 reflected in DNA methylation-based tools: implications and considerations for human population epigenetic studies

Background

The recently launched DNA methylation profiling platform, Illumina MethylationEPIC BeadChip Infinium microarray v2.0 (EPICv2), is highly correlated with measurements obtained from its predecessor MethylationEPIC BeadChip Infinium microarray v1.0 (EPICv1). However, the concordance between the two versions in the context of DNA methylation-based tools, including cell type deconvolution algorithms, epigenetic clocks, and inflammation and lifestyle biomarkers has not yet been investigated. To address this, we profiled DNA methylation on both EPIC versions using matched venous blood samples from individuals spanning early to late adulthood across four cohorts.

Findings

Within each cohort, samples primarily clustered by the EPIC version they were measured on. High concordance between EPIC versions at the array level, but variable concordance at the individual probe level was noted. Significant differences between versions in estimates from DNA methylation-based tools were observed, irrespective of the normalization method, with some nuanced differences across cohorts and tools. Adjusting for EPIC version or calculating estimates separately for each version largely mitigated these version-specific discordances.

Conclusions

Our work illustrates the importance of accounting for EPIC version differences in research scenarios, especially in meta-analyses and longitudinal studies, when samples profiled across different versions are harmonized. Alongside DNA methylation-based tools, our observations also have implications in interpretation of epigenome-wide association studies (EWAS) findings, when results obtained from one version are compared to another, particularly for probes that are poorly concordant between versions.

Longitudinal association of peripheral blood DNA methylation with liver fat content: distinguishing between predictors and biomarkers

BACKGROUND

Alterations in DNA methylation (DNAm) have been observed in patients with fatty liver, but whether they are cause or consequence remains unknown. The study aimed to investigate longitudinal association of epigenome-wide DNAm with liver fat content (LFC) in Chinese participants, and explore their temporal relationships.

METHODS

Data were obtained from 2 waves over a four-year time period of the Shanghai Changfeng Study (discovery, n = 407 and replication, n = 126). LFC and peripheral blood DNAm were repeatedly measured using quantitative hepatic ultrasonography and the 850 K Illumina EPIC BeadChip, respectively. Longitudinal and cross-sectional epigenome-wide association studies (EWASs) were conducted with linear mixed model and linear regression model, respectively. Meta-analysis was performed using METAL. Cross-lagged panel analysis (CLPA) was carried out to infer temporal relationships between the significant CpGs and LFC.

RESULTS

Longitudinal EWAS identified cg11024682 (SREBF1), cg06500161 (ABCG1), cg16740586 (ABCG1), cg15659943 (ABCA1) and cg00163198 (SNX19) significantly associated with LFC with P < 1e-7. Another 6 of the 22 previously reported CpGs were replicated in the present longitudinal EWAS. CLPA showed longitudinal effects of cg11024682 (SREBF1) (β = 0.14 [0.06, 0.23]), cg16740586 (ABCG1) (β = 0.17 [0.08, 0.25]), cg06500161 (ABCG1) (β = 0.12 [0.03, 0.20]), cg17901584 (DHCR24) (β = -0.10 [-0.18, -0.02]), cg00574958 (CPT1A) (β = -0.09 [-0.17, -0.01]), cg08309687 (LINC00649) (β = -0.11 [-0.19, -0.03]), and cg27243685 (ABCG1) (β = 0.09 [0.01, 0.18]) on subsequent LFC. The effects were attenuated when further adjusting for body mass index. High levels of LFC led to alterations in DNAm of cg15659943 (ABCA1) (β = 0.13 [0.04, 0.21]), cg07162647 (β = -0.11 [-0.19, -0.03]), cg06500161 (ABCG1) (β = 0.10 [0.02, 0.18]), and cg27243685 (ABCG1) (β = 0.10 [0.02, 0.18]).

CONCLUSIONS

Blood DNAm at SREBF1, ABCG1, DHCR24, CPT1A, and LINC00649 may be predictors of subsequent LFC change. The effects of DNAm at SREBF1 and ABCG1 on LFC were partially influenced by obesity. The findings have potential implications in understanding disease pathogenesis and highlight the potential of DNAm for early detection or intervention of fatty liver.

Mediation analysis in longitudinal study with high-dimensional methylation mediators

Mediation analysis has been widely utilized to identify potential pathways connecting exposures and outcomes. However, there remains a lack of analytical methods for high-dimensional mediation analysis in longitudinal data. To tackle this concern, we proposed an effective and novel approach with variable selection and the indirect effect (IE) assessment based on both linear mixed-effect model and generalized estimating equation. Initially, we employ sure independence screening to reduce the dimension of candidate mediators. Subsequently, we implement the Sobel test with the Bonferroni correction for IE hypothesis testing. Through extensive simulation studies, we demonstrate the performance of our proposed procedure with a higher F$_{1}$ score (0.8056 and 0.9983 at sample sizes of 150 and 500, respectively) compared with the linear method (0.7779 and 0.9642 at the same sample sizes), along with more accurate parameter estimation and a significantly lower false discovery rate. Moreover, we apply our methodology to explore the mediation mechanisms involving over 730 000 DNA methylation sites with potential effects between the paternal body mass index (BMI) and offspring growing BMI in the Shanghai sleeping birth cohort data, leading to the identification of two previously undiscovered mediating CpG sites.

Bronchial cell epigenetic aging in a human experimental study of short-term diesel and ozone exposures

Blood-based, observational, and cross-sectional epidemiological studies suggest that air pollutant exposures alter biological aging. In a single-blinded randomized crossover human experiment of 17 volunteers, we examined the effect of randomized 2-h controlled air pollution exposures on respiratory tissue epigenetic aging. Bronchial epithelial cell DNA methylation 24 h post-exposure was measured using the HumanMethylation450K BeadChip, and there was a minimum 2-week washout period between exposures. All 17 volunteers were exposed to ozone, but only 13 were exposed to diesel exhaust. Horvath DNAmAge [Pearson coefficient (r) = 0.64; median absolute error (MAE) = 2.7 years], GrimAge (r = 0.81; MAE = 13 years), and DNAm Telomere Length (DNAmTL) (r = -0.65) were strongly correlated with chronological age in this tissue. Compared to clean air, ozone exposure was associated with longer DNAmTL (median difference 0.11 kb, Fisher's exact P-value = .036). This randomized trial suggests a weak relationship of ozone exposure with DNAmTL in target respiratory cells. Still, causal relationships with long-term exposures need to be evaluated.

Prenatal and early-life air pollutant exposure and epigenetic aging acceleration

BACKGROUND

This study investigated the association of prenatal and early childhood exposure to air pollution with epigenetic age acceleration (EAA) at six years of age using the Environment and Development of Children Cohort (EDC Cohort) MATERIALS & METHODS: Air pollution, including particulate matter [< 2.5 µm (PM2.5) and < 10 µm (PM10) in an aerodynamic diameter], nitrogen dioxide (NO2), ozone (O3), carbon monoxide (CO), and sulfur dioxide (SO2) were estimated based on the residential address for two periods: 1) during the whole pregnancy, and 2) for one year before the follow-up in children at six years of age. The methylation levels in whole blood at six years of age were measured, and the methylation clocks, including Horvath's clock, Horvath's skin and blood clock, PedBE, and Wu's clock, were estimated. Multivariate linear regression models were constructed to analyze the association between EAA and air pollutants.

RESULTS

A total of 76 children in EDC cohort were enrolled in this study. During the whole pregnancy, interquartile range (IQR) increases in exposure to PM2.5 (4.56 μg/m3) and CO (0.156 ppm) were associated with 0.406 years and 0.799 years of EAA (Horvath's clock), respectively. An IQR increase in PM2.5 (4.76 μg/m3) for one year before the child was six years of age was associated with 0.509 years of EAA (Horvath's clock) and 0.289 years of EAA (Wu's clock). PM10 (4.30 μg/m3) and O3 (0.003 ppm) exposure in the period were also associated with EAA in Horvath's clock (0.280 years) and EAA in Horvath's skin and blood clock (0.163 years), respectively.

CONCLUSION

We found that prenatal and childhood exposure to ambient air pollutants is associated with EAA among children. The results suggest that air pollution could induce excess biological aging even in prenatal and early life.

Developing survival prediction models in colorectal cancer using epigenome-wide DNA methylation data from whole blood

While genome-wide association studies are valuable in identifying CRC survival predictors, the benefit of adding blood DNA methylation (blood-DNAm) to clinical features, including the TNM system, remains unclear. In a multi-site population-based patient cohort study of 2116 CRC patients with baseline blood-DNAm, we analyzed survival predictions using eXtreme Gradient Boosting with a 5-fold nested leave-sites-out cross-validation across four groups: traditional and comprehensive clinical features, blood-DNAm, and their combination. Model performance was assessed using time-dependent ROC curves and calibrations. During a median follow-up of 10.3 years, 1166 patients died. Although blood-DNAm-based predictive signatures achieved moderate performances, predictive signatures based on clinical features outperformed blood-DNAm signatures. The inclusion of blood-DNAm did not improve survival prediction over clinical features. M1 stage, age at blood collection, and N2 stage were the top contributors. Despite some prognostic value, incorporating blood DNA methylation did not enhance survival prediction of CRC patients beyond clinical features.

Uncovering novel regulatory variants in carbohydrate metabolism: a comprehensive multi-omics study of glycemic traits in the Indian population

Clinical biomarkers such as fasting glucose, HbA1c, and fasting insulin, which gauge glycemic status in the body, are highly influenced by diet. Indians are genetically predisposed to type 2 diabetes and their carbohydrate-centric diet further elevates the disease risk. Despite the combined influence of genetic and environmental risk factors, Indians have been inadequately explored in the studies of glycemic traits. Addressing this gap, we investigate the genetic architecture of glycemic traits at genome-wide level in 4927 Indians (without diabetes). Our analysis revealed numerous variants of sub-genome-wide significance, and their credibility was thoroughly assessed by integrating data from various levels. This identified key effector genes, ZNF470, DPP6, GXYLT2, PITPNM3, BEND7, and LORICRIN-PGLYRP3. While these genes were weakly linked with carbohydrate intake or glycemia earlier in other populations, our findings demonstrated a much stronger association in the Indian population. Associated genetic variants within these genes served as expression quantitative trait loci (eQTLs) in various gut tissues essential for digestion. Additionally, majority of these gut eQTLs functioned as methylation quantitative trait loci (meth-QTLs) observed in peripheral blood samples from 223 Indians, elucidating the underlying mechanism of their regulation of target gene expression. Specific co-localized eQTLs-meth-QTLs altered the binding affinity of transcription factors targeting crucial genes involved in glucose metabolism. Our study identifies previously unreported genetic variants that strongly influence the diet-glycemia relationship. These findings set the stage for future research into personalized lifestyle interventions integrating genetic insights with tailored dietary strategies to mitigate disease risk based on individual genetic profiles.

Genome-Wide DNA methylation profile analysis identifies differentially methylated loci associated with personal PM2.5 exposure in adults with asthma

Particulate matter with aerodynamic diameters ≤2.5 µm (PM2.5) is a major environmental risk factor for acute asthma exacerbation, and the underlying mechanism is not completely understood. Studies have indicated that DNA methylation is a potential mechanism linking PM2.5 to its health effects. We conducted a panel study involving 24 adult patients with asthma in Beijing,China between 2017 and 2019. PM2.5 and other atmospheric pollutant exposure data were repeatedly measured. Blood samples were collected for genome-wide DNA methylation analysis. A linear mixed-effects (LME) model was conducted to identify differentially methylated probes (DMPs) associated with PM2.5 exposure. After filtering out probes that did not meet the criteria through quality control, 811,001 CpG sites were included in the LME model, and 36 DMPs were strongly associated with personal PM2.5 exposure at false discovery rate (FDR) < 0.05, of which 22 and 14 DMPs negatively and positively correlated with personal PM2.5 exposure, respectively. Functional analysis revealed that DMPs affected smooth muscle cell contraction and development, extracellular matrix synthesis and secretion, T cell activation and differentiation, and inflammatory factor production. This study provides evidence linking personal PM2.5 exposure to genome-wide DNA methylation in adult patients with asthma. Identifying enrichment pathways can provide biological insights into the acute health effects of PM2.5.