UNASSIGNED: Maternal smoking has been linked to adverse health outcomes in newborns but the extent to which it impacts newborn health has not been quantified through an aggregated cord blood DNA methylation (DNAm) score. Here, we examine the feasibility of using cord blood DNAm scores leveraging large external studies as discovery samples to capture the epigenetic signature of maternal smoking and its influence on newborns in White European and South Asian populations.
UNASSIGNED: We first examined the association between individual CpGs and cigarette smoking during pregnancy, and smoking exposure in two White European birth cohorts (n=744). Leveraging established CpGs for maternal smoking, we constructed a cord blood epigenetic score of maternal smoking that was validated in one of the European-origin cohorts (n=347). This score was then tested for association with smoking status, secondary smoking exposure during pregnancy, and health outcomes in offspring measured after birth in an independent White European (n=397) and a South Asian birth cohort (n=504).
UNASSIGNED: Several previously reported genes for maternal smoking were supported, with the strongest and most consistent association signal from the GFI1 gene (6 CpGs with p<5 × 10-5). The epigenetic maternal smoking score was strongly associated with smoking status during pregnancy (OR = 1.09 [1.07, 1.10], p=5.5 × 10-33) and more hours of self-reported smoking exposure per week (1.93 [1.27, 2.58], p=7.8 × 10-9) in White Europeans. However, it was not associated with self-reported exposure (p>0.05) among South Asians, likely due to a lack of smoking in this group. The same score was consistently associated with a smaller birth size (-0.37±0.12 cm, p=0.0023) in the South Asian cohort and a lower birth weight (-0.043±0.013 kg, p=0.0011) in the combined cohorts.
UNASSIGNED: This cord blood epigenetic score can help identify babies exposed to maternal smoking and assess its long-term impact on growth. Notably, these results indicate a consistent association between the DNAm signature of maternal smoking and a small body size and low birth weight in newborns, in both White European mothers who exhibited some amount of smoking and in South Asian mothers who themselves were not active smokers.
UNASSIGNED: This study was funded by the Canadian Institutes of Health Research Metabolomics Team Grant: MWG-146332.

BACKGROUND: The plasma metabolome reflects the physiological state of various biological processes and can serve as a proxy for disease risk. Plasma metabolite variation, influenced by genetic and epigenetic mechanisms, can also affect the cellular microenvironment and blood cell epigenetics. The interplay between the plasma metabolome and the blood cell epigenome remains elusive. In this study, we performed an epigenome-wide association study (EWAS) of 1183 plasma metabolites in 693 participants from the LifeLines-DEEP cohort and investigated the causal relationships in DNA methylation-metabolite associations using bidirectional Mendelian randomization and mediation analysis.
RESULTS: After rigorously adjusting for potential confounders, including genetics, we identified five robust associations between two plasma metabolites (L-serine and glycine) and three CpG sites located in two independent genomic regions (cg14476101 and cg16246545 in PHGDH and cg02711608 in SLC1A5) at a false discovery rate of less than 0.05. Further analysis revealed a complex bidirectional relationship between plasma glycine/serine levels and DNA methylation. Moreover, we observed a strong mediating role of DNA methylation in the effect of glycine/serine on the expression of their metabolism/transport genes, with the proportion of the mediated effect ranging from 11.8 to 54.3%. This result was also replicated in an independent population-based cohort, the Rotterdam Study. To validate our findings, we conducted in vitro cell studies which confirmed the mediating role of DNA methylation in the regulation of PHGDH gene expression.
CONCLUSIONS: Our findings reveal a potential feedback mechanism in which glycine and serine regulate gene expression through DNA methylation.

BACKGROUND: While the association of chronological age with DNA methylation (DNAm) in whole blood has been extensively studied, the tissue-specificity of age-related DNAm changes remains an active area of research. Studies investigating the association of age with DNAm in tissues such as brain, skin, immune cells, fat, and liver have identified tissue-specific and non-specific effects, thus, motivating additional studies of diverse human tissue and cell types.
RESULTS: Here, we performed an epigenome-wide association study, leveraging DNAm data (Illumina EPIC array) from 961 tissue samples representing 9 tissue types (breast, lung, colon, ovary, prostate, skeletal muscle, testis, whole blood, and kidney) from the Genotype-Tissue Expression (GTEx) project. We identified age-associated CpG sites (false discovery rate < 0.05) in 8 tissues (all except skeletal muscle, n = 47). This included 162,002 unique hypermethylated and 90,626 hypomethylated CpG sites across all tissue types, with 130,137 (80%) hypermethylated CpGs and 74,703 (82%) hypomethylated CpG sites observed in a single tissue type. While the majority of age-associated CpG sites appeared tissue-specific, the patterns of enrichment among genomic features, such as chromatin states and CpG islands, were similar across most tissues, suggesting common mechanisms underlying cellular aging. Consistent with previous findings, we observed that hypermethylated CpG sites are enriched in regions with repressed polycomb signatures and CpG islands, while hypomethylated CpG sites preferentially occurred in non-CpG islands and enhancers. To gain insights into the functional effects of age-related DNAm changes, we assessed the correlation between DNAm and local gene expression changes to identify age-related expression quantitative trait methylation (age-eQTMs). We identified several age-eQTMs present in multiple tissue-types, including in the CDKN2A, HENMT1, and VCWE regions.
CONCLUSIONS: Overall, our findings will aid future efforts to develop biomarkers of aging and understand mechanisms of aging in diverse human tissue types.

Structural variation heavily influences the molecular landscape of cancer, in part by impacting DNA methylation-mediated transcriptional regulation. Here, using multi-omic datasets involving >2400 pediatric brain and central nervous system tumors of diverse histologies from the Children\'s Brain Tumor Network, we report hundreds of genes and associated CpG islands (CGIs) for which the nearby presence of somatic structural variant (SV) breakpoints is recurrently associated with altered expression or DNA methylation, respectively, including tumor suppressor genes ATRX and CDKN2A. Altered DNA methylation near enhancers associates with nearby somatic SV breakpoints, including MYC and MYCN. A subset of genes with SV-CGI methylation associations also have expression associations with patient survival, including BCOR, TERT, RCOR2, and PDLIM4. DNA methylation changes in recurrent or progressive tumors compared to the initial tumor within the same patient can predict survival in pediatric and adult cancers. Our comprehensive and pan-histology genomic analyses reveal mechanisms of noncoding alterations impacting cancer genes.

BACKGROUND: Renal cell carcinoma (RCC), one of the most fatal urologic tumors, accounts for approximately 3% of all adult cancers and exhibits a high metastatic index at diagnosis and a high rate of relapse. Radical or partial nephrectomy is a curative option for nonmetastatic RCCs. Targeted therapy has been shown to improve the survival of patients with metastatic RCCs. However, the underlying cellular and molecular events associated with RCC pathogenesis are not well known.
METHODS: To investigate the clinical role of the transcription factor activator protein (AP)-2α in RCC, methylated CpG island recovery assays and microarray analysis were employed. COBRA and RT‒qPCR assays were performed to assess AP-2α expression in RCC.
RESULTS: A negative correlation was noted between AP-2α mRNA expression levels and methylation status. Multivariate analyses showed that AP-2α mRNA was a major risk factor not only for overall and disease-free survival in RCC but also for disease-free survival in clear cell RCC.
CONCLUSIONS: Our results indicated that AP-2α expression was deregulated in RCC and associated with overall patient survival and disease-free survival. Such findings suggest that AP-2α might play an important role in the pathogenesis of RCC.

Circulating cell-free DNA (cfDNA) is emerging as an avenue for cancer detection, but the characteristics of cfDNA fragmentation in the blood are poorly understood. We evaluate the effect of DNA methylation and gene expression on genome-wide cfDNA fragmentation through analysis of 969 individuals. cfDNA fragment ends more frequently contained CCs or CGs, and fragments ending with CGs or CCGs are enriched or depleted, respectively, at methylated CpG positions. Higher levels and larger sizes of cfDNA fragments are associated with CpG methylation and reduced gene expression. These effects are validated in mice with isogenic tumors with or without the mutant IDH1, and are associated with genome-wide changes in cfDNA fragmentation in patients with cancer. Tumor-related hypomethylation and increased gene expression are associated with decrease in cfDNA fragment size that may explain smaller cfDNA fragments in human cancers. These results provide a connection between epigenetic changes and cfDNA fragmentation with implications for disease detection.

BACKGROUND: Diesel exhaust particles (DEP), which contain hazardous compounds, are emitted during the combustion of diesel. As approximately one-third of the vehicles worldwide use diesel, there are growing concerns about the risks posed by DEP to human health. Long-term exposure to DEP is associated with airway hyperresponsiveness, pulmonary fibrosis, and inflammation; however, the molecular mechanisms behind the effects of DEP on the respiratory tract are poorly understood. Such mechanisms can be addressed by examining transcriptional and DNA methylation changes. Although several studies have focused on the effects of short-term DEP exposure on gene expression, research on the transcriptional effects and genome-wide DNA methylation changes caused by long-term DEP exposure is lacking. Hence, in this study, we investigated transcriptional and DNA methylation changes in human adenocarcinoma alveolar basal epithelial A549 cells caused by prolonged exposure to DEP and determined whether these changes are concordant.
RESULTS: DNA methylation analysis using the Illumina Infinium MethylationEPIC BeadChips showed that the methylation levels of DEP-affected CpG sites in A549 cells changed in a dose-dependent manner; the extent of change increased with increasing dose reaching the statistical significance only in samples exposed to 30 µg/ml DEP. Four-week exposure to 30 µg/ml of DEP significantly induced DNA hypomethylation at 24,464 CpG sites, which were significantly enriched for DNase hypersensitive sites, genomic regions marked by H3K4me1 and H3K27ac, and several transcription factor binding sites. In contrast, 9,436 CpG sites with increased DNA methylation levels were significantly overrepresented in genomic regions marked by H3K27me3 as well as H3K4me1 and H3K27ac. In parallel, gene expression profiling by RNA sequencing demonstrated that long-term exposure to DEP altered the expression levels of 2,410 genes, enriching 16 gene sets including Xenobiotic metabolism, Inflammatory response, and Senescence. In silico analysis revealed that the expression levels of 854 genes correlated with the methylation levels of the DEP-affected cis-CpG sites.
CONCLUSIONS: To our knowledge, this is the first report of genome-wide transcriptional and DNA methylation changes and their associations in A549 cells following long-term exposure to DEP.

The time from conception through the first year of life is the most dynamic period in human development. This time period is particularly important for infants born very preterm (< 30 weeks gestation; VPT), as they experience a significant disruption in the normal developmental trajectories and are at heightened risk of experiencing developmental impairments and delays. Variations in the epigenetic landscape during this period may reflect this disruption and shed light on the interrelationships between aging, maturation, and the epigenome. We evaluated how gestational age (GA) and age since conception in neonates [post-menstrual age (PMA)], were related to DNA methylation in buccal cells collected at NICU discharge from VPT infants (n = 538). After adjusting for confounders and applying Bonferroni correction, we identified 2,366 individual CpGs associated with GA and 14,979 individual CpGs associated with PMA, as well as multiple differentially methylated regions. Pathway enrichment analysis identified pathways involved in axonogenesis and regulation of neuron projection development, among many other growth and developmental pathways (FDR q < 0.001). Our findings align with prior work, and also identify numerous novel associations, suggesting that genes important in growth and development, particularly neurodevelopment, are subject to substantial epigenetic changes during early development among children born VPT.

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

Integrating multi-omics data into predictive models has the potential to enhance accuracy, which is essential for precision medicine. In this study, we developed interpretable predictive models for multi-omics data by employing neural networks informed by prior biological knowledge, referred to as visible networks. These neural networks offer insights into the decision-making process and can unveil novel perspectives on the underlying biological mechanisms associated with traits and complex diseases. We tested the performance, interpretability and generalizability for inferring smoking status, subject age and LDL levels using genome-wide RNA expression and CpG methylation data from the blood of the BIOS consortium (four population cohorts, Ntotal = 2940). In a cohort-wise cross-validation setting, the consistency of the diagnostic performance and interpretation was assessed. Performance was consistently high for predicting smoking status with an overall mean AUC of 0.95 (95% CI: 0.90-1.00) and interpretation revealed the involvement of well-replicated genes such as AHRR, GPR15 and LRRN3. LDL-level predictions were only generalized in a single cohort with an R2 of 0.07 (95% CI: 0.05-0.08). Age was inferred with a mean error of 5.16 (95% CI: 3.97-6.35) years with the genes COL11A2, AFAP1, OTUD7A, PTPRN2, ADARB2 and CD34 consistently predictive. For both regression tasks, we found that using multi-omics networks improved performance, stability and generalizability compared to interpretable single omic networks. We believe that visible neural networks have great potential for multi-omics analysis; they combine multi-omic data elegantly, are interpretable, and generalize well to data from different cohorts.