Epigenetic Age Acceleration and Change in Frailty in MOBILIZE Boston

Age-associated changes in DNA methylation have been implicated as one mechanism to explain the development of frailty, however previous cross-sectional studies of epigenetic age acceleration (eAA) and frailty have had inconsistent findings. Few longitudinal studies have considered the association of eAA with change in frailty. We sought to determine the association between eAA and change in frailty in the MOBILIZE Boston cohort. Participants were assessed at two visits 12-18 months apart. Intrinsic, extrinsic, GrimAge, and PhenoAge eAA were assessed from whole blood DNA methylation at baseline using the Infinium 450k array. Frailty was assessed by a continuous frailty score based on the frailty phenotype and by frailty index (FI). Analysis was by correlation and linear regression with adjustment for age, sex, smoking status, and BMI. 395 participants with a frailty score and 431 with a FI had epigenetic and follow-up frailty measures. For the frailty score and FI cohorts, respectively, mean (SD) ages were 77.8 (5.49) and 77.9 (5.47), 232 (58.7%) and 257 (59.6%) were female. All participants with epigenetic data identified as white. Baseline frailty score was not correlated with intrinsic or extrinsic eAA, but was correlated with PhenoAge and, even after adjustment for covariates, GrimAge. Baseline FI was correlated with extrinsic, GrimAge, and PhenoAge eAA with and without adjustment. No eAA measure was associated with change in frailty, with or without adjustment. Our results suggest that no eAA measure was associated with change in frailty. Further studies should consider longer periods of follow-up and repeated eAA measurement.

Placental DNA methylation changes and the early prediction of autism in full-term newborns

Autism spectrum disorder (ASD) is associated with abnormal brain development during fetal life. Overall, increasing evidence indicates an important role of epigenetic dysfunction in ASD. The placenta is critical to and produces neurotransmitters that regulate fetal brain development. We hypothesized that placental DNA methylation changes are a feature of the fetal development of the autistic brain and importantly could help to elucidate the early pathogenesis and prediction of these disorders. Genome-wide methylation using placental tissue from the full-term autistic disorder subtype was performed using the Illumina 450K array. The study consisted of 14 cases and 10 control subjects. Significantly epigenetically altered CpG loci (FDR p-value <0.05) in autism were identified. Ingenuity Pathway Analysis (IPA) was further used to identify molecular pathways that were over-represented (epigenetically dysregulated) in autism. Six Artificial Intelligence (AI) algorithms including Deep Learning (DL) to determine the predictive accuracy of CpG markers for autism detection. We identified 9655 CpGs differentially methylated in autism. Among them, 2802 CpGs were inter- or non-genic and 6853 intragenic. The latter involved 4129 genes. AI analysis of differentially methylated loci appeared highly accurate for autism detection. DL yielded an AUC (95% CI) of 1.00 (1.00–1.00) for autism detection using intra- or intergenic markers by themselves or combined. The biological functional enrichment showed, four significant functions that were affected in autism: quantity of synapse, microtubule dynamics, neuritogenesis, and abnormal morphology of neurons. In this preliminary study, significant placental DNA methylation changes. AI had high accuracy for the prediction of subsequent autism development in newborns. Finally, biologically functional relevant gene pathways were identified that may play a significant role in early fetal neurodevelopmental influences on later cognition and social behavior.

Exposure to arsenic at different life-stages and DNA methylation meta-analysis in buccal cells and leukocytes

Background Arsenic (As) exposure through drinking water is a global public health concern. Epigenetic dysregulation including changes in DNA methylation (DNAm), may be involved in arsenic toxicity. Epigenome-wide association studies (EWAS) of arsenic exposure have been restricted to single populations and comparison across EWAS has been limited by methodological differences. Leveraging data from epidemiological studies conducted in Chile and Bangladesh, we use a harmonized data processing and analysis pipeline and meta-analysis to combine results from four EWAS. Methods DNAm was measured among adults in Chile with and without prenatal and early-life As exposure in PBMCs and buccal cells (N = 40, 850K array) and among men in Bangladesh with high and low As exposure in PBMCs (N = 32, 850K array; N = 48, 450K array). Linear models were used to identify differentially methylated positions (DMPs) and differentially variable positions (DVPs) adjusting for age, smoking, cell type, and sex in the Chile cohort. Probes common across EWAS were meta-analyzed using METAL, and differentially methylated and variable regions (DMRs and DVRs, respectively) were identified using comb-p. KEGG pathway analysis was used to understand biological functions of DMPs and DVPs. Results In a meta-analysis restricted to PBMCs, we identified one DMP and 23 DVPs associated with arsenic exposure; including buccal cells, we identified 3 DMPs and 19 DVPs (FDR < 0.05). Using meta-analyzed results, we identified 11 DMRs and 11 DVRs in PBMC samples, and 16 DMRs and 19 DVRs in PBMC and buccal cell samples. One region annotated to LRRC27 was identified as a DMR and DVR. Arsenic-associated KEGG pathways included lysosome, autophagy, and mTOR signaling, AMPK signaling, and one carbon pool by folate. Conclusions Using a two-step process of (1) harmonized data processing and analysis and (2) meta-analysis, we leverage four DNAm datasets from two continents of individuals exposed to high levels of As prenatally and during adulthood to identify DMPs and DVPs associated with arsenic exposure. Our approach suggests that standardizing analytical pipelines can aid in identifying biological meaningful signals.

IGHV-associated methylation signatures more accurately predict clinical outcomes of chronic lymphocytic leukemia patients than IGHV mutation load

Currently, no molecular biomarker indexes are used in standard care to make treatment decisions at diagnosis of chronic lymphocytic leukemia (CLL). We used Infinium MethylationEPIC array data from diagnostic blood samples of 114 CLL patients, and developed a patient stratification procedure based on methylation signatures associated with mutation load of the IGHV gene. This procedure allowed us to predict the time to treatment (TTT) with HR 8.34 (95% CI, 4.54-15.30), as opposed to HR 4.35 (95% CI, 2.60-7.28) for IGHV mutation status. Detailed evaluation of 17 discrepant cases between the two classification procedures showed that these cases were incorrectly classified using IGHV status. Moreover, methylation-based classification stratified patients with different overall survival (OS) (HR, 1.82; 95% CI, 1.07-3.09), which was not possible using IGHV status. Furthermore, we assessed the performance of the developed classification procedure using published HumanMethylation450 array data for 159 patients for which TTT, OS and relapse were available. Despite that 450K array methylation data did not contain all biomarkers used in our classification procedure, methylation signatures again stratified patients with significantly better accuracy than IGHV mutation load regarding all available clinical outcomes. Thus, stratification using IGHV-associated methylation signatures may provide improved prognostic power than IGHV mutation status.

A cross-cohort analysis of autosomal DNA methylation sex differences in the term placenta

Background Human placental DNA methylation (DNAme) data is a valuable resource for studying sex differences during gestation, as DNAme profiles after delivery reflect the cumulative effects of gene expression patterns and exposures across gestation. Here, we present an analysis of sex differences in autosomal DNAme in the uncomplicated term placenta (n = 343) using the Illumina 450K array. Results At a false discovery rate < 0.05 and a mean sex difference in DNAme beta value of > 0.10, we identified 162 autosomal CpG sites that were differentially methylated by sex and replicated in an independent cohort of samples (n = 293). Several of these differentially methylated CpG sites were part of larger correlated regions of sex differential DNAme. Although global DNAme levels did not differ by sex, the majority of significantly differentially methylated CpGs were more highly methylated in male placentae, the opposite of what is seen in differential methylation analyses of somatic tissues. Patterns of autosomal DNAme at these 162 CpGs were significantly associated with maternal age (in males) and newborn birthweight standard deviation (in females). Conclusions Our results provide a comprehensive analysis of sex differences in autosomal DNAme in the term human placenta. We report a list of high-confidence autosomal sex-associated differentially methylated CpGs and identify several key features of these loci that suggest their relevance to sex differences observed in normative and complicated pregnancies.

The genomic loci of specific human tRNA genes exhibit ageing-related DNA hypermethylation

The epigenome has been shown to deteriorate with age, potentially impacting on ageing-related disease. tRNA, while arising from only ˜46 kb (<0.002% genome), is the second most abundant cellular transcript. tRNAs also control metabolic processes known to affect ageing, through core translational and additional regulatory roles. Here, we interrogate the DNA methylation state of the genomic loci of human tRNA. We identify a genomic enrichment for age-related DNA hypermethylation at tRNA loci. Analysis in 4,350 MeDIP-seq peripheral-blood DNA methylomes (16–82 years), identifies 44 and 21 hypermethylating specific tRNAs at study-and genome-wide significance, respectively, contrasting with none hypomethylating. Validation and replication (450k array and independent targeted Bisuphite-sequencing) supported the hypermethylation of this functional unit. Tissue-specificity is a significant driver, although the strongest consistent signals, also independent of major cell-type change, occur in tRNA-iMet-CAT-1-4 and tRNA-Ser-AGA-2-6. This study presents a comprehensive evaluation of the genomic DNA methylation state of human tRNA genes and reveals a discreet hypermethylation with advancing age.

Intra-tumoral epigenetic heterogeneity and aberrant molecular clocks in hepatocellular carcinoma

There is limited understanding of the epigenetic drivers of tumor evolution in hepatocellular carcinoma (HCC). We quantify epigenetic intra-tumoral heterogeneity (ITH) using regional enhanced reduced-representation bisulfite sequencing (eRRBS) DNA methylation data from 47 early stage, treatment-naive HCC biopsies across 9 patients. Integrating these data with matching RNAseq, targeted DNA sequencing, tumor-infiltrating lymphocyte (TIL) and hepatitis-B viral (HBV) expression, we computed regional differential methylation (DM) ITH signatures across 19,327 promoter regions, and 654,133 CpG islands, while overlapping with known methylation age marker genes (240/354). We found substantial ITH signatures in promoter and enhancer sites across 4/9 patients highlighting novel molecular pathways of tumor progression not otherwise detectable from RNA analysis alone. Additionally, we identify an epigenetic tumoral aging measure that reflects a complex tumor fitness phenotype as a potential proxy for tumor evolution. In order to compute clinical associations with epigenetic tumoral age, we use 450k array data from 377 HCC patients in the TCGA-LIHC single-biopsy cohort to calculate tumoral age and find evidence implying that epigenetically old tumors have lower fitness yet higher TIL burden. Our data reveal a novel, unique epigenetic ITH axis in HCC tumors that furthers our understanding of tumor evolution and may serve as a potential avenue for enhancing patient stratification and treatment.

BMI is positively associated with accelerated epigenetic aging in twin pairs discordant for BMI

ABSTRACTBackgroundObesity is a heritable complex phenotype which can increase the risk of age-related outcomes. Biological age can be estimated from DNA methylation (DNAm) using various “epigenetic clocks.” Previous work suggests individuals with elevated weight also display accelerated aging, but results vary by epigenetic clock and population. Here, we utilize the new epigenetic clock GrimAge, which closely relates with mortality.ObjectivesWe aimed to assess the cross-sectional association of BMI with age acceleration in twins to limit confounding by genetics and shared environment.Methods and ResultsParticipants were from the Finnish Twin Cohort (FTC; n = 1424), including monozygotic (MZ) and dizygotic (DZ) twins, and DNAm was measured using the Illumina 450k array. Multivariate linear mixed effects models including MZ and DZ twins showed an accelerated epigenetic age of 1.02 months (p-value = 6.1 × 10−12) per 1-unit BMI increase. Additionally, heavier twins in a BMI-discordant MZ twin pair (ΔBMI > 3 kg/m2) had an epigenetic age 5.2 months older than their lighter co-twin (p-value = 0.0074). We also found a positive association between log(HOMA-IR) and age acceleration, confirmed by a meta-analysis of the FTC and two other Finnish cohorts (overall effect = 0.45 years, p-value = 0.0025) from adjusted models.ConclusionWe identified significant associations of BMI and insulin resistance with age acceleration based on GrimAge, which were not due to genetic effects on BMI and aging. Overall, these results support a role of BMI in aging, potentially in part due to the effects of insulin resistance.

A cross-cohort analysis of autosomal DNA methylation sex differences in the term placenta

ABSTRACTBackgroundHuman placental DNA methylation (DNAme) data is a valuable resource for studying sex differences during gestation, as DNAme profiles after delivery reflect the cumulative effects of gene expression patterns and exposures across gestation. Here, we present an analysis of sex differences in autosomal patterns of DNAme in the uncomplicated term placenta (n=343) using the Illumina 450K array.ResultsUsing a false discovery rate < 0.05 and a mean sex difference in DNAme beta value of > 0.10, we identified 162 autosomal CpG sites that were differentially methylated by sex, and that replicated in an independent cohort of samples (n=293). Several of these differentially methylated CpG sites were part of larger correlated regions of differential DNAme, and many also exhibited sex-specific DNAme variability. Although global DNAme levels did not differ by sex, the majority of significantly differentially methylated CpGs were more highly methylated in male placentae, the opposite of what is seen in differential methylation analyses of somatic tissues. Interestingly, patterns of autosomal DNAme at these significantly differentially methylated CpGs organized placental samples along a continuum, rather than into discrete male and female clusters, and sample position along the continuum was significantly associated with maternal age and newborn birthweight standard deviation.ConclusionsOur results provide a comprehensive analysis of sex differences in autosomal DNAme in the term human placenta. We report a list of high-confidence autosomal sex-associated differentially methylated CpGs, and identify several key features of these loci that suggest their relevance to sex differences observed in normative and complicated pregnancies.

Association between sperm mitochondarial DNA copy number and nuclear DNA methylation

Aim: Accumulating evidence associates sperm mitochondria DNA copy number (mtDNAcn) with male infertility and reproductive success. However, the mechanism underlying mtDNAcn variation is largely unknown. Patients & methods: Sperm mtDNAcn and genome-wide DNA methylation were assessed using triplex probe-based quantitative PCR and Illumina’s 450K array, respectively. Multivariable models assessed the association between sperm mtDNAcn and DNA methylation profiles of 47 men seeking infertility treatment. Results: A priori candidate-gene approach showed sperm mtDNAcn was associated with 16 CpGs located at/near POLG and TWNK genes. Unbiased genome-wide analysis revealed that sperm mtDNAcn was associated with 218 sperm differentially methylated regions (q <0.05), which displayed predominantly (94%) increases in methylation. Conclusion: Findings suggest that DNA methylation may play a role in regulating sperm mtDNAcn.