Abstract:DNA methylation (DNAm) is an important epigenetic process involved in the regulation of gene expression. While many studies have identified thousands of loci associated with age, few have differentiated between linear and non-linear DNAm trends with age. Non-linear trends could indicate early- or late-life gene regulatory processes. Using data from the Illumina 450K array on 336 human peripheral blood samples, we identified 21 CpG sites that associated with age (P<1.03E-7) and exhibited changing rates of DNAm … Show more
“…When restricting our analyses to CpGs that presented a mean difference > 5% (measured by the β value [68], see “Materials and methods”), we identified a total of 12,050 differentially methylated sites between populations (DMS) that mapped to 4818 genes. Because the age distributions of AFB and EUB individuals significantly differ (Wilcoxon P value = 10 −4 ; Additional file 1: Figure S2), and age might have a non-linear effect on DNA methylation [69], we also investigated with ANOVA the extent to which DNA methylation is non-linearly affected by age in our dataset. Our analyses showed that such effects had little to no impact on the population differences in DNA methylation detected (Additional file 2: Supplementary Note 1).Fig.…”
BackgroundDNA methylation is influenced by both environmental and genetic factors and is increasingly thought to affect variation in complex traits and diseases. Yet, the extent of ancestry-related differences in DNA methylation, their genetic determinants, and their respective causal impact on immune gene regulation remain elusive.ResultsWe report extensive population differences in DNA methylation between 156 individuals of African and European descent, detected in primary monocytes that are used as a model of a major innate immunity cell type. Most of these differences (~ 70%) are driven by DNA sequence variants nearby CpG sites, which account for ~ 60% of the variance in DNA methylation. We also identify several master regulators of DNA methylation variation in trans, including a regulatory hub nearby the transcription factor-encoding CTCF gene, which contributes markedly to ancestry-related differences in DNA methylation. Furthermore, we establish that variation in DNA methylation is associated with varying gene expression levels following mostly, but not exclusively, a canonical model of negative associations, particularly in enhancer regions. Specifically, we find that DNA methylation highly correlates with transcriptional activity of 811 and 230 genes, at the basal state and upon immune stimulation, respectively. Finally, using a Bayesian approach, we estimate causal mediation effects of DNA methylation on gene expression in ~ 20% of the studied cases, indicating that DNA methylation can play an active role in immune gene regulation.ConclusionUsing a system-level approach, our study reveals substantial ancestry-related differences in DNA methylation and provides evidence for their causal impact on immune gene regulation.Electronic supplementary materialThe online version of this article (10.1186/s13059-018-1601-3) contains supplementary material, which is available to authorized users.
“…When restricting our analyses to CpGs that presented a mean difference > 5% (measured by the β value [68], see “Materials and methods”), we identified a total of 12,050 differentially methylated sites between populations (DMS) that mapped to 4818 genes. Because the age distributions of AFB and EUB individuals significantly differ (Wilcoxon P value = 10 −4 ; Additional file 1: Figure S2), and age might have a non-linear effect on DNA methylation [69], we also investigated with ANOVA the extent to which DNA methylation is non-linearly affected by age in our dataset. Our analyses showed that such effects had little to no impact on the population differences in DNA methylation detected (Additional file 2: Supplementary Note 1).Fig.…”
BackgroundDNA methylation is influenced by both environmental and genetic factors and is increasingly thought to affect variation in complex traits and diseases. Yet, the extent of ancestry-related differences in DNA methylation, their genetic determinants, and their respective causal impact on immune gene regulation remain elusive.ResultsWe report extensive population differences in DNA methylation between 156 individuals of African and European descent, detected in primary monocytes that are used as a model of a major innate immunity cell type. Most of these differences (~ 70%) are driven by DNA sequence variants nearby CpG sites, which account for ~ 60% of the variance in DNA methylation. We also identify several master regulators of DNA methylation variation in trans, including a regulatory hub nearby the transcription factor-encoding CTCF gene, which contributes markedly to ancestry-related differences in DNA methylation. Furthermore, we establish that variation in DNA methylation is associated with varying gene expression levels following mostly, but not exclusively, a canonical model of negative associations, particularly in enhancer regions. Specifically, we find that DNA methylation highly correlates with transcriptional activity of 811 and 230 genes, at the basal state and upon immune stimulation, respectively. Finally, using a Bayesian approach, we estimate causal mediation effects of DNA methylation on gene expression in ~ 20% of the studied cases, indicating that DNA methylation can play an active role in immune gene regulation.ConclusionUsing a system-level approach, our study reveals substantial ancestry-related differences in DNA methylation and provides evidence for their causal impact on immune gene regulation.Electronic supplementary materialThe online version of this article (10.1186/s13059-018-1601-3) contains supplementary material, which is available to authorized users.
“…Another typical feature of aging is a state of chronic, low‐grade inflammation, also known as inflamm‐aging, which is characterized by elevated levels of proinflammatory cytokines (Franceschi et al, 2000; Sansoni et al, 2008). Considerable attention has been devoted to understanding the age‐related changes in the adaptive immune compartment, particularly in T cells (Goronzy, Hu, Kim, Jadhav, & Weyand, 2018; Johnson et al, 2017; Tserel et al, 2015; Ucar et al, 2017). However, many features of inflamm‐aging refer to a dysregulation of the innate immune system, which provides the first line of defense against invading pathogens and mediates signals to regulate the adaptive immune response.…”
Age-related changes at the cellular level include the dysregulation of metabolic and signaling pathways. Analyses of blood leukocytes have revealed a set of alterations that collectively lower their ability to fight infections and resolve inflammation later in life. We studied the transcriptomic, epigenetic, and metabolomic profiles of monocytes extracted from younger adults and individuals over the age of 65 years to map major age-dependent changes in their cellular physiology. We found that the monocytes from older persons displayed a decrease in the expression of ribosomal and mitochondrial protein genes and exhibited hypomethylation at the HLA class I locus.Additionally, we found elevated gene expression associated with cell motility, including the CX3CR1 and ARID5B genes, which have been associated with the development of atherosclerosis. Furthermore, the downregulation of two genes, PLA2G4Band ALOX15B, which belong to the arachidonic acid metabolism pathway involved in phosphatidylcholine conversion to anti-inflammatory lipoxins, correlated with increased phosphatidylcholine content in monocytes from older individuals. We found age-related changes in monocyte metabolic fitness, including reduced mitochondrial function and increased glycose consumption without the capacity to upregulate it during increased metabolic needs, and signs of increased oxidative stress and DNA damage. In conclusion, our results complement existing findings and elucidate the metabolic alterations that occur in monocytes during aging.We performed genome-wide mRNA expression profiling to identify the genes that are differentially expressed in CD14 + monocytes K E Y W O R D S aging, DNA methylation, glucose metabolism, monocytes, phosphatidylcholines, transcriptome * *
“…There are non-linear patterns in age-related DNA methylation 50 . To investigate if transformed data can remove the nonlinearity and hence improve the prediction accuracy, we selected eight DNA methylation cohorts with sample size larger than 600 to evaluate the impact of data transformation: LBC1921, LBC1936, GS, BSGS, SGPD, MND, GSE40279 and GSE42861.…”
DNA methylation is associated with age. The deviation of age predicted from DNA methylation from actual age has been proposed as a biomarker for ageing. However, a better prediction of chronological age implies less opportunity for biological age. Here we used 13,661 samples (from blood and saliva) in the age range of 2 to 104 years from 14 cohorts measured on Illumina HumanMethylation450/EPIC arrays to perform prediction analyses. We show that increasing the sample size achieves a smaller prediction error and higher correlations in test datasets. We demonstrate that smaller prediction errors provide a limit to how much variation in biological ageing can be captured by methylation and provide evidence that age predictors from small samples are prone to confounding by cell composition. Our predictor shows a similar or better performance in non-blood tissues including saliva, endometrium, breast, liver, adipose and muscle, compared with Horvath’s across-tissue age predictor.
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