3,569 research outputs found

    Curr Epidemiol Rep

    Get PDF
    Purpose of review:This review demonstrates the growing body of evidence connecting DNA methylation to prior exposure. It highlights the potential to use DNA methylation patterns as a feasible, stable, and accurate biomarker of past exposure, opening new opportunities for environmental and gene-environment interaction studies among existing banked samples.Recent findings:We present the evidence for association between past exposure, including prenatal exposures, and DNA methylation measured at a later time in the life course. We demonstrate the potential utility of DNA methylation-based biomarkers of past exposure using results from multiple studies of smoking as an example. Multiple studies show the ability to accurately predict prenatal smoking exposure based on DNA methylation measured at birth, in childhood, and even adulthood. Separate sets of DNA methylation loci have been used to predict past personal smoking exposure (postnatal) as well. Further, it appears that these two types of exposures, prenatal and previous personal exposure, can be isolated from each other. There is also a suggestion that quantitative methylation scores may be useful for estimating dose. We highlight the remaining needs for rigor in methylation biomarker development including analytic challenges as well as the need for development across multiple developmental windows, multiple tissue types, and multiple ancestries.Summary:If fully developed, DNA methylation-based biomarkers can dramatically shift our ability to carry out environmental and genetic-environmental epidemiology using existing biobanks, opening up unprecedented opportunities for environmental health.AS7659/AS/Autism Speaks/United StatesR01 ES017646/ES/NIEHS NIH HHS/United StatesR01 ES025531/ES/NIEHS NIH HHS/United StatesU01 DD001214/DD/NCBDD CDC HHS/United States2020-03-01T00:00:00Z31032172PMC64816777487vault:3518

    Understanding obesity: new insights from ANKRD26

    Get PDF
    Mounting evidence sustains the role of DNA methylation in determining obesity as well as the down-stream adverse responses to increased BMI. Thus, identifying new players and mechanisms relevant to obesity and its related endo-phenotypes and understanding whether and how DNA methylation changes may affect these targets is of particular importance. My colleagues and I have recently recognized the Ankyrin repeat domain 26 (Ankrd26) gene as an interesting and proper target to study. This work aims to i. provide clearer evidence in humans of the cause-effect relationship between ANKRD26 gene expression and DNA methylation and investigate the correlation of these changes to obesity-related endo-phenotypes, unhealthy metabolic states and cardio-metabolic risk. Also, I aim to ii. establish in vitro the obesity-induced hypothalamic regulation of Ankrd26 gene in terms of DNA methylation changes, and clarify the role of the Ankrd26 protein on the hypothalamic regulation of anorexigenic signals in vitro. i. Hyper-methylation at three specific CpG sites within the ANKRD26 promoter causes down-regulation of its gene expression and represents a common abnormality in obese patients, particularly if metabolically unhealthy. Furthermore, these mRNA and DNA methylation changes of ANKRD26 gene correlate to increased Body Mass Index (BMI), and raised levels of both pro-inflammatory molecules and cardio-metabolic risk-related factors in humans. ii. Down-regulation of Ankrd26 mRNA and protein expression occur in the hypothalamus of diet-induced obese mice compared to lean control mice. These changes are associated to hyper-methylation of a specific CpG site in the gene promoter. Furthermore, Ankrd26 protein is up-regulated by the treatments with both the hormones, insulin and leptin, and the drug Exendin-4 in murine hypothalamic mHypoE-N46 cells. Also, over-expression of Ankrd26 modulates MAPK signaling and neuropeptide gene expression, by increasing the mRNA of the anorexigenic POMC and CART and decreasing the mRNA of the orexigenic AgRP, in mHypoE-N46-Ankrd26 cells. In conclusion, the results showed in my PhD thesis demonstrated that down-regulation of the ANKRD26 gene and hyper-methylation at specific CpGs of its promoter are common abnormalities in obesity and mark adverse health outcomes. Also, my data demonstrate that Ankrd26 protein might play a pivotal role in the regulation of energy homeostasis in vitro, acting as down-stream effector of anorexigenic signals in hypothalamic neuronal cells

    PU.1 controls fibroblast polarization and tissue fibrosis

    Get PDF
    Fibroblasts are polymorphic cells with pleiotropic roles in organ morphogenesis, tissue homeostasis and immune responses. In fibrotic diseases, fibroblasts synthesize abundant amounts of extracellular matrix, which induces scarring and organ failure. By contrast, a hallmark feature of fibroblasts in arthritis is degradation of the extracellular matrix because of the release of metalloproteinases and degrading enzymes, and subsequent tissue destruction. The mechanisms that drive these functionally opposing pro-fibrotic and pro-inflammatory phenotypes of fibroblasts remain unknown. Here we identify the transcription factor PU.1 as an essential regulator of the pro-fibrotic gene expression program. The interplay between transcriptional and post-transcriptional mechanisms that normally control the expression of PU.1 expression is perturbed in various fibrotic diseases, resulting in the upregulation of PU.1, induction of fibrosis-associated gene sets and a phenotypic switch in extracellular matrix-producing pro-fibrotic fibroblasts. By contrast, pharmacological and genetic inactivation of PU.1 disrupts the fibrotic network and enables reprogramming of fibrotic fibroblasts into resting fibroblasts, leading to regression of fibrosis in several organs

    Computational Models for Transplant Biomarker Discovery.

    Get PDF
    Translational medicine offers a rich promise for improved diagnostics and drug discovery for biomedical research in the field of transplantation, where continued unmet diagnostic and therapeutic needs persist. Current advent of genomics and proteomics profiling called "omics" provides new resources to develop novel biomarkers for clinical routine. Establishing such a marker system heavily depends on appropriate applications of computational algorithms and software, which are basically based on mathematical theories and models. Understanding these theories would help to apply appropriate algorithms to ensure biomarker systems successful. Here, we review the key advances in theories and mathematical models relevant to transplant biomarker developments. Advantages and limitations inherent inside these models are discussed. The principles of key -computational approaches for selecting efficiently the best subset of biomarkers from high--dimensional omics data are highlighted. Prediction models are also introduced, and the integration of multi-microarray data is also discussed. Appreciating these key advances would help to accelerate the development of clinically reliable biomarker systems

    Many obesity-associated SNPs strongly associate with DNA methylation changes at proximal promoters and enhancers

    Get PDF
    Background: The mechanisms by which genetic variants, such as single nucleotide polymorphisms (SNPs), identified in genome-wide association studies act to influence body mass remain unknown for most of these SNPs, which continue to puzzle the scientific community. Recent evidence points to the epigenetic and chromatin states of the genome as having important roles. Methods: We genotyped 355 healthy young individuals for 52 known obesity-associated SNPs and obtained DNA methylation levels in their blood using the Illumina 450 K BeadChip. Associations between alleles and methylation at proximal cytosine residues were tested using a linear model adjusted for age, sex, weight category, and a proxy for blood cell type counts. For replication in other tissues, we used two open-access datasets (skin fibroblasts, n = 62; four brain regions, n = 121-133) and an additional dataset in subcutaneous and visceral fat (n = 149). Results: We found that alleles at 28 of these obesity-associated SNPs associate with methylation levels at 107 proximal CpG sites. Out of 107 CpG sites, 38 are located in gene promoters, including genes strongly implicated in obesity (MIR148A, BDNF, PTPMT1, NR1H3, MGAT1, SCGB3A1, HOXC12, PMAIP1, PSIP1, RPS10-NUDT3, RPS10, SKOR1, MAP2K5, SIX5, AGRN, IMMP1L, ELP4, ITIH4, SEMA3G, POMC, ADCY3, SSPN, LGR4, TUFM, MIR4721, SULT1A1, SULT1A2, APOBR, CLN3, SPNS1, SH2B1, ATXN2L, and IL27). Interestingly, the associated SNPs are in known eQTLs for some of these genes. We also found that the 107 CpGs are enriched in enhancers in peripheral blood mononuclear cells. Finally, our results indicate that some of these associations are not blood-specific as we successfully replicated four associations in skin fibroblasts. Conclusions: Our results strongly suggest that many obesity-associated SNPs are associated with proximal gene regulation, which was reflected by association of obesity risk allele genotypes with differential DNA methylation. This study highlights the importance of DNA methylation and other chromatin marks as a way to understand the molecular basis of genetic variants associated with human diseases and traits

    DNA methylation modules associate with incident cardiovascular disease and cumulative risk factor exposure

    Get PDF
    BACKGROUND: Epigenome-wide association studies using DNA methylation have the potential to uncover novel biomarkers and mechanisms of cardiovascular disease (CVD) risk. However, the direction of causation for these associations is not always clear, and investigations to-date have often failed to replicate at the level of individual loci. METHODS: Here, we undertook module- and region-based DNA methylation analyses of incident CVD in the Women's Health Initiative (WHI) and Framingham Heart Study Offspring Cohort (FHS) in order to find more robust epigenetic biomarkers for cardiovascular risk. We applied weighted gene correlation network analysis (WGCNA) and the Comb-p algorithm to find methylation modules and regions associated with incident CVD in the WHI dataset. RESULTS: We discovered two modules whose activation correlated with CVD risk and replicated across cohorts. One of these modules was enriched for development-related processes and overlaps strongly with epigenetic aging sites. For the other, we showed preliminary evidence for monocyte-specific effects and statistical links to cumulative exposure to traditional cardiovascular risk factors. Additionally, we found three regions (associated with the genes SLC9A1, SLC1A5, and TNRC6C) whose methylation associates with CVD risk. CONCLUSIONS: In sum, we present several epigenetic associations with incident CVD which reveal disease mechanisms related to development and monocyte biology. Furthermore, we show that epigenetic modules may act as a molecular readout of cumulative cardiovascular risk factor exposure, with implications for the improvement of clinical risk prediction.Trainee support was provided by the National Institutes of Health under award T32HL069772.S

    Refining epigenetic prediction of chronological and biological age

    Get PDF
    Background Epigenetic clocks can track both chronological age (cAge) and biological age (bAge). The latter is typically defined by physiological biomarkers and risk of adverse health outcomes, including all-cause mortality. As cohort sample sizes increase, estimates of cAge and bAge become more precise. Here, we aim to develop accurate epigenetic predictors of cAge and bAge, whilst improving our understanding of their epigenomic architecture. Methods First, we perform large-scale (N = 18,413) epigenome-wide association studies (EWAS) of chronological age and all-cause mortality. Next, to create a cAge predictor, we use methylation data from 24,674 participants from the Generation Scotland study, the Lothian Birth Cohorts (LBC) of 1921 and 1936, and 8 other cohorts with publicly available data. In addition, we train a predictor of time to all-cause mortality as a proxy for bAge using the Generation Scotland cohort (1214 observed deaths). For this purpose, we use epigenetic surrogates (EpiScores) for 109 plasma proteins and the 8 component parts of GrimAge, one of the current best epigenetic predictors of survival. We test this bAge predictor in four external cohorts (LBC1921, LBC1936, the Framingham Heart Study and the Women’s Health Initiative study). Results Through the inclusion of linear and non-linear age-CpG associations from the EWAS, feature pre-selection in advance of elastic net regression, and a leave-one-cohort-out (LOCO) cross-validation framework, we obtain cAge prediction with a median absolute error equal to 2.3 years. Our bAge predictor was found to slightly outperform GrimAge in terms of the strength of its association to survival (HRGrimAge = 1.47 [1.40, 1.54] with p = 1.08 × 10−52, and HRbAge = 1.52 [1.44, 1.59] with p = 2.20 × 10−60). Finally, we introduce MethylBrowsR, an online tool to visualise epigenome-wide CpG-age associations. Conclusions The integration of multiple large datasets, EpiScores, non-linear DNAm effects, and new approaches to feature selection has facilitated improvements to the blood-based epigenetic prediction of biological and chronological age
    corecore