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The majority of epigenetic epidemiology studies to date have generated genome-wide profiles from bulk tissues (e.g., whole blood) however these are vulnerable to confounding from variation in cellular composition. Proxies for cellular composition can be mathematically derived from the bulk tissue profiles using a deconvolution algorithm; however, there is no method to assess the validity of these estimates for a dataset where the true cellular proportions are unknown. In this study, we describe, validate and characterize a sample level accuracy metric for derived cellular heterogeneity variables. The CETYGO score captures the deviation between a sample's DNA methylation profile and its expected profile given the estimated cellular proportions and cell type reference profiles. We demonstrate that the CETYGO score consistently distinguishes inaccurate and incomplete deconvolutions when applied to reconstructed whole blood profiles. By applying our novel metric to >6,300 empirical whole blood profiles, we find that estimating accurate cellular composition is influenced by both technical and biological variation. In particular, we show that when using a common reference panel for whole blood, less accurate estimates are generated for females, neonates, older individuals and smokers. Our results highlight the utility of a metric to assess the accuracy of cellular deconvolution, and describe how it can enhance studies of DNA methylation that are reliant on statistical proxies for cellular heterogeneity. To facilitate incorporating our methodology into existing pipelines, we have made it freely available as an R package (https://github.com/ds420/CETYGO). [ABSTRACT FROM AUTHOR]

A genome-wide survey investigates the level of allelic skewing of DNA methylation in order to identify previously unknown differentially methylated regions (DMRs) mainly associated with DNA sequence variation acting in cis or genomic imprinting. A quantitative assessment of allele-specific DNA methylation (ASM) using SNP microarrays reveals that allelic skewing of DNA methylation is widespread across the genome, with likely heterogeneity between individuals and across tissues.

Abstract Background The COMT gene is located on chromosome 22q11, a region strongly implicated in the aetiology of several psychiatric disorders, in particular schizophrenia. Previous research has suggested that activity and expression of COMT is altered in schizophrenia, and is mediated by one or more polymorphisms within the gene, including the functional Val158Met polymorphism. Method In this study we examined the expression levels of COMT mRNA using quantitative RT-PCR in 60 post mortem cerebellum samples derived from individuals with schizophrenia, bipolar disorder, depression, and no history of psychopathology. Furthermore, we have examined the methylation status of two CpG sites in the promoter region of the gene. Results We found no evidence of altered COMT expression or methylation in any of the psychiatric diagnoses examined. We did, however, find evidence to suggest that genotype is related to COMT gene expression, replicating the findings of two previous studies. Specifically, val158met (rs165688; Val allele) rs737865 (G allele) and rs165599 (G allele) all showed reduced expression (P < 0.05). Finally, we observe a strong sexual dimorphism in COMT expression, with females exhibiting significantly greater levels of COMT mRNA. Conclusion The expression of COMT does not appear to be altered in the cerebellum of individuals suffering from schizophrenia, bipolar disorder or depression, but does appear to be influenced by single nucleotide polymorphisms within the gene.

Age-related changes in DNA methylation have been implicated in cellular senescence and longevity, yet the causes and functional consequences of these variants remain unclear. To elucidate the role of age-related epigenetic changes in healthy ageing and potential longevity, we tested for association between whole-blood DNA methylation patterns in 172 female twins aged 32 to 80 with age and age-related phenotypes. Twin-based DNA methylation levels at 26,690 CpG-sites showed evidence for mean genome-wide heritability of 18%, which was supported by the identification of 1,537 CpG-sites with methylation QTLs in cis at FDR 5%. We performed genome-wide analyses to discover differentially methylated regions (DMRs) for sixteen age-related phenotypes (ap-DMRs) and chronological age (a-DMRs). Epigenome-wide association scans (EWAS) identified age-related phenotype DMRs (ap-DMRs) associated with LDL (STAT5A), lung function (WT1), and maternal longevity (ARL4A, TBX20). In contrast, EWAS for chronological age identified hundreds of predominantly hyper-methylated age DMRs (490 a-DMRs at FDR 5%), of which only one (TBX20) was also associated with an age-related phenotype. Therefore, the majority of age-related changes in DNA methylation are not associated with phenotypic measures of healthy ageing in later life. We replicated a large proportion of a-DMRs in a sample of 44 younger adult MZ twins aged 20 to 61, suggesting that a-DMRs may initiate at an earlier age. We next explored potential genetic and environmental mechanisms underlying a-DMRs and ap-DMRs. Genome-wide overlap across cis-meQTLs, genotype-phenotype associations, and EWAS ap-DMRs identified CpG-sites that had cis-meQTLs with evidence for genotype-phenotype association, where the CpG-site was also an ap-DMR for the same phenotype. Monozygotic twin methylation difference analyses identified one potential environmentally-mediated ap-DMR associated with total cholesterol and LDL (CSMD1). Our results suggest that in a small set of genes DNA methylation may be a candidate mechanism of mediating not only environmental, but also genetic effects on age-related phenotypes. [ABSTRACT FROM AUTHOR]

Astrocytes are important regulatory cells in the brain that support neuronal function. However, their role in hypoglycemia counterregulation is poorly understood. Moreover, astrocytic transcriptional responses to hypoglycemia and the mechanisms by which recurrent hypoglycemia (RH) suppresses counterregulation are not understood. Using human primary astrocytes (HPA) in vitro we tested whether recurrent low glucose (RLG), to model RH, would regulate differential gene expression (DGE). HPA cells were exposed to 3-hour bouts of 2.5 or 0.1 mM glucose over 4 days and mRNA expression was quantified using RNA-sequencing and DGE measured using DESeq2. Acute low glucose (LG) differentially expressed 31 genes including the downregulation of mitochondrial genes (mitochondrially encoded NADH:ubiquinone oxidoreductase core subunit 2, subunit 4 and subunit 4L; MT-ND2, MT-ND4 and MT-ND4L) and endoplasmic reticulum stress associated genes (X-box binding protein 1; XBP1, and heat shock protein family A; Hsp70, member 1A, and member 5; HSPA1A and HSPA5). However, neither antecedent RLG (aRLG) nor RLG treated cells have significantly altered gene expression compared to control. LG treatment decreased the expression of XBP1 and increased thioredoxin interacting protein (TXNIP) and exostosin Like glycosyltransferase 3 (EXTL3) which were also similarly altered by low glucose compared to aRLG.MT-ND2, MT-ND4 and MT-ND4L genes are associated with diabetes and neurodegeneration. Furthermore, LG-induced reduction of XBP1 may also increase fatty acid oxidation by disinhibiting inositol-requiring kinase 1α. This corroborates previous work indicating low glucose increases fatty acid utilisation in astrocytes and the hypothalamus. While the functional impact of differential gene expression needs assessing, this provides evidence that these genes may be important in astrocytic responses to hypoglycemia. Disclosure: P.G. Weightman Potter: None. S.J. Washer: None. A. Jeffries: None. E.L. Dempster: None. C. Beall: None. Funding: Novo Nordisk UK Research Foundation; Diabetes UK; Mary Kinross Charitable Trust [ABSTRACT FROM AUTHOR]