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Grant, O. A.

Publications and source records attributed to Grant, O. A..

5 recordsLinked to original sources

Recalibrating the cerebellum DNA methylation clock: implications for ageing rates comparison

BackgroundDNA methylation (DNAm) based age clocks have been studied extensively as a biomarker of human ageing and risk factor for age-related diseases. Despite different tissues having vastly different rates of proliferation, it is still largely unknown whether they age at different rates. It was previously reported that the cerebellum ages slowly, however, this claim was drawn from a single clock using a small sample size and so warrants further investigation. ResultsWe collected the largest cerebellum DNAm dataset (N=752). We found the respective epigenetic ages are all severely underestimated by six representative DNAm age clocks, with the underestimation effects more pronounced in the four clocks whose training datasets do not include brain-related tissues. We identified 613 age-associated CpGs in the cerebellum, which accounts for only 14.5% of the number found in the middle temporal gyrus from the same population (N=404), of which only 201 CpGs are both age-associated in the two tissue types. We built a highly accurate age prediction model for the cerebellum named CerebellumClockspecific (Pearson correlation=0.941, MAD=3.18 years). Furthermore, based on the 201 age-associated CpGs, we built two other clocks CerebellumClockcommon and CortexClockcommon for the cerebellum and non-cerebellar brain cortex tissues separately, they both support that the cerebellum has a relative lower DNAm ageing rate. ConclusionsThe large underestimation for the cerebellum by previous clocks mainly reflects the improper usage of the age clocks. There exist strong and consistent ageing effects on the cerebellar methylome despite the cerebellum having unique age-dependent methylome changes. The DNAm clock based ageing rates comparisons are valid only upon models constructed on a small group of CpGs, therefore, more evidence is required to support the idea that different DNAm ageing rates represent different biological ageing rates.

bioinformatics↗

H4K16ac activates transcription of transposable elements and contribute to their cis regulatory function

Mammalian genomes harbour a large number of transposable elements (TEs) and their remnants. Many epigenetic repression mechanisms are known to silence TE transcription. However, TEs are upregulated during early development, neuronal lineage, and cancers, although the epigenetic factors contributing to the transcription of TEs have yet to be fully elucidated. Here we demonstrated that the male-specific lethal (MSL) complex mediated acetylation of histone H4 lysine 16 (H4K16ac) activates transcription of long interspersed nuclear elements (LINE1, L1) and long terminal repeats (LTRs). Furthermore, we show that the H4K16ac marked L1 and LTR subfamilies function as enhancers and are enriched with chromatin features associated with active enhancers and looping factors. L1 and LTRs enriched with histone acetylations are bound by chromatin looping factors and these regions loop with genes. CRISPR-based epigenetic perturbation and genetic deletion of L1s reveal that H4K16ac marked L1s and LTRs regulate the expression of genes in cis. Overall, TEs enriched with H4K16ac contribute to the cis-regulatory landscape of a significant portion of the mammalian genome by maintaining an active chromatin landscape at TEs. One Sentence SummaryH4K16ac activates LINE1 and ERV/LTR transcription and rewires the cis-regulatory landscape of a significant portion of the mammalian genome by increasing the transcriptional activity at TEs.

genomics↗

interpolatedXY: a two-step strategy to normalise DNA methylation microarray data avoiding sex bias

MotivationData normalization is an essential step to reduce technical variation within and between arrays. Due to the different karyotypes and the effects of X chromosome inactivation, females and males exhibit distinct methylation patterns on sex chromosomes, thus it poses a significant challenge to normalise sex chromosome data without introducing bias. Currently, existing methods do not provide unbiased solutions to normalise sex chromosome data, usually, they just process autosomal and sex chromosomes indiscriminately. ResultsHere, we demonstrate that ignoring this sex difference will lead to introducing artificial sex bias, especially for thousands of autosomal CpGs. We present a novel two-step strategy (interpolatedXY) to address this issue, which is applicable to all quantile-based normalisation methods. By this new strategy, the autosomal CpGs are first normalised independently by conventional methods, such as funnorm or dasen; then the corrected methylation values of sex chromosome linked CpGs are estimated as the weighted average of their nearest neighbours on autosomes. The proposed two-step strategy can also be applied to other non-quantile-based normalisation methods, as well as other array-based data types. Moreover, we propose a useful concept: the sex explained fraction of variance, to quantitatively measure the normalisation effect. AvailabilityThe proposed methods are available by calling the function adjustedDasen or adjustedFunnorm in the latest wateRmelon package (https://github.com/schalkwyk/wateRmelon), with methods compatible with all the major workflows, including minfi. Contactxzhai@essex.ac.uk; lschal@essex.ac.uk Supplementary informationSupplementary data are available at...

bioinformatics↗

Characterising sex differences of autosomal DNA methylation in whole blood using the Illumina EPIC array

Sex differences are known to play a role in disease etiology, progression and outcome. Previous studies have revealed autosomal epigenetic differences between males and females in some tissues, including differences in DNA methylation patterns. Here, we report for the first time an analysis of autosomal sex differences in DNAme using the Illumina EPIC array in human whole blood (n=1171). We identified 554 sex-associated differentially methylated CpG sites (saDMPs) with the majority found to be hypermethylated in females (70%). These saDMPs are enriched in CpG islands and CpG shores and located preferentially at 5UTRs, 3UTRs and enhancers. Additionally, we identified 311 significant sex associated differentially methylated regions (saDMRs). Transcription factor binding site enrichment revealed enrichment of transcription factors related to critical developmental processes and sex determination such as SRY and SOX9. Our study reports a reliable catalogue of sex associated CpG sites and elucidates several characteristics of these sites.

genomics↗

DNA methylation-based sex classifier to predict sex and identify sex chromosome aneuploidy

Sex is an important covariate of epigenome-wide association studies due to its strong influence on DNA methylation patterns across numerous genomic positions. Nevertheless, many samples on the Gene Expression Omnibus (GEO) frequently lack a sex annotation or are incorrectly labelled. Considering the influence that sex imposes on DNA methylation patterns, it is necessary to ensure that methods for filtering poor samples and checking of sex assignment are accurate and widely applicable. In this paper, we presented a novel method to predict sex using only DNA methylation density signals, which can be readily applied to almost all DNA methylation datasets of different formats (raw IDATs or text files with only density signals) uploaded to GEO. We identified 4345 significantly (p < 0.01) sex-associated CpG sites present on both 450K and EPIC arrays, and constructed a sex classifier based on the two first components of PCAs from the two sex chromosomes. The proposed method is constructed using whole blood samples and exhibits good performance across a wide range of tissues. We further demonstrated that our method can be used to identify samples with sex chromosome aneuploidy, this function is validated by five Turner syndrome cases and one Klinefelter syndrome case. The proposed method has been integrated into the wateRmelon Bioconductor package.

bioinformatics↗