Biology subjects

Shireby, G.

Publications and source records attributed to Shireby, G..

6 recordsLinked to original sources

bioRxiv2022

Epigenetic age acceleration is associated with oligodendrocyte proportions in MSA and control brain tissue

AimsEpigenetic clocks are widely applied as surrogates for biological age in different tissues and/or diseases, including several neurodegenerative diseases. Despite white matter (WM) changes often being observed in neurodegenerative diseases, no study has investigated epigenetic ageing in white matter. MethodsWe analysed the performances of two DNA methylation-based clocks, DNAmClockMulti and DNAmClockCortical, in post-mortem WM tissue from multiple subcortical regions and the cerebellum, and in oligodendrocyte-enriched nuclei. We also examined epigenetic ageing in control and multiple system atrophy (MSA) (WM and mixed WM and grey matter), as MSA is a neurodegenerative disease comprising pronounced WM changes and -synuclein aggregates in oligodendrocytes. ResultsEstimated DNA methylation (DNAm) ages showed strong correlations with chronological ages, even in WM (e.g., DNAmClockCortical, r = [0.80-0.97], p<0.05). However, performances and DNAm age estimates differed between clocks and brain regions. DNAmClockMulti significantly underestimated ages in all cohorts except in the MSA prefrontal cortex mixed tissue, whereas DNAmClockCortica tended towards age overestimations. Pronounced age overestimations in the oligodendrocyte-enriched cohorts (e.g., oligodendrocyte-enriched nuclei, p=6.1x10-5) suggested that this cell-type ages faster. Indeed, significant positive correlations were observed between estimated oligodendrocyte proportions and DNAm age acceleration estimated by DNAmClockCortica (r>0.31, p<0.05), and similar trends with DNAmClockMulti. Although increased age acceleration was observed in MSA compared to controls, no significant differences were observed upon adjustment for possible confounders (e.g., cell-type proportions). ConclusionsOur findings show that oligodendrocyte proportions positively influence epigenetic age acceleration across brain regions and highlight the need to further investigate this in ageing and neurodegeneration.

neuroscience↗

bioRxiv2022

Uncertainty quantification of reference based cellular deconvolution algorithms

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 characterise a sample level accuracy metric for derived cellular heterogeneity variables. The CETYGO score captures the deviation between a samples DNAm 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 the standard 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).

bioinformatics↗

bioRxiv2022

DNA methylation signatures of Alzheimer's disease neuropathology in the cortex are primarily driven by variation in non-neuronal cell-types

Alzheimers disease (AD) is a chronic neurodegenerative disease characterized by the progressive accumulation of amyloid-beta and neurofibrillary tangles of tau in the neocortex. Utilizing extensive neuropathology data from the Brains for Dementia Research (BDR) cohort we performed the most systematic epigenome-wide association study (EWAS) of multiple measures of AD neuropathology yet undertaken, profiling DNA methylation in two cortical regions from 631 donors. We meta-analyzed our results with those from previous studies of DNA methylation in AD cortex (total n = 2,013 donors), identifying 334 cortical differentially methylated positions (DMPs) associated with AD pathology including methylomic variation at novel loci not previously implicated in dementia. We subsequently characterized DNA methylation in purified nuclei populations - enriched for neurons, oligodendrocytes and microglia - exploring the extent to which cortex AD-associated DMPs reflect differences manifest in specific cell populations. We find that the majority of DMPs identified in bulk cortex tissue actually reflect DNA methylation differences occurring in non-neuronal cells, with dramatically increased effect sizes observed in microglia-enriched nuclei populations. Our study highlights the power of utilizing multiple measures of neuropathology to identify epigenetic signatures of AD and the importance of characterizing disease-associated variation in purified neural cell-types.

genetics↗

bioRxiv2022

Evaluation of nanopore sequencing for epigenetic epidemiology: a comparison with DNA methylation microarrays

Most epigenetic epidemiology to date has utilized microarrays to identify positions in the genome where variation in DNA methylation is associated with environmental exposures or disease. However, these profile less than 3% of DNA methylation sites in the human genome, potentially missing affected loci and preventing the discovery of disrupted biological pathways. Third generation sequencing technologies, including Nanopore sequencing, have the potential to revolutionise the generation of epigenetic data, not only by providing genuine genome-wide coverage but profiling epigenetic modifications direct from native DNA. Here we assess the viability of using Nanopore sequencing for epidemiology by performing a comparison with DNA methylation quantified using the most comprehensive microarray available, the Illumina EPIC array. We implemented a CRISPR-Cas9 targeted sequencing approach in concert with Nanopore sequencing to profile DNA methylation in three genomic regions to attempt to rediscover genomic positions that existing technologies have shown are differentially methylated in tobacco smokers. Using Nanopore sequencing reads, DNA methylation was quantified at 1,779 CpGs across three regions, providing a finer resolution of DNA methylation patterns compared to the EPIC array. The correlation of estimated levels of DNA methylation between platforms was high. Furthermore, we identified 12 CpGs where hypomethylation was significantly associated with smoking status, including 10 within the AHRR gene. In summary, Nanopore sequencing is a valid option for identifying genomic loci where large differences in DNAm are associated with a phenotype and has the potential to advance our understanding of the role differential methylation plays in the aetiology of complex disease.

neuroscience↗

bioRxiv2020

Recalibrating the Epigenetic Clock: Implications for Assessing Biological Age in the Human Cortex

Human DNA-methylation data have been used to develop biomarkers of ageing - referred to epigenetic clocks - that have been widely used to identify differences between chronological age and biological age in health and disease including neurodegeneration, dementia and other brain phenotypes. Existing DNA methylation clocks are highly accurate in blood but are less precise when used in older samples or on brain tissue. We aimed to develop a novel epigenetic clock that performs optimally in human cortex tissue and has the potential to identify phenotypes associated with biological ageing in the brain. We generated an extensive dataset of human cortex DNA methylation data spanning the life-course (n = 1,397, ages = 1 to 104 years). This dataset was split into training and testing samples (training: n = 1,047; testing: n = 350). DNA methylation age estimators were derived using a transformed version of chronological age on DNA methylation at specific sites using elastic net regression, a supervised machine learning method. The cortical clock was subsequently validated in a novel human cortex dataset (n = 1,221, ages = 41 to 104 years) and tested for specificity in a large whole blood dataset (n = 1,175, ages = 28 to 98 years). We identified a set of 347 DNA methylation sites that, in combination optimally predict age in the human cortex. The sum of DNA methylation levels at these sites weighted by their regression coefficients provide the cortical DNA methylation clock age estimate. The novel clock dramatically out-performed previously reported clocks in additional cortical datasets. Our findings suggest that previous associations between predicted DNA methylation age and neurodegenerative phenotypes might represent false positives resulting from clocks not robustly calibrated to the tissue being tested and for phenotypes that become manifest in older ages. The age distribution and tissue type of samples included in training datasets need to be considered when building and applying epigenetic clock algorithms to human epidemiological or disease cohorts.

genomics↗

bioRxiv2020

Meta-analysis of epigenome-wide association studies in Alzheime's disease highlights 220 differentially methylated loci across cortex

Epigenome-wide association studies of Alzheimers disease have highlighted neuropathology-associated DNA methylation differences, although existing studies have been limited in sample size and utilized different brain regions. Here, we combine data from six DNA methylomic studies of Alzheimers disease (N=1,453 unique individuals) to identify differential methylation associated with Braak stage in different brain regions and across cortex. We identify 236 CpGs in the prefrontal cortex, 95 CpGs in the temporal gyrus and ten CpGs in the entorhinal cortex at Bonferroni significance, with none in the cerebellum. Our cross-cortex meta-analysis (N=1,408 donors) identifies 220 CpGs associated with neuropathology, annotated to 121 genes, of which 84 genes have not been previously reported at this significance threshold. We have replicated our findings using two further DNA methylomic datasets consisting of a further > 600 unique donors. The meta-analysis summary statistics are available in our online data resource (www.epigenomicslab.com/ad-meta-analysis/).

genomics↗