Biology subjects

Dahir, A.

Publications and source records attributed to Dahir, A..

3 recordsLinked to original sources

bioRxiv2025

Methylomic signatures of tau and amyloid-beta in transgenic mouse models of Alzheimer's disease neuropathology

Alzheimers disease (AD) is characterized by progressive neurodegeneration driven by tau and amyloid-{beta} (A{beta}) pathology. Emerging evidence implicates a role for epigenetic modifications, particularly altered DNA methylation (DNAm), in AD pathogenesis. However, few studies have comprehensively investigated DNAm in experimental models. Here, we profile DNAm dynamics in two widely used transgenic mouse models of tau (rTg4510) and A{beta} (J20) neuropathology, focusing on variation in the entorhinal cortex and hippocampus. Using reduced representation bisulfite sequencing (RRBS) and methylation arrays across multiple disease stages, we identified widespread DNAm alterations associated with genotype and neuropathological burden. In rTg4510 mice, tau accumulation was linked to extensive DNAm remodeling at genes involved in neuronal plasticity and apoptosis (including Dcaf5, Creb3l4, and As3mt). J20 mice exhibited more modest changes annotated to immune-related genes, notably at Grk2, Ncam2, and Prmt8. Of note, tau-associated DNAm changes were more consistent across brain areas than those associated with A{beta} pathology. Comparison with DNAm data from human studies revealed that a subset of DNAm sites mirrored those observed in the human AD cortex, including hypermethylation at Ank1 and Prdm16. These findings provide evidence for pathology-associated epigenetic alterations in AD, highlight shared and distinct DNAm signatures of tau and A{beta}, and offer insight into molecular mechanisms that may precede overt neurodegeneration. Our work underscores the utility of epigenomic profiling in transgenic models and provides a foundation for identifying novel targets for early intervention in AD.

neuroscience↗

bioRxiv2021

Characterizing the properties of bisulfite sequencing data: maximizing power and sensitivity to identify between-group differences in DNA methylation

BackgroundThe combination of sodium bisulfite treatment with highly-parallel sequencing is a common method for quantifying DNA methylation across the genome. The power to detect between-group differences in DNA methylation using bisulfite-sequencing approaches is influenced by both experimental (e.g. read depth, missing data and sample size) and biological (e.g. mean level of DNA methylation and difference between groups) parameters. There is, however, no consensus about the optimal thresholds for filtering bisulfite sequencing data with implications for the reproducibility of findings in epigenetic epidemiology. ResultsWe used a large reduced representation bisulfite sequencing (RRBS) dataset to assess the distribution of read depth across DNA methylation sites and the extent of missing data. To investigate how various study variables influence power to identify DNA methylation differences between groups, we developed a framework for simulating bisulfite sequencing data. As expected, sequencing read depth, group size, and the magnitude of DNA methylation difference between groups all impacted upon statistical power. The influence on power was not dependent on one specific parameter, but reflected the combination of study-specific variables. As a resource to the community, we have developed a tool, POWEREDBiSeq, which utilizes our simulation framework to predict study-specific power for the identification of DNAm differences between groups, taking into account user-defined read depth filtering parameters and the minimum sample size per group. ConclusionsOur data-driven approach highlights the importance of filtering bisulfite-sequencing data by minimum read depth and illustrates how the choice of threshold is influenced by the specific study design and the expected differences between groups being compared. The POWEREDBiSeq tool can help users identify the level of data filtering needed to optimize power and aims to improve the reproducibility of bisulfite sequencing studies.

bioinformatics↗

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↗