Jennifer Imm

DNA methylation (DNAm) signatures are highly cell type-specific, yet most epigenome-wide association studies (EWAS) are performed on bulk tissue, potentially obscuring critical cell type-specific patterns. Existing computational tools for detecting cell type-specific DNAm changes are often limited by the accuracy of cell type deconvolution algorithms. Here, we introduce CEAM (Cell-type Enrichment Analysis for Methylation), a robust and interpretable framework for cell type enrichment analysis in DNA methylation data. CEAM applies over-representation analysis with cell type-specific CpG panels from Illumina EPIC arrays derived from nuclei-sorted cortical post-mortem brains from neurologically healthy aged individuals. The constructed CpG panels were systematically evaluated using both simulated datasets and published EWAS results from Alzheimer's disease, Lewy body disease, and multiple sclerosis. CEAM demonstrated resilience to shifts in cell type composition, a common confounder in EWAS, and remained robust across a wide range of differentially methylated positions, when upstream modeling of cell type composition was modeled with sufficient accuracy. Application to existing EWAS findings generated in neurodegenerative diseases revealed enrichment patterns concordant with established disease biology, confirming CEAM's biological relevance. The workflow is publicly available as an interactive Shiny app (https://um-dementia-systems-biology.shinyapps.io/CEAM/) enabling rapid, interpretable analysis of cell type-specific DNAm changes from bulk EWAS.

Alzheimer’s disease (AD) is characterized by neuroinflammation, yet the impact of concurrent systemic infections on the AD brain remains poorly understood. We investigated the molecular mechanisms underlying the central nervous system response to systemic infections in AD by analyzing RNA sequencing data generated in the prefrontal cortex from 202 post-mortem donors (113 AD, 89 controls), where we stratified by the presence of a respiratory infection at the time of death. We identified 763 significantly differentially expressed genes (DEGs) between AD and controls without infection, which were enriched for oxidative phosphorylation and neurodegenerative pathways. In contrast, 122 DEGs distinguished AD from controls during infection, with 57 genes uniquely altered in AD in the presence of infection, including MAPK4 , VAV3 , and POU3F4 , implicating infection-dependent mechanisms of vascular and immune regulation. Pathway activity analysis revealed that infection in AD suppresses some immune and vascular pathways, while enhancing transcriptional and developmental programs. Weighted gene co-expression network analysis uncovered three key modules: one module strongly associated with AD, enriched for aging and signal transduction; one module linked to both AD and infection, highlighting cytoskeletal remodelling and host–pathogen interactions; and one module specific to infection, enriched in astrocytes, pericytes, and endothelial cells, implicating blood–brain barrier dysfunction. These findings suggest that systemic respiratory infections reshape transcriptional programs in the AD brain, dampening immune effector pathways and engaging vascular and host–pathogen processes in blood–brain-barrier–associated cell types. Our results highlight the complex interplay between systemic infection, neuroinflammation, and vascular responses in AD.

DNA methylation signatures are highly cell type-specific, yet most epigenome-wide association studies (EWAS) are performed on bulk tissue, potentially obscuring critical cell type-specific patterns. Existing computational tools for detecting cell type-specific DNAm changes are often limited by the accuracy of cell type deconvolution algorithms. Here, we introduce CEAM (Cell-type Enrichment Analysis for Methylation), a robust and interpretable framework for cell type enrichment analysis in DNA methylation data. CEAM applies over-representation analysis with cell type-specific CpG panels from Illumina EPIC arrays derived from nuclei-sorted cortical post-mortem brains from neurologically healthy aged individuals. The constructed CpG panels were systematically evaluated using both simulated datasets and published EWAS results from Alzheimer’s disease, Lewy body disease, and multiple sclerosis. CEAM demonstrated resilience to shifts in cell type composition, a common confounder in EWAS, and remained accurate across a wide range of differentially methylated positions, underscoring its flexibility. Application to existing EWAS findings generated in neurodegenerative diseases revealed enrichment patterns concordant with established disease biology, confirming CEAM’s biological relevance. The workflow is publicly available as an interactive Shiny app ( https://um-dementia-systems-biology.shinyapps.io/CEAM/ ) enabling rapid, interpretable analysis of cell type-specific DNAm changes from bulk EWAS.

Lewy body (LB) diseases are an umbrella term encompassing a range of neurodegenerative conditions all characterized by the hallmark of intra-neuronal α-synuclein associated with the development of motor and cognitive dysfunction. In this study, we have conducted a large meta-analysis of DNA methylation across multiple cortical brain regions, in relation to increasing burden of LB pathology. Utilizing a combined dataset of 1,217 samples across 844 unique donors, we identified a set of 24 false discovery rate (FDR) significant loci that are differentially methylated in association with LB pathology, the most significant of which were located in UBASH3B and PTAFR , as well as an intergenic locus. Ontological enrichment analysis of our meta-analysis results highlights several neurologically relevant traits, including synaptic alterations. We leverage our data to compare DNA methylation signatures between different neurodegenerative pathologies and highlight a shared epigenetic profile across LB diseases, Alzheimer’s disease and Huntington’s disease, although the top-ranked loci show disease specificity. Utilizing summary statistics from previous large-scale genome-wide association studies we identified significant enrichment of DNA methylation differences with respect to increasing LB pathology in the SNCA genomic region. We identified specific relationships between genetic risk variants for LB-Dementia and Parkinson’s disease to methylation quantitative trait loci in the promoter region and expression of SNCA transcript isoforms.

Background: Dementia with Lewy bodies (DLB), Parkinson’s disease (PD) and Parkinson’s disease dementia (PDD) are termed Lewy body diseases (LBDs). These neurodegenerative diseases are classified by the accumulation of alpha‐synuclein in neurons, forming Lewy bodies (LB). Whilst pathologically similar, symptomatic staging is different. Previous genome wide association studies (GWAS) have identified classic Alzheimer’s disease (AD) and PD loci as risk loci in DLB. Therefore, we have used polygenic risk score (PRS) analysis to investigate the contribution of AD and PD variants in the LBDs.Method: DNA was extracted from bulk brain tissue from 481 individuals (162 controls, 95 DLB, 156 PDD and 68 PD) and genotyped on the Illumina global screening array. After a strict quality control and imputation pipeline, PRSs for multiple relevant traits using reference GWAS summary statistics were calculated using PRSice2.Result: Study groups have been sourced consisting of cases with PD, PDD and DLB based on LB deposition and clinical symptom staging. All groups, including controls are stratified for age and concomitant AD pathology. Assessing multiple PRSs for association with pathological development and clinical outcomes we have identified the contribution of genetic risk from related neurodegenerative and neuropsychiatric disorders in our cohort of PD, PDD and DLB samples.Conclusion: We have collated a well powered study cohort to interrogate the genetic basis of the Lewy body dementias and the relative contribution of AD and PD risk loci in the LBDs. We are currently generating methylation quantitative trait loci (mQTLs) for this dataset using previously generated matched DNA methylation data in the brain.

Background: Epigenome‐wide association studies (EWAS) of DNA methylation are being undertaken in Parkinson’s disease (PD) and Dementia with Lewy bodies (DLB) post‐mortem brain to identify novel disease mechanisms. However, there is considerable heterogeneity in clinical and neuropathological manifestation, as well as the contribution of post‐mortem factors, which can confound smaller studies. The heterozygous GBA1 (D409V/WT) mouse model of DLB presents α‐synuclein deposition, cognitive decline and cholinergic dysfunction. We have undertaken the first systematic investigation of DNA methylation in these mice to identify changes related to α‐synuclein deposition. We have also explored the effect of varying post‐mortem interval (PMI) on DNA methylation patterns, to allow us to better interpret human studies.Method: In total we profiled DNA methylation using the Illumina Infinium Mouse Methylation BeadChip array in 58 mice, corresponding to 28 GBA1 D409V/WT and 30 WT mice aged 8‐23 months. Within each age group we divided mice into 5 further groups to also explore the effect of PMI: +0hrs, +24hrs, +48hrs, +72hrs & +96 hrs respectively. The mice were culled and those in the +0hrs group were immediately dissected with the prefrontal cortex being flash frozen and stored in the freezer. Mice assigned to other PMI groups were culled and stored at 4 degrees for the relevant PMI before dissection and flash freezing. DNA was extracted, bisulfite treated and then profiled for DNA methylation.Result: Of the 58 mice that were sourced it was ensured that D409V/WT mice and WT mice were balanced between the 5 PMI groups. We have investigated the effect of phenotype, neuropathology and PMI on DNA methylation levels using linear regression models.Conclusion: Teasing apart the effect varying PMI has on DNA methylation in post mortem brain samples will be a great aid when analysing such data going forward in large human EWASs. We have collated an in vivo study cohort to investigate DNA methylation patterns in the heterozygous D409V/WT mouse compared to WT, and interrogated the effect PMI has on the quality of methylation data. More broadly our study of PMI will have relevance to interpreting the results of human EWAS of DLB and other dementias.