Peripheral blood DNA methylation differences in twin pairs discordant for Alzheimer's disease

Genome-wide association study reveals a locus in ADARB2 for complete freedom from headache in Danish Blood Donors

Headache disorders are the most common disorders of the nervous system. The lifetime prevalence of headache disorders show that some individuals never experience headache. The etiology of complete freedom from headache is not known. To assess genetic variants associated with complete freedom from headache, we performed a genome-wide association study of individuals who have never experienced a headache. We included 63,992 individuals (2,998 individuals with complete freedom from headache and 60,994 controls) from the Danish Blood Donor Study Genomic Cohort. Participants were included in two rounds, from 2015 to 2018 and in 2020. We discovered a genome-wide significant association, with the lead variant rs7904615[G] in ADARB2 (EAF = 27%, OR = 1.20 [1.13-1.27], p = 3.92 × 10-9). The genomic locus was replicated in a non-overlapping cohort of 13,032 individuals (539 individuals with complete freedom from headache and 12,493 controls) from the Danish Blood Donor Study Genomic Cohort (p < 0.05, two-sided). Participants for the replication were included from 2015 to 2020. In conclusion, we show that complete freedom from headache has a genetic component, and we suggest that ADARB2 is involved in complete freedom from headache. The genomic locus was specific for complete freedom from headache and was not associated with any primary headache disorders.

regionalpcs: improved discovery of DNA methylation associations with complex traits

We have developed the regional principal components (rPCs) method, a novel approach for summarizing gene-level methylation. rPCs address the challenge of deciphering complex epigenetic mechanisms in diseases like Alzheimer's disease (AD). In contrast to traditional averaging, rPCs leverage principal components analysis to capture complex methylation patterns across gene regions. Our method demonstrated a 54% improvement in sensitivity over averaging in simulations, offering a robust framework for identifying subtle epigenetic variations. Applying rPCs to the AD brain methylation data in ROSMAP, combined with cell type deconvolution, we uncovered 838 differentially methylated genes associated with neuritic plaque burden-significantly outperforming conventional methods. Integrating methylation quantitative trait loci (meQTL) with genome-wide association studies (GWAS) identified 17 genes with potential causal roles in AD, including MS4A4A and PICALM. Our approach is available in the Bioconductor package regionalpcs, opening avenues for research and facilitating a deeper understanding of the epigenetic landscape in complex diseases.

Association of Blood-Based DNA Methylation Markers With Late-Onset Alzheimer Disease: A Potential Diagnostic Approach

BACKGROUND AND OBJECTIVES

There is an urgent need to identify novel noninvasive biomarkers for Alzheimer disease (AD) diagnosis. Recent advances in blood-based measurements of phosphorylated tau (pTau) species are promising but still insufficient to address clinical needs. Epigenetics has been shown to be helpful to better understand AD pathogenesis. Epigenetic biomarkers have been successfully implemented in other medical disciplines, such as oncology. The objective of this study was to explore the diagnostic accuracy of a blood-based DNA methylation marker panel as a noninvasive tool to identify patients with late-onset Alzheimer compared with age-matched controls.

METHODS

A case-control study was performed. Blood DNA methylation levels at 46 cytosine-guanine sites (21 genes selected after a comprehensive literature search) were measured by bisulfite pyrosequencing in patients with "probable AD dementia" following National Institute on Aging and the Alzheimer's Association guidelines (2011) and age-matched and sex-matched controls recruited at Neurology Department-University Hospital of Navarre, Spain, selected by convenience sampling. Plasma pTau181 levels were determined by Simoa technology. Multivariable logistic regression analysis was performed to explore the optimal model to discriminate patients with AD from controls. Furthermore, we performed a stratified analysis by sex.

RESULTS

The final study cohort consisted of 80 patients with AD (age: median [interquartile range] 79 [11] years; 58.8% female) and 100 cognitively healthy controls (age 77 [10] years; 58% female). A panel including DNA methylation levels at NXN, ABCA7, and HOXA3 genes and plasma pTau181 significantly improved (area under the receiver operating characteristic curve 0.93, 95% CI 0.89-0.97) the diagnostic performance of a single pTau181-based model, adjusted for age, sex, and APOE ɛ4 genotype. The sensitivity and specificity of this panel were 83.30% and 90.00%, respectively. After sex-stratified analysis, HOXA3 DNA methylation levels showed consistent association with AD.

DISCUSSION

These results highlight the potential translational value of blood-based DNA methylation biomarkers for noninvasive diagnosis of AD.

REGISTRATION INFORMATION

Research Ethics Committee of the University Hospital of Navarre (PI17/02218).

Maximizing the value of twin studies in health and behaviour

In the classical twin design, researchers compare trait resemblance in cohorts of identical and non-identical twins to understand how genetic and environmental factors correlate with resemblance in behaviour and other phenotypes. The twin design is also a valuable tool for studying causality, intergenerational transmission, and gene-environment correlation and interaction. Here we review recent developments in twin studies, recent results from twin studies of new phenotypes and recent insights into twinning. We ask whether the results of existing twin studies are representative of the general population and of global diversity, and we conclude that stronger efforts to increase representativeness are needed. We provide an updated overview of twin concordance and discordance for major diseases and mental disorders, which conveys a crucial message: genetic influences are not as deterministic as many believe. This has important implications for public understanding of genetic risk prediction tools, as the accuracy of genetic predictions can never exceed identical twin concordance rates.

Differential expression of m5C RNA methyltransferase genes NSUN6 and NSUN7 in Alzheimer's disease and traumatic brain injury

Epigenetic processes have become increasingly relevant in understanding disease-modifying mechanisms. 5-Methylcytosine methylations of DNA (5mC) and RNA (m⁵C) have functional transcriptional and RNA translational consequences and are tightly regulated by writer, reader and eraser effector proteins. To investigate the involvement of 5mC/5hmC and m⁵C effector proteins contributing to the development of dementia neuropathology, RNA sequencing data of 31 effector proteins across four brain regions was examined in 56 aged non-affected and 51 Alzheimer's disease (AD) individuals obtained from the Aging, Dementia and Traumatic Brain Injury Study. Gene expression profiles were compared between AD and controls, between neuropathological Braak and CERAD scores and in individuals with a history of traumatic brain injury (TBI). We found an increase in the DNA methylation writers DNMT1, DNMT3A and DNMT3B messenger RNA (mRNA) and a decrease in the reader UHRF1 mRNA in AD samples across three brain regions whilst the DNA erasers GADD45B and AICDA showed changes in mRNA abundance within neuropathological load groupings. RNA methylation writers NSUN6 and NSUN7 showed significant expression differences with AD and, along with the reader ALYREF, differences in expression for neuropathologic ranking. A history of TBI was associated with a significant increase in the DNA readers ZBTB4 and MeCP2 (p < 0.05) and a decrease in NSUN6 (p < 0.001) mRNA. These findings implicate regulation of protein pathways disrupted in AD and TBI via multiple pre- and post-transcriptional mechanisms including potentially acting upon transfer RNAs, enhancer RNAs as well as nuclear-cytoplasmic shuttling and cytoplasmic translational control. The targeting of such processes provides new therapeutic avenues for neurodegenerative brain conditions.

ADmeth: A Manually Curated Database for the Differential Methylation in Alzheimer's Disease

Alzheimer's disease (AD) is the most common neurodegenerative disease. More and more evidence show that DNA methylation is closely related to the pathological mechanism of AD. Many AD-associated differentially methylated genes, regions and CpG sites have been identified in recent researches, which may have great potential in clinical research. However, there is no dedicated database to collect AD-related differential methylation up to now. To provide a reference to researchers, we design a database named ADmeth by manually curating relevant articles, which contains a total of 16,709 AD-related differentially methylated items identified from different brain regions and different cell types in the blood, involving 209 genes, 2,229 regions and 14,271 CpG sites. The ADmeth database provides user-friendly pages to search, submit and download data. We hope that the ADmeth database can facilitate researchers to select candidate AD-associated methylation markers in revealing the pathological mechanism of AD and promote the cell-free DNA based non-invasive diagnosis of AD. The ADmeth database is available at http://www.biobdlab.cn/ADmeth.

Sixteen-Year Longitudinal Evaluation of Blood-Based DNA Methylation Biomarkers for Early Prediction of Alzheimer's Disease

BACKGROUND

DNA methylation (DNAm), an epigenetic mark reflecting both inherited and environmental influences, has shown promise for Alzheimer's disease (AD) prediction.

OBJECTIVE

Testing long-term predictive ability (>15 years) of existing DNAm-based epigenetic age acceleration (EAA) measures and identifying novel early blood-based DNAm AD-prediction biomarkers.

METHODS

EAA measures calculated from Illumina EPIC data from blood were tested with linear mixed-effects models (LMMs) in a longitudinal case-control sample (50 late-onset AD cases; 51 matched controls) with prospective data up to 16 years before clinical onset, and post-onset follow-up. Novel DNAm biomarkers were generated with epigenome-wide LMMs, and Sparse Partial Least Squares Discriminant Analysis applied at pre- (10-16 years), and post-AD-onset time-points.

RESULTS

EAA did not differentiate cases from controls during the follow-up time (p > 0.05). Three new DNA biomarkers showed in-sample predictive ability on average 8 years pre-onset, after adjustment for age, sex, and white blood cell proportions (p-values: 0.022-<0.00001). Our longitudinally-derived panel replicated nominally (p = 0.012) in an external cohort (n = 146 cases, 324 controls). However, its effect size and discriminatory accuracy were limited compared to APOEɛ4-carriership (OR = 1.38 per 1 SD DNAm score increase versus OR = 13.58 for ɛ4-allele carriage; AUCs = 77.2% versus 87.0%). Literature review showed low overlap (n = 4) across 3275 AD-associated CpGs from 8 published studies, and no overlap with our identified CpGs.

Gene-environment interactions in Alzheimer disease: the emerging role of epigenetics

With the exception of a few monogenic forms, Alzheimer disease (AD) has a complex aetiology that is likely to involve multiple susceptibility genes and environmental factors. The role of environmental factors is difficult to determine and, until a few years ago, the molecular mechanisms underlying gene-environment (G × E) interactions in AD were largely unknown. Here, we review evidence that has emerged over the past two decades to explain how environmental factors, such as diet, lifestyle, alcohol, smoking and pollutants, might interact with the human genome. In particular, we discuss how various environmental AD risk factors can induce epigenetic modifications of key AD-related genes and pathways and consider how epigenetic mechanisms could contribute to the effects of oxidative stress on AD onset. Studies on early-life exposures are helping to uncover critical time windows of sensitivity to epigenetic influences from environmental factors, thereby laying the foundations for future primary preventative approaches. We conclude that epigenetic modifications need to be considered when assessing G × E interactions in AD.

Prediction of Alzheimer’s Disease Using Patterns of Methylation Levels in Key Immunologic-Related Genes

Background: DNA methylation is expected to become a kind of new diagnosis and treatment method of Alzheimer’s disease (AD). Neuroinflammation- and immune-related pathways represent one of the major genetic risk factors for AD. Objective: We aimed to investigate DNA methylation levels of 7 key immunologic-related genes in peripheral blood and appraise their applicability in the diagnosis of AD. Methods: Methylation levels were obtained from 222 participants (101 AD, 72 MC, 49 non-cognitively impaired controls). Logistic regression models for diagnosing AD were established after least absolute shrinkage and selection operator (LASSO) and best subset selection (BSS), evaluated by respondent working curve and decision curve analysis for sensitivity. Results: Six differentially methylated positions (DMPs) in the MCI group and 64 in the AD group were found, respectively. Among them, there were 2 DMPs in the MCI group and 30 DMPs in the AD group independent of age, gender, and APOE4 carriers (p <  0.05). AD diagnostic prediction models differentiated AD from normal controls both in a training dataset (LASSO: 8 markers, including methylation levels at ABCA7_1040077, CNR1_88166293, CX3CR1_39322324, LRRK2_40618505, LRRK2_40618493, NGFR_49496745, TARDBP_11070956, TARDBP_11070840, area under the curve [AUC] = 0.81; BSS: 2 markers, including methylation levels at ABCA7_1040077 and CX3CR1_39322324, AUC = 0.80) and a testing dataset (AUC = 0.84, AUC = 0.82, respectively). Conclusion: Our work indicated that methylation levels of 7 key immunologic-related genes (ABCA7, CNR1, CX3CR1, CSF1 R, LRRK2, NGFR, and TARDBP) in peripheral blood was altered in AD and the models including methylation of immunologic-related genes biomarkers improved prediction of AD.

LuxRep: a technical replicate-aware method for bisulfite sequencing data analysis

BACKGROUND

DNA methylation is commonly measured using bisulfite sequencing (BS-seq). The quality of a BS-seq library is measured by its bisulfite conversion efficiency. Libraries with low conversion rates are typically excluded from analysis resulting in reduced coverage and increased costs.

RESULTS

We have developed a probabilistic method and software, LuxRep, that implements a general linear model and simultaneously accounts for technical replicates (libraries from the same biological sample) from different bisulfite-converted DNA libraries. Using simulations and actual DNA methylation data, we show that including technical replicates with low bisulfite conversion rates generates more accurate estimates of methylation levels and differentially methylated sites. Moreover, using variational inference speeds up computation time necessary for whole genome analysis.

CONCLUSIONS

In this work we show that taking into account technical replicates (i.e. libraries) of BS-seq data of varying bisulfite conversion rates, with their corresponding experimental parameters, improves methylation level estimation and differential methylation detection.

Screening of Crucial Differentially-Methylated/Expressed Genes for Alzheimer's Disease

Background: We aimed to make an integrated analysis of published transcriptome and DNA methylation dataset to ascertain the key differentially methylated and differentially expressed genes for Alzherimer's disease (AD). Methods: Two gene expression microarrays and 1 gene methylation microarray were downloaded for identification of differentially expressed genes and differentially methylated genes. Then, we used various biological information databases to annotate the functions of the differentially-methylated/expressed genes, and screen out key genes and important signaling pathways. Finally, we validate the differentially-methylated/expressed genes in the additional online datasets and in blood from AD patients.Results: A total of 8 hub hypomethylated-high expression genes were obtained, including Rac family small GTPase 2, FGR proto-oncogene, Src family tyrosine kinase, LYN proto-oncogene, Src family tyrosine kinase, protein kinase C delta, myosin IF, integrin subunit alpha 5, semaphorin 4D, and growth arrest specific protein 7. Some enriched signaling pathways of hypomethylated-high expression genes were identified, including regulation of actin cytoskeleton, chemokine signaling pathway, Fc gamma R-mediated phagocytosis, and axon guidance. Conclusion: Differentially-methylated/expressed genes are likely to be associated with AD.

Large-scale plasma proteomic profiling identifies a high-performance biomarker panel for Alzheimer's disease screening and staging

INTRODUCTION

Blood proteins are emerging as candidate biomarkers for Alzheimer's disease (AD). We systematically profiled the plasma proteome to identify novel AD blood biomarkers and develop a high-performance, blood-based test for AD.

METHODS

We quantified 1160 plasma proteins in a Hong Kong Chinese cohort by high-throughput proximity extension assay and validated the results in an independent cohort. In subgroup analyses, plasma biomarkers for amyloid, tau, phosphorylated tau, and neurodegeneration were used as endophenotypes of AD.

RESULTS

We identified 429 proteins that were dysregulated in AD plasma. We selected 19 "hub proteins" representative of the AD plasma protein profile, which formed the basis of a scoring system that accurately classified clinical AD (area under the curve  = 0.9690-0.9816) and associated endophenotypes. Moreover, specific hub proteins exhibit disease stage-dependent dysregulation, which can delineate AD stages.

DISCUSSION

This study comprehensively profiled the AD plasma proteome and serves as a foundation for a high-performance, blood-based test for clinical AD screening and staging.

Placental DNA methylation marks are associated with maternal depressive symptoms during early pregnancy

Maternal depressive symptoms during pregnancy are a significant risk factor for adverse developmental and health outcomes of the offspring. The molecular mechanisms mediating the long-term effects of this exposure are not well understood. Previous studies have found association between prenatal exposure to maternal psychological distress and placental DNA methylation of candidate genes, which can influence placental barrier function and development of the fetus. Our objective in this study was to determine epigenome wide association of maternal depressive symptoms in early pregnancy with the placental DNA methylation. For this purpose we examined DNA methylomes of 92 placental samples by using reduced representation bisulfite sequencing. The placental samples were collected after deliveries of 39 girls and 59 boys, whose mothers had Edinburgh Postnatal Depression Score ranging from 0 to 19 at gestational week 14. According to our results maternal depressive symptoms are associated with DNA methylation of 2833 CpG sites, which are particularly over-represented in genic enhancers. The genes overlapping or nearest to these sites are functionally enriched for development of neurons and show expression enrichment in several regions of developing brain. The genomic regions harboring the DNA methylation marks are enriched for single nucleotide polymorphisms associated with mental disease trait class. Potential cellular signaling cascades mediating the effects include inflammatory and hormonal pathways. As a conclusion our results suggest that maternal depressive symptoms during early pregnancy are associated with DNA methylation marks in placenta in genes, which are important for the development and long-term health of the brain. Whether similar marks can be detected in exposed children remains to be elucidated in further studies.

Genome-wide DNA methylation and gene expression analyses in monozygotic twins identify potential biomarkers of depression

Depression is currently the leading cause of disability around the world. We conducted an epigenome-wide association study (EWAS) in a sample of 58 depression score-discordant monozygotic twin pairs, aiming to detect specific epigenetic variants potentially related to depression and further integrate with gene expression profile data. Association between the methylation level of each CpG site and depression score was tested by applying a linear mixed effect model. Weighted gene co-expression network analysis (WGCNA) was performed for gene expression data. The association of DNA methylation levels of 66 CpG sites with depression score reached the level of P < 1 × 10^-4. These top CpG sites were located at 34 genes, especially PTPRN2, HES5, GATA2, PRDM7, and KCNIP1. Many ontology enrichments were highlighted, including Notch signaling pathway, Huntington disease, p53 pathway by glucose deprivation, hedgehog signaling pathway, DNA binding, and nucleic acid metabolic process. We detected 19 differentially methylated regions (DMRs), some of which were located at GRIK2, DGKA, and NIPA2. While integrating with gene expression data, HELZ2, PTPRN2, GATA2, and ZNF624 were differentially expressed. In WGCNA, one specific module was positively correlated with depression score (r = 0.62, P = 0.002). Some common genes (including BMP2, PRDM7, KCNIP1, and GRIK2) and enrichment terms (including complement and coagulation cascades pathway, DNA binding, neuron fate specification, glial cell differentiation, and thyroid gland development) were both identified in methylation analysis and WGCNA. Our study identifies specific epigenetic variations which are significantly involved in regions, functional genes, biological function, and pathways that mediate depression disorder.

Alzheimer's Disorder: Epigenetic Connection and Associated Risk Factors

The gene based therapeutics and drug targets have shown incredible and appreciable advances in alleviating human sufferings and complexities. Epigenetics simply means above genetics or which controls the organism beyond genetics. At present it is very clear that all characteristics of an individual are not determined by DNA alone, rather the environment, stress, life style and nutrition play a vital part in determining the response of an organism. Thus, nature (genetic makeup) and nurture (exposure) play equally important roles in the responses observed, both at the cellular and organism levels. Epigenetics influence plethora of complications at cellular and molecular levels that includes cancer, metabolic and cardiovascular complications including neurological (psychosis) and neurodegenerative disorders (Alzheimer’s disease, Parkinson disease etc.). The epigenetic mechanisms include DNA methylation, histone modification and non coding RNA which have substantial impact on progression and pathways linked to Alzheimer’s disease. The epigenetic mechanism gets deregulated in Alzheimer’s disease and is characterized by DNA hyper methylation, deacetylation of histones and general repressed chromatin state which alter gene expression at the transcription level by upregulation, downregulation or silencing of genes. Thus, the processes or modulators of these epigenetic processes have shown vast potential as a therapeutic target in Alzheimer’s disease.

Artificial intelligence and leukocyte epigenomics: Evaluation and prediction of late-onset Alzheimer's disease

We evaluated the utility of leucocyte epigenomic-biomarkers for Alzheimer’s Disease (AD) detection and elucidates its molecular pathogeneses. Genome-wide DNA methylation analysis was performed using the Infinium MethylationEPIC BeadChip array in 24 late-onset AD (LOAD) and 24 cognitively healthy subjects. Data were analyzed using six Artificial Intelligence (AI) methodologies including Deep Learning (DL) followed by Ingenuity Pathway Analysis (IPA) was used for AD prediction. We identified 152 significantly (FDR p<0.05) differentially methylated intragenic CpGs in 171 distinct genes in AD patients compared to controls. All AI platforms accurately predicted AD with AUCs ≥0.93 using 283,143 intragenic and 244,246 intergenic/extragenic CpGs. DL had an AUC = 0.99 using intragenic CpGs, with both sensitivity and specificity being 97%. High AD prediction was also achieved using intergenic/extragenic CpG sites (DL significance value being AUC = 0.99 with 97% sensitivity and specificity). Epigenetically altered genes included CR1L & CTSV (abnormal morphology of cerebral cortex), S1PR1 (CNS inflammation), and LTB4R (inflammatory response). These genes have been previously linked with AD and dementia. The differentially methylated genes CTSV & PRMT5 (ventricular hypertrophy and dilation) are linked to cardiovascular disease and of interest given the known association between impaired cerebral blood flow, cardiovascular disease, and AD. We report a novel, minimally invasive approach using peripheral blood leucocyte epigenomics, and AI analysis to detect AD and elucidate its pathogenesis.

Promoter Methylation and Gene Expression of Pin1 Associated with the Risk of Alzheimer's Disease in Southern Chinese

BACKGROUND

Pin1 is a propyl cis-trans isomerase and it has been associated with age-atonset of Alzheimer's disease (AD) and other pathological characteristics of AD. DNA methylation is one of the gene regulation mechanisms and it might affect the gene expression.

OBJECTIVE

This study was aimed to examine the correlation between DNA methylation and gene expression of Pin1 and its effect on the risk of AD in a Chinese population.

METHODS

80 AD patients and 180 normal controls were recruited in this study and their cognitive functions were assessed. Pin1 gene expression and methylation were quantified by real-time RT-PCR and Melting Curve Analysis-Methylation assay (MCA-Meth), respectively.

RESULTS

Our finding revealed a positive correlation between methylation and gene expression of Pin1 (p=0.001) and increased Pin1 methylation was predisposed to the risk of AD (p<0.001). CG genotype of Pin1 SNP rs2287839 was associated with higher gene expression of Pin1 (p=0.036) and the effect was only prominent in normal controls as AD patients were already methylated at Pin1 promoter. Furthermore, methylation of Pin1 was associated with better performance in cognition (p=0.018).

CONCLUSION

Our result further supported the involvement of Pin1 in AD and the increased level of Pin1 might be a protective factor for AD.

Influence of Woodsmoke Exposure on Molecular Mechanisms Underlying Alzheimer's Disease: Existing Literature and Gaps in Our Understanding

Woodsmoke poses a significant health risk as a growing component of ambient air pollution in the United States. While there is a long history of association between woodsmoke exposure and diseases of the respiratory, circulatory, and cardiovascular systems, recent evidence has linked woodsmoke exposure to cognitive dysfunction, including Alzheimer’s disease dementia. Alzheimer’s disease is a progressive neurodegenerative disorder with largely idiopathic origins and no known cure. Here, we explore the growing body of literature which relates woodsmoke-generated and ambient air pollution particulate matter exposure to Alzheimer’s disease (AD) onset or exacerbation, in the context of an inflammation-centric view of AD. Epigenetic modifications, specifically changes in DNA methylation patterns, are well documented following woodsmoke exposure and have been shown to influence disease-favoring inflammatory cascades, induce oxidative stress, and modulate the immune response in vitro, in vivo, and in humans following exposure to air pollution. Though the current status of the literature does not allow us to draw definitive conclusions linking these events, this review highlights the need for additional work to fill gaps in our understanding of the directionality, causality, and susceptibility throughout the life course.

Plasma cell-free DNA methylation marks for episodic memory impairment: a pilot twin study

Decline in episodic memory performance usually causes the first clinical symptoms of Alzheimer’s disease. At present, Alzheimer’s disease can only be diagnosed at a very late stage when neurodegeneration and cognitive impairment is already irreversible. New early disease markers are needed for earlier and more efficient Alzheimer’s disease intervention. To identify early disease markers, we implemented a genome-wide bisulphite sequencing method for the analysis of plasma cell-free DNA methylation profiles and compared differences associated with episodic memory performance in Finnish twin pairs. A noticeable amount of cell-free DNA was present in plasma, however, the amounts as well as the genomic coverage of these fragments varied substantially between individuals. We found no significant markers associated with episodic memory performance in the twins’ plasma cell-free DNA methylation profiles. Furthermore, our results indicate that due to the low genomic coverage of cell-free DNA fragments and the variety in these fragments between individuals, the implemented genome-wide bisulphite sequencing method is not optimal for comparing cell-free DNA methylation differences between large groups of individuals.

Exploring Beyond the DNA Sequence: A Review of Epigenomic Studies of DNA and Histone Modifications in Dementia

Purpose of Review

Although genome-scale studies have identified many genetic variants associated with dementia, these do not account for all of disease incidence and so recently attention has turned to studying mechanisms of genome regulation. Epigenetic processes such as modifications to the DNA and histones alter transcriptional activity and have been hypothesized to be involved in the etiology of dementia. Here, we review the growing body of literature on dementia epigenomics, with a focus on novel discoveries, current limitations, and future directions for the field.

Recent Findings

It is through advances in genomic technology that large-scale quantification of epigenetic modifications is now possible in dementia. Most of the literature in the field has primarily focussed on exploring DNA modifications, namely DNA methylation, in postmortem brain samples from individuals with Alzheimer’s disease. However, recent studies have now begun to explore other epigenetic marks, such as histone modifications, investigating these signatures in both the brain and blood, and in a range of other dementias.

Summary

There is still a demand for more epigenomic studies to be conducted in the dementia field, particularly those assessing chromatin dynamics and a broader range of histone modifications. The field faces limitations in sample accessibility with many studies lacking power. Furthermore, the frequent use of heterogeneous bulk tissue containing multiple cell types further hinders data interpretation. Looking to the future, multi-omic studies, integrating many different epigenetic marks, with matched genetic, transcriptomic, and proteomic data, will be vital, particularly when undertaken in isolated cell populations, or ideally at the level of the single cell. Ultimately these studies could identify novel dysfunctional pathways and biomarkers for disease, which could lead to new therapeutic avenues.

Attempt to Predict A/T/N-Based Alzheimer's Disease Cerebrospinal Fluid Biomarkers Using a Peripheral Blood DNA Methylation Clock

Background:

Although aging is the strongest risk factor for the development of Alzheimer’s disease (AD), it remains uncertain if the blood DNA methylation clock, which reflects the effect of biological aging on DNA methylation (DNAme) status of blood cells, may be used as a surrogate biomarker for AD pathology in the central nervous system (CNS).

Objective:

We aimed to develop a practical model to predict for A/T/N-based AD biomarkers as the prediction targets using the aging acceleration of blood cells.

Methods:

We obtained data of North American ADNI study participants (n = 317) whose blood DNA methylation microarray (Illumina HumanMethylation EPIC Beadchips) and cerebrospinal fluid (CSF) AD biomarkers (Aβ, t-tau, and p-tau) were recorded simultaneously. Methylation clock was calculated to conduct machine learning, in order to predict binary statuses (+ or –) for A (corresponding to the lowered CSF Aβ), T (the elevated CSF p-tau), or N (the elevated CSF t-tau). The predictive performance of the models was evaluated by area under curve (AUC) in the test subset within ADNI.

Results:

Among the 317 included samples, 194 (61.2%) were A+, 247 (77.9%) were T+, and 104 (32.8%) were N+. The degree of blood aging acceleration showed weak positive correlation with the CSF Aβ levels, even after adjustment with APOE genotype and other covariates. However, the contribution of aging acceleration to improve the predictive performance of models was not significant for any of A+, T+, or N+.

Conclusion:

Our exploratory attempts could not demonstrate the substantial utility of the peripheral blood cells’ methylation clock as a predictor for A/T/N-based CSF biomarkers of AD, and further additional work should be conducted to determine whether the blood DNAme signatures including methylation clock have substantial utility in detecting underlying amyloid, tau or neurodegeneration pathology of AD.