Methylomic profiling implicates cortical deregulation of ANK1 in Alzheimer's disease

Decoding molecular mechanisms: brain aging and Alzheimer's disease

The complex morphological, anatomical, physiological, and chemical mechanisms within the aging brain have been the hot topic of research for centuries. The aging process alters the brain structure that affects functions and cognitions, but the worsening of such processes contributes to the pathogenesis of neurodegenerative disorders, such as Alzheimer's disease. Beyond these observable, mild morphological shifts, significant functional modifications in neurotransmission and neuronal activity critically influence the aging brain. Understanding these changes is important for maintaining cognitive health, especially given the increasing prevalence of age-related conditions that affect cognition. This review aims to explore the age-induced changes in brain plasticity and molecular processes, differentiating normal aging from the pathogenesis of Alzheimer's disease, thereby providing insights into predicting the risk of dementia, particularly Alzheimer's disease.

The late-onset Alzheimer's disease risk factor RHBDF2 is a modifier of microglial TREM2 proteolysis

The cell surface receptor TREM2 is a key genetic risk factor and drug target in Alzheimer's disease (AD). In the brain, TREM2 is expressed in microglia, where it undergoes proteolytic cleavage, linked to AD risk, but the responsible protease in microglia is still unknown. Another microglial-expressed AD risk factor is catalytically inactive rhomboid 2 (iRhom2, RHBDF2), which binds to and acts as a non-catalytic subunit of the metalloprotease ADAM17. A potential role in TREM2 proteolysis is not yet known. Using microglial-like BV2 cells, bone marrow-derived macrophages, and primary murine microglia, we identify iRhom2 as a modifier of ADAM17-mediated TREM2 shedding. Loss of iRhom2 increased TREM2 in cell lysates and at the cell surface and enhanced TREM2 signaling and microglial phagocytosis of the amyloid β-peptide (Aβ). This study establishes ADAM17 as a physiological TREM2 protease in microglia and suggests iRhom2 as a potential drug target for modulating TREM2 proteolysis in AD.

Evaluation of Illumina and Oxford Nanopore Sequencing for the Study of DNA Methylation in Alzheimer's Disease and Frontotemporal Dementia

DNA methylation is a critical epigenetic mechanism involved in numerous physiological processes. Alterations in DNA methylation patterns are associated with various brain disorders, including dementias such as Alzheimer's disease (AD) and frontotemporal dementia (FTD). Investigating these alterations is essential for understanding the pathogenesis and progression of these disorders. Among the various methods for detecting DNA methylation, DNA sequencing is one of the most widely employed. Specifically, two main sequencing approaches are commonly used for DNA methylation analysis: bisulfite sequencing and single-molecule long-read sequencing. In this review, we compared the performances of CpG methylation detection obtained using two popular sequencing platforms, Illumina for bisulfite sequencing and Oxford Nanopore (ON) for long-read sequencing. Our comparison considers several factors, including accuracy, efficiency, genomic regions, costs, wet-lab protocols, and bioinformatics pipelines. We provide insights into the strengths and limitations of both methods with a particular focus on their application in research on AD and FTD.

DNA methylation signature of a lifestyle-based resilience index for cognitive health

Cognitive resilience (CR) contributes to the variability in risk for developing and progressing in Alzheimer's disease (AD) among individuals. Beyond genetics, recent studies highlight the critical role of lifestyle factors in enhancing CR and delaying cognitive decline. DNA methylation (DNAm), an epigenetic mechanism influenced by both genetic and environmental factors, including CR-related lifestyle factors, offers a promising pathway for understanding the biology of CR. We studied DNAm changes associated with the Resilience Index (RI), a composite measure of lifestyle factors, using blood samples from the Healthy Brain Initiative (HBI) cohort. After corrections for multiple comparisons, our analysis identified 19 CpGs and 24 differentially methylated regions significantly associated with the RI, adjusting for covariates age, sex, APOE ε4, and immune cell composition. The RI-associated methylation changes are significantly enriched in pathways related to lipid metabolism, synaptic plasticity, and neuroinflammation, and highlight the connection between cardiovascular health and cognitive function. By identifying RI-associated DNAm, our study provided an alternative approach to discovering future targets and treatment strategies for AD, complementary to the traditional approach of identifying disease-associated variants directly. Furthermore, we developed a Methylation-based Resilience Score (MRS) that successfully predicted future cognitive decline in an external dataset from the Alzheimer's Disease Neuroimaging Initiative (ADNI), even after accounting for age, sex, APOE ε4, years of education, baseline diagnosis, and baseline MMSE score. Our findings are particularly relevant for a better understanding of epigenetic architecture underlying cognitive resilience. Importantly, the significant association between baseline MRS and future cognitive decline demonstrated that DNAm could be a predictive marker for AD, laying the foundation for future studies on personalized AD prevention.

Assessing DNA methylation detection for primary human tissue using Nanopore sequencing

DNA methylation most commonly occurs as 5-methylcytosine (5mC) in the human genome and has been associated with human diseases. Recent developments in single-molecule sequencing technologies (Oxford Nanopore Technologies [ONT] and Pacific Biosciences [PacBio]) have enabled readouts of long, native DNA molecules, including cytosine methylation. ONT recently upgraded their Nanopore sequencing chemistry and kits from the R9 to the R10 version, which yielded increased accuracy and sequencing throughput. However, the effects on methylation detection have not yet been documented. Here, we performed a series of computational analyses to characterize differences in Nanopore-based 5mC detection between the ONT R9 and R10 chemistries. We compared 5mC calls in R9 and R10 for three human genome data sets: a cell line, a frontal cortex brain sample, and a blood sample. We performed an in-depth analysis on CpG islands and homopolymer regions, and documented high concordance for methylation detection among sequencing technologies. The strongest correlation was observed between Nanopore R10 and Illumina bisulfite technologies for cell line-derived data sets. Subtle differences in methylation data sets between technologies can impact analysis tools such as differential methylation calling software. Our findings show that comparisons can be drawn between methylation data from different Nanopore chemistries using guided hypotheses. This work will facilitate comparison among Nanopore data cohorts derived using different chemistries from large-scale sequencing efforts, such as the NIH CARD Long Read Initiative.

Advancing Personalized Medicine in Alzheimer's Disease: Liquid Biopsy Epigenomics Unveil APOE ε4-Linked Methylation Signatures

Recent studies show that patients with Alzheimer's disease (AD) harbor specific methylation marks in the brain that, if accessible, could be used as epigenetic biomarkers. Liquid biopsy enables the study of circulating cell-free DNA (cfDNA) fragments originated from dead cells, including neurons affected by neurodegenerative processes. Here, we isolated and epigenetically characterized plasma cfDNA from 35 patients with AD and 35 cognitively healthy controls by using the Infinium® MethylationEPIC BeadChip array. Bioinformatics analysis was performed to identify differential methylation positions (DMPs) and regions (DMRs), including APOE ε4 genotype stratified analysis. Plasma pTau181 (Simoa) and cerebrospinal fluid (CSF) core biomarkers (Fujirebio) were also measured and correlated with differential methylation marks. Validation was performed with bisulfite pyrosequencing and bisulfite cloning sequencing. Epigenome-wide cfDNA analysis identified 102 DMPs associated with AD status. Most DMPs correlated with clinical cognitive and functional tests including 60% for Mini-Mental State Examination (MMSE) and 80% for Global Deterioration Scale (GDS), and with AD blood and CSF biomarkers. In silico functional analysis connected 30 DMPs to neurological processes, identifying key regulators such as SPTBN4 and APOE genes. Several DMRs were annotated to genes previously reported to harbor epigenetic brain changes in AD (HKR1, ZNF154, HOXA5, TRIM40, ATG16L2, ADAMST2) and were linked to APOE ε4 genotypes. Notably, a DMR in the HKR1 gene, previously shown to be hypermethylated in the AD hippocampus, was validated in cfDNA from an orthogonal perspective. These results support the feasibility of studying cfDNA to identify potential epigenetic biomarkers in AD. Thus, liquid biopsy could improve non-invasive AD diagnosis and aid personalized medicine by detecting epigenetic brain markers in blood.

Construction and evaluation of a diagnostic model for Alzheimer's disease based on mitophagy-related genes

Alzheimer's disease (AD) is the most common cause of dementia. Mitophagy fulfills crucial functions in neurodegenerative disorders and neuronal survival but the relationship between mitophagy and AD is unclear. Mitophagy correlation scores between AD samples and control samples were calculated using single-sample GSEA (ssGSEA) based on two datasets from gene expression omnibus (GEO) database. Mitophagy-related genes (MRGs) and differentially expressed genes (DEGs) in AD screened by WGCNA and "limma" package were intersected to take common genes. These overlapping genes were further compressed and used for diagnostic modeling by adopting the recursive feature elimination (RFE) and LASSO analysis. The reliability of the diagnostic model was verified based on the receiver operating characteristic (ROC) curve. Then, a transcription factor (TF)-mRNA regulatory network of these key genes was established. Lastly, ssGSEA was employed to examine the relationship between the identified genes and cellular pathways and immune cell infiltration. AD samples had notably lower mitophagy correlation scores than control samples. A total of 12 MRGs in the module with the greatest mitophagy connection with AD patients were identified. Functional enrichment analysis revealed that the DEGs were significantly enriched in synaptic function-related pathways. Based on GSE122063, a diagnostic prediction model was created and validated using two mitophagy-related genes (YWHAZ and NDE1), showing an area under ROC curve (AUC) greater than 0.7. This confirmed that the diagnostic model had a high predictive value. The TF-mRNA network showed that four TFs, namely, FOXC1, FOXL1, HOXA5 and GATA2, were regulated by both YWHAZ and NDE1 genes. Immune infiltration analysis revealed that NDE1 promoted the infiltration of most immune cells, while YWHAZ mainly inhibited the infiltration of most immune cells. The current findings improved our understanding of mitophagy in AD, contributing to future research and treatment development in AD.

Interrogating DNA methylation associated with Lewy body pathology in a cross brain-region and multi-cohort study

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 1239 samples across 855 unique donors, we identified a set of 30 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, inflammatory and vascular alterations. We leverage our summary statistics 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. Finally, utilizing summary statistics from previous large-scale genome-wide association studies we report FDR significant enrichment of DNA methylation differences with respect to increasing LB pathology in the SNCA genomic region, a gene previously associated with Parkinson's disease and dementia with Lewy bodies.

Multi-Ancestry Transcriptome-Wide Association Studies of Cognitive Function, White Matter Hyperintensity, and Alzheimer's Disease

Genetic variants increase the risk of neurocognitive disorders in later life, including vascular dementia (VaD) and Alzheimer's disease (AD), but the precise relationships between genetic risk factors and underlying disease etiologies are not well understood. Transcriptome-wide association studies (TWASs) can be leveraged to better characterize the genes and biological pathways underlying genetic influences on disease. To date, almost all existing TWASs on VaD and AD have been conducted using expression studies from individuals of a single genetic ancestry, primarily European. Using the joint likelihood-based inference framework in Multi-ancEstry TRanscriptOme-wide analysis (METRO), we leveraged gene expression data from European ancestry (EA) and African ancestry (AA) samples to identify genes associated with general cognitive function, white matter hyperintensity (WMH), and AD. Regions were fine-mapped using Fine-mapping Of CaUsal gene Sets (FOCUS). We identified 266, 23, 69, and 2 genes associated with general cognitive function, WMH, AD (using EA GWAS summary statistics), and AD (using AA GWAS), respectively (Bonferroni-corrected alpha = p < 2.9 × 10-6), some of which had been previously identified. Enrichment analysis showed that many of the identified genes were in pathways related to innate immunity, vascular dysfunction, and neuroinflammation. Further, the downregulation of ICA1L was associated with a higher WMH and with AD, indicating its potential contribution to overlapping AD and VaD neuropathology. To our knowledge, our study is the first TWAS on cognitive function and neurocognitive disorders that used expression mapping studies for multiple ancestries. This work may expand the benefits of TWASs beyond a single ancestry group and help to identify gene targets for pharmaceuticals or preventative treatments for dementia.

Epigenetic insights into neuropsychiatric and cognitive symptoms in Parkinson's disease: A DNA co-methylation network analysis

Parkinson's disease is a highly heterogeneous disorder, encompassing a complex spectrum of clinical presentation including motor, sleep, cognitive and neuropsychiatric symptoms. We aimed to investigate genome-wide DNA methylation networks in post-mortem Parkinson's disease brain samples and test for region-specific association with common neuropsychiatric and cognitive symptoms. Of traits tested, we identify a co-methylation module in the substantia nigra with significant correlation to depressive symptoms. Notably, expression of the genes annotated to the methylation loci present within this module are found to be significantly enriched in neuronal subtypes within the substantia nigra. These findings highlight the potential involvement of neuronal-specific changes within the substantia nigra with regards to depressive symptoms in Parkinson's disease.

Blood DNA methylation signature for incident dementia: Evidence from longitudinal cohorts

INTRODUCTION

Distinguishing between molecular changes that precede dementia onset and those resulting from the disease is challenging with cross-sectional studies.

METHODS

We studied blood DNA methylation (DNAm) differences and incident dementia in two large longitudinal cohorts: the Offspring cohort of the Framingham Heart Study (FHS) and the Alzheimer's Disease Neuroimaging Initiative (ADNI) study. We analyzed blood DNAm samples from > 1000 cognitively unimpaired subjects.

RESULTS

Meta-analysis identified 44 CpGs and 44 differentially methylated regions consistently associated with time to dementia in both cohorts. Our integrative analysis identified early processes in dementia, such as immune responses and metabolic dysfunction. Furthermore, we developed a methylation-based risk score, which successfully predicted future cognitive decline in an independent validation set, even after accounting for age, sex, apolipoprotein E ε4, years of education, baseline diagnosis, and baseline Mini-Mental State Examination score.

DISCUSSION

DNAm offers a promising source as a biomarker for dementia risk assessment.

HIGHLIGHTS

Blood DNA methylation (DNAm) differences at individual CpGs and differentially methylated regions are significantly associated with incident dementia. Pathway analysis revealed DNAm differences associated with incident dementia are significantly enriched in biological pathways involved in immune responses and metabolic processes. Out-of-sample validation analysis demonstrated that a methylation-based risk score successfully predicted future cognitive decline in an independent dataset, even after accounting for age, sex, apolipoprotein E ε4, years of education, baseline diagnosis, and baseline Mini-Mental State Examination score.

Integrative Epigenomic Landscape of Alzheimer's Disease Brains Reveals Oligodendrocyte Molecular Perturbations Associated with Tau

Alzheimer's disease (AD) brains are characterized by neuropathologic and biochemical changes that are highly variable across individuals. Capturing epigenetic factors that associate with this variability can reveal novel biological insights into AD pathophysiology. We conducted an epigenome-wide association study of DNA methylation (DNAm) in 472 AD brains with neuropathologic measures (Braak stage, Thal phase, and cerebral amyloid angiopathy score) and brain biochemical levels of five proteins (APOE, amyloid-β (Aβ)40, Aβ42, tau, and p-tau) core to AD pathogenesis. Using a novel regional methylation (rCpGm) approach, we identified 5,478 significant associations, 99.7% of which were with brain tau biochemical measures. Of the tau-associated rCpGms, 93 had concordant associations in external datasets comprising 1,337 brain samples. Integrative transcriptome-methylome analyses uncovered 535 significant gene expression associations for these 93 rCpGms. Genes with concurrent transcriptome-methylome perturbations were enriched in oligodendrocyte marker genes, including known AD risk genes such as BIN1 , myelination genes MYRF, MBP and MAG previously implicated in AD, as well as novel genes like LDB3 . We further annotated the top oligodendrocyte genes in an additional 6 brain single cell and 2 bulk transcriptome datasets from AD and two other tauopathies, Pick's disease and progressive supranuclear palsy (PSP). Our findings support consistent rCpGm and gene expression associations with these tauopathies and tau-related phenotypes in both bulk brain tissue and oligodendrocyte clusters. In summary, we uncover the integrative epigenomic landscape of AD and demonstrate tau-related oligodendrocyte gene perturbations as a common potential pathomechanism across different tauopathies.

A doubly robust estimator for continuous treatments in high dimensions

BACKGROUND

Generalized propensity score (GPS) methods have become popular for estimating causal relationships between a continuous treatment and an outcome in observational studies with rich covariate information. The presence of rich covariates enhances the plausibility of the unconfoundedness assumption. Nonetheless, it is also crucial to ensure the correct specification of both marginal and conditional treatment distributions, beyond the assumption of unconfoundedness.

METHOD

We address limitations in existing GPS methods by extending balance-based approaches to high dimensions and introducing the Generalized Outcome-Adaptive LASSO and Doubly Robust Estimate (GOALDeR). This novel approach integrates a balance-based method that is robust to the misspecification of distributions required for GPS methods, a doubly robust estimator that is robust to the misspecification of models, and a variable selection technique for causal inference that ensures an unbiased and statistically efficient estimation.

RESULTS

Simulation studies showed that GOALDeR was able to generate nearly unbiased estimates when either the GPS model or the outcome model was correctly specified. Notably, GOALDeR demonstrated greater precision and accuracy compared to existing methods and was slightly affected by the covariate correlation structure and ratio of sample size to covariate dimension. Real data analysis revealed no statistically significant dose-response relationship between epigenetic age acceleration and Alzheimer's disease.

CONCLUSION

In this study, we proposed GOALDeR as an advanced GPS method for causal inference in high dimensions, and empirically demonstrated that GOALDeR is doubly robust, with improved accuracy and precision compared to existing methods. The R package is available at https://github.com/QianGao-SXMU/GOALDeR .

Unveiling the Therapeutic Promise of Epigenetics in Vascular Cognitive Impairment and Vascular Dementia

Vascular dementia (VaD) is a progressive neurodegenerative disease characterized by cognitive decline and memory deficits. Despite its significant prevalence and impact, the pathophysiology of VaD remains poorly understood, and current treatments are limited to symptom management. Emerging evidence highlights the importance of lifestyle-associated risk factors in VaD, emphasizing the role of gene-environment interactions, particularly in the realm of epigenetics. While preclinical studies using animal models have provided valuable insights into epigenetic mechanisms, the translatability of these findings to human clinical settings remains limited, and research into VaD-specific epigenetics is still in its infancy. This review aims to elucidate the intricate interplay between epigenetics and VaD, shedding light on potential therapeutic interventions rooted in epigenetic mechanisms. By synthesizing insights from existing literature, we also discuss the challenges and opportunities in translating preclinical findings into clinically viable treatments, underscoring the need for further research to bridge the gap between animal models and human applications.

Transcriptome analysis of early- and late-onset Alzheimer's disease in Korean cohorts

INTRODUCTION

The molecular mechanisms underlying early-onset Alzheimer's disease (EOAD) and late-onset Alzheimer's disease (LOAD) remain incompletely understood, particularly in Asian populations.

METHODS

RNA-sequencing was carried out on blood samples from 248 participants in the Seoul National University Bundang Hospital cohort to perform differential gene expression (DGE) and weighted gene co-expression network analysis. Findings were replicated in an independent Korean cohort (N = 275).

RESULTS

DGE analysis identified 18 and 88 dysregulated genes in EOAD and LOAD, respectively. Network analysis identified a LOAD-associated module showing a significant enrichment in pathways related to mitophagy, 5' adenosine monophosphate-activated protein kinase signaling, and ubiquitin-mediated proteolysis. In the replication cohort, downregulation of SMOX and PLVAP in LOAD was replicated, and the LOAD-associated module was highly preserved. In addition, SMOX and PLVAP were associated with brain amyloid beta deposition.

DISCUSSION

Our findings suggest distinct molecular signatures for EOAD and LOAD in a Korean population, providing deeper understanding of their diagnostic potential and molecular mechanisms.

HIGHLIGHTS

Analysis identified 18 and 88 dysregulated genes in early-onset Alzheimer's disease (EOAD) and late-onset Alzheimer's disease (LOAD), respectively. Expression levels of SMOX and PLVAP were downregulated in LOAD. Expression levels of SMOX and PLVAP were associated with brain amyloid beta deposition. Pathways including mitophagy and 5' adenosine monophosphate-activated protein kinase signaling were enriched in a LOAD module. A LOAD module was highly preserved across two independent cohorts.

A brain DNA co-methylation network analysis of psychosis in Alzheimer's disease

INTRODUCTION

The presence of psychosis in Alzheimer's disease (AD) is suggested to be associated with distinct molecular and neuropathological profiles in the brain.

METHODS

We assessed brain DNA methylation in AD donors with psychosis (AD+P) and without psychosis (AD-P) using the EPIC array. Weighted gene correlation network analysis identified modules of co-methylated genes in a discovery cohort (PITT-ADRC: N = 113 AD+P, N = 40 AD-P), with validation in an independent cohort (BDR: N = 79 AD+P, N = 117 AD-P), with Gene Ontology and cell-type enrichment analysis. Genetic data were integrated to identify methylation quantitative trait loci (mQTLs), which were co-localized with GWAS for related traits.

RESULTS

We replicated one AD+P associated module, which was enriched for synaptic pathways and in excitatory and inhibitory neurons. mQTLs in this module co-localized with variants associated with schizophrenia and educational attainment.

DISCUSSION

This represents the largest epigenetic study of AD+P to date, identifying pleiotropic relationships between AD+P and related traits.

HIGHLIGHTS

DNA methylation was assessed in the prefrontal cortex in subjects with AD+P and AD-P. WGCNA identified six modules of co-methylated loci associated with AD+P in a discovery cohort. One of the modules was replicated in an independent cohort. This module was enriched for synaptic genes and in excitatory and inhibitory neurons. mQTLs mapping to genes in the module co-localized with GWAS loci for schizophrenia and educational attainment.

EpiAge: a next-generation sequencing-based ELOVL2 epigenetic clock for biological age assessment in saliva and blood across health and disease

This study introduces EpiAgePublic, a new method to estimate biological age using only three specific sites on the gene ELOVL2, known for its connection to aging. Unlike traditional methods that require complex and extensive data, our model uses a simpler approach that is well-suited for next-generation sequencing technology, which is a more advanced method of analyzing DNA methylation. This new model overcomes some of the common challenges found in older methods, such as errors due to sample quality and processing variations. We tested EpiAgePublic with a large and varied group of over 4,600 people to ensure its accuracy. It performed on par with, and sometimes better than, more complicated models that use much more data for age estimation. We examined its effectiveness in understanding how factors like HIV infection and stress affect aging, confirming its usefulness in real-world clinical settings. Our results prove that our simple yet effective model, EpiAgePublic, can capture the subtle signs of aging with high accuracy. We also used this model in a study involving patients with Alzheimer's Disease, demonstrating the practical benefits of next-generation sequencing in making precise age-related assessments. This study lays the groundwork for future research on aging mechanisms and assessing how different interventions might impact the aging process using this clock.

The Promise of Epigenetic Editing for Treating Brain Disorders

Brain disorders, especially neurodegenerative diseases, affect millions of people worldwide. There is no causal treatment available; therefore, there is an unmet clinical need for finding therapeutic options for these diseases. Epigenetic research has resulted in identification of various genomic loci with differential disease-specific epigenetic modifications, mainly DNA methylation. These biomarkers, although not yet translated into clinically approved options, offer therapeutic targets as epigenetic modifications are reversible. Indeed, clinical trials are designed to inhibit epigenetic writers, erasers, or readers using epigenetic drugs to interfere with epigenetic dysregulation in brain disorders. However, since such drugs elicit genome-wide effects and potentially cause toxicity, the recent developments in the field of epigenetic editing are gaining widespread attention. In this review, we provide examples of epigenetic biomarkers and epi-drugs, while describing efforts in the field of epigenetic editing, to eventually make a difference for the currently incurable brain disorders.

DNA Methylation in Alzheimer's Disease

To date, DNA methylation is the best characterized epigenetic modification in Alzheimer's disease. Involving the addition of a methyl group to the fifth carbon of the cytosine pyrimidine base, DNA methylation is generally thought to be associated with the silencing of gene expression. It has been hypothesized that epigenetics may mediate the interaction between genes and the environment in the manifestation of Alzheimer's disease, and therefore studies investigating DNA methylation could elucidate novel disease mechanisms. This chapter comprehensively reviews epigenomic studies, undertaken in human brain tissue and purified brain cell types, focusing on global methylation levels, candidate genes, epigenome wide approaches, and recent meta-analyses. We discuss key differentially methylated genes and pathways that have been highlighted to date, with a discussion on how new technologies and the integration of multiomic data may further advance the field.

Epigenetics in Neurodegenerative Diseases

Healthy brain functioning requires a continuous fine-tuning of gene expression, involving changes in the epigenetic landscape and 3D chromatin organization. Alzheimer's disease (AD), amyotrophic lateral sclerosis (ALS), and frontotemporal dementia (FTD) are three multifactorial neurodegenerative diseases (NDDs) that are partially explained by genetics (gene mutations and genetic risk factors) and influenced by non-genetic factors (i.e., aging, lifestyle, and environmental conditions). Examining comprehensive studies of global and locus-specific (epi)genomic and transcriptomic alterations in human and mouse brain samples at the cell-type resolution has uncovered important phenomena associated with AD. First, DNA methylation and histone marks at promoters contribute to transcriptional dysregulation of genes that are directly implicated in AD pathogenesis (i.e., APP), neuroplasticity and cognition (i.e., PSD95), and microglial activation (i.e., TREM2). Second, the presence of AD genetic risk variants in cell-type-specific distal enhancers (i.e., BIN1 in microglia) alters transcription, presumably by disrupting associated enhancer-promoter interactions and chromatin looping. Third, epigenomic erosion is associated with widespread transcriptional disruption and cell identity loss. And fourth, aging, high cholesterol, air pollution, and pesticides have emerged as potential drivers of AD by inducing locus-specific and global epigenetic modifications that impact key AD-related pathways. Epigenetic studies in ALS/FTD also provide evidence that genetic and non-genetic factors alter gene expression profiles in neurons and astrocytes through aberrant epigenetic mechanisms. We additionally overview the recent development of potential new therapeutic strategies involving (epi)genetic editing and the use of small chromatin-modifying molecules (epidrugs).

DNA methylation in peripheral blood leukocytes in late onset Alzheimer's disease

Background

Chronic systemic inflammation is implicated in Alzheimer's disease (AD) pathogenesis and has measurable effects on blood cells. There is increasing interest in non-invasive diagnostic tools that use blood-based biomarkers for AD, such as DNA methylation. Notably, DNA methylation changes in blood are also linked to systemic inflammation. The evaluation of DNA methylation profiles in peripheral blood leukocytes as potential biomarkers for AD is promising.

Objective

To determine DNA methylation patterns in blood for AD, and to explore specific blood CpG sites that act as surrogates for brain-tissue methylation.

Methods

DNA methylation data from peripheral blood leukocytes of AD patients and controls were obtained from the Gene Expression Omnibus (GSE59685 and GSE53740). Differential methylation analysis was performed for individual CpGs Differentially methylated positions (DMPs) and regions with multiple probes (DMRs) and the intersection analysis of DMPs and DMRs was conducted. Functional enrichment analysis highlights relevant biological processes. Furthermore, previously validated specific CpGs used as surrogate of brain tissue were explored.

Results

DNA methylation patterns included BTBD3, PGPEP1L, DUSP29, and MIB2 top genes ordered by statistical significance were found in the intersection of DMP and DMR. Differential methylation analyses revealed differentially methylated genes including HOXA-AS3, HOXA6, CACNA1A, KMT5A, MIDEAS, FAM234A, and KATNBL1P6. Gene enrichment analysis showed immune processes and intracellular signaling disruptions. Surrogate genes from brain found differentially methylated were PCDHGB1-3 and PCDHGA1-6.

Conclusions

This study identified DNA methylation patterns in peripheral blood leukocytes as potential biomarkers for AD. These findings offer insights into epigenetic mechanisms associated with systemic peripheral inflammation in AD.

Epigenetic Clocks: Beyond Biological Age, Using the Past to Predict the Present and Future

Predicting health trajectories and accurately measuring aging processes across the human lifespan remain profound scientific challenges. Assessing the effectiveness and impact of interventions targeting aging is even more elusive, largely due to the intricate, multidimensional nature of aging-a process that defies simple quantification. Traditional biomarkers offer only partial perspectives, capturing limited aspects of the aging landscape. Yet, over the past decade, groundbreaking advancements have emerged. Epigenetic clocks, derived from DNA methylation patterns, have established themselves as powerful aging biomarkers, capable of estimating biological age and assessing aging rates across diverse tissues with remarkable precision. These clocks provide predictive insights into mortality and age-related disease risks, effectively distinguishing biological age from chronological age and illuminating enduring questions in gerontology. Despite significant progress in epigenetic clock development, substantial challenges remain, underscoring the need for continued investigation to fully unlock their potential in the science of aging.

Long-read sequencing of hundreds of diverse brains provides insight into the impact of structural variation on gene expression and DNA methylation

Structural variants (SVs) drive gene expression in the human brain and are causative of many neurological conditions. However, most existing genetic studies have been based on short-read sequencing methods, which capture fewer than half of the SVs present in any one individual. Long-read sequencing (LRS) enhances our ability to detect disease-associated and functionally relevant structural variants (SVs); however, its application in large-scale genomic studies has been limited by challenges in sample preparation and high costs. Here, we leverage a new scalable wet-lab protocol and computational pipeline for whole-genome Oxford Nanopore Technologies sequencing and apply it to neurologically normal control samples from the North American Brain Expression Consortium (NABEC) (European ancestry) and Human Brain Collection Core (HBCC) (African or African admixed ancestry) cohorts. Through this work, we present a publicly available long-read resource from 351 human brain samples (median N50: 27 Kbp and at an average depth of ~40x genome coverage). We discover approximately 234,905 SVs and produce locally phased assemblies that cover 95% of all protein-coding genes in GRCh38. Utilizing matched expression datasets for these samples, we apply quantitative trait locus (QTL) analyses and identify SVs that impact gene expression in post-mortem frontal cortex brain tissue. Further, we determine haplotype-specific methylation signatures at millions of CpGs and, with this data, identify cis-acting SVs. In summary, these results highlight that large-scale LRS can identify complex regulatory mechanisms in the brain that were inaccessible using previous approaches. We believe this new resource provides a critical step toward understanding the biological effects of genetic variation in the human brain.

Graph Convolutional Network for AD and MCI Diagnosis Utilizing Peripheral DNA Methylation: Réseau de neurones en graphes pour le diagnostic de la MA et du TCL à l'aide de la méthylation de l'ADN périphérique

OBJECTIVE

Blood DNA methylation (DNAm) alterations have been widely reported in the onset and progression of mild cognitive impairment (MCI) and Alzheimer's disease (AD); however, DNAm is underutilized as a diagnostic biomarker for these diseases. We aimed to evaluate the diagnostic performance of DNAm for MCI and AD, both individually and in combination with well-established AD biosignatures.

METHODS

A total of 1,891 blood samples from Alzheimer's Disease Neuroimaging Initiative (ADNI) studies were used to identify potential candidate DNAm biomarkers. Multimodal clinical data from 635 samples (normal control (NC), n = 193; MCI, n = 352; AD, n = 90) in the TADPOLE dataset were utilized to construct eight different classification models using a graph convolutional network, a machine learning framework.

RESULTS

After feature selection, 17 DNAm sites were selected for subsequent analysis. Remarkable differences in DNAm levels were observed at the screened DNAm loci in all three cohorts. Adopting DNAm features into multimodal models significantly improved the classification performance for three dichotomous subtasks (NC vs. non-NC, MCI vs. non-MCI, and AD vs. non-AD), especially when combined with cerebrospinal fluid (CSF) features for NC (area under the curve (AUC): 0.8534) and MCI classification (AUC: 0.7675). A weak correlation between DNAm and both magnetic resonance imaging and CSF features in the NC and MCI cohorts suggests good complementarity between modalities (correlation coefficient ≤0.2).

CONCLUSIONS

Our study offers new insights into peripheral DNAm in MCI and AD and suggests promising diagnostic performance of models integrating epigenomics, imaging, or CSF biomarkers.

PLAIN LANGUAGE SUMMARY TITLE

Using Machine Learning and Blood-Based Genetic Markers to Help Diagnose Mild Cognitive Impairment and Alzheimer's Disease.

Epigenetic biomarkers in Alzheimer's disease: Diagnostic and prognostic relevance

Alzheimer's disease (AD) is a leading cause of cognitive decline in the aging population, presenting a critical need for early diagnosis and effective prognostic tools. Epigenetic modifications, including DNA methylation, histone modifications, and non-coding RNAs, have emerged as promising biomarkers for AD due to their roles in regulating gene expression and potential for reversibility. This review examines the current landscape of epigenetic biomarkers in AD, emphasizing their diagnostic and prognostic relevance. DNA methylation patterns in genes such as APP, PSEN1, and PSEN2 are highlighted for their strong associations with AD pathology. Alterations in DNA methylation at specific CpG sites have been consistently observed in AD patients, suggesting their utility in early detection. Histone modifications, such as acetylation and methylation, also play a crucial role in chromatin remodelling and gene expression regulation in AD. Dysregulated histone acetylation and methylation have been linked to AD progression, making these modifications valuable biomarkers. Non-coding RNAs, including microRNAs (miRNAs) and long non-coding RNAs (lncRNAs), further contribute to the epigenetic regulation in AD. miRNAs can modulate gene expression post-transcriptionally and have been found in altered levels in AD, while lncRNAs can influence chromatin structure and gene expression. The presence of these non-coding RNAs in biofluids like blood and cerebrospinal fluid positions them as accessible and minimally invasive biomarkers. Technological advancements in detecting and quantifying epigenetic modifications have propelled the field forward. Techniques such as next-generation sequencing, bisulfite sequencing, and chromatin immunoprecipitation assays offer high sensitivity and specificity, enabling the detailed analysis of epigenetic changes in clinical samples. These tools are instrumental in translating epigenetic research into clinical practice. This review underscores the potential of epigenetic biomarkers to enhance the early diagnosis and prognosis of AD, paving the way for personalized therapeutic strategies and improved patient outcomes. The integration of these biomarkers into clinical workflows promises to revolutionize AD management, offering hope for better disease monitoring and intervention.

Critical evaluation of the reliability of DNA methylation probes on the Illumina MethylationEPIC v1.0 BeadChip microarrays

DNA methylation (DNAm) plays a crucial role in a number of complex diseases. However, the reliability of DNAm levels measured using Illumina arrays varies across different probes. Previous research primarily assessed probe reliability by comparing duplicate samples between the 450k-450k or 450k-EPIC platforms, with limited investigations on Illumina EPIC v1.0 arrays. We conducted a comprehensive assessment of the EPIC v1.0 array probe reliability using 69 blood DNA samples, each measured twice, generated by the Alzheimer's Disease Neuroimaging Initiative study. We observed higher reliability in probes with average methylation beta values of 0.2 to 0.8, and lower reliability in type I probes or those within the promoter and CpG island regions. Importantly, we found that probe reliability has significant implications in the analyses of Epigenome-wide Association Studies (EWAS). Higher reliability is associated with more consistent effect sizes in different studies, the identification of differentially methylated regions (DMRs) and methylation quantitative trait locus (mQTLs), and significant correlations with downstream gene expression. Moreover, blood DNAm measurements obtained from probes with higher reliability are more likely to show concordance with brain DNAm measurements. Our findings, which provide crucial reliability information for probes on the EPIC v1.0 array, will serve as a valuable resource for future DNAm studies.

DNA Methylation Signature of a Lifestyle-based Resilience Index for Cognitive Health

Cognitive resilience (CR) contributes to the variability in risk for developing and progressing in Alzheimer's disease (AD) among individuals. Beyond genetics, recent studies highlight the critical role of lifestyle factors in enhancing CR and delaying cognitive decline. DNA methylation (DNAm), an epigenetic mechanism influenced by both genetic and environmental factors, including CR-related lifestyle factors, offers a promising pathway for understanding the biology of CR. We studied DNAm changes associated with the Resilience Index (RI), a composite measure of lifestyle factors, using blood samples from the Healthy Brain Initiative (HBI) cohort. After corrections for multiple comparisons, our analysis identified 19 CpGs and 24 differentially methylated regions significantly associated with the RI, adjusting for covariates age, sex, APOE ε4, and immune cell composition. The RI-associated methylation changes are significantly enriched in pathways related to lipid metabolism, synaptic plasticity, and neuroinflammation, and highlight the connection between cardiovascular health and cognitive function. By identifying RI-associated DNAm, our study provided an alternative approach to discovering future targets and treatment strategies for AD, complementary to the traditional approach of identifying disease-associated variants directly. Furthermore, we developed a Methylation-based Resilience Score (MRS) that successfully predicted future cognitive decline in an external dataset from the Alzheimer's Disease Neuroimaging Initiative (ADNI), even after accounting for age, sex, APOE ε4, years of education, baseline diagnosis, and baseline MMSE score. Our findings are particularly relevant for a better understanding of epigenetic architecture underlying cognitive resilience. Importantly, the significant association between baseline MRS and future cognitive decline demonstrated that DNAm could be a predictive marker for AD, laying the foundation for future studies on personalized AD prevention.

Blood DNA Methylation Signature for Incident Dementia: Evidence from Longitudinal Cohorts

INTRODUCTION

Distinguishing between molecular changes that precede dementia onset and those resulting from the disease is challenging with cross-sectional studies.

METHODS

We studied blood DNA methylation (DNAm) differences and incident dementia in two large longitudinal cohorts: the Offspring cohort of the Framingham Heart Study (FHS) and the Alzheimer's Disease Neuroimaging Initiative (ADNI) study. We analyzed blood DNAm samples from over 1,000 cognitively unimpaired subjects.

RESULTS

Meta-analysis identified 44 CpGs and 44 differentially methylated regions consistently associated with time to dementia in both cohorts. Our integrative analysis identified early processes in dementia, such as immune responses and metabolic dysfunction. Furthermore, we developed a Methylation-based Risk Score, which successfully predicted future cognitive decline in an independent validation set, even after accounting for age, sex, APOE ε4, years of education, baseline diagnosis, and baseline MMSE score.

DISCUSSION

DNA methylation offers a promising source of biomarker for early detection of dementia.

Nutrigenomics and neurological disorders: exploring diet-brain interactions for cognitive health

This review article investigates the intricate relationship between nutrigenomics and neurological disorders, highlighting how genetic variations affect an individual's response to nutrients. The study delves into the role of diet-related oxidative stress and the gut-brain axis in the progression and management of brain disorders such as Parkinson's disease, Alzheimer's disease, epilepsy, stroke, migraines, and depression. The review encompasses various clinical trials and introduces new trends and techniques, including omics and artificial intelligence, in identifying and managing neurological disorders. The main findings emphasize that personalized diet recommendations, tailored to an individual's genetic makeup, can significantly improve cognitive health and manage neurological conditions. The study concludes that further research in the field of nutrigenomics is essential to advancing personalized nutrition strategies for better neurological functioning, ultimately linking diet, genes, and brain health.

Unveiling DNA methylation in Alzheimer's disease: a review of array-based human brain studies

The intricacies of Alzheimer's disease pathogenesis are being increasingly illuminated by the exploration of epigenetic mechanisms, particularly DNA methylation. This review comprehensively surveys recent human-centered studies that investigate whole genome DNA methylation in Alzheimer's disease neuropathology. The examination of various brain regions reveals distinctive DNA methylation patterns that associate with the Braak stage and Alzheimer's disease progression. The entorhinal cortex emerges as a focal point due to its early histological alterations and subsequent impact on downstream regions like the hippocampus. Notably, ANK1 hypermethylation, a protein implicated in neurofibrillary tangle formation, was recurrently identified in the entorhinal cortex. Further, the middle temporal gyrus and prefrontal cortex were shown to exhibit significant hypermethylation of genes like HOXA3, RHBDF2, and MCF2L, potentially influencing neuroinflammatory processes. The complex role of BIN1 in late-onset Alzheimer's disease is underscored by its association with altered methylation patterns. Despite the disparities across studies, these findings highlight the intricate interplay between epigenetic modifications and Alzheimer's disease pathology. Future research efforts should address methodological variations, incorporate diverse cohorts, and consider environmental factors to unravel the nuanced epigenetic landscape underlying Alzheimer's disease progression.

Can salivary and skin microbiome become a biodetector for aging-associated diseases? Current insights and future perspectives

Growth and aging are fundamental elements of human development. Aging is defined by a decrease in physiological activities and higher illness vulnerability. Affected by lifestyle, environmental, and hereditary elements, aging results in disorders including cardiovascular, musculoskeletal, and neurological diseases, which accounted for 16.1 million worldwide deaths in 2019. Stress-induced cellular senescence, caused by DNA damage, can reduce tissue regeneration and repair, promoting aging. The root cause of many age-related disorders is inflammation, encouraged by the senescence-associated secretory phenotype (SASP). Aging's metabolic changes and declining immune systems raise illness risk via promoting microbiome diversity. Stable, individual-specific skin and oral microbiomes are essential for both health and disease since dysbiosis is linked with periodontitis and eczema. Present from birth to death, the human microbiome, under the influence of diet and lifestyle, interacts symbiotically with the body. Poor dental health has been linked to Alzheimer's and Parkinson's diseases since oral microorganisms and systemic diseases have important interactions. Emphasizing the importance of microbiome health across the lifetime, this study reviews the understanding of the microbiome's role in aging-related diseases that can direct novel diagnosis and treatment approaches.

An Effective DNA Methylation Biomarker Screening Mechanism for Amyotrophic Lateral Sclerosis (ALS) Based on Comorbidities and Gene Function Analysis

This study used epigenomic methylation differential expression analysis to identify primary biomarkers in patients with amyotrophic lateral sclerosis (ALS). We combined electronic medical record datasets from MIMIC-IV (United States) and NHIRD (Taiwan) to explore ALS comorbidities in depth and discover any comorbidity-related biomarkers. We also applied word2vec to these two clinical diagnostic medical databases to measure similarities between ALS and other similar diseases and evaluated the statistical assessment of the odds ratio to discover significant comorbidities for ALS subjects. Important and representative DNA methylation biomarker candidates could be effectively selected by cross-comparing similar diseases to ALS, comorbidity-related genes, and differentially expressed methylation loci for ALS subjects. The screened epigenomic and comorbidity-related biomarkers were clustered based on their genetic functions. The candidate DNA methylation biomarkers associated with ALS were comprehensively discovered. Gene ontology annotations were then applied to analyze and cluster the candidate biomarkers into three different groups based on gene function annotations. The results showed that a potential testing kit for ALS detection can be composed of SOD3, CACNA1H, and ERBB4 for effective early screening of ALS using blood samples. By developing an effective DNA methylation biomarker screening mechanism, early detection and prophylactic treatment of high-risk ALS patients can be achieved.

Scoping review of epigenetics on neurodegenerative diseases: research frontiers and publication status

Aims

Epigenetics has significantly evolved and emerged as important players in the pathogenesis of neurodegenerative diseases. However, a scientometric synthesis of such changes over time is currently lacking.

Methods

We conducted a comprehensive search of the Web of Science Core Collection from inception until November 5, 2022, using appropriate keywords. Our primary objective was to employ scientometric analysis to depict changes in keywords over time and to assess the structure and credibility of clusters. Additionally, we examined the network of research (countries, institutions, and authors) using CiteSpace and VOSviewer.

Results

We identified 25 clusters with well-structured networks (Q = 0.82) and highly credible clustering (S = 0.91) from 16,181 articles published between 1999 and 2022. Our findings are as follows: (a) the literature and research interest concerning the epigenetics of neurodegenerative diseases are continuously growing; (b) the three most productive countries are the USA, China, and Germany; (c) international collaborative relationships exist, alongside small, isolated collaboration networks of individual institutions.

Conclusion

The number and impact of global publications on the epigenetics of neurodegenerative diseases have expanded rapidly over the past 20 years. This review provides valuable guidelines for researchers interested in neurodegenerative diseases research.

Blood DNA methylomic signatures associated with CSF biomarkers of Alzheimer's disease in the EMIF-AD study

INTRODUCTION

We investigated blood DNA methylation patterns associated with 15 well-established cerebrospinal fluid (CSF) biomarkers of Alzheimer's disease (AD) pathophysiology, neuroinflammation, and neurodegeneration.

METHODS

We assessed DNA methylation in 885 blood samples from the European Medical Information Framework for Alzheimer's Disease (EMIF-AD) study using the EPIC array.

RESULTS

We identified Bonferroni-significant differential methylation associated with CSF YKL-40 (five loci) and neurofilament light chain (NfL; seven loci) levels, with two of the loci associated with CSF YKL-40 levels correlating with plasma YKL-40 levels. A co-localization analysis showed shared genetic variants underlying YKL-40 DNA methylation and CSF protein levels, with evidence that DNA methylation mediates the association between genotype and protein levels. Weighted gene correlation network analysis identified two modules of co-methylated loci correlated with several amyloid measures and enriched in pathways associated with lipoproteins and development.

DISCUSSION

We conducted the most comprehensive epigenome-wide association study (EWAS) of AD-relevant CSF biomarkers to date. Future work should explore the relationship between YKL-40 genotype, DNA methylation, and protein levels in the brain.

HIGHLIGHTS

Blood DNA methylation was assessed in the EMIF-AD MBD study. Epigenome-wide association studies (EWASs) were performed for 15 Alzheimer's disease (AD)-relevant cerebrospinal fluid (CSF) biomarker measures. Five Bonferroni-significant loci were associated with YKL-40 levels and seven with neurofilament light chain (NfL). DNA methylation in YKL-40 co-localized with previously reported genetic variation. DNA methylation potentially mediates the effect of single-nucleotide polymorphisms (SNPs) in YKL-40 on CSF protein levels.

Interplay of education and DNA methylation age on cognitive impairment: insights from the Health and Retirement Study

Few studies have assessed the association of educational attainment on dementia and cognitive impairment through DNA methylation age acceleration, while accommodating exposure-mediator interaction effects. We evaluated the mediation role of six epigenetic clocks with dementia, cognitive impairment non-dementia, and normal cognition, while accommodating exposure-mediator interaction effects. To understand the joint association of low education (≤12 years) and DNA methylation age acceleration (yes/no) in relation to cognitive impairment, we used weighted logistic regression, adjusting for chronological age, sex, race/ethnicity, and cell type composition. We performed four-way mediation and interaction decomposition analysis. Analyses were conducted on 2016 venous blood study participants from the Health and Retirement Study (N = 3724). Both GrimAge acceleration (OR = 1.6 95%CI 1.3-2.1) and low educational attainment (OR = 2.4 95%CI 1.9-3.0) were associated with higher odds of cognitive impairment in a mutually adjusted logistic model. We found additive interaction associations between low education and GrimAge acceleration on dementia. We observed that 6-8% of the association of education on dementia was mediated through GrimAge acceleration. While mediation effects were small, the portion of the association of education on dementia due to additive interaction with GrimAge acceleration was between 23.6 and 29.2%. These results support the interplay of social disadvantage and biological aging processes on impaired cognition.

Development of an epigenetic clock resistant to changes in immune cell composition

Epigenetic clocks are age predictors that use machine-learning models trained on DNA CpG methylation values to predict chronological or biological age. Increases in predicted epigenetic age relative to chronological age (epigenetic age acceleration) are connected to aging-associated pathologies, and changes in epigenetic age are linked to canonical aging hallmarks. However, epigenetic clocks rely on training data from bulk tissues whose cellular composition changes with age. Here, we found that human naive CD8+ T cells, which decrease in frequency during aging, exhibit an epigenetic age 15-20 years younger than effector memory CD8+ T cells from the same individual. Importantly, homogenous naive T cells isolated from individuals of different ages show a progressive increase in epigenetic age, indicating that current epigenetic clocks measure two independent variables, aging and immune cell composition. To isolate the age-associated cell intrinsic changes, we created an epigenetic clock, the IntrinClock, that did not change among 10 immune cell types tested. IntrinClock shows a robust predicted epigenetic age increase in a model of replicative senescence in vitro and age reversal during OSKM-mediated reprogramming.

MIAMI-AD (Methylation in Aging and Methylation in AD): an integrative knowledgebase that facilitates explorations of DNA methylation across sex, aging, and Alzheimer's disease

Alzheimer's disease (AD) is a common neurodegenerative disorder with a significant impact on aging populations. DNA methylation (DNAm) alterations have been implicated in both the aging processes and the development of AD. Given that AD affects more women than men, it is also important to explore DNAm changes that occur specifically in each sex. We created MIAMI-AD, a comprehensive knowledgebase containing manually curated summary statistics from 98 published tables in 38 studies, all of which included at least 100 participants. MIAMI-AD enables easy browsing, querying, and downloading DNAm associations at multiple levels-at individual CpG, gene, genomic regions, or genome-wide, in one or multiple studies. Moreover, it also offers tools to perform integrative analyses, such as comparing DNAm associations across different phenotypes or tissues, as well as interactive visualizations. Using several use case examples, we demonstrated that MIAMI-AD facilitates our understanding of age-associated CpGs in AD and the sex-specific roles of DNAm in AD. This open-access resource is freely available to the research community, and all the underlying data can be downloaded. MIAMI-AD facilitates integrative explorations to better understand the interplay between DNAm across aging, sex, and AD. Database URL: https://miami-ad.org/.

Insulin resistance as the molecular link between diabetes and Alzheimer's disease

Diabetes mellitus (DM) and Alzheimer's disease (AD) are two major health concerns that have seen a rising prevalence worldwide. Recent studies have indicated a possible link between DM and an increased risk of developing AD. Insulin, while primarily known for its role in regulating blood sugar, also plays a vital role in protecting brain functions. Insulin resistance (IR), especially prevalent in type 2 diabetes, is believed to play a significant role in AD's development. When insulin signalling becomes dysfunctional, it can negatively affect various brain functions, making individuals more susceptible to AD's defining features, such as the buildup of beta-amyloid plaques and tau protein tangles. Emerging research suggests that addressing insulin-related issues might help reduce or even reverse the brain changes linked to AD. This review aims to explore the rela-tionship between DM and AD, with a focus on the role of IR. It also explores the molecular mechanisms by which IR might lead to brain changes and assesses current treatments that target IR. Understanding IR's role in the connection between DM and AD offers new possibilities for treatments and highlights the importance of continued research in this interdisciplinary field.

APOE 5'UTR Methylation Pattern Analysis in Blood and Brain Tissue from Alzheimer's Disease Affected Patients

APOE ɛ4 allele is the major genetic risk factor for Alzheimer's Disease (AD). Furthermore, APOE methylation pattern has been described to be associated with the disease and to follow a bimodal pattern, with a hypermethylated CpG island and a hypomethylated promoter region. However, little is known about the methylation levels in the APOE 5'UTR region. Here, the methylation of two regions (R1 and R2) within APOE 5'UTR was investigated in both peripheral blood mononuclear cells (PBMCs) and hippocampus (HIC) samples to identify differentially methylated CpG sites and to associate clinical, genetic features and cerebrospinal fluid (CSF) biomarkers levels. DNA was extracted from PBMCs of 20 AD and 20 healthy controls (HC) and from 6 AD and 3 HC HIC samples. The methylation analysis was carried out by means of pyrosequencing. In AD PBMCs we found that R1 region displayed a higher methylation level, while the opposite trend was observed in R2. The presence of ɛ4 allele highlighted a marked increase in R1 methylation level and a decrease in R2. In AD PBMCs and HIC, age progression resulted to be associated with an increase in the methylation level of R1. Lastly, the methylation of a CpG site in R2 was found to be related to CSF biomarkers. Despite the lack of a statistical significance, the outcome from this exploratory analysis highlighted the presence of a difference in methylation in APOE 5'UTR in PBMCs of AD patients which seemed to be associated also with APOE genotype, age and CSF biomarkers level.

Epigenetics in Alzheimer's Disease: A Critical Overview

Epigenetic modifications have been implicated in a number of complex diseases as well as being a hallmark of organismal aging. Several reports have indicated an involvement of these changes in Alzheimer's disease (AD) risk and progression, most likely contributing to the dysregulation of AD-related gene expression measured by DNA methylation studies. Given that DNA methylation is tissue-specific and that AD is a brain disorder, the limitation of these studies is the ability to identify clinically useful biomarkers in a proxy tissue, reflective of the tissue of interest, that would be less invasive, more cost-effective, and easily obtainable. The age-related DNA methylation changes have also been used to develop different generations of epigenetic clocks devoted to measuring the aging in different tissues that sometimes suggests an age acceleration in AD patients. This review critically discusses epigenetic changes and aging measures as potential biomarkers for AD detection, prognosis, and progression. Given that epigenetic alterations are chemically reversible, treatments aiming at reversing these modifications will be also discussed as promising therapeutic strategies for AD.

A multi-platform analysis of human gingival crevicular fluid reveals ferroptosis as a relevant regulated cell death mechanism during the clinical progression of periodontitis

Ferroptosis is implicated in the pathogenesis of numerous chronic-inflammatory diseases, yet its association with progressive periodontitis remains unexplored. To investigate the involvement and significance of ferroptosis in periodontitis progression, we assessed sixteen periodontitis-diagnosed patients. Disease progression was clinically monitored over twelve weeks via weekly clinical evaluations and gingival crevicular fluid (GCF) collection was performed for further analyses. Clinical metrics, proteomic data, in silico methods, and bioinformatics tools were combined to identify protein profiles linked to periodontitis progression and to explore their potential connection with ferroptosis. Subsequent western blot analyses validated key findings. Finally, a single-cell RNA sequencing (scRNA-seq) dataset (GSE164241) for gingival tissues was analyzed to elucidate cellular dynamics during periodontitis progression. Periodontitis progression was identified as occurring at a faster rate than traditionally thought. GCF samples from progressing and non-progressing periodontal sites showed quantitative and qualitatively distinct proteomic profiles. In addition, specific biological processes and molecular functions during progressive periodontitis were revealed and a set of hub proteins, including SNCA, CA1, HBB, SLC4A1, and ANK1 was strongly associated with the clinical progression status of periodontitis. Moreover, we found specific proteins - drivers or suppressors - associated with ferroptosis (SNCA, FTH1, HSPB1, CD44, and GCLC), revealing the co-occurrence of this specific type of regulated cell death during the clinical progression of periodontitis. Additionally, the integration of quantitative proteomic data with scRNA-seq analysis suggested the susceptibility of fibroblasts to ferroptosis. Our analyses reveal proteins and processes linked to ferroptosis for the first time in periodontal patients, which offer new insights into the molecular mechanisms of progressive periodontal disease. These findings may lead to novel diagnostic and therapeutic strategies.

Genome-Wide DNA Methylation in Early-Onset-Dementia Patients Brain Tissue and Lymphoblastoid Cell Lines

Epigenetics, a potential underlying pathogenic mechanism of neurodegenerative diseases, has been in the scope of several studies performed so far. However, there is a gap in regard to analyzing different forms of early-onset dementia and the use of Lymphoblastoid cell lines (LCLs). We performed a genome-wide DNA methylation analysis on sixty-four samples (from the prefrontal cortex and LCLs) including those taken from patients with early-onset forms of Alzheimer's disease (AD) and frontotemporal dementia (FTD) and healthy controls. A beta regression model and adjusted p-values were used to obtain differentially methylated positions (DMPs) via pairwise comparisons. A correlation analysis of DMP levels with Clariom D array gene expression data from the same cohort was also performed. The results showed hypermethylation as the most frequent finding in both tissues studied in the patient groups. Biological significance analysis revealed common pathways altered in AD and FTD patients, affecting neuron development, metabolism, signal transduction, and immune system pathways. These alterations were also found in LCL samples, suggesting the epigenetic changes might not be limited to the central nervous system. In the brain, CpG methylation presented an inverse correlation with gene expression, while in LCLs, we observed mainly a positive correlation. This study enhances our understanding of the biological pathways that are associated with neurodegeneration, describes differential methylation patterns, and suggests LCLs are a potential cell model for studying neurodegenerative diseases in earlier clinical phases than brain tissue.

Role of Epigenetic Modulation in Neurodegenerative Diseases: Implications of Phytochemical Interventions

Epigenetics defines changes in cell function without involving alterations in DNA sequence. Neuroepigenetics bridges neuroscience and epigenetics by regulating gene expression in the nervous system and its impact on brain function. With the increase in research in recent years, it was observed that alterations in the gene expression did not always originate from changes in the genetic sequence, which has led to understanding the role of epigenetics in neurodegenerative diseases (NDDs) including Alzheimer's disease (AD) and Parkinson's disease (PD). Epigenetic alterations contribute to the aberrant expression of genes involved in neuroinflammation, protein aggregation, and neuronal death. Natural phytochemicals have shown promise as potential therapeutic agents against NDDs because of their antioxidant, anti-inflammatory, and neuroprotective effects in cellular and animal models. For instance, resveratrol (grapes), curcumin (turmeric), and epigallocatechin gallate (EGCG; green tea) exhibit neuroprotective effects through their influence on DNA methylation patterns, histone acetylation, and non-coding RNA expression profiles. Phytochemicals also aid in slowing disease progression, preserving neuronal function, and enhancing cognitive and motor abilities. The present review focuses on various epigenetic modifications involved in the pathology of NDDs, including AD and PD, gene expression regulation related to epigenetic alterations, and the role of specific polyphenols in influencing epigenetic modifications in AD and PD.

The broken Alzheimer's disease genome

The complex pathobiology of late-onset Alzheimer's disease (AD) poses significant challenges to therapeutic and preventative interventions. Despite these difficulties, genomics and related disciplines are allowing fundamental mechanistic insights to emerge with clarity, particularly with the introduction of high-resolution sequencing technologies. After all, the disrupted processes at the interface between DNA and gene expression, which we call the broken AD genome, offer detailed quantitative evidence unrestrained by preconceived notions about the disease. In addition to highlighting biological pathways beyond the classical pathology hallmarks, these advances have revitalized drug discovery efforts and are driving improvements in clinical tools. We review genetic, epigenomic, and gene expression findings related to AD pathogenesis and explore how their integration enables a better understanding of the multicellular imbalances contributing to this heterogeneous condition. The frontiers opening on the back of these research milestones promise a future of AD care that is both more personalized and predictive.

Aging phenotype in AD brain organoids: Track to success and challenges

Alzheimer's disease (AD) poses a complex challenge, with abnormal protein accumulation in the brain causing memory loss and cognitive decline. Traditional models fall short in AD research, prompting interest in 3D brain organoids (BOs) from human stem cells. These findings hold promise for unveiling the mechanisms of AD, especially in relation to aging. However, an understanding of the aging impact of AD remains elusive. BOs offer insight but face challenges. This review delves into the role of BOs in deciphering aging-related AD and acknowledges limitations. Strategies to enhance BOs for accurate aging modeling in AD brains are suggested. Strengthened by molecular advancements, BOs have the potential to uncover the aging phenotype, advancing AD research.

Genetic factors associated with suicidal behaviors and alcohol use disorders in an American Indian population

American Indians (AI) demonstrate the highest rates of both suicidal behaviors (SB) and alcohol use disorders (AUD) among all ethnic groups in the US. Rates of suicide and AUD vary substantially between tribal groups and across different geographical regions, underscoring a need to delineate more specific risk and resilience factors. Using data from over 740 AI living within eight contiguous reservations, we assessed genetic risk factors for SB by investigating: (1) possible genetic overlap with AUD, and (2) impacts of rare and low-frequency genomic variants. Suicidal behaviors included lifetime history of suicidal thoughts and acts, including verified suicide deaths, scored using a ranking variable for the SB phenotype (range 0-4). We identified five loci significantly associated with SB and AUD, two of which are intergenic and three intronic on genes AACSP1, ANK1, and FBXO11. Nonsynonymous rare and low-frequency mutations in four genes including SERPINF1 (PEDF), ZNF30, CD34, and SLC5A9, and non-intronic rare and low-frequency mutations in genes OPRD1, HSD17B3 and one lincRNA were significantly associated with SB. One identified pathway related to hypoxia-inducible factor (HIF) regulation, whose 83 nonsynonymous rare and low-frequency variants on 10 genes were significantly linked to SB as well. Four additional genes, and two pathways related to vasopressin-regulated water metabolism and cellular hexose transport, also were strongly associated with SB. This study represents the first investigation of genetic factors for SB in an American Indian population that has high risk for suicide. Our study suggests that bivariate association analysis between comorbid disorders can increase statistical power; and rare and low-frequency variant analysis in a high-risk population enabled by whole-genome sequencing has the potential to identify novel genetic factors. Although such findings may be population specific, rare functional mutations relating to PEDF and HIF regulation align with past reports and suggest a biological mechanism for suicide risk and a potential therapeutic target for intervention.

The association between DNA methylation and human height and a prospective model of DNA methylation-based height prediction

As a vital anthropometric characteristic, human height information not only helps to understand overall developmental status and genetic risk factors, but is also important for forensic DNA phenotyping. We utilized linear regression analysis to test the association between each CpG probe and the height phenotype. Next, we designed a methylation sequencing panel targeting 959 CpGs and subsequent height inference models were constructed for the Chinese population. A total of 11,730 height-associated sites were identified. By employing KPCA and deep neural networks, a prediction model was developed, of which the cross-validation RMSE, MAE and R2 were 5.62 cm, 4.45 cm and 0.64, respectively. Genetic factors could explain 39.4% of the methylation level variance of sites used in the height inference models. Collectively, we demonstrated an association between height and DNA methylation status through an EWAS analysis. Targeted methylation sequencing of only 959 CpGs combined with deep learning techniques could provide a model to estimate human height with higher accuracy than SNP-based prediction models.

Assessing methylation detection for primary human tissue using Nanopore sequencing

DNA methylation most commonly occurs as 5-methylcytosine (5-mC) in the human genome and has been associated with human diseases. Recent developments in single-molecule sequencing technologies (Oxford Nanopore Technologies (ONT) and Pacific Biosciences) have enabled readouts of long, native DNA molecules, including cytosine methylation. ONT recently upgraded their Nanopore sequencing chemistry and kits from R9 to the R10 version, which yielded increased accuracy and sequencing throughput. However the effects on methylation detection have not yet been documented. Here we performed a series of computational analyses to characterize differences in Nanopore-based 5mC detection between the ONT R9 and R10 chemistries. We compared 5mC calls in R9 and R10 for three human genome datasets: a cell line, a frontal cortex brain sample, and a blood sample. We performed an in-depth analysis on CpG islands and homopolymer regions, and documented high concordance for methylation detection among sequencing technologies. The strongest correlation was observed between Nanopore R10 and Illumina bisulfite technologies for cell line-derived datasets. Subtle differences in methylation datasets between technologies can impact analysis tools such as differential methylation calling software. Our findings show that comparisons can be drawn between methylation data from different Nanopore chemistries using guided hypotheses. This work will facilitate comparison among Nanopore data cohorts derived using different chemistries from large scale sequencing efforts, such as the NIH CARD Long Read Initiative.

Insights into ageing rates comparison across tissues from recalibrating cerebellum DNA methylation clock

DNA methylation (DNAm)-based age clocks have been studied extensively as a biomarker of human ageing and a 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 relatively small sample size and so warrants further investigation. We collected the largest cerebellum DNAm dataset (N = 752) to date. 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). From the 613 cerebellum age-associated CpGs, we built a highly accurate age prediction model for the cerebellum named CerebellumClockspecific (Pearson correlation=0.941, MAD=3.18 years). Ageing rate comparisons based on the two tissue-specific clocks constructed on the 201 overlapping age-associated CpGs support the cerebellum has younger DNAm age. Nevertheless, we built BrainCortexClock to prove a single DNAm clock is able to unbiasedly estimate DNAm ages of both cerebellum and cerebral cortex, when they are adequately and equally represented in the training dataset. Comparing ageing rates across tissues using DNA methylation multi-tissue clocks is flawed. The large underestimation of age prediction for cerebellums by previous clocks mainly reflects the improper usage of these age clocks. There exist strong and consistent ageing effects on the cerebellar methylome, and we suggest the smaller number of age-associated CpG sites in cerebellum is largely attributed to its extremely low average cell replication rates.

Quantifying the proportion of different cell types in the human cortex using DNA methylation profiles

BACKGROUND

Due to interindividual variation in the cellular composition of the human cortex, it is essential that covariates that capture these differences are included in epigenome-wide association studies using bulk tissue. As experimentally derived cell counts are often unavailable, computational solutions have been adopted to estimate the proportion of different cell types using DNA methylation data. Here, we validate and profile the use of an expanded reference DNA methylation dataset incorporating two neuronal and three glial cell subtypes for quantifying the cellular composition of the human cortex.

RESULTS

We tested eight reference panels containing different combinations of neuronal- and glial cell types and characterised their performance in deconvoluting cell proportions from computationally reconstructed or empirically derived human cortex DNA methylation data. Our analyses demonstrate that while these novel brain deconvolution models produce accurate estimates of cellular proportions from profiles generated on postnatal human cortex samples, they are not appropriate for the use in prenatal cortex or cerebellum tissue samples. Applying our models to an extensive collection of empirical datasets, we show that glial cells are twice as abundant as neuronal cells in the human cortex and identify significant associations between increased Alzheimer's disease neuropathology and the proportion of specific cell types including a decrease in NeuNNeg/SOX10Neg nuclei and an increase of NeuNNeg/SOX10Pos nuclei.

CONCLUSIONS

Our novel deconvolution models produce accurate estimates for cell proportions in the human cortex. These models are available as a resource to the community enabling the control of cellular heterogeneity in epigenetic studies of brain disorders performed on bulk cortex tissue.