Authors
Ophelia Lee
Abstract
Alzheimer’s disease (AD) is the most common form of dementia, characterized by progressive cognitive decline, amyloid-β plaque accumulation, and tau pathology. While extensive research has shed light on genetic contributors—such as mutations in APP, PSEN1, and PSEN2—these do not fully account for the disease’s clinical heterogeneity or sporadic forms. Epigenetic mechanisms, particularly DNA methylation, have emerged as vital regulatory processes that bridge genetic predispositions and environmental or lifestyle factors. DNA methylation patterns undergo significant changes during aging and in AD, influencing the expression of genes involved in synaptic plasticity, neuroinflammation, and neuronal metabolism.
This research paper provides a comprehensive exploration of DNA methylation in the context of Alzheimer’s disease. We begin by examining the fundamental biology of DNA methylation and its role in normal neuronal function. Next, we delve into the various methodologies used to analyze methylation changes, including bisulfite sequencing and array-based techniques, highlighting their strengths and limitations. We then map specific methylation patterns observed in AD pathology, discussing how they correlate with cognitive impairment, amyloid deposition, and tau hyperphosphorylation. The interplay between DNA methylation and key cellular processes—such as microglial activation, neuronal plasticity, and epigenetic drift—is also elucidated.
Furthermore, we assess how environmental and lifestyle factors—like diet, stress, and toxins—can modify the methylome, thereby impacting disease susceptibility and progression. Emerging biomarkers and therapeutic approaches targeting DNA methylation are reviewed, offering potential routes to early diagnosis and treatment. Finally, we address current challenges—such as heterogeneity in study designs and the complexity of gene-environment interactions—and propose future directions to refine our understanding and harness the potential of epigenetic interventions in Alzheimer’s disease.
Introduction
Alzheimer’s disease (AD) is the leading cause of dementia worldwide and poses a growing public health concern as life expectancies increase (Prince et al., 2015). Characterized by the presence of extracellular amyloid-β (Aβ) plaques and intracellular neurofibrillary tangles primarily composed of hyperphosphorylated tau, AD typically manifests as progressive memory impairment, cognitive deficits, and changes in behavior and personality. To date, the accumulation of Aβ and tau has been the focal point for the majority of AD research. However, despite decades of investigation, therapeutic interventions targeting these pathologies have achieved only limited success in halting or reversing disease progression (Cummings, Lee, Ritter, & Zhong, 2019).
One possible explanation for the inconsistent clinical outcomes and variable disease presentations, even among individuals with similar genetic backgrounds, lies in the realm of epigenetics (Liu, Van Groen, Kadish, & Li, 2008). Epigenetics refers to heritable modifications that influence gene expression without altering the primary DNA sequence. These include DNA methylation, post-translational modifications of histones, chromatin remodeling, and regulation by non-coding RNAs (Allis & Jenuwein, 2016). Among these, DNA methylation is one of the most extensively studied and dynamically regulated modifications in the brain (Klose & Bird, 2006).
Why Focus on DNA Methylation in Alzheimer’s Disease?
While genetic factors—most notably mutations in APP, PSEN1, and PSEN2, as well as the APOE ε4 allele—are associated with early-onset familial AD or increased risk in late-onset AD, they do not sufficiently explain the complexity and heterogeneity observed in the majority of sporadic cases (Bertram & Tanzi, 2012). Environmental influences (e.g., exposure to neurotoxins, diet, stress) and lifestyle factors (e.g., physical exercise, cognitive engagement) likely modulate gene expression through epigenetic mechanisms, contributing to the variable onset and progression of AD (Lardenoije et al., 2018). DNA methylation is particularly relevant because it can stably alter the transcriptional landscape over time while remaining potentially reversible, thus representing a promising therapeutic target.
Basic Mechanisms of DNA Methylation
DNA methylation typically occurs at the 5th carbon of the cytosine ring (5mC) within CpG dinucleotides and is catalyzed by a family of DNA methyltransferases (DNMTs), including DNMT1, DNMT3A, and DNMT3B (Moore, Le, & Fan, 2013). DNMT1 is primarily responsible for maintaining existing methylation patterns during DNA replication, whereas DNMT3A and DNMT3B are considered de novo methyltransferases that establish new methylation marks. In the central nervous system, DNA methylation patterns are intricately regulated during development and continue to change in response to neuronal activity, aging, and environmental stimuli (Lister et al., 2013).
Neurons exhibit a unique distribution of DNA methylation, including relatively high levels of 5-hydroxymethylcytosine (5hmC), generated by ten-eleven translocation (TET) enzymes (Kriaucionis & Heintz, 2009). 5hmC is particularly enriched in brain tissue and may serve as an intermediate in active DNA demethylation. These processes are essential for synaptic plasticity, learning, and memory (Sweatt, 2013). Given the dynamic nature of methylation and demethylation in neurons, even subtle dysregulations over time may have profound consequences for neuronal function and survival.
Aging and Epigenetic Drift
Aging is the most significant risk factor for Alzheimer’s disease, underscoring the need to examine how age-related epigenetic changes may predispose individuals to AD pathology (Rasmussen & Deberardinis, 2011). With advancing age, global methylation levels can shift—both increasing in certain regions and decreasing in others—a phenomenon termed “epigenetic drift” (Fraga & Esteller, 2007). This drift may lead to aberrant expression of genes involved in neuronal maintenance and repair, inflammation, and protein homeostasis. When superimposed on genetic susceptibilities, these epigenetic changes could accelerate the onset or progression of AD.
Aging is also associated with chronic oxidative stress and a pro-inflammatory milieu in the brain, factors that can influence DNMT activity and TET-mediated hydroxymethylation (Liu et al., 2019). As a result, the epigenome of older adults becomes increasingly unstable and susceptible to maladaptive modifications. While some changes may be protective—such as the methylation of pro-apoptotic genes—others can be deleterious, leading to reduced expression of key neuroprotective factors or increased expression of genes that promote neuroinflammation (Schafer et al., 2015).
Relevance of DNA Methylation to AD Pathological Hallmarks
Alzheimer’s disease is pathologically defined by Aβ plaques and neurofibrillary
tangles. Recent studies suggest that DNA methylation could influence both the
production and clearance of Aβ and the phosphorylation state of tau:
Amyloid-β Pathway: The amyloid precursor protein (APP) gene and enzymes involved in Aβ production (e.g., BACE1) may be regulated by methylation. Changes in methylation patterns near these genes could upregulate or downregulate amyloidogenic processing, thereby altering plaque burden (Chouliaras et al., 2013).
Tau Pathology: Tau phosphorylation is regulated by a variety of kinases and phosphatases, some of which are influenced by epigenetic mechanisms. Although less studied, emerging evidence suggests that aberrant methylation of genes encoding these enzymes might contribute to hyperphosphorylation of tau (Sanchez-Mut & Gräff, 2015).
This paper aims to provide an in-depth analysis of DNA methylation alterations in Alzheimer’s disease and to discuss how these changes may modulate disease etiology, progression, and potential therapeutic avenues. Specifically, we will:
Examine Methodologies: Evaluate the various experimental and computational methods used to detect and quantify DNA methylation changes in AD, including whole-genome bisulfite sequencing, MeDIP-seq, and targeted array-based methods.
Map Methylation Patterns: Present evidence of specific methylation profiles in genes related to synaptic function, neuroinflammation, and cellular metabolism.
Discuss Mechanistic Insights: Explore how dysregulated methylation influences cellular pathways, including Aβ production, tau phosphorylation, neuronal plasticity, and glial cell activation.
Evaluate Environmental and Lifestyle Factors: Delve into how external factors (e.g., toxins, diet, stress) interface with the methylome to influence AD risk.
Highlight Therapeutic Implications: Investigate emerging strategies to modulate DNA methylation pharmacologically or via lifestyle interventions, and evaluate their potential for disease-modifying effects.
Address Knowledge Gaps: Consider current limitations in our understanding of DNA methylation in AD, offering suggestions for future research trajectories.
By narrowing our focus to DNA methylation, we seek to provide a detailed mapping of this epigenetic landscape in Alzheimer’s disease, thereby clarifying its contributions to pathology and underscoring the potential for epigenetic-based diagnostic tools and treatments.
Significance for Precision Medicine
AD remains a complex disease with multiple subtypes, each influenced by various genetic and non-genetic factors (Scheltens et al., 2021). As the healthcare paradigm shifts toward personalized medicine, epigenetic biomarkers like DNA methylation signatures could offer more nuanced diagnostic categories. For example, patients exhibiting specific methylation changes might respond differently to certain therapies or could be more likely to benefit from lifestyle interventions. Such stratification could revolutionize the clinical management of AD, facilitating earlier diagnosis and more targeted treatment plans (Hampel et al., 2021).
Looking Ahead
The focus on DNA methylation in AD is rooted in a robust body of evidence indicating that methylation changes occur early in disease processes and may be reversible (De Jager et al., 2014). Yet, we are still in the early stages of translating these findings into clinical practice. As new technologies refine our ability to detect methylation changes at single-base resolution within specific brain cell types, we may identify precise epigenetic signatures that not only correlate with disease but also dictate therapeutic responsiveness.
In the subsequent sections, we will examine in detail how DNA methylation shapes Alzheimer’s disease pathology, the methods for mapping these changes, and the avenues for intervention that may one day transform AD from an incurable condition to a treatable—or even preventable—disorder.
Methodological Approaches to Studying DNA Methylation in Alzheimer’s Disease
The accurate and comprehensive mapping of DNA methylation changes in Alzheimer’s disease hinges on a variety of powerful methodologies. Each method has its strengths, limitations, and optimal use cases, influenced by factors such as genomic coverage, resolution, cost, and tissue availability. Understanding these methods is crucial for interpreting conflicting or nuanced results across different studies.
1. Bisulfite Conversion and Whole-Genome Bisulfite Sequencing (WGBS)
Overview: Sodium bisulfite treatment converts unmethylated cytosines to uracil (subsequently read as thymine during PCR), while methylated cytosines remain unchanged. Whole-genome bisulfite sequencing (WGBS) involves sequencing the entire genome after bisulfite conversion, offering single-base resolution of methylation patterns (Lister et al., 2009).
Advantages:
Comprehensive Coverage: WGBS covers nearly all CpG sites across the genome, allowing for a complete methylation profile.
Single-Base Resolution: The method distinguishes between methylated and unmethylated cytosines at an individual base level.
Quantitative: Methylation levels at each CpG can be quantitatively assessed.
Limitations:
High Cost and Complexity: WGBS is expensive and requires substantial computational infrastructure.
Tissue Heterogeneity: Samples from postmortem AD brain tissue can be heterogeneous, containing neurons, glia, and vascular cells. Without additional single-cell or cell-sorting techniques, the data may represent an averaged methylation profile.
DNA Quality: Postmortem brain samples may have variable DNA quality, affecting bisulfite conversion efficiency and sequencing accuracy (Stöger, 2006).
Applications in AD: WGBS has been used in pilot studies to map global methylation changes in different brain regions, identifying distinct methylation signatures in the hippocampus versus the cortex of AD patients (De Jager et al., 2014). These studies have shed light on the interplay between methylation status and gene expression changes relevant to synaptic function and neuroinflammation.
2. Reduced Representation Bisulfite Sequencing (RRBS)
Overview: RRBS is a cost-effective alternative to WGBS, enriching CpG-dense regions of the genome—such as promoters and CpG islands—by using restriction enzymes and size selection before bisulfite conversion (Meissner et al., 2005). This approach captures a fraction of the genome while still providing single-base resolution in CpG-rich areas.
Advantages:
Cost-Effective: Reduced sequencing volume compared to WGBS.
Focus on Regulatory Regions: Concentrates analysis on CpG islands, promoters, and other functionally relevant regions, making it ideal for investigating gene expression regulation.
Quantitative and High Resolution: Retains the single-base resolution of bisulfite-based methods.
Limitations:
Incomplete Coverage: RRBS targets only a subset of the genome, potentially missing methylation changes in CpG-poor regions that might still be relevant to AD pathogenesis.
Library Bias: Restriction enzyme-based fragmentation may introduce biases, depending on the enzyme used (Bock et al., 2010).
Applications in AD: RRBS has been employed in exploring promoter-associated methylation changes in genes implicated in neurodegeneration. For instance, studies using RRBS have identified differential methylation in genes related to synaptic plasticity and immune function in the hippocampus of AD patients (Lunnon et al., 2014).
3. Array-Based Methods (Infinium 450K and EPIC Arrays)
Overview: DNA methylation microarrays, such as Illumina’s Infinium HumanMethylation450 (450K) and MethylationEPIC (850K) arrays, use probes to interrogate hundreds of thousands of CpG sites across the genome (Bibikova et al., 2011). These arrays have been widely adopted due to their relative affordability and standardized protocols.
Advantages:
High Throughput: Enables large-scale epidemiological or case-control studies with hundreds to thousands of samples.
Cost-Effective: Significantly cheaper than WGBS, suitable for initial screening.
Good Coverage of Regulatory Regions: Includes promoters, enhancers, and gene bodies known to be relevant to gene expression regulation.
Limitations:
Limited CpG Coverage: The arrays target selected CpG sites, leaving much of the genome unexamined.
Potential Batch Effects: Array-based techniques can suffer from batch-to-batch and slide-to-slide variability, requiring stringent data normalization (Leek et al., 2010).
Lower Resolution: Because the coverage is probe-dependent, you cannot easily explore CpGs not included in the array design.
Applications in AD:
Large-scale epigenome-wide association studies (EWAS) of AD often use 450K or EPIC arrays to identify differentially methylated positions (DMPs) or regions (DMRs) in blood or brain tissues. Findings have consistently highlighted methylation differences in genes involved in neuronal signaling and immune response (Lunnon et al., 2014; De Jager et al., 2014). Although less comprehensive than WGBS, these arrays have been instrumental in generating testable hypotheses about candidate loci for further in-depth investigation.
4. MeDIP-Seq and MBD-Seq
Overview: Methylated DNA immunoprecipitation sequencing (MeDIP-seq) and methyl-CpG binding domain sequencing (MBD-Seq) are affinity enrichment techniques that enrich for methylated DNA fragments before sequencing (Jacinto et al., 2008). MeDIP-seq uses an antibody against 5mC, whereas MBD-Seq uses proteins that specifically bind methylated CpGs.
Advantages:
Enrichment of Methylated Regions: Allows for the targeted sequencing of methylated fragments, reducing the required sequencing depth.
Flexible: Adaptable to different experimental designs, suitable for comparative analyses.
Limitations:
Resolution: Lower resolution than bisulfite-based methods; typically identifies broad methylation domains rather than single CpGs.
Bias Toward High CpG Density: MeDIP-seq and MBD-Seq may preferentially capture regions with higher CpG content, potentially underrepresenting low-density regions (Bernstein, Meissner, & Lander, 2007).
Antibody Efficiency: The reliability depends on the specificity and efficiency of the antibody or MBD protein.
Applications in AD: Affinity enrichment methods have been used to map global changes in methylation across the AD genome, identifying hypermethylated clusters in pathways related to inflammation and metabolic dysfunction (Mastroeni et al., 2015). While they may not offer single-base resolution, they are useful for broad-scale methylation profiling and can guide subsequent high-resolution analyses.
5. Targeted Sequencing Approaches (Amplicon Bisulfite Sequencing)
Overview: Targeted bisulfite sequencing uses PCR amplification of specific genomic regions of interest—such as candidate genes associated with AD pathology—followed by bisulfite conversion and deep sequencing. This approach is cost-effective and provides high resolution in pre-selected regions (Xi & Guang, 2020)
Advantages:
High Depth of Coverage: Enables quantitative assessment of methylation at specific loci.
Focused: Ideal for hypothesis-driven studies examining known candidate genes or regulatory elements.
Sensitive: Can detect low-frequency methylation changes.
Limitations:
Limited Scope: Only interrogates predefined regions, so novel methylation sites remain undiscovered.
Primer Bias: Bisulfite conversion can complicate primer design, requiring careful validation to ensure specificity.
Applications in AD: Researchers have used targeted sequencing to examine methylation in genes such as BACE1, APOE, and BIN1 to confirm differential methylation patterns identified in broader screens (Shu, Wang, & Qu, 2016). This approach is particularly useful for validating array or WGBS findings in large patient cohorts.
6. Single-Cell and Spatially Resolved Methylation Analysis
Overview: Traditional bulk tissue analyses average methylation signals across multiple cell types, masking cell-type-specific changes. Newer techniques integrate single-cell isolation or spatial transcriptomics with bisulfite sequencing to assess methylation at the resolution of individual cells or discrete brain regions (Luo et al., 2017).
Advantages:
Cell-Type Specificity: Differentiate methylation patterns in neurons, microglia, astrocytes, and oligodendrocytes.
Spatial Resolution: Retain information on the anatomical context within the brain, critical for studying region-specific AD pathology.
Limitations:
Technical Complexity and Cost: Single-cell or spatially resolved techniques are expensive and require specialized instrumentation.
Reduced Genome Coverage: Sequencing depth may be compromised to accommodate single-cell resolution.
Applications in AD: Although still in its infancy, single-cell methylomics has the potential to unravel how distinct cell populations contribute to AD. For example, microglia are heavily implicated in AD pathology, and single-cell approaches could clarify how their methylation signatures change in response to Aβ plaques (Keren-Shaul et al., 2017).
Mapping DNA Methylation Patterns in Alzheimer’s Disease
With an understanding of the methods used to study DNA methylation, we now turn to the specific alterations observed in Alzheimer’s disease. While the data can sometimes appear heterogeneous across studies—due in part to variations in methodology, tissue source, and disease stage—several consistent patterns have begun to emerge.
1. Global Methylation Changes
Global Hypomethylation: Many studies report a general trend toward reduced global methylation in AD, particularly in later disease stages (Mastroeni et al., 2010). This hypomethylation could lead to the aberrant activation of genes that promote neuroinflammation or apoptotic pathways.
Global Hypermethylation: Conversely, other reports note hypermethylation in specific genomic regions (Bakulski et al., 2012). Discrepancies may arise from differences in brain region or disease severity. For instance, the entorhinal cortex might exhibit hypermethylation of certain regulatory elements, whereas the hippocampus experiences hypomethylation at other loci (Lunnon et al., 2014).
2. Gene-Specific Methylation Profiles
Amyloid-Related Genes:
APP (Amyloid Precursor Protein): The methylation of the APP promoter region can regulate its expression. Hypomethylation could lead to increased APP levels and greater Aβ production (Rao et al., 2012).
BACE1: This β-secretase enzyme is crucial for Aβ generation. Multiple studies suggest that BACE1 promoter hypermethylation correlates with reduced enzyme levels, while hypomethylation correlates with increased enzymatic activity (Fuso et al., 2012).
Tau-Related Genes:
MAPT (Microtubule-Associated Protein Tau): Although less extensively studied than amyloid-related genes, emerging data indicate that differential methylation in MAPT regulatory regions may affect tau expression and splicing, potentially influencing tau hyperphosphorylation (Sanchez-Mut & Gräff, 2015).
Neuroinflammation and Immune Response:
TNF-α, IL-1β, IL-6, TREM2: These inflammatory mediators and receptors can be upregulated in AD, partially due to changes in promoter methylation (Perri et al., 2017). Methylation-driven alterations in microglial genes may underlie the heightened inflammatory response observed in AD brains.
Synaptic Plasticity Genes:
BDNF (Brain-Derived Neurotrophic Factor): Essential for synaptic health, BDNF expression can be suppressed by promoter hypermethylation. Reduced BDNF levels have been linked to memory deficits in AD (Fukumoto et al., 2010).
Synapsin and Synaptic Vesicle Genes: Differential methylation in the promoter or enhancers of these genes may contribute to synaptic loss, one of the earliest AD pathologies (Haddick et al., 2017).
3. Tissue and Region-Specific Methylation Patterns
Alzheimer’s pathology progresses selectively, often beginning in the entorhinal cortex and hippocampus before spreading to other cortical areas (Braak & Braak, 1991). Epigenetic studies reflect these spatial dynamics:
Hippocampus: Changes in genes related to memory formation and synaptic plasticity are frequently observed (Lardenoije et al., 2018).
Prefrontal Cortex: Genes regulating cognitive function, decision-making, and higher-order processes may be differentially methylated (Sanchez-Mut et al., 2014).
Cerebellum: Though less impacted by classic AD pathology, some studies find methylation changes here, suggesting a broader epigenetic shift (Bradshaw et al., 2019).
It is crucial to account for cell-type composition when comparing across brain regions. Neuronal loss, astrocytic proliferation, and microglial activation can significantly shift the cellular landscape, confounding direct comparisons of methylation profiles unless cell-type-specific analyses are performed.
4. Disease Progression and Temporal Dynamics
DNA methylation changes often precede overt neuropathology. Longitudinal studies suggest that differentially methylated sites emerge early and become more pronounced over time (De Jager et al., 2014). In some cases, these changes correlate with mild cognitive impairment (MCI), a prodromal stage of AD. Identifying such early alterations could offer diagnostic value and an opportunity for early intervention.
However, the directionality of causation—whether these methylation changes drive pathology or result from ongoing neurodegenerative processes—remains an open question. Several lines of evidence point to a bidirectional relationship: AD pathology triggers epigenetic remodeling, and these epigenetic changes further exacerbate disease.
5. 5-hydroxymethylcytosine (5hmC) in AD
In addition to 5mC, the brain exhibits notable levels of 5hmC, which may serve as a stable epigenetic mark distinct from 5mC (Kriaucionis & Heintz, 2009). In AD, changes in 5hmC patterns have been reported in genes related to synaptic function and neuronal survival (Szulwach et al., 2011). Reduced TET enzyme activity could decrease 5hmC in regions critical for memory, potentially contributing to cognitive decline. However, 5hmC research in AD is still emerging, and further studies are needed to clarify its functional significance.
6. Cross-Talk with Other Epigenetic Modifications
DNA methylation does not act in isolation. Histone modifications, chromatin remodeling, and non-coding RNAs often interact to shape the transcriptional landscape (Berson et al., 2018). For instance, promoter hypermethylation can recruit MBD proteins that then interact with histone deacetylases (HDACs), leading to a closed chromatin conformation and gene repression. Unraveling these multi-layered interactions is essential for a complete picture of AD pathogenesis.
Mechanistic Insights: How Does DNA Methylation Influence AD Pathology?
Having outlined the patterns of DNA methylation observed in Alzheimer’s disease, we now delve into the mechanistic pathways by which these epigenetic alterations may exacerbate or mitigate pathology.
1. Regulation of Amyloid-β Production
APP Gene Expression:
Promoter Methylation: Methylation of APP promoter regions can suppress transcription, theoretically reducing the pool of precursor protein available for Aβ production (Rao et al., 2012). Conversely, hypomethylation might elevate APP levels, increasing Aβ generation.
Alternative Splicing: Emerging data indicate that epigenetic marks may also influence alternative splicing of APP, altering the ratio of amyloidogenic to non-amyloidogenic APP isoforms (Tanzi & Bertram, 2005).
BACE1 Expression:
Epigenetic Control of β-Secretase: BACE1 is essential for generating Aβ from APP. Studies have shown that BACE1 expression is epigenetically regulated, with hypomethylation of the BACE1 promoter correlating with increased enzyme activity (Fuso et al., 2012). Elevated BACE1 expression could tip the balance toward pathological Aβ formation.
Effect of Aβ on Epigenetic Machinery:
Feedback Loops: Excess Aβ can activate stress response pathways, leading to altered DNMT and TET activities (Chouliaras et al., 2013). This feedback loop may stabilize pathological methylation patterns that reinforce amyloidogenic processes.
2. Influence on Tau Pathology
Epigenetic Regulation of Tau Kinases and Phosphatases:
GSK-3β, CDK5: These kinases phosphorylate tau. Elevated expression or activity due to promoter hypomethylation could lead to excessive tau phosphorylation (Hernandez et al., 2013).
PP2A: The principal tau phosphatase may also be regulated by DNA methylation. Downregulation of PP2A could result in tau hyperphosphorylation (Sontag et al., 2004).
Direct Regulation of MAPT:
MAPT Promoter Methylation: Although data are still emerging, changes in MAPT promoter methylation could affect overall tau expression. Increased tau protein levels may facilitate the formation of neurofibrillary tangles (Sanchez-Mut & Gräff, 2015).
3. Neuroinflammation and Glial Activation
Neuroinflammation is a defining feature of AD pathology, driven largely by activated microglia and astrocytes (Heneka et al., 2015). DNA methylation can shape the inflammatory landscape in multiple ways:
Microglial Phenotypes: Microglia can adopt either pro-inflammatory (M1-like) or anti-inflammatory (M2-like) phenotypes. Methylation of key cytokine or chemokine genes may push microglia toward an M1-like state, exacerbating neuronal damage (Ponomarev et al., 2011).
Astrogliosis: Epigenetic dysregulation in astrocytes could affect their capacity to clear Aβ or support neuronal metabolism. Changes in methylation of genes involved in glutamate uptake or antioxidant defense may impact neuronal viability (Liddelow & Barres, 2017).
4. Synaptic Dysfunction
Synapse loss correlates more closely with cognitive decline in AD than does plaque or tangle burden (DeKosky & Scheff, 1990). Epigenetic modifications influencing synaptic genes—such as BDNF, glutamate receptors, and synapsin—can lead to deficits in neurotransmission:
BDNF: Methylation-induced silencing of BDNF has been linked to memory deficits in both rodent models and human AD brains (Fukumoto et al., 2010). Restoration of BDNF expression via epigenetic drugs could improve synaptic plasticity.
Glutamatergic and GABAergic Genes: Dysregulated methylation of receptor subunits (e.g., GRIN2B, GABRB2) can unbalance excitatory and inhibitory signaling, contributing to neuronal hyperexcitability or excitotoxicity (Chen et al., 2018).
5. Mitochondrial Dysfunction and Oxidative Stress
Neurons rely on mitochondrial integrity for ATP production and calcium buffering. In AD, accumulating evidence points to significant mitochondrial deficits (Cenini & Voos, 2019). DNA methylation may play a role through:
Nuclear-Encoded Mitochondrial Genes: Genes that regulate mitochondrial biogenesis and function, such as PGC-1α, can be hypermethylated, reducing their expression and exacerbating energy deficits (Shi et al., 2016).
Antioxidant Defenses: Epigenetic silencing of antioxidant enzymes (e.g., SOD2, CAT) could enhance oxidative damage, fueling further epigenetic dysregulation in a vicious cycle (Liu et al., 2015).
6. Cell Death Pathways
Neuronal loss is a hallmark of AD. Epigenetic modifications may tip the balance between pro-survival and pro-apoptotic gene expression:
Bcl-2 Family Genes: Methylation changes in genes encoding anti-apoptotic (Bcl-2) or pro-apoptotic (Bax, Bak) proteins can influence neuronal survival (Song et al., 2010).
Caspase Activation: Certain caspase genes may be subject to methylation-induced expression changes, intensifying the apoptotic cascade in AD (Sato et al., 2001).
Environmental and Lifestyle Intersections with DNA Methylation in AD
Genetic predispositions and endogenous aging processes undoubtedly contribute to AD, yet environmental exposures and lifestyle choices can significantly modulate the epigenome, influencing disease onset and trajectory.
1. Neurotoxic Exposures and Oxidative Stress
Heavy Metals (e.g., Lead, Cadmium, Mercury): Chronic exposure can induce oxidative stress and inflammation, altering DNMT activity and generating aberrant methylation patterns in neural cells (Bakulski et al., 2012). In older populations, cumulative exposure may heighten AD risk.
Pesticides and Air Pollutants: Epidemiological data indicate that individuals living in areas with high pollution or agricultural pesticide use have an elevated risk of dementia (Power et al., 2016). DNA methylation changes in genes responsible for detoxification and inflammatory responses may mediate this risk.
2. Diet and Nutritional Factors
Folate and B Vitamins: These micronutrients supply methyl groups via the one-carbon metabolism pathway (Mason & Miller, 1992). Insufficient folate or vitamin B12 can lead to global hypomethylation, while supplementation may maintain normal methylation patterns in aging populations (Fuso et al., 2012).
Polyphenols and Antioxidants: Diets rich in antioxidants (e.g., Mediterranean diet) have been associated with reduced AD risk (Singh et al., 2014). Certain compounds, such as curcumin and resveratrol, can modulate epigenetic regulators, potentially preserving synaptic function through sustained DNA methylation homeostasis (Tiwari et al., 2020).
High-Fat, High-Sugar Diets: Diet-induced metabolic disorders are risk factors for AD (Pasinetti & Eberstein, 2008). Epigenetic dysregulation of genes that govern insulin signaling, lipid metabolism, and neuroinflammation may underlie this relationship, amplifying pathological processes in the brain.
3. Stress and Glucocorticoid Pathways
Chronic psychological or physiological stress elevates glucocorticoid levels, influencing methylation at glucocorticoid response elements (Hunter et al., 2009). Prolonged stress can worsen AD pathology through neuronal atrophy and synaptic loss in stress-vulnerable areas like the hippocampus (Sapolsky et al., 1986). Epigenetic regulation of stress-responsive genes, such as NR3C1 (glucocorticoid receptor), may affect disease susceptibility and progression (Klengel & Binder, 2015).
4. Physical Activity and Cognitive Engagement
Physical exercise and cognitive stimulation have been shown to induce beneficial epigenetic modifications in the brain, including enhanced histone acetylation and normalized DNA methylation at synaptic plasticity genes (Gomez-Pinilla et al., 2011). Animal models suggest that exercise can mitigate some of the epigenetic and cognitive deficits in AD, potentially through BDNF promoter demethylation (Lu et al., 2009).
5. Socioeconomic and Psychosocial Factors
Stressors linked to low socioeconomic status—such as poor access to healthy foods, limited healthcare, and chronic stress—could accelerate epigenetic aging processes. Evidence indicates that social isolation also exerts epigenetic changes relevant to brain health, potentially increasing vulnerability to AD (Hawkley & Cacioppo, 2010).
Potential Biomarkers and Diagnostic Applications
One of the most promising aspects of DNA methylation research in AD lies in the quest for peripheral biomarkers. Early detection remains a key challenge in clinical practice, and methylation signatures in blood or other accessible tissues (e.g., saliva) could offer a minimally invasive alternative to costly or invasive diagnostic procedures.
1. Blood-Based Methylation Biomarkers
Feasibility and Advantages:
Ease of Collection: Blood draws are significantly less invasive than lumbar punctures or brain biopsies.
Systemic Reflection: Though not identical to brain tissue, certain methylation changes might be mirrored peripherally, reflecting systemic inflammatory or metabolic states relevant to AD (Lunnon et al., 2013).
Challenges:
Cell Type Heterogeneity: Blood comprises diverse cell types (e.g., T cells, B cells, monocytes), each with distinct methylomes. Adjusting for cell composition is crucial (Houseman et al., 2012).
Specificity: Peripheral methylation patterns can be influenced by numerous confounding factors, including infections, comorbidities, and medications.
Despite these challenges, EWAS in blood have identified candidate loci whose methylation status correlates with cognitive decline or neuropathological markers (De Jager et al., 2014). Replication in independent cohorts is essential for clinical translation.
2. CSF and Saliva as Alternative Sources
CSF (Cerebrospinal Fluid):
Closer to CNS Pathology: CSF bathes the brain and may carry cell-free DNA reflecting CNS methylation changes.
Invasiveness: Lumbar puncture is more invasive than a blood draw, limiting routine clinical use.
Saliva:
Non-Invasive Collection: Attractive for large-scale screenings.
Variable Sensitivity: Salivary DNA might be more influenced by oral microbiome and mucosal factors (Schrott et al., 2020).
3. Combining Methylation Markers with Other Modalities
Neuroimaging, Proteomics, and Metabolomics: Integrating methylation data with imaging markers (e.g., PET scans), cerebrospinal fluid biomarkers (Aβ42, tau), and proteomic/metabolomic profiles can enhance diagnostic accuracy (Hampel et al., 2021). Such multi-omic approaches may yield more robust AD risk stratification models.
Machine Learning and Predictive Models: Advanced computational techniques, including random forests and deep learning, can process high-dimensional methylation datasets to identify predictive signatures (Li et al., 2019). Validation and standardization remain critical for clinical application.
Epigenetic Therapeutic Implications
The plasticity of the epigenome presents a compelling opportunity for disease-modifying therapies in Alzheimer’s disease. While most established AD therapies focus on neurotransmitter modulation or amyloid processing, epigenetic strategies aim to recalibrate gene expression more holistically.
1. DNA Methyltransferase Inhibitors (DNMTi)
Classes of DNMTi:
Nucleoside Analogs: 5-azacytidine and 5-aza-2’-deoxycytidine incorporate into DNA, trapping DNMTs. Used primarily in oncology, they demethylate DNA and can reactivate silenced tumor suppressor genes (Christman, 2002).
Non-Nucleoside DNMTi: Hydralazine and procainamide offer less toxic profiles but with lower potency.
Rationale for AD:
Reactivation of Silenced Genes: DNMTi could restore the expression of neuroprotective genes (e.g., BDNF) silenced by hypermethylation (Fischer et al., 2007).
Risk and Challenges: Global demethylation may lead to oncogenic transformations or disrupt essential genes. Achieving brain-specific and gene-specific targeting remains a significant hurdle.
2. TET Activators and 5hmC Restoration
If reduced TET enzyme activity contributes to pathological changes in 5hmC, molecules enhancing TET function might restore normal hydroxymethylation levels. While less developed than DNMTi, these strategies aim for a more targeted approach—reversing specific detrimental methylation marks rather than causing widespread demethylation (Zhang et al., 2018).
3. Small Molecules Targeting MBD Proteins
MBD proteins (e.g., MeCP2) recognize methylated DNA and recruit repressive complexes (Nan et al., 1998). Disrupting MBD interactions could relieve repressive chromatin states at key neuronal genes. Although not yet tested extensively in AD models, MBD inhibitors offer a potential strategy to modulate gene expression (Ooi & Bestor, 2008).
4. Lifestyle Interventions and Nutraceuticals
Given the complexity and risks associated with pharmacological epigenetic modifiers, non-pharmacological strategies merit serious consideration:
Dietary Supplements: Folate, vitamin B12, and choline can maintain global methylation homeostasis. Polyphenolic compounds like EGCG (green tea) and curcumin may also favorably modulate DNMT activity and inflammatory gene methylation (Tiwari et al., 2020).
Exercise and Cognitive Training: Physical and mental activities can induce beneficial DNA methylation changes in genes regulating synaptic plasticity, potentially slowing AD progression (Gomez-Pinilla et al., 2011).
5. Gene Editing Approaches
CRISPR-based tools for epigenome editing have shown promise in laboratory settings. Fusion proteins combining inactivated Cas9 (dCas9) with DNMT3A or TET enzymes enable locus-specific modulation of methylation (Hilton et al., 2015). Although highly experimental, such precision tools could theoretically “correct” pathological methylation signatures at disease-relevant loci without altering the broader methylome.
Challenges:
Delivery: Efficient and safe delivery to the central nervous system remains difficult.
Specificity: Off-target effects could have unpredictable consequences in neuronal cells.
Challenges and Gaps in Our Current Understanding
Despite growing enthusiasm for the role of DNA methylation in Alzheimer’s disease, numerous hurdles must be addressed to translate epigenetic research into clinical practice.
1. Causality vs. Correlation
Most studies identify correlations between methylation changes and AD pathology. Establishing causality—i.e., whether methylation alterations drive disease or merely reflect downstream events—requires longitudinal, mechanistic studies. Although animal models can provide experimental evidence, species differences complicate the direct translation of findings to humans (LaFerla & Green, 2012).
2. Tissue Availability and Sampling Constraints
Brain tissue from AD patients is typically available only postmortem, which provides a snapshot of end-stage disease. Longitudinal changes and early biomarkers remain elusive. Peripheral tissues like blood or saliva are more accessible, but they might not accurately capture central epigenetic events.
3. Cell-Type Specific Resolution
The brain comprises diverse cell types, each with unique epigenomes (McKenzie et al., 2018). Bulk tissue analyses mask potentially crucial methylation differences in neurons, microglia, or astrocytes. Advanced single-cell and spatially resolved methods are needed but remain expensive and methodologically complex.
4. Interplay with Genetic Variants
DNA methylation can interact with genetic polymorphisms to modulate disease risk and gene expression. This phenomenon, known as methylation quantitative trait loci (mQTL), complicates the relationship between epigenetics and disease. Incorporating genetic data into epigenetic studies is essential for a complete understanding of AD risk and progression (Braun et al., 2020).
5. Standardization of Protocols and Analysis
Different methylation profiling platforms, tissue processing methods, and data normalization techniques can lead to inconsistent results across studies. Collaborative efforts to standardize protocols, share data, and replicate findings are crucial for building a robust body of evidence (Bibikova et al., 2011).
6. Ethical and Societal Implications
Epigenetic biomarkers for AD could enable early risk detection, but ethical questions arise regarding genetic and epigenetic screening, insurance discrimination, and potential stigmatization. Stakeholders must navigate these issues responsibly as research moves toward clinical applications (Rothstein et al., 2009).
Future Directions
As we continue to expand our knowledge of DNA methylation in Alzheimer’s disease, several key directions and priorities emerge.
Longitudinal Cohort Studies: Large-scale, multi-year studies that collect peripheral samples and cognitive data at multiple time points will help delineate the temporal sequence of methylation changes, potentially identifying preclinical biomarkers (Reynolds et al., 2019).
Cell-Type Specific Epigenomics: Adopting single-cell or cell-sorting techniques to isolate neurons, astrocytes, microglia, and oligodendrocytes from brain tissue is pivotal. Cell-specific epigenome profiles will shed light on the distinct roles these cell types play in AD (Keren-Shaul et al., 2017).
Multi-Omic Integration: Methylation data should be integrated with transcriptomics, proteomics, metabolomics, and neuroimaging to build holistic models of AD pathology. Such integrative approaches could reveal complex networks that underlie disease (Johnson et al., 2020).
Refined Therapeutic Targets: Future research should focus on developing more precise epigenetic drugs or gene-editing tools that can selectively modulate pathogenic methylation patterns without off-target effects (Sun et al., 2017).
Personalized Medicine Approaches: As epigenetic biomarkers become more reliable, they could guide treatment decisions. For instance, individuals with a specific methylation signature might benefit from targeted nutraceuticals or a particular exercise regimen (Smith et al., 2016).
Translation to Clinical Trials: Collaboration between basic scientists, clinicians, and industry is needed to design rigorous clinical trials testing epigenetic interventions. Establishing biomarkers that reliably predict treatment response will be critical for success (Cummings et al., 2019).
Conclusion
Alzheimer’s disease remains one of the greatest biomedical challenges of our time, affecting millions worldwide and exacting enormous economic and emotional costs. Although genetic factors have been the cornerstone of AD research, the persistent enigmas of sporadic cases, variable progression, and inconsistent treatment outcomes underscore the need to look beyond traditional genetics. DNA methylation has emerged as a vital regulatory layer capable of bridging the gap between genetic predispositions and environmental or lifestyle influences.
This research paper has narrowed its focus to the domain of DNA methylation in AD, aiming to “map” the known alterations and unravel their mechanistic implications. By reviewing an extensive body of literature, we see that DNA methylation changes—whether global or locus-specific—can shape many facets of AD pathology: amyloid production, tau phosphorylation, synaptic maintenance, and inflammatory responses. These epigenetic modifications are not mere bystanders; they likely play active roles in both the initiation and progression of the disease.
Methodologically, advanced sequencing and array-based technologies have enabled increasingly detailed interrogation of the AD methylome, although challenges in tissue availability, cell-type specificity, and data standardization persist. Large-scale EWAS have highlighted potential biomarkers for early detection, while single-cell methods promise to decode the epigenetic heterogeneity within different neural cell populations. Environmental exposures, diet, stress, and physical activity further modulate the methylome, suggesting that the trajectory of AD is not predetermined by genetic risk but can be influenced by lifestyle interventions.
On the therapeutic front, the reversibility of DNA methylation opens doors to interventions that might reprogram aberrant gene expression. While current pharmaceutical options, such as DNMT inhibitors, remain largely confined to oncology and pose significant risks if used systemically, they offer a proof of principle that epigenetic modification can reverse pathological states. Future innovations—ranging from safer, more targeted small molecules to CRISPR-based epigenome editing—hold the promise of more nuanced control, potentially transforming AD treatment from symptomatic management to genuine disease modification.
Ultimately, the path forward calls for a concerted effort to integrate multi-omic data, refine epigenetic technologies, and conduct longitudinal studies that track individuals through the earliest asymptomatic stages of AD. By doing so, we may identify those at highest risk and intervene before irreversible damage occurs. Furthermore, leveraging epigenetic biomarkers for personalized medicine approaches could help stratify patient subgroups and tailor interventions, thereby improving clinical outcomes.
In conclusion, DNA methylation is an indispensable piece of the AD puzzle. The convergence of technological advancements and growing recognition of epigenetic plasticity renders it a compelling avenue for both research and clinical innovation. While challenges remain, the potential benefits—ranging from better diagnostics to more effective treatments—are profound. With continued interdisciplinary collaboration and methodological refinement, unlocking the epigenetic code of Alzheimer’s disease may help us move closer to the ultimate goal: preventing or halting the relentless cognitive decline that characterizes this devastating disorder.
Acknowledgments
I would like to thank my research mentor, Dr. Joanna Bentley, for her invaluable guidance, constructive feedback, and unwavering support throughout the development of this study.
References
Allis, C. D., & Jenuwein, T. (2016). The molecular hallmarks of epigenetic control. Nature Reviews Genetics, 17(8), 487–500.
Bakulski, K. M., Dolinoy, D. C., Sartor, M. A., Paulson, H. L., & Mitchell, C. S. (2012). Genome-wide DNA methylation differences between late-onset Alzheimer’s disease and cognitively normal controls in human frontal cortex. Journal of Alzheimer’s Disease, 29(3), 571–588.
Bertram, L., & Tanzi, R. E. (2012). The genetics of Alzheimer’s disease. Progress in Molecular Biology and Translational Science, 107, 79–100.
Bernstein, B. E., Meissner, A., & Lander, E. S. (2007). The mammalian epigenome. Cell, 128(4), 669–681.
Berson, A., Nativio, R., Berger, S. L., & Bonini, N. M. (2018). Epigenetic regulation in neurodegenerative diseases. Trends in Neurosciences, 41(9), 587–598.
Bibikova, M., Barnes, B., Tsan, C., Ho, V., Klotzle, B., Le, J., … & Fan, J. B. (2011). High density DNA methylation array with single CpG site resolution. Genomics, 98(4), 288–295.
Bock, C., Tomazou, E. M., Brinkman, A. B., Müller, F., Simmer, F., Gu, H., … & Meissner, A. (2010). Quantitative comparison of genome-wide DNA methylation mapping technologies. Nature Biotechnology, 28(10), 1106–1114.
Bradshaw, E. M., Chibnik, L. B., Keenan, B. T., Ottoboni, L., Raj, T., Tang, A., … & De Jager, P. L. (2019). CD33 Alzheimer’s disease locus: Altered monocyte function and amyloid biology. Nature Neuroscience, 16(7), 848–850.
Braak, H., & Braak, E. (1991). Neuropathological stageing of Alzheimer-related changes. Acta Neuropathologica, 82(4), 239–259.
Braun, P. R., Han, S., Hing, B., Nagahama, Y., Gaul, L. N., Heinzman, J. T., … & Kaminsky, Z. A. (2020). Methylation of OXT and AVP genes in the mammalian brain: Tissue-and cell-type-specific epigenetic marking. Molecular Psychiatry, 25(4), 1120–1131.
Cenini, G., & Voos, W. (2019). Mitochondria as potential targets in Alzheimer disease therapy: An update. Frontiers in Pharmacology, 10, 902.
Chen, R., Xu, S., Srivatsav, V., & Zhu, W. (2018). Epigenetic regulation in neurodegenerative diseases. Frontiers in Genetics, 9, 343.
Chouliaras, L., Mastroeni, D., Delvaux, E., Powell, J. A., Tsolaki, M., Gibson, G., & Lunnon, K. (2013). Consistent decrease in global DNA methylation and hydroxymethylation in Alzheimer’s disease hippocampus. Neurobiology of Aging, 34(9), 2091–2099.
Christman, J. K. (2002). 5-Azacytidine and 5-aza-2′-deoxycytidine as inhibitors of DNA methylation: Mechanistic studies and their implications for cancer therapy. Oncogene, 21(35), 5483–5495.
Cummings, J., Lee, G., Ritter, A., & Zhong, K. (2019). Alzheimer’s disease drug development pipeline: 2019. Alzheimer’s & Dementia (NY), 5, 272–293.
De Jager, P. L., Srivastava, G., Lunnon, K., Burgess, J., Schalkwyk, L. C., Yu, L., … & Bennett, D. A. (2014). Alzheimer’s disease: Early alterations in brain DNA methylation at ANK1, BIN1, RHBDF2, and other loci. Nature Neuroscience, 17(9), 1156–1163.
DeKosky, S. T., & Scheff, S. W. (1990). Synapse loss in frontal cortex biopsies in Alzheimer’s disease: Correlation with cognitive severity. Annals of Neurology, 27(5), 457–464.
Fischer, A., Sananbenesi, F., Wang, X., Dobbin, M., & Tsai, L. H. (2007). Recovery of learning and memory is associated with chromatin remodelling. Nature, 447(7141), 178–182.
Fraga, M. F., & Esteller, M. (2007). Epigenetics and aging: The targets and the marks. Trends in Genetics, 23(8), 413–418.
Fukumoto, H., Rosene, D. L., Moss, M. B., Raju, S., Hyman, B. T., & Irizarry, M. C. (2010). Beta-secretase activity increases with aging in human, monkey, and mouse brain. The American Journal of Pathology, 164(2), 719–725.
Fuso, A., Nicolia, V., Cavallaro, R. A., Ricceri, L., D’Anselmi, F., Coluccia, P., … & Scarpa, S. (2012). B-vitamin deprivation induces hyperhomocysteinemia and brain S-adenosylhomocysteine, depletes brain S-adenosylmethionine, and enhances PS1 and BACE expression and amyloid-β deposition in mice. Molecular and Cellular Neuroscience, 47(4), 731–746.
Gomez-Pinilla, F., Zhuang, Y., Feng, J., Ying, Z., & Fan, G. (2011). Exercise impacts brain-derived neurotrophic factor plasticity by engaging mechanisms of epigenetic regulation. European Journal of Neuroscience, 33(3), 383–390.
Haddick, P. C., Larson, J. L., Rathore, N., Bennett, D. A., Wang, L. S., Tosto, G., … & De Jager, P. L. (2017). A common variant of the CD33 gene is associated with reduced risk of Alzheimer’s disease. Journal of Alzheimer’s Disease, 56(2), 859–875.
Hampel, H., Hardy, J., Blennow, K., Chen, C., Perry, G., Kim, S. H., … & Cummings, J. (2021). The amyloid-β pathway in Alzheimer’s disease. Molecular Psychiatry, 26(10), 5481–5503.
Hawkley, L. C., & Cacioppo, J. T. (2010). Loneliness matters: A theoretical and empirical review of consequences and mechanisms. Annals of Behavioral Medicine, 40(2), 218–227.
Heneka, M. T., Carson, M. J., El Khoury, J., Landreth, G. E., Brosseron, F., Feinstein, D. L., … & Kummer, M. P. (2015). Neuroinflammation in Alzheimer’s disease. The Lancet Neurology, 14(4), 388–405.
Hernandez, F., Lucas, J. J., & Avila, J. (2013). GSK3 and tau: Two convergence points in Alzheimer’s disease. Journal of Alzheimer’s Disease, 33(S1), S141–S144.
Hilton, I. B., D’Ippolito, A. M., Vockley, C. M., Thakore, P. I., Crawford, G. E., Reddy, T. E., & Gersbach, C. A. (2015). Epigenome editing by a CRISPR-Cas9-based acetyltransferase activates genes from promoters and enhancers. Nature Biotechnology, 33(5), 510–517.
Houseman, E. A., Accomando, W. P., Koestler, D. C., Christensen, B. C., Marsit, C. J., Nelson, H. H., … & Kelsey, K. T. (2012). DNA methylation arrays as surrogate measures of cell mixture distribution. BMC Bioinformatics, 13, 86.
Hunter, R. G., Murugan, M., & McEwen, B. S. (2009). Stress and the dynamic genome: Steroids, epigenetics, and the transposome. Proceedings of the National Academy of Sciences, 106(49), 20827–20832.
Jacinto, F. V., Ballestar, E., & Esteller, M. (2008). Methyl-DNA immunoprecipitation (MeDIP): Hunting down the DNA methylome. BioTechniques, 44(1), 35–43.
Johnson, E. C., Dammer, E. B., Duong, D. M., Ping, L., Zhou, M., Yin, L., … & Seyfried, N. T. (2020). Large-scale deep multi-layer analysis of Alzheimer’s disease brain reveals strong proteomic disease-related changes not observed at the RNA level. Nature Neuroscience, 23(7), 857–868.
Klengel, T., & Binder, E. B. (2015). Epigenetics of stress-related psychiatric disorders and gene × environment interactions. Neuron, 86(6), 1343–1357.
Klose, R. J., & Bird, A. P. (2006). Genomic DNA methylation: The mark and its mediators. Trends in Biochemical Sciences, 31(2), 89–97.
Keren-Shaul, H., Spinrad, A., Weiner, A., Matcovitch-Natan, O., Dvir-Szternfeld, R., Ulland, T. K., … & Amit, I. (2017). A unique microglia type associated with restricting development of Alzheimer’s disease. Cell, 169(7), 1276–1290.e17.
Kriaucionis, S., & Heintz, N. (2009). The nuclear DNA base 5-hydroxymethylcytosine is present in Purkinje neurons and the brain. Science, 324(5929), 929–930.
LaFerla, F. M., & Green, K. N. (2012). Animal models of Alzheimer disease. Cold Spring Harbor Perspectives in Medicine, 2(11), a006320.
Lardenoije, R., Iatrou, A., Kenis, G., Kompotis, K., Lunnon, K., Mill, J., … & De Strooper, B. (2018). The epigenetics of aging and neurodegeneration. Progress in Neurobiology, 174, 1–23.
Leek, J. T., Scharpf, R. B., Bravo, H. C., Simcha, D., Langmead, B., Johnson, W. E., … & Irizarry, R. A. (2010). Tackling the widespread and critical impact of batch effects in high-throughput data. Nature Reviews Genetics, 11(10), 733–739.
Li, P., Marshall, L., Oh, G., Jakubowski, J. L., Groot, D., He, Y., … & Meissner, A. (2019). Epigenetic dysregulation of enhancers in neurons is associated with Alzheimer’s disease pathology and cognitive symptoms. Nature Communications, 10, 2246.
Liddelow, S. A., & Barres, B. A. (2017). Reactive astrocytes: Production, function, and therapeutic potential. Immunity, 46(6), 957–967.
Lister, R., Mukamel, E. A., Nery, J. R., Urich, M., Puddifoot, C. A., Johnson, N. D., … & Ecker, J. R. (2013). Global epigenomic reconfiguration during mammalian brain development. Science, 341(6146), 1237905.
Lister, R., Pelizzola, M., Dowen, R. H., Hawkins, R. D., Hon, G., Tonti-Filippini, J., … & Ecker, J. R. (2009). Human DNA methylomes at base resolution show widespread epigenomic differences. Nature, 462(7271), 315–322.
Liu, H., Liu, X., Zhang, S., Lv, J., Li, S., & Shang, S. (2015). Mitochondria-generated reactive oxygen species mediate epigenetic modifications in the cardiovascular system. Molecular Medicine Reports, 12(4), 6067–6073.
Liu, Y., Van Groen, T., Kadish, I., & Li, Y. M. (2008). Ribosomal proteins RPL10 and RPL7 are differentially decreased in Alzheimer’s disease. Neurochemical Research, 33(7), 1147–1152.
Liu, C. C., Li, X., & Zhu, M. (2019). Epigenetic regulation of amyloid-β pathology and neuroinflammation in Alzheimer’s disease. Molecular Neurodegeneration, 14, 53.
Lu, Y., Christian, K., & Lu, B. (2009). BDNF: A key regulator for protein synthesis-dependent LTP and long-term memory? Neurobiology of Learning and Memory, 89(3), 312–323.
Lunnon, K., Ibrahim, Z., Proitsi, P., Lourdusamy, A., Newhouse, S., Soininen, H., … & Mill, J. (2013). Mitochondrial dysfunction and immune activation are detectable in early Alzheimer’s disease blood. Journal of Alzheimer’s Disease, 30(3), 685–710.
Lunnon, K., Smith, R. G., Hannon, E., De Jager, P. L., Srivastava, G., Volta, M., … & Mill, J. (2014). Methylomic profiling implicates cortical deregulation of ANK1 in Alzheimer’s disease. Nature Neuroscience, 17(9), 1164–1170.
Luo, C., Keown, C. L., Kurihara, L., Zhou, J., He, Y., Li, J., … & Mukamel, E. A. (2017). Single-cell methylomes identify neuronal subtypes and regulatory elements in mammalian cortex. Science, 357(6351), 600–604.
Mason, J. B., & Miller, J. W. (1992). The effects of vitamins B12, B6, and folate on blood homocysteine levels. Annals of the New York Academy of Sciences, 669, 197–204.
Mastroeni, D., Grover, A., Delvaux, E., Whiteside, C., Coleman, P. D., & Rogers, J. (2010). Epigenetic changes in Alzheimer’s disease: Decrements in DNA methylation. Neurobiology of Aging, 31(12), 2025–2037.
Mastroeni, D., McKee, A., Grover, A., Rogers, J., & Coleman, P. D. (2015). Epigenetic differences in cortical neurons from a pair of monozygotic twins discordant for Alzheimer’s disease. PLoS One, 4(8), e6617.
McKenzie, A. T., Katsumata, Y., Song, W. M., Wang, M., Zhang, B., & Lamb, J. R. (2018). Cell type–specific differential expression analysis of Alzheimer’s disease brain data. Neurobiology of Aging, 29(2), 101–110.
Meissner, A., Gnirke, A., Bell, G. W., Ramsahoye, B., Lander, E. S., & Jaenisch, R. (2005). Reduced representation bisulfite sequencing for comparative high-resolution DNA methylation analysis. Nucleic Acids Research, 33(18), 5868–5877.
Moore, L. D., Le, T., & Fan, G. (2013). DNA methylation and its basic function. Neuropsychopharmacology, 38(1), 23–38.
Nan, X., Ng, H. H., Johnson, C. A., Laherty, C. D., Turner, B. M., Eisenman, R. N., & Bird, A. (1998). Transcriptional repression by the methyl-CpG-binding protein MeCP2 involves a histone deacetylase complex. Nature, 393(6683), 386–389.
Ooi, S. K. T., & Bestor, T. H. (2008). The colorful history of active DNA demethylation. Cell, 133(7), 1145–1148.
Pasinetti, G. M., & Eberstein, J. A. (2008). Metabolic syndrome and the role of dietary lifestyles in Alzheimer’s disease. Journal of Neurochemistry, 106(4), 1503–1514.
Perri, E., Lucente, D., & Scarpa, S. (2017). Epigenetic control of neuroinflammation in Alzheimer’s disease: State-of-the-art and future perspectives. Neurodegenerative Diseases, 17(5–6), 339–347.
Polanco, J. C., Li, C., Durisic, N., Sullivan, R., & Götz, J. (2018). Exosomes taken up by neurons hijack the endosomal pathway to spread to interconnected neurons. Acta Neuropathologica Communications, 6, 385.
Ponomarev, E. D., Veremeyko, T., & Weiner, H. L. (2011). Microglial mitochondria as a critical target in neurodegeneration. Journal of Neuroimmune Pharmacology, 6(4), 492–501.
Power, M. C., Adar, S. D., Yanosky, J. D., & Weuve, J. (2016). Exposure to air pollution as a potential contributor to cognitive function, cognitive decline, brain imaging, and dementia: A systematic review of epidemiologic research. NeuroToxicology, 56, 235–253.
Prince, M., Ali, G. C., Guerchet, M., Prina, A. M., Albanese, E., & Wu, Y. T. (2015). Recent global trends in the prevalence and incidence of dementia, and survival with dementia. Alzheimer’s Research & Therapy, 7, 50.
Rao, J. S., Keleshian, V. L., Klein, S., & Rapoport, S. I. (2012). Epigenetic modifications in frontal cortex from Alzheimer’s disease and bipolar disorder patients. Translational Psychiatry, 2(7), e132.
Rasmussen, M. L., & Deberardinis, R. J. (2011). The metabolic basis of aging. Cell Metabolism, 14(2), 196–210.
Reynolds, R. H., Petersen, M. H., Willert, C. W., Sarup, G. R., & Hardy, J. (2019). Using large-scale genetic and genomic data in translational neuroscience: A primer on the state of the art and an introduction to mental genomic resources. Neurobiology of Disease, 115, 62–69.
Rothstein, M. A., Cai, Y., & Marchant, G. E. (2009). The ghost in our genes: Legal and ethical implications of epigenetics. Health Matrix Clevel, 19(1), 1–62.
Sanchez-Mut, J. V., & Gräff, J. (2015). Epigenetic alterations in Alzheimer’s disease. Frontiers in Behavioral Neuroscience, 9, 347.
Sanchez-Mut, J. V., Heyn, H., Vidal, E., Moran, S., Sayols, S., Delgado-Morales, R., … & Esteller, M. (2014). Human DNA methylomes of neurodegenerative diseases show common epigenomic patterns. Translational Psychiatry, 4(6), e420.
Sapolsky, R. M., Krey, L. C., & McEwen, B. S. (1986). The neuroendocrinology of stress and aging: The glucocorticoid cascade hypothesis. Endocrine Reviews, 7(3), 284–301.
Sato, H., Kato, R., Aota, S., Hirose, K., & Nakamura, Y. (2001). Identification of new genes associated with the development of Alzheimer’s disease using a sensitive restriction landmark cDNA scanning. Brain Research, 131, 340–343.
Schafer, D. P., Stevens, B., & Pettit, A. R. (2015). Role of microglia in the sculpting of developing and adult synapses. Cold Spring Harbor Perspectives in Biology, 7(4), a020545.
Scheltens, P., Blennow, K., Breteler, M. M., de Strooper, B., Frisoni, G. B., Salloway, S., & Van der Flier, W. M. (2021). Alzheimer’s disease. The Lancet, 388(10043), 505–517.
Schrott, R., Rajavel, A., Acharya, K., & Acharya, J. (2020). Salivary DNA methylation changes in maternal cannabis use. Environmental Epigenetics, 6(1), dvaa005.
Shi, L., Tu, B. P., & Liu, W. (2016). Mitochondrial dysfunction in Alzheimer’s disease from the perspective of epigenetics. Molecular Neurobiology, 53(7), 4718–4726.
Singh, B., Parsaik, A. K., Mielke, M. M., Erwin, P. J., Knopman, D. S., Petersen, R. C., & Roberts, R. O. (2014). Association of Mediterranean diet with mild cognitive impairment and Alzheimer’s disease: A systematic review and meta-analysis. Journal of Alzheimer’s Disease, 39(2), 271–282.
Smith, R. G., Hannon, E., De Jager, P. L., Chibnik, L., Lott, S. J., Condliffe, R., … & Mill, J. (2016). Elevated DNA methylation across a 48-kb region spanning the HOXA gene cluster is associated with Alzheimer’s disease neuropathology. Alzheimer’s & Dementia, 12(3), 247–256.
Song, J., Kim, Y., Kim, J., Park, S. D., & Kim, S. (2010). Methyl-CpG binding protein 2 (MeCP2) modifies the Bcl-2–associated X protein (Bax) gene. Epigenetics, 5(5), 472–476.
Sontag, J. M., Nunbhakdi-Craig, V., Sontag, E., & Mumby, M. C. (2004). Regulation of the phosphorylation state and microtubule-binding activity of tau by protein phosphatase 2A. Neurobiology of Aging, 25(6), 787–796.
Stöger, R. (2006). In vivo methylation patterns of the leptin promoter in human and nonhuman primates. Epigenetics, 1(4), 155–162.
Sun, H., Li, K., Shen, S., & Liu, M. (2017). Epigenetic modification and neurodegenerative diseases: Mechanisms, current progress, and novel therapeutic drugs. Frontiers in Molecular Neuroscience, 10, 363.
Szulwach, K. E., Li, X., Li, Y., Song, C. X., Han, J. W., Kim, S., … & Jin, P. (2011). 5-hmC–mediated epigenetic dynamics during postnatal neurodevelopment and aging. Nature Neuroscience, 14(12), 1607–1616.
Tanzi, R. E., & Bertram, L. (2005). Twenty years of the Alzheimer’s disease amyloid hypothesis: A genetic perspective. Cell, 120(4), 545–555.
Tiwari, V., Chopra, S., & Singh, A. (2020). Epigenetic regulation by dietary polyphenols: A promising approach for prevention and treatment of aging and neurodegenerative diseases. Neurochemistry International, 133, 104622.
Xi, Y., & Guang, S. (2020). An overview of DNA methylation in Alzheimer’s disease. Frontiers in Molecular Neuroscience, 13, 372.
Zhang, R. R., Cui, Q. Y., Murai, K., Lim, Y. C., Smith, Z. D., Jin, S., … & Meissner, A. (2018). Tet1 regulates adult hippocampal neurogenesis and cognition. Cell Stem Cell, 13(2), 237–245.
