DNA methylation and inflammation marker profiles associated with a history of depression

Epigenetic Aging in Major Depressive Disorder: Clocks, Mechanisms and Therapeutic Perspectives

Depression, a chronic mental disorder characterized by persistent sadness, loss of interest, and difficulty in daily tasks, impacts millions globally with varying treatment options. Antidepressants, despite their long half-life and minimal effectiveness, leave half of patients undertreated, highlighting the need for new therapies to enhance well-being. Epigenetics, which studies genetic changes in gene expression or cellular phenotype without altering the underlying Deoxyribonucleic Acid (DNA) sequence, is explored in this article. This article delves into the intricate relationship between epigenetic mechanisms and depression, shedding light on how environmental stressors, early-life adversity, and genetic predispositions shape gene expression patterns associated with depression. We have also discussed Histone Deacetylase (HDAC) inhibitors, which enhance cognitive function and mood regulation in depression. Non-coding RNAs, (ncRNAs) such as Long Non-Coding RNAs (lncRNAs) and micro RNA (miRNAs), are highlighted as potential biomarkers for detecting and monitoring major depressive disorder (MDD). This article also emphasizes the reversible nature of epigenetic modifications and their influence on neuronal growth processes, underscoring the dynamic interplay between genetics, environment, and epigenetics in depression development. It explores the therapeutic potential of targeting epigenetic pathways in treating clinical depression. Additionally, it examines clinical findings related to epigenetic clocks and their role in studying depression and biological aging.

Correlation between immune-related genes and depression-like features in an animal model and in humans

A growing body of evidence suggests that immune-related genes play pivotal roles in the pathophysiology of depression. In the present study, we investigated a plausible connection between gene expression, DNA methylation, and brain structural changes in the pathophysiology of depression using a combined approach of murine and human studies. We ranked the immobility behaviors of 30 outbred Crl:CD1 (ICR) mice in the forced swim test (FST) and harvested their prefrontal cortices for RNA sequencing. Of the 24,532 analyzed genes, 141 showed significant correlations with FST immobility time, as determined through linear regression analysis with p ≤ 0.01. The identified genes were mostly involved in immune responses, especially interferon signaling pathways. Moreover, induction of virus-like neuroinflammation in the brains of two separate mouse cohorts (n = 30 each) using intracerebroventricular polyinosinic:polycytidylic acid injection resulted in increased immobility during FST and similar expression of top immobility-correlated genes. In human blood samples, candidate gene (top 5%) expression profiling using DNA methylation analysis found the interferon-related USP18 (cg25484698, p = 7.04 × 10-11, Δβ = 1.57 × 10-2; cg02518889, p = 2.92 × 10-3, Δβ =  - 8.20 × 10-3) and IFI44 (cg07107453, p = 3.76 × 10-3, Δβ =  - 4.94 × 10-3) genes to be differentially methylated between patients with major depressive disorder (n = 350) and healthy controls (n = 161). Furthermore, cortical thickness analyses using T1-weighted images revealed that the DNA methylation scores for USP18 were negatively correlated with the thicknesses of several cortical regions, including the prefrontal cortex. Our results reveal the important role of the interferon pathway in depression and suggest USP18 as a potential candidate target. The results of the correlation analysis between transcriptomic data and animal behavior carried out in this study provide insights that could enhance our understanding of depression in humans.

A comparison of feature selection methodologies and learning algorithms in the development of a DNA methylation-based telomere length estimator

BACKGROUND

The field of epigenomics holds great promise in understanding and treating disease with advances in machine learning (ML) and artificial intelligence being vitally important in this pursuit. Increasingly, research now utilises DNA methylation measures at cytosine-guanine dinucleotides (CpG) to detect disease and estimate biological traits such as aging. Given the challenge of high dimensionality of DNA methylation data, feature-selection techniques are commonly employed to reduce dimensionality and identify the most important subset of features. In this study, our aim was to test and compare a range of feature-selection methods and ML algorithms in the development of a novel DNA methylation-based telomere length (TL) estimator. We utilised both nested cross-validation and two independent test sets for the comparisons.

RESULTS

We found that principal component analysis in advance of elastic net regression led to the overall best performing estimator when evaluated using a nested cross-validation analysis and two independent test cohorts. This approach achieved a correlation between estimated and actual TL of 0.295 (83.4% CI [0.201, 0.384]) on the EXTEND test data set. Contrastingly, the baseline model of elastic net regression with no prior feature reduction stage performed less well in general-suggesting a prior feature-selection stage may have important utility. A previously developed TL estimator, DNAmTL, achieved a correlation of 0.216 (83.4% CI [0.118, 0.310]) on the EXTEND data. Additionally, we observed that different DNA methylation-based TL estimators, which have few common CpGs, are associated with many of the same biological entities.

CONCLUSIONS

The variance in performance across tested approaches shows that estimators are sensitive to data set heterogeneity and the development of an optimal DNA methylation-based estimator should benefit from the robust methodological approach used in this study. Moreover, our methodology which utilises a range of feature-selection approaches and ML algorithms could be applied to other biological markers and disease phenotypes, to examine their relationship with DNA methylation and predictive value.

Relationship between telomere shortening and early subjective depressive symptoms and cognitive complaints in older adults

Telomere length (TL) has been reported to be associated with depression and cognitive impairment in elderly. Early detection of depression and cognitive impairment is important to delay disease progression. Therefore, we aimed to identify whether TL is associated with early subjective depressive symptoms and cognitive complaints among healthy elderly subjects. This study was a multicenter, outcome assessor-blinded, 24-week, randomized controlled trial (RCT). Measurement of questionnaire and physical activity scores and blood sample analyses were performed at baseline and after six months of follow-up in all study participants. Linear regression analyses were performed to identify whether early subjective depressive symptoms, cognitive complaints, and several blood biomarkers are associated with TL. Altogether, 137 relatively healthy elderly individuals (60-79 years old) were enrolled in this prospective RCT. We observed an approximate decrease of 0.06 and 0.11-0.14 kbps of TL per one point increase in the geriatric depression scale and cognitive complaint interview scores, respectively, at baseline and after six months of follow-up. We also found an approximate decrease of 0.08-0.09 kbps of TL per one point increase in interleukin (IL)-6 levels at baseline and after six months of follow-up. Our study showed that both early subjective depressive symptoms and cognitive complaints were associated with a relatively shorter TL in relatively healthy elderly individuals. In addition, based on our findings, we believe that IL-6 plays an important role in the relationship between shortening TL and early subjective depressive symptoms and cognitive complaints.

The Role of Inflammation in the Pathophysiology of Depression and Suicidal Behavior: Implications for Treatment

Depression and suicidal behavior are 2 complex psychiatric conditions of significant public health concerns due to their debilitating nature. The need to enhance contemporary treatments and preventative approaches for these illnesses not only calls for distillation of current views on their pathogenesis but also provides an impetus for further elucidation of their novel etiological determinants. In this regard, inflammation has recently been recognized as a potentially important contributor to the development of depression and suicidal behavior. This review highlights key evidence that supports the presence of dysregulated neurometabolic and immunologic signaling and abnormal interaction with microbial species as putative etiological hallmarks of inflammation in depression as well as their contribution to the development of suicidal behavior. Furthermore, therapeutic insights addressing candidate mechanisms of pathological inflammation in these disorders are proposed.

Co-expression network of mRNA and DNA methylation in first-episode and drug-naive adolescents with major depressive disorder

OBJECTIVE

We explored the DNA methylation and messenger RNA (mRNA) co-expression network and hub genes in first-episode, drug-naive adolescents with major depressive disorder (MDD). To preliminarily explore whether adolescent MDD has unique mechanisms compared with adult MDD.

METHODS

We compared DNA methylation and mRNA profiles of peripheral blood mononuclear cells from four first-episode and drug-naive adolescents with MDD and five healthy adolescent controls (HCs). We performed differential expression analysis, constructed co-expression network, and screened the hub genes. And enrichment analysis was performed based on Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). We also downloaded DNA methylation and mRNA datasets of adults with MDD (GSE113725/GSE38206) from the GEO database, and performed differential expression and enrichment analysis.

RESULTS

Our clinical data showed that 3034 methylation sites and 4190 mRNAs were differentially expressed in first-episode, drug-naive adolescents MDD patients compared with HCs. 19 hub genes were screened out according to the high degree value in the co-expression network. The results from the GEO database showed that compared with adult HCs, there were 290 methylation sites and 127 mRNAs were differentially expressed in adult MDD patients.

CONCLUSION

Compared with adolescent HCs and adult MDD patients, the DNA methylation and mRNA expression patterns of first-episode, drug-naive adolescent MDD patients were different. The co-expression network of DNA methylation and mRNA and the screened hub genes may play an important role in the pathogenesis of MDD in first-episode, drug-naive adolescents. Compared with adult MDD, adolescent MDD is more enriched in metabolism in terms of function and pathways.

The role of DNA methylation in progression of neurological disorders and neurodegenerative diseases as well as the prospect of using DNA methylation inhibitors as therapeutic agents for such disorders

Genome-wide studies related to neurological disorders and neurodegenerative diseases have pointed to the role of epigenetic changes such as DNA methylation, histone modification, and noncoding RNAs. DNA methylation machinery controls the dynamic regulation of methylation patterns in discrete brain regions.

Objective

This review aims to describe the role of DNA methylation in inhibiting and progressing neurological and neurodegenerative disorders and therapeutic approaches.

Methods

A Systematic search of PubMed, Web of Science, and Cochrane Library was conducted for all qualified studies from 2000 to 2022.

Results

For the current need of time, we have focused on the DNA methylation role in neurological and neurodegenerative diseases and the expression of genes involved in neurodegeneration such as Alzheimer's, Depression, and Rett Syndrome. Finally, it appears that the various epigenetic changes do not occur separately and that DNA methylation and histone modification changes occur side by side and affect each other. We focused on the role of modification of DNA methylation in several genes associated with depression (NR3C1, NR3C2, CRHR1, SLC6A4, BDNF, and FKBP5), Rett syndrome (MECP2), Alzheimer's, depression (APP, BACE1, BIN1 or ANK1) and Parkinson's disease (SNCA), as well as the co-occurring modifications to histones and expression of non-coding RNAs. Understanding these epigenetic changes and their interactions will lead to better treatment strategies.

Conclusion

This review captures the state of understanding of the epigenetics of neurological and neurodegenerative diseases. With new epigenetic mechanisms and targets undoubtedly on the horizon, pharmacological modulation and regulation of epigenetic processes in the brain holds great promise for therapy.

DNA Methylation in Depression and Depressive-Like Phenotype: Biomarker or Target of Pharmacological Intervention?

Major depressive disorder (MDD) is a debilitating psychiatric disorder, the third leading global cause of disability. Regarding aetiopathogenetic mechanisms involved in the onset of depressive disorders, the interaction between genetic vulnerability traits and environmental factors is believed to play a major role. Although much is still to be elucidated about the mechanisms through which the environment can interact with genetic background shaping the disease risk, there is a general agreement about a key role of epigenetic marking. In this narrative review, we focused on the association between changes in DNA methylation patterns and MDD or depressive-like phenotype in animal models, as well as mechanisms of response to antidepressant drugs. We discussed studies presenting DNA methylation changes at specific genes of interest and profiling analyses in both patients and animal models of depression. Overall, we collected evidence showing that DNA methylation could not only be considered as a promising epigenetic biomarker of pathology but could also help in predicting antidepressant treatment efficacy. Finally, we discussed the hypothesis that specific changes in DNA methylation signature could play a role in aetiopathogenetic processes as well as in the induction of antidepressant effect.

Elevated BICD2 DNA methylation in blood of major depressive disorder patients and reduction of depressive-like behaviors in hippocampal Bicd2-knockdown mice

Major depressive disorder (MDD) is a prevalent and devastating mental illness. To date, the diagnosis of MDD is largely dependent on clinical interviews and questionnaires and still lacks a reliable biomarker. DNA methylation has a stable and reversible nature and is likely associated with the course and therapeutic efficacy of complex diseases, which may play an important role in the etiology of a disease. Here, we identified and validated a DNA methylation biomarker for MDD from four independent cohorts of the Chinese Han population. First, we integrated the analysis of the DNA methylation microarray (n = 80) and RNA expression microarray data (n = 40) and identified BICD2 as the top-ranked gene. In the replication phase, we employed the Sequenom MassARRAY method to confirm the DNA hypermethylation change in a large sample size (n = 1,346) and used the methylation-sensitive restriction enzymes and a quantitative PCR approach (MSE-qPCR) and qPCR method to confirm the correlation between DNA hypermethylation and mRNA down-regulation of BICD2 (n = 60). The results were replicated in the peripheral blood of mice with depressive-like behaviors, while in the hippocampus of mice, Bicd2 showed DNA hypomethylation and mRNA/protein up-regulation. Hippocampal Bicd2 knockdown demonstrates antidepressant action in the chronic unpredictable mild stress (CUMS) mouse model of depression, which may be mediated by increased BDNF expression. Our study identified a potential DNA methylation biomarker and investigated its functional implications, which could be exploited to improve the diagnosis and treatment of MDD.

Antidepressant and anxiolytic-like effects of ayahuasca in rats subjected to LPS-induced neuroinflammation

The objective of this study was to evaluate the behavioral response of ayahuasca in rats submitted to neuroinflammation through the intraperitoneal application of lipopolysaccharide (0.63 mg/kg/mL). Eighty animals, male, about 90 days old, were divided into control and LPS groups and later into prevention and treatment subgroups. The prevention subgroup was administered ayahuasca or saline solution, via gavage, at a dose of 4 mL/kg one hour before applying LPS or saline, while the treatment subgroup received the same dose of the respective substances 24 h after intraperitoneal applications. Behavioral parameters were evaluated using open field (anxiety-like) and forced swimming (depressive-like) tests. A decrease in LPS/AYA rats in the prevention and treatment subgroups regarding anxiety-like behavior was observed. As for the depressive-like behavior, there was a decrease in the group induced to the disease model, both in the prevention subgroup (when compared to the SAL/SAL, SAL/AYA, and LPS/AYA with LPS/SAL groups) and in the treatment (when comparing SAL/SAL and LPS/AYA with LPS/SAL). This study concludes the anxiolytic and antidepressant potential of ayahuasca in an animal model of neuroinflammation, possibly due to the antineuroinflammatory effects already reported of the compound.

Associations Between DNA Methylation Age Acceleration, Depressive Symptoms, and Cardiometabolic Traits in African American Mothers From the InterGEN Study

Background

African American women (AAW) have a high risk of both cardiometabolic (CM) illness and depressive symptoms. Depressive symptoms co-occur in individuals with CM illness at higher rates than the general population, and accelerated aging may explain this. In this secondary analysis, we examined associations between age acceleration; depressive symptoms; and CM traits (hypertension, diabetes mellitus [DM], and obesity) in a cohort of AAW.

Methods

Genomic and clinical data from the InterGEN cohort (n = 227) were used. Age acceleration was based on the Horvath method of DNA methylation (DNAm) age estimation. Accordingly, DNAm age acceleration (DNAm AA) was defined as the residuals from a linear regression of DNAm age on chronological age. Spearman's correlations, linear and logistic regression examined associations between DNAm AA, depressive symptoms, and CM traits.

Results

DNAm AA did not associate with total depressive symptom scores. DNAm AA correlated with specific symptoms including self-disgust/self-hate (-0.13, 95% CI -0.26, -0.01); difficulty with making decisions (-0.15, 95% CI -0.28, -0.02); and worry over physical health (0.15, 95% CI 0.02, 0.28), but were not statistically significant after multiple comparison correction. DNAm AA associated with obesity (0.08, 95% CI 1.02, 1.16), hypertension (0.08, 95% CI 1.01, 1.17), and DM (0.20, 95% CI 1.09, 1.40), after adjustment for potential confounders.

Conclusions

Associations between age acceleration and depressive symptoms may be highly nuanced and dependent on study design contexts. Factors other than age acceleration may explain the connection between depressive symptoms and CM traits. AAW with CM traits may be at increased risk of accelerated aging.

The molecular pathophysiology of mood disorders: From the analysis of single molecular layers to multi-omic integration

Next-generation sequencing now enables the rapid and affordable production of reliable biological data at multiple molecular levels, collectively referred to as "omics". To maximize the potential for discovery, computational biologists have created and adapted integrative multi-omic analytical methods. When applied to diseases with traceable pathophysiology such as cancer, these new algorithms and statistical approaches have enabled the discovery of clinically relevant molecular mechanisms and biomarkers. In contrast, these methods have been much less applied to the field of molecular psychiatry, although diagnostic and prognostic biomarkers are similarly needed. In the present review, we first briefly summarize main findings from two decades of studies that investigated single molecular processes in relation to mood disorders. Then, we conduct a systematic review of multi-omic strategies that have been proposed and used more recently. We also list databases and types of data available to researchers for future work. Finally, we present the newest methodologies that have been employed for multi-omics integration in other medical fields, and discuss their potential for molecular psychiatry studies.

Association of DNA Methylation of the NLRP3 Gene with Changes in Cortical Thickness in Major Depressive Disorder

The Nod-like receptor pyrin containing 3 (NLRP3) inflammasome has been reported to be a convergent point linking the peripheral immune response induced by psychological stress and neuroinflammatory processes in the brain. We aimed to identify differences in the methylation profiles of the NLRP3 gene between major depressive disorder (MDD) patients and healthy controls (HCs). We also investigated the correlation of the methylation score of loci in NLRP3 with cortical thickness in the MDD group using magnetic resonance imaging (MRI) data. A total of 220 patients with MDD and 82 HCs were included in the study, and genome-wide DNA methylation profiling of the NLRP3 gene was performed. Among the total sample, 88 patients with MDD and 74 HCs underwent T1-weighted structural MRI and were included in the neuroimaging–methylation analysis. We identified five significant differentially methylated positions (DMPs) in NLRP3. In the MDD group, the methylation scores of cg18793688 and cg09418290 showed significant positive or negative correlations with cortical thickness in the occipital, parietal, temporal, and frontal regions, which showed significant differences in cortical thickness between the MDD and HC groups. Our findings suggest that NLRP3 DNA methylation may predispose to depression-related brain structural changes by increasing NLRP3 inflammasome-related neuroinflammatory processes in MDD.

Current Advances in Coptidis Rhizoma for Gastrointestinal and Other Cancers

Cancer is a serious disease with an increasing number of reported cases and high mortality worldwide. Gastrointestinal cancer defines a group of cancers in the digestive system, e.g., liver cancer, colorectal cancer, and gastric cancer. Coptidis Rhizoma (C. Rhizoma; Huanglian, in Chinese) is a classical Chinese medicinal botanical drug for the treatment of gastrointestinal disorders and has been shown to have a wide variety of pharmacological activity, including antifungal, antivirus, anticancer, antidiabetic, hypoglycemic, and cardioprotective effects. Recent studies on C. Rhizoma present significant progress on its anticancer effects and the corresponding mechanisms as well as its clinical applications. Herein, keywords related to C. Rhizoma, cancer, gastrointestinal cancer, and omics were searched in PubMed and the Web of Science databases, and more than three hundred recent publications were reviewed and discussed. C. Rhizoma extract along with its main components, berberine, palmatine, coptisine, magnoflorine, jatrorrhizine, epiberberine, oxyepiberberine, oxyberberine, dihydroberberine, columbamine, limonin, and derivatives, are reviewed. We describe novel and classic anticancer mechanisms from various perspectives of pharmacology, pharmaceutical chemistry, and pharmaceutics. Researchers have transformed the chemical structures and drug delivery systems of these components to obtain better efficacy and bioavailability of C. Rhizoma. Furthermore, C. Rhizoma in combination with other drugs and their clinical application are also summarized. Taken together, C. Rhizoma has broad prospects as a potential adjuvant candidate against cancers, making it reasonable to conduct additional preclinical studies and clinical trials in gastrointestinal cancer in the future.

Integrated co-expression network analysis uncovers novel tissue-specific genes in major depressive disorder and bipolar disorder

Tissue-specific gene expression has been found to be associated with multiple complex diseases including cancer, metabolic disease, aging, etc. However, few studies of brain-tissue-specific gene expression patterns have been reported, especially in psychiatric disorders. In this study, we performed joint analysis on large-scale transcriptome multi-tissue data to investigate tissue-specific expression patterns in major depressive disorder (MDD) and bipolar disorder (BP). We established the strategies of identifying tissues-specific modules, annotated pathways for elucidating biological functions of tissues, and tissue-specific genes based on weighted gene co-expression network analysis (WGCNA) and robust rank aggregation (RRA) with transcriptional profiling data from different human tissues and genome wide association study (GWAS) data, which have been expanded into overlapping tissue-specific modules and genes sharing with MDD and BP. Nine tissue-specific modules were identified and distributed across the four tissues in the MDD and six modules in the BP. In general, the annotated biological functions of differentially expressed genes (DEGs) in blood were mainly involved in MDD and BP progression through immune response, while those in the brain were in neuron and neuroendocrine response. Tissue-specific genes of the prefrontal cortex (PFC) in MDD-, such as IGFBP2 and HTR1A, were involved in disease-related functions, such as response to glucocorticoid, taste transduction, and tissue-specific genes of PFC in BP-, such as CHRM5 and LTB4R2, were involved in neuroactive ligand-receptor interaction. We also found PFC tissue-specific genes including SST and CRHBP were shared in MDD-BP, SST was enriched in neuroactive ligand-receptor interaction, and CRHBP shown was related to the regulation of hormone secretion and hormone transport.

Assessing the co-variability of DNA methylation across peripheral cells and tissues: Implications for the interpretation of findings in epigenetic epidemiology

Most epigenome-wide association studies (EWAS) quantify DNA methylation (DNAm) in peripheral tissues such as whole blood to identify positions in the genome where variation is statistically associated with a trait or exposure. As whole blood comprises a mix of cell types, it is unclear whether trait-associated DNAm variation is specific to an individual cellular population. We collected three peripheral tissues (whole blood, buccal epithelial and nasal epithelial cells) from thirty individuals. Whole blood samples were subsequently processed using fluorescence-activated cell sorting (FACS) to purify five constituent cell-types (monocytes, granulocytes, CD4⁺ T cells, CD8⁺ T cells, and B cells). DNAm was profiled in all eight sample-types from each individual using the Illumina EPIC array. We identified significant differences in both the level and variability of DNAm between different sample types, and DNAm data-derived estimates of age and smoking were found to differ dramatically across sample types from the same individual. We found that for the majority of loci variation in DNAm in individual blood cell types was only weakly predictive of variance in DNAm measured in whole blood, although the proportion of variance explained was greater than that explained by either buccal or nasal epithelial samples. Covariation across sample types was much higher for DNAm sites influenced by genetic factors. Overall, we observe that DNAm variation in whole blood is additively influenced by a combination of the major blood cell types. For a subset of sites, however, variable DNAm detected in whole blood can be attributed to variation in a single blood cell type providing potential mechanistic insight about EWAS findings. Our results suggest that associations between whole blood DNAm and traits or exposures reflect differences in multiple cell types and our data will facilitate the interpretation of findings in epigenetic epidemiology.

As epigenetic variation is cell-type specific, an ongoing challenge in epigenetic epidemiology is how to interpret studies performed using bulk tissue (for example, whole blood) which comprises a mix of different cell types. In this study, we identified major differences in DNA methylation (DNAm) across multiple peripheral tissues and different blood cell types, with each sample type being characterized by a unique signature across multiple genomic loci. We demonstrate how these differences influence commonly used prediction scores derived from DNAm data for age and tobacco smoking, with estimates for the same individual being highly variable across tissues and cell types. Our results enabled us to assess the extent to which variable DNAm in each individual blood cell type relates to variation measured in whole blood. We found that although individual blood cell types predict more of the variation in DNAm in whole blood compared to buccal and nasal epithelial cells, the actual proportion of variance explained is relatively small, except for at sites where DNAm is under genetic control. Our data indicate that for most sites variation in multiple blood cell types additively combines to drive variation in DNAm measured in whole blood. Of note, for a subset of sites, variation in DNAm detected in whole blood can be attributed to a specific blood cell type, potentially facilitating the interpretation of EWAS findings.

The impact of psychopathology, social adversity and stress-relevant DNA methylation on prospective risk for post-traumatic stress: A machine learning approach

BACKGROUND

A range of factors have been identified that contribute to greater incidence, severity, and prolonged course of post-traumatic stress disorder (PTSD), including: comorbid and/or prior psychopathology; social adversity such as low socioeconomic position, perceived discrimination, and isolation; and biological factors such as genomic variation at glucocorticoid receptor regulatory network (GRRN) genes. This complex etiology and clinical course make identification of people at higher risk of PTSD challenging. Here we leverage machine learning (ML) approaches to identify a core set of factors that may together predispose persons to PTSD.

METHODS

We used multiple ML approaches to assess the relationship among DNA methylation (DNAm) at GRRN genes, prior psychopathology, social adversity, and prospective risk for PTS severity (PTSS).

RESULTS

ML models predicted prospective risk of PTSS with high accuracy. The Gradient Boost approach was the top-performing model with mean absolute error of 0.135, mean square error of 0.047, root mean square error of 0.217, and R2 of 95.29%. Prior PTSS ranked highest in predicting the prospective risk of PTSS, accounting for >88% of the prediction. The top ranked GRRN CpG site was cg05616442, in AKT1, and the top ranked social adversity feature was loneliness.

CONCLUSION

Multiple factors including prior PTSS, social adversity, and DNAm play a role in predicting prospective risk of PTSS. ML models identified factors accounting for increased PTSS risk with high accuracy, which may help to target risk factors that reduce the likelihood or course of PTSD, potentially pointing to approaches that can lead to early intervention.

LIMITATION

One of the limitations of this study is small sample size.

Integrated Analysis of Methylomic and Transcriptomic Data to Identify Potential Diagnostic Biomarkers for Major Depressive Disorder

Major depressive disorder (MDD) is a mental illness with high incidence and complex etiology, that poses a serious threat to human health and increases the socioeconomic burden. Currently, high-accuracy biomarkers for MDD diagnosis are urgently needed. This paper aims to identify novel blood-based diagnostic biomarkers for MDD. Whole blood DNA methylation data and gene expression data from the Gene Expression Omnibus database are downloaded. Then, differentially expressed/methylated genes (DEGs/DMGs) are identified. In addition, we made a systematic analysis of the DNA methylation on 5′-C-phosphate-G-3′ (CpGs) in all of the gene regions, as well as different gene regions, and then we defined a “dominant” region. Subsequently, integrated analysis is employed to identify the robust MDD-related blood biomarkers. Finally, a gene expression classifier and a methylation classifier are constructed using the random forest algorithm and the leave-one-out cross-validation method. Our results demonstrate that DEGs are mainly involved in the inflammatory response-associated pathways, while DMGs are primarily concentrated in the neurodevelopment- and neuroplasticity-associated pathways. Our integrated analysis identified 46 hypo-methylated and up-regulated (hypo-up) genes and 71 hyper-methylated and down-regulated (hyper-down) genes. One gene expression classifier and two DNA methylation classifiers, based on the CpGs in all of the regions or in the dominant regions are constructed. The gene expression classifier possessed the best predictive ability, followed by the DNA methylation classifiers, based on the CpGs in both the dominant regions and all of the regions. In summary, the integrated analysis of DNA methylation and gene expression has identified 46 hypo-up genes and 71 hyper-down genes, which could be used as diagnostic biomarkers for MDD.

Distinct epigenetic signatures between adult-onset and late-onset depression

The heterogeneity of major depressive disorder (MDD) is attributed to the fact that diagnostic criteria (e.g., DSM-5) are only based on clinical symptoms. The discovery of blood biomarkers has the potential to change the diagnosis of MDD. The purpose of this study was to identify blood biomarkers of DNA methylation by strategically subtyping patients with MDD by onset age. We analyzed genome-wide DNA methylation of patients with adult-onset depression (AOD; age ≥ 50 years, age at depression onset < 50 years; N = 10) and late-onset depression (LOD; age ≥ 50 years, age at depression onset ≥ 50 years; N = 25) in comparison to that of 30 healthy subjects. The methylation profile of the AOD group was not only different from that of the LOD group but also more homogenous. Six identified methylation CpG sites were validated by pyrosequencing and amplicon bisulfite sequencing as potential markers for AOD in a second set of independent patients with AOD and healthy control subjects (N = 11). The combination of three specific methylation markers achieved the highest accuracy (sensitivity, 64%; specificity, 91%; accuracy, 77%). Taken together, our findings suggest that DNA methylation markers are more suitable for AOD than for LOD patients.

Anti-inflammatory activity of ayahuasca: therapeutical implications in neurological and psychiatric diseases

Ayahuasca is a decoction with psychoactive properties, used for millennia for therapeutic and religious purposes by indigenous groups and the population of amazonian countries. As described in this narrative review, it is essentially constituted by β-carbolines and tryptamines, and it has therapeutic effects on behavioral disorders due to the inhibition of the monoamine oxidase enzyme and the activation of 5-hydroxytryptamine receptors, demonstrated through preclinical and clinical studies. It was recently observed that the pharmacological response presented by ayahuasca is linked to its anti-inflammatory action, attributed mainly to dimethyltryptamines (N, N-dimethyltryptamine and 5-methoxy-N, N-dimethyltryptamine), which act as endogenous systemic regulators of inflammation and immune homeostasis, also through sigma-1 receptors. Therefore, since neuroinflammation is among the main pathophysiological mechanisms related to the development of neurological and psychiatric diseases, we suggest, based on the available evidence, that ayahuasca is a promising and very safe therapeutic strategy since extremely high doses are required to reach toxicity. However, even so, additional studies are needed to confirm such evidence, as well as the complete elucidation of the mechanisms involved.

Incidence of depression in relation to transportation noise exposure and noise annoyance in the SAPALDIA study

Prospective evidence on the risk of depression in relation to transportation noise exposure and noise annoyance is limited and mixed. We aimed to investigate the associations of long-term exposure to source-specific transportation noise and noise annoyance with incidence of depression in the SAPALDIA (Swiss cohort study on air pollution and lung and heart diseases in adults) cohort. We investigated 4,581 SAPALDIA participants without depression in the year 2001/2002. Corresponding one-year mean road, railway and aircraft day-evening-night noise (Lden) was calculated at the most exposed façade of the participants' residential floors, and transportation noise annoyance was assessed on an 11-point scale. Incident cases of depression were identified in 2010/2011, and comprised participants reporting physician diagnosis, intake of antidepressant medication or having a short form-36 mental health score < 50. We used robust Poisson regressions to estimate the mutually adjusted relative risks (RR) and 95% confidence intervals (CI) of depression, independent of traffic-related air pollution and other potential confounders. Incidence of depression was 11 cases per 1,000 person-years. In single exposure models, we observed positive but in part, statistically non-significant associations (per 10 dB) of road traffic Lden [RR: 1.06 (0.93, 1.22)] and aircraft Lden [RR: 1.19 (0.93, 1.53)], and (per 1-point difference) of noise annoyance [RR: 1.05 (1.02, 1.08)] with depression risk. In multi-exposure model, noise annoyance effect remained unchanged, with weaker effects of road traffic Lden [(RR: 1.02 (0.89, 1.17)] and aircraft Lden [(RR: 1.17 (0.90, 1.50)]. However, there were statistically significant indirect effects of road traffic Lden [(β: 0.02 (0.01, 0.03)] and aircraft Lden [β: 0.01 (0.002, 0.02)] via noise annoyance. There were no associations with railway Lden in the single and multi-exposure models [(RR_{both models}: 0.88 (0.75, 1.03)]. We made similar findings among 2,885 non-movers, where the effect modification and cumulative risks were more distinct. Noise annoyance effect in non-movers was stronger among the insufficiently active (RR: 1.09; 95%CI: 1.02, 1.17; p_interaction = 0.07) and those with daytime sleepiness [RR: 1.07 (1.02, 1.12); p_interaction = 0.008]. Cumulative risks of Lden in non-movers showed additive tendencies for the linear cumulative risk [(RR_{per 10dB of combined sources}: 1.31 (0.90, 1.91)] and the categorical cumulative risk [(RR_{triple- vs. zero-source ≥45 dB}: 2.29 (1.02, 5.14)], and remained stable to noise annoyance. Transportation noise level and noise annoyance may jointly and independently influence the risk of depression. Combined long-term exposures to noise level seems to be most detrimental, largely acting via annoyance. The moderation of noise annoyance effect by daytime sleepiness and physical activity further contribute to clarifying the involved mechanisms. More evidence is needed to confirm these findings for effective public health control of depression and noise exposure burden.

The Bidirectional Relationship of Depression and Inflammation: Double Trouble

Depression represents the number one cause of disability worldwide and is often fatal. Inflammatory processes have been implicated in the pathophysiology of depression. It is now well established that dysregulation of both the innate and adaptive immune systems occur in depressed patients and hinder favorable prognosis, including antidepressant responses. In this review, we describe how the immune system regulates mood and the potential causes of the dysregulated inflammatory responses in depressed patients. However, the proportion of never-treated major depressive disorder (MDD) patients who exhibit inflammation remains to be clarified, as the heterogeneity in inflammation findings may stem in part from examining MDD patients with varied interventions. Inflammation is likely a critical disease modifier, promoting susceptibility to depression. Controlling inflammation might provide an overall therapeutic benefit, regardless of whether it is secondary to early life trauma, a more acute stress response, microbiome alterations, a genetic diathesis, or a combination of these and other factors.

Methylome profiling of young adults with depression supports a link with immune response and psoriasis

BACKGROUND

Currently the leading cause of global disability, clinical depression is a heterogeneous condition characterised by low mood, anhedonia and cognitive impairments. Its growing incidence among young people, often co-occurring with self-harm, is of particular concern. We recently reported very high rates of depression among first year university students in Northern Ireland, with over 25% meeting the clinical criteria, based on DSM IV. However, the causes of depression in such groups remain unclear, and diagnosis is hampered by a lack of biological markers. The aim of this exploratory study was to examine DNA methylation patterns in saliva samples from individuals with a history of depression and matched healthy controls.

RESULTS

From our student subjects who showed evidence of a total lifetime major depressive event (MDE, n = 186) we identified a small but distinct subgroup (n = 30) with higher risk scores on the basis of co-occurrence of self-harm and attempted suicide. Factors conferring elevated risk included being female or non-heterosexual, and intrinsic factors such as emotional suppression and impulsiveness. Saliva samples were collected and a closely matched set of high-risk cases (n = 16) and healthy controls (n = 16) similar in age, gender and smoking status were compared. These showed substantial differences in DNA methylation marks across the genome, specifically in the late cornified envelope (LCE) gene cluster. Gene ontology analysis showed highly significant enrichment for immune response, and in particular genes associated with the inflammatory skin condition psoriasis, which we confirmed using a second bioinformatics approach. We then verified methylation gains at the LCE gene cluster at the epidermal differentiation complex and at MIR4520A/B in our cases in the laboratory, using pyrosequencing. Additionally, we found loss of methylation at the PSORSC13 locus on chromosome 6 by array and pyrosequencing, validating recent findings in brain tissue from people who had died by suicide. Finally, we could show that similar changes in immune gene methylation preceded the onset of depression in an independent cohort of adolescent females.

CONCLUSIONS

Our data suggests an immune component to the aetiology of depression in at least a small subgroup of cases, consistent with the accumulating evidence supporting a relationship between inflammation and depression. Additionally, DNA methylation changes at key loci, detected in saliva, may represent a valuable tool for identifying at-risk subjects.

A methylation study of long-term depression risk

Recurrent and chronic major depressive disorder (MDD) accounts for a substantial part of the disease burden because this course is most prevalent and typically requires long-term treatment. We associated blood DNA methylation profiles from 581 MDD patients at baseline with MDD status 6 years later. A resampling approach showed a highly significant association between methylation profiles in blood at baseline and future disease status (P = 2.0 × 10^-16). Top MWAS results were enriched specific pathways, overlapped with genes found in GWAS of MDD disease status, autoimmune disease and inflammation, and co-localized with eQTLS and (genic enhancers of) of transcription sites in brain and blood. Many of these findings remained significant after correction for multiple testing. The major themes emerging were cellular responses to stress and signaling mechanisms linked to immune cell migration and inflammation. This suggests that an immune signature of treatment-resistant depression is already present at baseline. We also created a methylation risk score (MRS) to predict MDD status 6 years later. The AUC of our MRS was 0.724 and higher than risk scores created using a set of five putative MDD biomarkers, genome-wide SNP data, and 27 clinical, demographic and lifestyle variables. Although further studies are needed to examine the generalizability to different patient populations, these results suggest that methylation profiles in blood may present a promising avenue to support clinical decision making by providing empirical information about the likelihood MDD is chronic or will recur in the future.

Genome-Wide DNA Methylation in Peripheral Blood and Long-Term Exposure to Source-Specific Transportation Noise and Air Pollution: The SAPALDIA Study

BACKGROUND

Few epigenome-wide association studies (EWAS) on air pollutants exist, and none have been done on transportation noise exposures, which also contribute to environmental burden of disease.

OBJECTIVE

We performed mutually independent EWAS on transportation noise and air pollution exposures.

METHODS

We used data from two time points of the Swiss Cohort Study on Air Pollution and Lung and Heart Diseases in Adults (SAPALDIA) from 1,389 participants contributing 2,542 observations. We applied multiexposure linear mixed-effects regressions with participant-level random intercept to identify significant Cytosine-phosphate-Guanine (CpG) sites and differentially methylated regions (DMRs) in relation to 1-y average aircraft, railway, and road traffic day-evening-night noise (Lden); nitrogen dioxide (NO 2 ); and particulate matter (PM) with aerodynamic diameter < 2.5 μ m (PM 2.5 ). We performed candidate (CpG-based; cross-systemic phenotypes, combined into "allostatic load") and agnostic (DMR-based) pathway enrichment tests, and replicated previously reported air pollution EWAS signals.

RESULTS

We found no statistically significant CpGs at false discovery rate < 0.05 . However, 14, 48, 183, 8, and 71 DMRs independently associated with aircraft, railway, and road traffic Lden; NO 2 ; and PM 2.5 , respectively, with minimally overlapping signals. Transportation Lden and air pollutants tendentially associated with decreased and increased methylation, respectively. We observed significant enrichment of candidate DNA methylation related to C-reactive protein and body mass index (aircraft, road traffic Lden, and PM 2.5 ), renal function and "allostatic load" (all exposures). Agnostic functional networks related to cellular immunity, gene expression, cell growth/proliferation, cardiovascular, auditory, embryonic, and neurological systems development were enriched. We replicated increased methylation in cg08500171 (NO 2 ) and decreased methylation in cg17629796 (PM 2.5 ).

CONCLUSIONS

Mutually independent DNA methylation was associated with source-specific transportation noise and air pollution exposures, with distinct and shared enrichments for pathways related to inflammation, cellular development, and immune responses. These findings contribute in clarifying the pathways linking these exposures and age-related diseases but need further confirmation in the context of mediation analyses. https://doi.org/10.1289/EHP6174.

Cell Type-Specific Methylome-wide Association Studies Implicate Neurotrophin and Innate Immune Signaling in Major Depressive Disorder

BACKGROUND

We sought to characterize methylation changes in brain and blood associated with major depressive disorder (MDD). As analyses of bulk tissue may obscure association signals and hamper the biological interpretation of findings, these changes were studied on a cell type-specific level.

METHODS

In 3 collections of human postmortem brain (n = 206) and 1 collection of blood samples (N = 1132) of MDD cases and controls, we used epigenomic deconvolution to perform cell type-specific methylome-wide association studies within subpopulations of neurons/glia for the brain data and granulocytes/T cells/B cells/monocytes for the blood data. Sorted neurons/glia from a fourth postmortem brain collection (n = 58) were used for validation purposes.

RESULTS

Cell type-specific methylome-wide association studies identified multiple findings in neurons/glia that were detected across brain collections and were reproducible in physically sorted nuclei. Cell type-specific analyses in blood samples identified methylome-wide significant associations in T cells, monocytes, and whole blood that replicated findings from a past methylation study of MDD. Pathway analyses implicated p75 neurotrophin receptor/nerve growth factor signaling and innate immune toll-like receptor signaling in MDD. Top results in neurons, glia, bulk brain, T cells, monocytes, and whole blood were enriched for genes supported by genome-wide association studies for MDD and other psychiatric disorders.

CONCLUSIONS

We both replicated and identified novel MDD-methylation associations in human brain and blood samples at a cell type-specific level. Our results provide mechanistic insights into how the immune system may interact with the brain to affect MDD susceptibility. Importantly, our findings involved associations with MDD in human samples that implicated many closely related biological pathways. These disease-linked sites and pathways represent promising new therapeutic targets for MDD.

Genome-Wide DNA Methylation Patterns in Persistent Attention-Deficit/Hyperactivity Disorder and in Association With Impulsive and Callous Traits

Attention-deficit/hyperactivity disorder (ADHD) is a neurodevelopmental disorder that often persists into adulthood. ADHD and related personality traits, such as impulsivity and callousness, are caused by genetic and environmental factors and their interplay. Epigenetic modifications of DNA, including methylation, are thought to mediate between such factors and behavior and may behave as biomarkers for disorders. Here, we set out to study DNA methylation in persistent ADHD and related traits. We performed epigenome-wide association studies (EWASs) on peripheral whole blood from participants in the NeuroIMAGE study (age range 12-23 years). We compared participants with persistent ADHD (n = 35) with healthy controls (n = 19) and with participants with remittent ADHD (n = 19). Additionally, we performed EWASs of impulsive and callous traits derived from the Conners Parent Rating Scale and the Callous-Unemotional Inventory, respectively, across all participants. For every EWAS, the linear regression model analyzed included covariates for age, sex, smoking scores, and surrogate variables reflecting blood cell type composition and genetic background. We observed no epigenome-wide significant differences in single CpG site methylation between participants with persistent ADHD and healthy controls or participants with remittent ADHD. However, epigenome-wide analysis of differentially methylated regions provided significant findings showing that hypermethylated regions in the APOB and LPAR5 genes were associated with ADHD persistence compared to ADHD remittance (p = 1.68 * 10-24 and p = 9.06 * 10-7, respectively); both genes are involved in cholesterol signaling. Both findings appeared to be linked to genetic variation in cis. We found neither significant epigenome-wide single CpG sites nor regions associated with impulsive and callous traits; the top-hits from these analyses were annotated to genes involved in neurotransmitter release and the regulation of the biological clock. No link to genetic variation was observed for these findings, which thus might reflect environmental influences. In conclusion, in this pilot study with a small sample size, we observed several DNA-methylation-disorder/trait associations of potential significance for ADHD and the related behavioral traits. Although we do not wish to draw conclusions before replication in larger, independent samples, cholesterol signaling and metabolism may be of relevance for the onset and/or persistence of ADHD.

Using Openly Accessible Resources to Strengthen Causal Inference in Epigenetic Epidemiology of Neurodevelopment and Mental Health

The recent focus on the role of epigenetic mechanisms in mental health has led to several studies examining the association of epigenetic processes with psychiatric conditions and neurodevelopmental traits. Some studies suggest that epigenetic changes might be causal in the development of the psychiatric condition under investigation. However, other scenarios are possible, e.g., statistical confounding or reverse causation, making it particularly challenging to derive conclusions on causality. In the present review, we examine the evidence from human population studies for a possible role of epigenetic mechanisms in neurodevelopment and mental health and discuss methodological approaches on how to strengthen causal inference, including the need for replication, (quasi-)experimental approaches and Mendelian randomization. We signpost openly accessible resources (e.g., "MR-Base" "EWAS catalog" as well as tissue-specific methylation and gene expression databases) to aid the application of these approaches.

A Natural Isoquinoline Alkaloid With Antitumor Activity: Studies of the Biological Activities of Berberine

Coptis, a traditional medicinal plant, has been used widely in the field of traditional Chinese medicine for many years. More recently, the chemical composition and bioactivity of Coptis have been studied worldwide. Berberine is a main component of Rhizoma Coptidis. Modern medicine has confirmed that berberine has pharmacological activities, such as anti-inflammatory, analgesic, antimicrobial, hypolipidemic, and blood pressure-lowering effects. Importantly, the active ingredient of berberine has clear inhibitory effects on various cancers, including colorectal cancer, lung cancer, ovarian cancer, prostate cancer, liver cancer, and cervical cancer. Cancer, ranked as one of the world’s five major incurable diseases by WHO, is a serious threat to the quality of human life. Here, we try to outline how berberine exerts antitumor effects through the regulation of different molecular pathways. In addition, the berberine-mediated regulation of epigenetic mechanisms that may be associated with the prevention of malignant tumors is described. Thus, this review provides a theoretical basis for the biological functions of berberine and its further use in the clinical treatment of cancer.

Cytokine Research in Depression: Principles, Challenges, and Open Questions

Cytokines have been implicated in the pathology of depression. Currently, the evidence is based on cross-sectional studies and meta-analytic research comparing blood concentrations of T helper type 1 (TH1), T helper type 2 (TH2), pro-inflammatory or anti-inflammatory cytokines of patients with a depressive disorder to those of healthy controls. Additionally, multiple longitudinal studies have investigated cytokine levels during antidepressant treatment. According to the current literature, it seems that peripheral levels of interleukin (IL)-6, IL-10, IL-12, IL-13, and tumor necrosis factor (TNF)-α are elevated and that interferon (IFN)-γ levels are lower in patients with depression compared to healthy controls. However, the overlap of cytokine values between acutely depressed patients, remitted and recovered patients and healthy controls is considerable. Thus, the discriminative power of cytokine concentrations between depressed and non-depressed people is likely weak. Treatment with certain antidepressants appears to decrease peripheral levels of IL-6, IL-10, and TNF-α. However, weight gain-inducing psychopharmacological substances, such as the antidepressant mirtazapine, have been reported to potentially increase the production of pro-inflammatory cytokines. Even though cytokines are often discussed as biomarkers for depression, they have also been shown to be altered in other psychiatric disorders. Moreover, many environmental, social, psychological, biological, and medical factors are also associated with cytokine changes. Thus, cytokine alterations seem extremely unspecific. The interpretation of the results of these studies remains a challenge because it is unknown which type of cells are most responsible for cytokine changes measured in the blood nor have the main target cells or target tissues been identified. The same cytokine can be produced by multiple cell types, and the same cell can produce various cytokines. Additionally, redundancy, synergy, antagonism, and signaling cascades of cytokine signaling must be considered. Cytokines might not be associated with the diagnosis of depression according to the currently used diagnostic manuals, but rather with specific subtypes of depression, or with depressive symptoms across different psychiatric diagnoses. Therefore, the currently available diagnostic systems may not be the ideal starting point for psychiatric cytokine research.

Mood Disorders, Accelerated Aging, and Inflammation: Is the Link Hidden in Telomeres?

Mood disorders are associated with an increased risk of aging-related diseases, which greatly contribute to the excess morbidity and mortality observed in affected individuals. Clinical and molecular findings also suggest that mood disorders might be characterized by a permanent state of low-grade inflammation. At the cellular level, aging translates into telomeres shortening. Intriguingly, inflammation and telomere shortening show a bidirectional association: a pro-inflammatory state seems to contribute to aging and telomere dysfunction, and telomere attrition is able to induce low-grade inflammation. Several independent studies have reported shorter telomere length and increased levels of circulating inflammatory cytokines in mood disorders, suggesting a complex interplay between altered inflammatory–immune responses and telomere dynamics in the etiopathogenesis of these disorders. In this review, we critically discuss studies investigating the role of telomere attrition and inflammation in the pathogenesis and course of mood disorders, and in pharmacological treatments with psychotropic medications.

Enhanced Molecular Appreciation of Psychiatric Disorders Through High-Dimensionality Data Acquisition and Analytics

The initial diagnosis, molecular investigation, treatment, and posttreatment care of major psychiatric disorders (schizophrenia and bipolar depression) are all still significantly hindered by the current inability to define these disorders in an explicit molecular signaling manner. High-dimensionality data analytics, using large datastreams from transcriptomic, proteomic, or metabolomic investigations, will likely advance both the appreciation of the molecular nature of major psychiatric disorders and simultaneously enhance our ability to more efficiently diagnose and treat these debilitating conditions. High-dimensionality data analysis in psychiatric research has been heterogeneous in aims and methods and limited by insufficient sample sizes, poorly defined case definitions, methodological inhomogeneity, and confounding results. All of these issues combine to constrain the conclusions that can be extracted from them. Here, we discuss possibilities for overcoming methodological challenges through the implementation of transcriptomic, proteomic, or metabolomics signatures in psychiatric diagnosis and offer an outlook for future investigations. To fulfill the promise of intelligent high-dimensionality data-based differential diagnosis in mental disease diagnosis and treatment, future research will need large, well-defined cohorts in combination with state-of-the-art technologies.

Study Design and Rationale for the Mood and Methylation Study: A Platform for Multi-Omics Investigation of Depression in Twins

Major depression is a complex disorder with no single, direct causal mechanism. Morbidity has been linked to genetic processes, developmental history, and unique environmental exposures. Epigenetic mechanisms, especially DNA methylation, are also likely important factors in the pathogenesis of major depressive disorder (MDD). A community-based twin sample has many advantages for epigenetic studies, given the shared genetic and developmental histories of same-sex twin pairs. This article describes the rationale and study design for the Mood and Methylation Study in which 133 twin pairs (101 monozygotic and 32 dizygotic), both discordant and concordant for lifetime history of MDD, were evaluated on a large number of variables related to MDD. The twins also provided blood samples for an epigenome-wide association study of differentially methylated regions (DMR) relevant to MDD. Although MDD is typically considered a disorder of the central nervous system, it is unfeasible to obtain a large sample of brain tissues. However, epigenetic variation is not limited to the affected tissue but can also be detected in peripheral blood leukocytes. Thus, this study focused on monocytes for the major analyses. Additional plans for the study include gene expression analysis from the same set of twins using RNA-seq and validation of significant DMRs in postmortem brain tissues from a separate sample. Moreover, sufficient samples have been collected to perform future ‘multi-omic’ analyses, including metabolome, microbiome, and transcriptome. Our long-term goal is to understand how epigenomic and other ‘omic’ factors can be manipulated for diagnostic, preventive, and therapeutic purposes for MDD and its related conditions.