DNA methylation meta-analysis reveals cellular alterations in psychosis and markers of treatment-resistant schizophrenia

Association between the copy number variations of Methyl-CpG binding domain family and schizophrenia

Schizophrenia is recognized as one of the most severe psychiatric disorders, with its pathogenesis likely involving genetic, epigenetic, developmental, and environmental factors. Members of the Methyl-CpG Binding Domain (MBD) Family play a crucial role in the regulation of genomic DNA methylation, and studies have implicated the association between MBD family and neurodevelopmental disorders. Copy number variations (CNVs) are a significant genetic basis for human genomic variation, also playing a critical role in the genetic processes of schizophrenia. Therefore, we aimed to evaluate the susceptibility of MBD family CNVs to schizophrenia by exploring and validating them in two separate populations using CNVplex™ and qPCR methods, and to explore the relationship between MBD family CNVs and clinical phenotypes in the overall population using chi-square tests and Fisher's exact tests. Results suggest that an increase in MBD1 gene copy number and a deficiency in MBD2 gene copy number may be associated with the risk of schizophrenia. The deficiency in MBD2 gene copy number may increase the risk of delusion of reference and delusion of persecutory in the overall sample, as well as in males. This research provides preliminary evidence supporting the association between MBD family CNVs and schizophrenia, highlighting the potential role of the MBD family in the pathogenesis of schizophrenia.

Applicability of epigenetic age models to next-generation methylation arrays

BACKGROUND

Epigenetic clocks are mathematical models used to estimate epigenetic age based on DNA methylation at specific CpG sites. As new methylation microarrays are developed and older models discontinued, existing epigenetic clocks might become obsolete. Here, we explored the effects of the changes introduced in the new EPICv2 DNA methylation array on existing epigenetic clocks.

METHODS

We tested the performance of four epigenetic clocks on the probeset of the EPICv2 array using a dataset of 10,835 samples. We developed a new epigenetic age prediction model compatible across the 450 k, EPICv1, and EPICv2 microarrays and validated it on 2095 samples. We estimated technical noise and intra-subject variation using two datasets with repeated sampling. We used data from (i) cancer survivors who had undergone different therapies, (ii) breast cancer patients and controls, and (iii) an exercise-based interventional study, to test the ability of our model to detect alterations in epigenetic age acceleration in response to theoretically antiaging interventions.

RESULTS

The results of the four epiclocks tested are significantly distorted by the EPICv2 probeset, causing an average difference of up to 25 years. Our new model produced highly accurate chronological age predictions, comparable to a state-of-the-art epiclock. The model reported the lowest epigenetic age acceleration in normal populations, as well as the lowest variation across technical replicates and repeated samples from the same subjects. Finally, our model reproduced previous results of increased epigenetic age acceleration in cancer patients and in survivors treated with radiation therapy, and no changes from exercise-based interventions.

CONCLUSION

Existing epigenetic clocks require updates for full EPICv2 compatibility. Our new model translates the capabilities of state-of-the-art epigenetic clocks to the EPICv2 platform and is cross-compatible with older microarrays. The characterization of epigenetic age prediction variation provides useful metrics to contextualize the relevance of epigenetic age alterations. The analysis of data from subjects influenced by radiation, cancer, and exercise-based interventions shows that despite being good predictors of chronological age, neither a pathological state like breast cancer, a hazardous environmental factor (radiation), nor exercise (a beneficial intervention) caused significant changes in the values of the "epigenetic age" determined by these first-generation models.

Longitudinal changes in DNA methylation associated with clozapine use in treatment-resistant schizophrenia from two international cohorts

The second-generation antipsychotic clozapine is used as a medication for treatment-resistant schizophrenia. It has previously been associated with epigenetic changes in pre-clinical rodent models and cross-sectional studies of treatment-resistant schizophrenia. Cross-sectional studies are susceptible to confounding, however, and cannot disentangle the effects of diagnosis and medication. We therefore profiled DNA methylation in sequential blood samples (n = 126) from two independent cohorts of patients (n = 38) with treatment-resistant schizophrenia spectrum disorders who commenced clozapine after study enrolment and were followed up for up to six months. We identified significant non-linear changes in cell-type proportion estimates derived from DNA methylation data - specifically B-cells - associated with time on clozapine. Mixed effects regression models were used to identify changes in DNA methylation at specific sites associated with time on clozapine, identifying 37 differentially methylated positions (DMPs) (p < 5 × 10-5) in a linear model and 90 DMPs in a non-linear quadratic model. We compared these results to data from our previous epigenome-wide association study (EWAS) meta-analysis of psychosis, finding evidence that many previously identified DMPs associated with schizophrenia and treatment-resistant schizophrenia might reflect exposure to clozapine. In conclusion, our results indicate that clozapine exposure is associated with changes in DNA methylation and cellular composition. Our study shows that medication effects might confound many case-control studies of neuropsychiatric disorders performed in blood.

Schizophrenia is associated with altered DNA methylation variance

Varying combinations of genetic and environmental risk factors are thought to underpin phenotypic heterogeneity between individuals in psychiatric conditions such as schizophrenia. While epigenome-wide association studies in schizophrenia have identified extensive alteration of mean DNA methylation levels, less is known about the location and impact of DNA methylation variance, which could contribute to phenotypic and treatment response heterogeneity. To explore this question, we conducted the largest meta-analysis of blood DNA methylation variance in schizophrenia to date, leveraging three cohorts comprising 1036 individuals with schizophrenia and 954 non-psychiatric controls. Surprisingly, only a small proportion (0.1%) of the 213 variably methylated positions (VMPs) associated with schizophrenia (Benjamini-Hochberg FDR < 0.05) were shared with differentially methylated positions (DMPs; sites with mean changes between cases and controls). These blood-derived VMPs were found to be overrepresented in genes previously associated with schizophrenia and amongst brain-enriched genes, with evidence of concordant changes at VMPs in the cerebellum, hippocampus, prefrontal cortex, or striatum. Epigenetic covariance was also observed with respect to clinically significant metrics including age of onset, cognitive deficits, and symptom severity. We also uncovered a significant VMP in individuals with first-episode psychosis (n = 644) from additional cohorts and a non-psychiatric comparison group (n = 633). Collectively, these findings suggest schizophrenia is associated with significant changes in DNA methylation variance, which may contribute to individual-to-individual heterogeneity.

Map of epigenetic age acceleration: A worldwide analysis

We present a systematic analysis of epigenetic age acceleration based on by far the largest collection of publicly available DNA methylation data for healthy samples (93 datasets, 23 K samples), focusing on the geographic (25 countries) and ethnic (31 ethnicities) aspects around the world. We employed the most popular epigenetic tools for assessing age acceleration and examined their quality metrics and ability to extrapolate to epigenetic data from different tissue types and age ranges different from the training data of these models. In most cases, the models proved to be inconsistent with each other and showed different signs of age acceleration, with the PhenoAge model tending to systematically underestimate and different versions of the GrimAge model tending to systematically overestimate the age prediction of healthy subjects. Referring to data availability and consistency, most countries and populations are still not represented in GEO, moreover, different datasets use different criteria for determining healthy controls. Because of this, it is difficult to fully isolate the contribution of "geography/environment", "ethnicity" and "healthiness" to epigenetic age acceleration. Among the explored metrics, only the DunedinPACE, which measures aging rate, appears to adequately reflect the standard of living and socioeconomic indicators in countries, although it has a limited application to blood methylation data only. Invariably, by epigenetic age acceleration, males age faster than females in most of the studied countries and populations.

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

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

Sex effects on DNA methylation affect discovery in epigenome-wide association study of schizophrenia

Sex differences in the epidemiology and clinical characteristics of schizophrenia are well-known; however, the molecular mechanisms underlying these differences remain unclear. Further, the potential advantages of sex-stratified meta-analyses of epigenome-wide association studies (EWAS) of schizophrenia have not been investigated. Here, we performed sex-stratified EWAS meta-analyses to investigate whether sex stratification improves discovery, and to identify differentially methylated regions (DMRs) in schizophrenia. Peripheral blood-derived DNA methylation data from 1519 cases of schizophrenia (male n = 989, female n = 530) and 1723 controls (male n = 997, female n = 726) from three publicly available datasets, and the TOP cohort were meta-analyzed to compare sex-specific, sex-stratified, and sex-adjusted EWAS. The predictive power of each model was assessed by polymethylation score (PMS). The number of schizophrenia-associated differentially methylated positions identified was higher for the sex-stratified model than for the sex-adjusted one. We identified 20 schizophrenia-associated DMRs in the sex-stratified analysis. PMS from sex-stratified analysis outperformed that from sex-adjusted analysis in predicting schizophrenia. Notably, PMSs from the sex-stratified and female-only analyses, but not those from sex-adjusted or the male-only analyses, significantly predicted schizophrenia in males. The findings suggest that sex-stratified EWAS meta-analyses improve the identification of schizophrenia-associated epigenetic changes and highlight an interaction between sex and schizophrenia status on DNA methylation. Sex-specific DNA methylation may have potential implications for precision psychiatry and the development of stratified treatments for schizophrenia.

Decoding the Epigenetic Landscape: Insights into 5mC and 5hmC Patterns in Mouse Cortical Cell Types

The DNA modifications, 5-methylcytosine (5mC) and 5-hydroxymethylcytosine (5hmC), represent powerful epigenetic regulators of temporal and spatial gene expression. Yet, how the cooperation of these genome-wide, epigenetic marks determine unique transcriptional signatures across different brain cell populations is unclear. Here we applied Nanopore sequencing of native DNA to obtain a complete, genome-wide, single-base resolution atlas of 5mC and 5hmC modifications in neurons, astrocytes and microglia in the mouse cortex (99% genome coverage, 40 million CpG sites). In tandem with RNA sequencing, analysis of 5mC and 5hmC patterns across cell types reveals astrocytes drive uniquely high brain 5hmC levels and support two decades of research regarding methylation patterns, gene expression and alternative splicing, benchmarking this resource. As such, we provide the most comprehensive DNA methylation data in mouse brain as an interactive, online tool (NAM-Me, https://olsenlab.shinyapps.io/NAMME/) to serve as a resource dataset for those interested in the methylome landscape.

Accelerated Pace of Aging in Schizophrenia: Five Case-Control Studies

BACKGROUND

Schizophrenia is associated with increased risk of developing multiple aging-related diseases, including metabolic, respiratory, and cardiovascular diseases, and Alzheimer's and related dementias, leading to the hypothesis that schizophrenia is accompanied by accelerated biological aging. This has been difficult to test because there is no widely accepted measure of biological aging. Epigenetic clocks are promising algorithms that are used to calculate biological age on the basis of information from combined cytosine-phosphate-guanine sites (CpGs) across the genome, but they have yielded inconsistent and often negative results about the association between schizophrenia and accelerated aging. Here, we tested the schizophrenia-aging hypothesis using a DNA methylation measure that is uniquely designed to predict an individual's rate of aging.

METHODS

We brought together 5 case-control datasets to calculate DunedinPACE (Pace of Aging Calculated from the Epigenome), a new measure trained on longitudinal data to detect differences between people in their pace of aging over time. Data were available from 1812 psychosis cases (schizophrenia or first-episode psychosis) and 1753 controls. Mean chronological age was 38.9 (SD = 13.6) years.

RESULTS

We observed consistent associations across datasets between schizophrenia and accelerated aging as measured by DunedinPACE. These associations were not attributable to tobacco smoking or clozapine medication.

CONCLUSIONS

Schizophrenia is accompanied by accelerated biological aging by midlife. This may explain the wide-ranging risk among people with schizophrenia for developing multiple different age-related physical diseases, including metabolic, respiratory, and cardiovascular diseases, and dementia. Measures of biological aging could prove valuable for assessing patients' risk for physical and cognitive decline and for evaluating intervention effectiveness.

An improved epigenetic counter to track mitotic age in normal and precancerous tissues

The cumulative number of stem cell divisions in a tissue, known as mitotic age, is thought to be a major determinant of cancer-risk. Somatic mutational and DNA methylation (DNAm) clocks are promising tools to molecularly track mitotic age, yet their relationship is underexplored and their potential for cancer risk prediction in normal tissues remains to be demonstrated. Here we build and validate an improved pan-tissue DNAm counter of total mitotic age called stemTOC. We demonstrate that stemTOC's mitotic age proxy increases with the tumor cell-of-origin fraction in each of 15 cancer-types, in precancerous lesions, and in normal tissues exposed to major cancer risk factors. Extensive benchmarking against 6 other mitotic counters shows that stemTOC compares favorably, specially in the preinvasive and normal-tissue contexts. By cross-correlating stemTOC to two clock-like somatic mutational signatures, we confirm the mitotic-like nature of only one of these. Our data points towards DNAm as a promising molecular substrate for detecting mitotic-age increases in normal tissues and precancerous lesions, and hence for developing cancer-risk prediction strategies.

Antidepressant Exposure and DNA Methylation: Insights from a Methylome-Wide Association Study

Importance

Understanding antidepressant mechanisms could help design more effective and tolerated treatments.

Objective

Identify DNA methylation (DNAm) changes associated with antidepressant exposure.

Design

Case-control methylome-wide association studies (MWAS) of antidepressant exposure were performed from blood samples collected between 2006-2011 in Generation Scotland (GS). The summary statistics were tested for enrichment in specific tissues, gene ontologies and an independent MWAS in the Netherlands Study of Depression and Anxiety (NESDA). A methylation profile score (MPS) was derived and tested for its association with antidepressant exposure in eight independent cohorts, alongside prospective data from GS.

Setting

Cohorts; GS, NESDA, FTC, SHIP-Trend, FOR2107, LBC1936, MARS-UniDep, ALSPAC, E-Risk, and NTR.

Participants

Participants with DNAm data and self-report/prescription derived antidepressant exposure.

Main Outcomes and Measures

Whole-blood DNAm levels were assayed by the EPIC/450K Illumina array (9 studies, N exposed = 661, N unexposed = 9,575) alongside MBD-Seq in NESDA (N exposed = 398, N unexposed = 414). Antidepressant exposure was measured by self- report and/or antidepressant prescriptions.

Results

The self-report MWAS (N = 16,536, N exposed = 1,508, mean age = 48, 59% female) and the prescription-derived MWAS (N = 7,951, N exposed = 861, mean age = 47, 59% female), found hypermethylation at seven and four DNAm sites (p < 9.42x10 -8 ), respectively. The top locus was cg26277237 ( KANK1, p self-report = 9.3x10 -13 , p prescription = 6.1x10 -3 ). The self-report MWAS found a differentially methylated region, mapping to DGUOK-AS1 ( p adj = 5.0x10 -3 ) alongside significant enrichment for genes expressed in the amygdala, the "synaptic vesicle membrane" gene ontology and the top 1% of CpGs from the NESDA MWAS (OR = 1.39, p < 0.042). The MPS was associated with antidepressant exposure in meta-analysed data from external cohorts (N studies = 9, N = 10,236, N exposed = 661, f3 = 0.196, p < 1x10 -4 ).

Conclusions and Relevance

Antidepressant exposure is associated with changes in DNAm across different cohorts. Further investigation into these changes could inform on new targets for antidepressant treatments.

3 Key Points

Question: Is antidepressant exposure associated with differential whole blood DNA methylation?Findings: In this methylome-wide association study of 16,536 adults across Scotland, antidepressant exposure was significantly associated with hypermethylation at CpGs mapping to KANK1 and DGUOK-AS1. A methylation profile score trained on this sample was significantly associated with antidepressant exposure (pooled f3 [95%CI]=0.196 [0.105, 0.288], p < 1x10 -4 ) in a meta-analysis of external datasets. Meaning: Antidepressant exposure is associated with hypermethylation at KANK1 and DGUOK-AS1 , which have roles in mitochondrial metabolism and neurite outgrowth. If replicated in future studies, targeting these genes could inform the design of more effective and better tolerated treatments for depression.

A multi-organization epigenetic age prediction based on a channel attention perceptron networks

DNA methylation indicates the individual's aging, so-called Epigenetic clocks, which will improve the research and diagnosis of aging diseases by investigating the correlation between methylation loci and human aging. Although this discovery has inspired many researchers to develop traditional computational methods to quantify the correlation and predict the chronological age, the performance bottleneck delayed access to the practical application. Since artificial intelligence technology brought great opportunities in research, we proposed a perceptron model integrating a channel attention mechanism named PerSEClock. The model was trained on 24,516 CpG loci that can utilize the samples from all types of methylation identification platforms and tested on 15 independent datasets against seven methylation-based age prediction methods. PerSEClock demonstrated the ability to assign varying weights to different CpG loci. This feature allows the model to enhance the weight of age-related loci while reducing the weight of irrelevant loci. The method is free to use for academics at www.dnamclock.com/#/original.

Influence of maternal folate depletion on Art3 DNA methylation in the murine adult brain; potential consequences for brain and neurocognitive health

The developmental origins of health and disease hypothesis suggest early-life environment impacts health outcomes throughout the life course. In particular, epigenetic marks, including DNA methylation, are thought to be key mechanisms through which environmental exposures programme later-life health. Adequate maternal folate status before and during pregnancy is essential in the protection against neural tube defects, but data are emerging that suggest early-life folate exposures may also influence neurocognitive outcomes in childhood and, potentially, thereafter. Since folate is key to the supply of methyl donors for DNA methylation, we hypothesize that DNA methylation may be a mediating mechanism through which maternal folate influences neurocognitive outcomes. Using bisulphite sequencing, we measured DNA methylation of five genes (Art3, Rsp16, Tspo, Wnt16, and Pcdhb6) in the brain tissue of adult offspring of dams who were depleted of folate (n = 5, 0.4 mg folic acid/kg diet) during pregnancy (~19-21 days) and lactation (mean 22 days) compared with controls (n = 6, 2 mg folic acid/kg diet). Genes were selected as methylation of their promoters had previously been found to be altered by maternal folate intake in mice and humans across the life course, and because they have potential associations with neurocognitive outcomes. Maternal folate depletion was significantly associated with Art3 gene hypomethylation in subcortical brain tissue of adult mice at 28 weeks of age (mean decrease 6.2%, P = .03). For the other genes, no statistically significant differences were found between folate depleted and control groups. Given its association with neurocognitive outcomes, we suggest Art3 warrants further study in the context of lifecourse brain health. We have uncovered a potential biomarker that, once validated in accessible biospecimens and human context, may be useful to track the impact of early-life folate exposure on later-life neurocognitive health, and potentially be used to develop and monitor the effects of interventions.

Alteration of DNA Methylation and Epigenetic Scores Associated With Features of Schizophrenia and Common Variant Genetic Risk

BACKGROUND

Unpacking molecular perturbations associated with features of schizophrenia is a critical step toward understanding phenotypic heterogeneity in this disorder. Recent epigenome-wide association studies have uncovered pervasive dysregulation of DNA methylation in schizophrenia; however, clinical features of the disorder that account for a large proportion of phenotypic variability are relatively underexplored.

METHODS

We comprehensively analyzed patterns of DNA methylation in a cohort of 381 individuals with schizophrenia from the deeply phenotyped Australian Schizophrenia Research Bank. Epigenetic changes were investigated in association with cognitive status, age of onset, treatment resistance, Global Assessment of Functioning scores, and common variant polygenic risk scores for schizophrenia. We subsequently explored alterations within genes previously associated with psychiatric illness, phenome-wide epigenetic covariance, and epigenetic scores.

RESULTS

Epigenome-wide association studies of the 5 primary traits identified 662 suggestively significant (p < 6.72 × 10-5) differentially methylated probes, with a further 432 revealed after controlling for schizophrenia polygenic risk on the remaining 4 traits. Interestingly, we uncovered many probes within genes associated with a variety of psychiatric conditions as well as significant epigenetic covariance with phenotypes and exposures including acute myocardial infarction, C-reactive protein, and lung cancer. Epigenetic scores for treatment-resistant schizophrenia strikingly exhibited association with clozapine administration, while epigenetic proxies of plasma protein expression, such as CCL17, MMP10, and PRG2, were associated with several features of schizophrenia.

CONCLUSIONS

Our findings collectively provide novel evidence suggesting that several features of schizophrenia are associated with alteration of DNA methylation, which may contribute to interindividual phenotypic variation in affected individuals.

Methylome-wide and meQTL analysis helps to distinguish treatment response from non-response and pathogenesis markers in schizophrenia

Schizophrenia is a complex condition with entwined genetic and epigenetic risk factors, posing a challenge to disentangle the intermixed pathological and therapeutic epigenetic signatures. To resolve this, we performed 850K methylome-wide and 700K genome-wide studies on the same set of schizophrenia patients by stratifying them into responders, non-responders, and drug-naïve patients. The key genes that signified the response were followed up using real-time gene expression studies to understand the effect of antipsychotics at the gene transcription level. The study primarily implicates hypermethylation in therapeutic response and hypomethylation in the drug-non-responsive state. Several differentially methylated sites and regions colocalized with the schizophrenia genome-wide association study (GWAS) risk genes and variants, supporting the convoluted gene-environment association. Gene ontology and protein-protein interaction (PPI) network analyses revealed distinct patterns that differentiated the treatment response from drug resistance. The study highlights the strong involvement of several processes related to nervous system development, cell adhesion, and signaling in the antipsychotic response. The ability of antipsychotic medications to alter the pathology by modulating gene expression or methylation patterns is evident from the general increase in the gene expression of response markers and histone modifiers and the decrease in class II human leukocyte antigen (HLA) genes following treatment with varying concentrations of medications like clozapine, olanzapine, risperidone, and haloperidol. The study indicates a directional overlap of methylation markers between pathogenesis and therapeutic response, thereby suggesting a careful distinction of methylation markers of pathogenesis from treatment response. In addition, there is a need to understand the trade-off between genetic and epigenetic observations. It is suggested that methylomic changes brought about by drugs need careful evaluation for their positive effects on pathogenesis, course of disease progression, symptom severity, side effects, and refractoriness.

Prenatal cocaine exposure and its influence on pediatric epigenetic clocks and epigenetic scores in humans

The investigation of the effects of prenatal cocaine exposure (PCE) on offspring has been inconsistent, with few studies investigating biological outcomes in humans. We profiled genome-wide DNA methylation (DNAm) of umbilical cord blood (UCB) from newborns with (n = 35) and without (n = 47) PCE. We used DNAm data to (1) assess pediatric epigenetic clocks at birth and (2) to estimate epigenetic scores (ES) for lifetime disorders. We generated gestational epigenetic age estimates (DNAmGA) based on Knight and Bohlin epigenetic clocks. We also investigated the association between DNAmGA and UCB serum brain-derived neurotrophic factor (BDNF) levels. Considering the large-scale DNAm data availability and existing evidence regarding PCE as a risk for health problems later in life, we generated ES for tobacco smoking, psychosis, autism, diabetes, and obesity. A gene ontology (GO) analysis on the CpGs included in the ES with group differences was performed. PCE was associated with lower DNAmGA in newborns, and this effect remained significant when controlling for potential confounders, such as blood cell type composition predicted by DNAm and obstetric data. DNAmGA was negatively correlated with BDNF levels in the serum of UCB. Higher tobacco smoking, psychosis, and diabetes ES were found in the PCE group. The GO analysis revealed GABAergic synapses as a potential pathway altered by PCE. Our findings of decelerated DNAmGA and ES for adverse phenotypes associated with PCE, suggest that the effects of gestational cocaine exposure on the epigenetic landscape of human newborns are detectable at birth.

Divergent epigenetic responses to perinatal asphyxia in severe mental disorders

Epigenetic modifications influenced by environmental exposures are molecular sources of phenotypic heterogeneity found in schizophrenia and bipolar disorder and may contribute to shared etiopathogenetic mechanisms of these two disorders. Newborns who experienced perinatal asphyxia have suffered reduced oxygen delivery to the brain around the time of birth, which increases the risk of later psychiatric diagnosis. This study aimed to investigate DNA methylation in blood cells for associations with a history of perinatal asphyxia, a neurologically harmful condition occurring within the biological environment of birth. We utilized prospective data from the Medical Birth Registry of Norway to identify incidents of perinatal asphyxia in 643 individuals with schizophrenia or bipolar disorder and 676 healthy controls. We performed an epigenome wide association study to distinguish differentially methylated positions associated with perinatal asphyxia. We found an interaction between methylation and exposure to perinatal asphyxia on case-control status, wherein having a history of perinatal asphyxia was associated with an increase of methylation in healthy controls and a decrease of methylation in patients on 4 regions of DNA important for brain development and function. The differentially methylated regions were observed in genes involved in oligodendrocyte survival and axonal myelination and functional recovery (LINGO3); assembly, maturation and maintenance of the brain (BLCAP;NNAT and NANOS2) and axonal transport processes and neural plasticity (SLC2A14). These findings are consistent with the notion that an opposite epigenetic response to perinatal asphyxia, in patients compared with controls, may contribute to molecular mechanisms of risk for schizophrenia and bipolar disorder.

Epigenetic profile of the immune system associated with symptom severity and treatment response in schizophrenia

BACKGROUND

Environmental modification of genetic information (epigenetics) is often invoked to explain interindividual differences in the phenotype of schizophrenia. In clinical practice, such variability is most prominent in the symptom profile and the treatment response. Epigenetic regulation of immune function is of particular interest, given the therapeutic relevance of this mechanism in schizophrenia.

METHODS

We analyzed the DNA methylation data of immune-relevant genes in patients with schizophrenia whose disease duration was less than 3 years, with previous lifetime antipsychotic treatment of no more than 2 weeks total.

RESULTS

A total of 441 patients met the inclusion criteria. Core symptoms were consistently associated with 206 methylation positions, many of which had previously been implicated in inflammatory responses. Of these, 24 methylation positions were located either in regulatory regions or near the CpG islands of 20 genes, including the SRC gene, which is a key player in glutamatergic signalling. These symptom-associated immune genes were enriched in neuronal development functions, such as neuronal migration and glutamatergic synapse. Compared with using only clinical information (including scores on the Positive and Negative Syndrome Scale), integrating methylation data into the model significantly improved the predictive ability (as indicated by area under the curve) for response to 8 weeks of antipsychotic treatment.

LIMITATIONS

We focused on a small number of methylation probes (immune-centred search) and lacked nutritional data and direct brain-based measures.

CONCLUSION

Epigenetic modifications of the immune system are associated with symptom severity at onset and subsequent treatment response in schizophrenia.

Comprehensive evaluation of the implementation of episignatures for diagnosis of neurodevelopmental disorders (NDDs)

Episignatures are popular tools for the diagnosis of rare neurodevelopmental disorders. They are commonly based on a set of differentially methylated CpGs used in combination with a support vector machine model. DNA methylation (DNAm) data often include missing values due to changes in data generation technology and batch effects. While many normalization methods exist for DNAm data, their impact on episignature performance have never been assessed. In addition, technologies to quantify DNAm evolve quickly and this may lead to poor transposition of existing episignatures generated on deprecated array versions to new ones. Indeed, probe removal between array versions, technologies or during preprocessing leads to missing values. Thus, the effect of missing data on episignature performance must also be carefully evaluated and addressed through imputation or an innovative approach to episignatures design. In this paper, we used data from patients suffering from Kabuki and Sotos syndrome to evaluate the influence of normalization methods, classification models and missing data on the prediction performances of two existing episignatures. We compare how six popular normalization methods for methylarray data affect episignature classification performances in Kabuki and Sotos syndromes and provide best practice suggestions when building new episignatures. In this setting, we show that Illumina, Noob or Funnorm normalization methods achieved higher classification performances on the testing sets compared to Quantile, Raw and Swan normalization methods. We further show that penalized logistic regression and support vector machines perform best in the classification of Kabuki and Sotos syndrome patients. Then, we describe a new paradigm to build episignatures based on the detection of differentially methylated regions (DMRs) and evaluate their performance compared to classical differentially methylated cytosines (DMCs)-based episignatures in the presence of missing data. We show that the performance of classical DMC-based episignatures suffers from the presence of missing data more than the DMR-based approach. We present a comprehensive evaluation of how the normalization of DNA methylation data affects episignature performance, using three popular classification models. We further evaluate how missing data affect those models' predictions. Finally, we propose a novel methodology to develop episignatures based on differentially methylated regions identification and show how this method slightly outperforms classical episignatures in the presence of missing data.

Sex-specific DNA methylation in saliva from the multi-ethnic Future of Families and Child Wellbeing Study

The prevalence and severity of many diseases differs by sex, potentially due to sex-specific patterns in DNA methylation. Autosomal sex-specific differences in DNA methylation have been observed in cord blood and placental tissue but are not well studied in saliva or in diverse populations. We sought to characterize sex-specific DNA methylation on autosomal chromosomes in saliva samples from children in the Future of Families and Child Wellbeing Study, a multi-ethnic prospective birth cohort containing an oversampling of Black, Hispanic and low-income families. DNA methylation from saliva samples was analysed on 796 children (50.6% male) at both ages 9 and 15 with DNA methylation measured using the Illumina HumanMethylation 450k array. An epigenome-wide association analysis of the age 9 samples identified 8,430 sex-differentiated autosomal DNA methylation sites (P < 2.4 × 10-7), of which 76.2% had higher DNA methylation in female children. The strongest sex-difference was in the cg26921482 probe, in the AMDHD2 gene, with 30.6% higher DNA methylation in female compared to male children (P < 1 × 10-300). Treating the age 15 samples as an internal replication set, we observed highly consistent results between the ages 9 and 15 measurements, indicating stable and replicable sex-differentiation. Further, we directly compared our results to previously published DNA methylation sex differences in both cord blood and saliva and again found strong consistency. Our findings support widespread and robust sex-differential DNA methylation across age, human tissues, and populations. These findings help inform our understanding of potential biological processes contributing to sex differences in human physiology and disease.

Uncertainty quantification of reference-based cellular deconvolution algorithms

The majority of epigenetic epidemiology studies to date have generated genome-wide profiles from bulk tissues (e.g., whole blood) however these are vulnerable to confounding from variation in cellular composition. Proxies for cellular composition can be mathematically derived from the bulk tissue profiles using a deconvolution algorithm; however, there is no method to assess the validity of these estimates for a dataset where the true cellular proportions are unknown. In this study, we describe, validate and characterize a sample level accuracy metric for derived cellular heterogeneity variables. The CETYGO score captures the deviation between a sample's DNA methylation profile and its expected profile given the estimated cellular proportions and cell type reference profiles. We demonstrate that the CETYGO score consistently distinguishes inaccurate and incomplete deconvolutions when applied to reconstructed whole blood profiles. By applying our novel metric to >6,300 empirical whole blood profiles, we find that estimating accurate cellular composition is influenced by both technical and biological variation. In particular, we show that when using a common reference panel for whole blood, less accurate estimates are generated for females, neonates, older individuals and smokers. Our results highlight the utility of a metric to assess the accuracy of cellular deconvolution, and describe how it can enhance studies of DNA methylation that are reliant on statistical proxies for cellular heterogeneity. To facilitate incorporating our methodology into existing pipelines, we have made it freely available as an R package (https://github.com/ds420/CETYGO).

The Cutting Edge of Epigenetic Clocks: In Search of Mechanisms Linking Aging and Mental Health

Individuals with psychiatric disorders are at increased risk of age-related diseases and early mortality. Recent studies demonstrate that this link between mental health and aging is reflected in epigenetic clocks, aging biomarkers based on DNA methylation. The reported relationships between epigenetic clocks and mental health are mostly correlational, and the mechanisms are poorly understood. Here, we review recent progress concerning the molecular and cellular processes underlying epigenetic clocks as well as novel technologies enabling further studies of the causes and consequences of epigenetic aging. We then review the current literature on how epigenetic clocks relate to specific aspects of mental health, such as stress, medications, substance use, health behaviors, and symptom clusters. We propose an integrated framework where mental health and epigenetic aging are each broken down into multiple distinct processes, which are then linked to each other, using stress and schizophrenia as examples. This framework incorporates the heterogeneity and complexity of both mental health conditions and aging, may help reconcile conflicting results, and provides a basis for further hypothesis-driven research in humans and model systems to investigate potentially causal mechanisms linking aging and mental health.

DNA methylation and the opposing NMDAR dysfunction in schizophrenia and major depression disorders: a converging model for the therapeutic effects of psychedelic compounds in the treatment of psychiatric illness

Psychedelic compounds are being increasingly explored as a potential therapeutic option for treating several psychiatric conditions, despite relatively little being known about their mechanism of action. One such possible mechanism, DNA methylation, is a process of epigenetic regulation that changes gene expression via chemical modification of nitrogenous bases. DNA methylation has been implicated in the pathophysiology of several psychiatric conditions, including schizophrenia (SZ) and major depressive disorder (MDD). In this review, we propose alterations to DNA methylation as a converging model for the therapeutic effects of psychedelic compounds, highlighting the N-methyl D-aspartate receptor (NMDAR), a crucial mediator of synaptic plasticity with known dysfunction in both diseases, as an example and anchoring point. We review the established evidence relating aberrant DNA methylation to NMDAR dysfunction in SZ and MDD and provide a model asserting that psychedelic substances may act through an epigenetic mechanism to provide therapeutic effects in the context of these disorders.

Genome-wide DNA methylation analysis in families with multiple individuals diagnosed with schizophrenia and intellectual disability

OBJECTIVES

Schizophrenia (SZ) and intellectual disability (ID) are both included in the continuum of neurodevelopmental disorders (NDDs). DNA methylation is known to be important in the occurrence of NDDs. The family study is conducive to eliminate the effects of relative epigenetic backgrounds, and to screen for differentially methylated positions (DMPs) and regions (DMRs) that are truly associated with NDDs.

METHODS

Four monozygotic twin families were recruited, and both twin individuals suffered from NDDs (either SZ, ID, or SZ plus ID). Genome-wide methylation analysis was performed in all samples and each family. DMPs and DMRs between NDD patients and unaffected individuals were identified. Functional and pathway enrichment analyses were performed on the annotated genes.

RESULTS

Two significant DMPs annotated to CYP2E1 were found in all samples. In Family One, 1476 DMPs mapped to 880 genes, and 162 DMRs overlapping with 153 unique genes were recognised. Our results suggested that the altered methylation levels of FYN, STAT3, RAC1, and NR4A2 were associated with the development of SZ and ID. Neurodevelopment and the immune system may participate in the occurrence of SZ and ID.

CONCLUSIONS

Our findings suggested that DNA methylation participated in the development of NDDs by affecting neurodevelopment and the immune system.

Accurate age prediction from blood using a small set of DNA methylation sites and a cohort-based machine learning algorithm

Chronological age prediction from DNA methylation sheds light on human aging, health, and lifespan. Current clocks are mostly based on linear models and rely upon hundreds of sites across the genome. Here, we present GP-age, an epigenetic non-linear cohort-based clock for blood, based upon 11,910 methylomes. Using 30 CpG sites alone, GP-age outperforms state-of-the-art models, with a median accuracy of ∼2 years on held-out blood samples, for both array and sequencing-based data. We show that aging-related changes occur at multiple neighboring CpGs, with implications for using fragment-level analysis of sequencing data in aging research. By training three independent clocks, we show enrichment of donors with consistent deviation between predicted and actual age, suggesting individual rates of biological aging. Overall, we provide a compact yet accurate alternative to array-based clocks for blood, with applications in longitudinal aging research, forensic profiling, and monitoring epigenetic processes in transplantation medicine and cancer.

A meta-analysis of immune-cell fractions at high resolution reveals novel associations with common phenotypes and health outcomes

BACKGROUND

Changes in cell-type composition of tissues are associated with a wide range of diseases and environmental risk factors and may be causally implicated in disease development and progression. However, these shifts in cell-type fractions are often of a low magnitude, or involve similar cell subtypes, making their reliable identification challenging. DNA methylation profiling in a tissue like blood is a promising approach to discover shifts in cell-type abundance, yet studies have only been performed at a relatively low cellular resolution and in isolation, limiting their power to detect shifts in tissue composition.

METHODS

Here we derive a DNA methylation reference matrix for 12 immune-cell types in human blood and extensively validate it with flow-cytometric count data and in whole-genome bisulfite sequencing data of sorted cells. Using this reference matrix, we perform a directional Stouffer and fixed effects meta-analysis comprising 23,053 blood samples from 22 different cohorts, to comprehensively map associations between the 12 immune-cell fractions and common phenotypes. In a separate cohort of 4386 blood samples, we assess associations between immune-cell fractions and health outcomes.

RESULTS

Our meta-analysis reveals many associations of cell-type fractions with age, sex, smoking and obesity, many of which we validate with single-cell RNA sequencing. We discover that naïve and regulatory T-cell subsets are higher in women compared to men, while the reverse is true for monocyte, natural killer, basophil, and eosinophil fractions. Decreased natural killer counts associated with smoking, obesity, and stress levels, while an increased count correlates with exercise and sleep. Analysis of health outcomes revealed that increased naïve CD4 + T-cell and N-cell fractions associated with a reduced risk of all-cause mortality independently of all major epidemiological risk factors and baseline co-morbidity. A machine learning predictor built only with immune-cell fractions achieved a C-index value for all-cause mortality of 0.69 (95%CI 0.67-0.72), which increased to 0.83 (0.80-0.86) upon inclusion of epidemiological risk factors and baseline co-morbidity.

CONCLUSIONS

This work contributes an extensively validated high-resolution DNAm reference matrix for blood, which is made freely available, and uses it to generate a comprehensive map of associations between immune-cell fractions and common phenotypes, including health outcomes.

Genome-wide discovery of circulating cell-free DNA methylation biomarkers for colorectal cancer detection

BACKGROUND

Colorectal polyp is known a precursor of colorectal cancer (CRC) that holds an increased risk for progression to CRC. Circulating cell-free DNA (cfDNA) methylation has shown favorable performance in the detection and monitoring the malignant progression in a variety of cancers.

RESULTS

To discover cfDNA methylation markers for the diagnosis of CRC, we first performed a genome-wide analysis between eight CRC and eight polyp tissues using the Infinium HumanMethylationEPIC BeadChip. We identified 7008 DMCs, and after filtering, we validated 39 DMCs by MethylTarget sequencing in 62 CRC and 56 polyp tissues. A panel of four CpGs (cg04486886, cg06712559, cg13539460, and cg27541454) was selected as the methylation marker in tissue by LASSO and random forest models. A diagnosis prediction model was built based on the four CpGs, and the methylation diagnosis score (md-score) can effectively discriminate tissues with CRC from polyp patients (AUROC > 0.9). Finally, the cg27541454 was confirmed hypermethylated in CRC (AUC = 0.85) in the plasma validation cohort.

CONCLUSIONS

Our findings suggest that the md-score could robustly detect CRC from polyp tissues, and cg27541454 may be a promising candidate noninvasive biomarker for CRC early diagnosis.

YBX1-Mediated DNA Methylation-Dependent SHANK3 Expression in PBMCs and Developing Cortical Interneurons in Schizophrenia

Schizophrenia (SCZ) is a severe psychiatric and neurodevelopmental disorder. The pathological process of SCZ starts early during development, way before the first onset of psychotic symptoms. DNA methylation plays an important role in regulating gene expression and dysregulated DNA methylation is involved in the pathogenesis of various diseases. The methylated DNA immunoprecipitation-chip (MeDIP-chip) is performed to investigate genome-wide DNA methylation dysregulation in peripheral blood mononuclear cells (PBMCs) of patients with first-episode SCZ (FES). Results show that the SHANK3 promoter is hypermethylated, and this hypermethylation (HyperM) is negatively correlated with the cortical surface area in the left inferior temporal cortex and positively correlated with the negative symptom subscores in FES. The transcription factor YBX1 is further found to bind to the HyperM region of SHANK3 promoter in induced pluripotent stem cells (iPSCs)-derived cortical interneurons (cINs) but not glutamatergic neurons. Furthermore, a direct and positive regulatory effect of YBX1 on the expression of SHANK3 is confirmed in cINs using shRNAs. In summary, the dysregulated SHANK3 expression in cINs suggests the potential role of DNA methylation in the neuropathological mechanism underlying SCZ. The results also suggest that HyperM of SHANK3 in PBMCs can serve as a potential peripheral biomarker of SCZ.

Differences in white blood cell proportions between schizophrenia cases and controls are influenced by medication and variations in time of day

Cases with schizophrenia (SCZ) and healthy controls show differences in white blood cell (WBC) counts and blood inflammation markers. Here, we investigate whether time of blood draw and treatment with psychiatric medications are related to differences in estimated WBC proportions between SCZ cases and controls. DNA methylation data from whole blood was used to estimate proportions of six subtypes of WBCs in SCZ patients (n = 333) and healthy controls (n = 396). We tested the association of case-control status with estimated cell-type proportions and the neutrophil-to-lymphocyte ratio (NLR) in 4 models: with/without adjusting for time of blood draw, and then compared results from blood samples drawn during a 12-h (07:00-19:00) or 7-h (07:00-14:00) period. We also investigated WBC proportions in a subgroup of medication-free patients (n = 51). Neutrophil proportions were significantly higher in SCZ cases (mean=54.1%) vs. controls (mean=51.1%; p = <0.001), and CD8⁺T lymphocyte proportions were lower in SCZ cases (mean=12.1%) vs. controls (mean=13.2%; p = 0.001). The effect sizes in the 12-h sample (07:00-19:00) showed a significant difference between SCZ vs. controls for neutrophils, CD4⁺T, CD8⁺T, and B-cells, which remained significant after adjusting for time of blood draw. In the samples matched for time of blood draw during 07.00-14.00, we also observed an association with neutrophils, CD4⁺T, CD8⁺T, and B-cells that was unaffected by further adjustment for time of blood draw. In the medication-free patients, we observed differences that remained significant in neutrophils (p = 0.01) and CD4⁺T (p = 0.01) after adjusting for time of day. The association of SCZ with NLR was significant in all models (range: p < 0.001 to p = 0.03) in both medicated and unmedicated patients. In conclusion, controlling for pharmacological treatment and circadian cycling of WBC is necessary for unbiased estimates in case-control studies. Nevertheless, the association of WBC with SCZ remains, even after adjusting for the time of day.

An international research agenda for clozapine-resistant schizophrenia

Treatment-resistant symptoms occur in about a third of patients with schizophrenia and are associated with a substantial reduction in their quality of life. The development of new treatment options for clozapine-resistant schizophrenia constitutes a crucial, unmet need in psychiatry. Additionally, an overview of past and possible future research avenues to optimise the early detection, diagnosis, and management of clozapine-resistant schizophrenia is unavailable. In this Health Policy, we discuss the ongoing challenges associated with clozapine-resistant schizophrenia faced by patients and health-care providers worldwide to improve the understanding of this condition. We then revisit several clozapine guidelines, the diagnostic tests and treatment options for clozapine-resistant schizophrenia, and currently applied research approaches in clozapine-resistant schizophrenia. We also suggest methodologies and targets for future research, divided into innovative nosology-oriented field trials (eg, examining dimensional symptom staging), translational approaches (eg, genetics), epidemiological research (eg, real-world studies), and interventional studies (eg, non-traditional trial designs incorporating lived experiences and caregivers' perspectives). Finally, we note that low-income and middle-income countries are under-represented in studies on clozapine-resistant schizophrenia and propose an agenda to guide multinational research on the cause and treatment of clozapine-resistant schizophrenia. We hope that this research agenda will empower better global representation of patients living with clozapine-resistant schizophrenia and ultimately improve their functional outcomes and quality of life.

Exploring the mediation of DNA methylation across the epigenome between childhood adversity and First Episode of Psychosis-findings from the EU-GEI study

ABTRACT

Studies conducted in psychotic disorders have shown that DNA-methylation (DNAm) is sensitive to the impact of Childhood Adversity (CA). However, whether it mediates the association between CA and psychosis is yet to be explored. Epigenome wide association studies (EWAS) using the Illumina Infinium-Methylation EPIC array in peripheral blood tissue from 366 First-episode of psychosis and 517 healthy controls was performed. Adversity scores were created for abuse, neglect and composite adversity with the Childhood Trauma Questionnaire (CTQ). Regressions examining (I) CTQ scores with psychosis; (II) with DNAm EWAS level and (III) between DNAm and caseness, adjusted for a variety of confounders were conducted. Divide-Aggregate Composite-null Test for the composite null-hypothesis of no mediation effect was conducted. Enrichment analyses were conducted with missMethyl package and the KEGG database. Our results show that CA was associated with psychosis (Composite: OR = 1.68; p = <0.001; abuse: OR = 2.16; p < 0.001; neglect: OR = 2.27; p = <0.001). None of the CpG sites significantly mediated the adversity-psychosis association after Bonferroni correction (p < 8.1 × 10-8). However, 28, 34 and 29 differentially methylated probes associated with 21, 27, 20 genes passed a less stringent discovery threshold (p < 5 × 10-5) for composite, abuse and neglect respectively, with a lack of overlap between abuse and neglect. These included genes previously associated to psychosis in EWAS studies, such as PANK1, SPEG TBKBP1, TSNARE1 or H2R. Downstream gene ontology analyses did not reveal any biological pathways that survived false discovery rate correction. Although at a non-significant level, DNAm changes in genes previously associated with schizophrenia in EWAS studies may mediate the CA-psychosis association. These results and associated involved processes such as mitochondrial or histaminergic disfunction, immunity or neural signalling requires replication in well powered samples. The lack of overlap between mediating genes associated with abuse and neglect suggests differential biological trajectories linking CA subtypes and psychosis.

The molecular pathology of schizophrenia: an overview of existing knowledge and new directions for future research

Despite enormous efforts employing various approaches, the molecular pathology in the schizophrenia brain remains elusive. On the other hand, the knowledge of the association between the disease risk and changes in the DNA sequences, in other words, our understanding of the genetic pathology of schizophrenia, has dramatically improved over the past two decades. As the consequence, now we can explain more than 20% of the liability to schizophrenia by considering all analyzable common genetic variants including those with weak or no statistically significant association. Also, a large-scale exome sequencing study identified single genes whose rare mutations substantially increase the risk for schizophrenia, of which six genes (SETD1A, CUL1, XPO7, GRIA3, GRIN2A, and RB1CC1) showed odds ratios larger than ten. Based on these findings together with the preceding discovery of copy number variants (CNVs) with similarly large effect sizes, multiple disease models with high etiological validity have been generated and analyzed. Studies of the brains of these models, as well as transcriptomic and epigenomic analyses of patient postmortem tissues, have provided new insights into the molecular pathology of schizophrenia. In this review, we overview the current knowledge acquired from these studies, their limitations, and directions for future research that may redefine schizophrenia based on biological alterations in the responsible organ rather than operationalized criteria.

Psychotic disorders as a framework for precision psychiatry

People with psychotic disorders can show marked interindividual variations in the onset of illness, responses to treatment and relapse, but they receive broadly similar clinical care. Precision psychiatry is an approach that aims to stratify people with a given disorder according to different clinical outcomes and tailor treatment to their individual needs. At present, interindividual differences in outcomes of psychotic disorders are difficult to predict on the basis of clinical assessment alone. Therefore, current research in psychosis seeks to build models that predict outcomes by integrating clinical information with a range of biological measures. Here, we review recent progress in the application of precision psychiatry to psychotic disorders and consider the challenges associated with implementing this approach in clinical practice.

Maternal SARS-CoV-2 exposure alters infant DNA methylation

Background

Infection during pregnancy can increase the risk of neurodevelopmental disorders in offspring. The impact of maternal SARS-CoV-2 infection on infant neurodevelopment is poorly understood. The maternal immune response to infection may be mimicked in rodent models of maternal immune activation which recapitulate altered neurodevelopment and behavioural disturbances in the offspring. In these models, epigenetic mechanisms, in particular DNA methylation, are one pathway through which this risk is conferred in utero to offspring. We hypothesised that in utero exposure to SARS-CoV-2 in humans may alter infant DNA methylation, particularly in genes associated with neurodevelopment. We aimed to test this hypothesis in a pilot sample of children in Victoria, Australia, who were exposed in utero to SARS-CoV-2.

Methods

DNA was extracted from buccal swab specimens from (n = 4) SARS-CoV-2 in utero exposed and (n = 4) non-exposed infants and methylation status assessed across 850,000 methylation sites using an Illumina EPIC BeadChip. We also conducted an exploratory enrichment analysis using Gene Ontology annotations.

Results

1962 hypermethylated CpG sites were identified with an unadjusted p-value of 0.05, where 1133 CpGs mapped to 959 unique protein coding genes, and 716 hypomethylated CpG sites mapped to 559 unique protein coding genes in SARS-CoV-2 exposed infants compared to non-exposed. One differentially methylated position (cg06758191), located in the gene body of AFAP1 that was hypomethylated in the SARS-CoV-2 exposed cohort was significant after correction for multiple testing (FDR-adjusted p-value <0.00083). Two significant differentially methylated regions were identified; a hypomethylated intergenic region located in chromosome 6p proximal to the genes ZP57 and HLA-F (fwer <0.004), and a hypomethylated region in the promoter and body of the gene GAREM2 (fwer <0.036). Gene network enrichment analysis revealed differential methylation in genes corresponding to pathways relevant to neurodevelopment, including the ERBB pathway.

Conclusion

These pilot data suggest that exposure to SARS-CoV-2 in utero differentially alters methylation of genes in pathways that play a role in human neurodevelopment.

Biological hypotheses, risk factors, and biomarkers of schizophrenia

Both the discovery of biomarkers of schizophrenia and the verification of biological hypotheses of schizophrenia are an essential part of the process of understanding the etiology of this mental disorder. Schizophrenia has long been considered a neurodevelopmental disease whose symptoms are caused by impaired synaptic signal transduction and brain neuroplasticity. Both the onset and chronic course of schizophrenia are associated with risk factors-induced disruption of brain function and the establishment of a new homeostatic setpoint characterized by biomarkers. Different risk factors and biomarkers can converge to the same symptoms of schizophrenia, suggesting that the primary cause of the disease can be highly individual. Schizophrenia-related biomarkers include measurable biochemical changes induced by stress (elevated allostatic load), mitochondrial dysfunction, neuroinflammation, oxidative and nitrosative stress, and circadian rhythm disturbances. Here is a summary of selected valid biological hypotheses of schizophrenia formulated based on risk factors and biomarkers, neurodevelopment, neuroplasticity, brain chemistry, and antipsychotic medication. The integrative neurodevelopmental-vulnerability-neurochemical model is based on current knowledge of the neurobiology of the onset and progression of the disease and the effects of antipsychotics and psychotomimetics and reflects the complex and multifactorial nature of schizophrenia.

DNA methylation signature aberration as potential biomarkers in treatment-resistant schizophrenia: Constructing a methylation risk score using a machine learning method

Treatment-resistant schizophrenia (TRS) is defined as a non-response to at least two trials of antipsychotic medication with an adequate dose and duration. We aimed to evaluate the discriminant abilities of DNA methylation probes and methylation risk score between treatment-resistant schizophrenia and non-treatment-resistant schizophrenia. This study recruited 96 schizophrenia patients (TRS and non-TRS) and 56 healthy controls (HC). Participants were divided into a discovery set and a validation set. In the discovery set, we conducted genome-wide methylation analysis (human MethylationEPIC 850K BeadChip) on the subject's blood DNA and discriminated significant methylation signatures, then verified these methylation signatures in the validation set. Based on genome-wide scans of TRS versus non-TRS, thirteen differentially methylated probes were identified at FDR <0.05 and >20% differences in DNA methylation β-values. Next, we selected six probes within gene coding regions (LOC404266, LOXL2, CERK, CHMP7, and SLC17A9) to conduct verification in the validation set using quantitative methylation-specific PCR (qMSP). These six methylation probes showed satisfactory discrimination between TRS patients and non-TRS patients, with an AUC ranging from 0.83 to 0.92, accuracy ranging from 77.8% to 87.3%, sensitivity ranging from 80% to 90%, and specificity ranging from 65.6% to 85%. This methylation risk score model showed satisfactory discrimination between TRS patients and non-TRS patients, with an accuracy of 88.3%. These findings support that methylation signatures may be used as an indicator of TRS vulnerability and provide a model for the clinical use of methylation to identify TRS.

Regulation of DNA Methylation by Cannabidiol and Its Implications for Psychiatry: New Insights from In Vivo and In Silico Models

Cannabidiol (CBD) is a non-psychotomimetic compound present in cannabis sativa. Many recent studies have indicated that CBD has a promising therapeutic profile for stress-related psychiatric disorders, such as anxiety, schizophrenia and depression. Such a diverse profile has been associated with its complex pharmacology, since CBD can target different neurotransmitter receptors, enzymes, transporters and ion channels. However, the precise contribution of each of those mechanisms for CBD effects is still not yet completely understood. Considering that epigenetic changes make the bridge between gene expression and environment interactions, we review and discuss herein how CBD affects one of the main epigenetic mechanisms associated with the development of stress-related psychiatric disorders: DNA methylation (DNAm). Evidence from in vivo and in silico studies indicate that CBD can regulate the activity of the enzymes responsible for DNAm, due to directly binding to the enzymes and/or by indirectly regulating their activities as a consequence of neurotransmitter-mediated signaling. The implications of this new potential pharmacological target for CBD are discussed in light of its therapeutic and neurodevelopmental effects.

A systematic assessment of cell type deconvolution algorithms for DNA methylation data

We performed systematic assessment of computational deconvolution methods that play an important role in the estimation of cell type proportions from bulk methylation data. The proposed framework methylDeConv (available as an R package) integrates several deconvolution methods for methylation profiles (Illumina HumanMethylation450 and MethylationEPIC arrays) and offers different cell-type-specific CpG selection to construct the extended reference library which incorporates the main immune cell subsets, epithelial cells and cell-free DNAs. We compared the performance of different deconvolution algorithms via simulations and benchmark datasets and further investigated the associations of the estimated cell type proportions to cancer therapy in breast cancer and subtypes in melanoma methylation case studies. Our results indicated that the deconvolution based on the extended reference library is critical to obtain accurate estimates of cell proportions in non-blood tissues.

A correlation map of genome-wide DNA methylation patterns between paired human brain and buccal samples

Epigenome-wide association studies (EWAS) assessing the link between DNA methylation (DNAm) and phenotypes related to structural brain measures, cognitive function, and neurodegenerative diseases are becoming increasingly more popular. Due to the inaccessibility of brain tissue in humans, several studies use peripheral tissues such as blood, buccal swabs, and saliva as surrogates. To aid the functional interpretation of EWAS findings in such settings, there is a need to assess the correlation of DNAm variability across tissues in the same individuals. In this study, we performed a correlation analysis between DNAm data of a total of n = 120 matched post-mortem buccal and prefrontal cortex samples. We identified nearly 25,000 (3% of approximately 730,000) cytosine-phosphate-guanine (CpG) sites showing significant (false discovery rate q < 0.05) correlations between buccal and PFC samples. Correlated CpG sites showed a preponderance to being located in promoter regions and showed a significant enrichment of being determined by genetic factors, i.e. methylation quantitative trait loci (mQTL), based on buccal and dorsolateral prefrontal cortex mQTL databases. Our novel buccal-brain DNAm correlation map will provide a valuable resource for future EWAS using buccal samples for studying DNAm effects on phenotypes relating to the brain. All correlation results are made freely available to the public online.

Disease classification for whole-blood DNA methylation: Meta-analysis, missing values imputation, and XAI

BACKGROUND

DNA methylation has a significant effect on gene expression and can be associated with various diseases. Meta-analysis of available DNA methylation datasets requires development of a specific workflow for joint data processing.

RESULTS

We propose a comprehensive approach of combined DNA methylation datasets to classify controls and patients. The solution includes data harmonization, construction of machine learning classification models, dimensionality reduction of models, imputation of missing values, and explanation of model predictions by explainable artificial intelligence (XAI) algorithms. We show that harmonization can improve classification accuracy by up to 20% when preprocessing methods of the training and test datasets are different. The best accuracy results were obtained with tree ensembles, reaching above 95% for Parkinson's disease. Dimensionality reduction can substantially decrease the number of features, without detriment to the classification accuracy. The best imputation methods achieve almost the same classification accuracy for data with missing values as for the original data. XAI approaches have allowed us to explain model predictions from both populational and individual perspectives.

CONCLUSIONS

We propose a methodologically valid and comprehensive approach to the classification of healthy individuals and patients with various diseases based on whole-blood DNA methylation data using Parkinson's disease and schizophrenia as examples. The proposed algorithm works better for the former pathology, characterized by a complex set of symptoms. It allows to solve data harmonization problems for meta-analysis of many different datasets, impute missing values, and build classification models of small dimensionality.

Peripheral biomarkers of treatment-resistant schizophrenia: Genetic, inflammation and stress perspectives

Treatment-resistant schizophrenia (TRS) often results in severe disability and functional impairment. Currently, the diagnosis of TRS is largely exclusionary and emphasizes the improvement of symptoms that may not be detected early and treated according to TRS guideline. As the gold standard, clozapine is the most prescribed selection for TRS. Therefore, how to predict TRS in advance is critical for forming subsequent treatment strategy especially clozapine is used during the early stage of TRS. Although mounting studies have identified certain clinical factors and neuroimaging characteristics associated with treatment response in schizophrenia, the predictors for TRS remain to be explored. Biomarkers, particularly for peripheral biomarkers, show great potential in predicting TRS in view of their predictive validity, noninvasiveness, ease of testing and low cost that would enable their widespread use. Recent evidence supports that the pathogenesis of TRS may be involved in abnormal neurotransmitter systems, inflammation and stress. Due to the heterogeneity of TRS and the lack of consensus in diagnostic criteria, it is difficult to compare extensive results among different studies. Based on the reported neurobiological mechanisms that may be associated with TRS, this paper narratively reviews the updates of peripheral biomarkers of TRS, from genetic and other related perspectives. Although current evidence regarding biomarkers in TRS remains fragmentary, when taken together, it can help to better understand the neurobiological interface of clinical phenotypes and psychiatric symptoms, which will enable individualized prediction and therapy for TRS in the long run.

Immune system disruptions implicated in whole blood epigenome-wide association study of depression among Parkinson's disease patients

Although Parkinson's Disease (PD) is typically described in terms of motor symptoms, depression is a common feature. We explored whether depression influences blood-based genome-wide DNA methylation (DNAm) in 692 subjects from a population-based PD case-control study, using both a history of clinically diagnosed depression and current depressive symptoms measured by the geriatric depression scale (GDS). While PD patients in general had more immune activation and more accelerated epigenetic immune system aging than controls, the patients experiencing current depressive symptoms (GDS≥5) showed even higher levels of both markers than patients without current depressive symptoms (GDS<5). For PD patients with a history of clinical depression compared to those without, we found no differences in immune cell composition. However, a history of clinical depression among patients was associated with differentially methylated CpGs. Epigenome-wide association analysis (EWAS) revealed 35 CpGs associated at an FDR≤0.05 (569 CpGs at FDR≤0.10, 1718 CpGs at FDR≤0.15). Gene set enrichment analysis implicated immune system pathways, including immunoregulatory interactions between lymphoid and non-lymphoid cells (p-adj = 0.003) and cytokine-cytokine receptor interaction (p-adj = 0.004). Based on functional genomics, 25 (71%) of the FDR≤0.05 CpGs were associated with genetic variation at 45 different methylation quantitative trait loci (meQTL). Twenty-six of the meQTLs were also expression QTLs (eQTLs) associated with the abundance of 53 transcripts in blood and 22 transcripts in brain (substantia nigra, putamen basal ganglia, or frontal cortex). Notably, cg15199181 was strongly related to rs823114 (SNP-CpG p-value = 3.27E-310), a SNP identified in a PD meta-GWAS and related to differential expression of PM20D1, RAB29, SLC41A1, and NUCKS1. The entire set of genes detected through functional genomics was most strongly overrepresented for interferon-gamma-mediated signaling pathway (enrichment ratio = 18.8, FDR = 4.4e-03) and T cell receptor signaling pathway (enrichment ratio = 13.2, FDR = 4.4e-03). Overall, the current study provides evidence of immune system involvement in depression among Parkinson's patients.

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Parkinson's disease (PD) is associated with clinical depression prior to PD onset and depressive symptoms after PD diagnosis.

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Epigenome-wide analysis revealed CpGs related to current depressive symptoms and a history of clinical depression among PD patients.

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Patients experiencing current depressive symptoms had the highest epigenetic-based neutrophil-to-lymphocyte ratio on average.

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Patients with a history of clinical depression had differentially methylated CpGs in genes enriched for immune system pathways.

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Many of the depression associated CpGs were linked to differential expression through meQTL/eQTLs, which included GWAS variants.

eQTL analysis of laying hens divergently selected for feather pecking identifies KLF14 as a potential key regulator for this behavioral disorder

Feather pecking in chickens is a damaging behavior, seriously impacting animal welfare and leading to economic losses. Feather pecking is a complex trait, which is partly under genetic control. Different hypotheses have been proposed to explain the etiology of feather pecking and notably, several studies have identified similarities between feather pecking and human mental disorders such as obsessive-compulsive disorder and schizophrenia. This study uses transcriptomic and phenotypic data from 167 chickens to map expression quantitative trait loci and to identify regulatory genes with a significant effect on this behavioral disorder using an association weight matrix approach. From 70 of the analyzed differentially expressed genes, 11,790 genome wide significantly associated variants were detected, of which 23 showed multiple associations (≥15). These were located in proximity to a number of genes, which are transcription regulators involved in chromatin binding, nucleic acid metabolism, protein translation and putative regulatory RNAs. The association weight matrix identified 36 genes and the two transcription factors: SP6 (synonym: KLF14) and ENSGALG00000042129 (synonym: CHTOP) as the most significant, with an enrichment of KLF14 binding sites being detectable in 40 differentially expressed genes. This indicates that differential expression between animals showing high and low levels of feather pecking was significantly associated with a genetic variant in proximity to KLF14. This multiallelic variant was located 652 bp downstream of KLF14 and is a deletion of 1-3 bp. We propose that a deletion downstream of the transcription factor KLF14 has a negative impact on the level of T cells in the developing brain of high feather pecking chickens, which leads to developmental and behavioral abnormalities. The lack of CD4 T cells and gamma-Aminobutyric acid (GABA) receptors are important factors for the increased propensity of laying hens to perform feather pecking. As such, KLF14 is a clear candidate regulator for the expression of genes involved in the pathogenic development. By further elucidating the regulatory pathways involved in feather pecking we hope to take significant steps forward in explaining and understanding other mental disorders, not just in chickens.

ABCB9 polymorphism rs61955196 is associated with schizophrenia in a Chinese Han population

BACKGROUND

Schizophrenia (SCZ) is a complex disease which can be affected by both genetic and environmental factors. Prenatal famine exposure may cause changes in DNA methylation levels of genes. Meanwhile, maternal nutrition during pregnancy is a pivotal environmental factor in the development of SCZ. DNA methylation may be an intermediate factor mediating exposure to famine during pregnancy and SCZ, and DNA methylation quantitative trait loci might serve as a promising tool for linking SCZ and prenatal famine.

AIM

To analyze the association between prenatal famine exposure and SCZ risk in Northeast Han Chinese through analysis of DNA methylation related loci.

METHODS

A total of 954 Han Chinese from Northeast China were recruited, including 443 patients with SCZ and 511 healthy controls. The participants were further divided into famine (born in 1960-1962) and non-famine (born in 1963-1965) groups to investigate the effect of prenatal famine exposure. Four single-nucleotide polymorphisms (SNPs) selected according to the relevant literature were genotyped, namely, rs11917047 in PTPRG, rs2239681 in IGF2, rs3842756 in INSIGF, and rs61955196 in ABCB9. DNA were extracted from peripheral blood samples, and the genotypes of these SNP loci were detected using the improved Multiple Ligase Detection Reaction multiple SNP typing technique. The associations of the DNA methylation related SNPs with SCZ risk and prenatal famine, and their interactions were analyzed using logistic regression analysis and generalized multifactor dimensionality reduction (GMDR) software.

RESULTS

Based on the sequencing data, genotype distributions and allele frequencies of the four selected SNPs were determined. All genotype frequencies of the four SNPs in the healthy control group were tested for deviation from Hardy-Weinberg equilibrium (P > 0.05). Logistic regression analysis showed that rs61955196 was significantly associated with SCZ risk in the log-additive model [odds ratio (OR): 1.22; 95% confidence interval (CI): 1.01-1.48; P = 0.040]. We also found that the rs61955196 allele was related with an enhanced risk of SCZ (G>C, OR: 1.22; 95%CI: 1.01-1.47; P = 0.042). However, no associations were observed between rs11917047, rs2239681, or rs3842756 and SCZ risk. Under the optimal genetic model, no significant association of famine with the four SNPs was seen. Though the gene–gene interactions between rs2239681 and rs61955196 were found in GMDR analysis, none of the gene-gene interactions and gene-famine interactions were associated with the risk of SCZ.

CONCLUSION

Our study suggested that rs61955196 in ABCB9 is associated with SCZ susceptibility in Northeast Han Chinese, providing insight into genetic effects on SCZ.

Can epigenetics shine a light on the biological pathways underlying major mental disorders?

A significant proportion of the global burden of disease can be attributed to mental illness. Despite important advances in identifying risk factors for mental health conditions, the biological processing underlying causal pathways to disease onset remain poorly understood. This represents a limitation to implement effective prevention and the development of novel pharmacological treatments. Epigenetic mechanisms have emerged as mediators of environmental and genetic risk factors which might play a role in disease onset, including childhood adversity (CA) and cannabis use (CU). Particularly, human research exploring DNA methylation has provided new and promising insights into the role of biological pathways implicated in the aetio-pathogenesis of psychiatric conditions, including: monoaminergic (Serotonin and Dopamine), GABAergic, glutamatergic, neurogenesis, inflammatory and immune response and oxidative stress. While these epigenetic changes have been often studied as disease-specific, similarly to the investigation of environmental risk factors, they are often transdiagnostic. Therefore, we aim to review the existing literature on DNA methylation from human studies of psychiatric diseases (i) to identify epigenetic modifications mapping onto biological pathways either transdiagnostically or specifically related to psychiatric diseases such as Eating Disorders, Post-traumatic Stress Disorder, Bipolar and Psychotic Disorder, Depression, Autism Spectrum Disorder and Anxiety Disorder, and (ii) to investigate a convergence between some of these epigenetic modifications and the exposure to known risk factors for psychiatric disorders such as CA and CU, as well as to other epigenetic confounders in psychiatry research.

Identification of COVID-19-Associated DNA Methylation Variations by Integrating Methylation Array and scRNA-Seq Data at Cell-Type Resolution

Single-cell transcriptome studies have revealed immune dysfunction in COVID-19 patients, including lymphopenia, T cell exhaustion, and increased levels of pro-inflammatory cytokines, while DNA methylation plays an important role in the regulation of immune response and inflammatory response. The specific cell types of immune responses regulated by DNA methylation in COVID-19 patients will be better understood by exploring the COVID-19 DNA methylation variation at the cell-type level. Here, we developed an analytical pipeline to explore single-cell DNA methylation variations in COVID-19 patients by transferring bulk-tissue-level knowledge to the single-cell level. We discovered that the methylation variations in the whole blood of COVID-19 patients showed significant cell-type specificity with remarkable enrichment in gamma-delta T cells and presented a phenomenon of hypermethylation and low expression. Furthermore, we identified five genes whose methylation variations were associated with several cell types. Among them, S100A9, AHNAK, and CX3CR1 have been reported as potential COVID-19 biomarkers previously, and the others (TRAF3IP3 and LFNG) are closely associated with the immune and virus-related signaling pathways. We propose that they might serve as potential epigenetic biomarkers for COVID-19 and could play roles in important biological processes such as the immune response and antiviral activity.

Reelin Alterations, Behavioral Phenotypes, and Brain Anomalies in Schizophrenia: A Systematic Review of Insights From Rodent Models

Reelin is an extracellular matrix glycoprotein reduced in brain regions (the prefrontal cortex and the hippocampus) of patients with schizophrenia. There are diverse rodent models of schizophrenia that mimic patient symptoms based on various causal theories; however, likely shared reelin alterations have not yet been systematically assessed in those models. A systematic review of the literature was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) model. Articles focused on psychotic disorders or schizophrenia and their relationship with reelin in rodent models were selected. Data (first author, publication year, results, both open field and prepulse inhibition test results, and type of reelin alteration) were extracted in duplicate by two independent reviewers. The 37 reviewed articles reported about various schizophrenia models and their reelin alterations, brain morphology, and behavioral defects. We conclude that reelin is an altered preclinical biomarker common to all models included, mainly prenatal or genetic models, and a key protein in schizophrenia disease, making the reelin signaling pathway in prenatal stages a target of special interest for future preclinical and clinical studies. All models presented at least one of the four described reelin alteration types.

Systematic Review Registration: [https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42021210568], identifier [CRD42021210568].

The role of alcohol intake in the pharmacogenetics of treatment with clozapine

Clozapine (CLZ) is an atypical antipsychotic reserved for patients with refractory psychosis, but it is associated with a significant risk of severe adverse reactions (ADRs) that are potentiated with the concomitant use of alcohol. Additionally, pharmacogenetic studies have explored the influence of several genetic variants in CYP450, receptors and transporters involved in the interindividual response to CLZ. Herein, we systematically review the current multiomics knowledge behind the interaction between CLZ and alcohol intake, and how its concomitant use might modulate the pharmacogenetics. CYP1A2*1F, *1C and other alleles not yet discovered could support a precision medicine approach for better therapeutic effects and fewer CLZ ADRs. CLZ monitoring systems should be amended and include alcohol intake to protect patients from severe CLZ ADRs.

The Epigenetics of Psychosis: A Structured Review with Representative Loci

The evidence for an environmental component in chronic psychotic disorders is strong and research on the epigenetic manifestations of these environmental impacts has commenced in earnest. In reviewing this research, the focus is on three genes as models for differential methylation, MCHR1, AKT1 and TDO2, each of which have been investigated for genetic association with psychotic disorders. Environmental factors associated with psychotic disorders, and which interact with these model genes, are explored in depth. The location of transcription factor motifs relative to key methylation sites is evaluated for predicted gene expression results, and for other sites, evidence is presented for methylation directing alternative splicing. Experimental results from key studies show differential methylation: for MCHR1, in psychosis cases versus controls; for AKT1, as a pre-existing methylation pattern influencing brain activation following acute administration of a psychosis-eliciting environmental stimulus; and for TDO2, in a pattern associated with a developmental factor of risk for psychosis, in all cases the predicted expression impact being highly dependent on location. Methylation induced by smoking, a confounding variable, exhibits an intriguing pattern for all three genes. Finally, how differential methylation meshes with Darwinian principles is examined, in particular as it relates to the "flexible stem" theory of evolution.