Epigenome-wide association study of alcohol consumption in N = 8161 individuals and relevance to alcohol use disorder pathophysiology: identification of the cystine/glutamate transporter SLC7A11 as a top target.

Alcohol misuse is common in many societies worldwide and is associated with extensive morbidity and mortality, often leading to alcohol use disorders (AUD) and alcohol-related end-organ damage. The underlying mechanisms contributing to the development of AUD are largely unknown; however, growing evidence suggests that alcohol consumption is strongly associated with alterations in DNA methylation. Identification of alcohol-associated methylomic variation might provide novel insights into pathophysiology and novel treatment targets for AUD. Here we performed the largest single-cohort epigenome-wide association study (EWAS) of alcohol consumption to date (N = 8161) and cross-validated findings in AUD populations with relevant endophenotypes, as well as alcohol-related animal models. Results showed 2504 CpGs significantly associated with alcohol consumption (Bonferroni p value < 6.8 × 10-8) with the five leading probes located in SLC7A11 (p = 7.75 × 10-108), JDP2 (p = 1.44 × 10-56), GAS5 (p = 2.71 × 10-47), TRA2B (p = 3.54 × 10-42), and SLC43A1 (p = 1.18 × 10-40). Genes annotated to associated CpG sites are implicated in liver and brain function, the cellular response to alcohol and alcohol-associated diseases, including hypertension and Alzheimer's disease. Two-sample Mendelian randomization confirmed the causal relationship of consumption on AUD risk (inverse variance weighted (IVW) p = 5.37 × 10-09). A methylation-based predictor of alcohol consumption was able to discriminate AUD cases in two independent cohorts (p = 6.32 × 10-38 and p = 5.41 × 10-14). The top EWAS probe cg06690548, located in the cystine/glutamate transporter SLC7A11, was replicated in an independent cohort of AUD and control participants (N = 615) and showed strong hypomethylation in AUD (p < 10-17). Decreased CpG methylation at this probe was consistently associated with clinical measures including increased heavy drinking days (p < 10-4), increased liver function enzymes (GGT (p = 1.03 × 10-21), ALT (p = 1.29 × 10-6), and AST (p = 1.97 × 10-8)) in individuals with AUD. Postmortem brain analyses documented increased SLC7A11 expression in the frontal cortex of individuals with AUD and animal models showed marked increased expression in liver, suggesting a mechanism by which alcohol leads to hypomethylation-induced overexpression of SLC7A11. Taken together, our EWAS discovery sample and subsequent validation of the top probe in AUD suggest a strong role of abnormal glutamate signaling mediated by methylomic variation in SLC7A11. Our data are intriguing given the prominent role of glutamate signaling in brain and liver and might provide an important target for therapeutic intervention.

Epigenome-wide association study and multi-tissue replication of individuals with alcohol use disorder: evidence for abnormal glucocorticoid signaling pathway gene regulation.

Alcohol use disorder (AUD) is a chronic debilitating disorder with limited treatment options and poorly defined pathophysiology. There are substantial genetic and epigenetic components; however, the underlying mechanisms contributing to AUD remain largely unknown. We conducted the largest DNA methylation epigenome-wide association study (EWAS) analyses currently available for AUD (total N = 625) and employed a top hit replication (N = 4798) using a cross-tissue/cross-phenotypic approach with the goal of identifying novel epigenetic targets relevant to AUD. Results show that a network of differentially methylated regions in glucocorticoid signaling and inflammation-related genes were associated with alcohol use behaviors. A top probe consistently associated across all cohorts was located in the long non-coding RNA growth arrest specific five gene (GAS5) (p < 10-24). GAS5 has been implicated in regulating transcriptional activity of the glucocorticoid receptor and has multiple functions related to apoptosis, immune function and various cancers. Endophenotypic analyses using peripheral cortisol levels and neuroimaging paradigms showed that methylomic variation in GAS5 network-related probes were associated with stress phenotypes. Postmortem brain analyses documented increased GAS5 expression in the amygdala of individuals with AUD. Our data suggest that alcohol use is associated with differential methylation in the glucocorticoid system that might influence stress and inflammatory reactivity and subsequently risk for AUD.

Genetic Association and Expression Analyses of the Phosphatidylinositol-4-Phosphate 5-Kinase (PIP5K1C) Gene in Alcohol Use Disorder-Relevance for Pain Signaling and Alcohol Use.

BACKGROUND: The gene encoding phosphatidylinositol-4-phosphate 5-kinase (PIP5K1C) has been recently implicated in pain regulation. Interestingly, a recent cross-tissue and cross-phenotypic epigenetic analysis identified the same gene in alcohol use disorder (AUD). Given the high comorbidity between AUD and chronic pain, we hypothesized that genetic variation in PIP5K1C might contribute to susceptibility to AUD. METHODS: We conducted a case-control association study of genetic variants in PIP5K1C. Association analyses of 16 common PIP5K1C single nucleotide polymorphisms (SNPs) were conducted in cases and controls of African (427 cases and 137 controls) and European ancestry (488 cases and 324 controls) using standard methods. In addition, given the prominent role of the opioid system in pain signaling, we investigated the effects of acute alcohol exposure on PIP5K1C expression in humanized transgenic mice for the µ-opioid receptor that included the OPRM1 A118G polymorphism, a widely used mouse model to study analgesic response to opioids in pain. PIP5K1C expression was measured in the thalamus and basolateral amygdala (BLA) in mice after short-term administration (single 2 g/kg dose) of alcohol or saline using immunohistochemistry and analyzed by 2-way analysis of variance. RESULTS: In the case-control association study using an NIAAA discovery sample, 8 SNPs in PIP5K1C were significantly associated with AUD in the African ancestry (AA) group (p < 0.05 after correction; rs4807493, rs10405681, rs2074957, rs10432303, rs8109485, rs1476592, rs10419980, and rs4432372). However, a replication analysis using an independent sample (N = 3,801) found no significant associations after correction for multiple testing. In the humanized transgenic mouse model with the OPRM1 polymorphism, PIP5K1C expression was significantly different between alcohol and saline-treated mice, regardless of genotype, in both the thalamus (p < 0.05) and BLA (p < 0.01). CONCLUSIONS: Our discovery sample shows that genetic variants in PIP5K1C are associated with AUD in the AA group, and acute alcohol exposure leads to up-regulation of PIP5K1C, potentially explaining a mechanism underlying the increased risk for chronic pain conditions in individuals with AUD.

Tumor Targeting via siRNA-COG3 to Suppress Tumor Progression in Mice and Inhibit Cancer Metastasis and Angiogenesis in Ovarian Cancer Cell Lines.

BACKGROUND: The COG complex is implicated in the tethering of retrograde intra-Golgi vesicles, which involves vesicular tethering and SNAREs. SNARE complexes mediate the invasion and metastasis of cancer cells through MMPs which activate growth factors for ECM fragments by binding to integrin receptors. Increasing MMPs is in line with YKL40 since YKL40 is linked to promoting angiogenesis through VEGF and can increase ovarian cancer (OC) resistance to chemotropic and cell migration. OBJECTIVE: The aim of this study is an assessment of siRNA-COG3 on proliferation, invasion, and apoptosis of OC cells. In addition, siRNA-COG3 may prevent the growth of OC cancer in mice with tumors. METHODS: Primary OC cell lines will be treated with siRNA-COG3 to assay YKL40 and identified angiogenesis by Tube-like structure formation in HOMECs. The Golgi morphology was analyzed using Immunofluorescence microscopy. Furthermore, the effects of siRNA-COG3 on the proliferation and apoptosis of cells were evaluated using MTT and TUNEL assays. Clones of the HOSEpiC OC cell line were subcutaneously implanted in FVB/N mice. Mice were treated after two weeks of injection of cells using siRNA-COG3. Tumor development suppression was detected by D-luciferin. RT-PCR and western blotting analyses were applied to determine COG3, MT1- MMP, SNAP23, and YKL40 expression to investigate the effects of COG3 gene knockdown. RESULTS: siRNA-COG3 exhibited a substantial effect in suppressing tumor growth in mice. It dramatically reduced OC cell proliferation and triggered apoptosis (all p < 0.01). Inhibition of COG3, YKL-40, and MT1-MPP led to suppression of angiogenesis and reduction of microvessel density through SNAP23 in OC cells. CONCLUSION: Overall, by knockdown of the COG3 gene, MT1-MMP and YKL40 were dropped, leading to suppressed angiogenesis along with decreasing migration and proliferation. SiRNACOG3 may be an ideal agent to consider for clinical trial assessment therapy for OC, especially when an antiangiogenic SNAR-pathway targeting drug.

BioTile, a Perl based tool for the identification of differentially enriched regions in tiling microarray data.

BACKGROUND: Genome-wide tiling array experiments are increasingly used for the analysis of DNA methylation. Because DNA methylation patterns are tissue and cell type specific, the detection of differentially methylated regions (DMRs) with small effect size is a necessary feature of tiling microarray 'peak' finding algorithms, as cellular heterogeneity within a studied tissue may lead to a dilution of the phenotypically relevant effects. Additionally, the ability to detect short length DMRs is necessary as biologically relevant signal may occur in focused regions throughout the genome. RESULTS: We present a free open-source Perl application, Binding Intensity Only Tile array analysis or "BioTile", for the identification of differentially enriched regions (DERs) in tiling array data. The application of BioTile to non-smoothed data allows for the identification of shorter length and smaller effect-size DERs, while correcting for probe specific variation by inversely weighting on probe variance through a permutation corrected meta-analysis procedure employed at identified regions. BioTile exhibits higher power to identify significant DERs of low effect size and across shorter genomic stretches as compared to other peak finding algorithms, while not sacrificing power to detect longer DERs. CONCLUSION: BioTile represents an easy to use analysis option applicable to multiple microarray platforms, allowing for its integration into the analysis workflow of array data analysis.

Role of S100 and YKL40 on Intraventricular Cerebral Hemorrhages in the Preterm Infant and the Neuroprotective Role of miR-138-siRNAs-HIF-1a and miR-21-siRNAs-HVCN1 in Neonatal Mice with Nerve Injury.

BACKGROUND: Epilepsy and intraventricular-cerebral hemorrhage is a common complication irreversible in preterm infants. Inflammation leads to an increase in intracellular calcium, acidosis, and oxygen usage, and finally, may damage brain cells. Increases in HIF-1a and HVCN1 can reduce the complications of oxygen consumption and acidosis in infants with intraventricular hemorrhage (IVH). On the other hand, decreases in S100B can shield nerve cells from apoptosis and epilepsy by reducing brain damage. OBJECTIVE: In this research, we investigated how miR-138-siRNAs-HIF-1a and miR-21-siRNAs-HVCN1 affect apoptosis in hypoxic mice. METHODS: On the first and third days after delivery, the YKL40, HIF-1a, HVCN1, and S100b genes were compared between two groups of preterm infants with and without maternal inflammation. Afterward, the miRNAs were transfected into cell lines to monitor variations in YKL40, HIF-1a, HVCN1, and S100b gene expression and nerve cell apoptosis. We changed the expression of S100b, HVCN1, and HIF-1a genes by using specific siRNAs injected into mice. Using real-time PCR, Western blotting, flow cytometry (FCM), and immunofluorescence, and changes in gene expression were evaluated (IHC). RESULTS: HVCN1 gene expression showed a strong negative correlation with epilepsy in both groups of infants (P<0.001). Significant correlations between epilepsy and the expression levels of the S100b, YKL40, and HIF-1a genes were found (P<0.001). According to FCM, after transfecting miRNA-431 and miRNA-34a into cell lines, the apoptosis index (A.I.) were 41.6 3.3 and 34.5 5.2%, respectively, while the A.I. were 9.6 2.7 and 7.1 4.2% after transfecting miRNA-21 and miRNA-138. MiR-138-siRNAs-HIF-1a and miR-21-siRNAs-HVCN1 were simultaneously injected into hypoxic mice, and IHC double-labeling revealed that this reduced apoptosis and seizures compared to the hypoxic group. CONCLUSION: Our findings demonstrate that miR-138-siRNAs-HIF-1a and miR-21-siRNAs-HVCN1 injections prevent cerebral ischemia-induced brain damage in hypoxia mice by increasing HVCN1 and HIF-1a and decreasing S100b, which in turn lessens apoptosis and epilepsy in hypoxic mice.

Developing a biomarker for restless leg syndrome using genome wide DNA methylation data.

This study reports on an epigenetic biomarker for restless leg syndrome (RLS) developed using whole genome DNA methylation data. Lymphocyte-derived DNA methylation was examined in 15 subjects with and without RLS (discovery cohort). T-tests and linear regressions were used followed by a principal component analysis (PCA). The principal component model from the discovery cohort was used to predict RLS status in a peripheral blood (N = 24; including 12 cases and 12 controls) and a post-mortem neural tissue (N = 71; including 36 cases and 35 controls) replication cohort as well as iron deficiency anemia status in a publicly available dataset (N = 71, 59 cases with iron deficiency anemia, 12 controls). Using receiver-operating characteristic analysis the optimum biomarker model - that included 49 probes - predicted RLS status in the blood-based replication cohort with an area under the curve (AUC) of 87.5% (confidence interval = 71.9%-100%). In the neural tissue samples, the model predicted RLS status with an AUC of 73.4% (confidence interval = 61.5%-85.3%). An AUC of 83% was found for predictions of iron deficiency anemia. Thus, the blood-based biomarker model reported here and built with epigenome-wide data showed reasonable replicability in lymphocytes and neural tissue samples. A limitation of this study is that we could not determine the metabolic or neurobiological pathways linking epigenetic changes with RLS. Further research is needed to fine-tune this model for prospective predictions of RLS and to enable translation for clinical use.

A machine learning approach predicts future risk to suicidal ideation from social media data.

Machine learning analysis of social media data represents a promising way to capture longitudinal environmental influences contributing to individual risk for suicidal thoughts and behaviors. Our objective was to generate an algorithm termed "Suicide Artificial Intelligence Prediction Heuristic (SAIPH)" capable of predicting future risk to suicidal thought by analyzing publicly available Twitter data. We trained a series of neural networks on Twitter data queried against suicide associated psychological constructs including burden, stress, loneliness, hopelessness, insomnia, depression, and anxiety. Using 512,526 tweets from N = 283 suicidal ideation (SI) cases and 3,518,494 tweets from 2655 controls, we then trained a random forest model using neural network outputs to predict binary SI status. The model predicted N = 830 SI events derived from an independent set of 277 suicidal ideators relative to N = 3159 control events in all non-SI individuals with an AUC of 0.88 (95% CI 0.86-0.90). Using an alternative approach, our model generates temporal prediction of risk such that peak occurrences above an individual specific threshold denote a ~7 fold increased risk for SI within the following 10 days (OR = 6.7 ± 1.1, P = 9 × 10-71). We validated our model using regionally obtained Twitter data and observed significant associations of algorithm SI scores with county-wide suicide death rates across 16 days in August and in October, 2019, most significantly in younger individuals. Algorithmic approaches like SAIPH have the potential to identify individual future SI risk and could be easily adapted as clinical decision tools aiding suicide screening and risk monitoring using available technologies.

Educational attainment reduces the risk of suicide attempt among individuals with and without psychiatric disorders independent of cognition: a bidirectional and multivariable Mendelian randomization study with more than 815,000 participants.

Rates of suicidal behavior are increasing in the United States and identifying causal risk factors continues to be a public health priority. Observational literature has shown that educational attainment (EA) and cognitive performance (CP) influence suicide attempt risk; however, the causal nature of these relationships is unknown. Using summary statistics from genome-wide association studies (GWAS) of EA, CP, and suicide attempt risk with > 815,000 combined white participants of European ancestry, we performed multivariable Mendelian randomization (MR) to disentangle the effects of EA and CP on attempted suicide. In single-variable MR (SVMR), EA and CP appeared to reduce suicide attempt risk (EA odds ratio (OR) per standard deviation (SD) increase in EA (4.2 years), 0.524, 95% CI, 0.412-0.666, P = 1.07 × 10-7; CP OR per SD increase in standardized score, 0.714, 95% CI, 0.577-0.885, P = 0.002). Conversely, bidirectional analyses found no effect of a suicide attempt on EA or CP. Using various multivariable MR (MVMR) models, EA seems to be the predominant risk factor for suicide attempt risk with the independent effect (OR, 0.342, 95% CI, 0.206-0.568, P = 1.61 × 10-4), while CP had no effect (OR, 1.182, 95% CI, 0.842-1.659, P = 0.333). In additional MVMR analyses accounting simultaneously for potential behavioral and psychiatric mediators (tobacco smoking; alcohol consumption; and self-reported nerves, tension, anxiety, or depression), the effect of EA was little changed (OR, 0.541, 95% CI, 0.421-0.696, P = 3.33 × 10-6). Consistency of results across complementary MR methods accommodating different assumptions about genetic pleiotropy strengthened causal inference. Our results show that even after accounting for psychiatric disorders and behavioral mediators, EA, but not CP, may causally influence suicide attempt risk among white individuals of European ancestry, which could have important implications for health policy and programs aimed at reducing the increasing rates of suicide. Future work is necessary to examine the EA-suicide relationship populations of different ethnicities.

Epigenetic aging is accelerated in alcohol use disorder and regulated by genetic variation in APOL2.

To investigate the potential role of alcohol use disorder (AUD) in aging processes, we employed Levine's epigenetic clock (DNAm PhenoAge) to estimate DNA methylation age in 331 individuals with AUD and 201 healthy controls (HC). We evaluated the effects of heavy, chronic alcohol consumption on epigenetic age acceleration (EAA) using clinical biomarkers, including liver function test enzymes (LFTs) and clinical measures. To characterize potential underlying genetic variation contributing to EAA in AUD, we performed genome-wide association studies (GWAS) on EAA, including pathway analyses. We followed up on relevant top findings with in silico expression quantitative trait loci (eQTL) analyses for biological function using the BRAINEAC database. There was a 2.22-year age acceleration in AUD compared to controls after adjusting for gender and blood cell composition (p = 1.85 × 10-5). This association remained significant after adjusting for race, body mass index, and smoking status (1.38 years, p = 0.02). Secondary analyses showed more pronounced EAA in individuals with more severe AUD-associated phenotypes, including elevated gamma-glutamyl transferase (GGT) and alanine aminotransferase (ALT), and higher number of heavy drinking days (all ps < 0.05). The genome-wide meta-analysis of EAA in AUD revealed a significant single nucleotide polymorphism (SNP), rs916264 (p = 5.43 × 10-8), in apolipoprotein L2 (APOL2) at the genome-wide level. The minor allele A of rs916264 was associated with EAA and with increased mRNA expression in hippocampus (p = 0.0015). Our data demonstrate EAA in AUD and suggest that disease severity further accelerates epigenetic aging. EAA was associated with genetic variation in APOL2, suggesting potential novel biological mechanisms for age acceleration in AUD.

DNA methylation biomarkers prospectively predict both antenatal and postpartum depression.

We sought to replicate and expand upon previous work demonstrating antenatal TTC9B and HP1BP3 gene DNA methylation is prospectively predictive of postpartum depression (PPD) with ~80% accuracy. In a preterm birth study from Emory, Illumina MethylEPIC microarray derived 1st but not 3rd trimester biomarker models predicted 3rd trimester Edinburgh Postnatal Depression Scale (EPDS) scores ≥ 13 with an AUC=0.8 (95% CI: 0.63-0.8). Bisulfite pyrosequencing derived biomarker methylation was generated using bisulfite pyrosequencing across all trimesters in a pregnancy cohort at UC Irvine and in 3rd trimester from an independent Johns Hopkins pregnancy cohort. A support vector machine model incorporating 3rd trimester EPDS scores, TTC9B, and HP1BP3 methylation status predicted 4 week to 6 week postpartum EPDS ≥ 13 from 3rd trimester blood in the UC Irvine cohort (AUC=0.78, 95% CI: 0.64-0.78) and from the Johns Hopkins cohort (AUC=0.84, 95% CI: 0.72-0.97), both independent of previous psychiatric diagnosis. Technical replicate predictions in a subset of the Johns Hopkins cohort exhibited strong cross experiment correlation. This study confirms the PPD prediction model has the potential to be developed into a clinical tool enabling the identification of pregnant women at future risk of PPD who may benefit from clinical intervention.

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.

SLC9B1 methylation predicts fetal intolerance of labor.

Fetal intolerance of labor is a common indication for delivery by Caesarean section. Diagnosis is based on the presence of category III fetal heart rate tracing, which is an abnormal heart tracing associated with increased likelihood of fetal hypoxia and metabolic acidemia. This study analyzed data from 177 unique women who, during their prenatal visits (7-15 weeks and/or 24-32 weeks) to Atlanta area prenatal care clinics, consented to provide blood samples for DNA methylation (HumanMethylation450 BeadChip) and gene expression (Human HT-12 v4 Expression BeadChip) analyses. We focused on 57 women aged 18-36 (mean 25.4), who had DNA methylation data available from their second prenatal visit. DNA methylation patterns at CpG sites across the genome were interrogated for associations with fetal intolerance of labor. Four CpG sites (P value <8.9 × 10-9, FDR <0.05) in gene SLC9B1, a Na+/H+ exchanger, were associated with fetal intolerance of labor. DNA methylation and gene expression were negatively associated when examined longitudinally during pregnancy using a linear mixed-effects model. Positive predictive values of methylation of these four sites ranged from 0.80 to 0.89, while negative predictive values ranged from 0.91 to 0.92. The four CpG sites were also associated with fetal intolerance of labor in an independent cohort (the Johns Hopkins Prospective PPD cohort). Therefore, fetal intolerance of labor could be accurately predicted from maternal blood samples obtained between 24-32 weeks gestation. Fetal intolerance of labor may be accurately predicted from maternal blood samples obtained between 24-32 weeks gestation by assessing DNA methylation patterns of SLC9B1. The identification of pregnant women at elevated risk for fetal intolerance of labor may allow for the development of targeted treatments or management plans.

DNA methylation age is accelerated in alcohol dependence.

Alcohol dependence (ALC) is a chronic, relapsing disorder that increases the burden of chronic disease and significantly contributes to numerous premature deaths each year. Previous research suggests that chronic, heavy alcohol consumption is associated with differential DNA methylation patterns. In addition, DNA methylation levels at certain CpG sites have been correlated with age. We used an epigenetic clock to investigate the potential role of excessive alcohol consumption in epigenetic aging. We explored this question in five independent cohorts, including DNA methylation data derived from datasets from blood (n = 129, n = 329), liver (n = 92, n = 49), and postmortem prefrontal cortex (n = 46). One blood dataset and one liver tissue dataset of individuals with ALC exhibited positive age acceleration (p < 0.0001 and p = 0.0069, respectively), whereas the other blood and liver tissue datasets both exhibited trends of positive age acceleration that were not significant (p = 0.83 and p = 0.57, respectively). Prefrontal cortex tissue exhibited a trend of negative age acceleration (p = 0.19). These results suggest that excessive alcohol consumption may be associated with epigenetic aging in a tissue-specific manner and warrants further investigation using multiple tissue samples from the same individuals.

Single nucleotide extension technology for quantitative site-specific evaluation of metC/C in GC-rich regions.

The development and use of high throughput technologies for detailed mapping of methylated cytosines (metC) is becoming of increasing importance for the expanding field of epigenetics. The single nucleotide primer extension reaction used for genotyping of single nucleotide polymorphisms has been recently adapted to interrogate the bisulfite modification induced 'quantitative' C/T polymorphism that corresponds to metC/C in the native DNA. In this study, we explored the opportunity to investigate C/T (and G/A) ratios using the Applied Biosystems (ABI) SNaPshot technology. The main effort of this study was dedicated to addressing the complexities in the analysis of DNA methylation in GC-rich regions where interrogation of the target cytosine can be confounded by variable degrees of methylation in other cytosines (resulting in variable C/T or G/A ratios after treatment with bisulfite) in the annealing site of the interrogating primer. In our studies, the mismatches of the SNaPshot primer with the target DNA sequence resulted in a biasing effect of up to 70% while these effects decreased as the location of the polymorphic site moved upstream of the target cytosine. We demonstrated that the biasing effect can be corrected with the SNaPshot primers containing degenerative C/T and G/A nucleotides. A series of experiments using various permutations of quantitative C/T and G/A polymorphisms at various positions of the target DNA sequence demonstrated that SNaPshot is able to accurately report cytosine methylation levels with <5% average SD from the true values. Given the relative simplicity of the method and the possibility to multiplex C/T and G/A interrogations, the SNaPshot approach may become a useful tool for large-scale mapping of metC.

Role of epigenetic factors in the development of mental illness throughout life.

Psychiatric disease is believed to result from a combination of genetic vulnerability and environmental influence. At the crux are epigenetic modifications, which mediate the influence of environment on the genome. Twin and genome-wide association studies demonstrate a wide range of heritabilities across psychiatric disorders, while epidemiological and animal models implicate distinct developmental windows where environmental factors may interact with genetic vulnerability to confer risk. Certain developmental periods appear to be more prone to these influences including during gestation, in the early postnatal period, and during periods of major hormonal rearrangement. Here we review the role of environmental factors capable of epigenetic reprogramming during these periods and present evidence for the link between these modifications and disease. The cross tissue relevance of environmentally induced epigenetic change and its utility for identifying peripheral biomarkers is discussed.

Epigenetic assimilation in the aging human brain.

BACKGROUND: Epigenetic drift progressively increases variation in DNA modification profiles of aging cells, but the finale of such divergence remains elusive. In this study, we explored the dynamics of DNA modification and transcription in the later stages of human life. RESULTS: We find that brain tissues of older individuals (>75 years) become more similar to each other, both epigenetically and transcriptionally, compared with younger individuals. Inter-individual epigenetic assimilation is concurrent with increasing similarity between the cerebral cortex and the cerebellum, which points to potential brain cell dedifferentiation. DNA modification analysis of twins affected with Alzheimer's disease reveals a potential for accelerated epigenetic assimilation in neurodegenerative disease. We also observe loss of boundaries and merging of neighboring DNA modification and transcriptomic domains over time. CONCLUSIONS: Age-dependent epigenetic divergence, paradoxically, changes to convergence in the later stages of life. The newly described phenomena of epigenetic assimilation and tissue dedifferentiation may help us better understand the molecular mechanisms of aging and the origins of diseases for which age is a risk factor.

High Precision DNA Modification Analysis of HCG9 in Major Psychosis.

New epigenetic technologies may uncover etiopathogenic mechanisms of major psychosis. In this study, we applied padlock probe-based ultra-deep bisulfite sequencing for fine mapping of modified cytosines of the HLA complex group 9 (nonprotein coding) gene in the postmortem brains of individuals affected with schizophrenia or bipolar disorder and unaffected controls. Significant differences between patients and controls were detected in both CpG and CpH modifications. In addition, we identified epigenetic age effects, DNA modification differences between sense and anti-sense strands, and demonstrated how DNA modification data can be used in clustering of patient populations. Our findings revealed new epigenetic complexities but also highlighted the potential of DNA modification approaches in the search of heterogeneous causes of major psychiatric disease.

DNA modification study of major depressive disorder: beyond locus-by-locus comparisons.

BACKGROUND: Major depressive disorder (MDD) exhibits numerous clinical and molecular features that are consistent with putative epigenetic misregulation. Despite growing interest in epigenetic studies of psychiatric diseases, the methodologies guiding such studies have not been well defined. METHODS: We performed DNA modification analysis in white blood cells from monozygotic twins discordant for MDD, in brain prefrontal cortex, and germline (sperm) samples from affected individuals and control subjects (total N = 304) using 8.1K CpG island microarrays and fine mapping. In addition to the traditional locus-by-locus comparisons, we explored the potential of new analytical approaches in epigenomic studies. RESULTS: In the microarray experiment, we detected a number of nominally significant DNA modification differences in MDD and validated selected targets using bisulfite pyrosequencing. Some MDD epigenetic changes, however, overlapped across brain, blood, and sperm more often than expected by chance. We also demonstrated that stratification for disease severity and age may increase the statistical power of epimutation detection. Finally, a series of new analytical approaches, such as DNA modification networks and machine-learning algorithms using binary and quantitative depression phenotypes, provided additional insights on the epigenetic contributions to MDD. CONCLUSIONS: Mapping epigenetic differences in MDD (and other psychiatric diseases) is a complex task. However, combining traditional and innovative analytical strategies may lead to identification of disease-specific etiopathogenic epimutations.