Multi-omics analysis of the molecular response to glucocorticoids - insights into shared genetic risk from psychiatric to medical disorders.

BACKGROUND: Alterations in the effects of glucocorticoids have been implicated in mediating some of the negative health effects associated with chronic stress, including increased risk for psychiatric disorders as well as cardiovascular and metabolic diseases. This study investigates how genetic variants influence gene expression and DNA methylation (DNAm) in response to glucocorticoid receptor (GR)-activation, and their association with disease risk. METHODS: We measured DNAm (n=199) and gene expression (n=297) in peripheral blood before and after GR-activation with dexamethasone, with matched genotype data available for all samples. A comprehensive molecular quantitative trait locus (QTL) analysis was conducted, mapping GR-response methylation (me)QTLs, GR-response expression (e)QTLs, and GR-response expression quantitative trait methylation (eQTM). A multi-level network analysis was employed to map the complex relationships between the transcriptome, epigenome, and genetic variation. RESULTS: We identified 3,772 GR-response meCpGs corresponding to 104,828 local GR-response meQTLs that did not strongly overlap with baseline meQTLs. eQTM and eQTL analyses revealed distinct genetic influences on gene expression and DNAm. Multi-level network analysis uncovered GR-response network trio QTLs, characterized by SNP-CpG-transcript combinations where meQTLs act as both eQTLs and eQTMs. GR-response trio variants were enriched in GWAS for psychiatric, respiratory, autoimmune and cardiovascular diseases and conferred a higher relative heritability per SNP than GR-response meQTL and baseline QTL SNP. CONCLUSIONS: Genetic variants modulating the molecular effects of glucocorticoids are associated with psychiatric as well as medical diseases and not uncovered in baseline QTL analyses.

Dissecting depression symptoms: Multi-omics clustering uncovers immune-related subgroups and cell-type specific dysregulation.

In a subset of patients with mental disorders, such as depression, low-grade inflammation and altered immune marker concentrations are observed. However, these immune alterations are often assessed by only one data type and small marker panels. Here, we used a transdiagnostic approach and combined data from two cohorts to define subgroups of depression symptoms across the diagnostic spectrum through a large-scale multi-omics clustering approach in 237 individuals. The method incorporated age, body mass index (BMI), 43 plasma immune markers and RNA-seq data from peripheral mononuclear blood cells (PBMCs). Our initial clustering revealed four clusters, including two immune-related depression symptom clusters characterized by elevated BMI, higher depression severity and elevated levels of immune markers such as interleukin-1 receptor antagonist (IL-1RA), C-reactive protein (CRP) and C-C motif chemokine 2 (CCL2 or MCP-1). In contrast, the RNA-seq data mostly differentiated a cluster with low depression severity, enriched in brain related gene sets. This cluster was also distinguished by electrocardiography data, while structural imaging data revealed differences in ventricle volumes across the clusters. Incorporating predicted cell type proportions into the clustering resulted in three clusters, with one showing elevated immune marker concentrations. The cell type proportion and genes related to cell types were most pronounced in an intermediate depression symptoms cluster, suggesting that RNA-seq and immune markers measure different aspects of immune dysregulation. Lastly, we found a dysregulation of the SERPINF1/VEGF-A pathway that was specific to dendritic cells by integrating immune marker and RNA-seq data. This shows the advantages of combining different data modalities and highlights possible markers for further stratification research of depression symptoms.

Single-nucleus transcriptomic profiling of human orbitofrontal cortex reveals convergent effects of aging and psychiatric disease.

Aging is a complex biological process and represents the largest risk factor for neurodegenerative disorders. The risk for neurodegenerative disorders is also increased in individuals with psychiatric disorders. Here, we characterized age-related transcriptomic changes in the brain by profiling ~800,000 nuclei from the orbitofrontal cortex from 87 individuals with and without psychiatric diagnoses and replicated findings in an independent cohort with 32 individuals. Aging affects all cell types, with LAMP5+LHX6+ interneurons, a cell-type abundant in primates, by far the most affected. Disrupted synaptic transmission emerged as a convergently affected pathway in aged tissue. Age-related transcriptomic changes overlapped with changes observed in Alzheimer's disease across multiple cell types. We find evidence for accelerated transcriptomic aging in individuals with psychiatric disorders and demonstrate a converging signature of aging and psychopathology across multiple cell types. Our findings shed light on cell-type-specific effects and biological pathways underlying age-related changes and their convergence with effects driven by psychiatric diagnosis.

longmixr: a tool for robust clustering of high-dimensional cross-sectional and longitudinal variables of mixed data types.

SUMMARY: Accurate clustering of mixed data, encompassing binary, categorical, and continuous variables, is vital for effective patient stratification in clinical questionnaire analysis. To address this need, we present longmixr, a comprehensive R package providing a robust framework for clustering mixed longitudinal data using finite mixture modeling techniques. By incorporating consensus clustering, longmixr ensures reliable and stable clustering results. Moreover, the package includes a detailed vignette that facilitates cluster exploration and visualization. AVAILABILITY AND IMPLEMENTATION: The R package is freely available at https://cran.r-project.org/package=longmixr with detailed documentation, including a case vignette, at https://cellmapslab.github.io/longmixr/.

Stress-induced brain responses are associated with BMI in women.

Overweight and obesity are associated with altered stress reactivity and increased inflammation. However, it is not known whether stress-induced changes in brain function scale with BMI and if such associations are driven by peripheral cytokines. Here, we investigate multimodal stress responses in a large transdiagnostic sample using predictive modeling based on spatio-temporal profiles of stress-induced changes in activation and functional connectivity. BMI is associated with increased brain responses as well as greater negative affect after stress and individual response profiles are associated with BMI in females (pperm < 0.001), but not males. Although stress-induced changes reflecting BMI are associated with baseline cortisol, there is no robust association with peripheral cytokines. To conclude, alterations in body weight and energy metabolism might scale acute brain responses to stress more strongly in females compared to males, echoing observational studies. Our findings highlight sex-dependent associations of stress with differences in endocrine markers, largely independent of peripheral inflammation.

Associations of psychiatric disease and ageing with FKBP5 expression converge on superficial layer neurons of the neocortex.

Identification and characterisation of novel targets for treatment is a priority in the field of psychiatry. FKBP5 is a gene with decades of evidence suggesting its pathogenic role in a subset of psychiatric patients, with potential to be leveraged as a therapeutic target for these individuals. While it is widely reported that FKBP5/FKBP51 mRNA/protein (FKBP5/1) expression is impacted by psychiatric disease state, risk genotype and age, it is not known in which cell types and sub-anatomical areas of the human brain this occurs. This knowledge is critical to propel FKBP5/1-targeted treatment development. Here, we performed an extensive, large-scale postmortem study (n = 1024) of FKBP5/1, examining neocortical areas (BA9, BA11 and ventral BA24/BA24a) derived from subjects that lived with schizophrenia, major depression or bipolar disorder. With an extensive battery of RNA (bulk RNA sequencing, single-nucleus RNA sequencing, microarray, qPCR, RNAscope) and protein (immunoblot, immunohistochemistry) analysis approaches, we thoroughly investigated the effects of disease state, ageing and genotype on cortical FKBP5/1 expression including in a cell type-specific manner. We identified consistently heightened FKBP5/1 levels in psychopathology and with age, but not genotype, with these effects strongest in schizophrenia. Using single-nucleus RNA sequencing (snRNAseq; BA9 and BA11) and targeted histology (BA9, BA24a), we established that these disease and ageing effects on FKBP5/1 expression were most pronounced in excitatory superficial layer neurons of the neocortex, and this effect appeared to be consistent in both the granular and agranular areas examined. We then found that this increase in FKBP5 levels may impact on synaptic plasticity, as FKBP5 gex levels strongly and inversely correlated with dendritic mushroom spine density and brain-derived neurotrophic factor (BDNF) levels in superficial layer neurons in BA11. These findings pinpoint a novel cellular and molecular mechanism that has potential to open a new avenue of FKBP51 drug development to treat cognitive symptoms in psychiatric disorders.

DiffBrainNet: Differential analyses add new insights into the response to glucocorticoids at the level of genes, networks and brain regions.

Genome-wide gene expression analyses are invaluable tools for studying biological and disease processes, allowing a hypothesis-free comparison of expression profiles. Traditionally, transcriptomic analysis has focused on gene-level effects found by differential expression. In recent years, network analysis has emerged as an important additional level of investigation, providing information on molecular connectivity, especially for diseases associated with a large number of linked effects of smaller magnitude, like neuropsychiatric disorders. Here, we describe how combined differential expression and prior-knowledge-based differential network analysis can be used to explore complex datasets. As an example, we analyze the transcriptional responses following administration of the glucocorticoid/stress receptor agonist dexamethasone in 8 mouse brain regions important for stress processing. By applying a combination of differential network- and expression-analyses, we find that these explain distinct but complementary biological mechanisms of the glucocorticoid responses. Additionally, network analysis identifies new differentially connected partners of risk genes and can be used to generate hypotheses on molecular pathways affected. With DiffBrainNet (http://diffbrainnet.psych.mpg.de), we provide an analysis framework and a publicly available resource for the study of the transcriptional landscape of the mouse brain which can identify molecular pathways important for basic functioning and response to glucocorticoids in a brain-region specific manner.

Network Embedding Across Multiple Tissues and Data Modalities Elucidates the Context of Host Factors Important for COVID-19 Infection.

COVID-19 is a heterogeneous disease caused by SARS-CoV-2. Aside from infections of the lungs, the disease can spread throughout the body and damage many other tissues, leading to multiorgan failure in severe cases. The highly variable symptom severity is influenced by genetic predispositions and preexisting diseases which have not been investigated in a large-scale multimodal manner. We present a holistic analysis framework, setting previously reported COVID-19 genes in context with prepandemic data, such as gene expression patterns across multiple tissues, polygenetic predispositions, and patient diseases, which are putative comorbidities of COVID-19. First, we generate a multimodal network using the prior-based network inference method KiMONo. We then embed the network to generate a meaningful lower-dimensional representation of the data. The input data are obtained via the Genotype-Tissue Expression project (GTEx), containing expression data from a range of tissues with genomic and phenotypic information of over 900 patients and 50 tissues. The generated network consists of nodes, that is, genes and polygenic risk scores (PRS) for several diseases/phenotypes, as well as for COVID-19 severity and hospitalization, and links between them if they are statistically associated in a regularized linear model by feature selection. Applying network embedding on the generated multimodal network allows us to perform efficient network analysis by identifying nodes close by in a lower-dimensional space that correspond to entities which are statistically linked. By determining the similarity between COVID-19 genes and other nodes through embedding, we identify disease associations to tissues, like the brain and gut. We also find strong associations between COVID-19 genes and various diseases such as ischemic heart disease, cerebrovascular disease, and hypertension. Moreover, we find evidence linking PTPN6 to a range of comorbidities along with the genetic predisposition of COVID-19, suggesting that this kinase is a central player in severe cases of COVID-19. In conclusion, our holistic network inference coupled with network embedding of multimodal data enables the contextualization of COVID-19-associated genes with respect to tissues, disease states, and genetic risk factors. Such contextualization can be exploited to further elucidate the biological importance of known and novel genes for severity of the disease in patients.

Effects of stressful life-events on DNA methylation in panic disorder and major depressive disorder.

BACKGROUND: Panic disorder (PD) is characterized by recurrent panic attacks and higher affection of women as compared to men. The lifetime prevalence of PD is about 2-3% in the general population leading to tremendous distress and disability. Etiologically, genetic and environmental factors, such as stress, contribute to the onset and relapse of PD. In the present study, we investigated epigenome-wide DNA methylation (DNAm) in respond to a cumulative, stress-weighted life events score (wLE) in patients with PD and its boundary to major depressive disorder (MDD), frequently co-occurring with symptoms of PD. METHODS: DNAm was assessed by the Illumina HumanMethylation450 BeadChip. In a meta-analytic approach, epigenome-wide DNAm changes in association with wLE were first analyzed in two PD cohorts (with a total sample size of 183 PD patients and 85 healthy controls) and lastly in 102 patients with MDD to identify possible overlapping and opposing effects of wLE on DNAm. Additionally, analysis of differentially methylated regions (DMRs) was conducted to identify regional clusters of association. RESULTS: Two CpG-sites presented with p-values below 1 × 10-05 in PD: cg09738429 (p = 6.40 × 10-06, located in an intergenic shore region in next proximity of PYROXD1) and cg03341655 (p = 8.14 × 10-06, located in the exonic region of GFOD2). The association of DNAm at cg03341655 and wLE could be replicated in the independent MDD case sample indicating a diagnosis independent effect. Genes mapping to the top hits were significantly upregulated in brain and top hits have been implicated in the metabolic system. Additionally, two significant DMRs were identified for PD only on chromosome 10 and 18, including CpG-sites which have been reported to be associated with anxiety and other psychiatric phenotypes. CONCLUSION: This first DNAm analysis in PD reveals first evidence of small but significant DNAm changes in PD in association with cumulative stress-weighted life events. Most of the top associated CpG-sites are located in genes implicated in metabolic processes supporting the hypothesis that environmental stress contributes to health damaging changes by affecting a broad spectrum of systems in the body.

Cell-Type-Specific Impact of Glucocorticoid Receptor Activation on the Developing Brain: A Cerebral Organoid Study.

OBJECTIVE: A fine-tuned balance of glucocorticoid receptor (GR) activation is essential for organ formation, with disturbances influencing many health outcomes. In utero, glucocorticoids have been linked to brain-related negative outcomes, with unclear underlying mechanisms, especially regarding cell-type-specific effects. An in vitro model of fetal human brain development, induced human pluripotent stem cell (hiPSC)-derived cerebral organoids, was used to test whether cerebral organoids are suitable for studying the impact of prenatal glucocorticoid exposure on the developing brain. METHODS: The GR was activated with the synthetic glucocorticoid dexamethasone, and the effects were mapped using single-cell transcriptomics across development. RESULTS: The GR was expressed in all cell types, with increasing expression levels through development. Not only did its activation elicit translocation to the nucleus and the expected effects on known GR-regulated pathways, but also neurons and progenitor cells showed targeted regulation of differentiation- and maturation-related transcripts. Uniquely in neurons, differentially expressed transcripts were significantly enriched for genes associated with behavior-related phenotypes and disorders. This human neuronal glucocorticoid response profile was validated across organoids from three independent hiPSC lines reprogrammed from different source tissues from both male and female donors. CONCLUSIONS: These findings suggest that excessive glucocorticoid exposure could interfere with neuronal maturation in utero, leading to increased disease susceptibility through neurodevelopmental processes at the interface of genetic susceptibility and environmental exposure. Cerebral organoids are a valuable translational resource for exploring the effects of glucocorticoids on early human brain development.

Sex differences in the genetic regulation of the blood transcriptome response to glucocorticoid receptor activation.

Substantial sex differences have been reported in the physiological response to stress at multiple levels, including the release of the stress hormone, cortisol. Here, we explore the genomic variants in 93 females and 196 males regulating the initial transcriptional response to cortisol via glucocorticoid receptor (GR) activation. Gene expression levels in peripheral blood were obtained before and after GR-stimulation with the selective GR agonist dexamethasone to identify differential expression following GR-activation. Sex stratified analyses revealed that while the transcripts responsive to GR-stimulation were mostly overlapping between males and females, the quantitative trait loci (eQTLs) regulation differential transcription to GR-stimulation was distinct. Sex-stratified eQTL SNPs (eSNPs) were located in different functional genomic elements and sex-stratified transcripts were enriched within postmortem brain transcriptional profiles associated with Major Depressive Disorder (MDD) specifically in males and females in the cingulate cortex. Female eSNPs were enriched among SNPs linked to MDD in genome-wide association studies. Finally, transcriptional sensitive genetic profile scores derived from sex-stratified eSNPS regulating differential transcription to GR-stimulation were predictive of depression status and depressive symptoms in a sex-concordant manner in a child and adolescent cohort (n = 584). These results suggest the potential of eQTLs regulating differential transcription to GR-stimulation as biomarkers of sex-specific biological risk for stress-related psychiatric disorders.

Polygenic risk for immuno-metabolic markers and specific depressive symptoms: A multi-sample network analysis study.

BACKGROUND: About every fourth patient with major depressive disorder (MDD) shows evidence of systemic inflammation. Previous studies have shown inflammation-depression associations of multiple serum inflammatory markers and multiple specific depressive symptoms. It remains unclear, however, if these associations extend to genetic/lifetime predisposition to higher inflammatory marker levels and what role metabolic factors such as Body Mass Index (BMI) play. It is also unclear whether inflammation-symptom associations reflect direct or indirect associations, which can be disentangled using network analysis. METHODS: This study examined associations of polygenic risk scores (PRSs) for immuno-metabolic markers (C-reactive protein [CRP], interleukin [IL]-6, IL-10, tumour necrosis factor [TNF]-α, BMI) with seven depressive symptoms in one general population sample, the UK Biobank study (n = 110,010), and two patient samples, the Munich Antidepressant Response Signature (MARS, n = 1058) and Sequenced Treatment Alternatives to Relieve Depression (STAR*D, n = 1143) studies. Network analysis was applied jointly for these samples using fused graphical least absolute shrinkage and selection operator (FGL) estimation as primary analysis and, individually, using unregularized model search estimation. Stability of results was assessed using bootstrapping and three consistency criteria were defined to appraise robustness and replicability of results across estimation methods, network bootstrapping, and samples. RESULTS: Network analysis results displayed to-be-expected PRS-PRS and symptom-symptom associations (termed edges), respectively, that were mostly positive. Using FGL estimation, results further suggested 28, 29, and six PRS-symptom edges in MARS, STAR*D, and UK Biobank samples, respectively. Unregularized model search estimation suggested three PRS-symptom edges in the UK Biobank sample. Applying our consistency criteria to these associations indicated that only the association of higher CRP PRS with greater changes in appetite fulfilled all three criteria. Four additional associations fulfilled at least two consistency criteria; specifically, higher CRP PRS was associated with greater fatigue and reduced anhedonia, higher TNF-α PRS was associated with greater fatigue, and higher BMI PRS with greater changes in appetite and anhedonia. Associations of the BMI PRS with anhedonia, however, showed an inconsistent valence across estimation methods. CONCLUSIONS: Genetic predisposition to higher systemic inflammatory markers are primarily associated with somatic/neurovegetative symptoms of depression such as changes in appetite and fatigue, consistent with previous studies based on circulating levels of inflammatory markers. We extend these findings by providing evidence that associations are direct (using network analysis) and extend to genetic predisposition to immuno-metabolic markers (using PRSs). Our findings can inform selection of patients with inflammation-related symptoms into clinical trials of immune-modulating drugs for MDD.

Versatile knowledge guided network inference method for prioritizing key regulatory factors in multi-omics data.

Constantly decreasing costs of high-throughput profiling on many molecular levels generate vast amounts of multi-omics data. Studying one biomedical question on two or more omic levels provides deeper insights into underlying molecular processes or disease pathophysiology. For the majority of multi-omics data projects, the data analysis is performed level-wise, followed by a combined interpretation of results. Hence the full potential of integrated data analysis is not leveraged yet, presumably due to the complexity of the data and the lacking toolsets. We propose a versatile approach, to perform a multi-level fully integrated analysis: The Knowledge guIded Multi-Omics Network inference approach, KiMONo ( https://github.com/cellmapslab/kimono ). KiMONo performs network inference by using statistical models for combining omics measurements coupled to a powerful knowledge-guided strategy exploiting prior information from existing biological sources. Within the resulting multimodal network, nodes represent features of all input types e.g. variants and genes while edges refer to knowledge-supported and statistically derived associations. In a comprehensive evaluation, we show that our method is robust to noise and exemplify the general applicability to the full spectrum of multi-omics data, demonstrating that KiMONo is a powerful approach towards leveraging the full potential of data sets for detecting biomarker candidates.

Dissecting the Association Between Inflammation, Metabolic Dysregulation, and Specific Depressive Symptoms: A Genetic Correlation and 2-Sample Mendelian Randomization Study.

Importance: Observational studies highlight associations of C-reactive protein (CRP), a general marker of inflammation, and interleukin 6 (IL-6), a cytokine-stimulating CRP production, with individual depressive symptoms. However, it is unclear whether inflammatory activity is associated with individual depressive symptoms and to what extent metabolic dysregulation underlies the reported associations. Objective: To explore the genetic overlap and associations between inflammatory activity, metabolic dysregulation, and individual depressive symptoms. GWAS Data Sources: Genome-wide association study (GWAS) summary data of European individuals, including the following: CRP levels (204 402 individuals); 9 individual depressive symptoms (3 of which did not differentiate between underlying diametrically opposite symptoms [eg, insomnia and hypersomnia]) as measured with the Patient Health Questionnaire 9 (up to 117 907 individuals); summary statistics for major depression, including and excluding UK Biobank participants, resulting in sample sizes of 500 199 and up to 230 214 individuals, respectively; insomnia (up to 386 533 individuals); body mass index (BMI) (up to 322 154 individuals); and height (up to 253 280 individuals). Design: In this genetic correlation and 2-sample mendelian randomization (MR) study, linkage disequilibrium score (LDSC) regression was applied to infer single-nucleotide variant-based heritability and genetic correlation estimates. Two-sample MR tested potential causal associations of genetic variants associated with CRP levels, IL-6 signaling, and BMI with depressive symptoms. The study dates were November 2019 to April 2020. Results: Based on large GWAS data sources, genetic correlation analyses revealed consistent false discovery rate (FDR)-controlled associations (genetic correlation range, 0.152-0.362; FDR P = .006 to P < .001) between CRP levels and depressive symptoms that were similar in size to genetic correlations of BMI with depressive symptoms. Two-sample MR analyses suggested that genetic upregulation of IL-6 signaling was associated with suicidality (estimate [SE], 0.035 [0.010]; FDR plus Bonferroni correction P = .01), a finding that remained stable across statistical models and sensitivity analyses using alternative instrument selection strategies. Mendelian randomization analyses did not consistently show associations of higher CRP levels or IL-6 signaling with other depressive symptoms, but higher BMI was associated with anhedonia, tiredness, changes in appetite, and feelings of inadequacy. Conclusions and Relevance: This study reports coheritability between CRP levels and individual depressive symptoms, which may result from the potentially causal association of metabolic dysregulation with anhedonia, tiredness, changes in appetite, and feelings of inadequacy. The study also found that IL-6 signaling is associated with suicidality. These findings may have clinical implications, highlighting the potential of anti-inflammatory approaches, especially IL-6 blockade, as a putative strategy for suicide prevention.

A polyepigenetic glucocorticoid exposure score at birth and childhood mental and behavioral disorders.

BACKGROUND: Maternal depression and anxiety during pregnancy may enhance fetal exposure to glucocorticoids (GCs) and harm neurodevelopment. We tested whether a novel cross-tissue polyepigenetic biomarker indicative of in utero exposure to GC is associated with mental and behavioral disorders and their severity in children, possibly mediating the associations between maternal prenatal depressive and anxiety symptoms and these child outcomes. METHODS: Children (n = 814) from the Prediction and Prevention of Preeclampsia and Intrauterine Growth Restriction (PREDO) study were followed-up from birth to age 7.1-10.7 years. A weighted polyepigenetic GC exposure score was calculated based on the methylation profile of 24 CpGs from umbilical cord blood. Child diagnosis of mental and behavioral disorder (n = 99) and its severity, defined as the number of days the child had received treatment (all 99 had received outpatient treatment and 8 had been additionally in inpatient treatment) for mental or behavioral disorder as the primary diagnosis, came from the Care Register for Health Care. Mothers (n = 408) reported on child total behavior problems at child's age of 2.3-5.8 years and their own depressive and anxiety symptoms during pregnancy (n = 583). RESULTS: The fetal polyepigenetic GC exposure score at birth was not associated with child hazard of mental and behavioral disorder (HR = 0.82, 95% CI 0.54; 1.24, p = 0.35) or total behavior problems (unstandardized beta = -0.10, 95% CI -0.31; 0.10, p = 0.33). However, for one standard deviation decrease in the polyepigenetic score, the child had spent 2.94 (95%CI 1.59; 5.45, p < 0.001) more days in inpatient or outpatient treatment with any mental and behavioral disorder as the primary diagnosis. Criteria for mediation tests were not met. CONCLUSIONS: These findings suggest that fetal polyepigenetic GC exposure score at birth was not associated with any mental or behavioral disorder diagnosis or mother-rated total behavior problems, but it may contribute to identifying children at birth who are at risk for more severe mental or behavioral disorders.

DeepWAS: Multivariate genotype-phenotype associations by directly integrating regulatory information using deep learning.

Genome-wide association studies (GWAS) identify genetic variants associated with traits or diseases. GWAS never directly link variants to regulatory mechanisms. Instead, the functional annotation of variants is typically inferred by post hoc analyses. A specific class of deep learning-based methods allows for the prediction of regulatory effects per variant on several cell type-specific chromatin features. We here describe "DeepWAS", a new approach that integrates these regulatory effect predictions of single variants into a multivariate GWAS setting. Thereby, single variants associated with a trait or disease are directly coupled to their impact on a chromatin feature in a cell type. Up to 61 regulatory SNPs, called dSNPs, were associated with multiple sclerosis (MS, 4,888 cases and 10,395 controls), major depressive disorder (MDD, 1,475 cases and 2,144 controls), and height (5,974 individuals). These variants were mainly non-coding and reached at least nominal significance in classical GWAS. The prediction accuracy was higher for DeepWAS than for classical GWAS models for 91% of the genome-wide significant, MS-specific dSNPs. DSNPs were enriched in public or cohort-matched expression and methylation quantitative trait loci and we demonstrated the potential of DeepWAS to generate testable functional hypotheses based on genotype data alone. DeepWAS is available at https://github.com/cellmapslab/DeepWAS.

Glucocorticoid exposure during hippocampal neurogenesis primes future stress response by inducing changes in DNA methylation.

Prenatal stress exposure is associated with risk for psychiatric disorders later in life. This may be mediated in part via enhanced exposure to glucocorticoids (GCs), which are known to impact neurogenesis. We aimed to identify molecular mediators of these effects, focusing on long-lasting epigenetic changes. In a human hippocampal progenitor cell (HPC) line, we assessed the short- and long-term effects of GC exposure during neurogenesis on messenger RNA (mRNA) expression and DNA methylation (DNAm) profiles. GC exposure induced changes in DNAm at 27,812 CpG dinucleotides and in the expression of 3,857 transcripts (false discovery rate [FDR] ≤ 0.1 and absolute fold change [FC] expression ≥ 1.15). HPC expression and GC-affected DNAm profiles were enriched for changes observed during human fetal brain development. Differentially methylated sites (DMSs) with GC exposure clustered into 4 trajectories over HPC differentiation, with transient as well as long-lasting DNAm changes. Lasting DMSs mapped to distinct functional pathways and were selectively enriched for poised and bivalent enhancer marks. Lasting DMSs had little correlation with lasting expression changes but were associated with a significantly enhanced transcriptional response to a second acute GC challenge. A significant subset of lasting DMSs was also responsive to an acute GC challenge in peripheral blood. These tissue-overlapping DMSs were used to compute a polyepigenetic score that predicted exposure to conditions associated with altered prenatal GCs in newborn's cord blood DNA. Overall, our data suggest that early exposure to GCs can change the set point of future transcriptional responses to stress by inducing lasting DNAm changes. Such altered set points may relate to differential vulnerability to stress exposure later in life.

Identification of dynamic glucocorticoid-induced methylation changes at the FKBP5 locus.

BACKGROUND: Epigenetic mechanisms may play a major role in the biological embedding of early-life stress (ELS). One proposed mechanism is that glucocorticoid (GC) release following ELS exposure induces long-lasting alterations in DNA methylation (DNAm) of important regulatory genes of the stress response. Here, we investigate the dynamics of GC-dependent methylation changes in key regulatory regions of the FKBP5 locus in which ELS-associated DNAm changes have been reported. RESULTS: We repeatedly measured DNAm in human peripheral blood samples from 2 independent cohorts exposed to the GC agonist dexamethasone (DEX) using a targeted bisulfite sequencing approach, complemented by data from Illumina 450K arrays. We detected differentially methylated CpGs in enhancers co-localizing with GC receptor binding sites after acute DEX treatment (1 h, 3 h, 6 h), which returned to baseline levels within 23 h. These changes withstood correction for immune cell count differences. While we observed main effects of sex, age, body mass index, smoking, and depression symptoms on FKBP5 methylation levels, only the functional FKBP5 SNP (rs1360780) moderated the dynamic changes following DEX. This genotype effect was observed in both cohorts and included sites previously shown to be associated with ELS. CONCLUSION: Our study highlights that DNAm levels within regulatory regions of the FKBP5 locus show dynamic changes following a GC challenge and suggest that factors influencing the dynamics of this regulation may contribute to the previously reported alterations in DNAm associated with current and past ELS exposure.

Epigenetic upregulation of FKBP5 by aging and stress contributes to NF-κB-driven inflammation and cardiovascular risk.

Aging and psychosocial stress are associated with increased inflammation and disease risk, but the underlying molecular mechanisms are unclear. Because both aging and stress are also associated with lasting epigenetic changes, a plausible hypothesis is that stress along the lifespan could confer disease risk through epigenetic effects on molecules involved in inflammatory processes. Here, by combining large-scale analyses in human cohorts with experiments in cells, we report that FKBP5, a protein implicated in stress physiology, contributes to these relations. Across independent human cohorts (total n > 3,000), aging synergized with stress-related phenotypes, measured with childhood trauma and major depression questionnaires, to epigenetically up-regulate FKBP5 expression. These age/stress-related epigenetic effects were recapitulated in a cellular model of replicative senescence, whereby we exposed replicating human fibroblasts to stress (glucocorticoid) hormones. Unbiased genome-wide analyses in human blood linked higher FKBP5 mRNA with a proinflammatory profile and altered NF-κB-related gene networks. Accordingly, experiments in immune cells showed that higher FKBP5 promotes inflammation by strengthening the interactions of NF-κB regulatory kinases, whereas opposing FKBP5 either by genetic deletion (CRISPR/Cas9-mediated) or selective pharmacological inhibition prevented the effects on NF-κB. Further, the age/stress-related epigenetic signature enhanced FKBP5 response to NF-κB through a positive feedback loop and was present in individuals with a history of acute myocardial infarction, a disease state linked to peripheral inflammation. These findings suggest that aging/stress-driven FKBP5-NF-κB signaling mediates inflammation, potentially contributing to cardiovascular risk, and may thus point to novel biomarker and treatment possibilities.

Stress dynamically regulates co-expression networks of glucocorticoid receptor-dependent MDD and SCZ risk genes.

Early-life adversity is an important risk factor for major depressive disorder (MDD) and schizophrenia (SCZ) that interacts with genetic factors to confer disease risk through mechanisms that are still insufficiently understood. One downstream effect of early-life adversity is the activation of glucocorticoid receptor (GR)-dependent gene networks that drive acute and long-term adaptive behavioral and cellular responses to stress. We have previously shown that genetic variants that moderate GR-induced gene transcription (GR-response eSNPs) are significantly enriched among risk variants from genome-wide association studies (GWASs) for MDD and SCZ. Here, we show that the 63 transcripts regulated by these disease-associated functional genetic variants form a tight glucocorticoid-responsive co-expression network (termed GCN). We hypothesized that changes in the correlation structure of this GCN may contribute to early-life adversity-associated disease risk. Therefore, we analyzed the effects of different qualities of social support and stress throughout life on GCN formation across distinct brain regions using a translational mouse model. We observed that different qualities of social experience substantially affect GCN structure in a highly brain region-specific manner. GCN changes were predominantly found in two functionally interconnected regions, the ventral hippocampus and the hypothalamus, two brain regions previously shown to be of relevance for the stress response, as well as psychiatric disorders. Overall, our results support the hypothesis that a subset of genetic variants may contribute to risk for MDD and SCZ by altering circuit-level effects of early and adult social experiences on GCN formation and structure.