Ketamine and the neurobiology of depression: Toward next-generation rapid-acting antidepressant treatments.

Ketamine has emerged as a transformative and mechanistically novel pharmacotherapy for depression. Its rapid onset of action, efficacy for treatment-resistant symptoms, and protection against relapse distinguish it from prior antidepressants. Its discovery emerged from a reconceptualization of the neurobiology of depression and, in turn, insights from the elaboration of its mechanisms of action inform studies of the pathophysiology of depression and related disorders. It has been 25 y since we first presented our ketamine findings in depression. Thus, it is timely for this review to consider what we have learned from studies of ketamine and to suggest future directions for the optimization of rapid-acting antidepressant treatment.

iHerd: an integrative hierarchical graph representation learning framework to quantify network changes and prioritize risk genes in disease.

Different genes form complex networks within cells to carry out critical cellular functions, while network alterations in this process can potentially introduce downstream transcriptome perturbations and phenotypic variations. Therefore, developing efficient and interpretable methods to quantify network changes and pinpoint driver genes across conditions is crucial. We propose a hierarchical graph representation learning method, called iHerd. Given a set of networks, iHerd first hierarchically generates a series of coarsened sub-graphs in a data-driven manner, representing network modules at different resolutions (e.g., the level of signaling pathways). Then, it sequentially learns low-dimensional node representations at all hierarchical levels via efficient graph embedding. Lastly, iHerd projects separate gene embeddings onto the same latent space in its graph alignment module to calculate a rewiring index for driver gene prioritization. To demonstrate its effectiveness, we applied iHerd on a tumor-to-normal GRN rewiring analysis and cell-type-specific GCN analysis using single-cell multiome data of the brain. We showed that iHerd can effectively pinpoint novel and well-known risk genes in different diseases. Distinct from existing models, iHerd's graph coarsening for hierarchical learning allows us to successfully classify network driver genes into early and late divergent genes (EDGs and LDGs), emphasizing genes with extensive network changes across and within signaling pathway levels. This unique approach for driver gene classification can provide us with deeper molecular insights. The code is freely available at https://github.com/aicb-ZhangLabs/iHerd. All other relevant data are within the manuscript and supporting information files.

Genetic liability to suicidal thoughts and behaviors and risk of suicide attempt in US military veterans: moderating effects of cumulative trauma burden.

BACKGROUND: Little is known about environmental factors that may influence associations between genetic liability to suicidality and suicidal behavior. METHODS: This study examined whether a suicidality polygenic risk score (PRS) derived from a large genome-wide association study (N = 122,935) was associated with suicide attempts in a population-based sample of European-American US military veterans (N = 1664; 92.5% male), and whether cumulative lifetime trauma exposure moderated this association. RESULTS: Eighty-five veterans (weighted 6.3%) reported a history of suicide attempt. After adjusting for sociodemographic and psychiatric characteristics, suicidality PRS was associated with lifetime suicide attempt (odds ratio 2.65; 95% CI 1.37-5.11). A significant suicidality PRS-by-trauma exposure interaction emerged, such that veterans with higher levels of suicidality PRS and greater trauma burden had the highest probability of lifetime suicide attempt (16.6%), whereas the probability of attempts was substantially lower among those with high suicidality PRS and low trauma exposure (1.4%). The PRS-by-trauma interaction effect was enriched for genes implicated in cellular and developmental processes, and nervous system development, with variants annotated to the DAB2 and SPNS2 genes, which are implicated in inflammatory processes. Drug repurposing analyses revealed upregulation of suicide gene-sets in the context of medrysone, a drug targeting chronic inflammation, and clofibrate, a triacylglyceride level lowering agent. CONCLUSION: Results suggest that genetic liability to suicidality is associated with increased risk of suicide attempt among veterans, particularly in the presence of high levels of cumulative trauma exposure. Additional research is warranted to investigate whether incorporation of genomic information may improve suicide prediction models.

Positive modulation of N-methyl-D-aspartate receptors in the mPFC reduces the spontaneous recovery of fear.

N-methyl-D-aspartate receptor (NMDAR) modulators have recently received increased attention as potential therapeutics for posttraumatic stress disorder (PTSD). Here, we tested a novel NMDAR-positive modulator, NYX-783, in the following two rodent models of PTSD: an auditory fear-conditioning model and a single-prolonged stress (SPS) model. We examined the ability of NYX-783 to reduce subsequent fear-based behaviors by measuring enhanced fear extinction and reduced spontaneous recovery (spontaneous return of fear) in male mice. NYX-783 administration significantly reduced spontaneous recovery in both PTSD models and enhanced fear extinction in the SPS model. Furthermore, NYX-783 increased the NMDA-induced inward currents of excitatory and inhibitory neurons in the infralimbic medial prefrontal cortex (IL mPFC) and that the GluN2B subunit of NMDARs on pyramidal neurons in the IL mPFC is required for its effect on spontaneous recovery. The downstream expression of brain-derived neurotrophic factor was required for NYX-783 to achieve its behavioral effect. These results elucidate the cellular targets of NYX-783 and the molecular mechanisms underlying the inhibition of spontaneous recovery. These preclinical findings support the hypothesis that NYX-783 may have therapeutic potential for PTSD treatment and may be particularly useful for inhibiting spontaneous recovery.

Venus: An efficient virus infection detection and fusion site discovery method using single-cell and bulk RNA-seq data.

Early and accurate detection of viruses in clinical and environmental samples is essential for effective public healthcare, treatment, and therapeutics. While PCR detects potential pathogens with high sensitivity, it is difficult to scale and requires knowledge of the exact sequence of the pathogen. With the advent of next-gen single-cell sequencing, it is now possible to scrutinize viral transcriptomics at the finest possible resolution-cells. This newfound ability to investigate individual cells opens new avenues to understand viral pathophysiology with unprecedented resolution. To leverage this ability, we propose an efficient and accurate computational pipeline, named Venus, for virus detection and integration site discovery in both single-cell and bulk-tissue RNA-seq data. Specifically, Venus addresses two main questions: whether a tissue/cell type is infected by viruses or a virus of interest? And if infected, whether and where has the virus inserted itself into the human genome? Our analysis can be broken into two parts-validation and discovery. Firstly, for validation, we applied Venus on well-studied viral datasets, such as HBV- hepatocellular carcinoma and HIV-infection treated with antiretroviral therapy. Secondly, for discovery, we analyzed datasets such as HIV-infected neurological patients and deeply sequenced T-cells. We detected viral transcripts in the novel target of the brain and high-confidence integration sites in immune cells. In conclusion, here we describe Venus, a publicly available software which we believe will be a valuable virus investigation tool for the scientific community at large.

Cortical Transcriptomic Alterations in Association With Appetitive Neuropeptides and Body Mass Index in Posttraumatic Stress Disorder.

BACKGROUND: The molecular pathology underlying posttraumatic stress disorder (PTSD) remains unclear mainly due to a lack of human PTSD postmortem brain tissue. The orexigenic neuropeptides ghrelin, neuropeptide Y, and hypocretin were recently implicated in modulating negative affect. Drawing from the largest functional genomics study of human PTSD postmortem tissue, we investigated whether there were molecular changes of these and other appetitive molecules. Further, we explored the interaction between PTSD and body mass index (BMI) on gene expression. METHODS: We analyzed previously reported transcriptomic data from 4 prefrontal cortex regions from 52 individuals with PTSD and 46 matched neurotypical controls. We employed gene co-expression network analysis across the transcriptomes of these regions to uncover PTSD-specific networks containing orexigenic genes. We utilized Ingenuity Pathway Analysis software for pathway annotation. We identified differentially expressed genes (DEGs) among individuals with and without PTSD, stratified by sex and BMI. RESULTS: Three PTSD-associated networks (P < .01) contained genes in signaling families of appetitive molecules: 2 in females and 1 in all subjects. We uncovered DEGs (P < .05) between PTSD and control subjects stratified by sex and BMI with especially robust changes in males with PTSD with elevated vs normal BMI. Further, we identified putative upstream regulators (P < .05) driving these changes, many of which were enriched for involvement in inflammation. CONCLUSIONS: PTSD-associated cortical transcriptomic modules contain transcripts of appetitive genes, and BMI further interacts with PTSD to impact expression. DEGs and inferred upstream regulators of these modules could represent targets for future pharmacotherapies for obesity in PTSD.

Ketamine rapidly reverses stress-induced impairments in GABAergic transmission in the prefrontal cortex in male rodents.

Dysfunction of medial prefrontal cortex (mPFC) in association with imbalance of inhibitory and excitatory neurotransmission has been implicated in depression. However, the precise cellular mechanisms underlying this imbalance, particularly for GABAergic transmission in the mPFC, and the link with the rapid acting antidepressant ketamine remains poorly understood. Here we determined the influence of chronic unpredictable stress (CUS), an ethologically validated model of depression, on synaptic markers of GABA neurotransmission, and the influence of a single dose of ketamine on CUS-induced synaptic deficits in mPFC of male rodents. The results demonstrate that CUS decreases GABAergic proteins and the frequency of inhibitory post synaptic currents (IPSCs) of layer V mPFC pyramidal neurons, concomitant with depression-like behaviors. In contrast, a single dose of ketamine can reverse CUS-induced deficits of GABA markers, in conjunction with reversal of CUS-induced depressive-like behaviors. These findings provide further evidence of impairments of GABAergic synapses as key determinants of depressive behavior and highlight ketamine-induced synaptic responses that restore GABA inhibitory, as well as glutamate neurotransmission.

Altered metabotropic glutamate receptor 5 markers in PTSD: In vivo and postmortem evidence.

Posttraumatic stress disorder (PTSD) is a prevalent and highly disabling disorder, but there is currently no targeted pharmacological treatment for it. Dysfunction of the glutamate system has been implicated in trauma and stress psychopathology, resulting in a growing interest in modulation of the glutamate system for the treatment of PTSD. Specifically, the metabotropic glutamate receptor 5 (mGluR5) represents a promising treatment target. We used [18F]FPEB, a radioligand that binds to the mGluR5, and positron emission tomography (PET) to quantify in vivo mGluR5 availability in human PTSD vs. healthy control (HCs) subjects. In an independent sample of human postmortem tissue, we investigated expression of proteins that have a functional relationship with mGluR5 and glucocorticoids in PTSD. We observed significantly higher cortical mGluR5 availability in PTSD in vivo and positive correlations between mGluR5 availability and avoidance symptoms. In the postmortem sample, we observed up-regulation of SHANK1, a protein that anchors mGluR5 to the cell surface, as well as decreased expression of FKBP5, implicating aberrant glucocorticoid functioning in PTSD. Results of this study provide insight into molecular mechanisms underlying PTSD and suggest that mGluR5 may be a promising target for mechanism-based treatments aimed at mitigating this disorder.

Molecular and cellular studies of PTSD: Postmortem transcriptome analysis and novel therapeutic targets.

The neurobiology of fear memory and extinction has been the subject of extensive research efforts that have increased our understanding of the brain regions, circuitry, and the cellular and molecular determinants of fear memory processes. However, the inability to access and directly study the brains of PTSD patients has made it difficult to translate the rodent fear memory studies to understand the neurobiological underpinnings of PTSD. The formation of a PTSD brain repository has recently been undertaken to address this issue. This will allow for high throughput gene expression and proteome analysis that can be coupled with epigenetic and genomic approaches to characterize the molecular alterations underlying PTSD. Preliminary studies using next generation RNA sequencing have identified PTSD specific gene expression alterations in the prefrontal cortex (PFC). The approaches used for transcriptome analysis and early findings regarding two glucocorticoid regulated genes of interest, FKBP5 and SGK1 are discussed, and the consequences of altered SGK1 are presented. Altered SGK1 could contribute to synaptic alterations in PFC subregions that could contribute to loss of inhibitory control and extinction of fear memories. Based on these findings, we discuss new studies demonstrating that ketamine can increase synapse number in the PFC and enhance the extinction of fear memory in rodent models and improve symptoms in PTSD patients. Continued molecular and cellular characterization of postmortem brain tissue of PTSD subjects will further define the neurobiology of PTSD and identify novel targets for safe and more efficacious treatments.

Ketamine accelerates fear extinction via mTORC1 signaling.

Impaired fear extinction contributes to the persistence of post-traumatic stress disorder (PTSD), and can be utilized for the study of novel therapeutic agents. Glutamate plays an important role in the formation of traumatic memories, and in the pathophysiology and treatment of PTSD, highlighting several possible drug targets. Recent clinical studies demonstrate that infusion of ketamine, a glutamate NMDA receptor antagonist, rapidly and significantly reduces symptom severity in PTSD patients. In the present study, we examine the mechanisms underlying the actions of ketamine in a rodent model of fear conditioning, extinction, and renewal. Rats received ketamine or saline 24h after fear conditioning and were then subjected to extinction-training on each of the following three days. Ketamine administration enhanced extinction on the second day of training (i.e., reduced freezing behavior to cue) and produced a long-lasting reduction in freezing on exposure to cue plus context 8days later. Additionally, ketamine and extinction exposure increased levels of mTORC1 in the medial prefrontal cortex (mPFC), a region involved in the acquisition and retrieval of extinction, and infusion of the selective mTORC1 inhibitor rapamycin into the mPFC blocked the effects of ketamine on extinction. Ketamine plus extinction also increased cFos in the mPFC and administration of a glutamate-AMPA receptor antagonist blocked the effects of ketamine. These results support the hypothesis that ketamine produces long-lasting mTORC1/protein synthesis and activity dependent effects on neuronal circuits that enhance the expression of extinction and could represent a novel approach for the treatment of PTSD.

Molecular and Cellular Effects of Traumatic Stress: Implications for PTSD.

PURPOSE OF REVIEW: Posttraumatic stress disorder (PTSD) is characterized by hyperarousal and recurrent stressful memories after an emotionally traumatic event. Extensive research has been conducted to identify the neurobiological determinants that underlie the pathophysiology of PTSD. In this review, we examine evidence regarding the molecular and cellular pathophysiology of PTSD focusing on two primary brain regions: the vmPFC and the amygdala. RECENT FINDINGS: This discussion includes a review of the molecular alterations related to PTSD, focusing mainly on changes to glucocorticoid receptor signaling. We also examine postmortem gene expression studies that have been conducted to date and the molecular changes that have been observed in peripheral blood studies of PTSD patients. Causal, mechanistic evidence is difficult to obtain in human studies, so we also review preclinical models of PTSD. Integration of peripheral blood and postmortem studies with preclinical models of PTSD has begun to reveal the molecular changes occurring in patients with PTSD. These findings indicate that the pathophysiology of PTSD includes disruption of glucocorticoid signaling and inflammatory systems and occurs at the level of altered gene expression. We will assess the impact of these findings on the future of PTSD molecular research.

Tuning parameters for polygenic risk score methods using GWAS summary statistics from training data.

Various polygenic risk scores (PRS) methods have been proposed to combine the estimated effects of single nucleotide polymorphisms (SNPs) to predict genetic risks for common diseases, using data collected from genome-wide association studies (GWAS). Some methods require external individual-level GWAS dataset for parameter tuning, posing privacy and security-related concerns. Leaving out partial data for parameter tuning can also reduce model prediction accuracy. In this article, we propose PRStuning, a method that tunes parameters for different PRS methods using GWAS summary statistics from the training data. PRStuning predicts the PRS performance with different parameters, and then selects the best-performing parameters. Because directly using training data effects tends to overestimate the performance in the testing data, we adopt an empirical Bayes approach to shrinking the predicted performance in accordance with the genetic architecture of the disease. Extensive simulations and real data applications demonstrate PRStuning's accuracy across PRS methods and parameters.

The impact of chronic pain on brain gene expression.

Background: Chronic pain affects one fifth of American adults, contributing significant public health burden. Chronic pain mechanisms can be further understood through investigating brain gene expression. Methods: We tested differentially expressed genes (DEGs) in chronic pain, migraine, lifetime fentanyl and oxymorphone use, and with chronic pain genetic risk in four brain regions (dACC, DLPFC, MeA, BLA) and imputed cell type expression data from 304 postmortem donors. We compared findings across traits and with independent transcriptomics resources, and performed gene-set enrichment. Results: We identified two chronic pain DEGs: B4GALT and VEGFB in bulk dACC. We found over 2000 (primarily BLA microglia) chronic pain cell type DEGs. Findings were enriched for mouse microglia pain genes, and for hypoxia and immune response. Cross-trait DEG overlap was minimal. Conclusions: Chronic pain-associated gene expression is heterogeneous across cell type, largely distinct from that in pain-related traits, and shows BLA microglia are a key cell type.

Novel hippocampal genes involved in enhanced susceptibility to chronic pain-induced behavioral emotionality.

Altered mood and psychiatric disorders are commonly associated with chronic pain conditions; however, brain mechanisms linking pain and comorbid clinical depression are still largely unknown. In this study, we aimed to identify whether key genes/cellular mechanisms underlie susceptibility/resiliency to development of depressive-like behaviors during chronic pain state. Genome-wide RNA-seq analysis was used to examine the transcriptomic profile of the hippocampus, a limbic brain region that regulates mood and stress responses, from male rats exposed to chronic inflammatory pain. Pain-exposed animals were separated into either 'resilient' or 'susceptible' to development of enhanced behavioral emotionality based on behavioral testing. RNA-seq bioinformatic analysis, followed by validation using qPCR, revealed dysregulation of hippocampal genes involved in neuroinflammation, cell cycle/neurogenesis and blood-brain barrier integrity. Specifically, ADAM Metallopeptidase Domain 8 (Adam8) and Aurora Kinase B (Aurkb), genes with functional roles in activation of the NLRP3 inflammasome and microgliosis, respectively, were significantly upregulated in the hippocampus of 'susceptible' animals expressing increased behavioral emotionality. In addition, genes associated with blood-brain barrier integrity, such as the Claudin 4 (Cldn4), a tight junction protein and a known marker of astrocyte activation, were also significantly dysregulated between 'resilient' or 'susceptible' pain groups. Furthermore, differentially expressed genes (DEGs) were further characterized in rodents stress models to determine whether their hippocampal dysregulation is driven by common stress responses vs. affective pain processing. Altogether these results continue to strengthen the connection between dysregulation of hippocampal genes involved in neuroinflammatory and neurodegenerative processes with increased behavioral emotionality often expressed in chronic pain state.

Uncommon Protein-Coding Variants Associated With Suicide Attempt in a Diverse Sample of U.S. Army Soldiers.

BACKGROUND: Suicide is a societal and public health concern of global scale. Identifying genetic risk factors for suicide attempt can characterize underlying biology and enable early interventions to prevent deaths. Recent studies have described common genetic variants for suicide-related behaviors. Here, we advance this search for genetic risk by analyzing the association between suicide attempt and uncommon variation exome-wide in a large, ancestrally diverse sample. METHODS: We sequenced whole genomes of 13,584 soldiers from the Army STARRS (Army Study to Assess Risk and Resilience in Servicemembers), including 979 individuals with a history of suicide attempt. Uncommon, nonsilent protein-coding variants were analyzed exome-wide for association with suicide attempt using gene-collapsed and single-variant analyses. RESULTS: We identified 19 genes with variants enriched in individuals with history of suicide attempt, either through gene-collapsed or single-variant analysis (Bonferroni padjusted < .05). These genes were CIB2, MLF1, HERC1, YWHAE, RCN2, VWA5B1, ATAD3A, NACA, EP400, ZNF585A, LYST, RC3H2, PSD3, STARD9, SGMS1, ACTR6, RGS7BP, DIRAS2, and KRTAP10-1. Most genes had variants across multiple genomic ancestry groups. Seventeen of these genes were expressed in healthy brain tissue, with 9 genes expressed at the highest levels in the brain versus other tissues. Brains from individuals deceased from suicide aberrantly expressed RGS7BP (padjusted = .035) in addition to nominally significant genes including YWHAE and ACTR6, all of which have reported associations with other mental disorders. CONCLUSIONS: These results advance the molecular characterization of suicide attempt behavior and support the utility of whole-genome sequencing for complementing the findings of genome-wide association studies in suicide research.

Tuning Parameters for Polygenic Risk Score Methods Using GWAS Summary Statistics from Training Data.

Predicting genetic risks for common diseases may improve their prevention and early treatment. In recent years, various additive-model-based polygenic risk scores (PRS) methods have been proposed to combine the estimated effects of single nucleotide polymorphisms (SNPs) using data collected from genome-wide association studies (GWAS). Some of these methods require access to another external individual-level GWAS dataset to tune the hyperparameters, which can be difficult because of privacy and security-related concerns. Additionally, leaving out partial data for hyperparameter tuning can reduce the predictive accuracy of the constructed PRS model. In this article, we propose a novel method, called PRStuning, to automatically tune hyperparameters for different PRS methods using only GWAS summary statistics from the training data. The core idea is to first predict the performance of the PRS method with different parameter values, and then select the parameters with the best prediction performance. Because directly using the effects observed from the training data tends to overestimate the performance in the testing data (a phenomenon known as overfitting), we adopt an empirical Bayes approach to shrinking the predicted performance in accordance with the estimated genetic architecture of the disease. Results from extensive simulations and real data applications demonstrate that PRStuning can accurately predict the PRS performance across PRS methods and parameters, and it can help select the best-performing parameters.

Integrating genetics and transcriptomics to study major depressive disorder: a conceptual framework, bioinformatic approaches, and recent findings.

Major depressive disorder (MDD) is a complex and heterogeneous psychiatric syndrome with genetic and environmental influences. In addition to neuroanatomical and circuit-level disturbances, dysregulation of the brain transcriptome is a key phenotypic signature of MDD. Postmortem brain gene expression data are uniquely valuable resources for identifying this signature and key genomic drivers in human depression; however, the scarcity of brain tissue limits our capacity to observe the dynamic transcriptional landscape of MDD. It is therefore crucial to explore and integrate depression and stress transcriptomic data from numerous, complementary perspectives to construct a richer understanding of the pathophysiology of depression. In this review, we discuss multiple approaches for exploring the brain transcriptome reflecting dynamic stages of MDD: predisposition, onset, and illness. We next highlight bioinformatic approaches for hypothesis-free, genome-wide analyses of genomic and transcriptomic data and their integration. Last, we summarize the findings of recent genetic and transcriptomic studies within this conceptual framework.

Decoding the role of transcriptomic clocks in the human prefrontal cortex.

Aging is a complex process with interindividual variability, which can be measured by aging biological clocks. Aging clocks are machine-learning algorithms guided by biological information and associated with mortality risk and a wide range of health outcomes. One of these aging clocks are transcriptomic clocks, which uses gene expression data to predict biological age; however, their functional role is unknown. Here, we profiled two transcriptomic clocks (RNAAgeCalc and knowledge-based deep neural network clock) in a large dataset of human postmortem prefrontal cortex (PFC) samples. We identified that deep-learning transcriptomic clock outperforms RNAAgeCalc to predict transcriptomic age in the human PFC. We identified associations of transcriptomic clocks with psychiatric-related traits. Further, we applied system biology algorithms to identify common gene networks among both clocks and performed pathways enrichment analyses to assess its functionality and prioritize genes involved in the aging processes. Identified gene networks showed enrichment for diseases of signal transduction by growth factor receptors and second messenger pathways. We also observed enrichment of genome-wide signals of mental and physical health outcomes and identified genes previously associated with human brain aging. Our findings suggest a link between transcriptomic aging and health disorders, including psychiatric traits. Further, it reveals functional genes within the human PFC that may play an important role in aging and health risk.

Profiling neuronal methylome and hydroxymethylome of opioid use disorder in the human orbitofrontal cortex.

Opioid use disorder (OUD) is influenced by genetic and environmental factors. While recent research suggests epigenetic disturbances in OUD, this is mostly limited to DNA methylation (5mC). DNA hydroxymethylation (5hmC) has been widely understudied. We conducted a multi-omics profiling of OUD in a male cohort, integrating neuronal-specific 5mC and 5hmC as well as gene expression profiles from human postmortem orbitofrontal cortex (OUD = 12; non-OUD = 26). Single locus methylomic analysis and co-methylation analysis showed a higher number of OUD-associated genes and gene networks for 5hmC compared to 5mC; these were enriched for GPCR, Wnt, neurogenesis, and opioid signaling. 5hmC marks also showed a higher correlation with gene expression patterns and enriched for GWAS of psychiatric traits. Drug interaction analysis revealed interactions with opioid-related drugs, some used as OUD treatments. Our multi-omics findings suggest an important role of 5hmC and reveal loci epigenetically dysregulated in OFC neurons of individuals with OUD.

Common genetic variation impacts stress response in the brain.

To explain why individuals exposed to identical stressors experience divergent clinical outcomes, we determine how molecular encoding of stress modifies genetic risk for brain disorders. Analysis of post-mortem brain (n=304) revealed 8557 stress-interactive expression quantitative trait loci (eQTLs) that dysregulate expression of 915 eGenes in response to stress, and lie in stress-related transcription factor binding sites. Response to stress is robust across experimental paradigms: up to 50% of stress-interactive eGenes validate in glucocorticoid treated hiPSC-derived neurons (n=39 donors). Stress-interactive eGenes show brain region- and cell type-specificity, and, in post-mortem brain, implicate glial and endothelial mechanisms. Stress dysregulates long-term expression of disorder risk genes in a genotype-dependent manner; stress-interactive transcriptomic imputation uncovered 139 novel genes conferring brain disorder risk only in the context of traumatic stress. Molecular stress-encoding explains individualized responses to traumatic stress; incorporating trauma into genomic studies of brain disorders is likely to improve diagnosis, prognosis, and drug discovery.