Sex Differences in the Human Brain Transcriptome of Cases With Schizophrenia

Large-scale transcriptomic analyses of major depressive disorder reveal convergent dysregulation of synaptic pathways in excitatory neurons

Major Depressive Disorder (MDD) is a common, complex disorder that is a leading cause of disability worldwide and a significant risk factor for suicide. In this study, we have performed the largest molecular analysis of MDD in postmortem human brains (846 samples across 458 individuals) in the subgenual Anterior Cingulate Cortex (sACC) and the Amygdala, two regions central to mood regulation and the pathophysiology of MDD. We found extensive expression differences, particularly at the level of specific transcripts, with prominent enrichment for genes associated with the vesicular functioning, the postsynaptic density, GTPase signaling, and gene splicing. We find associated transcriptional features in 107 of 243 genome-wide significant loci for MDD and, through integrative analyses, highlight convergence of genetic risk, gene expression, and network-based analyses on dysregulated glutamatergic signaling and synaptic vesicular functioning. Together, these results provide an initial mechanistic understanding of MDD and highlight potential targets for novel drug discovery.

Benchmark of cellular deconvolution methods using a multi-assay dataset from postmortem human prefrontal cortex

Cellular deconvolution of bulk RNA-sequencing data using single cell/nuclei RNA-seq reference data is an important strategy for estimating cell type composition in heterogeneous tissues, such as the human brain. Here, we generate a multi-assay dataset in postmortem human dorsolateral prefrontal cortex from 22 tissue blocks, including bulk RNA-seq, reference snRNA-seq, and orthogonal measurement of cell type proportions with RNAScope/ImmunoFluorescence. We use this dataset to evaluate six deconvolution algorithms. Bisque and hspe were the most accurate methods. The dataset, as well as the Mean Ratio gene marker finding method, is made available in the DeconvoBuddies R/Bioconductor package.

Single-Cell Transcriptional Profiling Reveals Cell Type-Specific Sex-Dependent Molecular Patterns of Schizophrenia

Schizophrenia (SCZ) is a debilitating psychiatric disorder marked by alterations in cognition and social behavior, resulting in profound impacts on individuals and society. Although sex-dependent disparities in the epidemiology of SCZ are well established, the biological molecular basis of these disparities remains poorly understood. Investigating cell type-specific transcriptomic profiles is critical for identifying regulatory components underlying sex-dependent molecular dysregulation in SCZ, which could serve as targets for sex-specific therapeutic interventions. To address this, we systematically analyzed publicly available single-nucleus RNA sequencing datasets to characterize cell type-specific sex-dependent gene expression profiles in the prefrontal cortex of SCZ cases. Functional enrichment analyses revealed sex-dependent dysregulation patterns of SCZ at the pathway level. Furthermore, we constructed cell type-specific gene regulatory networks for males and females, identifying SCZ-associated transcription factors that interact with sex hormones and their receptors. By incorporating drug screening results from the Connectivity Map, we established disease-gene-drug connections, elucidating sex-dependent molecular mechanisms of SCZ from the single-gene to the regulatory network level. Our findings delineate the molecular patterns of sex-dependent disparities in SCZ, uncover regulatory mechanisms driving SCZ-associated sex-dependent dysregulation, and illustrate the signal flow through which the biological sex influences downstream cellular pathways in SCZ cases. Our study provides significant evidence supporting the neuroprotective role of estrogen in the pathophysiology of female SCZ cases, while also establishing a robust foundation for the development of sex-specific therapeutic approaches for both sexes.

Multi-level therapeutic actions of cannabidiol in ketamine-induced schizophrenia psychopathology in male rats

Repeated administration of ketamine (KET) has been used to model schizophrenia-like symptomatology in rodents, but the psychotomimetic neurobiological and neuroanatomical underpinnings remain elusive. In parallel, the unmet need for a better treatment of schizophrenia requires the development of novel therapeutic strategies. Cannabidiol (CBD), a major non-addictive phytocannabinoid has been linked to antipsychotic effects with unclear mechanistic basis. Therefore, this study aims to clarify the neurobiological substrate of repeated KET administration model and to evaluate CBD's antipsychotic potential and neurobiological basis. CBD-treated male rats with and without prior repeated KET administration underwent behavioral analyses, followed by multilevel analysis of different brain areas including dopaminergic and glutamatergic activity, synaptic signaling, as well as electrophysiological recordings for the assessment of corticohippocampal and corticostriatal network activity. Repeated KET model is characterized by schizophrenia-like symptomatology and alterations in glutamatergic and dopaminergic activity mainly in the PFC and the dorsomedial striatum (DMS), through a bi-directional pattern. These observations are accompanied by glutamatergic/GABAergic deviations paralleled to impaired function of parvalbumin- and cholecystokinin-positive interneurons, indicative of excitation/inhibition (E/I) imbalance. Moreover, CBD counteracted the schizophrenia-like behavioral phenotype as well as reverted prefrontal abnormalities and ventral hippocampal E/I deficits, while partially modulated dorsostriatal dysregulations. This study adds novel insights to our understanding of the KET-induced schizophrenia-related brain pathology, as well as the CBD antipsychotic action through a region-specific set of modulations in the corticohippocampal and costicostrtiatal circuitry of KET-induced profile contributing to the development of novel therapeutic strategies focused on the ECS and E/I imbalance restoration.

Human-specific protein-coding and lncRNA genes cast sex-biased genes in the brain and their relationships with brain diseases

BACKGROUND

Gene expression shows sex bias in the brain as it does in other organs. Since female and male humans exhibit noticeable differences in emotions, logical thinking, movement, spatial orientation, and even the incidence of neurological disorders, sex biases in the brain are especially interesting, but how they are determined, whether they are conserved or lineage specific, and what the consequences of the biases are, remain poorly explored and understood.

METHODS

Based on RNA-seq datasets from 16  and 14 brain regions in humans and macaques across developmental periods and from patients with brain diseases, we used linear mixed models (LMMs) to differentiate variations in gene expression caused by factors of interest and confounding factors and identify four types of sex-biased genes. Effect size and confidence in each effect were measured upon the local false sign rate (LFSR). We utilized the biomaRt R package to acquire orthologous genes in humans and macaques from the BioMart Ensembl website. Transcriptional regulation of sex-biased genes by sex hormones and lncRNAs were analyzed using the CellOracle, GENIE3, and Longtarget programs. Sex-biased genes' functions were revealed by gene set enrichment analysis using multiple methods.

RESULTS

Lineage-specific sex-biased genes greatly determine the distinct sex biases in human and macaque brains. In humans, those encoding proteins contribute directly to immune-related functions, and those encoding lncRNAs intensively regulate the expression of other sex-biased genes, especially genes with immune-related functions. The identified sex-specific differentially expressed genes (ssDEGs) upon gene expression in disease and normal samples also indicate that protein-coding ssDEGs are conserved in humans and macaques but that lncRNA ssDEGs are not conserved. The results answer the above questions, reveal an intrinsic relationship between sex biases in the brain and sex-biased susceptibility to brain diseases, and will help researchers investigate human- and sex-specific ncRNA targets for brain diseases.

CONCLUSIONS

Human-specific genes greatly cast sex-biased genes in the brain and their relationships with brain diseases, with protein-coding genes contributing to immune response related functions and lncRNA genes critically regulating sex-biased genes. The high proportions of lineage-specific lncRNAs in mammalian genomes indicate that sex biases may have evolved rapidly in not only the brain but also other organs.

Schizophrenia diagnosis based on diverse epoch size resting-state EEG using machine learning

Schizophrenia is a severe mental disorder that impairs a person's mental, social, and emotional faculties gradually. Detection in the early stages with an accurate diagnosis is crucial to remedying the patients. This study proposed a new method to classify schizophrenia disease in the rest state based on neurologic signals achieved from the brain by electroencephalography (EEG). The datasets used consisted of 28 subjects, 14 for each group, which are schizophrenia and healthy control. The data was collected from the scalps with 19 EEG channels using a 250 Hz frequency. Due to the brain signal variation, we have decomposed the EEG signals into five sub-bands using a band-pass filter, ensuring the best signal clarity and eliminating artifacts. This work was performed with several scenarios: First, traditional techniques were applied. Secondly, augmented data (additive white Gaussian noise and stretched signals) were utilized. Additionally, we assessed Minimum Redundancy Maximum Relevance (MRMR) as the features reduction method. All these data scenarios are applied with three different window sizes (epochs): 1, 2, and 5 s, utilizing six algorithms to extract features: Fast Fourier Transform (FFT), Approximate Entropy (ApEn), Log Energy entropy (LogEn), Shannon Entropy (ShnEn), and kurtosis. The L2-normalization method was applied to the derived features, positively affecting the results. In terms of classification, we applied four algorithms: K-nearest neighbor (KNN), support vector machine (SVM), quadratic discriminant analysis (QDA), and ensemble classifier (EC). From all the scenarios, our evaluation showed that SVM had remarkable results in all evaluation metrics with LogEn features utilizing a 1-s window size, impacting the diagnosis of Schizophrenia disease. This indicates that an accurate diagnosis of schizophrenia can be achieved through the right features and classification model selection. Finally, we contrasted our results to recently published works using the same and a different dataset, where our method showed a notable improvement.

Analysis of risk factors for hospital-acquired pneumonia in schizophrenia

Background

Hospital-acquired pneumonia is one of the most important causes of recurrent illness, disease progression, and even death during hospitalization. Patients with schizophrenia have the special characteristics of their disease, and at the same time, the occurrence of hospital-acquired pneumonia is more common among patients with schizophrenia due to the prolonged stay in closed wards, accompanied by various factors such as age, gender, and nutritional status.

Methods

The PubMed, Web of Science, Cochrane Library, China National Knowledge Infrastructure (CNKI), and China Biomedical Literature Database (CBM) databases were searched with a timeframe of build to February 2024 to collect studies on factors influencing hospital-acquired pneumonia in patients with schizophrenia. Two researchers independently screened the literature, extracted data, and analyzed them.

Results

A total of 5 papers including 85246 patients were included in the literature, which suggested that benzodiazepines (especially the use of clozapine), combination of antipsychotics, mood stabilizers, modified electroconvulsive therapy (MECT), duration of hospitalization, underlying diseases, hyperglycemia, and salivation/dysphagia were important risk factors for hospital-acquired pneumonia in schizophrenia patients, and that advanced age, smoking and alcohol drinking Older age, smoking and drinking habits, malnutrition, and underlying diseases are also risk factors for hospital-acquired pneumonia.

Conclusions

Patients with schizophrenia are at a higher risk of developing hospital-acquired pneumonia, so identifying the risk factors associated with hospital-acquired pneumonia and evaluating them comprehensively and promptly during hospitalization facilitates the development of early interventions, which are essential for improving the prognosis of patients with schizophrenia.

Cell-type-specific effects of age and sex on human cortical neurons

Altered transcriptional and epigenetic regulation of brain cell types may contribute to cognitive changes with advanced age. Using single-nucleus multi-omic DNA methylation and transcriptome sequencing (snmCT-seq) in frontal cortex from young adult and aged donors, we found widespread age- and sex-related variation in specific neuron types. The proportion of inhibitory SST- and VIP-expressing neurons was reduced in aged donors. Excitatory neurons had more profound age-related changes in their gene expression and DNA methylation than inhibitory cells. Hundreds of genes involved in synaptic activity, including EGR1, were less expressed in aged adults. Genes located in subtelomeric regions increased their expression with age and correlated with reduced telomere length. We further mapped cell-type-specific sex differences in gene expression and X-inactivation escape genes. Multi-omic single-nucleus epigenomes and transcriptomes provide new insight into the effects of age and sex on human neurons.

The impact of sex on gene expression in the brain of schizophrenic patients: a systematic review and meta-analysis of transcriptomic studies

BACKGROUND

Schizophrenia is a severe neuropsychiatric disorder characterized by altered perception, mood, and behavior that profoundly impacts patients and society despite its relatively low prevalence. Sex-based differences have been described in schizophrenia epidemiology, symptomatology and outcomes. Different studies explored the impact of schizophrenia in the brain transcriptome, however we lack a consensus transcriptomic profile that considers sex and differentiates specific cerebral regions.

METHODS

We performed a systematic review on bulk RNA-sequencing studies of post-mortem brain samples. Then, we fulfilled differential expression analysis on each study and summarized their results with regions-specific meta-analyses (prefrontal cortex and hippocampus) and a global all-studies meta-analysis. Finally, we used the consensus transcriptomic profiles to functionally characterize the impact of schizophrenia in males and females by protein-protein interaction networks, enriched biological processes and dysregulated transcription factors.

RESULTS

We discovered the sex-based dysregulation of 265 genes in the prefrontal cortex, 1.414 genes in the hippocampus and 66 genes in the all-studies meta-analyses. The functional characterization of these gene sets unveiled increased processes related to immune response functions in the prefrontal cortex in male and the hippocampus in female schizophrenia patients and the overexpression of genes related to neurotransmission and synapses in the prefrontal cortex of female schizophrenia patients. Considering a meta-analysis of all brain regions available, we encountered the relative overexpression of genes related to synaptic plasticity and transmission in females and the overexpression of genes involved in organizing genetic information and protein folding in male schizophrenia patients. The protein-protein interaction networks and transcription factors activity analyses supported these sex-based profiles.

CONCLUSIONS

Our results report multiple sex-based transcriptomic alterations in specific brain regions of schizophrenia patients, which provides new insight into the role of sex in schizophrenia. Moreover, we unveil a partial overlapping of inflammatory processes in the prefrontal cortex of males and the hippocampus of females.

Transcriptomic sex differences in postmortem brain samples from patients with psychiatric disorders

Many psychiatric disorders exhibit sex differences, but the underlying mechanisms remain poorly understood. We analyzed transcriptomics data from 2160 postmortem adult prefrontal cortex brain samples from the PsychENCODE consortium in a sex-stratified study design. We compared transcriptomics data of postmortem brain samples from patients with schizophrenia (SCZ), bipolar disorder (BD), and autism spectrum disorder (ASD) with transcriptomics data of postmortem control brains from individuals without a known history of psychiatric disease. We found that brain samples from females with SCZ, BD, and ASD showed a higher burden of transcriptomic dysfunction than did brain samples from males with these disorders. This observation was supported by the larger number of differentially expressed genes (DEGs) and a greater magnitude of gene expression changes observed in female versus male brain specimens. In addition, female patient brain samples showed greater overall connectivity dysfunction, defined by a higher proportion of gene coexpression modules with connectivity changes and higher connectivity burden, indicating a greater degree of gene coexpression variability. We identified several gene coexpression modules enriched in sex-biased DEGs and identified genes from a genome-wide association study that were involved in immune and synaptic functions across different brain cell types. We found a number of genes as hubs within these modules, including those encoding SCN2A, FGF14, and C3. Our results suggest that in the context of psychiatric diseases, males and females exhibit different degrees of transcriptomic dysfunction and implicate immune and synaptic-related pathways in these sex differences.

Sex affects transcriptional associations with schizophrenia across the dorsolateral prefrontal cortex, hippocampus, and caudate nucleus

Schizophrenia is a complex neuropsychiatric disorder with sexually dimorphic features, including differential symptomatology, drug responsiveness, and male incidence rate. Prior large-scale transcriptome analyses for sex differences in schizophrenia have focused on the prefrontal cortex. Analyzing BrainSeq Consortium data (caudate nucleus: n = 399, dorsolateral prefrontal cortex: n = 377, and hippocampus: n = 394), we identified 831 unique genes that exhibit sex differences across brain regions, enriched for immune-related pathways. We observed X-chromosome dosage reduction in the hippocampus of male individuals with schizophrenia. Our sex interaction model revealed 148 junctions dysregulated in a sex-specific manner in schizophrenia. Sex-specific schizophrenia analysis identified dozens of differentially expressed genes, notably enriched in immune-related pathways. Finally, our sex-interacting expression quantitative trait loci analysis revealed 704 unique genes, nine associated with schizophrenia risk. These findings emphasize the importance of sex-informed analysis of sexually dimorphic traits, inform personalized therapeutic strategies in schizophrenia, and highlight the need for increased female samples for schizophrenia analyses.

Whole-brain connections of glutamatergic neurons in the mouse lateral habenula in both sexes

BACKGROUND

The lateral habenula (LHb) is an epithalamus nucleus that is evolutionarily conserved and involved in various physiological functions, such as encoding value signals, integrating emotional information, and regulating related behaviors. The cells in the LHb are predominantly glutamatergic and have heterogeneous functions in response to different stimuli. The circuitry connections of the LHb glutamatergic neurons play a crucial role in integrating a wide range of events. However, the circuitry connections of LHb glutamatergic neurons in both sexes have not been thoroughly investigated.

METHODS

In this study, we injected Cre-dependent retrograde trace virus and anterograde synaptophysin-labeling virus into the LHb of adult male and female Vglut2-ires-Cre mice, respectively. We then quantitatively analyzed the input and output of the LHb glutamatergic connections in both the ipsilateral and contralateral whole brain.

RESULTS

Our findings showed that the inputs to LHbvGlut2 neurons come from more than 30 brain subregions, including the cortex, striatum, pallidum, thalamus, hypothalamus, midbrain, pons, medulla, and cerebellum with no significant differences between males and females. The outputs of LHbvGlut2 neurons targeted eight large brain regions, primarily focusing on the midbrain and pons nuclei, with distinct features in presynaptic bouton across different brain subregions. While correlation and cluster analysis revealed differences in input and collateral projection features, the input-output connection pattern of LHbvGlut2 neurons in both sexes was highly similar.

CONCLUSIONS

This study provides a systematic and comprehensive analysis of the input and output connections of LHbvGlut2 neurons in male and female mice, shedding light on the anatomical architecture of these specific cell types in the mouse LHb. This structural understanding can help guide further investigations into the complex functions of the LHb.

Benchmark of cellular deconvolution methods using a multi-assay reference dataset from postmortem human prefrontal cortex

Background

Cellular deconvolution of bulk RNA-sequencing (RNA-seq) data using single cell or nuclei RNA-seq (sc/snRNA-seq) reference data is an important strategy for estimating cell type composition in heterogeneous tissues, such as human brain. Computational methods for deconvolution have been developed and benchmarked against simulated data, pseudobulked sc/snRNA-seq data, or immunohistochemistry reference data. A major limitation in developing improved deconvolution algorithms has been the lack of integrated datasets with orthogonal measurements of gene expression and estimates of cell type proportions on the same tissue sample. Deconvolution algorithm performance has not yet been evaluated across different RNA extraction methods (cytosolic, nuclear, or whole cell RNA), different library preparation types (mRNA enrichment vs. ribosomal RNA depletion), or with matched single cell reference datasets.

Results

A rich multi-assay dataset was generated in postmortem human dorsolateral prefrontal cortex (DLPFC) from 22 tissue blocks. Assays included spatially-resolved transcriptomics, snRNA-seq, bulk RNA-seq (across six library/extraction RNA-seq combinations), and RNAScope/Immunofluorescence (RNAScope/IF) for six broad cell types. The Mean Ratio method, implemented in the DeconvoBuddies R package, was developed for selecting cell type marker genes. Six computational deconvolution algorithms were evaluated in DLPFC and predicted cell type proportions were compared to orthogonal RNAScope/IF measurements.

Conclusions

Bisque and hspe were the most accurate methods, were robust to differences in RNA library types and extractions. This multi-assay dataset showed that cell size differences, marker genes differentially quantified across RNA libraries, and cell composition variability in reference snRNA-seq impact the accuracy of current deconvolution methods.

Sex differences in brain cell-type specific chromatin accessibility in schizophrenia

Our understanding of the sex-specific role of the non-coding genome in serious mental illness remains largely incomplete. To address this gap, we explored sex differences in 1,393 chromatin accessibility profiles, derived from neuronal and non-neuronal nuclei of two distinct cortical regions from 234 cases with serious mental illness and 235 controls. We identified sex-specific enhancer-promoter interactions and showed that they regulate genes involved in X-chromosome inactivation (XCI). Examining chromosomal conformation allowed us to identify sex-specific cis- and trans-regulatory domains (CRDs and TRDs). Co-localization of sex-specific TRDs with schizophrenia common risk variants pinpointed male-specific regulatory regions controlling a number of metabolic pathways. Additionally, enhancers from female-specific TRDs were found to regulate two genes known to escape XCI, (XIST and JPX), underlying the importance of TRDs in deciphering sex differences in schizophrenia. Overall, these findings provide extensive characterization of sex differences in the brain epigenome and disease-associated regulomes.

dcSBM: A federated constrained source-based morphometry approach for multivariate brain structure mapping

The examination of multivariate brain morphometry patterns has gained attention in recent years, especially for their powerful exploratory capabilities in the study of differences between patients and controls. Among the many existing methods and tools for the analysis of brain anatomy based on structural magnetic resonance imaging data, data-driven source-based morphometry (SBM) focuses on the exploratory detection of such patterns. Here, we implement a semi-blind extension of SBM, called constrained source-based morphometry (constrained SBM), which enables the extraction of maximally independent reference-alike sources using the constrained independent component analysis (ICA) approach. To do this, we combine SBM with a set of reference components covering the full brain, derived from a large independent data set (UKBiobank), to provide a fully automated SBM framework. This also allows us to implement a federated version of constrained SBM (cSBM) to allow analysis of data that is not locally accessible. In our proposed decentralized constrained source-based morphometry (dcSBM), the original data never leaves the local site. Each site operates constrained ICA on its private local data using a common distributed computation platform. Next, an aggregator/master node aggregates the results estimated from each local site and applies statistical analysis to estimate the significance of the sources. Finally, we utilize two additional multisite patient data sets to validate our model by comparing the resulting group difference estimates from both cSBM and dcSBM.

Epigenome-wide meta-analysis of prenatal maternal stressful life events and newborn DNA methylation

Prenatal maternal stressful life events are associated with adverse neurodevelopmental outcomes in offspring. Biological mechanisms underlying these associations are largely unknown, but DNA methylation likely plays a role. This meta-analysis included twelve non-overlapping cohorts from ten independent longitudinal studies (N = 5,496) within the international Pregnancy and Childhood Epigenetics consortium to examine maternal stressful life events during pregnancy and DNA methylation in cord blood. Children whose mothers reported higher levels of cumulative maternal stressful life events during pregnancy exhibited differential methylation of cg26579032 in ALKBH3. Stressor-specific domains of conflict with family/friends, abuse (physical, sexual, and emotional), and death of a close friend/relative were also associated with differential methylation of CpGs in APTX, MyD88, and both UHRF1 and SDCCAG8, respectively; these genes are implicated in neurodegeneration, immune and cellular functions, regulation of global methylation levels, metabolism, and schizophrenia risk. Thus, differences in DNA methylation at these loci may provide novel insights into potential mechanisms of neurodevelopment in offspring.

The impact of genetic risk for schizophrenia on eating disorder clinical presentations

A growing body of literature recognizes associations between eating disorders (EDs) and schizophrenia and suggests that familial liability to schizophrenia in individuals with anorexia nervosa (AN) reveals distinct patterns of clinical outcomes. To further investigate the influence of schizophrenia genetic liability among individuals with EDs, we evaluated the associations between schizophrenia polygenic risk scores (PRS) and clinical presentations of individuals with EDs including their overall health condition and ED-related symptoms. Using data from two previous studies of the genetics of EDs comprising 3,573 Anorexia Nervosa Genetics Initiative (ANGI) cases and 696 Binge Eating Genetics Initiative (BEGIN) cases born after 1973 and linked to the Swedish National Patient Register, we examined the association of schizophrenia PRS on ED clinical features, psychiatric comorbidities, and somatic and mental health burden. Among ANGI cases, higher schizophrenia PRS was statistically significantly associated with higher risk of major depressive disorder (MDD) measured by hazard ratio (HR) with 95% confidence interval (CI) (HR [95% CI]: 1.07 [1.02, 1.13]) and substance abuse disorder (SUD) (HR [95% CI]: 1.14 [1.03, 1.25]) after applying multiple testing correction. Additionally, higher schizophrenia PRS was associated with decreased clinical impairment assessment scores (-0.56, 95% CI: [-1.04, -0.08]) at the conventional significance level (p < 0.05). Further, in BEGIN cases, higher schizophrenia PRS was statistically significantly associated with earlier age at first ED symptom (-0.35 year, 95% CI: [-0.64, -0.06]), higher ED symptom scores (0.16, 95% CI: [0.04, 0.29]), higher risk of MDD (HR [95% CI]: 1.18 [1.04, 1.34]) and SUD (HR [95% CI]: 1.36 [1.07, 1.73]). Similar, but attenuated, patterns held in the subgroup of exclusively AN vs other eating disorder (OED) cases. These results suggest a similar pattern of influence of schizophrenia PRS for AN and OED cases in terms of psychiatric comorbidities, but a different pattern in terms of ED-related clinical features. The disparity of the effect of schizophrenia PRS on AN vs OED merits further investigation.

Comparison of gene expression in living and postmortem human brain

Molecular mechanisms of neuropsychiatric disorders are challenging to study in human brain. For decades, the preferred model has been to study postmortem human brain samples despite the limitations they entail. A recent study generated RNA sequencing data from biopsies of prefrontal cortex from living patients with Parkinson's Disease and compared gene expression to postmortem tissue samples, from which they found vast differences between the two. This led the authors to question the utility of postmortem human brain studies. Through re-analysis of the same data, we unexpectedly found that the living brain tissue samples were of much lower quality than the postmortem samples across multiple standard metrics. We also performed simulations that illustrate the effects of ignoring RNA degradation in differential gene expression analyses, showing the effects can be substantial and of similar magnitude to what the authors find. For these reasons, we believe the authors' conclusions are unjustified. To the contrary, while opportunities to study gene expression in the living brain are welcome, evidence that this eclipses the value of postmortem analyses is not apparent.

Functional genomic mechanisms of opioid action and opioid use disorder: a systematic review of animal models and human studies

In the past two decades, over-prescription of opioids for pain management has driven a steep increase in opioid use disorder (OUD) and death by overdose, exerting a dramatic toll on western countries. OUD is a chronic relapsing disease associated with a lifetime struggle to control drug consumption, suggesting that opioids trigger long-lasting brain adaptations, notably through functional genomic and epigenomic mechanisms. Current understanding of these processes, however, remain scarce, and have not been previously reviewed systematically. To do so, the goal of the present work was to synthesize current knowledge on genome-wide transcriptomic and epigenetic mechanisms of opioid action, in primate and rodent species. Using a prospectively registered methodology, comprehensive literature searches were completed in PubMed, Embase, and Web of Science. Of the 2709 articles identified, 73 met our inclusion criteria and were considered for qualitative analysis. Focusing on the 5 most studied nervous system structures (nucleus accumbens, frontal cortex, whole striatum, dorsal striatum, spinal cord; 44 articles), we also conducted a quantitative analysis of differentially expressed genes, in an effort to identify a putative core transcriptional signature of opioids. Only one gene, Cdkn1a, was consistently identified in eleven studies, and globally, our results unveil surprisingly low consistency across published work, even when considering most recent single-cell approaches. Analysis of sources of variability detected significant contributions from species, brain structure, duration of opioid exposure, strain, time-point of analysis, and batch effects, but not type of opioid. To go beyond those limitations, we leveraged threshold-free methods to illustrate how genome-wide comparisons may generate new findings and hypotheses. Finally, we discuss current methodological development in the field, and their implication for future research and, ultimately, better care.

A higher dysregulation burden of brain DNA methylation in female patients implicated in the sex bias of Schizophrenia

Sex differences are pervasive in schizophrenia (SCZ), but the extent and magnitude of DNA methylation (DNAm) changes underlying these differences remain uncharacterized. In this study, sex-stratified differential DNAm analysis was performed in postmortem brain samples from 117 SCZ and 137 controls, partitioned into discovery and replication datasets. Three differentially methylated positions (DMPs) were identified (adj.p < 0.05) in females and 29 DMPs in males without overlap between them. Over 81% of these sex-stratified DMPs were directionally consistent between sexes but with different effect sizes. Females experienced larger magnitude of DNAm changes and more DMPs (based on data of equal sample size) than males, contributing to a higher dysregulation burden of DNAm in females SCZ. Additionally, despite similar proportions of female-related DMPs (fDMPs, 8%) being under genetic control compared with males (10%), significant enrichment of DMP-related single nucleotide polymorphisms (SNPs) in signals of genome-wide association studies was identified only in fDMPs. One DMP in each sex connected the SNPs and gene expression of CALHM1 in females and CCDC149 in males. PPI subnetworks revealed that both female- and male-related differential DNAm interacted with synapse-related dysregulation. Immune-related pathways were unique for females and neuron-related pathways were associated with males. This study reveals remarkable quantitative differences in DNAm-related sexual dimorphism in SCZ and that females have a higher dysregulation burden of SCZ-associated DNAm than males.

DeepGAMI: deep biologically guided auxiliary learning for multimodal integration and imputation to improve genotype-phenotype prediction

BACKGROUND

Genotypes are strongly associated with disease phenotypes, particularly in brain disorders. However, the molecular and cellular mechanisms behind this association remain elusive. With emerging multimodal data for these mechanisms, machine learning methods can be applied for phenotype prediction at different scales, but due to the black-box nature of machine learning, integrating these modalities and interpreting biological mechanisms can be challenging. Additionally, the partial availability of these multimodal data presents a challenge in developing these predictive models.

METHOD

To address these challenges, we developed DeepGAMI, an interpretable neural network model to improve genotype-phenotype prediction from multimodal data. DeepGAMI leverages functional genomic information, such as eQTLs and gene regulation, to guide neural network connections. Additionally, it includes an auxiliary learning layer for cross-modal imputation allowing the imputation of latent features of missing modalities and thus predicting phenotypes from a single modality. Finally, DeepGAMI uses integrated gradient to prioritize multimodal features for various phenotypes.

RESULTS

We applied DeepGAMI to several multimodal datasets including genotype and bulk and cell-type gene expression data in brain diseases, and gene expression and electrophysiology data of mouse neuronal cells. Using cross-validation and independent validation, DeepGAMI outperformed existing methods for classifying disease types, and cellular and clinical phenotypes, even using single modalities (e.g., AUC score of 0.79 for Schizophrenia and 0.73 for cognitive impairment in Alzheimer's disease).

CONCLUSION

We demonstrated that DeepGAMI improves phenotype prediction and prioritizes phenotypic features and networks in multiple multimodal datasets in complex brains and brain diseases. Also, it prioritized disease-associated variants, genes, and regulatory networks linked to different phenotypes, providing novel insights into the interpretation of gene regulatory mechanisms. DeepGAMI is open-source and available for general use.

Sex-based influential factors for dental caries in patients with schizophrenia

BACKGROUND

Schizophrenia is a common mental disorder that seriously affects patients' daily lives and brings heavy psychological and economic burdens to their families and society. The oral problems of patients with schizophrenia are gradually gaining attention, among which dental caries are among the most common oral diseases. Sex differences may be related not only to the various clinical symptoms of schizophrenia but also to different oral hygiene statuses; therefore, the main purpose of this paper is to investigate sex differences related to influencing factors for dental caries in patients with schizophrenia.

METHOD

Inpatients with schizophrenia over 18 years old were included in this study, and multidimensional indicators such as demographics, symptom and cognitive impairment assessments, medications, and the caries index of decayed, missing, and filled teeth (DMFT) were collected. An analysis of sex-based influential factors for dental caries in schizophrenia patients was performed.

RESULTS

Four-hundred and ninety-six patients with schizophrenia were included, with a mean age of 46.73 ± 12.23 years, of which 142 were females and 354 were males. The mean DMFT was significantly higher in males (8.81 ± 8.50) than in females (5.63 ± 6.61, p < 0.001), and the odd ratio of caries in males to females was significantly higher as well (OR = 2.305, p < 0.001). The influential factors of caries in male patients were independently associated with age and smoking status, in which current smokers were at the highest risk for developing caries, and different smoking statuses had various influencing factors for caries. The influencing factors for caries in female patients were independently associated with age, antipsychotic dose, PANSS-positive symptoms, and MMSE levels.

CONCLUSION

Our findings suggest sex differences exist among influential factors for caries in patients with schizophrenia. These risk factors may even be associated with and affect the treatment and prognosis of psychiatric symptoms in patients. Therefore, oral hygiene management of patients with schizophrenia should be enhanced. These differential factors provide new visions and ideas for formulating individual interventions, treatments, and care priorities.

Investigating trait variability of gene co-expression network architecture in brain by controlling for genomic risk of schizophrenia

The effect of schizophrenia (SCZ) genetic risk on gene expression in brain remains elusive. A popular approach to this problem has been the application of gene co-expression network algorithms (e.g., WGCNA). To improve reliability with this method it is critical to remove unwanted sources of variance while also preserving biological signals of interest. In this WCGNA study of RNA-Seq data from postmortem prefrontal cortex (78 neurotypical donors, EUR ancestry), we tested the effects of SCZ genetic risk on co-expression networks. Specifically, we implemented a novel design in which gene expression was adjusted by linear regression models to preserve or remove variance explained by biological signal of interest (GWAS genomic scores for SCZ risk-(GS-SCZ), and genomic scores- GS of height (GS-Ht) as a negative control), while removing variance explained by covariates of non-interest. We calculated co-expression networks from adjusted expression (GS-SCZ and GS-Ht preserved or removed), and consensus between them (representative of a "background" network free of genomic scores effects). We then tested the overlap between GS-SCZ preserved modules and background networks reasoning that modules with reduced overlap would be most affected by GS-SCZ biology. Additionally, we tested these modules for convergence of SCZ risk (i.e., enrichment in PGC3 SCZ GWAS priority genes, enrichment in SCZ risk heritability and relevant biological ontologies. Our results highlight key aspects of GS-SCZ effects on brain co-expression networks, specifically: 1) preserving/removing SCZ genetic risk alters the co-expression modules; 2) biological pathways enriched in modules affected by GS-SCZ implicate processes of transcription, translation and metabolism that converge to influence synaptic transmission; 3) priority PGC3 SCZ GWAS genes and SCZ risk heritability are enriched in modules associated with GS-SCZ effects. Overall, our results indicate that gene co-expression networks that selectively integrate information about genetic risk can reveal novel combinations of biological pathways involved in schizophrenia.

Sex differences in brain protein expression and disease

Most complex human traits differ by sex, but we have limited insight into the underlying mechanisms. Here, we investigated the influence of biological sex on protein expression and its genetic regulation in 1,277 human brain proteomes. We found that 13.2% (1,354) of brain proteins had sex-differentiated abundance and 1.5% (150) of proteins had sex-biased protein quantitative trait loci (sb-pQTLs). Among genes with sex-biased expression, we found 67% concordance between sex-differentiated protein and transcript levels; however, sex effects on the genetic regulation of expression were more evident at the protein level. Considering 24 psychiatric, neurologic and brain morphologic traits, we found that an average of 25% of their putatively causal genes had sex-differentiated protein abundance and 12 putatively causal proteins had sb-pQTLs. Furthermore, integrating sex-specific pQTLs with sex-stratified genome-wide association studies of six psychiatric and neurologic conditions, we uncovered another 23 proteins contributing to these traits in one sex but not the other. Together, these findings begin to provide insights into mechanisms underlying sex differences in brain protein expression and disease.

Insights into Sex and Gender Differences in Brain and Psychopathologies Using Big Data

The societal implication of sex and gender (SG) differences in brain are profound, as they influence brain development, behavior, and importantly, the presentation, prevalence, and therapeutic response to diseases. Technological advances have enabled speed up identification and characterization of SG differences during development and in psychopathologies. The main aim of this review is to elaborate on new technological advancements, such as genomics, imaging, and emerging biobanks, coupled with bioinformatics analyses of data generated from these technologies have facilitated the identification and characterization of SG differences in the human brain through development and psychopathologies. First, a brief explanation of SG concepts is provided, along with a developmental and evolutionary context. We then describe physiological SG differences in brain activity and function, and in psychopathologies identified through imaging techniques. We further provide an overview of insights into SG differences using genomics, specifically taking advantage of large cohorts and biobanks. We finally emphasize how bioinformatics analyses of big data generated by emerging technologies provides new opportunities to reduce SG disparities in health outcomes, including major challenges.

A sex-specific genome-wide association study of depression phenotypes in UK Biobank

There are marked sex differences in the prevalence, phenotypic presentation and treatment response for major depression. While genome-wide association studies (GWAS) adjust for sex differences, to date, no studies seek to identify sex-specific markers and pathways. In this study, we performed a sex-stratified genome-wide association analysis for broad depression with the UK Biobank total participants (N = 274,141), including only non-related participants, as well as with males (N = 127,867) and females (N = 146,274) separately. Bioinformatics analyses were performed to characterize common and sex-specific markers and associated processes/pathways. We identified 11 loci passing genome-level significance (P < 5 × 10-8) in females and one in males. In both males and females, genetic correlations were significant between the broad depression GWA and other psychopathologies; however, correlations with educational attainment and metabolic features including body fat, waist circumference, waist-to-hip ratio and triglycerides were significant only in females. Gene-based analysis showed 147 genes significantly associated with broad depression in the total sample, 64 in the females and 53 in the males. Gene-based analysis revealed "Regulation of Gene Expression" as a common biological process, but suggested sex-specific molecular mechanisms. Finally, sex-specific polygenic risk scores (PRSs) for broad depression outperformed total and the opposite sex PRSs in the prediction of broad major depressive disorder. These findings provide evidence for sex-dependent genetic pathways for clinical depression as well as for health conditions comorbid with depression.

Deep proteomics identifies shared molecular pathway alterations in synapses of patients with schizophrenia and bipolar disorder and mouse model

Synaptic dysfunction is implicated in the pathophysiology of schizophrenia (SCZ) and bipolar disorder (BP). We use quantitative mass spectrometry to carry out deep, unbiased proteomic profiling of synapses purified from the dorsolateral prefrontal cortex of 35 cases of SCZ, 35 cases of BP, and 35 controls. Compared with controls, SCZ and BP synapses show substantial and similar proteomic alterations. Network analyses reveal upregulation of proteins associated with autophagy and certain vesicle transport pathways and downregulation of proteins related to synaptic, mitochondrial, and ribosomal function in the synapses of individuals with SCZ or BP. Some of the same pathways are similarly dysregulated in the synaptic proteome of mutant mice deficient in Akap11, a recently discovered shared risk gene for SCZ and BP. Our work provides biological insights into molecular dysfunction at the synapse in SCZ and BP and serves as a resource for understanding the pathophysiology of these disorders.

Stem Cell Models for Context-Specific Modeling in Psychiatric Disorders

Genome-wide association studies reveal the complex polygenic architecture underlying psychiatric disorder risk, but there is an unmet need to validate causal variants, resolve their target genes(s), and explore their functional impacts on disorder-related mechanisms. Disorder-associated loci regulate transcription of target genes in a cell type- and context-specific manner, which can be measured through expression quantitative trait loci. In this review, we discuss methods and insights from context-specific modeling of genetically and environmentally regulated expression. Human induced pluripotent stem cell-derived cell type and organoid models have uncovered context-specific psychiatric disorder associations by investigating tissue-, cell type-, sex-, age-, and stressor-specific genetic regulation of expression. Techniques such as massively parallel reporter assays and pooled CRISPR (clustered regularly interspaced short palindromic repeats) screens make it possible to functionally fine-map genome-wide association study loci and validate their target genes at scale. Integration of disorder-associated contexts with these patient-specific human induced pluripotent stem cell models makes it possible to uncover gene by environment interactions that mediate disorder risk, which will ultimately improve our ability to diagnose and treat psychiatric disorders.

Sex differences in factors influencing hospital-acquired pneumonia in schizophrenia patients receiving modified electroconvulsive therapy

BACKGROUND

Sex differences may be presented in the clinical features or symptoms of schizophrenia patients but also affect the occurrence of hospital-acquired pneumonia (HAP). Modified electroconvulsive therapy (mECT) is a common treatment method for schizophrenia, used in combination with antipsychotics. This retrospective research explores the sex difference in HAP affecting patients with schizophrenia who have received mECT treatment during hospitalization.

METHODS

We included schizophrenia inpatients treated with mECT and antipsychotics between January 2015 and April 2022. Blood-related and demographic data collected on admission were analyzed. Influencing factors of HAP in male and female groups were assessed separately.

RESULTS

A total of 951 schizophrenia patients treated with mECT were enrolled in the study, including 375 males and 576 females, of which 62 patients experienced HAP during hospitalization. The risk period of HAP in these patients was found to be the first day after each mECT treatment and the first three sessions of mECT treatment. Statistically significant differences in the incidence of HAP were identified in male vs. female groups, with an incidence in men about 2.3 times higher than that in women (P < 0.001). Lower total cholesterol (Z = -2.147, P = 0.032) and the use of anti-parkinsonian drugs (χ2 = 17.973, P < 0.001) were found to be independent risk factors of HAP in male patients, while lower lymphocyte count (Z = -2.408, P = 0.016), hypertension (χ2 = 9.096, P = 0.003), and use of sedative-hypnotic drugs (χ2 = 13.636, P < 0.001) were identified in female patients.

CONCLUSION

Influencing factors of HAP in schizophrenia patients treated with mECT have gender differences. The first day after each mECT treatment and the first three sessions of mECT treatment were identified to have the greatest risk for HAP development. Therefore, it would be imperative to monitor clinical management and medications during this period according to these gender differences.

Sex-Specific Differences in the Transcriptome of the Human Dorsolateral Prefrontal Cortex in Schizophrenia

Schizophrenia presents clinical and biological differences between males and females. This study investigated transcriptional profiles in the dorsolateral prefrontal cortex (DLPFC) using postmortem data from the largest RNA-sequencing (RNA-seq) database on schizophrenic cases and controls. Data for 154 male and 113 female controls and 160 male and 93 female schizophrenic cases were obtained from the CommonMind Consortium. In the RNA-seq database, the principal component analysis showed that sex effects were small in schizophrenia. After we analyzed the impact of sex-specific differences on gene expression, the female group showed more significantly changed genes compared with the male group. Based on the gene ontology analysis, the female sex-specific genes that changed were overrepresented in the mitochondrion, ATP (phosphocreatine and adenosine triphosphate)-, and metal ion-binding relevant biological processes. An ingenuity pathway analysis revealed that the differentially expressed genes related to schizophrenia in the female group were involved in midbrain dopaminergic and γ-aminobutyric acid (GABA)-ergic neurons and microglia. We used methylated DNA-binding domain-sequencing analyses and microarray to investigate the DNA methylation that potentially impacts the sex differences in gene transcription using a maternal immune activation (MIA) murine model. Among the sex-specific positional genes related to schizophrenia in the PFC of female offspring from MIA, the changes in the methylation and transcriptional expression of loci ACSBG1 were validated in the females with schizophrenia in independent postmortem samples by real-time PCR and pyrosequencing. Our results reveal potential genetic risks in the DLPFC for the sex-dependent prevalence and symptomology of schizophrenia.

A Higher Dysregulation Burden of Brain DNA Methylation in Female Patients Implicated in the Sex Bias of Schizophrenia

Sex differences are pervasive in schizophrenia (SCZ), but the extent and magnitude of DNA methylation (DNAm) changes underlying these differences remain uncharacterized. In this study, sex-stratified differential DNAm analysis was performed in postmortem brain samples from 117 SCZ and 137 controls, partitioned into discovery and replication datasets. Three differentially methylated positions (DMPs) were identified (adj. p  < 0.05) in females and 29 DMPs in males without overlap between them. Over 81% of these sex-stratified DMPs were directionally consistent between sexes but with different effect sizes. Down-sampling analysis revealed more DMPs in females than in males when the sample sizes matched. Females had higher DNAm levels in healthy individuals and larger magnitude of DNAm changes in patients than males. Despite similar proportions of female-related DMPs (fDMPs, 8%) being under genetic control compared with males (10%), significant enrichment of DMP-related SNPs in signals of genome-wide association studies was identified only in fDMPs. One DMP in each sex connected the SNPs and gene expression of CALHM1 in females and CCDC149 in males. PPI subnetworks revealed that both female- and male-related differential DNAm interacted with synapse-related dysregulation. Immune-related pathways were unique for females and neuron-related pathways were associated with males. This study reveals remarkable quantitative differences in DNAm-related sexual dimorphism in SCZ and that females have a higher dysregulation burden of SCZ-associated DNAm than males.

Association analysis of risk genes identified by SCHEMA with schizophrenia in the Chinese Han population

BACKGROUND

Schizophrenia is a chronic brain disorder. Previously, the Schizophrenia Exome Sequencing Meta-analysis consortium identified 10 highest risk genes related to schizophrenia. This study aimed to analyze the relationship between the 10 highest risk genes identified by the SCHEMA and schizophrenia in a Chinese population.

METHODS

A total of 225 variants in 10 genes were screened in a Chinese population of 6836 using a customized array. All variants were annotated through the Variant Effect Predictor tool, and the functional impacts of missense variants were assessed based on sorting intolerant from tolerant and PolyPhen-2 scores. The SHEsisPlus tool was used to analyze the association between risk genes and schizophrenia at the locus and gene levels.

RESULTS

At the locus level, no missense variants significantly related to schizophrenia were found, but we detected three missense variants that appeared only in cases, including TRIO p. Arg1185Gln, RB1CC1 p. Arg1514Cys, and HERC1 p. Val4517Leu. At the gene level, five genes (TRIO, RB1CC1, HERC1, GRIN2A, and CACAN1G) with more than one variant analyzed were kept for the gene-level association analysis. Only the association between RB1CC1 and schizophrenia reached a significant level (OR = 1.634; 95% CI, 1.062-2.516; P = 0.025).

CONCLUSION

In this study, we determined that RB1CC1 might be a risk gene for schizophrenia in the Chinese population. Our results provide new evidence for recognizing the correlation of these risk genes with the Chinese schizophrenia population.

In-depth investigations of the molecular basis underlying sex differences among middle-aged and elderly schizophrenia populations

Sex can influence almost all aspects of schizophrenia. However, the molecular mechanisms underlying sex differences in schizophrenia remain poorly understood. In this project, the dataset GSE107638 containing neuronal RNA-seq data and age/sex information of individuals with or without schizophrenia were retrieved. Schizophrenia samples were divided into young male (M-1), young female (F-1), middle-aged and elderly male (M-2) and middle-aged and elderly female (F-2) groups. Next, green/yellow/turquoise modules related to the M-2 trait and turquoise module correlated with the F-2 trait were identified by weighted correlation network analysis (WGCNA) analysis (soft thresholding power: 13; min module size: 200). Crucial genes in the M-2 green, M-2 turquoise and F-2 turquoise modules were identified by WGCNA, gene significance/module membership, and protein-protein interaction (PPI) analysis. Moreover, 2067 and 934 differentially expressed genes (|log2 fold-change| ≥0.58 and P-value < 0.05) in M-2 and F-2 schizophrenia subgroups versus same-age and same-sex counterparts were identified, respectively. Additionally, 82 core genes in the M-2 turquoise module and 4 hub genes in the F-2 turquoise module were differentially expressed in M-2 and F-2 schizophrenia subgroups versus their counterparts, respectively. Among the 82 hub genes, 15 genes were found to be correlated with neuronal development by the Kyoto Encyclopedia of Genes and Genomes enrichment analysis. Also, 2 potential PPI networks related to neuronal development were identified. Taken together, multiple potential hub genes and 2 potential neurobiological networks related to schizophrenia sex differences and disease progression were identified among middle-aged and elderly schizophrenia populations.

The sexual brain, genes, and cognition: A machine-predicted brain sex score explains individual differences in cognitive intelligence and genetic influence in young children

Sex impacts the development of the brain and cognition differently across individuals. However, the literature on brain sex dimorphism in humans is mixed. We aim to investigate the biological underpinnings of the individual variability of sexual dimorphism in the brain and its impact on cognitive performance. To this end, we tested whether the individual difference in brain sex would be linked to that in cognitive performance that is influenced by genetic factors in prepubertal children (N = 9,658, ages 9-10 years old; the Adolescent Brain Cognitive Development study). To capture the interindividual variability of the brain, we estimated the probability of being male or female based on the brain morphometry and connectivity features using machine learning (herein called a brain sex score). The models accurately classified the biological sex with a test ROC-AUC of 93.32%. As a result, a greater brain sex score correlated significantly with greater intelligence (p_fdr  < .001, η p 2 $$ {\eta}_p^2 $$  = .011-.034; adjusted for covariates) and higher cognitive genome-wide polygenic scores (GPSs) (p_fdr  < .001, η p 2 $$ {\eta}_p^2 $$  < .005). Structural equation models revealed that the GPS-intelligence association was significantly modulated by the brain sex score, such that a brain with a higher maleness score (or a lower femaleness score) mediated a positive GPS effect on intelligence (indirect effects = .006-.009; p = .002-.022; sex-stratified analysis). The finding of the sex modulatory effect on the gene-brain-cognition relationship presents a likely biological pathway to the individual and sex differences in the brain and cognitive performance in preadolescence.

Chromatin domain alterations linked to 3D genome organization in a large cohort of schizophrenia and bipolar disorder brains

Chromosomal organization, scaling from the 147-base pair (bp) nucleosome to megabase-ranging domains encompassing multiple transcriptional units, including heritability loci for psychiatric traits, remains largely unexplored in the human brain. In this study, we constructed promoter- and enhancer-enriched nucleosomal histone modification landscapes for adult prefrontal cortex from H3-lysine 27 acetylation and H3-lysine 4 trimethylation profiles, generated from 388 controls and 351 individuals diagnosed with schizophrenia (SCZ) or bipolar disorder (BD) (n = 739). We mapped thousands of cis-regulatory domains (CRDs), revealing fine-grained, 104-106-bp chromosomal organization, firmly integrated into Hi-C topologically associating domain stratification by open/repressive chromosomal environments and nuclear topography. Large clusters of hyper-acetylated CRDs were enriched for SCZ heritability, with prominent representation of regulatory sequences governing fetal development and glutamatergic neuron signaling. Therefore, SCZ and BD brains show coordinated dysregulation of risk-associated regulatory sequences assembled into kilobase- to megabase-scaling chromosomal domains.

Sex Differences in Psychosis: Focus on Animal Models

Most psychiatric illnesses, such as schizophrenia, show profound sex differences in incidence, clinical presentation, course, and outcome. Fortunately, more recently the literature on sex differences and (to a lesser extent) effects of sex steroid hormones is expanding, and in this review we have focused on such studies in psychosis, both from a clinical/epidemiological and preclinical/animal model perspective. We begin by briefly describing the clinical evidence for sex differences in schizophrenia epidemiology, symptomatology, and pathophysiology. We then detail sex differences and sex hormone effects in behavioral animal models of psychosis, specifically psychotropic drug-induced locomotor hyperactivity and disruption of prepulse inhibition. We expand on the preclinical data to include developmental and genetic models of psychosis, such as the maternal immune activation model and neuregulin transgenic animals, respectively. Finally, we suggest several recommendations for future studies, in order to facilitate a better understanding of sex differences in the development of psychosis.