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Identifying genes whose expression is associated with schizophrenia (SCZ) risk by transcriptome-wide association studies (TWAS) facilitates downstream experimental studies. Here, we integrated multiple published datasets of TWAS (including FUSION, PrediXcan, summary-data-based Mendelian randomization (SMR), joint-tissue imputation approach with Mendelian randomization (MR-JTI)), gene coexpression, and differential gene expression analysis to prioritize SCZ candidate genes for functional study. Convergent evidence prioritized Propionyl-CoA Carboxylase Subunit Beta (PCCB), a nuclear-encoded mitochondrial gene, as an SCZ risk gene. However, the PCCB’s contribution to SCZ risk has not been investigated before. Using dual luciferase reporter assay, we identified that SCZ-associated SNP rs35874192, an eQTL SNP for PCCB, showed differential allelic effects on transcriptional activities. PCCB knockdown in human forebrain organoids (hFOs) followed by RNA-seq revealed dysregulation of genes enriched with multiple neuronal functions including gamma-aminobutyric acid (GABA)-ergic synapse, as well as genes dysregulated in postmortem brains of SCZ patients or in cerebral organoids derived from SCZ patients. The metabolomic and mitochondrial function analyses confirmed the deceased GABA levels resulted from reduced tricarboxylic acid cycle in PCCB knockdown hFOs. Multielectrode array recording analysis showed that PCCB knockdown in hFOs resulted into SCZ-related phenotypes including hyper-neuroactivities and decreased synchronization of neural network. In summary, this study utilized hFOs-based multi-omics data and revealed that PCCB downregulation may contribute to SCZ risk through regulating GABAergic system, highlighting the mitochondrial function in SCZ.

GGC repeat expansions within NOTCH2NLC have been identified as the genetic cause of neuronal intranuclear inclusion disease (NIID). To understand the molecular pathogenesis of NIID, here, we established both a transgenic mouse model and a human neural progenitor cells (hNPCs) model. Expression of the NOTCH2NLC with expanded GGC repeats produced widespread intranuclear and perinuclear polyglycine (polyG), polyalanine (polyA), and polyarginine (polyR) inclusions, leading to behavioral deficits and severe neurodegeneration, which faithfully mimicked the clinical and pathological features associated with NIID. Furthermore, conserved alternative splicing events were identified between the NIID mouse and hNPC models, among which was the enrichment of the binding motifs of hnRNPM, an RNA binding protein known as alternative splicing regulator. Expanded NOTCH2NLC-polyG and NOTCH2NLC-polyA could interact with and sequester hnRNPM, while overexpression of hnRNPM could ameliorate the cellular toxicity. These results together suggested that dysfunction of hnRNPM could play an important role in the molecular pathogenesis of NIID.

Microglia are resident immune cells in the central nervous system, playing critical roles in brain development and homeostasis. Increasing evidence has implicated microglia dysfunction in the pathogenesis of various brain disorders ranging from psychiatric disorders to neurodegenerative diseases. Using a human cell-based model to illuminate the functional mechanisms of microglia will promote pathological studies and drug development. The recently developed microglia-containing human brain organoids (MC-HBOs), in-vitro three-dimensional cell cultures that recapitulate key features of the human brain, have provided a new avenue to model brain development and pathology. However, MC-HBOs generated from different methods differ in the origin, proportion, and fidelity of microglia within the organoids, and may have produced inconsistent results. To help researchers to develop a robust and reproducible model that recapitulates in-vivo signatures of human microglia to study brain development and pathology, this review summarized the current methods used to generate MC-HBOs and provided opinions on the use of MC-HBOs for disease modeling and functional studies.

Schizophrenia (SCZ) is a neuropsychiatric disorder with aberrant expression of multiple genes. However, identifying its exact causal genes remains a considerable challenge. The brain-specific transcription factor POU3F2 (POU domain, class 3, transcription factor 2) has been recognized as a risk factor for SCZ, but our understanding of its target genes and pathogenic mechanisms are still limited. Here we report that POU3F2 regulates 42 SCZ-related genes in knockdown and RNA-sequencing experiments of human neural progenitor cells (NPCs). Among those SCZ-related genes, TRIM8 (Tripartite motif containing 8) is located in SCZ-associated genetic locus and is aberrantly expressed in patients with SCZ. Luciferase reporter and electrophoretic mobility shift assays (EMSA) showed that POU3F2 induces TRIM8 expression by binding to the SCZ-associated SNP (single nucleotide polymorphism) rs5011218, which affects POU3F2-binding efficiency at the promoter region of TRIM8. We investigated the cellular functions of POU3F2 and TRIM8 as they co-regulate several pathways related to neural development and synaptic function. Knocking down either POU3F2 or TRIM8 promoted the proliferation of NPCs, inhibited their neuronal differentiation, and impaired the excitatory synaptic transmission of NPC-derived neurons. These results indicate that POU3F2 regulates TRIM8 expression through the SCZ-associated SNP rs5011218, and both genes may be involved in the etiology of SCZ by regulating neural development and synaptic function.

Valproic acid (VPA) exposure as an environmental factor that confers risk of autism spectrum disorder (ASD), its functional mechanisms in the human brain remain unclear since relevant studies are currently restricted to two-dimensional cell cultures and animal models. To identify mechanisms by which VPA contribute to ASD risk in human, here we used human forebrain organoids (hFOs), in vitro derived three-dimensional cell cultures that recapitulate key human brain developmental features. We identified that VPA exposure in hFOs affected the expression of genes enriched in neural development, synaptic transmission, oxytocin signaling, calcium, and potassium signaling pathways, which have been implicated in ASD. Genes (e.g., CAMK4, CLCN4, DPP10, GABRB3, KCNB1, PRKCB, SCN1A, and SLC24A2) that affected by VPA were significantly overlapped with those dysregulated in brains or organoids derived from ASD patients, and known ASD risk genes, as well as genes in ASD risk-associated gene coexpression modules. Single-cell RNA sequencing analysis showed that VPA exposure affected the expression of genes in choroid plexus, excitatory neuron, immature neuron, and medial ganglionic eminence cells annotated in hFOs. Microelectrode array further identified that VPA exposure in hFOs disrupted synaptic transmission. Taken together, this study connects VPA exposure to ASD pathogenesis using hFOs, which is valuable for illuminating the etiology of ASD and screening for potential therapeutic targets.

Neuropsychiatric disorders affect hundreds of millions of patients and families worldwide. To decode the molecular framework of these diseases, many studies use human postmortem brain samples. These studies reveal brain-specific genetic and epigenetic patterns via high-throughput sequencing technologies. Identifying best practices for the collection of postmortem brain samples, analyzing such large amounts of sequencing data, and interpreting these results are critical to advance neuropsychiatry. We provide an overview of human brain banks worldwide, including progress in China, highlighting some well-known projects using human postmortem brain samples to understand molecular regulation in both normal brains and those with neuropsychiatric disorders. Finally, we discuss future research strategies, as well as state-of-the-art statistical and experimental methods that are drawn upon brain bank resources to improve our understanding of the agents of neuropsychiatric disorders. Keywords: Neuropsychiatric disorders, Brain bank, Postmortem brain, Expression quantitative trait loci, GWAS interpretation

Epidemiological studies have shown an increased prevalence of cancer in some patients with neurodevelopmental disorder (NDD); however, the genetic mechanisms regarding how cancer-related genes (CRGs) contribute to NDD remain unclear. We performed bioinformatic analyses on 219 CRGs from OMIM and de novo mutations (DNMs) from 16,498 patients with different NDDs and 3391 controls. Our results showed that autism spectrum disorder, undiagnosed neurodevelopmental disorder, congenital heart disease and intellectual disability, but not epileptic encephalopathy and schizophrenia, harboured significantly more putative functional DNMs in CRGs, compared with controls, providing genetic evidence supporting previous epidemiological surveys. We further detected 26 CRGs with recurrent putative functional DNMs that showed high expression in the human brain during the prenatal stage and in non-brain organs in adults. The proteins coded by the 26 CRGs and known NDD candidate genes formed a functional network that is involved in brain development and tumorigenesis. Overall, we proposed 39 cancer-targeting drugs that could be investigated for treating patients with NDD, which would be potentially cost-effective. In conclusion, DNMs contribute to specific NDDs and there may be a shared genetic basis between NDDs and cancer, highlighting the importance of considering cancer-targeting drugs with potential curative effects in patients with NDDs. KEY MESSAGES: • The contribution of DNMs in NDD is consistent with epidemiological surveys. • We highlighted 26 CRGs, including nine genes with more than five functional DNMs. • Specific expression patterns underlie the genetic mechanism of CRGs in NDD. • Specific functional networks underlie the genetic mechanism of CRGs in NDD. • The shared genetic aetiology suggests potential mutual treatment strategies.

Genome-wide association studies (GWASs) have identified numerous single nucleotide polymorphisms (SNPs) associated with bipolar disorder (BD), but what the causal variants are and how they contribute to BD is largely unknown. In this study, we used FUMA, a GWAS annotation tool, to pinpoint potential causal variants and genes from the latest BD GWAS findings, and performed integrative analyses, including brain expression quantitative trait loci (eQTL), gene coexpression network, differential gene expression, protein-protein interaction, and brain intermediate phenotype association analysis to identify the functions of a prioritized gene and its connection to BD. Convergent lines of evidence prioritized protein-coding gene G Protein Nucleolar 3 (GNL3) as a BD risk gene, with integrative analyses revealing GNL3's roles in cell proliferation, neuronal functions, and brain phenotypes. We experimentally revealed that BD-related eQTL SNPs rs10865973, rs12635140, and rs4687644 regulate GNL3 expression using dual luciferase reporter assay and CRISPR interference experiment in human neural progenitor cells. We further identified that GNL3 knockdown and overexpression led to aberrant neuronal proliferation and differentiation, using two-dimensional human neural cell cultures and three-dimensional forebrain organoid model. This study gathers evidence that BD-related genetic variants regulate GNL3 expression which subsequently affects neuronal proliferation and differentiation.

A number of studies indicate that rare copy number variations (CNVs) contribute to the risk of schizophrenia (SCZ). Most of these studies have focused on protein-coding genes residing in the CNVs. Here, we investigated long noncoding RNAs (lncRNAs) within 10 SCZ risk-associated CNV deletion regions (CNV-lncRNAs) and examined their potential contribution to SCZ risk. We used RNA sequencing transcriptome data derived from postmortem brain tissue from control individuals without psychiatric disease as part of the PsychENCODE BrainGVEX and Developmental Capstone projects. We carried out weighted gene coexpression network analysis to identify protein-coding genes coexpressed with CNV-lncRNAs in the human brain. We identified one neuronal function-related coexpression module shared by both datasets. This module contained a lncRNA called DGCR5 within the 22q11.2 CNV region, which was identified as a hub gene. Protein-coding genes associated with SCZ genome-wide association study signals, de novo mutations, or differential expression were also contained in this neuronal module. Using DGCR5 knockdown and overexpression experiments in human neural progenitor cells derived from human induced pluripotent stem cells, we identified a potential role for DGCR5 in regulating certain SCZ-related genes.

Schizophrenia and bipolar disorder are complex psychiatric diseases with risks contributed by multiple genes. Dysregulation of gene expression has been implicated in these disorders, but little is known about such dysregulation in the human brain. We analyzed three transcriptome datasets from 394 postmortem brain tissue samples from patients with schizophrenia or bipolar disorder or from healthy control individuals without a known history of psychiatric disease. We built genome-wide coexpression networks that included microRNAs (miRNAs). We identified a coexpression network module that was differentially expressed in the brain tissue from patients compared to healthy control individuals. This module contained genes that were principally involved in glial and neural cell genesis and glial cell differentiation, and included schizophrenia risk genes carrying rare variants. This module included five miRNAs and 545 mRNAs, with six transcription factors serving as hub genes in this module. We found that the most connected transcription factor gene POU3F2, also identified on a genome-wide association study for bipolar disorder, could regulate the miRNA hsa-miR-320e and other putative target mRNAs. These regulatory relationships were replicated using PsychENCODE/BrainGVEX datasets and validated by knockdown and overexpression experiments in SH-SY5Y cells and human neural progenitor cells in vitro. Thus, we identified a brain gene expression module that was enriched for rare coding variants in genes associated with schizophrenia and that contained the putative bipolar disorder risk gene POU3F2 The transcription factor POU3F2 may be a key regulator of gene expression in this disease-associated gene coexpression module.

A number of studies indicate that rare copy number variations (CNVs) contribute to the risk of schizophrenia (SCZ). Most of these studies have focused on protein-coding genes residing in the CNVs. Here, we investigated long non-coding RNAs (lncRNAs) within ten SCZ risk-associated CNV deletion regions (CNV-lncRNAs) and examined their potential contribution to SCZ risk. We used RNA-Seq transcriptomics data derived from postmortem brain tissue from control individuals without psychiatric disease as part of the PsychENCODE BrainGVEX and Developmental Capstone projects. We carried out weighted gene coexpression network analysis (WGCNA) to identify protein-coding genes coexpressed with CNV-lncRNAs in the human brain. We identified one neuronal function-related coexpression module shared by both data sets. This module contained a lncRNA within the 22q11.2 CNV region called DGCR5, which was identified as a hub gene. Protein-coding genes associated with SCZ GWAS signals, de novo mutations or differential expression were also contained in this neuronal module. Using DGCR5 knockdown and overexpression experiments in human neural progenitor cells derived from human induced pluripotent stem cells, we identified a potential role for DGCR5 in regulating certain SCZ-related genes.

Background Schizophrenia and bipolar disorder are complex mental disorders, with risks contributed by multiple genes. Recently, genome-wide systemic approaches have been used to reveal the associations of hundreds of SNPs with those disorders. Dysregulation of gene expression has been implied, but little is known about such regulation systems in the human brain. Several methods have been developed to achieve measurement of large-scale or multi-level data on interactions in biological systems. Additional biological regulators, such as transcriptional factors (TFs), enhancers, and microRNAs (miRNAs) binding information may help us to unveil the underlying regulatory mechanisms in the networks and suggest causal relationships. Methods We analyzed two data sets using brain tissues from 51 patients with schizophrenia or bipolar disorder and 24 healthy controls. We applied whole genome transcriptome profiling using Affymetrix Human Gene 1.0 ST Array (Affymetrix, Santa Clara, CA) and miRNA sequencing on the Illumina 1G Genome Analyzer. Using weighted gene co-expression network analysis, we integrated gene expression data of mRNA and miRNA from the same brain collections, built mRNA-miRNA co-expression networks and detected differentially expressed modules. In silico predicted binding relationships of TFs and miRNAs were used to validate the putative regulations suggested by co-expression patterns. Using SNP genotype data, expression quantitive trait loci (eQTL) and Network Edge Orienting (NEO) analysis, we resolved causal relationships among the regulators and their targets. We further validated the predicted regulations using RNA interference knockdown experimentally. Results We identified a module differentially expressed between cases and controls (p = 7.6e-4, FDR q = 0.01). This module were enriched for neuron differentiation (p = 4.8e-7, FDR q = 8.5e-4) and neuron development (p = 2.3e-5, FDR q = 4.0e-2), and contained five miRNAs and 501 mRNA genes. Six TFs also served as hub genes in these modules (POU2F1, EPAS1, PAX6, ZNF423, SOX5 and SOX9). The co-expression-suggested regulatory relationships were consistent with the binding relationships predicted by databases. Focusing on those regulations containing TFs and miRNAs, we resolved a regulation cascade from SNP variants (rs16853832) to TF (POU2F1) to miRNA (hsa-mir-320e) to target genes (NR2E1) and ultimately, to disease risks. Discussion We revealed POU2F1 as a key regulator in a neuron differentiation/developmental module associated with disease, and revealed a putative cascade regulation effect. This study showed that we can utilize multi-dimensional data to construct co-expression networks. Causal relationships can be resolved among SNPs, regulatory molecules and their downstream target genes through data integration. Novel genes and their corresponding regulations underlying the disease risks could be revealed.

Background Gene expression of specific genes change over a lifespan, and also vary by tissue or cell types. Such variations are critical for normal structure and functions of the human brain. Deviation from a normal range likely contributes to etiology and pathology of neuropsychiatric disorders. But such normal range has never been defined. Additionally, gene coexpression may reflect regulatory organization relationships among genes. We also considered that gene co-expression might guide complex connection variations in different brain regions and development stages. Changes of co-expression have also been implicated in multiple psychiatric disorders. To capture the variations of individual genes and related co-expression modules in normal human brains, we are developing a novel database, named Brain Gene Expression Database (BrainExDB), serving the needs of common reference data of normal expression levels, variations, and co-expression organizations. Methods We collected 15 brain regions and six cell types of 1,258 brain samples from existing public databases and our data, including the Gene Expression Omnibus database, ArrayExpress, Genotype-Tissue Expression project, Brain Cloud, and Stanley. Stringent quality control was applied to remove low-quality data. Both microarray and RNA-seq data have been included. For studies containing samples of different ages and different brain regions or cell types, data were partitioned by their spatiotemporal groups. All gene expression data were normalized into ranking orders to represent their expression levels so that different studies were comparable. Each gene was recorded for its average rank order and variance of expression by brain regions, cell types, and age ranges whichever available. Finally, we analyzed the data by the weighted gene co-expression network analysis to get the co-expression modules. The hub genes and members of co-expression modules were also databased for query. By comparing data across different studies, we will also identify marker genes that significantly differ by cell types, brain regions, age, and sex. Results We will build a human brain expression database featuring gene expression levels and variance in brain cells and regions throughout a lifespan. The database will provide the marker gene list of cell type-, brain region-, age-, and sex-specific genes. The database will also provide gene co-expression module data. Data can be queried by the list of genes, with filters for specific brain regions, cell types, age range, and sex. Discussion We are compiling the biggest datasets of human brain gene expression, aiming to develop reference data that can be used in studying gene expression changes in neuropsychiatric brains. For the time-being, its immediate value is to offer the comprehensive information about expression levels and variance of specific candidate genes in specific brain regions, cell types, age range, and sex. The stability and validity for predicting case states will need to be evaluated by subset data of those patient brains in the same data collection, and from other sources. Furthermore, the marker gene lists and co-expression modules can be used for gene set analyses, and deconvolution analysis of mixed data.

Schizophrenia is a complex psychiatric disorder with strong genetic background. Studies demonstrated that rare Copy Number Variations (CNVs) contribute to the risk of schizophrenia. More attention has been put to protein-coding genes residing in those CNVs. However, the exact mechanisms still remain unclear. In this study, we explored the potential roles of long non-coding RNAs (lncRNAs) inside CNV deletions (CNV-lncRNAs) in the risk of schizophrenia. We retrieved lncRNAs mapped to the CNV deletion regions known to increase risks of developing schizophrenia. Seven such regions were repeatedly reported by large case-control studies, including 1q21.1, 3q29, 15q11.2, 15q13.3, 17p12, 17q12, and 22q11.2. We carried out weighted gene co-expression network analysis (WGCNA) using RNA-seq data from Genotype-Tissue Expression (GTEx) and BrainSpan projects to look for co-expression modules that harbor CNV-lncRNAs. We identified one male reproduction-related module in male individuals and one neuronal functions-related module both in male and female individuals. In addition, the neuronal functions module comprised of protein-coding genes that were involved in several ion channel activities including calcium and potassium channel activities, which were known to be related to schizophrenia. Pathway analysis of these two modules further suggested that CNV-lncRNAs were involved in the olfactory transduction, neuroactive ligand-receptor and calcium signaling pathways. The CNV-lncRNA co-expression patterns were preserved through the different development and aging time points, and across different tissue types of human body. Our results suggested that lncRNAs inside those rare CNVs might play significant temporal and spatial roles in regulating other protein-coding genes and subsequently contribute to schizophrenia risk.