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Telencephalic organoids generated from human pluripotent stem cells (hPSCs) are a promising system for studying the distinct features of the developing human brain and the underlying causes of many neurological disorders. While organoid technology is steadily advancing, many challenges remain, including potential batch-to-batch and cell-line-to-cell-line variability, and structural inconsistency. Here, we demonstrate that a major contributor to cortical organoid quality is the way hPSCs are maintained prior to differentiation. Optimal results were achieved using particular fibroblast-feeder-supported hPSCs rather than feeder-independent cells, differences that were reflected in their transcriptomic states at the outset. Feeder-supported hPSCs displayed activation of diverse transforming growth factor β (TGFβ) superfamily signaling pathways and increased expression of genes connected to naive pluripotency. We further identified combinations of TGFβ-related growth factors that are necessary and together sufficient to impart broad telencephalic organoid competency to feeder-free hPSCs and enhance the formation of well-structured brain tissues suitable for disease modeling.

Brain organoids represent a powerful tool for studying human neurological diseases, particularly those that affect brain growth and structure. However, many diseases manifest with clear evidence of physiological and network abnormality in the absence of anatomical changes, raising the question of whether organoids possess sufficient neural network complexity to model these conditions. Here, we explore the network-level functions of brain organoids using calcium sensor imaging and extracellular recording approaches that together reveal the existence of complex network dynamics reminiscent of intact brain preparations. We demonstrate highly abnormal and epileptiform-like activity in organoids derived from induced pluripotent stem cells from individuals with Rett syndrome, accompanied by transcriptomic differences revealed by single-cell analyses. We also rescue key physiological activities with an unconventional neuroregulatory drug, pifithrin-α. Together, these findings provide an essential foundation for the utilization of brain organoids to study intact and disordered human brain network formation and illustrate their utility in therapeutic discovery.

Description, Postnatal immune activation leads to social deficits in a mouse model of TSC by abnormal interferon production in microglia., There is growing evidence that prenatal immune activation contributes to neuropsychiatric disorders. Here, we show that early postnatal immune activation resulted in profound impairments in social behavior, including in social memory in adult male mice heterozygous for a gene responsible for tuberous sclerosis complex (Tsc2+/−), a genetic disorder with high prevalence of autism. Early postnatal immune activation did not affect either wild-type or female Tsc2+/− mice. We demonstrate that these memory deficits are caused by abnormal mammalian target of rapamycin–dependent interferon signaling and impairments in microglia function. By mining the medical records of more than 3 million children followed from birth, we show that the prevalence of hospitalizations due to infections in males (but not in females) is associated with future development of autism spectrum disorders (ASD). Together, our results suggest the importance of synergistic interactions between strong early postnatal immune activation and mutations associated with ASD.

Over the past decade, large-scale genetic studies have successfully identified hundreds of genetic variants robustly associated with risk for psychiatric disorders. However, mechanistic insight -- and clinical translation -- continues to lag the pace of risk variant identification, hindered by the sheer number of targets, their predominant non-coding localization, as well as pervasive pleiotropy and incomplete penetrance. Successful next steps require identification of ‘causal’ genetic variants and their proximal biological consequences; placing variants within biologically-defined functional contexts, reflecting specific molecular pathways, cell-types, circuits, and developmental windows; and characterizing the downstream, convergent neurobiological impact of polygenicity within an individual. Here, we discuss opportunities and challenges of high-throughput transcriptomic profiling in human brain, and how transcriptomics can help pinpoint mechanisms underlying genetic risk for psychiatric disorders at a scale necessary to tackle daunting levels of polygenicity. These include transcriptome-wide association studies (TWAS) and related approaches for candidate risk gene prioritization through integration of GWAS with expression quantitative trait loci (eQTL). We outline transcriptomic results informing our understanding of the brain-level molecular pathology of psychiatric disorders, including autism (ASD), bipolar disorder (BD), major depression (MDD), and schizophrenia (SCZ). Finally, we discuss systems-level approaches for integration of distinct genetic, genomic and phenotypic levels, including combining spatially-resolved gene expression and human neuroimaging maps. Results highlight the importance of understanding gene expression (dys)regulation across human brain development as a major contributor to psychiatric disease pathogenesis, from common variants acting as expression quantitative trait loci (eQTL) to rare variants enriched for gene expression regulatory pathways.

Gene-environment interactions (GxE) can be fundamental in applications ranging from functional genomics to precision medicine and is a conjectured source of substantial heritability. However, unbiased methods to profile GxE genome-wide are nascent and, as we show, cannot accommodate general environment variables, modest sample sizes, heterogeneous noise, and binary traits. To address this gap, we propose a simple, unifying mixed model for gene-environment interaction (GxEMM). In simulations and theory, we show that GxEMM can dramatically improve estimates and eliminate false positives when the assumptions of existing methods fail. We apply GxEMM to a range of human and model organism datasets and find broad evidence of context-specific genetic effects, including GxSex, GxAdversity, and GxDisease interactions across thousands of clinical and molecular phenotypes. Overall, GxEMM is broadly applicable for testing and quantifying polygenic interactions, which can be useful for explaining heritability and invaluable for determining biologically relevant environments.

Background 22q11.2 copy number variants are among the most highly penetrant genetic risk variants for developmental neuropsychiatric disorders such as schizophrenia (SCZ) and autism spectrum disorder (ASD). However, the specific mechanisms through which they confer risk remain unclear. Methods Using a functional genomics approach, we integrated transcriptomic data from the developing human brain, genome-wide association findings for SCZ and ASD, protein interaction data, and gene expression signatures from SCZ and ASD postmortem cortex to 1) organize genes into the developmental cellular and molecular systems within which they operate, 2) identify neurodevelopmental processes associated with polygenic risk for SCZ and ASD across the allelic frequency spectrum, and 3) elucidate pathways and individual genes through which 22q11.2 copy number variants may confer risk for each disorder. Results Polygenic risk for SCZ and ASD converged on partially overlapping neurodevelopmental modules involved in synaptic function and transcriptional regulation, with ASD risk variants additionally enriched for modules involved in neuronal differentiation during fetal development. The 22q11.2 locus formed a large protein network during development that disproportionately affected SCZ-associated and ASD-associated neurodevelopmental modules, including loading highly onto synaptic and gene regulatory pathways. SEPT5, PI4KA, and SNAP29 genes are candidate drivers of 22q11.2 synaptic pathology relevant to SCZ and ASD, and DGCR8 and HIRA are candidate drivers of disease-relevant alterations in gene regulation. Conclusions This approach offers a powerful framework to identify neurodevelopmental processes affected by diverse risk variants for SCZ and ASD and elucidate mechanisms through which highly penetrant, multigene copy number variants contribute to disease risk.

Autism spectrum disorder (ASD) manifests as alterations in complex human behaviors including social communication and stereotypies. In addition to genetic risks, the gut microbiome differs between typically-developing (TD) and ASD individuals, though it remains unclear whether the microbiome contributes to symptoms. We transplanted gut microbiota from human donors with ASD or TD controls into germ-free mice, and reveal that colonization with ASD microbiota is sufficient to induce hallmark autistic behaviors. The brains of mice colonized with ASD microbiota display alternative splicing of ASD-relevant genes. Microbiome and metabolome profiles of mice harboring human microbiota predict that specific bacterial taxa and their metabolites modulate ASD behaviors. Indeed, treatment of an ASD mouse model with candidate microbial metabolites improves behavioral abnormalities and modulates neuronal excitability in the brain. We propose that the gut microbiota regulates behaviors in mice via production of neuroactive metabolites, suggesting that gut-brain connections contribute to the pathophysiology of ASD.

There is mounting evidence that seemingly diverse psychiatric disorders share genetic etiology, but the biological substrates mediating this overlap are not well characterized. Here we leverage the unique Integrative Psychiatric Research Consortium (iPSYCH) study, a nationally representative cohort ascertained through clinical psychiatric diagnoses indicated in Danish national health registers. We confirm previous reports of individual and cross-disorder single-nucleotide polymorphism heritability for major psychiatric disorders and perform a cross-disorder genome-wide association study. We identify four novel genome-wide significant loci encompassing variants predicted to regulate genes expressed in radial glia and interneurons in the developing neocortex during mid-gestation. This epoch is supported by partitioning cross-disorder single-nucleotide polymorphism heritability, which is enriched at regulatory chromatin active during fetal neurodevelopment. These findings suggest that dysregulation of genes that direct neurodevelopment by common genetic variants may result in general liability for many later psychiatric outcomes.

INTRODUCTION Strong genetic associations have been found for a number of psychiatric disorders. However, understanding the underlying molecular mechanisms remains challenging. RATIONALE To address this challenge, the PsychENCODE Consortium has developed a comprehensive online resource and integrative models for the functional genomics of the human brain. RESULTS The base of the pyramidal resource is the datasets generated by PsychENCODE, including bulk transcriptome, chromatin, genotype, and Hi-C datasets and single-cell transcriptomic data from ~32,000 cells for major brain regions. We have merged these with data from Genotype-Tissue Expression (GTEx), ENCODE, Roadmap Epigenomics, and single-cell analyses. Via uniform processing, we created a harmonized resource, allowing us to survey functional genomics data on the brain over a sample size of 1866 individuals. From this uniformly processed dataset, we created derived data products. These include lists of brain-expressed genes, coexpression modules, and single-cell expression profiles for many brain cell types; ~79,000 brain-active enhancers with associated Hi-C loops and topologically associating domains; and ~2.5 million expression quantitative-trait loci (QTLs) comprising ~238,000 linkage-disequilibrium–independent single-nucleotide polymorphisms and of other types of QTLs associated with splice isoforms, cell fractions, and chromatin activity. By using these, we found that >88% of the cross-population variation in brain gene expression can be accounted for by cell fraction changes. Furthermore, a number of disorders and aging are associated with changes in cell-type proportions. The derived data also enable comparison between the brain and other tissues. In particular, by using spectral analyses, we found that the brain has distinct expression and epigenetic patterns, including a greater extent of noncoding transcription than other tissues. The top level of the resource consists of integrative networks for regulation and machine-learning models for disease prediction. The networks include a full gene regulatory network (GRN) for the brain, linking transcription factors, enhancers, and target genes from merging of the QTLs, generalized element-activity correlations, and Hi-C data. By using this network, we link disease genes to genome-wide association study (GWAS) variants for psychiatric disorders. For schizophrenia, we linked 321 genes to the 142 reported GWAS loci. We then embedded the regulatory network into a deep-learning model to predict psychiatric phenotypes from genotype and expression. Our model gives a ~6-fold improvement in prediction over additive polygenic risk scores. Moreover, it achieves a ~3-fold improvement over additive models, even when the gene expression data are imputed, highlighting the value of having just a small amount of transcriptome data for disease prediction. Lastly, it highlights key genes and pathways associated with disorder prediction, including immunological, synaptic, and metabolic pathways, recapitulating de novo results from more targeted analyses. CONCLUSION Our resource and integrative analyses have uncovered genomic elements and networks in the brain, which in turn have provided insight into the molecular mechanisms underlying psychiatric disorders. Our deep-learning model improves disease risk prediction over traditional approaches and can be extended with additional data types (e.g., microRNA and neuroimaging). A comprehensive functional genomic resource for the adult human brain. The resource forms a three-layer pyramid. The bottom layer includes sequencing datasets for traits, such as schizophrenia. The middle layer represents derived datasets, including functional genomic elements and QTLs. The top layer contains integrated models, which link genotypes to phenotypes. DSPN, Deep Structured Phenotype Network; PC1 and PC2, principal components 1 and 2; ref, reference; alt, alternate; H3K27ac, histone H3 acetylation at lysine 27.

Change history: In this Letter, the labels for splicing events A3SS and A5SS were swapped in column D of Supplementary Table 3a and b. This has been corrected online.

Background Genetic studies in autism have pinpointed a heterogeneous group of loci and genes. Further, environment may be an additional factor conferring susceptibility to autism. Transcriptome studies investigate quantitative differences in gene expression between patient-derived tissues and control. These studies may pinpoint genes relevant to pathophysiology yet circumvent the need to understand genetic architecture or gene-by-environment interactions leading to disease. Methods We conducted alternate gene set enrichment analyses using differentially expressed genes from a previously published RNA-seq study of post-mortem autism cerebral cortex. We used three previously published microarray datasets for validation and one of the microarray datasets for additional differential expression analysis. The RNA-seq study used 26 autism and 33 control brains in differential gene expression analysis, and the largest microarray dataset contained 15 autism and 16 control post-mortem brains. Results While performing a gene set enrichment analysis of genes differentially expressed in the RNA-seq study, we discovered that genes associated with mitochondrial function were downregulated in autism cerebral cortex, as compared to control. These genes were correlated with genes related to synaptic function. We validated these findings across the multiple microarray datasets. We also did separate differential expression and gene set enrichment analyses to confirm the importance of the mitochondrial pathway among downregulated genes in post-mortem autism cerebral cortex. Conclusions We found that genes related to mitochondrial function were differentially expressed in autism cerebral cortex and correlated with genes related to synaptic transmission. Our principal findings replicate across all datasets investigated. Further, these findings may potentially replicate in other diseases, such as in schizophrenia. Electronic supplementary material The online version of this article (10.1186/s11689-018-9237-x) contains supplementary material, which is available to authorized users.

Autism spectrum disorder (ASD) involves substantial genetic contributions. These contributions are profoundly heterogeneous but may converge on common pathways that are not yet well understood(1–3). Here, through post-mortem genome-wide transcriptome analysis of the largest cohort of samples analysed so far, to our knowledge(4–7), we interrogate the noncoding transcriptome, alternative splicing, and upstream molecular regulators to broaden our understanding of molecular convergence in ASD. Our analysis reveals ASD-associated dysregulation of primate-specific long noncoding RNAs (lncRNAs), downregulation of the alternative splicing of activity-dependent neuron-specific exons, and attenuation of normal differences in gene expression between the frontal and temporal lobes. Our data suggest that SOX5, a transcription factor involved in neuron fate specification, contributes to this reduction in regional differences. We further demonstrate that a genetically defined subtype of ASD, chromosome 15q11.2–13.1 duplication syndrome (dup15q), shares the core transcriptomic signature observed in idiopathic ASD. Co-expression network analysis reveals that individuals with ASD show age-related changes in the trajectory of microglial and synaptic function over the first two decades, and suggests that genetic risk for ASD may influence changes in regional cortical gene expression. Our findings illustrate how diverse genetic perturbations can lead to phenotypic convergence at multiple biological levels in a complex neuropsychiatric disorder.

Three-dimensional physical interactions within chromosomes dynamically regulate gene expression in a tissue-specific manner1–3. However, the 3D organization of chromosomes during human brain development and its role in regulating gene networks dysregulated in neurodevelopmental disorders, such as autism or schizophrenia4–6, are unknown. Here we generate high-resolution 3D maps of chromatin contacts during human corticogenesis, permitting large-scale annotation of previously uncharacterized regulatory relationships relevant to the evolution of human cognition and disease. Our analyses identify hundreds of genes that physically interact with enhancers gained on the human, many of which are under purifying selection and associated with human cognitive function. We integrate chromatin contacts with non-coding variants identified in schizophrenia genome-wide association studies (GWAS), highlighting multiple new candidate schizophrenia risk genes and pathways, including transcription factors involved in neurogenesis, as well as cholinergic signalling, several of which are supported by independent expression quantitative trait loci and gene expression analyses. Genome editing in human neural progenitors suggests that one of these distal schizophrenia GWAS loci regulates FOXG1 expression, supporting its potential role as a novel schizophrenia risk gene. This work provides a framework for understanding the impact of non-coding regulatory elements on human brain development and the evolution of cognition, and highlights novel mechanisms underlying neuropsychiatric disorders.

Hundreds of genetic loci increasing risk for neuropsychiatric disorders have recently been identified. This success, perhaps paradoxically, has posed challenges for therapeutic development, which are amplified by the highly polygenic and pleiotropic nature of these genetic contributions. Success requires understanding the biological impact of single genetic variants and predicting their effects within an individual. Comprehensive functional genomic annotation of risk loci provides a framework for interpretation of neurobiological impact, requiring experimental validation with in vivo or in vitro model systems. Systems-level, integrative pathway analyses are beginning to elucidate the additive, polygenic contributions of risk variants on specific cellular, molecular, developmental, or circuit-level processes. Although most neuropsychiatric disease modeling has focused on genes disrupted by rare, large-effect-size mutations, common smaller-effect-size variants may also provide solid therapeutic targets to inform precision medicine approaches. Here we enumerate the promise and challenges of a genomics-driven approach to uncovering neuropsychiatric disease mechanisms and facilitating therapeutic development.