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Despite extensive genetic and neuroimaging studies, detailed cellular mechanisms underlying schizophrenia and bipolar disorder remain poorly understood. Recent progress in single-cell RNA sequencing (scRNA-seq) technologies enables identification of cell-type-specific pathophysiology. However, its application to psychiatric disorders is challenging because of methodological difficulties in analyzing human brains and the confounds due to a lifetime of illness. Brain organoids derived from induced pluripotent stem cells (iPSCs) of the patients are a powerful avenue to investigate the pathophysiological processes. Here, we generated iPSC-derived cerebral organoids from monozygotic twins discordant for psychosis. scRNA-seq analysis of the organoids revealed enhanced GABAergic specification and reduced cell proliferation following diminished Wnt signaling in the patient, which was confirmed in iPSC-derived forebrain neuronal cells. Two additional monozygotic twin pairs discordant for schizophrenia also confirmed the excess GABAergic specification of the patients’ neural progenitor cells. With a well-controlled genetic background, our data suggest that unbalanced specification of excitatory and inhibitory neurons during cortical development underlies psychoses.

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. Schizophrenia research has traditionally overlooked sex differences. Here, the authors show the importance of sex-based analysis across multi-brain regions by identifying sex-specific genes and genetic interactions in schizophrenia and sex-specific risk.

Ancestral differences in genomic variation affect the regulation of gene expression; however, most gene expression studies have been limited to European ancestry samples or adjusted to identify ancestry-independent associations. Here, we instead examined the impact of genetic ancestry on gene expression and DNA methylation in the postmortem brain tissue of admixed Black American neurotypical individuals to identify ancestry-dependent and ancestry-independent contributions. Ancestry-associated differentially expressed genes (DEGs), transcripts and gene networks, while notably not implicating neurons, are enriched for genes related to the immune response and vascular tissue and explain up to 26% of heritability for ischemic stroke, 27% of heritability for Parkinson disease and 30% of heritability for Alzheimer’s disease. Ancestry-associated DEGs also show general enrichment for the heritability of diverse immune-related traits but depletion for psychiatric-related traits. We also compared Black and non-Hispanic white Americans, confirming most ancestry-associated DEGs. Our results delineate the extent to which genetic ancestry affects differences in gene expression in the human brain and the implications for brain illness risk. Studying gene expression in admixed Black Americans, Benjamin et al. reveal genetic ancestry-linked differences impacting immune and vascular genes and potentially influencing neurological disease risk. These findings highlight the importance of considering ancestry in brain research.

Most studies of gene expression in the brains of individuals with schizophrenia have focused on cortical regions, but subcortical nuclei such as the striatum are prominently implicated in the disease, and current antipsychotic drugs target the striatum’s dense dopaminergic innervation. Here, we performed a comprehensive analysis of the genetic and transcriptional landscape of schizophrenia in the postmortem caudate nucleus of the striatum of 443 individuals (245 neurotypical individuals, 154 individuals with schizophrenia and 44 individuals with bipolar disorder), 210 from African and 233 from European ancestries. Integrating expression quantitative trait loci analysis, Mendelian randomization with the latest schizophrenia genome-wide association study, transcriptome-wide association study and differential expression analysis, we identified many genes associated with schizophrenia risk, including potentially the dopamine D2 receptor short isoform. We found that antipsychotic medication has an extensive influence on caudate gene expression. We constructed caudate nucleus gene expression networks that highlight interactions involving schizophrenia risk. These analyses provide a resource for the study of schizophrenia and insights into risk mechanisms and potential therapeutic targets. In this work, the authors transcriptionally and genetically profile 443 caudate nucleus samples, including 154 with schizophrenia, highlighting new genes associated with schizophrenia risk, including the presynaptic DRD2 isoform.

By using genetic admixture in the multi-omic analysis of postmortem brains from Black Americans, we show that genetic ancestry influences gene expression in the brain. Notably, we find enrichment of ancestry-associated genes for immune response and vascular function, but not neuronal function. Our findings have potential implications for stroke, Parkinson’s disease and Alzheimer’s disease.

Most studies of gene expression in the brains of individuals with schizophrenia have focused on cortical regions, but subcortical nuclei such as the striatum are prominently implicated in the disease, and current antipsychotic drugs target the striatum’s dense dopaminergic innervation. Here, we performed a comprehensive analysis of the genetic and transcriptional landscape of schizophrenia in the postmortem caudate nucleus of the striatum of 443 individuals (245 neurotypical controls, 154 patients with schizophrenia, and 44 with bipolar disorder), 210 from African and 233 from European ancestries. Integrating expression quantitative trait loci (eQTLs) analysis, Mendelian Randomization with the latest schizophrenia GWAS, transcriptome wide association study (TWAS), and differential expression analysis, we identified many genes associated with schizophrenia risk, including potentially the dopamine D2 receptor short isoform. We find that antipsychotic medication has an extensive influence on caudate gene expression. We construct caudate nucleus gene expression networks that highlight interactions involving schizophrenia risk. These analyses provide a resource for the study of schizophrenia and insights into risk mechanisms and potential therapeutic targets.

Local ancestry inference is a powerful technique in genetics, revealing population history and the genetic basis of diseases. It is particularly valuable for improving eQTL discovery and fine-mapping in admixed populations. Despite the widespread use of the RFMix software for local ancestry inference, large-scale genomic studies face challenges of high memory consumption and processing times when handling RFMix output files. Here, I present RFMix-reader, a new Python-based parsing software, designed to streamline the analysis of large-scale local ancestry datasets. This software prioritizes computational efficiency and memory optimization, leveraging GPUs when available for additional speed boosts. By overcoming these data processing hurdles, RFMix-reader empowers researchers to unlock the full potential of local ancestry data for understanding human health and health disparities. RFMix-reader is freely available on PyPI at https://pypi.org/project/RFMix-reader/, implemented in Python 3, and supported on Linux, Windows, and Mac OS.

Ancestral differences in genomic variation are determining factors in gene regulation; however, most gene expression studies have been limited to European ancestry samples or adjusted for ancestry to identify ancestry-independent associations. We instead examined the impact of genetic ancestry on gene expression and DNA methylation (DNAm) in admixed African/Black American neurotypical individuals to untangle effects of genetic and environmental factors. Ancestry-associated differentially expressed genes (DEGs), transcripts, and gene networks, while notably not implicating neurons, are enriched for genes related to immune response and vascular tissue and explain up to 26% of heritability for ischemic stroke, 27% of heritability for Parkinson's disease, and 30% of heritability for Alzhemier's disease. Ancestry-associated DEGs also show general enrichment for heritability of diverse immune-related traits but depletion for psychiatric-related traits. The cell-type enrichments and direction of effects vary by brain region. These DEGs are less evolutionarily constrained and are largely explained by genetic variations; roughly 15% are predicted by DNAm variation implicating environmental exposures. We also compared Black and White Americans, confirming most of these ancestry-associated DEGs. Our results highlight how environment and genetic background affect genetic ancestry differences in gene expression in the human brain and affect risk for brain illness.Ancestral differences in genomic variation are determining factors in gene regulation; however, most gene expression studies have been limited to European ancestry samples or adjusted for ancestry to identify ancestry-independent associations. We instead examined the impact of genetic ancestry on gene expression and DNA methylation (DNAm) in admixed African/Black American neurotypical individuals to untangle effects of genetic and environmental factors. Ancestry-associated differentially expressed genes (DEGs), transcripts, and gene networks, while notably not implicating neurons, are enriched for genes related to immune response and vascular tissue and explain up to 26% of heritability for ischemic stroke, 27% of heritability for Parkinson's disease, and 30% of heritability for Alzhemier's disease. Ancestry-associated DEGs also show general enrichment for heritability of diverse immune-related traits but depletion for psychiatric-related traits. The cell-type enrichments and direction of effects vary by brain region. These DEGs are less evolutionarily constrained and are largely explained by genetic variations; roughly 15% are predicted by DNAm variation implicating environmental exposures. We also compared Black and White Americans, confirming most of these ancestry-associated DEGs. Our results highlight how environment and genetic background affect genetic ancestry differences in gene expression in the human brain and affect risk for brain illness.

Technology advances have generated larger omics datasets with applications for machine learning. Even so, in many datasets, the number of measured features greatly exceeds the number of observations or experimental samples. Dynamic recursive feature elimination (RFE) provides a flexible feature elimination framework to tackle this problem and to gain biological insight by selecting feature sets that are relevant for prediction. Here, we developed dRFEtools that implements dynamic RFE, and show that it reduces computational time with high accuracy compared to RFE. Given a prediction task on a dataset, dRFEtools identifies a minimal, non-redundant, set of features and a functionally redundant set of features leading to higher prediction accuracy compared to RFE. We demonstrate dRFEtools' ability to identify biologically relevant information from genomic data using RNA-Seq and genotype data from the BrainSeq Consortium. dRFEtools provides an interpretable and flexible tool to gain biological insights from omics data using machine learning. Competing Interest Statement The authors have declared no competing interest. Footnotes * https://pypi.org/project/drfetools/

Primary human trophoblast stem cells (TSCs) and TSCs derived from human pluripotent stem cells (hPSCs) can potentially model placental processes in vitro. Yet, the pluripotent states and factors involved in the differentiation of hPSCs to TSCs remain poorly understood. In this study, we demonstrate that the primed pluripotent state can generate TSCs by activating pathways such as Epidermal Growth Factor (EGF) and Wingless-related integration site (WNT), and by suppressing tumor growth factor beta (TGFβ), histone deacetylases (HDAC), and Rho-associated protein kinase (ROCK) signaling pathways, all without the addition of exogenous Bone morphogenetic protein 4 (BMP4)—a condition we refer to as the TS condition. We characterized this process using temporal single-cell RNA sequencing to compare TS conditions with differentiation protocols involving BMP4 activation alone or BMP4 activation in conjunction with WNT inhibition. The TS condition consistently produced a stable, proliferative cell type that closely mimics first-trimester placental cytotrophoblasts, marked by the activation of endogenous retroviral genes and the absence of amnion expression. This was observed across multiple cell lines, including various primed induced pluripotent stem cell (iPSC) and embryonic stem cell (ESC) lines. Primed-derived TSCs can proliferate for over 30 passages and further specify into multinucleated syncytiotrophoblasts and extravillous trophoblast cells. Our research establishes that the differentiation of primed hPSCs to TSC under TS conditions triggers the induction of TMSB4X , BMP5/7 , GATA3 , and TFAP2A without progressing through a naive state. These findings propose that the primed hPSC state is part of a continuum of potency with the capacity to differentiate into TSCs through multiple routes.