INTRODUCTION Our understanding of the pathophysiology of psychiatric disorders, including autism spectrum disorder (ASD), schizophrenia (SCZ), and bipolar disorder (BD), lags behind other fields of medicine. The diagnosis and study of these disorders currently depend on behavioral, symptomatic characterization. Defining genetic contributions to disease risk allows for biological, mechanistic understanding but is challenged by genetic complexity, polygenicity, and the lack of a cohesive neurobiological model to interpret findings. RATIONALE The transcriptome represents a quantitative phenotype that provides biological context for understanding the molecular pathways disrupted in major psychiatric disorders. RNA sequencing (RNA-seq) in a large cohort of cases and controls can advance our knowledge of the biology disrupted in each disorder and provide a foundational resource for integration with genomic and genetic data. RESULTS Analysis across multiple levels of transcriptomic organization—gene expression, local splicing, transcript isoform expression, and coexpression networks for both protein-coding and noncoding genes—provides an in-depth view of ASD, SCZ, and BD molecular pathology. More than 25% of the transcriptome exhibits differential splicing or expression in at least one disorder, including hundreds of noncoding RNAs (ncRNAs), most of which have unexplored functions but collectively exhibit patterns of selective constraint. Changes at the isoform level, as opposed to the gene level, show the largest effect sizes and genetic enrichment and the greatest disease specificity. We identified coexpression modules associated with each disorder, many with enrichment for cell type–specific markers, and several modules significantly dysregulated across all three disorders. These enabled parsing of down-regulated neuronal and synaptic components into a variety of cell type– and disease-specific signals, including multiple excitatory neuron and distinct interneuron modules with differential patterns of disease association, as well as common and rare genetic risk variant enrichment. The glial-immune signal demonstrates shared disruption of the blood-brain barrier and up-regulation of NFkB-associated genes, as well as disease-specific alterations in microglial-, astrocyte-, and interferon-response modules. A coexpression module associated with psychiatric medication exposure in SCZ and BD was enriched for activity-dependent immediate early gene pathways. To identify causal drivers, we integrated polygenic risk scores and performed a transcriptome-wide association study and summary-data–based Mendelian randomization. Candidate risk genes—5 in ASD, 11 in BD, and 64 in SCZ, including shared genes between SCZ and BD—are supported by multiple methods. These analyses begin to define a mechanistic basis for the composite activity of genetic risk variants. CONCLUSION Integration of RNA-seq and genetic data from ASD, SCZ, and BD provides a quantitative, genome-wide resource for mechanistic insight and therapeutic development at Resource.PsychENCODE.org. These data inform the molecular pathways and cell types involved, emphasizing the importance of splicing and isoform-level gene regulatory mechanisms in defining cell type and disease specificity, and, when integrated with genome-wide association studies, permit the discovery of candidate risk genes. The PsychENCODE cross-disorder transcriptomic resource. Human brain RNA-seq was integrated with genotypes across individuals with ASD, SCZ, BD, and controls, identifying pervasive dysregulation, including protein-coding, noncoding, splicing, and isoform-level changes. Systems-level and integrative genomic analyses prioritize previously unknown neurogenetic mechanisms and provide insight into the molecular neuropathology of these disorders. Most genetic risk for psychiatric disease lies in regulatory regions, implicating pathogenic dysregulation of gene expression and splicing. However, comprehensive assessments of transcriptomic organization in diseased brains are limited. In this work, we integrated genotypes and RNA sequencing in brain samples from 1695 individuals with autism spectrum disorder (ASD), schizophrenia, and bipolar disorder, as well as controls. More than 25% of the transcriptome exhibits differential splicing or expression, with isoform-level changes capturing the largest disease effects and genetic enrichments. Coexpression networks isolate disease-specific neuronal alterations, as well as microglial, astrocyte, and interferon-response modules defining previously unidentified neural-immune mechanisms. We integrated genetic and genomic data to perform a transcriptome-wide association study, prioritizing disease loci likely mediated by cis effects on brain expression. This transcriptome-wide characterization of the molecular pathology across three major psychiatric disorders provides a comprehensive resource for mechanistic insight and therapeutic development.

中文翻译：

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自闭症谱系障碍（ASD）、精神分裂症和双相情感障碍中全转录组异构体水平失调


引言 我们对精神疾病病理生理学的理解，包括自闭症谱系障碍 (ASD)、精神分裂症 (SCZ) 和双相情感障碍 (BD)，落后于其他医学领域。目前这些疾病的诊断和研究取决于行为、症状特征。定义遗传对疾病风险的贡献可以实现生物学、机制上的理解，但受到遗传复杂性、多基因性和缺乏一致的神经生物学模型来解释研究结果的挑战。基本原理转录组代表了一种定量表型，为理解主要精神疾病中被破坏的分子途径提供了生物学背景。对大量病例和对照进行 RNA 测序 (RNA-seq) 可以增进我们对每种疾病所破坏的生物学的了解，并为基因组和遗传数据的整合提供基础资源。结果跨多个转录组组织水平（基因表达、局部剪接、转录同工型表达以及蛋白质编码和非编码基因的共表达网络）的分析提供了 ASD、SCZ 和 BD 分子病理学的深入视图。超过 25% 的转录组在至少一种疾病中表现出差异剪接或表达，其中包括数百种非编码 RNA (ncRNA)，其中大多数具有未开发的功能，但共同表现出选择性限制模式。与基因水平相反，亚型水平的变化显示出最大的效应大小和遗传富集以及最大的疾病特异性。我们确定了与每种疾病相关的共表达模块，其中许多模块富含细胞类型特异性标记物，并且一些模块在所有三种疾病中均显着失调。 这些能够将下调的神经元和突触成分解析为各种细胞类型和疾病特异性信号，包括多个兴奋性神经元和具有不同疾病关联模式的不同中间神经元模块，以及常见和罕见的遗传风险变异富集。胶质细胞免疫信号表明血脑屏障的共同破坏和 NFkB 相关基因的上调，以及小胶质细胞、星形胶质细胞和干扰素反应模块的疾病特异性改变。 SCZ 和 BD 中与精神科药物暴露相关的共表达模块丰富了活动依赖性即早期基因通路。为了确定因果驱动因素，我们整合了多基因风险评分，并进行了全转录组关联研究和基于汇总数据的孟德尔随机化。多种方法支持候选风险基因——ASD 中的 5 个、BD 中的 11 个、SCZ 中的 64 个，包括 SCZ 和 BD 之间的共享基因。这些分析开始定义遗传风险变异的复合活性的机制基础。结论 RNA-seq 和来自 ASD、SCZ 和 BD 的遗传数据的整合为 Resource.PsychENCODE.org 的机制洞察和治疗开发提供了定量的全基因组资源。这些数据告知了所涉及的分子途径和细胞类型，强调了剪接和异构体水平基因调控机制在定义细胞类型和疾病特异性方面的重要性，并且当与全基因组关联研究相结合时，可以发现候选风险基因。 PsychENCODE 跨疾病转录组资源。 人脑 RNA-seq 与 ASD、SCZ、BD 和对照个体的基因型整合，识别普遍的失调，包括蛋白质编码、非编码、剪接和亚型水平的变化。系统级和综合基因组分析优先考虑以前未知的神经发生机制，并提供对这些疾病的分子神经病理学的见解。精神疾病的大多数遗传风险存在于调控区域，这意味着基因表达和剪接的致病性失调。然而，对患病大脑转录组组织的综合评估是有限的。在这项工作中，我们整合了 1695 名自闭症谱系障碍 (ASD)、精神分裂症和躁郁症患者以及对照者的大脑样本中的基因型和 RNA 测序。超过 25% 的转录组表现出差异剪接或表达，同种型水平的变化捕获了最大的疾病影响和遗传富集。共表达网络隔离了疾病特异性神经元改变，以及小胶质细胞、星形胶质细胞和干扰素反应模块，定义了以前未识别的神经免疫机制。我们整合了遗传和基因组数据来进行全转录组关联研究，优先考虑可能由大脑表达顺式效应介导的疾病位点。这种对三种主要精神疾病的分子病理学的全转录组表征为机制洞察和治疗开发提供了全面的资源。