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Transcriptome-Wide Isoform-Level Dysregulation in ASD, Schizophrenia, and Bipolar Disorder Michael J

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Transcriptome-Wide Isoform-Level Dysregulation in ASD, Schizophrenia, and Bipolar Disorder Michael J RESEARCH | PSYCHENCODE ◥ local splicing, transcript isoform expression, and RESEARCH ARTICLE SUMMARY coexpression networks for both protein-coding and noncoding genes—provides an in-depth view of ASD, SCZ, and BD molecular pathology. PSYCHIATRIC GENOMICS More than 25% of the transcriptome exhibits differential splicing or expression in at least one disorder, including hundreds of noncod- Transcriptome-wide isoform-level ing RNAs (ncRNAs), most of which have un- explored functions but collectively exhibit dysregulation in ASD, schizophrenia, patterns of selective constraint. Changes at the ◥ isoform level, as opposed and bipolar disorder ON OUR WEBSITE to the gene level, show the Read the full article largest effect sizes and ge- Michael J. Gandal*, Pan Zhang, Evi Hadjimichael, Rebecca L. Walker, Chao Chen, at http://dx.doi. netic enrichment and the Shuang Liu, Hyejung Won, Harm van Bakel, Merina Varghese, Yongjun Wang, org/10.1126/ greatest disease specific- science.aat8127 ity. We identified coexpres- Annie W. Shieh, Jillian Haney, Sepideh Parhami, Judson Belmont, Minsoo Kim, .................................................. Patricia Moran Losada, Zenab Khan, Justyna Mleczko, Yan Xia, Rujia Dai, sion modules associated Daifeng Wang, Yucheng T. Yang, Min Xu, Kenneth Fish, Patrick R. Hof, with each disorder, many with enrichment for – Jonathan Warrell, Dominic Fitzgerald, Kevin White, Andrew E. Jaffe, cell type specific markers, and several modules Downloaded from PsychENCODE Consortium†, Mette A. Peters, Mark Gerstein, Chunyu Liu*, significantly dysregulated across all three disor- Lilia M. Iakoucheva*, Dalila Pinto*, Daniel H. Geschwind* ders. These enabled parsing of down-regulated neuronal and synaptic components into a vari- ety of cell type– and disease-specific signals, INTRODUCTION: Our understanding of the RATIONALE: The transcriptome represents a including multiple excitatory neuron and dis- pathophysiology of psychiatric disorders, including quantitative phenotype that provides biological tinct interneuron modules with differential autism spectrum disorder (ASD), schizophrenia context for understanding the molecular path- patterns of disease association, as well as com- http://science.sciencemag.org/ (SCZ), and bipolar disorder (BD), lags behind ways disrupted in major psychiatric disorders. mon and rare genetic risk variant enrichment. other fields of medicine. The diagnosis and RNA sequencing (RNA-seq) in a large cohort of The glial-immune signal demonstrates shared study of these disorders currently depend on cases and controls can advance our knowledge disruption of the blood-brain barrier and up- behavioral, symptomatic characterization. De- of the biology disrupted in each disorder and regulation of NFkB-associated genes, as well fining genetic contributions to disease risk provide a foundational resource for integration as disease-specific alterations in microglial-, allows for biological, mechanistic understand- with genomic and genetic data. astrocyte-, and interferon-response modules. ing but is challenged by genetic complexity, A coexpression module associated with psychi- polygenicity, and the lack of a cohesive neuro- RESULTS: Analysis across multiple levels of atric medication exposure in SCZ and BD was biological model to interpret findings. transcriptomic organization—gene expression, enriched for activity-dependent immediate early gene pathways. To identify causal drivers, we on December 19, 2018 Transcriptomic Framework Biological Insight integrated polygenic risk scores and performed a transcriptome-wide association study and ASD (n=51) Isoform-Level Specificity summary-data–based Mendelian randomization. SCZ (n=559) isoform A A isoM1: Neuron BD (n=222) Gene ASD Candidate risk genes—5 in ASD, 11 in BD, and Control (n=936) B isoM2: Astrocyte 64 in SCZ, including shared genes between SCZ isoform B SCZ and BD—are supported by multiple methods. Genotypes RNA-Seq These analyses begin to define a mechanistic basis Local Splicing Alterations for the composite activity of genetic risk variants. Gene ASD SCZ Transcript Δ CONCLUSION: Integration of RNA-seq and Isoform genetic data from ASD, SCZ, and BD provides a Local Noncoding RNA Dysregulation quantitative, genome-wide resource for mech- Splicing anistic insight and therapeutic development at Resource.PsychENCODE.org. These data inform the molecular pathways and cell types involved, Network-Level Integration emphasizing the importance of splicing and Gene isoform-level gene regulatory mechanisms in Isoform Altered Neural-Immune Trajectories ncRNA defining cell type and disease specificity, and, Interferon Microglia NFkB Hub response when integrated with genome-wide association ASD BD studies, permit the discovery of candidate risk SCZ genes.▪ The list of author affiliations is available in the full article online. The PsychENCODE cross-disorder transcriptomic resource. *Corresponding author. Email: [email protected] Human brain RNA-seq was (M.J.G.); [email protected] (C.L.); [email protected] (L.M.I.); integrated with genotypes across individuals with ASD, SCZ, BD, and controls, identifying [email protected] (D.P.); [email protected] (D.H.G.) pervasive dysregulation, including protein-coding, noncoding, splicing, and isoform-level changes. †PsychENCODE Consortium authors and affiliations are listed in the supplementary materials. Systems-level and integrative genomic analyses prioritize previously unknown neurogenetic Cite this article as M. J. Gandal et al., Science 362, mechanisms and provide insight into the molecular neuropathology of these disorders. eaat8127 (2018). DOI: 10.1126/science.aat8127 Gandal et al., Science 362, 1265 (2018) 14 December 2018 1of1 RESEARCH | PSYCHENCODE ◥ of understanding transcriptional regulation and RESEARCH ARTICLE noncoding genome function, several consortia (8, 10–12) have undertaken large-scale efforts to provide maps of the transcriptome and its PSYCHIATRIC GENOMICS genetic and epigenetic regulation across human tissues. Although some have included central nervous system (CNS) tissues, a more compre- Transcriptome-wide isoform-level hensive analysis focusing on the brain in both healthy and disease states is necessary to ac- celerate our understanding of the molecular dysregulation in ASD, schizophrenia, mechanisms of these disorders (13–16). We present results of the analysis of RNA se- and bipolar disorder quencing (RNA-seq) data from the PsychENCODE Consortium (16), integrating genetic and genomic Michael J. Gandal1,2,3,4*, Pan Zhang5, Evi Hadjimichael6,7,8,9, Rebecca L. Walker2,3,4, data from more than 2000 well-curated, high- Chao Chen10,11, Shuang Liu12, Hyejung Won2,3,4,13,14, Harm van Bakel7, quality postmortem brain samples from individ- Merina Varghese9,15, Yongjun Wang16, Annie W. Shieh17, Jillian Haney1,2,3, uals with SCZ, BD, and ASD, as well as controls Sepideh Parhami1,2,3, Judson Belmont6,7,8,9, Minsoo Kim1,4, Patricia Moran Losada5, (17). We provide a comprehensive resource of Zenab Khan7, Justyna Mleczko18, Yan Xia10,17, Rujia Dai10,17, Daifeng Wang19, disease-relevant gene expression changes and Yucheng T. Yang12, Min Xu12, Kenneth Fish18, Patrick R. Hof 9,15, 20, transcriptional networks in the postnatal human Downloaded from Jonathan Warrell12, Dominic Fitzgerald21, Kevin White21,22,23, Andrew E. Jaffe24,25, brain (see Resource.PsychENCODE.org for data PsychENCODE Consortium†, Mette A. Peters26, Mark Gerstein12, Chunyu Liu10,17,27*, and annotations). Data were generated across 18 19 20 Lilia M. Iakoucheva5*, Dalila Pinto6,7,8,9*, Daniel H. Geschwind1,2,3,4* eight studies ( , , ), uniformly processed, and combined through a consolidated genomic 21 Most genetic risk for psychiatric disease lies in regulatory regions, implicating pathogenic data processing pipeline ( ) (fig. S1), yielding a dysregulation of gene expression and splicing. However, comprehensive assessments of total of 2188 samples passing quality control transcriptomic organization in diseased brains are limited. In this work, we integrated (QC) for this analysis, representing frontal and http://science.sciencemag.org/ genotypes and RNA sequencing in brain samples from 1695 individuals with autism spectrum temporal cerebral cortices from 1695 individuals disorder (ASD), schizophrenia, and bipolar disorder, as well as controls. More than 25% of across the human life span, including 279 tech- the transcriptome exhibits differential splicing or expression, with isoform-level changes nical replicates (fig. S2). Extensive QC steps were capturing the largest disease effects and genetic enrichments. Coexpression networks isolate taken within and across individual studies, re- disease-specific neuronal alterations, as well as microglial, astrocyte, and interferon-response sulting in the detection of 16,541 protein-coding modules defining previously unidentified neural-immune mechanisms. We integrated genetic and 9233 noncoding genes based on Gencode v19 21 and genomic data to perform a transcriptome-wide association study, prioritizing disease annotations ( ) (fig. S3). There was substan- loci likely mediated by cis effects on brain expression.This transcriptome-wide characterization tial heterogeneity in RNA-seq methodologies of the molecular pathology across three major psychiatric disorders provides a comprehensive across cohorts, which was accounted for by in- resource for mechanistic insight and therapeutic development. cluding 28 surrogate variables and aggregate sequencing metrics as covariates in downstream on December
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