Mapping the role of long noncoding RNAs in gene regulatory networks in schizophrenia

绘制长非编码 RNA 在精神分裂症基因调控网络中的作用

• 批准号: 10078634
• 负责人: Dalila Pinto
• 金额: $ 77.02万
• 依托单位: ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-12-01 至 2022-11-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10078634
• 关键词: Affect; Algorithms; Alternative Splicing; Architecture; Autopsy; Brain; Chromatin; Chromosomes; Code; Data; Development; Disease; Elements; Enhancers; Epigenetic Process; Etiology; Exons; Future; Gene Expression; Gene Expression Regulation; Gene Structure; Genes; Genetic; Genetic Risk; Genetic Transcription; Genome; Genomic Segment; Genomics; Genotype; Goals; Heritability; Hi-C; Human; In Situ; In Vitro; Intercistronic Region; Knock-out; Length; Link; Linkage Disequilibrium; Maps; Mediation; Mental disorders; Messenger RNA; Neurodevelopmental Disorder; Neurons; Pathologic; Pathway Analysis; Patients; Prefrontal Cortex; Protein Isoforms; Proteins; Quantitative Trait Loci; RNA; RNA Splicing; Regulator Genes; Reporting; Resolution; Resources; Role; Sampling; Schizophrenia; Site; Specimen; Susceptibility Gene; Technology; Testing; Tissues; Transcript; Untranslated RNA; Variant; autism spectrum disorder; brain tissue; case control; causal variant; cohort; differential expression; diverse data; experimental study; genome wide association study; genomic locus; histone modification; improved; in silico; induced pluripotent stem cell; insight; neurodevelopment; novel; psychiatric genomics; rare variant; risk variant; schizophrenia risk; screening; severe psychiatric disorder; small hairpin RNA; therapeutic target; transcriptome; transcriptome sequencing

Schizophrenia (SCZ) is a common and debilitating psychiatric disorder that imposes tremendous personal and societal burdens, and studies have demonstrated substantial heritability reflecting common and rare alleles at many loci. Most genetic or mechanistic studies of SCZ still focus predominantly on protein-coding genes; however, the majority of SCZ risk variants reside in noncoding regions of the genome. Long noncoding RNAs (lncRNAs) account for a significant fraction of functional noncoding elements and (like enhancers) are enriched for SCZ risk variants, but so far remain largely uncharacterized. Though the functions of most lncRNAs are unknown, many have now been implicated in the regulation of gene expression and chromatin architecture, and there is emerging evidence that lncRNAs are important during neurodevelopment. As such, there is an urgent need to understand their role in SCZ. Comprehensive profiling of lncRNAs has remained challenging because they are typically expressed at low levels compared to other transcripts. We therefore propose here to leverage new RNA Capture technologies, which we have developed and applied to control and autism brains, to deeply profile the lncRNA transcriptome in a uniquely large resource of diverse data types from post-mortem dorsolateral prefrontal cortex (DLPFC) brain samples of 350 SCZ cases and 350 matched controls. By deep short-read sequencing of samples enriched for lncRNAs using specific capture probes, we will identify lncRNAs that are dysregulated in SCZ cases (Aim 1). We will integrate our noncoding expression data with existing standard RNA-Seq data generated for the same samples by the CommonMinds Consortium (CMC) to construct coding/noncoding co-expression networks to identify key regulatory lncRNAs whose dysregulation may contribute to SCZ risk. Network analyses will be supported by the availability of high-resolution chromosome confirmation capture maps (Hi-C) to identify direct interactions between lncRNAs and their targets (Aim 2). Finally, in silico regulatory lncRNA predictions will be validated in-situ and in-vitro by mapping their complete genomic loci using full-length transcript sequencing technology, and analyzing the effect of lncRNA perturbations on target gene expression and regulatory interactions in neural cells derived from human induced pluripotent stem cells (hiPSCs) (Aim 3). As part of all these analyses we will also identify and integrate lncRNA gene expression quantitative trait loci (lncQTL) with existing PsychENCODE epigenetic histone modifications and open-chromatin QTLs for the same samples, to assess the mechanistic impacts of SCZ risk variants. Together these results will not only improve our understanding of the role of lncRNAs in SCZ etiology, potentially providing therapeutic targets, but also provide a robust framework for future noncoding RNA studies in any disease context.

精神分裂症（SCZ）是一种常见的和使人衰弱的精神疾病， 社会负担，研究表明，大量的遗传性反映了常见和罕见的等位基因， 很多地方。大多数SCZ的遗传学或机制研究仍然主要集中在蛋白质编码基因上; 然而，大多数SCZ风险变体位于基因组的非编码区。长非编码rna （lncRNA）占功能性非编码元件的很大一部分，并且（像增强子一样）富集 对于SCZ风险变体，但到目前为止仍然基本上没有特征。尽管大多数lncRNA的功能 未知的，许多现在已经涉及到基因表达和染色质结构的调节， 有证据表明lncRNA在神经发育过程中是重要的。因此，有一个紧迫的 我们需要了解他们在SCZ中的角色。 lncRNA的全面分析仍然具有挑战性，因为它们通常以低水平表达。 与其他成绩单相比。因此，我们在此提出利用新的RNA捕获技术， 我们已经开发并应用于控制和自闭症大脑，以深入分析lncRNA转录组 在来自死后背外侧前额叶皮层（DLPFC）大脑的各种数据类型的独特的大型资源中， 350例SCZ病例和350例匹配对照的样本。通过对富集以下物质的样品进行深度短读测序， 使用特异性捕获探针，我们将鉴定SCZ病例中失调的lncRNA（目的1）。 我们将整合我们的非编码表达数据与现有的标准RNA-Seq数据产生的相同， CommonMinds Consortium（CMC）的样本构建编码/非编码共表达网络， 鉴定其失调可能导致SCZ风险的关键调节性lncRNA。网络分析将 支持高分辨率染色体确认捕获图（Hi-C）的可用性，以确定直接 lncRNA与其靶标之间的相互作用（Aim 2）。最后，在计算机上调控lncRNA预测将是 通过使用全长转录物测序绘制其完整基因组基因座来在原位和体外验证 技术，并分析lncRNA扰动对靶基因表达和调控的影响。 本发明的目的是提供在源自人诱导多能干细胞（hiPSC）的神经细胞中的相互作用（Aim 3）。作为所有 通过这些分析，我们还将鉴定并整合lncRNA基因表达数量性状位点（lncQTL）， 现有的PsychENCODE表观遗传组蛋白修饰和开放染色质QTL的相同样品， 评估SCZ风险变体的机械影响。这些成果不仅将改善我们的 了解lncRNA在SCZ病因学中的作用，可能提供治疗靶点，但也提供 这是未来在任何疾病背景下进行非编码RNA研究的一个强大框架。