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: 10318590
• 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 2024-11-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10318590
• 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; gene network; gene regulatory network; 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）是一种常见且令人衰弱的精神疾病，造成了巨大的个人和 社会负担和研究表明，反映出常见和稀有等位基因的实质性遗传力 许多基因座。 SCZ的大多数遗传学或机械研究仍然主要集中在蛋白质编码基因上。 但是，大多数SCZ风险变体都存在于基因组的非编码区域。长的非编码RNA （LNCRNA）占功能性非编码元素的很大一部分，并且（如增强剂）被丰富 对于SCZ风险变体而言，但到目前为止，基本上仍然没有表征。虽然大多数lncrnas的功能是 未知，许多人现在与基因表达和染色质结构的调节有关，以及 有新兴的证据表明LNCRNA在神经发育过程中很重要。因此，有一个紧急的 需要了解它们在SCZ中的作用。 LNCRNA的全面分析仍然具有挑战性，因为它们通常以低表达 与其他成绩单相比。因此，我们在这里建议利用新的RNA捕获技术， 我们已经开发并应用于控制和自闭症的大脑，以深层介绍LNCRNA转录组 在验尸前外侧前额叶皮层（DLPFC）大脑的独特资源中 350个SCZ病例和350个匹配对照的样品。通过深入的样品的深度简短读取测序 LNCRNA使用特定的捕获探针，我们将识别在SCZ病例中失调的LNCRNA（AIM 1）。 我们将将非编码表达数据与现有的标准RNA-seq数据集成 通过CommonMinds联盟（CMC）样本以构建编码/非编码共表达网络 确定其功能失调可能导致SCZ风险的关键调节性LNCRNA。网络分析将是 由高分辨率染色体确认捕获地图（HI-C）的可用性支持以识别直接 LNCRNA及其目标之间的相互作用（AIM 2）。最后，在计算机调节性lncRNA中将进行预测 通过使用全长转录本测序绘制其完整的基因组基因座，通过验证了原位和体外验证 技术，并分析lncRNA扰动对靶基因表达和调节性的影响 来自人类诱导多能干细胞（HIPSC）的神经细胞的相互作用（AIM 3）。作为所有人的一部分 这些分析我们还将识别和整合LNCRNA基因表达定量性状基因座（LNCQTL） 现有的Psychencode表观遗传组蛋白修饰和相同样本的开染色质QTL， 评估SCZ风险变体的机械影响。这些结果不仅会改善我们的 了解LNCRNA在SCZ病因中的作用，可能提供治疗靶标，但也提供 在任何疾病背景下，用于未来非编码RNA研究的强大框架。

项目成果

Evaluating performance and applications of sample-wise cell deconvolution methods on human brain transcriptomic data.

评估样本细胞反卷积方法在人脑转录组数据上的性能和应用。

• DOI: 10.1101/2023.03.13.532468
• 发表时间: 2023
• 期刊: bioRxiv : the preprint server for biology
• 作者: Dai,Rujia;Chu,Tianyao;Zhang,Ming;Wang,Xuan;Jourdon,Alexandre;Wu,Feinan;Mariani,Jessica;Vaccarino,FloraM;Lee,Donghoon;Fullard,JohnF;Hoffman,GabrielE;Roussos,Panos;Wang,Yue;Wang,Xusheng;Pinto,Dalila;Wang,SidneyH;Zhang,Ch
• 通讯作者: Zhang,Ch