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

精神分裂症(SCZ)是一种常见的、使人衰弱的精神疾病，它对个人和 社会负担，研究表明，相当大的遗传力反映了常见和罕见的等位基因。 有很多个轨迹。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风险变异存在于基因组的非编码区。长的非编码RNA (LncRNAs)占功能非编码元件的很大一部分，并且(如增强子)被丰富 对于SCZ风险变种，但到目前为止基本上仍未确定其特征。尽管大多数lncRNA的功能是 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，以及 越来越多的证据表明，lncRNAs在神经发育过程中很重要。因此，有一个紧急情况 需要了解他们在SCZ中的角色。 对lncRNA的全面分析仍然具有挑战性，因为它们通常在低水平表达 与其他成绩单相比的水平。因此，我们建议利用新的RNA捕获技术， 我们已经开发并应用于控制和自闭症大脑，以深入描述lncRNA转录组 在来自死后背外侧前额叶皮质(DLPFC)大脑的不同数据类型的独特大型资源中 350例SCZ病例和350例配对对照的样本。通过对样品进行深度短读测序 使用特定的捕获探针，我们将识别在SCZ病例中表达失调的lncRNAs(目标1)。 我们将把我们的非编码表达数据与为其生成的现有标准RNA-Seq数据相集成 由CommonMinds Consortium(CMC)构建编码/非编码共表达网络的样本 确定其调控失调可能导致SCZ风险的关键调控LncRNA。网络分析将是 由高分辨率染色体确认捕获图谱(Hi-C)的可用性支持，以直接识别 LncRNA与其靶标之间的相互作用(目标2)。最后，在电子计算机中，调节性的lncRNA预测将是 利用全长转录本测序定位它们的完整基因组座位，以验证原位和体外验证 技术，并分析了lncRNA扰动对靶基因表达和调控的影响 人类诱导多能干细胞(HiPSCs)衍生的神经细胞之间的相互作用(目标3)。作为一切的一部分 这些分析我们还将识别和整合lncRNA基因表达数量性状基因座(LncQTL)与 相同样本的现有表观编码组蛋白修饰和开放染色质QTL，以 评估SCZ风险变量的机械影响。总而言之，这些结果不仅将改善我们的 了解lncRNAs在SCZ病因学中的作用，可能提供治疗靶点，但也提供 为未来在任何疾病背景下进行非编码RNA研究提供了一个强大的框架。

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.