Comprehensive Human Expressed Sequences in Brain (CHESS-BRAIN) and their roles in neuropsychiatric illness

项目摘要 在过去的十年里，RNA测序技术的广泛使用使科学家们能够发现一个遥远的 人类基因组编码的基因和转录物的数量比我们所知的更大、更丰富， 十年前至少90%的人类基因具有多种异构体，包括剪接变体、替代位点、 转录起始和终止，外显子跳跃事件，等等。人类转录本的数量 在标准基因数据库中的数据已经大幅增长，从21世纪末的约40，000到今天的超过200，000， 它可能仍远未完成。我们以前的工作使用外显子-外显子剪接点和其他片段 转录本已经证明了人脑中未注释但表达的基因的临床相关性， 包括与精神分裂症及其遗传风险的关联。该项目将试图发现和 表征新的基因异构体收集从健康和患病的大脑，使用最新的 用于转录组组装的计算方法和广泛收集的脑RNA-seq数据集。的 该项目分为三个目标：首先，我们将开发新的算法来组装RNA-seq数据 从已经使用核糖体RNA耗尽测序的样品中， 人类大脑研究，但不用于大多数其他RNA-seq实验，而是使用polyA+ 丰富。我们将实现这些方法作为RNA-seq的HISAT和StringTie系统的扩展 校准和组装，这两个都是在PI和co-PI的实验室开发的。我们将应用这些 改进的方法，从人脑组织中提取数千个公开可用的RNA-seq样本， 新的“CHESS-BRAIN”（脑中的人类表达序列综合）基因注释数据库。这 努力还将确定哪些转录物是组织特异性的和脑区域特异性的;即，表达 与其他组织相比，在脑组织和各种脑区域中的水平显著更高或更低。在 第二个目标，我们将使用这些方法来定量基因表达水平在数百死后的大脑 来自被诊断患有精神分裂症（SCZD）、重性抑郁症（MDD）、双相情感障碍（躁郁症）和抑郁症的受试者的RNA-seq样品 自闭症谱系障碍（ASD）和创伤后应激障碍（PTSD），我们将 与匹配的对照进行比较，以确定这些疾病中未注释的转录的贡献。在 我们的第三个目标是在整个CHESS-脑中进行表达数量性状基因座（eQTL）定位。 数据集，无论是在大脑区域和诊断，以确定未注释的基因调控 转录本，包括编码和非编码转录本。这项分析将确定基因和转录本 其表达水平在不同组织和疾病中显著变化。我们将联合收割机结合这些结果， 确定与每种精神疾病的遗传风险相关的新转录本。

Project Summary The widespread use of RNA sequencing technology over the past decade has allowed scientists to discover a far larger and richer repertoire of genes and transcripts encoded by the human genome than were known just a decade ago. At least 90% of human genes have multiple isoforms, including splicing variants, alternative sites of transcription initiation and termination, exon skipping events, and more. The number of human transcripts in standard gene databases has grown enormously, from ~40,000 in the late 2000s to over 200,000 today, but it is still likely far from complete. Our previous work using exon-exon splice junctions and other fragmentary transcripts has demonstrated the clinical relevance of unannotated but expressed genes in the human brain, including associations with schizophrenia and its genetic risk. This project will attempt to discover and characterize novel gene isoforms collected from both healthy and diseased brains, using the latest computational methods for transcriptome assembly and an extensive collection of brain RNA-seq datasets. The project is organized into three aims: first, we will develop new algorithms designed to assemble RNA-seq data from samples that have been sequenced using ribosomal RNA depletion, a technique that is widely used in human brain studies but that is not used in most other RNA-seq experiments, which instead use polyA+ enrichment. We will implement these methods as extensions to the HISAT and StringTie systems for RNA-seq alignment and assembly, both of which were developed in the PI's and co-PI's labs. We will then apply these improved methods to thousands of publicly available RNA-seq samples from human brain tissue to create a new "CHESS-BRAIN" (Comprehensive Human Expressed Sequences in Brain) gene annotation database. This effort will also determine which transcripts are tissue-specific and brain-region specific; i.e., expressed at significantly higher or lower levels in brain tissues and in various brain regions as compared to other tissues. In the second aim, we will use these methods to quantify gene expression levels in hundreds of post-mortem brain RNA-seq samples from subjects diagnosed with schizophrenia (SCZD), major depression (MDD), bipolar disorder (BPD), autism spectrum disorder (ASD), and post-traumatic stress disorder (PTSD), whom we will compare to matched controls to identify the contribution of unannotated transcription in these disorders. In our third aim we will perform expression quantitative trait loci (eQTL) mapping across the entire CHESS-brain dataset, both within and across brain regions and diagnoses, to identify genetic regulation of unannotated transcripts, including both coding and noncoding transcripts. This analysis will identify genes and transcripts whose expression levels change significantly in different tissues and diseases. We will combine these results to identify novel transcripts associated with genetic risk for each of the psychiatric disorders.