Protein network of high risk copy number variants for psychiatric disorders

描述（由申请人提供）：神经精神疾病，如自闭症、精神分裂症、双相情感障碍和智力残疾是社会的主要负担。我们目前对其潜在病理生理学的了解仍然有限。然而，遗传因素的贡献已被清楚地证明。本研究的目的是通过研究与这些疾病有关的高危遗传因素编码的蛋白质之间的相互作用网络，发现自闭症、精神分裂症和相关疾病之间共有的生物学机制。 现在已经确定，罕见的拷贝数变异体（CNV）：（1）在精神障碍的风险中起重要作用;（2）许多高风险的CNV基因突变体（CNV）在精神障碍的风险中起重要作用。 CNVs跨越疾病边界，并与几种精神疾病有关。CNV通常涉及多个基因，并且这些大量的功能异质性基因如何促成病理尚不完全清楚。 为了进一步了解CNV对精神疾病的贡献，我们建议研究来自高风险罕见CNV的基因如何在蛋白质水平上相互作用。对连接CNV基因的网络的了解将有助于更好地了解它们在不同疾病中的病理影响。使用构建的网络，我们将测试以下假设：（1）交叉疾病CNV共享相互作用的蛋白质伴侣，可以解释不同疾病的共同病因;（2）交叉疾病CNV具有一组独特的相互作用伴侣，可以解释疾病之间的差异。 我们选择了11个高风险的CNV（包含169个基因），这些CNV与两种或多种精神疾病密切相关。对这169个基因的二进制蛋白质-蛋白质相互作用（PPIs）的文献检索表明，33%的基因在公共数据库中没有PPIs注释，只有3.5%的基因相互作用。然而，已经反复证明，文献PPI偏向于高度研究的蛋白质，不完整，并且通常不像通常假设的那样可靠。在这里，我们建议对来自高风险交叉疾病CNV的169个基因进行无偏的蛋白质相互作用筛选。我们的具体目标如下：（1）组装169个开放阅读框（ORF）克隆的文库，其对应于11个赋予精神疾病高风险的CNV的基因;（2）构建和验证交叉疾病CNV相互作用组;（3）鉴定和进行交叉疾病CNV共有或独有的相互作用伴侣的后续功能研究。

DESCRIPTION (provided by applicant): Neuropsychiatric disorders such as autism, schizophrenia, bipolar disorder and intellectual disability are major burden to society. Our current knowledge of their underlying pathophysiology remains limited. However, a contribution of genetic factors has been clearly demonstrated. The goal of this study is to discover shared biological mechanisms between autism, schizophrenia and related disorders through investigation of the networks of interactions between proteins encoded by the high risk genetic factors implicated in these disorders. It is now firmly established that rare Copy Number Variants (CNVs): (1) play significant role in the risk of psychiatric disorders; (2) many high-risk CNVs cross disorder boundaries and are implicated in several psychiatric disorders. CNVs generally involve multiple genes, and how this large number of functionally heterogeneous genes contributes to the pathology is not completely understood. To advance our understanding of CNV contribution to psychiatric diseases, we propose to investigate how the genes from high risk rare CNVs interact on a protein level. The knowledge of the networks connecting CNV genes will help to better understand their pathological impact in different disorders. Using the constructed networks, we will test the hypotheses that: (1) cross-disorder CNVs share interacting protein partners that may explain shared etiology of different disorders; (2) cross-disorder CNVs have a unique set of interacting partners that may explain the differences between disorders. We have selected 11 high risk CNVs (containing 169 genes) that are firmly implicated in two or more psychiatric disorders for this study. Literature search fr binary protein-protein interactions (PPIs) for these 169 genes demonstrated that 33% of them have no PPIs annotated in the public databases, and only 3.5% of them interact with each other. However, it has been repeatedly demonstrated that literature PPIs are biased toward highly studied proteins, incomplete, and often are not as reliable as commonly assumed. Here, we are proposing to perform an unbiased protein interaction screen for 169 genes from high risk cross-disorder CNVs. Our Specific Aims are as follows: (1) Assemble a library of 169 open reading frame (ORF) clones corresponding to genes from 11 CNVs that confer high risk to psychiatric disorders; (2) Build and validate the cross-disorder CNV interactome; (3) Identify and perform follow-up functional studies of the interacting partners that are shared by or are unique to cross-disorder CNVs.