Protein network of high risk copy number variants for psychiatric disorders

描述（由申请人提供）：自闭症、精神分裂症、双相情感障碍和智力障碍等神经精神疾病是社会的主要负担。我们目前对其潜在病理生理学的了解仍然有限。然而，遗传因素的作用已被清楚地证明。本研究的目的是通过研究自闭症、精神分裂症和相关疾病中涉及的高风险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的共同生物学机制。现在已经确定，罕见的拷贝数变异（CNVs）：(1)在精神疾病的风险中起重要作用；(2)高风险多

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.