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RAS 病:在基因组、相互作用组和表型组水平上的表现

RASopathies: Presentation at the Genome, Interactome, and Phenome Levels.

作者信息

Pevec Urska, Rozman Neva, Gorsek Blaz, Kunej Tanja

机构信息

Department of Animal Science, Biotechnical Faculty, University of Ljubljana, Domzale, Slovenia.

出版信息

Mol Syndromol. 2016 May;7(2):72-9. doi: 10.1159/000445733. Epub 2016 Apr 21.

Abstract

Clinical symptoms often reflect molecular correlations between mutated proteins. Alignment between interactome and phenome levels reveals new disease genes and connections between previously unrelated diseases. Despite a great potential for novel discoveries, this approach is still rarely used in genomics. In the present study, we analyzed the data of 6 syndromes belonging to the RASopathy class of disorders (RASopathies) and presented them as a model to study associations between genome, interactome, and phenome levels. Causative genes and clinical symptoms were collected from OMIM and NCBI GeneReviews databases for 6 syndromes: Noonan, Noonan syndrome with multiple lentigines, neurofibromatosis type 1, cardiofaciocutaneous, and Legius and Costello syndrome. The STRING tool was used for the identification of protein interactions. Six RASopathy syndromes were found to be associated with 12 causative genes. We constructed an interactome of RASopathy proteins and their neighbors and developed a database of 328 clinical symptoms. The collected data was presented at genome, interactome, and phenome levels and as an integrated network of all 3 data types. The present study provides a baseline for future studies of associations between interactome and phenome in RASopathies and could serve as a novel approach to analyze phenotypically and genetically related diseases.

摘要

临床症状往往反映了突变蛋白之间的分子关联。相互作用组与表型组水平之间的比对揭示了新的疾病基因以及先前不相关疾病之间的联系。尽管具有巨大的新发现潜力,但这种方法在基因组学中仍很少使用。在本研究中,我们分析了属于RAS病类疾病(RASopathies)的6种综合征的数据,并将其作为研究基因组、相互作用组和表型组水平之间关联的模型呈现。从OMIM和NCBI GeneReviews数据库收集了6种综合征的致病基因和临床症状:努南综合征、伴有多发雀斑的努南综合征、1型神经纤维瘤病、心面皮肤综合征以及勒吉尤斯综合征和科斯特洛综合征。使用STRING工具来识别蛋白质相互作用。发现6种RAS病综合征与12个致病基因相关。我们构建了RAS病蛋白及其相邻蛋白的相互作用组,并开发了一个包含328种临床症状的数据库。收集的数据在基因组、相互作用组和表型组水平呈现,并作为所有三种数据类型的整合网络呈现。本研究为未来RAS病中相互作用组与表型组之间关联的研究提供了基线,并可作为分析表型和遗传相关疾病的新方法。

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