Mapping gene associations in human mitochondria using clinical disease phenotypes.
Mapping gene associations in human mitochondria using clinical disease phenotypes.
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DOI：
10.1371/journal.pcbi.1000374
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发表时间：
2009-04
影响因子：
4.3
通讯作者：
Davis RW
中科院分区：
文献类型：
--
作者：
Scharfe C;Lu HH;Neuenburg JK;Allen EA;Li GC;Klopstock T;Cowan TM;Enns GM;Davis RW
核基因编码大多数线粒体蛋白质，它们的突变导致多种多样的和使人衰弱的临床疾病。迄今为止，已经记录了1，200个这些线粒体基因，而没有相关临床表型的标准化目录。这样的目录将有助于开发分析人类表型数据的方法，以确定许多基因和疾病之间的基因型-表型关系，并支持线粒体疾病的临床诊断。在这里，我们建立了174个线粒体疾病基因的临床表型目录，并研究疾病和基因的关联。临床体征和症状等表型特征从全文医学文章中手动注释，并基于分层MeSH本体进行分类。每个基因的表型特征的这种分类允许不同基因之间的疾病比较。反过来，我们能够测量疾病基因的表型关联，我们计算了基于其共同表型特征的定量值。结果表明，具有更多相似表型的基因具有更强的功能相互作用的倾向，证明了表型相似性值在疾病基因网络分析中的有用性。然后，我们构建了一个线粒体基因的功能网络，发现非疾病基因的连接性高于疾病基因，并且疾病基因之间存在相互作用的趋势。利用这些差异，我们提出了168个候选基因，类似于线粒体疾病基因的特征相互作用模式。通过他们的网络协会，候选人被进一步优先用于特定疾病的研究，如视神经病和帕金森病。大多数线粒体疾病表型涉及几个临床类别，包括神经系统、代谢和胃肠道疾病，这可能表明线粒体系统内基因缺陷的影响。附带的知识库（http：//www.mitophenome.org/）支持临床疾病和相关基因的研究。成功的疾病基因鉴定的一个重要先决条件是以最小的模糊性评估特定的临床性状或表型。即使有多年的经验，识别和诊断线粒体疾病仍然是临床医学的一个主要障碍。支持临床医生的计算工具不仅有助于识别受影响的个体，还可以指导对这些疾病的遗传和生物学原因的研究。在这项研究中，我们对174个疾病基因的临床特征、体征和症状进行了分析和分类，然后根据其共同的表型特征识别基因相似性。我们证明，共享更相似的表型的基因有更强的功能相互作用的倾向，证明了疾病基因网络分析的表型相似性值的有用性。我们对线粒体基因的大型功能网络的研究揭示了区分疾病和非疾病基因的独特特性。疾病基因显示出较低的平均总连接性，但倾向于相互作用;我们用这一发现预测了168种高概率疾病候选者。附带的知识库允许在疾病和基因信息之间轻松导航。我们相信开源格式将支持和鼓励进一步的研究，这将有利于这个和其他人类表型组项目。
Nuclear genes encode most mitochondrial proteins, and their mutations cause diverse and debilitating clinical disorders. To date, 1,200 of these mitochondrial genes have been recorded, while no standardized catalog exists of the associated clinical phenotypes. Such a catalog would be useful to develop methods to analyze human phenotypic data, to determine genotype-phenotype relations among many genes and diseases, and to support the clinical diagnosis of mitochondrial disorders. Here we establish a clinical phenotype catalog of 174 mitochondrial disease genes and study associations of diseases and genes. Phenotypic features such as clinical signs and symptoms were manually annotated from full-text medical articles and classified based on the hierarchical MeSH ontology. This classification of phenotypic features of each gene allowed for the comparison of diseases between different genes. In turn, we were then able to measure the phenotypic associations of disease genes for which we calculated a quantitative value that is based on their shared phenotypic features. The results showed that genes sharing more similar phenotypes have a stronger tendency for functional interactions, proving the usefulness of phenotype similarity values in disease gene network analysis. We then constructed a functional network of mitochondrial genes and discovered a higher connectivity for non-disease than for disease genes, and a tendency of disease genes to interact with each other. Utilizing these differences, we propose 168 candidate genes that resemble the characteristic interaction patterns of mitochondrial disease genes. Through their network associations, the candidates are further prioritized for the study of specific disorders such as optic neuropathies and Parkinson disease. Most mitochondrial disease phenotypes involve several clinical categories including neurologic, metabolic, and gastrointestinal disorders, which might indicate the effects of gene defects within the mitochondrial system. The accompanying knowledgebase (http://www.mitophenome.org/) supports the study of clinical diseases and associated genes. An important prerequisite for successful disease gene identification is the assessment, with minimal ambiguity, of a particular clinical trait or phenotype. Even with years of experience, recognizing and diagnosing mitochondrial diseases is still a major hurdle in clinical medicine. Computational tools supporting clinicians not only help identify affected individuals, but also guide studies of the genetic and biological causes of these disorders. In this study we dissect and categorize individual clinical features, signs, and symptoms of 174 disease genes and then identify gene similarities based on their shared phenotypic features. We demonstrate that genes sharing more similar phenotypes have a stronger tendency for functional interactions, proving the usefulness of phenotype similarity values in disease gene network analysis. Our study of a large functional network of mitochondrial genes revealed distinct properties that differentiate disease and non-disease genes. Disease genes showed a lower average total connectivity but a tendency to interact with each other; a finding that we used to predict 168 high-probability disease candidates. The accompanying knowledgebase allows for easy navigation between disease and gene information. We believe the open source format will support and encourage further research that will benefit this and other human phenome projects.
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影响因子：
46.9
作者：
Butte, AJ;Kohane, IS
通讯作者：
Kohane, IS
影响因子：
5.5
作者：
Benard, G.;Faustin, B.;Rossignol, R.
通讯作者：
Rossignol, R.
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影响因子：
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作者：
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影响因子：
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11.2
作者：
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Tulinius, M