Fontaine Jean-Fred, Priller Josef, Spruth Eike, Perez-Iratxeta Carol, Andrade-Navarro Miguel A
Max Delbrück Center for Molecular Medicine, Germany; Faculty of Biology, Johannes Gutenberg University Mainz, Mainz, Germany.
Charité - Universitätsmedizin Berlin, Department of Neuropsychiatry and Laboratory of Molecular Psychiatry, Berlin, Germany; Cluster of Excellence "NeuroCure" and Berlin Institute of Health (BIH), Berlin, Germany.
Methods. 2015 Mar;74:90-6. doi: 10.1016/j.ymeth.2014.11.022. Epub 2014 Dec 5.
Clinical evaluation of patients and diagnosis of disorder is crucial to make decisions on appropriate therapies. In addition, in the case of genetic disorders resulting from gene abnormalities, phenotypic effects may guide basic research on the mechanisms of a disorder to find the mutated gene and therefore to propose novel targets for drug therapy. However, this approach is complicated by two facts. First, the relationship between genes and disorders is not simple: one gene may be related to multiple disorders and a disorder may be caused by mutations in different genes. Second, recognizing relevant phenotypes might be difficult for clinicians working with patients of closely related complex disorders. Neuropsychiatric disorders best illustrate these difficulties since phenotypes range from metabolic to behavioral aspects, the latter extremely complex. Based on our clinical expertise on five neurodegenerative disorders, and from the wealth of bibliographical data on neuropsychiatric disorders, we have built a resource to infer associations between genes, chemicals, phenotypes for a total of 31 disorders. An initial step of automated text mining of the literature related to 31 disorders returned thousands of enriched terms. Fewer relevant phenotypic terms were manually selected by clinicians as relevant to the five neural disorders of their expertise and used to analyze the complete set of disorders. Analysis of the data indicates general relationships between neuropsychiatric disorders, which can be used to classify and characterize them. Correlation analyses allowed us to propose novel associations of genes and drugs with disorders. More generally, the results led us to uncovering mechanisms of disease that span multiple neuropsychiatric disorders, for example that genes related to synaptic transmission and receptor functions tend to be involved in many disorders, whereas genes related to sensory perception and channel transport functions are associated with fewer disorders. Our study shows that starting from expertise covering a limited set of neurological disorders and using text and data mining methods, meaningful and novel associations regarding genes, chemicals and phenotypes can be derived for an expanded set of neuropsychiatric disorders. Our results are intended for clinicians to help them evaluate patients, and for basic scientists to propose new gene targets for drug therapies. This strategy can be extended to virtually all diseases and takes advantage of the ever increasing amount of biomedical literature.
对患者进行临床评估和疾病诊断对于决定适当的治疗方法至关重要。此外，对于由基因异常导致的遗传性疾病，表型效应可能会指导对疾病机制的基础研究，以找到突变基因，从而提出新的药物治疗靶点。然而，这种方法因两个事实而变得复杂。首先，基因与疾病之间的关系并不简单：一个基因可能与多种疾病相关，而一种疾病可能由不同基因的突变引起。其次，对于处理密切相关的复杂疾病患者的临床医生来说，识别相关表型可能很困难。神经精神疾病最能说明这些困难，因为其表型范围从代谢方面到行为方面，后者极其复杂。基于我们对五种神经退行性疾病的临床专业知识，以及神经精神疾病丰富的文献数据，我们构建了一个资源库，用于推断总共31种疾病的基因、化学物质和表型之间的关联。对与31种疾病相关的文献进行自动文本挖掘的第一步返回了数千个富集术语。临床医生手动选择了较少的与他们专业领域的五种神经疾病相关的相关表型术语，并用于分析所有疾病。数据分析表明了神经精神疾病之间的一般关系，可用于对它们进行分类和表征。相关性分析使我们能够提出基因和药物与疾病的新关联。更普遍地说，结果使我们发现了跨越多种神经精神疾病的疾病机制，例如与突触传递和受体功能相关的基因往往涉及许多疾病，而与感觉感知和通道运输功能相关的基因与较少的疾病相关。我们的研究表明，从涵盖有限一组神经疾病的专业知识出发，使用文本和数据挖掘方法，可以为更广泛的神经精神疾病得出关于基因、化学物质和表型的有意义的新关联。我们的结果旨在帮助临床医生评估患者，并帮助基础科学家提出新的药物治疗基因靶点。这种策略几乎可以扩展到所有疾病，并利用不断增加的生物医学文献。
