Research Programme on Biomedical Informatics (GRIB), IMIM (Hospital del Mar Research Institute), Universitat Pompeu Fabra, Barcelona, Spain.
PLoS One. 2011;6(6):e20284. doi: 10.1371/journal.pone.0020284. Epub 2011 Jun 14.
Scientists have been trying to understand the molecular mechanisms of diseases to design preventive and therapeutic strategies for a long time. For some diseases, it has become evident that it is not enough to obtain a catalogue of the disease-related genes but to uncover how disruptions of molecular networks in the cell give rise to disease phenotypes. Moreover, with the unprecedented wealth of information available, even obtaining such catalogue is extremely difficult.
We developed a comprehensive gene-disease association database by integrating associations from several sources that cover different biomedical aspects of diseases. In particular, we focus on the current knowledge of human genetic diseases including mendelian, complex and environmental diseases. To assess the concept of modularity of human diseases, we performed a systematic study of the emergent properties of human gene-disease networks by means of network topology and functional annotation analysis. The results indicate a highly shared genetic origin of human diseases and show that for most diseases, including mendelian, complex and environmental diseases, functional modules exist. Moreover, a core set of biological pathways is found to be associated with most human diseases. We obtained similar results when studying clusters of diseases, suggesting that related diseases might arise due to dysfunction of common biological processes in the cell.
For the first time, we include mendelian, complex and environmental diseases in an integrated gene-disease association database and show that the concept of modularity applies for all of them. We furthermore provide a functional analysis of disease-related modules providing important new biological insights, which might not be discovered when considering each of the gene-disease association repositories independently. Hence, we present a suitable framework for the study of how genetic and environmental factors, such as drugs, contribute to diseases.
The gene-disease networks used in this study and part of the analysis are available at http://ibi.imim.es/DisGeNET/DisGeNETweb.html#Download.
长期以来，科学家们一直试图理解疾病的分子机制，以设计预防和治疗策略。对于某些疾病，人们已经清楚地认识到，仅仅获得与疾病相关的基因目录是不够的，还需要揭示细胞内分子网络的紊乱如何导致疾病表型。此外，随着前所未有的丰富信息的出现，即使获得这样的目录也是极其困难的。
我们通过整合涵盖疾病不同生物医学方面的多个来源的关联，开发了一个全面的基因-疾病关联数据库。特别是，我们关注当前人类遗传疾病的知识，包括孟德尔、复杂和环境疾病。为了评估人类疾病模块性的概念，我们通过网络拓扑和功能注释分析，对人类基因-疾病网络的新兴特性进行了系统研究。结果表明，人类疾病具有高度共享的遗传起源，并表明对于大多数疾病，包括孟德尔、复杂和环境疾病，存在功能模块。此外，发现一组核心的生物学途径与大多数人类疾病相关。当研究疾病聚类时，我们得到了类似的结果，这表明相关疾病可能是由于细胞内常见生物过程的功能障碍而产生的。
我们首次在一个综合的基因-疾病关联数据库中纳入孟德尔、复杂和环境疾病，并表明模块性的概念适用于所有这些疾病。我们进一步对疾病相关模块进行了功能分析，提供了重要的新生物学见解，如果独立考虑每个基因-疾病关联存储库，可能不会发现这些见解。因此，我们提出了一个合适的框架，用于研究遗传和环境因素（如药物）如何导致疾病。
本研究中使用的基因-疾病网络和部分分析结果可在 http://ibi.imim.es/DisGeNET/DisGeNETweb.html#Download 获得。
