Network-based elucidation of human disease similarities reveals common functional modules enriched for pluripotent drug targets.
Network-based elucidation of human disease similarities reveals common functional modules enriched for pluripotent drug targets.
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DOI：
10.1371/journal.pcbi.1000662
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发表时间：
2010-02-05
影响因子：
4.3
通讯作者：
Butte AJ
中科院分区：
文献类型：
--
作者：
Suthram S;Dudley JT;Chiang AP;Chen R;Hastie TJ;Butte AJ
目前阐明疾病之间关系的工作在很大程度上是基于对疾病基因的已有知识。因此，这些研究在发现新的和未知的疾病关系方面受到限制。我们通过对疾病相关mRNA表达数据和人类蛋白质相互作用网络的综合分析，提出了第一个比较和对比疾病的定量框架。我们确定了人类蛋白质网络中的4,620个功能模块，并提供了一个定量指标来记录它们在54种疾病中的反应，从而得出138种疾病之间的显著相似性。14种重要的疾病相关性也有共同的药物，这支持了类似疾病可以用相同药物治疗的假设，使我们能够预测现有药物的新用途。最后，我们还确定了至少一半疾病中失调的59个模块，代表了常见的疾病状态“特征”。这些模块在已知的药物靶标基因中显著富集。有趣的是，已知靶向这些基因/蛋白质的药物比靶向其他基因/蛋白质的药物治疗更多的疾病，这突出了这些核心模块作为主要治疗机会的重要性。许多人类疾病通过共同的原因甚至共同的病理相互关联。长期以来，人们一直利用对这些关系的了解，用相同的疗法来治疗类似的疾病。然而，大多数发现这些关系的传统方法都依赖于主观的衡量标准，如症状的相似性，或不完整的知识，如基因突变。在这里，我们提出了第一种整合高通量数据集（如mRNA表达和大规模蛋白质-蛋白质相互作用网络）的方法，以系统和定量的方式发现人类疾病的关系。我们发现了从肺癌、精神分裂症和疟疾等54种人类疾病之间的138个显著的病理相似性。我们还发现了一组细胞内的共同途径和过程，这些途径和过程在至少一半的疾病中是失调的。我们推断，这些过程对应于人体系统对疾病状态的共同反应。有趣的是，我们发现这些途径中的许多蛋白质已经被已知是现有药物的靶标。事实上，与这些蛋白质相对应的药物治疗的疾病比预期的要多得多，这突出了这些常见的分子病理途径作为主要治疗机会的重要性。
Current work in elucidating relationships between diseases has largely been based on pre-existing knowledge of disease genes. Consequently, these studies are limited in their discovery of new and unknown disease relationships. We present the first quantitative framework to compare and contrast diseases by an integrated analysis of disease-related mRNA expression data and the human protein interaction network. We identified 4,620 functional modules in the human protein network and provided a quantitative metric to record their responses in 54 diseases leading to 138 significant similarities between diseases. Fourteen of the significant disease correlations also shared common drugs, supporting the hypothesis that similar diseases can be treated by the same drugs, allowing us to make predictions for new uses of existing drugs. Finally, we also identified 59 modules that were dysregulated in at least half of the diseases, representing a common disease-state “signature”. These modules were significantly enriched for genes that are known to be drug targets. Interestingly, drugs known to target these genes/proteins are already known to treat significantly more diseases than drugs targeting other genes/proteins, highlighting the importance of these core modules as prime therapeutic opportunities. Many human diseases are related to each other through shared causes or even shared pathology. Knowledge of these relationships has long been exploited to treat similar diseases with the same therapies. However, most of the traditional approaches to discover these relationships have depended on subjective measures, such as similarity in symptoms, or incomplete knowledge, such as genes with mutations. Here we present the first approach integrating high-throughput datasets such as mRNA expression and large-scale protein-protein interaction networks to discover human disease relationships in a systematic and quantitative way. We discover 138 significant pathological similarities between 54 human diseases ranging from lung cancer, schizophrenia, and malaria. We also discovered a set of common pathways and processes within the cell that are dysregulated in at least half of the diseases. We infer that these processes correspond to a common response of the human system to a disease state. Interestingly, we find that many of the proteins in these pathways are already known to be targets of existing drugs. In fact, the drugs corresponding to these proteins are known to treat significantly more diseases than expected by chance highlighting the importance of these common molecular pathological pathways as prime therapeutic opportunities.
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影响因子：
14.9
作者：
Keshava Prasad TS;Goel R;Kandasamy K;Keerthikumar S;Kumar S;Mathivanan S;Telikicherla D;Raju R;Shafreen B;Venugopal A;Balakrishnan L;Marimuthu A;Banerjee S;Somanathan DS;Sebastian A;Rani S;Ray S;Harrys Kishore CJ;Kanth S;Ahmed M;Kashyap MK;Mohmood R;Ramachandra YL;Krishna V;Rahiman BA;Mohan S;Ranganathan P;Ramabadran S;Chaerkady R;Pandey A
通讯作者：
Pandey A
影响因子：
1.5
作者：
Kim, Seong-Ho;Yi, Soojin V.
通讯作者：
Yi, Soojin V.
影响因子：
46.9
作者：
Lage, Kasper;Karlberg, E. Olof;Brunak, Soren
通讯作者：
Brunak, Soren
影响因子：
64.8
作者：
Rual, JF;Venkatesan, K;Vidal, M
通讯作者：
Vidal, M
影响因子：
6.1
作者：
Jeon, YT;Kim, JW;Lee, HP
通讯作者：
Lee, HP