Identification of drug-specific pathways based on gene expression data: application to drug induced lung injury
Identification of drug-specific pathways based on gene expression data: application to drug induced lung injury
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DOI：
10.1039/c4ib00294f
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发表时间：
2015-01-01
影响因子：
2.5
通讯作者：
Bai, Jane P. F.
中科院分区：
文献类型：
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作者：
Melas, Ioannis N.;Sakellaropoulos, Theodore;Bai, Jane P. F.
识别在特定生物学背景下起作用的信号通路是系统生物学中的一个主要挑战，可能有助于复杂疾病的研究和药物发现的各个方面。最近的方法试图以PPI网络的形式将基因表达数据与蛋白质连接性的先验知识相结合，并基于手头的数据使用计算方法来识别具有功能的蛋白质-蛋白质-相互作用(PPI)网络的子集。然而，无向网络的使用限制了可以得出的机械性洞察，因为它不允许从一个节点到下一个节点进行机械性的信号转导。为了解决这一重要问题，我们使用有向信令网络作为表示蛋白质连通性的支架，并实现了整数线性规划(ILP)公式来建模网络中从一个节点到下一个节点的信号转导规则。然后，我们优化了网络结构，以最好地匹配手头的基因表达数据。我们以药物所致肺损伤为例，说明了ILP模型的实用性。我们根据200种肺毒性药物的基因表达谱确定了它们的作用模式，并随后合并了药物特异性通路，构建了一个信号网络，揭示了药物诱导的肺病(DILD)的机制。我们进一步证明了DILD网络的预测能力和生物学相关性，将其应用于识别具有相关药理机制的治疗肺损伤的药物。
Identification of signaling pathways that are functional in a specific biological context is a major challenge in systems biology, and could be instrumental to the study of complex diseases and various aspects of drug discovery. Recent approaches have attempted to combine gene expression data with prior knowledge of protein connectivity in the form of a PPI network, and employ computational methods to identify subsets of the protein-protein-interaction (PPI) network that are functional, based on the data at hand. However, the use of undirected networks limits the mechanistic insight that can be drawn, since it does not allow for following mechanistically signal transduction from one node to the next. To address this important issue, we used a directed, signaling network as a scaffold to represent protein connectivity, and implemented an Integer Linear Programming (ILP) formulation to model the rules of signal transduction from one node to the next in the network. We then optimized the structure of the network to best fit the gene expression data at hand. We illustrated the utility of ILP modeling with a case study of drug induced lung injury. We identified the modes of action of 200 lung toxic drugs based on their gene expression profiles and, subsequently, merged the drug specific pathways to construct a signaling network that captured the mechanisms underlying Drug Induced Lung Disease (DILD). We further demonstrated the predictive power and biological relevance of the DILD network by applying it to identify drugs with relevant pharmacological mechanisms for treating lung injury.