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posted on 06.07.2016, 17:24 by Yin-ying Chen, Ya-nan Yu, Ying-ying Zhang, Bing Li, Jun Liu, Dong-feng Li, Ping Wu, Jie Wang, Zhong Wang, Yong-yan Wang

Drug-target networks were constructed by integrating gene expression data and protein interaction data, and then functional modules were identified using the AP, MCL and MCODE algorithms, respectively. The results of module identification were then optimized based on the minimum entropy criterion. Enrichment analysis of GO biological processes and KEGG pathways was performed with the DAVID 6.7 software program. The similarity or overlap between modules was calculated using SimiNEF. Then, five different types of modular allostery were identified. We defined the five types of AMs as follows. (1) AMs. Most modules showed partial overlap (0<Snef <100%) between vehicle-treated and compound-treated groups, as well as between various compound-treated groups, and thus they were referred to as AMs. (2) Conserved allosteric modules (CAMs, AMC). If the similarity between vehicle-treated group and a compound-treated group reached 100% (Snef = 100%), these modules were referred to as CAMs. (3) Generated allosteric modules (GAMs, AMG). If a module was not found in the vehicle-treated group but could be identified in compound-treated groups, we defined it as a GAM. (4) Disappeared allosteric modules (DAMs, AMD). If a module was found in the vehicle-treated group but could not be identified in compound-treated groups, we defined it as a DAM. (5) Watershed allosteric module (WAM, AMW). 0<Snef <100%, the first overlapped module between vehicle-treated and compound-treated groups, as well as between various compound-treated groups, was referred to as the WAM. V = Vehicle, C = Compound. ‘√’ or ‘×’ represents its appearance ‘yes’ or ‘no’ in the group, respectively. Finally, two modules could be validated using RT-PCR and Western blotting, respectively.

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