School of Biomedical Informatics,
The University of Texas Health Science Center at Houston
A drug performs its function via a cascade to transfer chemical signals from drug binding proteins to signal recipient transcription factors (TFs). The cascade is complicated, which involves multiple signaling pathways acting in the network mode. Reconstruction of signaling pathway networks is vital for the identification of drug targets and off-targets, which in turn facilitates our understanding the mode of drug action and drug development. However, it is challenging to abstract multiple signaling pathways involved in the drug action into one system. In this talk, I will present our recent study to address this challenge. We developed a computational framework to comprehensively integrate multiple data sets to generate one signaling pathway network (SPNetwork) for one drug. Here, we utilized the drug metformin as a proof of principle. Given the available data and the nature of signal transduction cascades, we compiled metformin upstream genes and metformin downstream TFgenes. Then, by overlaying them to human SPNetwork we compiled and applying random walk algorithms through longitudinal and lateral movements, we generated one metformin-specific SPNetwork. By examining the disease genes and genotyping data of multiple GWAS data in the network, we found that the network was significantly enriched with genes with mutation signals in the pathology of type 2 diabetes (T2D) and cancer, and in the metformin therapy to reduce the cancer risk of T2D patients. Furthermore, we built a crosstalk subnetwork for T2D and cancer for illustrating the metformin action. Our topological and functional analyses revealed that seven genes might play important roles in metformin signal transduction, including some results that are supported by previous studies. Thus, this study provides a valuable source for understanding the molecular mechanism of metformin action. The observation of critical components in the signaling pathway network, with partial verification from previous studies, demonstrates that our approach is promising for the identification of novel and key components in signaling pathways of metformin, potentially, other drugs.