Abstract
Due to the diverse medicinal effects, polyphenols are among the most intensively studied natural products. However, it is a great challenge to elucidate the polypharmacological mechanisms of polyphenols. To address this challenge, we establish a method for identifying multiple targets of chemical agents through analyzing the module profiles of gene expression upon chemical treatments. By using FABIA algorithm, we have performed a biclustering analysis of gene expression profiles derived from Connectivity Map (cMap), and clustered the profiles into 49 gene modules. This allowed us to define a 49 dimensional binary vector to characterize the gene module profiles, by which we can compare the expression profiles for each pair of chemical agents with Tanimoto coefficient. For the agent pairs with similar gene expression profiles, we can predict the target of one agent from the other. Drug target enrichment analysis indicated that this method is efficient to predict the multiple targets of chemical agents. By using this method, we identify 148 targets for 20 polyphenols derived from cMap. A large part of the targets are validated by experimental observations. The results show that the medicinal effects of polyphenols are far beyond their well-known antioxidant activities. This method is also applicable to dissect the polypharmacology of other natural products.
Highlights
Since reactive oxygen species (ROS), e.g., superoxide radical, hydrogen peroxide, and hydroxyl radical, are involved in the pathogenesis of many diseases, such as cancer, neurodegenerative diseases and atherosclerosis [1], antioxidants in particular polyphenolic antioxidants, have been widely expected to exert prophylactic or therapeutic effects on these diseases [2,3,4,5]
The Connectivity Map (cMap)-derived 1309 agents and expression profiles have been grouped into 49 gene modules by FABIA algorithm [19], which consist of 5921 probes, much greater than those used in the conventional microarray analysis [15,16]
Each chemical agent in cMap has a gene module profile, which is defined by a 49 dimensional binary vector, with 1 or 0 representing the presence or not of the module (Table S1)
Summary
Since reactive oxygen species (ROS), e.g., superoxide radical, hydrogen peroxide, and hydroxyl radical, are involved in the pathogenesis of many diseases, such as cancer, neurodegenerative diseases and atherosclerosis [1], antioxidants in particular polyphenolic antioxidants, have been widely expected to exert prophylactic or therapeutic effects on these diseases [2,3,4,5]. The evolutionary consideration suggested that natural polyphenols have evolved an excellent scaffold with well-balanced rigidity and flexibility to adapt to different structures of enzymes in the biosynthetic pipeline, which enables the compounds to bind various proteins [12] This finding implies that natural polyphenols have inherent potential to exert polypharmacological effects other than redox modulation [13]. The existent methods for gene expression profile analysis normally use limited signature genes (usually corresponding to ~500 probes out of 22,000+), which lose valuable information These methods are efficient to reveal a single MoA or target for a certain drug, rather than its polypharmacological mechanisms [16]. We first establish a gene module-based target identification method and use this method to further elucidate the polypharmacological mechanisms for the 20 polyphenols
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