Natural medicines have attracted wide attention in recent years. It is of great significance to clarify the pharmacological mechanisms of natural medicines. In prior studies, we established a method for elucidating pharmacological mechanisms of natural products contained in connectivity map (cMap), in terms of module profiles of gene expression in chemical treatments. In this study, we explore whether this methodology is applicable to dissecting the pharmacological mechanisms of natural medicines beyond the agents contained in cMap. First, the gene expression profiles of curcumin (a typical isolated natural medicine) and Si-Wu-Tang (a classic traditional Chinese medicine formula) treatments were merged with those of cMap-derived 1309 agents, respectively. Then, a biclustering analysis was performed using FABIA method to identify gene modules. The biological functions of gene modules provide preliminary insights into pharmacological mechanisms of both natural medicines. The module profile can be characterized by a binary vector, which allowed us to compare the expression profiles of natural medicines with those of cMap-derived agents. Accordingly, we predicted a series of pharmacological effects for curcumin and Si-Wu-Tang by the indications of cMap-covered drugs. Most predictions were supported by experimental observations, suggesting the potential use of this method in natural medicine dissection.

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.