Next Article in Journal
A Novel Human TGF-β1 Fusion Protein in Combination with rhBMP-2 Increases Chondro-Osteogenic Differentiation of Bone Marrow Mesenchymal Stem Cells
Next Article in Special Issue
DADS Suppresses Human Esophageal Xenograft Tumors through RAF/MEK/ERK and Mitochondria-Dependent Pathways
Previous Article in Journal
Development of Laser Ionization Techniques for Evaluation of the Effect of Cancer Drugs Using Imaging Mass Spectrometry
Previous Article in Special Issue
PSNO: Predicting Cysteine S-Nitrosylation Sites by Incorporating Various Sequence-Derived Features into the General Form of Chou’s PseAAC
Article Menu

Export Article

Open AccessArticle
Int. J. Mol. Sci. 2014, 15(7), 11245-11254;

Elucidating Polypharmacological Mechanisms of Polyphenols by Gene Module Profile Analysis

National Key Laboratory of Crop Genetic Improvement, Agricultural Bioinformatics Key Laboratory of Hubei Province, Huazhong Agricultural University, Wuhan 430070, China
These authors contributed equally to this work.
Author to whom correspondence should be addressed.
Received: 13 May 2014 / Revised: 4 June 2014 / Accepted: 17 June 2014 / Published: 25 June 2014
(This article belongs to the Special Issue Molecular Science for Drug Development and Biomedicine)
Full-Text   |   PDF [239 KB, uploaded 30 June 2014]   |  


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. View Full-Text
Keywords: polypharmacology; polyphenol; biclustering analysis; target polypharmacology; polyphenol; biclustering analysis; target

Graphical abstract

This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

Supplementary material


Share & Cite This Article

MDPI and ACS Style

Li, B.; Xiong, M.; Zhang, H.-Y. Elucidating Polypharmacological Mechanisms of Polyphenols by Gene Module Profile Analysis. Int. J. Mol. Sci. 2014, 15, 11245-11254.

Show more citation formats Show less citations formats

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Int. J. Mol. Sci. EISSN 1422-0067 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top