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Int. J. Mol. Sci. 2014, 15(7), 11245-11254; doi:10.3390/ijms150711245
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.
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 , antioxidants in particular polyphenolic antioxidants, have been widely expected to exert prophylactic or therapeutic effects on these diseases [2,3,4,5]. However, a large number of researches indicated that the strong in vitro antioxidant activities of polyphenols can not be translated into in vivo therapeutic effects [5,6,7,8,9]. This antioxidant paradox was primarily explained by the poor bioavailability of exogenous polyphenols . Our analysis about the biological roles of polyphenols revealed that they were evolved for filtering UV light rather than scavenging intense ROS, which provided an evolutionary explanation to the weak in vivo radical-scavenging potential of polyphenols . The evolutionary consideration also 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 . This finding implies that natural polyphenols have inherent potential to exert polypharmacological effects other than redox modulation . However, how to elucidate the polypharmacological mechanisms of natural polyphenols is a great challenge, because the conventional methods to dissect drug mode of action (MoA) are laborious and low throughput .
Recently, gene expression-based analysis showed great potential in identifying drug targets [15,16,17]. But 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. In addition, these methods are efficient to reveal a single MoA or target for a certain drug, rather than its polypharmacological mechanisms . Since gene expression signatures related to different biological activities cluster into different modules , we speculate that the polypharmacological mechanisms of polyphenols may be better dissected in terms of module profiles of gene expression.
In a previous analysis about connectivity map (cMap), which contains 7056 expression profiles of 5 different human cell lines treated with 1309 agents (including 20 polyphenols), we generated 49 gene modules by using biclustering approach FABIA (factor analysis for bicluster acquisition) . Through analyzing the biological functions of the modules, we revealed that some polyphenols exert polypharmacological effects through activating transcription factors, such as estrogen receptors, nuclear factor (erythroid-derived 2)-like 2, and peroxisome proliferator-activated receptor gamma. In this study, we first establish a gene module-based target identification method and then use this method to further elucidate the polypharmacological mechanisms for the 20 polyphenols.
2. Results and Discussion
In a prior research, the cMap-derived 1309 agents and expression profiles have been grouped into 49 gene modules by FABIA algorithm , which consist of 5921 probes, much greater than those used in the conventional microarray analysis [15,16]. Thus, 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). This allows us to calculate Tanimoto coefficient for each pair of the compounds to characterize the similarity of their gene expression profiles. The bigger the Tanimoto coefficient is, the more similar biological effects of the compound pairs are expected. For the compound pairs with similar gene module profiles, if one has the MoA and/or target information, we can predict the medicinal behaviors of the other. A total of 856,086 pairwise Tanimoto coefficients were calculated for the 1309 compounds in the cMap dataset (Table S2). The top 1% and 5% coefficients are higher than 0.45 and 0.33, respectively (Figure 1).
To evaluate the effectiveness of this parameter in target identification, we performed a target enrichment test. First, by searching DrugBank  and Therapeutic Target Database (TTD) , we retrieved 573 approved drugs from 1309 agents, which hit 536 targets. Then, we found that 209 targets were shared by at least two drugs. These targets and corresponding 476 drugs can be used to assess the target enrichment significance. Although the drug targets collected by DrugBank and TTD may be incomplete and may be indirect targets, these information have been successfully used by previous studies to evaluate the target enrichment efficiency . 113,050 pairwise Tanimoto coefficients were calculated for the 476 drugs. The drug pairs with Tanimoto coefficients of higher than 0.33 were used to estimate the probability of target sharing by hypergeometric test. The results showed that 78 targets of 128 drugs can be enriched (q < 0.05) (Table S3). It is noteworthy that 96 of 128 drugs have multiple targets (≥2), for which the average ratio of target enrichment reaches 68.75% (66/96) (Table S3). In particular, the 7 targets of chlorpromazine, 8 targets of maprotiline, and 14 targets of imipramine were completely enriched (Table S3). Thus, the present method has great potential to predict MoA and targets of chemical agents, especially to dissect the polypharmacological mechanisms of natural products.
The cMap-derived 1309 agents involve four kinds of polyphenols, i.e., flavonoids (16 agents), monolignols (2 agents) and stilbenoids (1 agent), phenylpropanoids (1 agent). The gene module profiles of these polyphenols show that they are involved in more gene modules than other agents (14.85 ± 4.80 vs. 11.85 ± 5.42, p < 0.01, t-test), suggesting that polyphenols indeed have more complex biological functions than others. The most common modules covered by the 20 polyphenols include module 11 (with occurrence of 14), module 18 (with occurrence of 13), module 25 (with occurrence of 13), module 7 (with occurrence of 12), and module 3 (with occurrence of 12). According to the previously enriched biological functions of 49 gene modules , the major functions associated with these modules are protein transport, protein location, cytoskeleton organization, cell motion, purine and pyrimidine metabolism, oxidative phosphorylation, cell cycle, RNA processing, ubiquitin-dependent protein catabolic process and translational elongation. By searching in GeneDecks , it was found that four of the five common modules (modules 3, 11, 18 and 25) are tightly linked to cancer and tumors (p < 0.0001).
There are 93 drugs that are similar to the 20 polyphenols in terms of gene expression module profile (with Tanimoto coefficients > 0.45), which correspond to 148 targets and provide meaningful clues to clarifying the polypharmacology for these polyphenols (Table S4). In the predicted medicinal effects, anti-neoplastic is most popular (with occurrence of 17 in 93 drugs), in good agreement with the above finding that cancer is linked to most common gene modules.
Table 1, Table 2, Table 3 and Table 4 list the predicted targets of four most intensively studied polyphenols, including genistein (a representative component of soybean), quercetin (one of most widely distributed flavonoids), resveratrol (a representative component of red wine), and (−)-catechin (a representative component of green tea). It can be seen that antineoplastic and antihypertensive are the most common predicted activities of the four polyphenols, which agree well with the health benefits of their dietary sources. For instance, accumulating evidence indicated that high soybean intake and regular green tea drinking are associated with low incidence rates of human cancers and hypertension [23,24,25,26,27,28]. In addition, a large part (50%) of the predicted targets of these polyphenols are validated by experiments, most (92.3%) of which are direct targets (Table 1, Table 2, Table 3 and Table 4). These results strongly warrant the experimental evaluation of other predicted targets.
It is intriguing to note that phosphodiesterase enzymes (PDEs) and estrogen receptor are predicted targets for three of four polyphenols. This finding agrees well with the opinion that plant polyphenols collectively behave as phytoestrogens and can inhibit several isoforms of PDEs [29,30,31]. A major progress in recent natural medicine research was the identification of PDEs as the target of resveratrol . The present analysis highlights the similar pharmacological mechanisms underlying genistein and quercetin.
|Imatinib||Antineoplastic Agents||Platelet-derived growth factor receptor a|||
|Proto-oncogene tyrosine-protein kinase ABL1 a|||
|Mast/stem cell growth factor receptor a|||
|Raloxifene||Antihypocalcemic Agents||Estrogen receptor a|||
|Iloprost||Antihypertensive Agents||Prostaglandin E2 receptor, EP2 subtype b|||
|cAMP-specific 3',5'-cyclic phosphodiesterase a|||
|Prostacyclin receptor c|||
|Cisapride||Anti-Ulcer Agents||5-Hydroxytryptamine 4 receptor||-|
|Fluticasone||Anti-inflammatory Agents||Glucocorticoid receptor a|||
|Diethylstilbestrol||Antineoplastic Agents||Estrogen receptor a|||
|Finasteride||Anti-baldness Agents||Steroid-5-alpha reductase a|||
|Sulindac sulfide||Rheumatoid arthritis||-||-|
|Prednisone||Anti-inflammatory Agents||Glucocorticoid receptor a|||
|Estradiol||Anti-menopausal Agents||Estrogen receptor a|||
a as direct targets of genistein; b as indirect target of genistein which increases prostaglandin release; c as indirect target of genistein which increases prostacyclin release.
|Tolazoline||Adrenergic alpha-Antagonists||Alpha adrenergic receptor||-|
|Tamoxifen||Antineoplastic Agents||Estrogen receptor a|||
|Bone Density Conservation Agents|
|Finasteride||Anti-baldness Agents||Steroid-5-alpha reductase||-|
|Skin and Mucous Membrane Agents|
|Sulindac sulfide||Rheumatoid arthritis||-||-|
|Iloprost||Antihypertensive Agents||Prostaglandin E2 receptor, EP2 subtype||-|
|cAMP-specific 3',5'-cyclic phosphodiesterase a|||
|Raloxifene||Antihypocalcemic Agents||Estrogen receptor a|||
|Bone Density Conservation Agents|
|Apomorphine||Antiparkinson Agents||Dopamine receptor a|||
|5-Hydroxytryptamine receptor a|||
|Fluticasone||Anti-inflammatory Agents||Glucocorticoid receptor||-|
|Tocainide||Anti-Arrhythmia Agents||Sodium channel protein type 5 subunit alpha a|||
a as direct targets of quercetin.
|Reserpine||Antihypertensive Agents||Synaptic vesicular amine transporter||-|
|Mercaptopurine||Antineoplastic Agents||Hypoxanthine-guanine phosphoribosyltransferase||-|
|Daunorubicin||Antineoplastic Agents||DNA topoisomerase||-|
|Terfenadine||Anti-Allergic Agents||Histamine H1 receptor||-|
|Antiarrhythmic Agents||Potassium voltage-gated channel subfamily H member 2 a|||
|Muscarinic acetylcholine receptor M3||-|
|Fluphenazine||Antipsychotic Agents||Dopamine receptor||-|
|Dipyridamole||Vasodilator Agents||Adenosine deaminase||-|
|cGMP-specific 3',5'-cyclic phosphodiesterase a|||
|Rescinnamine||Antihypertensive Agents||Angiotensin-converting enzyme a|||
|Trifluoperazine||Antipsychotic Agents||Dopamine receptor||-|
|Metixene||Antiparkinson Agents||Muscarinic acetylcholine receptor||-|
a as direct targets of resveratrol.
|Letrozole||Antineoplastic Agents||Cytochrome P450 19A1 a|||
|Triprolidine||Anti-Allergic Agents||Histamine H1 receptor|
|Pindolol||Antihypertensive Agents||Adrenergic receptor||-|
|Vasodilator Agents||5-hydroxytryptamine receptor||-|
|Norfloxacin||Anti-Bacterial Agents||DNA topoisomerase 2-alpha a|||
|Prilocaine||Anesthetics||Sodium channel protein type 5 subunit alpha||-|
|Estradiol||Anti-menopausal Agents||Estrogen receptor a|||
|Doxycycline||Anti-Bacterial Agents||30S ribosomal protein||-|
|Bendroflumethiazide||Antihypertensive Agents||Solute carrier family 12 member 3||-|
|Calcium-activated potassium channel subunit alpha 1||-|
|Theophylline||Bronchodilator Agents||Adenosine A1 receptor||-|
|Vasodilator Agents||cGMP-specific 3',5'-cyclic phosphodiesterase a|||
|Naltrexone||Anti-craving Agents||Opioid receptor a|||
a as direct targets of (−)-catechin.
3.1. Tanimoto Coefficient Calculation
Tanimoto coefficient (TC) was calculated with a perl program to compare the gene module profiles of each compound pair.
3.2. Drug Target Enrichment
Hypergeometric test was used to assess the drug target enrichment significance. The Equation (2) was derived by computing the extreme tail probabilities:
Natural products (NPs) have made important contributions to safe guarding human health. Not only ancient humans depended on NPs to cure various diseases, modern pharmaceutical industry also benefit from NPs to find hits, leads and drugs . Therefore, it is of great significance to elucidate the therapeutic mechanisms of NPs. However, this is a big challenge, because NPs usually hit multiple targets with relatively weak affinity and the conventional target identification methods are laborious and low throughput .
In this study, we established a gene module-based target identification method. Because gene modules cover more gene probes, this method is more efficient than conventional microarray analysis methods in information extraction. Therefore, this method enables the discovery of richer information about the medicinal effects of chemical agents, which is very helpful to clarify the polypharmacological mechanisms of polyphenols and other NPs. Moreover, this method may be used to predict targets for NPs beyond those contained in cMap, so it is expected to find more and more applications in the omics era, because the NP-related microarray data are rapidly accumulated.
Supplementary FilesSupplementary File 1
We are grateful to Qiang Zhu for helpful discussions. This work was supported by the National Basic Research Program of China (973 project, grant 2010CB126100), the National Natural Science Foundation of China (grant 21173092) and the Natural Science Foundation of Hubei Province (grant 2013CFA016).
Bin Li and Min Xiong performed research, analyzed data, and wrote the paper. Hong-Yu Zhang designed research, analyzed data, and wrote the paper.
Conflicts of Interest
The authors declare no conflict of interest.
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