Leihong Wu

A Network Pharmacology Study of Chinese Medicine QiShenYiQi to Reveal Its Underlying Multi-Compound, Multi-Target, Multi-Pathway Mode of Action

Chinese medicine is a complex system guided by traditional Chinese medicine (TCM) theories, which has proven to be especially effective in treating chronic and complex diseases. However, the underlying modes of action (MOA) are not always systematically investigated. Herein, a systematic study was designed to elucidate the multi-compound, multi-target and multi-pathway MOA of a Chinese medicine, QiShenYiQi (QSYQ), on myocardial infarction. QSYQ is composed of Astragalus membranaceus (Huangqi), Salvia miltiorrhiza (Danshen), Panax notoginseng (Sanqi), and Dalbergia odorifera (Jiangxiang). Male Sprague Dawley rat model of myocardial infarction were administered QSYQ intragastrically for 7 days while the control group was not treated. The differentially expressed genes (DEGs) were identified from myocardial infarction rat model treated with QSYQ, followed by constructing a cardiovascular disease (CVD)-related multilevel compound-target-pathway network connecting main compounds to those DEGs supported by literature evidences and the pathways that are functionally enriched in ArrayTrack. 55 potential targets of QSYQ were identified, of which 14 were confirmed in CVD-related literatures with experimental supporting evidences. Furthermore, three sesquiterpene components of QSYQ, Trans-nerolidol, (3S,6S,7R)-3,7,11-trimethyl-3,6-epoxy-1,10-dodecadien-7-ol and (3S,6R,7R)-3,7,11-trimethyl-3,6-epoxy-1,10-dodecadien-7-ol from Dalbergia odorifera T. Chen, were validated experimentally in this study. Their anti-inflammatory effects and potential targets including extracellular signal-regulated kinase-1/2, peroxisome proliferator-activated receptor-gamma and heme oxygenase-1 were identified. Finally, through a three-level compound-target-pathway network with experimental analysis, our study depicts a complex MOA of QSYQ on myocardial infarction.

Identification of the effective constituents for anti-inflammatory activity of Ju-Zhi-Jiang-Tang, an ancient traditional Chinese medicine formula

The anti-inflammatory constituents of Ju-Zhi-Jiang-Tang (JZJT), a formula used for thousands of years in China, were identified by LC-MS and pharmacological activity evaluation. In this study, the whole extract of formula was separated into multiple components to facilitate the analytical process. To characterize their contributions to pharmacological activity of formula, activity indexes of constituents were proposed and calculated for the first time, which integrated the chemical and pharmacological information of multiple components. Among the 151 constituents detected in JZJT by LC-Q-TOF-MS and LC-IT-MS, a total number of 108 constituents were identified unambiguously or tentatively, including eighteen potential novel compounds. And, the structures of some constituents were confirmed by NMR. According to their activity indexes, polymethoxy flavones were indicated as the major active constituents responsible for the anti-inflammatory activity of JZJT. To verify the feasibility of activity indexes in predicting the active constituents, nine compounds with positive and negative index values were selected to validate their anti-inflammatory activity in vitro. The results showed that two polymethoxy flavones with higher positive index values, i.e., nobiletin and tangeretin can significantly exert anti-inflammatory effects, while other compounds with negative values did not show any activity. In conclusion, our results indicated the proposed approach might be an efficient and rapid way to identify active constituents of TCM formulae.

A Network Pharmacology Approach to Evaluating the Efficacy of Chinese Medicine Using Genome-Wide Transcriptional Expression Data

The research of multicomponent drugs, such as in Chinese Medicine, on both mechanism dissection and drug discovery is challenging, especially the approaches to systematically evaluating the efficacy at a molecular level. Here, we presented a network pharmacology-based approach to evaluating the efficacy of multicomponent drugs by genome-wide transcriptional expression data and applied it to Shenmai injection (SHENMAI), a widely used Chinese Medicine composed of red ginseng (RG) and Radix Ophiopogonis (RO) in clinically treating myocardial ischemia (MI) diseases. The disease network, MI network in this case, was constructed by combining the protein-protein interactions (PPI) involved in the MI enriched pathways. The therapeutic efficacy of SHENMAI, RG, and RO was therefore evaluated by a network parameter, namely, network recovery index (NRI), which quantitatively evaluates the overall recovery rate in MI network. The NRI of SHENMAI, RG, and RO were 0.876, 0.494, and 0.269 respectively, which indicated SHENMAI exerts protective effects and the synergistic effect of RG and RO on treating myocardial ischemia disease. The successful application of SHENMAI implied that the proposed network pharmacology-based approach could help researchers to better evaluate a multicomponent drug on a systematic and molecular level.

Identifying roles of "Jun-Chen-Zuo-Shi" component herbs of QiShenYiQi formula in treating acute myocardial ischemia by network pharmacology.

BackgroundThe role of “Jun-Chen-Zuo-Shi” (also known as “sovereign-minister-assistant-courier”) component herbs of Chinese medicine is not fully understood. This study aims to test the “Jun-Chen-Zuo-Shi” rule with the QiShenYiQi formula (QSYQ) on treating acute myocardial ischemia (AMI) by a network pharmacology approach.MethodsAn Acute Myocardial Ischemia (AMI) specific Organism Disturbed Network (AMI-ODN), was constructed by integrating data of disease-associated genes, protein-protein interaction and microarray experiments. A network-based index, Network Recovery Index for Organism Disturbed Network (NRI-ODN), was developed to measure the therapeutic efficacy of QSYQ and its ingredients, i.e., the ability to recover disturbed AMI network model back to normal state.ResultsThe whole formula of QSYQ got a NRI-ODN score of 864.48, which outperformed all individual herbs. Additionally, the primary component herbs, Radix Astragalus membranaceus and Radix Salvia miltiorrrhiza showed NRI-DON score of 680.27 and 734.31 respectively, which meant a better performance to recover disturbed AMI network than the supplementary component herbs, Panax notoginseng and Dalbergia sissoo did (545.76 and 584.88, respectively).ConclusionAMI-ODN model and NRI-ODN identified the possible roles of “Jun-Chen-Zuo-Shi” component herbs of QSYQ in treating AMI at molecular network and pathway level.

Toxicogenomic analysis of the particle dose- and size-response relationship of silica particles-induced toxicity in mice

This study investigated the relationship between particle size and toxicity of silica particles (SP) with diameters of 30, 70, and 300 nm, which is essential to the safe design and application of SP. Data obtained from histopathological examinations suggested that SP of these sizes can all induce acute inflammation in the liver. In vivo imaging showed that intravenously administrated SP are mainly present in the liver, spleen and intestinal tract. Interestingly, in gene expression analysis, the cellular response pathways activated in the liver are predominantly conserved independently of particle dose when the same size SP are administered or are conserved independently of particle size, surface area and particle number when nano- or submicro-sized SP are administered at their toxic doses. Meanwhile, integrated analysis of transcriptomics, previous metabonomics and conventional toxicological results support the view that SP can result in inflammatory and oxidative stress, generate mitochondrial dysfunction, and eventually cause hepatocyte necrosis by neutrophil-mediated liver injury.

Relating anatomical therapeutic indications by the ensemble similarity of drug sets.

The anatomical therapeutic chemical (ATC) system is a world standard to define drug indications. Despite its broad applications in pharmaceutical and biomedical research, only a few studies that examine the relationships among ATC classes have been published. Here we present a similarity-based approach, named the indication similarity ensemble approach (iSEA), that innovatively correlates ATC classes by their drug set similarity. Our study demonstrated that iSEA was capable of relating ATC classes, and these relationships could accurately assign the right indications for approved drugs and make reasonable predictions about possible clinical indications for unclassified drugs, which would provide valuable information for drug repositioning. Additionally, on the basis of iSEA, we constructed the first ATC relationship network to reflect correlations among ATCs from a network view, which would further render novel insight to understand the intrinsic relationships in the ATC system.

Consensus Ranking Approach to Understanding the Underlying Mechanism With QSAR

Constructing a highly predictive model and exploiting the underlying mechanism associated with a specific property of chemicals are the two main goals of quantitative structure-activity relationship analysis (QSAR). However, the latter has long been carried out as a byproduct of model construction. Here we confirmed for the first time in this study that conventional descriptor selection methods designed to develop a best predictive model are likely not suitable for mechanistic analysis, i.e., the selected descriptors strongly depended on the selection of chemicals in the training sets. As an alternative, a consensus ranking protocol was proposed to select a robust descriptor set for mechanistic analysis, which can successfully overcome the above shortcoming. Moreover, the consistently inferior model performance using descriptors selected for mechanistic analysis suggested the irreplaceable role of model development in achieving models with the best predictive capability.

LTMap: a web server for assessing the potential liver toxicity by genome-wide transcriptional expression data

Toxicogenomics (TGx) has played a significant role in mechanistic research related with hepatotoxicity as well as liver toxicity prediction. Currently, several large‐scale preclinical TGx data sets were made freely accessible to the public, such as Open TG‐GATEs. With the availability of a sufficient amount of microarray data, it is important to integrate this information to provide new insights into the risk assessment of potential drug‐induced liver toxicity. Here we developed a web server for evaluating the potential liver toxicity based on genome‐wide transcriptomics data, namely LTMap. In LTMap, researchers could compare signatures of query compounds against a pregenerated signature database of 20 123 Affymetrix arrays associated with about 170 compounds retrieved from the largest public toxicogenomics data set Open TG‐GATEs. Results from this comparison may lead to the unexpected discovery of similar toxicological responses between chemicals. We validated our computational approach for similarity comparison using three example drugs. Our successful applications of LTMap in these case studies demonstrated its utility in revealing the connection of chemicals according to similar toxicological behaviors. Furthermore, a user‐friendly web interface is provided by LTMap to browse and search toxicogenomics data (http://tcm.zju.edu.cn/ltmap). Copyright

Determination of Minimum Training Sample Size for Microarray-Based Cancer Outcome Prediction–An Empirical Assessment

The promise of microarray technology in providing prediction classifiers for cancer outcome estimation has been confirmed by a number of demonstrable successes. However, the reliability of prediction results relies heavily on the accuracy of statistical parameters involved in classifiers. It cannot be reliably estimated with only a small number of training samples. Therefore, it is of vital importance to determine the minimum number of training samples and to ensure the clinical value of microarrays in cancer outcome prediction. We evaluated the impact of training sample size on model performance extensively based on 3 large-scale cancer microarray datasets provided by the second phase of MicroArray Quality Control project (MAQC-II). An SSNR-based (scale of signal-to-noise ratio) protocol was proposed in this study for minimum training sample size determination. External validation results based on another 3 cancer datasets confirmed that the SSNR-based approach could not only determine the minimum number of training samples efficiently, but also provide a valuable strategy for estimating the underlying performance of classifiers in advance. Once translated into clinical routine applications, the SSNR-based protocol would provide great convenience in microarray-based cancer outcome prediction in improving classifier reliability.

T2D@ZJU: a knowledgebase integrating heterogeneous connections associated with type 2 diabetes mellitus

Type 2 diabetes mellitus (T2D), affecting >90% of the diabetic patients, is one of the major threats to human health. A comprehensive understanding of the mechanisms of T2D at molecular level is essential to facilitate the related translational research. Here, we introduce a comprehensive and up-to-date knowledgebase for T2D, i.e. T2D@ZJU. T2D@ZJU contains three levels of heterogeneous connections associated with T2D, which is retrieved from pathway databases, protein–protein interaction databases and literature, respectively. In current release, T2D@ZJU contains 1078 T2D related entities such as proteins, protein complexes, drugs and others together with their corresponding relationships, which include 3069 manually curated connections, 14 893 protein–protein interactions and 26 716 relationships identified by text-mining technology. Moreover, T2D@ZJU provides a user-friendly web interface for users to browse and search data. A Cytoscape Web-based interactive network browser is available to visualize the corresponding network relationships between T2D-related entities. The functionality of T2D@ZJU is shown by means of several case studies. Database URL: http://tcm.zju.edu.cn/t2d