Uncovering the role of traditional Chinese medicine in immune-metabolic balance of gastritis from the perspective of Cold and Hot: Jin Hong Tablets as a case study.

Chronic gastritis (CG) is a common inflammatory disease of chronic inflammatory lesion of gastric mucosa and in the diagnosis of gastritis in traditional Chinese medicine (TCM), CG can be classified into Cold ZHENG (syndrome in TCM) and Hot ZHENG. However, the molecular features of Cold/Hot ZHENG in CG and the mechanism of Cold/Hot herbs in formulae for CG remained unclear. In this study, we collected a transcriptomics data including 35 patients of Cold/Hot ZHENG CG and 3 scRNA-seq CG samples. And 25 formulae for CG and 89 herbs recorded in these formulae were also collected. We conduct a comprehensive analysis based on the combination of transcriptomics datasets and machine learning algorithms, to discover biomarkers for Cold/Hot ZHENG CG. Then the target profiles of the collected formulae and Cold/Hot herbs were predicted to uncover the features and biomarkers of them against Cold/Hot ZHENG CG. These biomarkers suggest that Hot ZHENG CG might be characterized by over-inflammation and exuberant metabolism, and Cold ZHENG CG showed a trend of suppression in immune regulation and energy metabolism. Biomarkers and specific pathways of Hot herbs tend to regulate immune responses and energy metabolism, while those of Cold herbs are more likely to participate in anti-inflammatory effects. Finally, the findings were verified based on public transcriptomics datasets, as well as transcriptomics and ELISA detection, taking Jin Hong tablets as a case study. Biomarkers like leptin and IL-6 together with proportions of immune cells showed significant changes after the intervention. These findings might reflect the mechanism and build a bridge between macro and micro views of Cold/Hot ZHENG as well as Cold/Hot herbs.

Resveratrol Inhibits Nasopharyngeal Carcinoma (NPC) by Targeting the MAPK Signaling Pathway.

BACKGROUND: With conventional cancer treatments facing limitations, interest in plant-derived natural products as potential alternatives is increasing. Although resveratrol has demonstrated antitumor effects in various cancers, its impact and mechanism on nasopharyngeal carcinoma remain unclear. OBJECTIVE: This study aimed to systematically investigate the anti-cancer effects of resveratrol on nasopharyngeal carcinoma using a combination of experimental pharmacology, network pharmacology, and molecular docking approaches. METHODS: CCK-8, scratch wound, and transwell assays were employed to confirm the inhibitory effect of resveratrol on the proliferation, migration, and invasion of nasopharyngeal carcinoma cells. H&E and TUNEL stainings were used to observe the morphological changes and apoptosis status of resveratrol-treated cells. The underlying mechanisms were elucidated using a network pharmacology approach. Immunohistochemistry and Western blotting were utilized to validate key signaling pathways. RESULTS: Resveratrol inhibited the proliferation, invasion, and migration of nasopharyngeal carcinoma cells, ultimately inducing apoptosis in a time- and dose-dependent manner. Network pharmacology analysis revealed that resveratrol may exert its anti-nasopharyngeal carcinoma effect mainly through the MAPK pathway. Immunohistochemistry results from clinical cases showed MAPK signaling activation in nasopharyngeal carcinoma tissues compared to adjacent tissues. Western blotting validated the targeting effect of resveratrol, demonstrating significant inhibition of the MAPK signaling pathway. Furthermore, molecular docking supported its multi-target role with MAPK, TP53, PIK3CA, SRC, etc. Conclusion: Resveratrol has shown promising potential in inhibiting human nasopharyngeal carcinoma cells by primarily targeting the MAPK pathway. These findings position resveratrol as a potential therapeutic agent for nasopharyngeal carcinoma.

Optimizing Genomic Control in Hit Network-Target Set Model Associations with Lung Adenocarcinoma.

Background: Hit network-target sets (HNSs), compiled sets of different network nodes of the same type, are available and play a significant role in cancer development but are notoriously more difficult to select than a single target. This is due to a combination of challenges attributed to the differential of node interactions, node heterogeneity, and the limitations of node-hit information. Methods: In this study, we constructed a lung adenocarcinoma regulatory network using TCGA data and obtained different HNSs of driver nodes (DNs), core modules (CMs) and core nodes (CNs) through three kinds of methods. Then, the optimized HNS (OHNS) was obtained by integrating CMs, CNs and DNs, and the performance of different HNSs was evaluated according to network structure importance, control capability, and clinical value. Results: We found that the OHNS has two main advantages, the central location of the network and the ability to control the network, and it plays an important role in the disease network through its multifaceted capabilities. Three unique pathways were discovered in the OHNS, which is consistent with previous experiments. Additionally, 13 genes were predicted to play roles in risk prognosis, disease drivers, and cell perturbation effects of lung adenocarcinoma, of which 12 may be candidates for new drugs and biomarkers of lung adenocarcinoma. Conclusion: This study can help us understand and control a network more effectively to determine the development trend of a disease, design effective multitarget drugs, and guide the therapeutic community to optimize appropriate strategies according to different research aims in cancer treatment.

Network pharmacology: towards the artificial intelligence-based precision traditional Chinese medicine.

Network pharmacology (NP) provides a new methodological perspective for understanding traditional medicine from a holistic perspective, giving rise to frontiers such as traditional Chinese medicine network pharmacology (TCM-NP). With the development of artificial intelligence (AI) technology, it is key for NP to develop network-based AI methods to reveal the treatment mechanism of complex diseases from massive omics data. In this review, focusing on the TCM-NP, we summarize involved AI methods into three categories: network relationship mining, network target positioning and network target navigating, and present the typical application of TCM-NP in uncovering biological basis and clinical value of Cold/Hot syndromes. Collectively, our review provides researchers with an innovative overview of the methodological progress of NP and its application in TCM from the AI perspective.

Identify Biomarkers and Design Effective Multi-Target Drugs in Ovarian Cancer: Hit Network-Target Sets Model Optimizing.

Epithelial ovarian cancer (EOC) is highly aggressive with poor patient outcomes, and a deeper understanding of ovarian cancer tumorigenesis could help guide future treatment development. We proposed an optimized hit network-target sets model to systematically characterize the underlying pathological mechanisms and intra-tumoral heterogeneity in human ovarian cancer. Using TCGA data, we constructed an epithelial ovarian cancer regulatory network in this study. We use three distinct methods to produce different HNSs for identification of the driver genes/nodes, core modules, and core genes/nodes. Following the creation of the optimized HNS (OHNS) by the integration of DN (driver nodes), CM (core module), and CN (core nodes), the effectiveness of various HNSs was assessed based on the significance of the network topology, control potential, and clinical value. Immunohistochemical (IHC), qRT-PCR, and Western blotting were adopted to measure the expression of hub genes and proteins involved in epithelial ovarian cancer (EOC). We discovered that the OHNS has two key advantages: the network's central location and controllability. It also plays a significant role in the illness network due to its wide range of capabilities. The OHNS and clinical samples revealed the endometrial cancer signaling, and the PI3K/AKT, NER, and BMP pathways. MUC16, FOXA1, FBXL2, ARID1A, COX15, COX17, SCO1, SCO2, NDUFA4L2, NDUFA, and PTEN hub genes were predicted and may serve as potential candidates for new treatments and biomarkers for EOC. This research can aid in better capturing the disease progression, the creation of potent multi-target medications, and the direction of the therapeutic community in the optimization of effective treatment regimens by various research objectives in cancer treatment.

[A new approach for optimizing empirical prescriptions of famous physicians based on network target: taking Qingluo Decoction as an example].

Based on the network target approach and technology, this study proposed for the first time a novel optimization method for Chinese medicine formulae. Moreover, with Qingluo Decoction as an example, a method for the research and development of Chinese medicine, which combines scientific methodology and experience of famous doctors, was developed. Specifically, based on the composition of Qingluo Decoction, this study used the using network target for intelligent and quantitative analysis on drug actions(UNIQ) to predict the medicinals that targeted the key pathways of rheumatoid arthritis(RA) such as angiogenesis. Then, combining the experience of the first national Chinese medical master LI Ji-ren and Aihui famous Chinese medicine doctor LI Yan and Chinese medicine theory, this study developed a novel angiogenesis-targeted prescription modified Qingluo Decoction(MQLD). Afterward, the clinical efficacy and mechanism of MQLD were verified. The results showed that 27 medicinals with significant regulatory effect on angiogenesis-related key signaling pathways were identified by UNIQ, among which 6 were selected by the Chinese medicine physicians to develop the MQLD. Clinical trials demonstrated that the clinical efficacy of MQLD, in terms of either American College of Rheumatology 20% improvement and 50% improvement criteria(ACR20, ACR50) or TCM syndrome evaluation, was better than that of Qingluo Decoction. Experimental study revealed that MQLD can inhibit RA angiogenesis by acting on the vascular endothelial growth factor(VEGF) pathway, nuclear factor κB(NF-κB) pathway, inflammatory cytokine release, and immune cell regulation. Taken together, this study developed a new formula MQLD with improved clinical efficacy, precise applicable clinical settings, and authorized patent through the network target technology, thus providing a new way for the precise development of Chinese medicine and preservation of the experience of famous physicians.

[Traditional Chinese medicine network pharmacology: development in new era under guidance of network pharmacology evaluation method guidance].

Traditional Chinese medicine(TCM) has unique advantages in the prevention and treatment of diseases owing to its holistic view and more than 2 000 years of experience in the clinical use of natural medicine. The "holistic" characteristic of TCM gives birth to a new generation of research paradigm featuring "network" and "system", which has been developing rapidly in the era of biomedical big data and artificial intelligence. Network pharmacology, a representative research field, provides new ideas and methods for the research of the interdiscipline of artificial intelligence and medicine, the analysis of massive biomedical data, and the transformation from data to knowledge. TCM plays an important role in proposing the core theory of "network target" and promoting the establishment and development of network pharmacology, and has taken the lead in formulating the first international standard of network pharmacology--Network Pharmacology Evaluation Method Guidance. In terms of theory, network target can systematically link drugs and diseases and quantitatively interpret the overall regulatory mechanism of drugs. In the aspect of method, network pharmacology is developing towards a research model that combines computational, experimental, and clinical approaches. This review introduces the resent important progress of TCM network pharmacology in predicting drug targets, understanding the biological basis of drugs and diseases, and searching for disease and syndrome biomarkers. Under the guidance of Network Pharmacology Evaluation Method Guidance, the development of network pharmacology is expected to become more and more standardized and healthy. Network target will help produce more high-quality research outcomes in TCM and effectively boost the modernization and internationalization of TCM.

[Biological connotation of four traditional Chinese medicine syndromes of rheumatoid arthritis based on "disease-syndrome-symptom" association network].

The present study explored the biological connotation of traditional Chinese medicine(TCM) syndromes of rheumatoid arthritis(RA) from the "disease-syndrome-symptom" association network. RA patients with four TCM syndromes(dampness-heat obstruction, phlegm-stasis obstruction, Qi-blood deficiency, and liver and kidney deficiency), three for each type, were assigned as the RA TCM syndrome group, and three healthy volunteers as the normal control group. The differential gene sets of four syndromes were screened out through transcriptome expression profiling and bioinformatics mining. The relevant gene sets of syndrome-related clinical symptoms were collected from TCMIP v2.0(http://www.tcmip.cn/). The "disease-syndrome-symptom" association networks of four RA syndromes were established by using the intersection genes of syndrome-related differential genes and symptom-related genes, and the key network target genes of each syndrome were screened out and the corresponding biological functions were mined through topological feature calculation and enrichment analysis. The genes associated with clinical symptoms such as vasculitis, joint pain, and fever in the damp-heat obstruction syndrome ranked the top, and the key network target genes of this syndrome were most significantly enriched in the pathways related to material and energy metabolism and thermal reaction biological processes. The clinical symptom-related genes of the phlegm-stasis obstruction syndrome were most significantly enriched in the pathways related to "immunity-inflammation", nervous system regulation, and sensory response. The clinical symptoms such as hypoglycemia, hypotension, weight loss, palpitation, and arrhythmia in Qi-blood deficiency syndrome were predominant, and its key network target genes were most significantly enriched in the pathways related to the nervous system and "immunity-inflammation" response. The abnormal symptoms in the liver and kidney in the liver and kidney deficiency syndrome were commonly seen, and its key network target genes were most significantly enriched in the "immunity-inflammation" regulatory pathways, and liver and kidney development and metabolic response. In conclusion, the differences and connections of the biological basis between different TCM syndromes of RA are in line with the theoretical interpretation of TCM on the etiology and pathogenesis of RA. This study summarized the objective essence of syndromes to a certain extent from the "disease-syndrome-symptom" association network and is expected to provide a theoretical basis for the discovery of serum biomarkers of RA syndromes.

Deciphering the Pharmacological Mechanisms of Guizhi-Fuling Capsule on Primary Dysmenorrhea Through Network Pharmacology.

Guizhi-Fuling capsule (GZFLC), originated from a classical traditional Chinese herbal formula Guizhi-Fuling Wan, has been clinically used for primary dysmenorrhea in China. Nonetheless, the underlying pharmacological mechanisms of GZFLC remain unclear. The integration of computational and experimental methods of network pharmacology might be a promising way to decipher the mechanisms. In this study, the target profiles of 51 representative compounds of GZFLC were first predicted by a high-accuracy algorithm, drugCIPHER-CS, and the network target of GZFLC was identified. Then, potential functional modules of GZFLC on primary dysmenorrhea were investigated using functional enrichment analysis. Potential bioactive compounds were recognized by hierarchical clustering analysis of GZFLC compounds and first-line anti-dysmenorrhea drugs. Furthermore, the potential anti-dysmenorrhea mechanisms of GZFLC were verified through enzyme activity assays and immunofluorescence tests. Moreover, effects of GZFLC on primary dysmenorrhea were evaluated in oxytocin-induced dysmenorrhea murine model. In the network target analysis, GZFLC may act on five functional modules of pain, inflammation, endocrine, blood circulation and energy metabolism. Integrating computational and experimental approaches, we found that GZFLC significantly inhibited the writhing response and reduced the degree of uterine lesions in oxytocin-induced dysmenorrhea murine model. Furthermore, GZFLC may partially alleviate primary dysmenorrhea by inhibiting cyclooxygenase 2 (COX2) and downregulating MAPK signaling pathway. Consequently, GZFLC presented pain relief and sustained benefits for primary dysmenorrhea. This study could provide a scientific approach for deciphering pharmacological mechanisms of herbal formulae through network pharmacology.

TCM network pharmacology: A new trend towards combining computational, experimental and clinical approaches.

Traditional Chinese medicine (TCM) is a precious treasure of the Chinese nation and has unique advantages in the prevention and treatment of diseases. The holistic view of TCM coincides with the new generation of medical research paradigm characterized by network and system. TCM gave birth to a new method featuring holistic and systematic "network target", a core theory and method of network pharmacology. TCM is also an important research object of network pharmacology. TCM network pharmacology, which aims to understand the network-based biological basis of complex diseases, TCM syndromes and herb treatments, plays a critical role in the origin and development process of network pharmacology. This review introduces new progresses of TCM network pharmacology in recent years, including predicting herb targets, understanding biological foundation of diseases and syndromes, network regulation mechanisms of herbal formulae, and identifying disease and syndrome biomarkers based on biological network. These studies show a trend of combining computational, experimental and clinical approaches, which is a promising direction of TCM network pharmacology research in the future. Considering that TCM network pharmacology is still a young research field, it is necessary to further standardize the research process and evaluation indicators to promote its healthy development.

An integrated approach to uncover quality markers of Traditional Chinese medicine underlying chemical profiling, network target selection and metabolomics approach: Guan-Xin-Jing capsule as a model.

The "multi-component and multi-target" property of traditional Chinese medicine (TCM) makes the quality control more difficult, which remains a key concern hindering the space of progress in its modernization and globalization. This study aims to provide a new strategy to discover quality markers related to the efficacy and stability of TCM based on the bioactive markers finding and differential markers finding, Guan-Xin-Jing capsule (GXJC) was used as a model. Firstly, the UHPLC-QTOF-MS/MS was performed to identify the chemical profiling of GXJC and 148 compounds were unequivocally or tentatively identified. Subsequently, the compounds with relatively low intensity, low oral bioavailability and unsuited drug likeness were removed. In addition, a network based approach by integrating candidate compound genes and disease genes was applied to further filter out the potential active compounds against coronary heart disease (CHD) and finally 7 compounds were identified as the potential bioactive markers. Moreover, to ensure the batch-to-batch consistency of GXJC, an untargeted UHPLC-QTOF-MS/MS based metabolomics approach and multivariate statistical analysis was performed and 5 compounds were identified as the potential differential markers to illustrate the discrepancy among 27 batches of GXJC. Using these strategies above, a total of 11 chemical compounds in GXJC were screened out as quality markers(Q-markers). The information generated with our strategy should be more helpful to guide the selection of the Q-markers and lay the foundation for quality control of TCM.

Network-based cancer precision medicine: A new emerging paradigm.

The complex interactions in biological systems have been shown to affect the response to single-targeted therapies which were initially developed under the "reductionist paradigm" of cancer precision medicine. To address these fundamental challenges, great efforts have been dedicated from a network perspective to explore the mechanisms underlying tumorigenesis and progression and to extend our understanding of cancer as a complex disease, which is exploiting new advances in cancer diagnosis, prevention, and treatment. This review summarizes recent progress of network applications in cancer precision medicine research, including biomarker identification, cancer patient stratification and network target recognition, highlights network-based systematic integrations across macro and micro networks, and discusses the tremendous potential of this new emerging network-based "systems paradigm" for precision medicine, which would ultimately make substantial progress for fighting cancer.

A strategy for the discovery and validation of toxicity quality marker of Chinese medicine based on network toxicology.

BACKGROUND: Considering that the quality control indicators in Chinese medicine (CM) are disconnected from safety and effectiveness, Prof. Chang-xiao Liu et al. has proposed a concept regarding the quality marker (Q-marker) of CM to promote the healthy development of the CM industry and improve the CM quality control method. PURPOSE: In this study, we proposed a strategy to discover and verify the toxicity Q-marker of CM based on network toxicology. METHODS: First, traditional biochemical pathology indicators and sensitive biomarkers were used to predict the toxicity of CM. Next, the chemical composition of toxic CMs and their metabolites were rapidly identified by multidimensional detection techniques. Subsequently, the interaction network between "toxicity - toxic chemical composition - toxic target - effect pathway" was built through network toxicology, and the potential toxicity Q-marker of CM was initially screened. Finally, the chemical properties of toxicity Q-markers were verified by traceability and testability. RESULTS: Based on the predicted results of network toxicology, the toxic compounds of CM were preliminarily identified, and the toxic mechanism was comprehensively interpreted. In the context of definite biological properties and chemical properties, the toxicity Q-marker was finally confirmed. CONCLUSION: This extensive review provides a study method for the toxicity Q-marker of CM, which helps to systemically and thoroughly reveal the internal toxicity mechanism of CM. The in-depth study of the toxicity Q-marker provides the material basis and technical support for the safety evaluation of CM.

Network Pharmacology to Unveil the Biological Basis of Health-Strengthening Herbal Medicine in Cancer Treatment.

Health-strengthening (Fu-Zheng) herbs is a representative type of traditional Chinese medicine (TCM) widely used for cancer treatment in China, which is in contrast to pathogen eliminating (Qu-Xie) herbs. However, the commonness in the biological basis of health-strengthening herbs remains to be holistically elucidated. In this study, an innovative high-throughput research strategy integrating computational and experimental methods of network pharmacology was proposed, and 22 health-strengthening herbs were selected for the investigation. Additionally, 25 pathogen-eliminating herbs were included for comparison. First, based on network-based, large-scale target prediction, we analyzed the target profiles of 1446 TCM compounds. Next, the actions of 166 compounds on 420 antitumor or immune-related genes were measured using a unique high-throughput screening strategy by high-throughput sequencing, referred to as HTS². Furthermore, the structural information and the antitumor activity of the compounds in health-strengthening and pathogen-eliminating herbs were compared. Using network pharmacology analysis, we discovered that: (1) Functionally, the predicted targets of compounds from health strengthening herbs were enriched in both immune-related and antitumor pathways, similar to those of pathogen eliminating herbs. As a case study, galloylpaeoniflorin, a compound in a health strengthening herb Radix Paeoniae Alba (Bai Shao), was found to exert antitumor effects both in vivo and in vitro. Yet the inhibitory effects of the compounds from pathogen eliminating herbs on tumor cells proliferation as a whole were significantly stronger than those in health-strengthening herbs (p < 0.001). Moreover, the percentage of assay compounds in health-strengthening herbs with the predicted targets enriched in the immune-related pathways (e.g., natural killer cell mediated cytotoxicity and antigen processing and presentation) were significantly higher than that in pathogen-eliminating herbs (p < 0.05). This finding was supported by the immune-enhancing effects of a group of compounds from health-strengthening herbs indicated by differentially expressed genes in the HTS² results. (2) Compounds in the same herb may exhibit the same or distinguished mechanisms in cancer treatment, which was demonstrated as the compounds influence pathway gene expressions in the same or opposite directions. For example, acetyl ursolic acid and specnuezhenide in a health-strengthening herb Fructus Ligustri lucidi (Nv Zhen Zi) both upregulated gene expressions in T cell receptor signaling pathway. Together, this study suggested greater potentials in tumor immune microenvironment regulation and tumor prevention than in direct killing tumor cells of health-strengthening herbs generally, and provided a systematic strategy for unveiling the commonness in the biological basis of health-strengthening herbs in cancer treatment.

Corrigendum: Yin-Chen-Hao Tang Attenuates Severe Acute Pancreatitis in Rat: An Experimental Verification of In silico Network Target Prediction.

[This corrects the article DOI: 10.3389/fphar.2016.00378.].

Identifying potential quality markers of Xin-Su-Ning capsules acting on arrhythmia by integrating UHPLC-LTQ-Orbitrap, ADME prediction and network target analysis.

BACKGROUND: Quality marker (Q-markers) has been proposed as a novel concept for quality evaluation and standard elaboration of traditional Chinese medicine (TCM). Xin-Su-Ning capsule (XSNC) has been extensively used for the treatment of arrhythmia with the satisfactory therapeutic effects in clinics. However, it is lack of reliable and effective Q-markers of this prescription. PURPOSE: To identify potential Q-markers of XSNC against arrhythmia. STUDY DESIGN: An integrative pharmacology-based investigation was performed. METHODS: Ultra-high-pressure liquid chromatography coupled with linear ion trap-Orbitrap tandem mass spectrometry (UHPLC-LTQ-Orbitrap) was performed to identify the preliminary chemical profile of XSNC in a rapid and high-throughput manner. Then, in silico Absorption-Distribution-Metabolism-Excretion (ADME) models were utilized to screen candidate active chemical compounds characterized by drug-likeness features. In addition, drug target-disease gene interaction network was constructed, and network features were calculated to identify key candidate targets and the potential Q-markers of XSNC against arrhythmia. RESULTS: A total of 41 chemical compounds with good drug-likeness and more chances to be absorbed into body were identified as the candidate bioactive chemical compounds which might offer contributions to the therapeutic effects of XSNC against arrhythmia in vivo. Following the prediction of 921 XSNC putative targets and the construction of XSNC putative target-known therapeutic target of arrhythmia interaction network, 315 hub nodes with high connectivity were selected. Functionally, the hub nodes were involved into modulation of cardiac sympatho-vagal balance, regulation of energy production and metabolism, as well as angiogenesis and vascular circulation during the development and progression of arrhythmia. Moreover, 63 major hubs with network topological importance were chosen as XSNC candidate targets against arrhythmia. Furthermore, berberine, palmatine, scopoletin, liquiritigenin, naringenin, formononetin, nobiletin, tangeretin, 5-demethylnobiletin, kushenol E and kurarinone hitting the corresponding XSNC candidate targets were screened out to be the potential Q-markers of XSNC against arrhythmia. CONCLUSION: Our integrative pharmacology-based approach combining UHPLC-LTQ-Orbitrap, in silico ADME prediction and network target analysis may be efficient to identify potential Q-markers of TCM prescriptions. Our data showed that berberine, palmatine, scopoletin, liquiritigenin, naringenin, formononetin, nobiletin, tangeretin, 5-demethylnobiletin, kushenol E and kurarinone might function as candidate markers for qualitative evaluation of XSNC.

Matrine Is Identified as a Novel Macropinocytosis Inducer by a Network Target Approach.

Comprehensively understanding pharmacological functions of natural products is a key issue to be addressed for the discovery of new drugs. Unlike some single-target drugs, natural products always exert diverse therapeutic effects through acting on a "network" that consists of multiple targets, making it necessary to develop a systematic approach, e.g., network pharmacology, to reveal pharmacological functions of natural products and infer their mechanisms of action. In this work, to identify the "network target" of a natural product, we perform a functional analysis of matrine, a marketed drug in China extracted from a medical herb Ku-Shen (Radix Sophorae Flavescentis). Here, the network target of matrine was firstly predicted by drugCIPHER, a genome-wide target prediction method. Based on the network target of matrine, we performed a functional gene set enrichment analysis to computationally identify the potential pharmacological functions of matrine, most of which are supported by the literature evidence, including neurotoxicity and neuropharmacological activities of matrine. Furthermore, computational results demonstrated that matrine has the potential for the induction of macropinocytosis and the regulation of ATP metabolism. Our experimental data revealed that the large vesicles induced by matrine are consistent with the typical characteristics of macropinosome. Our verification results also suggested that matrine could decrease cellular ATP level. These findings demonstrated the availability and effectiveness of the network target strategy for identifying the comprehensive pharmacological functions of natural products.

[Exploiture and application of an internet-based Computation Platform for Integrative Pharmacology of Traditional Chinese Medicine].

Recently, integrative pharmacology(IP) has become a pivotal paradigm for the modernization of traditional Chinese medicines(TCM) and combinatorial drugs discovery, which is an interdisciplinary science for establishing the in vitro and in vivo correlation between absorption, distribution, metabolism, and excretion/pharmacokinetic(ADME/PK) profiles of TCM and the molecular networks of disease by the integration of the knowledge of multi-disciplinary and multi-stages. In the present study, an internet-based Computation Platform for IP of TCM(TCM-IP, www.tcmip.cn) is established to promote the development of the emerging discipline. Among them, a big data of TCM is an important resource for TCM-IP including Chinese Medicine Formula Database, Chinese Medical Herbs Database, Chemical Database of Chinese Medicine, Target Database for Disease and Symptoms, et al. Meanwhile, some data mining and bioinformatics approaches are critical technology for TCM-IP including the identification of the TCM constituents, ADME prediction, target prediction for the TCM constituents, network construction and analysis, et al. Furthermore, network beautification and individuation design are employed to meet the consumer's requirement. We firmly believe that TCM-IP is a very useful tool for the identification of active constituents of TCM and their involving potential molecular mechanism for therapeutics, which would wildly applied in quality evaluation, clinical repositioning, scientific discovery based on original thinking, prescription compatibility and new drug of TCM, et al.