ABSTRACT
Objective:Metabolomics was used to analyze the brain tissue samples of model mice with chronic unpredictable mild stress (CUMS) depression, in order to find out the differential metabolites related to depression and to explore the possible antidepressant mechanism of iridoid part of Valerianae Jatamansi Rhizoma et Radix (IEFV). Method:Forty-two Kunming mice were randomly divided into 6 groups, including the normal group, the model group, the fluoxetine group (2.5 mg·kg-1) and the IEFV low, medium, and high dose groups (doses were 5.73, 11.47, 22.94 mg·kg-1, respectively). The behavioral and biochemical indicators of CUMS model mice were used for pharmacodynamic evaluation with IEFV and a positive drug (fluoxetine) as the intervention drugs. Then, the effect of IEFV on endogenous substances of the brain tissue in CUMS model mice were analyzed by nuclear magnetic resonance hydrogen spectrum (1H-NMR) metabolomics, and multivariate statistical analysis was used to identify the differential metabolites and to enrich the metabolic pathways involved in the differential metabolites. Result:After modeling, the immobility time of the model mice increased significantly, their sucrose preference rate and the excitatory neurotransmitters [5-hydroxytryptamine (5-HT) and norepinephrine (NE)] decreased significantly, indicating the success of modeling. The depression was relieved after IEFV administration, mainly manifested by the recovery of the immobility time, sucrose preference rate and the excitatory neurotransmitters (5-HT and NE). Principal component analysis (PCA) of endogenous metabolites in brain tissue showed that the model group could be significantly separated from the normal group, while the IEFV groups and fluoxetine group all showed a trend of deviating from the model group to the normal group, which was consistent with the behavioral results. The results of orthogonal partial least squares discriminant analysis (OPLS-DA) showed that there were 16 different metabolites between the model group and the normal group, including 12 water-soluble metabolites and 4 liposoluble metabolites. Seven potential metabolism pathways were obtained through MetPA analysis, including metabolism of phenylalanine, metabolism of phenylalanine, tyrosine and tryptophan, metabolism of taurine and hypotaurine acid, metabolism of alanine, aspartic acid and glutamic acid, biosynthesis of valine, leucine and isoleucine, metabolism of D-glutamine and D-glutamate and tricarboxylic acid cycle (TCA). IEFV-high dose group could significantly recall 11 differential metabolites. Conclusion:IEFV may play an antidepressant role mainly by affecting energy metabolism, amino acid metabolism and neurotransmitter levels, which provides a reference for further study on the antidepressant mechanism of IEFV.
ABSTRACT
Objective: To predict the possible quality markers of ginseng in the treatment of heart failure. Methods: The ginseng chemical information was used to predict its putative targets related to heart failure by TCMIP V2.0 and the protein-protein interaction (PPI) network was constructed. The key targets of drug intervention on heart failure were enriched. The interaction network of chemical components-key targets-pathways was constructed to obtain the main components acting on these key targets, which are related to drug efficacy. According to the five principles of quality markers identification, we analyzed the quality markers of ginseng in the treatment of heart failure. Results: A total of 63 key targets were obtained for ginseng in the treatment of heart failure, including 63 putative drug targets and two targets related to disease. ATP1A1 and ADCY2 are the common targets associated with the drug and disease. The common targets of ATP1A1 and ADCY2 may be the key targets of drug acting on disease. The main components of ginseng acting on these common targets were screened out, and then we have determined the possible quality markers of ginseng for the treatment of heart failure based on the five principles of quality markers. Conclusion: We obtained the possible quality markers of ginseng in the treatment of heart failure, including ginsenoside Rg1, ginsenoside Re, ginsenoside Rf, and ginsenoside Rb2, which provided the basis for our deeper research of ginseng in the treatment of heart failure.
ABSTRACT
Shengmaiyin is widely used in the treatment of arrhythmia and has achieved a good effect. Due to the complexity of traditional Chinese medical formula, the pharmacological mechanism of Shengmaiyin in the treatment of arrhythmia is unclear. In this study, we used the internet-based Computation Platform (www.tcmip.cn) to explore the molecular mechanism. Shengmaiyin was found to treat the arrhythmia by modulating the pathway related to energy metabolism such as carbon metabolism, purine metabolism, carbohydrate metabolism, or by regulating the level of ATP. In this study, we find that the main active drug component in Shengmaiyin may be ginseng.
ABSTRACT
Integrative pharmacology (IP) is a discipline that studies the interaction, integration and principle of action of multiple components with the body, emphasizing the integrations of multi-level and multi-link, such as "whole and part", " and ", " process and activity evaluation". After four years of development and practice, the theory and method of IP has received extensive attention and application.In order to better promote the development of IP, this paper systematically reviews the concepts, research contents, research methods and application fields about IP.
ABSTRACT
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.