• Users Online: 137
  • Print this page
  • Email this page

 
Table of Contents
REVIEW ARTICLE
Year : 2020  |  Volume : 6  |  Issue : 3  |  Page : 260-270

Strategy of systems biology for visualizing the “Black box” of traditional Chinese medicine


1 Institute of Interdisciplinary Integrative Medicine Research, Shanghai University of Traditional Chinese Medicine, Shanghai, China
2 Institute of Interdisciplinary Integrative Medicine Research, Shanghai University of Traditional Chinese Medicine; Department of Phytochemistry, College of Pharmacy, Second Military Medical University, Shanghai, China

Date of Submission02-Jan-2020
Date of Acceptance01-May-2020
Date of Web Publication05-Aug-2020

Correspondence Address:
Prof. Hou-Kai Li
Institute of Interdisciplinary Integrative Medicine Research, Shanghai University of Traditional Chinese Medicine, Shanghai 201203
China
Prof. Wei-Dong Zhang
Institute of Interdisciplinary Integrative Medicine Research, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China; Department of Phytochemistry, College of Pharmacy, Second Military Medical University, Shanghai 200433
China
Login to access the Email id

Source of Support: None, Conflict of Interest: None


DOI: 10.4103/wjtcm.wjtcm_31_20

Rights and Permissions
  Abstract 


Traditional Chinese medicine (TCM) has been practiced for thousands of years in China. TCM formula, usually composed of several or even dozens of herbal medicines, is the main form of TCM practicing, which is extremely complex due to multiple components and therapeutic targets, especially the characteristics of formula compatibility. Thus, it is an enormous challenge for the modernization of TCM. Systems biology is a strategy for investigating the complex interactions between genes, mRNA, proteins, and metabolites by using integrated omics approaches. In recent years, systems biology has been increasingly adopted in TCM study. This review comprehensively summarized status of syndrome and application of TCM formulae in clinical and preclinical studies and discussed the advances of systems biology in TCM research. Then, a “Disease-Syndrome-Formulae-Effect” strategy was proposed for TCM research. Combination of systems biology and “Disease-Syndrome-Formulae-Effect” strategy provided a novel approach to understand the complex interactions among biological systems, drugs, and complex diseases from a network perspective, thus facilitating the modernization of TCM. The objective of this manuscript is to provide comprehensive and up-to-date review on the application of systems biology in TCM research, as well as the perspective of TCM modernization with systems biology.

Keywords: Formula, omics, systems biology, traditional Chinese medicine


How to cite this article:
Gu Y, Wu GS, Li HK, Zhang WD. Strategy of systems biology for visualizing the “Black box” of traditional Chinese medicine. World J Tradit Chin Med 2020;6:260-70

How to cite this URL:
Gu Y, Wu GS, Li HK, Zhang WD. Strategy of systems biology for visualizing the “Black box” of traditional Chinese medicine. World J Tradit Chin Med [serial online] 2020 [cited 2020 Dec 1];6:260-70. Available from: https://www.wjtcm.net/text.asp?2020/6/3/260/291403




  Introduction Top


“One target, one component” mode is classical mode for drug development for dozens of years, especially in Western medicine. However, this classical strategy encounters huge challenge nowadays because there are few diseases that are due to the abnormity of single gene.[1],[2],[3],[4] Recently, multitargeted agents have been used to treat disease with better efficacy and safety, shifting the therapeutic approach from a “one target, one drug” mode to a “network target, multiple component therapeutics” mode.[5],[6],[7] For example, a combination of metformin and aspirin can produce a synergistic effect on pancreatic cancer by action at different targets.[8] Sunitinib is a multitargeted kinase inhibitor used for renal cell carcinoma therapy.[9] Thus, a network-targeted combination would be a better therapy for the treatment of some diseases. The human body is not only a complex and highly connected system but also dynamically adjusts within the boundary, that is, the so-called homeostasis.[10] A certain physiological or pathological phenomenon should not be treated as a separate entity, but rather it should be treated and prevented from a holistic perspective, which is similar with the “holistic view” in TCM.[11]

TCM has been developed and practiced for thousands of years in China, which has made great contribution for the prevention and treatment of disease in China and even Asian countries.[4],[12],[13],[14],[15] There are a variety of theories in TCM such as “Yin-Yang” and “Five Elements.[16] The practice of TCM is mainly through prescription of formulas, which usually compose two or more medicines (including herbs, animals, and minerals) and contain hundreds or even thousands of components.[17],[18],[19] Actually, TCM formula is a “black box” in terms of its complex components and mechanisms. It is almost impossible to understand the mechanism of TCM with the reductionism-oriented approaches.

Although some advances have been made such as active component and therapeutic target identification of TCM, there is still a long way to go for elucidating the exact mechanisms underlying TCM practice. As a result, systems biology is highly valued for bridging TCM with modern science.[20] Systems biology is focused on studying the interactions among molecules, cells, and organs and integrates different types of biological information (genomes, RNAs, proteins, metabolites, phenotypes, and so on) based on high-throughput platforms and computational and mathematical modeling.[21] High-throughput techniques and information-rich assays can be used to decipher the pharmacodynamic basis and quality control of formulae[18],[19] and shift the research paradigm from “one target, one component” mode to “network target, multicomponent” mode.[5] In this review, we mainly discussed the characteristics of TCM theory and omics-based systems biology approaches. We also proposed a model of “Disease-Syndrome-Formulae-Effect” for TCM study.


  Characteristics of Traditional Chinese Medicine Top


TCM is a systemic medicine and views human body as a wholeness [Figure 1].[22] There are two core characteristics of TCM: (1) Holism. TCM believes that the human body is an organic whole, which is composed of organs and tissues. Each tissue and organ have its unique functions to maintain the health of body; thus, TCM treatment emphasizes restoration of homeostasis of the whole person instead of curing the ill organs and (2) syndrome differentiation and treatment (meaning TCM treatment according to syndrome differentiation). Syndrome, also called “Zheng” in Chinese, is another core characteristic in TCM, which is different with the “syndrome of disease” in Western medicine. Syndrome or Zheng is a phenotypic summary during the development of disease, which usually reflects the general health status of the patients and varies accompanied with the progression of disease, that is, the same disease may have different syndromes[23],[24] and same syndrome in different diseases.[25],[26] Therefore, syndrome differentiation of disease lays the foundation for personalized disease therapy, in that different diseases could be treated with the same method on the basis of identical syndrome, and the same disease could be treated with different ways due to diversified syndromes present.
Figure 1: The workflow in the diagnosis and treatment of traditional Chinese medicine

Click here to view


At present, most clinical used medicines are single target oriented, with inevitable side effects[1],[2] and limited efficacy.[3],[4] Actually, the etiology of most diseases is far more complicated than supposed to be single gene related.[27],[28] The principle of TCM formulae usually includes the four functional components: Jun (), Chen (), Zuo (), and Shi (). Medicines of Jun target the main cause of the disease, while those of Chen are supposed to increase the efficacy of Jun drug and relieve the secondary symptoms. Medicines of Zuo facilitate the therapeutic effects of Jun and Chen and reduce the toxic or side effects of these medicines. Shi () usually promotes the absorption of all components and orchestrates the components to the targets.[29] The formula is based on the compatibility of TCM and it is not the simple combination of medicines. TCM formula is usually better for reducing adverse effects and toxicity or improving therapeutic efficacy than the equivalent doses of individual ingredients[30] by targeting multitargets of disease simultaneously,[31],[32] which is called synergetic effect. These properties indicate that there are multifaceted and mutual interactions existed among the complex components in the formulae with nonlinear and complex characteristics. The dynamic process of formulaein vivo is important for affecting TCM efficacy. As a result, studies on TCM formulae should take the static and dynamic processes of TCM formulae into consideration, that is, determining the chemical components of TCM formulae and the metabolized forms in vivo. However, it is still a great challenge to unveil the therapeutic mechanism of TCM formulae.


  Systems Biology Top


Systems biology is an integrated discipline, which studies all the elements in the system such as genes, mRNA, proteins, and small molecules and their dynamic alterations under specific conditions.[20],[21],[33],[34] The main advantage of systems biology is taking all the elements as a whole and integrating all levels from gene to cell, tissue, and even individual.[33] On the other hand, it also reveals the dynamic change under different conditions and time periods.[35] The goal of systems biology is to describe the biological functions, phenotypes, and behaviors[36] with various omics techniques including genomics, transcriptomics, proteomics, metabolomics, and metagenomics with computational and mathematical modeling.[37] It aims to apply a holistic and “top-down” approach to study the whole system by measuring information as many as possible rather than the strategy of “down-up,” which divides the system into various parts.[38] Systems biology is believed to be a bridge linking TCM with modern sciences. The integration of the data from multiomics with bioinformatics is the main process for systems biology study, which is summarized in [Table 1].[36] These omics technologies not only observe the identification and quantification of the biomolecules at different levels but also analyze the complex interactions based on massive data. Genomics, transcriptomics, and proteomics decipher the alterations at DNA, RNA, or protein levels, respectively.[37] In addition, epigenomics has been applied in the quality assessment and identification of “authentic” TCMs and investigating the mechanism of its formation.[39] Some efforts have been made to investigate the TCM characteristics with epigenetics approach such as holism, yi-yang balance,[40] and syndrome classification,[41] suggesting that the efficacy of TCM formulae is associated with regulation on epigenetics.[40] In addition, phenomics was also applied in TCM research, which tried to explain the relationship between phenotype and genotype.[42] Moreover, SymMap presents the symptom-herb knowledge, and the TCM symptoms map to the modern medicine symptoms,[43] which would make the definitions of TCM symptoms and potential mechanism clearer. The transcriptome and proteome can not only be used as the approach for biomarker discovery and identification of therapeutic targets as well.[44],[45],[46],[47] Metabolites are components of the end products of gene expression and are produced by the action of metabolic enzymes, and therefore, metabolomics can sensitively reflect the changes in body.[48] In addition, the gut microbiota is associated with the development of various diseases, especially metabolic diseases such as diabetes,[49] obesity,[50] and nonalcoholic fatty liver disease.[51] Moreover, increasing evidence has revealed that gut microbiota plays an important role in mediating TCM activity.[12],[49] Microbiota may contribute to the transformation or production of metabolites facilitating the absorption of TCM components. TCM formulae are mainly administrated orally, and therefore unavoidably interact with gut microbiota.[52] For example, the microbiota is capable of metabolizing daidzein, an isoflavone phytoestrogen in soy, to equol and O-desmethylangolensin, which are more biologically active in treating breast and prostate cancers.[53] Studies have shown that the efficacy of TCM treatment is associated with the modification on gut microbiota. For example, Gegen Qinlian Decoction (GGQLD) was used for the treatment of type 2 diabetes (T2D) in the clinic, in which 187 T2D patients were divided into high-, moderate-, low-dose GGQLD or the placebo groups. There were 17 and 9 GGQLD-enriched species negatively correlated with fasting blood glucose or glycated hemoglobin, respectively,[54] suggesting the involvement of gut microbiota in its efficacy. Dahuang Mudan Decoction (DHMDD) alleviated the pathological changes of dextran sulfate sodium-induced ulcerative colitis. DHMDD significantly promoted Firmicutes and Actinobacteria abundance, while markedly reduced Proteobacteria and Bacteroidetes levels. Treatment with DHMDD increased the abundance of Butyricicoccus pullicaecorum, a butyrate-producing bacterium. Meanwhile, the concentration of short-chain fatty acid (SCFA) restored by DHMDD in intestinal tract.[55] These results suggested that the effect of TCM was associated with gut microbiota modulation. Biological molecules do not function separately; moreover, the molecules and the related interaction will form different types of network which are essential for acknowledging the function and mechanism of the complex system including gene interaction, gene–protein interaction, protein interaction, and metabolic and gut microbiota network. These complex networks construct the phenotype together [Figure 2]. It is anticipated that omics techniques will promote our understanding of the diagnosis and treatment of disease. Undoubtedly, a bulk of information generated by omics represents a huge challenge. To achieve this goal, bioinformatics approaches should be applied for strengthening the relationship among different omics and explaining the biologically relevant information. Different from other “omics,” network pharmacology aims to provide the joint analysis of the massive data from omics and even construct drug-target-disease networks, which shifts the research strategy from a “single component, single target” to “multicomponent, multitargets.”[56] Then, it will be possible to have a more comprehensive understanding of the mechanisms underlying the multicomponent and multitarget effects of TCM with these omics tools. Databases for TCM information, software, biomolecular, and phenotype information could be a good resource for providing information to underlie the TCM. The detailed information of common databases and software is shown in [Table 2].
Table 1: Summary of omics technologies

Click here to view
Figure 2: The molecular interaction networks. The interactions including different genes, proteins, and molecules from the different levels in the living system: gene interaction network, gene–protein interaction network, protein interaction network, metabolic network, gut microbiota, and phenotype

Click here to view
Table 2: Network pharmacology-related public databases and software for traditional Chinese medicine study

Click here to view


Systems biology brings amounts of data for the diagnosis and treatment for diseases; meanwhile, the explanation for biological meaning has been a great challenge. However, data quality and reproducibility are demanding. The error rate has an attendance to increase when combining the multiple omics data due to the incompatibility and the quality of the public database. In spite of the current situation, more software, database, and algorithm are being explored.[86]


  “Disease-Syndrome-Formula-Effect” Network of Traditional Chinese Medicine Top


Disease-related network

In biological system, there are various interactions at different levels among genes, proteins, metabolites, gut microbiota, and so on, which are closely related to the occurrence of diseases. There are very few cases of diseases that are caused by the alteration of single factor such as the SNP of single gene or malfunction of single protein.[87] Taking stroke for example, it resulted from the abnormality of a variety of genetic and environmental factors.[88] Previous publication summarized that there were a dozen of genes as the monogenic cause of ischemic stroke such as NOTCH3, HTRA1, TREX1, HBB, CBS, GLA, ABCC6, COL3A1, FBN1, tRNA Leu, APP, CST3, and COL4A1.[89] In addition, some proteins are considered as the potential therapeutic targets for stroke including 70-kDa heat shock protein,[90] C1q/TNF-related protein 9,[91] and thioredoxin-interacting protein.[92] Protein carbonyl derivatives is related to oxidation stress and formation of amino acid such as histidine, lysine, cysteine, proline, arginine, and threonine,[91],[92],[93] and some amino acids were reduced in ischemic stroke patients.[94] In addition to genomics and transcriptomics evidence, metagenomics provided novel view for understanding the etiology of heart disease. For example, increased SCFA-producing bacteria including Odoribacter, Akkermansia, Ruminococcaceae_UCG005, and Victivallis have been detected in cerebral ischemic stroke patients, suggesting that gut microbiota is involved in development of stroke.[95] Therefore, elucidation of disease-related network from genes to metabolites, as well as gut microbiota, is necessary for seeking effective therapeutic options in clinic.

Syndrome-related network

TCM syndrome is descriptive for the pathophysiological status of patients, which is composed of a set of abnormal symptoms in the human body such as the appearance of tongue, pulse, symptoms, and signs. Physicians of TCM can make diagnosis based on these abnormal syndrome appearances.[96],[97] Interestingly, a particular TCM syndrome can be present in different diseases (same syndrome for different diseases), which is the basis for the TCM theory of “different diseases with identical therapy.” On the other hand, different syndromes can be observed on the same disease (same disease with different syndromes), which is the basis for TCM theory of “same disease with different therapy.” However, the syndrome differentiation is dependent on the clinical observation and the physicians' experience via four diagnostic ways of TCM: looking, listening and smelling, asking, and touching, in TCM practice. However, these traditional practices are rather subjective and personal experience oriented, which makes the diagnosis be inconsistent or even contradictory among different TCM physicians on the same patient. Thus, it is a huge challenge for realization of TCM practice standardization.

Chronic hepatitis B (CHB) is a typical virus-infected disease by HBV. In TCM, CHB patients are usually stratified into three different syndromes including liver–gallbladder dampness–heat syndrome, liver depression and spleen-deficiency syndrome, and liver–kidney yin deficiency syndrome. The diagnosis of these different syndromes for CHB patients is necessary for TCM physicians performing different therapies with TCM theory. Meanwhile, lots of biological markers including genes, miRNA, proteins, and metabolites corresponding to the relevant syndromes have also been identified using omics approaches, which are supposed to be either potential markers for CHB diagnosis or the biological basis of TCM syndromes.[24],[98],[99],[100] Importantly, the dynamic changes of TCM syndrome is another problem in the syndrome differentiation study. For instance, miRNA could contribute to TCM syndrome classification such as the progression from excessive to deficient syndromes in CHB[99] as well as DNBs PLG and F12.[101] In addition, the fact of different diseases with the same syndrome is another important characteristic of TCM. For example, either coronary heart disease (CHD) or chronic renal failure (CRF) is characterized with dampness syndrome in TCM. Metabolomics study showed that patients of CHD or CRF with dampness syndrome also had common metabolic features in serum metabolic profiles compared to healthy subjects,[25] suggesting the metabolic basis for the rationale of different diseases with identical syndrome in TCM.

Therefore, the differentiation of TCM syndromes of diseases will provide practical information of disease therapy, in particular personalized therapy. Nevertheless, more omics-based studies are needed to uncover the biological basis of TCM syndromes.

Syndrome-based traditional Chinese medicine therapy with formulae

Based on the above discussion, the relationship between TCM syndrome and disease is the rationale for pursuing a syndrome-based TCM therapy in addition to disease-based therapy. There are usually two strategies for syndrome-based TCM therapy, i.e., same disease with different therapies and same therapy for different diseases. Taking Huanglian Jiedu decoction (HLJDD) for example, HLJDD is a classical TCM formula consisting of Coptidis Rhizoma, Scutellariae Radix, Phellodendri Chinensis Cortex, and Gardeniae Fructus. The main efficacy of HLJDD is heat-clearing and detoxifying to improve heat and blood-stasis syndrome (HBSS). HBSS is characterized with abnormal changes in blood rheology, microcirculation, hemodynamic changes, and tissue hyperplasia in patients.[11] HLJDD has been implicated in therapy for cardiovascular- and cerebrovascular-related diseases. Earlier studies suggested that HLJDD could change gene expression involved in regulating smooth muscle contraction, Ca(2+) homeostasis, and NO pathway with reduced hypertension.[102] Further studies showed that it exerted neuroprotective effects, regulated glutamate/GABA-glutamine, enhanced cholinergic neurons function, reduced oxidative stress and inflammatory, induced protective autophagy through regulating mitogen-activated kinase signals after cerebral ischemia.[103],[104],[105],[106] In addition, HLJDD is also used for the treatment of diseases that are characterized with HBSS such as hyperglycemia, T2D diseases, and Alzheimer's disease by attenuating inflammation and restoring gut dysbiosis.[49],[107],[108],[109] Thus, the characteristic of TCM syndrome is the basis for disease therapy with formulae including same disease with different therapies and same therapy for different diseases as well.

Moreover, some researchers have used integrated omics to comprehensively analyze the mechanism of formula treatment. For example, transcriptomic and proteomic combination was utilized to understand the antifibrotic effects of Fuzheng Huayu (FZHY) in CCl4-induced liver fibrosis in rats. After FZHY treatment, ten core genes/proteins were found, of which Ugt2a3, Cyp2b1, and Cyp3a18 involved in retinol metabolism.[110] Similarly, antifibrosis mechanism of gypenoside, a saponin extract derived from Gynostemma pentaphyllum (Thunb.) Makino., was evaluated by integrated proteomics and metabolomics. Gypenoside may altered glycolysis metabolism and protected against the damage of aldehydes and lipid peroxidation via the upregulation of ALDH.[111] Besides, TCM syndrome differentiation and formula treatment may be based on the molecular subtyping such as CYP1A2-G2964A locus for syndrome-based FZHY efficacy in HBC.[112]

“Disease-Syndrome-Formulae-Effect”

Based on the previous studies, we proposed a new strategy “Disease-Syndrome-Formula-Effect” network for TCM study [Figure 3]. In this model, the key basis is the “same disease with different syndromes” according to TCM theory. Actually, most diseases are characterized with different syndromes at different stages, which is the basis for adopting different therapies by TCM physicians on patients with identical disease. In Western medicine, the standardized clinical treatment guidelines are the main principles for disease therapy on patients with the same disease, which usually neglecting the fact of personalized difference in pathophysiology. In contrary, TCM physicians can judge the differences in syndromes of patients who suffer same disease, and then personalized medicines could be prescribed by TCM physicians according to their specific syndromes. Therefore, it is critical to construct both the disease and syndromes network using systems biology strategy, in which the disease- and syndrome-specific patterns in alteration of genes, proteins, metabolites, and even gut microbiome are necessary for understanding the etiology of disease from a holistic view. Meanwhile, formula is the main form for TCM therapy. The huge challenge of TCM modernization is the difficulty of determining the exact active components within formulae that is composed of several or even dozens of herbal medicines. There are usually hundreds or thousands of chemical components within formulae, and it is very hard to distinguish which of them are the key players. Fortunately, the advances of high-throughput analytical instruments and bioinformatics have provided reliable techniques to identify the chemicals either in TCM formulae or presence in blood as much as possible, which is the first step for understanding the mechanisms underlying each TCM formula. Therefore, the qualitative and quantitative determination of chemicals within TCM formulae and in circulation by systems biology approaches lay the solid foundation for research on TCM. Consequently, it is realistic for TCM modernization by transforming the massive data from disease, syndrome, and formulae studies into “Disease-Syndrome-Formulae-Effect” network on the basis of application of omics approaches.
Figure 3: The main schematic diagram “Disease-Syndrome-Formula-Effect” network in traditional Chinese medicine study

Click here to view


Taking Buyang Huanwu decoction (BYHWD) and Xuefu Zhuyu decoction (XFZYD) in CHD therapy with either qi-deficiency and blood-stasis syndrome (QDBSS) or qi-stagnation and blood-stasis (QSBSS) syndrome, respectively, for example, the chemical components of the formulae were first determined through data and literature mining, as well as experiments validation,in vitro and in vivo.[113],[114],[115],[116] Second, a QDBSS rat model was built, in which about 50 differential expression genes were determined involved in inflammation and immunoregulation.[117],[118] At the metabolic level, NMR analysis revealed that formate, creatinine, 2-oxoglutarate, citrate, taurine, trimethylamine-N-oxide, succinate, and hippurate were significantly altered in QDBSS model.[119] Meanwhile, QSBSS is another main syndrome of CHD, in which about 104 long non-coding RNAs, 2 circular RNAs, and 697 genes were significantly altered.[120] Proteomics revealed that JAK-STAT pathway-related and immune-related proteins were differently expressed in QSBSS rats.[121] The comparison between QDBSS and QSBSS in CHD indicated that intracellular adhesion molecule-1 (ICAM-1) was higher in QDBSS than QSBSS.[122]

Evidence showed that BYHWD was effective in treating QDBSS via inhibiting C-reactive protein and regulating endothelium-derived vasoactive factors.[123] The cardioprotective effects of BYHWD included targeting angiogenesis via Cav-1/VEGF signaling pathway with expression upregulation of Cav-1, VEGF, VEGFR2, and p-ERK, as well as inactivation of Tgf-β/Smads and MAPKs signaling triggered fibrosis.[124],[125] A clinical trial has shown that XFZYD could treat QSBSS and its related diseases such as CHD.[126] Metabolomics analysis indicated that the mechanism of XFZYD might be involved in energy metabolism, lipid metabolism (e.g. phospholipid and polyunsaturated fatty acid), amino acid metabolism, and bile acid metabolism pathways.[127],[128],[129] These studies provided proof-of-concept evidence of TCM therapies on the basis of syndrome differentiation, which highlight the significance of “Disease-Syndrome-Formulae-Effect” strategy.


  Conclusions and Perspectives Top


TCM is a “black box” in terms of its complicated components and therapeutic mechanisms. Currently, very few evidences have been acquired for understanding this “Black box” as a whole. Systems biology is increasingly applied in TCM research due to their common principle of holism. The fast advances of omics approaches enhance the capacity for visualizing the “black box” of TCM with systems biology strategy. The proposed “Disease-Syndrome-Formula-Effect” network is a promising model for TCM research, and further, more investigations are needed for elucidating the therapeutic mechanisms underlying TCM formulae and theory. Systems biology could elucidate the mechanism of disease and syndrome from multiple dimensions and form a network of all factors for analysis from the aspects of genes, proteins, metabolites, and microorganisms, rather than a single target [Figure 4].
Figure 4: The relationship between traditional Chinese medicine researches and systems biology

Click here to view


In this paper, we proposed the model of “Disease-Syndrome-Formulae-Effect” network for TCM study. It is necessary to choose classical TCM formulae (included in the Chinese Pharmacopoeia) with long-term clinical application to interpret the modern scientific connotation using multidisciplinary methods. In addition to the determination of active components in TCM formulae, the endogenous metabolites alteration should be included because many metabolites are functional, in addition to as endpoints of metabolism under disease or therapy. On the one hand, it should be noted that the study on TCM formulae should be under the guidance of TCM theory. On the other hand, systems biology is practical for bridging the theory of TCM with modern science. In fact, there are many animal oriented or evenin vitro experiments on TCM, which definitely provide experiment-based evidence for understanding TCM theory or mechanism underlying TCM formulae. However, the TCM research should particularly emphasize the significance of clinic-based studies by incorporating holistic and reductive approaches together. Many studies on TCM have been carried out by adopting omics approaches individually or in combination; we should pay more attention to integrating the knowledge from molecule, cell, tissue, organ, to system levels to obtain the system dynamic changes. As a result, we proposed the strategy of constructing the “Disease-Syndrome-Formulae-Effect” network with systems biology approaches, which is practical for visualizing the “black box” of TCM from molecular to holistic perspective.

Financial support and sponsorship

This work was funded by the National Key Research and Development Program of China (2017YFC1700200 and 2020YFC0845400), Professor of Chang Jiang Scholars Program (No. 81520108030), and Program for Professor of Special Appointment (Eastern Scholar) at Shanghai Institutions of Higher Learning from Shanghai Municipal Education Commission.

Conflicts of interest

There are no conflicts of interest.



 
  References Top

1.
Kola I, Landis J. Can the pharmaceutical industry reduce attrition rates? Nat Rev Drug Discov 2004;3:711-5.  Back to cited text no. 1
    
2.
Hopkins AL. Network pharmacology: The next paradigm in drug discovery. Nat Chem Biol 2008;4:682-90.  Back to cited text no. 2
    
3.
Xu L, Zhao W, Wang D, Ma X. Chinese medicine in the battle against obesity and metabolic diseases. Front Physiol 2018;9:850.  Back to cited text no. 3
    
4.
Chen J, Zheng L, Hu Z, Wang F, Huang S, Li Z, et al. Metabolomics reveals effect of Zishen Jiangtang pill, a Chinese herbal product on high-fat diet-induced type 2 diabetes mellitus in mice. Front Pharmacol 2019;10:256.  Back to cited text no. 4
    
5.
Zhou X, Seto SW, Chang D, Kiat H, Razmovski-Naumovski V, Chan K, et al. Synergistic effects of Chinese herbal medicine: A comprehensive review of methodology and current research. Front Pharmacol 2016;7:201.  Back to cited text no. 5
    
6.
Ravikumar B, Aittokallio T. Improving the efficacy-safety balance of polypharmacology in multi-target drug discovery. Expert Opin Drug Discov 2018;13:179-92.  Back to cited text no. 6
    
7.
Li S, Fan TP, Jia W, Lu A, Zhang W. Network pharmacology in traditional chinese medicine. Evid Based Complement Alternat Med 2014;2014:138460.  Back to cited text no. 7
    
8.
Yue W, Yang CS, DiPaola RS, Tan XL. Repurposing of metformin and aspirin by targeting AMPK-mTOR and inflammation for pancreatic cancer prevention and treatment. Cancer Prev Res (Phila) 2014;7:388-97.  Back to cited text no. 8
    
9.
Papaetis GS, Syrigos KN. Sunitinib: A multitargeted receptor tyrosine kinase inhibitor in the era of molecular cancer therapies. BioDrugs 2009;23:377-89.  Back to cited text no. 9
    
10.
Wang J, van der Heijden R, Spruit S, Hankermeier T, Chan K, van der Greef J, et al. Quality and safety of Chinese herbal medicines guided by a systems biology perspective. J Ethnopharmacol 2009;126:31-41.  Back to cited text no. 10
    
11.
Jiang WY. Therapeutic wisdom in traditional Chinese medicine: A perspective from modern science. Trends Pharmacol Sci 2005;26:558-63.  Back to cited text no. 11
    
12.
Kong YH, Shi Q, Han N, Zhang L, Zhang YY, Gao TX, et al. Structural modulation of gut Microbiota in rats with allergic bronchial asthma treated with recuperating lung decoction. Biomed Environ Sci 2016;29:574-83.  Back to cited text no. 12
    
13.
Fan X, Li X, Lv S, Wang Y, Zhao Y, Luo G. Comparative proteomics research on rat MSCs differentiation induced by Shuanglong Formula. J Ethnopharmacol 2010;131:575-80.  Back to cited text no. 13
    
14.
Chen FP, Chen TJ, Kung YY, Chen YC, Chou LF, Chen FJ, et al. Use frequency of traditional Chinese medicine in Taiwan. BMC Health Serv Res 2007;7:26.  Back to cited text no. 14
    
15.
Hosbach I, Neeb G, Hager S, Kirchhoff S, Kirschbaum B. In defence of traditional Chinese herbal medicine. Anaesthesia 2003;58:282-3.  Back to cited text no. 15
    
16.
Van Hal M, Green M S. Acupuncture, Treasure Island (FL); 2019.  Back to cited text no. 16
    
17.
Ke H, Tong W, Xue R, Lu X, Fan X. Characterization of chemical constituents and identification of absorbed components and metabolites in rat plasma of Fu-Ke-Zai-Zao pills by ultraperformance liquid chromatography quadrupole time-of-flight mass spectrometry. J Sep Sci 2019;42:1842-52.  Back to cited text no. 17
    
18.
Wu R, Lin S, Wang J, Tian S, Ke X, Qu Y, et al. Rapid characterization of chemical constituents and metabolites of Qi-Jing-Sheng-Bai granule by using UHPLC-Q-TOF-MS. J Sep Sci 2018;41:1960-72.  Back to cited text no. 18
    
19.
Jiang P, Lu Y, Chen D. Qualitative and quantitative analysis of multiple components for quality control of Deng-Zhan-Sheng-Mai capsules by ultra high performance liquid chromatography tandem mass spectrometry method coupled with chemometrics. J Sep Sci 2017;40:612-24.  Back to cited text no. 19
    
20.
Hood L. A personal view of molecular technology and how it has changed biology. J Proteome Res 2002;1:399-409.  Back to cited text no. 20
    
21.
Weston AD, Hood L. Systems biology, proteomics, and the future of health care: Toward predictive, preventative, and personalized medicine. J Proteome Res 2004;3:179-96.  Back to cited text no. 21
    
22.
Zhang A, Sun H, Wang Z, Sun W, Wang P, Wang X. Metabolomics: Towards understanding traditional Chinese medicine. Planta Med 2010;76:2026-35.  Back to cited text no. 22
    
23.
Gao S, Chen LY, Wang P, Liu LM, Chen Z. MicroRNA expression in salivary supernatant of patients with pancreatic cancer and its relationship with ZHENG. Biomed Res Int 2014;2014:756347.  Back to cited text no. 23
    
24.
Lu YY, Chen QL, Guan Y, Guo ZZ, Zhang H, Zhang W, et al. Study of ZHENG differentiation in hepatitis B-caused cirrhosis: A transcriptional profiling analysis. BMC Complement Altern Med 2014;14:371.  Back to cited text no. 24
    
25.
Hao Y, Yuan X, Qian P, Bai G, Wang Y. The serum analysis of dampness syndrome in patients with coronary heart disease and chronic renal failure based on the theory of “same syndromes in different diseases”. Biomed Res Int 2017;2017:3805806.  Back to cited text no. 25
    
26.
Ji Q, Wang W, Luo Y, Cai F, Lu Y, Deng W, et al. Characteristic proteins in the plasma of postoperative colorectal and liver cancer patients with Yin deficiency of liver-kidney syndrome. Oncotarget 2017;8:103223-35.  Back to cited text no. 26
    
27.
Mocchegiani E, Costarelli L, Giacconi R, Cipriano C, Muti E, Tesei S, et al. Nutrient-gene interaction in ageing and successful ageing. A single nutrient (zinc) and some target genes related to inflammatory/immune response. Mech Ageing Dev 2006;127:517-25.  Back to cited text no. 27
    
28.
Wang Q, Shi G, Zhang Y, Lu F, Xie D, Wen C, et al. Deciphering the potential pharmaceutical mechanism of GUI-ZHI-FU-LING-WAN on systemic sclerosis based on systems biology approaches. Sci Rep 2019;9:355.  Back to cited text no. 28
    
29.
Qiu J. Traditional medicine: A culture in the balance. Nature 2007;448:126-8.  Back to cited text no. 29
    
30.
Jia W, Gao WY, Yan YQ, Wang J, Xu ZH, Zheng WJ, et al. The rediscovery of ancient Chinese herbal formulas. Phytother Res 2004;18:681-6.  Back to cited text no. 30
    
31.
Zhao P, Li J, Yang L, Li Y, Tian Y, Li S. Integration of transcriptomics, proteomics, metabolomics and systems pharmacology data to reveal the therapeutic mechanism underlying Chinese herbal Bufei Yishen formula for the treatment of chronic obstructive pulmonary disease. Mol Med Rep 2018;17:5247-57.  Back to cited text no. 31
    
32.
Cai FF, Bian YQ, Wu R, Sun Y, Chen XL, Yang MD, et al. Yinchenhao decoction suppresses rat liver fibrosis involved in an apoptosis regulation mechanism based on network pharmacology and transcriptomic analysis. Biomed Pharmacother 2019;114:108863.  Back to cited text no. 32
    
33.
Xu WW, Ren QL, Hong DD. Analysis of dampness heat syndrome from the perspective of systems biology and holistic view of traditional Chinese medicine. Guid J Tradit Chin Med Pharm 2019;25:5-9.  Back to cited text no. 33
    
34.
O'Connor JE, Herrera G, Martínez-Romero A, de Oyanguren FS, Díaz L, Gomes A, et al. Systems Biology and immune aging. Immunol Lett 2014;162:334-45.  Back to cited text no. 34
    
35.
Ideker T, Galitski T, Hood L. A new approach to decoding life: Systems biology. Annu Rev Genomics Hum Genet 2001;2:343-72.  Back to cited text no. 35
    
36.
Cai FF, Zhou WJ, Wu R, Su SB. Systems biology approaches in the study of Chinese herbal formulae. Chin Med 2018;13:65.  Back to cited text no. 36
    
37.
Joyce AR, Palsson BØ. The model organism as a system: Integrating 'omics' data sets. Nat Rev Mol Cell Biol 2006;7:198-210.  Back to cited text no. 37
    
38.
Kell DB, Oliver SG. Here is the evidence, now what is the hypothesis? The complementary roles of inductive and hypothesis-driven science in the post-genomic era. Bioessays 2004;26:99-105.  Back to cited text no. 38
    
39.
Xin T, Zhang Y, Pu X, Gao R, Xu Z, Song J. Trends in herbgenomics. Sci China Life Sci 2019;62:288-308.  Back to cited text no. 39
    
40.
Hu XQ, Su SB. An overview of epigenetics in Chinese medicine researches. Chin J Integr Med 2017;23:714-20.  Back to cited text no. 40
    
41.
Chen QL, Lu YY, Zhang GB, Song YN, Zhou QM, Zhang H, et al. Characteristic analysis from excessive to deficient syndromes in hepatocarcinoma underlying miRNA array data. Evid Based Complement Alternat Med 2013;2013:324636.  Back to cited text no. 41
    
42.
Li X, Zhou X, Peng Y, Liu B, Zhang R, Hu J, et al. Network based integrated analysis of phenotype-genotype data for prioritization of candidate symptom genes. Biomed Res Int 2014;2014:435853.  Back to cited text no. 42
    
43.
Wu Y, Zhang F, Yang K, Fang S, Bu D, Li H, et al. SymMap: An integrative database of traditional Chinese medicine enhanced by symptom mapping. Nucleic Acids Res 2019;47:D1110-7.  Back to cited text no. 43
    
44.
Titz B, Gadaleta RM, Lo Sasso G, Elamin A, Ekroos K, Ivanov NV, et al. Proteomics and lipidomics in inflammatory bowel disease research: From mechanistic insights to biomarker identification. Int J Mol Sci 2018;19:2775.  Back to cited text no. 44
    
45.
Yang Y, Wei J, Huang X, Wu M, Lv Z, Tong P, et al. iTRAQ-based proteomics of chronic renal failure rats after fushengong decoction treatment reveals haptoglobin and alpha-1-antitrypsin as potential biomarkers. Evid Based Complement Alternat Med 2017;2017:1480514.  Back to cited text no. 45
    
46.
Wang J, Yu G. A systems biology approach to characterize biomarkers for blood stasis syndrome of unstable angina patients by integrating microRNA and messenger RNA expression profiling. Evid Based Complement Alternat Med 2013;2013:510208.  Back to cited text no. 46
    
47.
Hanjin C, Tao L, Pengfei L, Ali Y, Huajun Z, Jiekun L, et al. Altered long noncoding RNA and messenger RNA expression in experimental intracerebral hemorrhage-A preliminary study. Cell Physiol Biochem 2018;45:1284-301.  Back to cited text no. 47
    
48.
Shi J, Cao B, Wang XW, Aa JY, Duan JA, Zhu XX, et al. Metabolomics and its application to the evaluation of the efficacy and toxicity of traditional Chinese herb medicines. J Chromatogr B Analyt Technol Biomed Life Sci 2016;1026:204-16.  Back to cited text no. 48
    
49.
Chen M, Liao Z, Lu B, Wang M, Lin L, Zhang S, et al. Huang-lian-jie-du-decoction ameliorates hyperglycemia and insulin resistant in association with gut microbiota modulation. Front Microbiol 2018;9:2380.  Back to cited text no. 49
    
50.
Liu S, Li F, Zhang X. Structural modulation of gut microbiota reveals Coix seed contributes to weight loss in mice. Appl Microbiol Biotechnol 2019;103:5311-21.  Back to cited text no. 50
    
51.
Tang W, Yao X, Xia F, Yang M, Chen Z, Zhou B, et al. Modulation of the gut microbiota in rats by hugan qingzhi tablets during the treatment of high-fat-diet-induced nonalcoholic fatty liver disease. Oxid Med Cell Longev 2018;2018:7261619.  Back to cited text no. 51
    
52.
Chen L, Li H. Targeting microbiome: New opportunities and challenges in the study of pharmacodynamic mechanism of traditional Chinese medicine. SH J TCM 2020;54:15-20.  Back to cited text no. 52
    
53.
Atkinson C, Frankenfeld CL, Lampe JW. Gut bacterial metabolism of the soy isoflavone daidzein: Exploring the relevance to human health. Exp Biol Med (Maywood) 2005;230:155-70.  Back to cited text no. 53
    
54.
Xu J, Lian F, Zhao L, Zhao Y, Chen X, Zhang X, et al. Structural modulation of gut microbiota during alleviation of type 2 diabetes with a Chinese herbal formula. ISME J 2015;9:552-62.  Back to cited text no. 54
    
55.
Luo S, Wen R, Wang Q, Zhao Z, Nong F, Fu Y, et al. Rhubarb Peony Decoction ameliorates ulcerative colitis in mice by regulating gut microbiota to restoring Th17/Treg balance. J Ethnopharmacol 2019;231:39-49.  Back to cited text no. 55
    
56.
Lee S. Systems biology-A pivotal research methodology for understanding the mechanisms of traditional medicine. J Pharmacopuncture 2015;18:11-8.  Back to cited text no. 56
    
57.
Chen X, Zhou H, Liu YB, Wang JF, Li H, Ung CY, et al. Database of traditional Chinese medicine and its application to studies of mechanism and to prescription validation. Brit J Pharmacol 2006;149:1092-103.  Back to cited text no. 57
    
58.
Huang L, Xie D, Yu Y, Liu H, Shi Y, Shi T, et al. TCMID 2.0: A comprehensive resource for TCM. Nucleic Acids Res 2018;46:D1117-20.  Back to cited text no. 58
    
59.
Chen CY. TCM Database@Taiwan: The world's largest traditional Chinese medicine database for drug screening in silico. PLoS One 2011;6:e15939.  Back to cited text no. 59
    
60.
Fang YC, Huang HC, Chen HH, Juan HF. TCMGeneDIT: A database for associated traditional Chinese medicine, gene and disease information using text mining. BMC Complement Altern Med 2008;8:58.  Back to cited text no. 60
    
61.
Ru J, Li P, Wang J, Zhou W, Li B, Huang C, et al. TCMSP: A database of systems pharmacology for drug discovery from herbal medicines. J Cheminform 2014;6:13.  Back to cited text no. 61
    
62.
Kim SK, Nam S, Jang H, Kim A, Lee JJ. TM-MC: A database of medicinal materials and chemical compounds in Northeast Asian traditional medicine. BMC Complement Altern Med 2015;15:218.  Back to cited text no. 62
    
63.
Tao W, Li B, Gao S, Bai Y, Shar PA, Zhang W, et al. CancerHSP: Anticancer herbs database of systems pharmacology. Sci Rep 2015;5:11481.  Back to cited text no. 63
    
64.
Huang J, Zheng Y, Wu W, Xie T, Yao H, Pang X, et al. CEMTDD: The database for elucidating the relationships among herbs, compounds, targets and related diseases for Chinese ethnic minority traditional drugs. Oncotarget 2015;6:17675-84.  Back to cited text no. 64
    
65.
Xu HY, Zhang YQ, Liu ZM, Chen T, Lv CY, Tang SH, et al. ETCM: An encyclopaedia of traditional Chinese medicine. Nucleic Acids Res 2019;47:D976-82.  Back to cited text no. 65
    
66.
Ye H, Ye L, Kang H, Zhang D, Tao L, Tang K, et al. HIT: Linking herbal active ingredients to targets. Nucleic Acids Res 2011;39:D1055-9.  Back to cited text no. 66
    
67.
Zhang RZ, Yu SJ, Bai H, Ning K. TCM-Mesh: The database and analytical system for network pharmacology analysis for TCM preparations. Sci Rep 2017;7:2821.  Back to cited text no. 67
    
68.
Wishart DS, Feunang YD, Guo AC, Lo EJ, Marcu A, Grant JR, et al. DrugBank 5.0: A major update to the DrugBank database for 2018. Nucleic Acids Res 2018;46(D1):D1074-82.  Back to cited text no. 68
    
69.
Szklarczyk D, Santos A, von Mering C, Jensen LJ, Bork P, Kuhn M. STITCH 5: Augmenting protein-chemical interaction networks with tissue and affinity data. Nucleic Acids Res 2016;44:D380-4.  Back to cited text no. 69
    
70.
Mendez D, Gaulton A, Bento AP, Chambers J, De Veij M, Félix E, et al. ChEMBL: Towards direct deposition of bioassay data. Nucleic Acids Res 2019;47:D930-D940.  Back to cited text no. 70
    
71.
Kim S, Chen J, Cheng T, Gindulyte A, He J, He S, et al. PubChem 2019 update: Improved access to chemical data. Nucleic Acids Res 2019;47:D1102-9.  Back to cited text no. 71
    
72.
Keshava Prasad TS, Goel R, Kandasamy K, Keerthikumar S, Kumar S, Mathivanan S, et al. Human protein reference database-2009 update. Nucleic Acids Res 2009;37:D767-72.  Back to cited text no. 72
    
73.
Licata L, Briganti L, Peluso D, Perfetto L, Iannuccelli M, Galeota E, et al. MINT, the molecular interaction database: 2012 update. Nucleic Acids Res 2012;40:D857-61.  Back to cited text no. 73
    
74.
Szklarczyk D, Gable AL, Lyon D, Junge A, Wyder S, Huerta-Cepas J, et al. STRING v11: Protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res 2019;47:D607-13.  Back to cited text no. 74
    
75.
Salwinski L, Miller CS, Smith AJ, Pettit FK, Bowie JU, Eisenberg D. The Database of Interacting Proteins: 2004 update. Nucleic Acids Res 2004;32:D449-51.  Back to cited text no. 75
    
76.
Oughtred R, Stark C, Breitkreutz BJ, Rust J, Boucher L, Chang C, et al. The BioGRID interaction database: 2019 update. Nucleic Acids Res 2019;47:D529-41.  Back to cited text no. 76
    
77.
Croft D, O'Kelly G, Wu G, Haw R, Gillespie M, Matthews L, et al. Reactome: A database of reactions, pathways and biological processes. Nucleic Acids Res 2011;39:D691-7.  Back to cited text no. 77
    
78.
Orchard S, Ammari M, Aranda B, Breuza L, Briganti L, Broackes-Carter F, et al. The MIntAct project-IntAct as a common curation platform for 11 molecular interaction databases. Nucleic Acids Res 2014;42:D358-63.  Back to cited text no. 78
    
79.
Chen JY, Pandey R, Nguyen TM. HAPPI-2: A Comprehensive and high-quality map of human annotated and predicted protein interactions. BMC Genomics 2017;18:182.  Back to cited text no. 79
    
80.
Amberger JS, Bocchini CA, Scott AF, Hamosh A. OMIM.org: Leveraging knowledge across phenotype-gene relationships. Nucleic Acids Res 2019;47:D1038-43.  Back to cited text no. 80
    
81.
Bodenreider O. The unified medical language system (UMLS): Integrating biomedical terminology. Nucleic Acids Res 2004;32:D267-70.  Back to cited text no. 81
    
82.
Köhler S, Vasilevsky NA, Engelstad M, Foster E, McMurry J, Aymé S, et al. The human phenotype ontology in 2017. Nucleic Acids Res 2017;45:D865-76.  Back to cited text no. 82
    
83.
Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, et al. Cytoscape: A software environment for integrated models of biomolecular interaction networks. Genome Res 2003;13:2498-504.  Back to cited text no. 83
    
84.
Dohleman BS. Exploratory social network analysis with Pajek. Psychometrika 2006;71:605-6.  Back to cited text no. 84
    
85.
Chang KW, Tsai TY, Chen KC, Yang SC, Huang HJ, Chang TT, et al. iSMART: An integrated cloud computing web server for traditional Chinese medicine for online virtual screening, de novo evolution and drug design. J Biomol Struct Dyn 2011;29:243-50.  Back to cited text no. 85
    
86.
Mei H, Xia T, Feng G, Zhu J, Lin SM, Qiu Y. Opportunities in systems biology to discover mechanisms and repurpose drugs for CNS diseases. Drug Discov Today 2012;17:1208-16.  Back to cited text no. 86
    
87.
Chauhan G, Debette S. Genetic risk factors for ischemic and hemorrhagic stroke. Curr Cardiol Rep 2016;18:124.  Back to cited text no. 87
    
88.
Kim JY, Han Y, Lee JE, Yenari MA. The 70-kDa heat shock protein (Hsp70) as a therapeutic target for stroke. Expert Opin Ther Targets 2018;22:191-9.  Back to cited text no. 88
    
89.
Yang C, Fan F, Sawmiller D, Tan J, Wang Q, Xiang Y. C1q/TNF-related protein 9: A novel therapeutic target in ischemic stroke? J Neurosci Res 2019;97:128-36.  Back to cited text no. 89
    
90.
Ishrat T, Mohamed IN, Pillai B, Soliman S, Fouda AY, Ergul A, et al. Thioredoxin-interacting protein: A novel target for neuroprotection in experimental thromboembolic stroke in mice. Mol Neurobiol 2015;51:766-78.  Back to cited text no. 90
    
91.
Uchida K, Stadtman ER. Covalent attachment of 4-hydroxynonenal to glyceraldehyde-3-phosphate dehydrogenase. A possible involvement of intra- and intermolecular cross-linking reaction. J Biol Chem 1993;268:6388-93.  Back to cited text no. 91
    
92.
Saito A, Hayashi T, Okuno S, Nishi T, Chan PH. Modulation of proline-rich akt substrate survival signaling pathways by oxidative stress in mouse brains after transient focal cerebral ischemia. Stroke 2006;37:513-7.  Back to cited text no. 92
    
93.
Cai Z, Yan LJ. Protein oxidative modifications: Beneficial roles in disease and health. J Biochem Pharmacol Res 2013;1:15-26.  Back to cited text no. 93
    
94.
Wang D, Kong J, Wu J, Wang X, Lai M. GC-MS-based metabolomics identifies an amino acid signature of acute ischemic stroke. Neurosci Lett 2017;642:7-13.  Back to cited text no. 94
    
95.
Li N, Wang X, Sun C, Wu X, Lu M, Si Y, et al. Change of intestinal microbiota in cerebral ischemic stroke patients. BMC Microbiol 2019;19:191.  Back to cited text no. 95
    
96.
Bian ZX, Xu H, Lu AP, Lee MS, Cheung H. Insights of Chinese medicine syndrome study: From current status to future prospects. Chin J Integr Med 2014;20:326-31.  Back to cited text no. 96
    
97.
Hu J, Liu B. The basic theory, diagnostic, and therapeutic system of traditional Chinese medicine and the challenges they bring to statistics. Stat Med 2012;31:602-5.  Back to cited text no. 97
    
98.
Guo Z, Yu S, Guan Y, Li YY, Lu YY, Zhang H, et al. Molecular mechanisms of same TCM syndrome for different diseases and different TCM syndrome for same disease in chronic hepatitis B and liver cirrhosis. Evid Based Complement Alternat Med 2012;2012:120350.  Back to cited text no. 98
    
99.
Chen QL, Lu YY, Zhang GB, Song YN, Zhou QM, Zhang H, et al. Progression from excessive to deficient syndromes in chronic hepatitis B: A dynamical network analysis of miRNA array data. Evid Based Complement Alternat Med 2013;2013:945245.  Back to cited text no. 99
    
100.
Liang ZL, Zhang XY, Wang F, Zhang K, Liu HF, Liu HL. Understanding molecular mechanisms of Rhodiola rosea for the treatment of acute mountain sickness through computational approaches (a STROBE-compliant article). Medicine (Baltimore) 2018;97:e11886.  Back to cited text no. 100
    
101.
Lu Y, Fang Z, Zeng T, Li M, Chen Q, Zhang H, et al. Chronic hepatitis B: Dynamic change in Traditional Chinese Medicine syndrome by dynamic network biomarkers. Chin Med 2019;14:52.  Back to cited text no. 101
    
102.
Yue GH, Zhuo SY, Xia M, Zhang Z, Gao YW, Luo Y. Effect of huanglian jiedu decoction on thoracic aorta gene expression in spontaneous hypertensive rats. Evid Based Complement Alternat Med 2014;2014:565784.  Back to cited text no. 102
    
103.
Ye Y, Huang C, Jiang L, Shen X, Zhu S, Rao Y, et al. Huanglian-Jie-Du-Tang extract protects against chronic brain injury after focal cerebral ischemia via hypoxia-inducible-factor-1α-regulated vascular endothelial growth factor signaling in mice. Biol Pharm Bull 2012;35:355-61.  Back to cited text no. 103
    
104.
Zhu B, Cao H, Sun L, Li B, Guo L, Duan J, et al. Metabolomics-based mechanisms exploration of Huang-Lian Jie-Du decoction on cerebral ischemia via UPLC-Q-TOF/MS analysis on rat serum. J Ethnopharmacol 2018;216:147-56.  Back to cited text no. 104
    
105.
Wang PR, Wang JS, Yang MH, Kong LY. Neuroprotective effects of Huang-Lian-Jie-Du-Decoction on ischemic stroke rats revealed by (1)H NMR metabolomics approach. J Pharm Biomed Anal 2014;88:106-16.  Back to cited text no. 105
    
106.
Wang PR, Wang JS, Zhang C, Song XF, Tian N, Kong LY. Huang-Lian-Jie-Du-Decotion induced protective autophagy against the injury of cerebral ischemia/reperfusion via MAPK-mTOR signaling pathway. J Ethnopharmacol 2013;149:270-80.  Back to cited text no. 106
    
107.
Zhang XJ, Deng YX, Shi QZ, He MY, Chen B, Qiu XM. Hypolipidemic effect of the Chinese polyherbal Huanglian Jiedu decoction in type 2 diabetic rats and its possible mechanism. Phytomedicine 2014;21:615-23.  Back to cited text no. 107
    
108.
Ye YL, Zhong K, Liu DD, Xu J, Pan BB, Li X, et al. Huanglian-jie-du-tang extract ameliorates depression-like behaviors through BDNF-TrkB-CREB pathway in rats with chronic unpredictable stress. Evid Based Complement Alternat Med 2017;2017:7903918.  Back to cited text no. 108
    
109.
Sun LM, Zhu BJ, Cao HT, Zhang XY, Zhang QC, Xin GZ, et al. Explore the effects of Huang-Lian-Jie-Du-Tang on Alzheimer's disease by UPLC-QTOF/MS-based plasma metabolomics study. J Pharm Biomed Anal 2018;151:75-83.  Back to cited text no. 109
    
110.
Dong S, Cai FF, Chen QL, Song YN, Sun Y, Wei B, et al. Chinese herbal formula Fuzheng Huayu alleviates CCl-induced liver fibrosis in rats: A transcriptomic and proteomic analysis. Acta Pharmacol Sin 2018;39:930-41.  Back to cited text no. 110
    
111.
Song YN, Dong S, Wei B, Liu P, Zhang YY, Su SB. Metabolomic mechanisms of gypenoside against liver fibrosis in rats: An integrative analysis of proteomics and metabolomics data. PLoS One 2017;12:e0173598.  Back to cited text no. 111
    
112.
Li QY, Guo ZZ, Deng X, Xu LM, Gao YQ, Zhang W, et al. Curative Effects of ZHENG-Based Fuzheng-Huayu Tablet on Hepatitis B Caused Cirrhosis Related to CYP1A2 Genetic Polymorphism. Evid Based Complement Alternat Med 2013;2013:302131.  Back to cited text no. 112
    
113.
Liu EH, Qi LW, Peng YB, Cheng XL, Wu Q, Li P, et al. Rapid separation and identification of 54 major constituents in Buyang Huanwu decoction by ultra-fast HPLC system coupled with DAD-TOF/MS. Biomed Chromatogr 2009;23:828-42.  Back to cited text no. 113
    
114.
Yang D, Cai S, Liu H, Guo X, Li C, Shang M, et al. On-line identification of the constituents of Buyang Huanwu decoction in pig serum using combined HPLC-DAD-MS techniques. J Chromatogr B Analyt Technol Biomed Life Sci 2006;831:288-302.  Back to cited text no. 114
    
115.
Fu C, Xia Z, Liu Y, Lu H, Zhang Z, Wang Y, et al. Qualitative analysis of major constituents from Xue Fu Zhu Yu Decoction using ultra high performance liquid chromatography with hybrid ion trap time-of-flight mass spectrometry. J Sep Sci 2016;39:3457-68.  Back to cited text no. 115
    
116.
Zhang L, Jiang Z, Yang J, Li Y, Wang Y, Chai X. Chemical material basis study of Xuefu Zhuyu decoction by ultra-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry. J Food Drug Anal 2015;23:811-20.  Back to cited text no. 116
    
117.
Liu Y, Li S H, Li X H, Zhang H G, Zhou J Z, Jia Y. Study on different expression of genes associated with inflammatory and immune response in rats with qi deficency and blood stasis syndrome. Chin J Informat TCM 2008;15:33-6.  Back to cited text no. 117
    
118.
Miao L, Liu JX, Ren JX, Pan YH. Primary proteomics analysis of rat serum proteins related with syndrome of Qi-deficiency and blood stasis. Chin J Experiment Tradit Med Formulae 2009;15:42-6.  Back to cited text no. 118
    
119.
Li L, Wang JN, Reng JX, Xiang JF, Tang YL, Liu JX, Han D. Nuclear magnetic resonance spectroscopy metabolomics of urine in rats with Qi deficiency and blood stasis syndrome. Sci Bulletin 2007;52:1758-62.  Back to cited text no. 119
    
120.
Chen G, Gao J, He H, Liu C, Liu Y, Li J, et al. Identification of differentially expressed non-coding RNAs and mRNAs involved in Qi stagnation and blood stasis syndrome. Exp Ther Med 2019;17:1206-23.  Back to cited text no. 120
    
121.
Miao L. Proteomic profiles in animal models with syndrome of blood stasis. China Academy Chin Med Sci 2008;41-4.  Back to cited text no. 121
    
122.
Ma CY, Liu JH, Liu JX, Shi DZ, Xu ZY, Wang SP, et al. Relationship between two blood stasis syndromes and inflammatory factors in patients with acute coronary syndrome. Chin J Integr Med 2017;23:845-9.  Back to cited text no. 122
    
123.
Zhang H, Wang WR, Lin R, Zhang JY, Ji QL, Lin QQ, et al. Buyang Huanwu decoction ameliorates coronary heart disease with Qi deficiency and blood stasis syndrome by reducing CRP and CD40 in rats. J Ethnopharmacol 2010;130:98-102.  Back to cited text no. 123
    
124.
Zhu JZ, Bao XY, Zheng Q, Tong Q, Zhu PC, Zhuang Z, et al. Buyang huanwu decoction exerts cardioprotective effects through targeting angiogenesis via caveolin-1/VEGF signaling pathway in mice with acute myocardial infarction. Oxid Med Cell Longev 2019;2019:4275984.  Back to cited text no. 124
    
125.
Chen H, Song H, Liu X, Tian J, Tang W, Cao T, et al. Buyanghuanwu Decoction alleviated pressure overload induced cardiac remodeling by suppressing Tgf-β/Smads and MAPKs signaling activated fibrosis. Biomed Pharmacother 2017;95:461-8.  Back to cited text no. 125
    
126.
He H, Chen G, Gao J, Liu Y, Zhang C, Liu C, et al. Xue-Fu-Zhu-Yu capsule in the treatment of qi stagnation and blood stasis syndrome: A study protocol for a randomised controlled pilot and feasibility trial. Trials 2018;19:515.  Back to cited text no. 126
    
127.
Yi M, Li Q, Zhao Y, Nie S, Wu N, Wang D. Metabolomics study on the therapeutic effect of traditional Chinese medicine Xue-Fu-Zhu-Yu decoction in coronary heart disease based on LC-Q-TOF/MS and GC-MS analysis. Drug Metab Pharmacokinet 2019;34:340-9.  Back to cited text no. 127
    
128.
Zhao Y, Nie S, Yi M, Wu N, Wang W, Zhang Z, et al. UPLC-QTOF/MS-based metabolomics analysis of plasma reveals an effect of Xue-Fu-Zhu-Yu capsules on blood-stasis syndrome in CHD rats. J Ethnopharmacol 2019;241:111908.  Back to cited text no. 128
    
129.
Tao T, He T, Wang X, Liu X. Metabolic profiling analysis of patients with coronary heart disease undergoing xuefu zhuyu decoction treatment. Front Pharmacol 2019;10:985.  Back to cited text no. 129
    


    Figures

  [Figure 1], [Figure 2], [Figure 3], [Figure 4]
 
 
    Tables

  [Table 1], [Table 2]



 

Top
 
  Search
 
    Similar in PUBMED
   Search Pubmed for
   Search in Google Scholar for
 Related articles
    Access Statistics
    Email Alert *
    Add to My List *
* Registration required (free)  

 
  In this article
Abstract
Introduction
Characteristics ...
Systems Biology
“Disease-S...
Conclusions and ...
References
Article Figures
Article Tables

 Article Access Statistics
    Viewed450    
    Printed48    
    Emailed0    
    PDF Downloaded59    
    Comments [Add]    

Recommend this journal