Uncovering the mechanism of Maxing Ganshi Decoction on asthma from a systematic perspective: A network pharmacology study

Published: 26 November 2018 Wenjie Song, Shenglou Ni, Yanling Fu & Yun Wang Scientific Reportsvolume 8, Article number: 17362 (2018) | Download Citation Abstract Maxing Ganshi Decoction (MXGSD) is used widely for asthma over thousands of years, but its underlying pharmacological mechanisms remain unclear. In this study, systematic and comprehensive network pharmacology was utilized for the first time to reveal the potential pharmacological mechanisms of MXGSD on asthma. Specifically, we collected 141 bioactive components from the 600 components in MXGSD, which shared 52 targets common to asthma-related ones. In-depth network analysis of these 52 common targets indicated that asthma might be a manifestation of systemic neuro-immuno-inflammatory dysfunction in the respiratory system, and MXGSD could treat asthma through relieving airway inflammation, improving airway remodeling, and increasing drug responsiveness. After further cluster and enrichment analysis of the protein-protein interaction network of MXGSD bioactive component targets and asthma-related targets, we found that the neurotrophin signaling pathway, estrogen signaling pathway, PI3K-Akt signaling pathway, and ErbB signaling pathway might serve as the key points and principal pathways of MXGSD gene therapy for asthma from a systemic and holistic perspective, and also provides a novel idea for the development of new drugs for asthma. Introduction Bronchial asthma (hereinafter referred to as asthma) is the most common chronic respiratory disease in the world1. It affects all age groups, especially in children, and has a high morbidity rate2. With increasing smoking and allergy rates, air pollution, and an aging population, the incidence of asthma is rising year-on-year3,4,5. Epidemiological surveys from different countries put the prevalence of asthma at between 1% to 21%, translating to an increase by approximately 30% in the past 20 years6. In 2015, the World Health Organization estimated that 334 million people (4.9% of the world’s population) suffer from asthma, and 250,000 deaths each year, remaining the top twenty causes of disability in children globally7. Apart from clinical manifestations such as wheezing, shortness of breath, chest tightness, and cough caused by the chronic airway inflammation in asthma8,9,10, complications such as respiratory failure, cardiovascular diseases, kidney diseases, sudden death11,12,13,14,15, are also frequently encountered, which not only endangers human health, increases economic burden, but also affects societal productivity2,16. Research has shown asthma to be a polygenic inherited respiratory disease closely related to gene-environment interactions and has significant individual differences in clinical manifestations and pathological changes17, but its complex pathogenesis has not been fully elucidated. Well-acknowledged mechanisms include chronic airway inflammation, airway hyper-responsiveness, reversible airway restriction and airway remodeling caused by airway immuno-inflammation and neuro-modulation18. Therefore, clinical drugs like anti-inflammatories, bronchodilators, and inhaled corticosteroids are used mainly for asthma. However, their side effects and unknown pathogens that cannot be identified in time are reasons for the limitations of these drugs in the clinic19,20. Thus, safe and effective individualized gene therapy may be a future trend in the treatment of asthma21. Traditional Chinese Medicine (TCM) has been used widely in clinical practice for over thousands of years and has the advantages of being “simple, convenient, economic and effective”. Furthermore, treatment based on pattern identification is also in accordance with the concept of individualized gene therapy in modern precision medicine22. Therefore, finding a solution for asthma in the domain of TCM is necessary. Our previous studies confirmed that Maxing Ganshi Decoction (MXGSD) is effective in relieving clinical symptoms of asthma, improving lung function, shortening treatment course, bettering quality of life, and reducing mortality rate23, which has been confirmed by other studies24,25,26. MXGSD, a well-known formula from the Treatise on Febrile and Miscellaneous Diseases (Shang Han Za Bing Lun) by famous Chinese physician Zhang Zhongjing, comprises four Chinese herbal ingredients: Mahuang, Xingren, Gancao, and Shigao in a 5:5:3:6 ratio27. Research has found MXGSD to have therapeutic effects on asthma by reducing airway inflammation, regulating mucus secretion, controlling airway smooth muscle cell proliferation, and improving hypoxia28,29,30,31,32, but its underlying pharmacological mechanism is not fully clear. As the same neuro-immuno-inflammatory cascade reactions are common in different tissues and organs, and can interact with each other, a single organ cannot be viewed separately from the whole body. For this reason, we speculate that MXGSD is effective for asthma via the regulation of the body’s different systems and homeostasis. TCM formulas have the characteristics of being multi-component, multi-target and multi-pathway, but currently, most studies are still confined to the traditional research method of “single-drug, single-target, and single-pathway”33,34,35. Network pharmacology is a comprehensive method that includes chemo-informatics, bio-informatics, network biology and traditional pharmacology, and provides a novel strategy to uncover the bioactive components and underlying mechanisms of TCM formulas from a systemic and holistic perspective36. Therefore, we first applied network pharmacology to identify bioactive components and targets of MXGSD, seek common targets for asthma, and understand the underlying mechanism of the common targets. We then used topological analysis to construct a core protein-protein interaction (PPI) network of MXGSD and asthma, classified the core PPI network based on cluster analysis and further explored the core pharmacological mechanisms of MXGSD in treating asthma. Materials and Methods Data sources Components of MXGSD Data on the four herbs in MXGSD were mainly obtained from the TCM systems pharmacology database (TCMSP, http://ibts.hkbu.edu.hk/LSP/tcmsp.php, updated on May. 31, 2014) and the TCM database@Taiwan (TCM@Taiwan, http://tcm.cmu.edu.tw, updated on Mar. 25, 2014), the two largest pharmacological data platforms for TCM. They contain all herbs, chemical components and pharmacokinetic properties (namely, absorption, distribution, metabolism and excretion or ADME) information in the Pharmacopoeia of the People’s Republic of China (2010 edition)37,38. In addition, the databases of the China National Knowledge Infrastructure, Wanfang, and Pubmed were also used to supplement any other omitted components. Finally, 600 components from MXGSD were collected, including 230, 85, 280 and 5 from Mahuang, Xingren, Gancao, and Shigao, respectively (Supplementary Table S1). Asthma-related targets Data on the asthma-related targets were obtained from five databases: (i) We obtained 174 asthma-related targets from the Online Mendelian Inheritance in Man (OMIM, http://omim.org/, updated on Jan. 3, 2018), an information platform mainly designed for human genes and genetic diseases39. (ii) We obtained 81 asthma-related targets from the Therapeutic Target Database (TTD, http://systemsdock.unit.osit.jp/iddp/home/index, updated on Sep. 15, 2017), which contains substantial information about the therapeutic proteins, gene targets, related diseases, pathways and corresponding drugs40. (iii) We obtained 367 asthma-related targets from the Genetic Association Database (GAD, https://geneticassociationdb.nih.gov/, updated on Sep. 1, 2014), which provides genes associated with complex diseases and disorders41. (iv) We obtained 85 asthma-related targets from the Pharmacogenomics Knowledge Implementation (PharmGkb, https://www.pharmgkb.org/, updated on Dec. 28, 2017), which mainly details relationships between actionable genes and clinical drugs42. (v) We obtained 35 asthma-related targets from the Drugbank database (https://www.drugbank.ca, updated on Dec. 20, 2017), which offers target genes from FDA-approved drugs43. After removing duplicates, 546 asthma-related targets were collected altogether (Supplementary Table S2). Data preprocessing Screening bioactive components of MXGSD Oral administration is the most common route for TCM herbs to take effect in the gastrointestinal tract. Therefore, we selected two ADME-related models, bioavailability (OB) and drug-likeness (DL), for the bioactive components screening of MXGSD, which plays an important role in pharmacodynamics studies. The screening criteria are: OB ≥ 20% and DL ≥ 0.1844. In this study, 141 bioactive components of MXGSD were included, 11 from Mahuang, 10 from Xingren, 115 from Gancao, and 5 from Shigao (Supplementary Table S3). Bioactive component-target prediction for MXGSD According to different component-target prediction principles, target prediction techniques and methods can be divided into four types: (i) ligand-based prediction (chemical similarity search and pharmacophore model); (ii) receptor-based prediction (molecular docking); (iii) machine learning prediction (the database must have a clear correspondence between the molecules and targets, and the target names must be standardized), and (iv) combined prediction45. Based on our previous experience and considering the limitations of experimental conditions for molecular docking and machine learning predictions, we chose ligand-based prediction method for the subsequent study43,46. First, we chose three common chemical component databases of TCMSP, PubChem Project (PubChem, https://pubchem.ncbi.nlm.nih.gov/, updated on Oct. 26, 2017), and ZINC (http://zinc15.docking.org, updated on Mar. 14, 2016) as the main sources of structure information on chemical components, from which we obtained the detailed information about the bioactive components of MXGSD, including canonical smiles, molecular structures, and their “mol2” files. Second, we chose four public databases of TCMSP, Swiss Target Prediction (http://www.swisstargetprediction.ch/), STITCH (http://stitch.embl.de/), and Pharm-Mapper (http://lilab.ecust.edu.cn/pharmmapper/index.php, updated on Nov. 27, 2017), as the main sources of component-target data, which predicts targets depending on the chemical similarities and pharmacophore models of its component. After that, we obtained the target protein names of MXGSD bioactive components. Finally, by converting target protein names of MXGSD bioactive components into gene names with the species limited into “Homo sapiens” by UniProt Knowledgebase (UniProtKB, http://www.uniprot.org/), we established the component-target data table (Supplementary Table S4). Screening common targets for MXGSD and asthma After screening the common targets of MXGSD and asthma (Supplementary Table S5), we explored the underlying mechanisms of these common targets with gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis (Supplementary Table S6). PPI network construction Proteins often work together with other molecules, such as lipids, nucleic acids, and other proteins47. The PPI network is a network structure that physically connects proteins through molecular docking in cells48, and can be visually analyzed using Bisogenet, a Cytoscape plugin. The data sources came from 6 main PPI databases, namely, Database of Interacting Proteins (DIP), Biological General Repository for Interaction Datasets (BioGRID), Human Protein Reference Database (HPRD), IntAct Molecular Interaction Database (IntAct), Molecular INTeraction Database (MINT), and Biomolecular Interaction Network Database (BIND)49. PPI networks of MXGSD bioactive component-targets and asthma-related targets were then constructed. Central network evaluation Central network evaluation is a topological method of defining the core central network. First, the two PPI networks of MXGSD bioactive component-targets and asthma-related targets were compared, before being analyzed and evaluated by CytoNCA, a Cytoscape plugin. Each node in the area of intersection was assessed with its eight typical central attributes: betweenness centrality (BC), closeness centrality (CC), degree centrality (DC), eigenvector centrality (EC), local average connectivity-based method (LAC), network centrality (NC), subgraph centrality (SC), and information centrality (IC)50. However, using the Cytoscape software, only six of the eight typical center attributes of CytoNCA can be used to calculate the required data, namely BC, CC, DC, EC, LAC and NC. Therefore, we selected “DC ≥ 2 × median DC” as the primary screening criteria, before finding the shortest average distance (CC), shortest paths (BC), highest eigenvector score (EC), largest average local connectivity (LAC), and biggest aggregation coefficients (NC) as the core targets51,52 (Supplementary Table S7). If the initial screening criteria was to satisfy six conditions at the same time, the degree of targets found cannot be considered core targets. Cluster analysis Clusters refer to highly interconnected regions distilled from different, complex objects with similar underlying properties. Cluster analysis as an important classification method clearly shows the inherent laws in a PPI network53. Many algorithms in the cluster analysis of PPI network in Cytoscape have been reported, but previous research confirmed that the network stability of the module generated by the Molecular Complex Detection (MCODE) algorithm is superior54, so we chose MCODE to perform cluster analysis for the PPI network (Supplementary Table S8). Enrichment analysis of GO and KEGG pathway GO (including biological process (BP), cell component (CC), and molecular functions (MF)) and KEGG pathway enrichment analysis are the common methods used to describe the characteristics of candidate targets55. We utilized the Database for Annotation Visualization and Integrated Discovery (DAVID, https://david.nicifcrf.gov/, updated in Mar. 2017), an online platform for the high-throughput functional annotation bioinformatics, to perform functional annotation and enrichment analysis, and recognized GO terms with Bonferroni <0.0534 (Supplementary Table S9) and KEGG pathways with P-value < 0.0535 as significant (Supplementary Table S10). Results MXGSD component-target network We retrieved Chinese herbal components from two broad categories of databases: natural product databases (Fig. 1A) and biomedical literature (Fig. 1B). In total, 600 components were collected, including 230, 85, 280, and 5 from the four herbs: Mahuang, Xingren, Gancao and Shigao, respectively. Among these components, O-benzoyl-L-(+)-pseudoephedrine, ()-N-methylpseudoephedrine, ()-N-methylephedrine, amygdalin, glycyrrhizin, CaSO4·nH2O (n = 0 or 2), Fe, Mn, and Zn were retrieved manually for further study owing to their reported properties56,57,58. The components were screened by two ADME-related models, OB and DL, and 141 bioactive components were included (Fig. 1C), of which 7.80% (11/141) from Mahuang, 7.09% (10/141) from Xingren, 81.56% (115/141) from Gancao, and 3.55% (5/141) from Shigao (Fig. 1D). Of these 141 bioactive components, three groups from Mahuang, Xingren, and Shigao overlapped significantly, which indicates that different herbs in a formula can share the same or similar components and targets with synergistic effects. Figure 1 Figure 1 MXGSD component-targets network. Two broad categories database: (A) natural product databases and (B) biomedical literatures. (C) 600 constituent components (green section), and 141 bioactive components screened by two ADME-related models (blue section stands for the components of OB ≥20%, yellow section stands for DL ≥0.18). (D) Distributions of different herbs. (E) Construction of MXGSD bioactive component-putative targets visual network. Full size image Generally speaking, the therapeutic and preventive effects of a compound formula rely on the synergies between the multiple components, targets and pathways59. Therefore, we then explored the effective targets of the 141 MXGSD bioactive components with ligand-based prediction strategy. After removing duplicates, we finally identified 186 potential targets from 118 out of 141 bioactive components. The number of these targets in Mahuang, Xingren, Gancao, and Shigao was 112, 62, 139, and 34 respectively. To further understand MXGSD component-targets network from a systematic and holistic view, we constructed a network map using Cytoscape, version 3.2.1 (http://www.cytoscape.org)60 (Fig. 1E). The network contained 300 nodes and 2512 edges altogether. The degree of node represented the number of edges or targets linking with the node based on the topological analysis. In this network, we identified 56 candidate components with a median ≥21 degrees, which also proved synergistic effects in different herbs and components. Specifically, we found that quercetin, kaempferol, and apigenin acted on 172, 106 and 46 targets, respectively, and became the crucial bioactive components in MXGSD. Some studies suggested that quercetin could reduce airway inflammation and relax airway smooth muscle (ASM) by regulating some key pathways, such as mast cell signaling pathway and calcium ion pathway, which in turn manages asthma61,62,63. This may also explain the pleiotropic effects of traditional Chinese medical herbs. Asthma-related target network Asthma is a polygenic genetic predisposing disease. Research on gene and gene-environment interaction is beneficial to revealing the pathogenesis of asthmatic diseases. In this study, we collected a total of 546 asthma-related targets (Fig. 2A) recorded in genomic databases of human diseases (Fig. 2B). Among these, MXGSD shared 52 common targets with asthma (Fig. 2C,D). Figure 2 Figure 2 Asthma-related targets network. (A) Five disease gene target databases. (B) Asthma-target network, containing 542 nodes and 545 edges. (C) 52 common targets between asthma and MXGSD. (D) 52 common target network, containing 164 nodes and 764 edges. (E) GO and (F) KEGG pathway (bubble diagram on left, pie diagram on right) enrichment analysis of MXGSD on asthma. Full size image To gain insights into the pharmacological mechanisms of MXGSD on asthma, we performed GO and KEGG pathway analysis on the 52 common targets, and identified 19 BPs, 17 CCs, 6 MFs (Fig. 2E) and 32 KEGG pathways (Fig. 2F) of significance. The results were mainly enriched in airway inflammatory response, airway remodeling, and drug reaction, which were the same as the present pathologic mechanism of asthma. In addition, we also confirmed that MXGSD inhibits inflammation through the regulation of cholinergic, adrenergic, and other receptors with the involvement of nitric oxide biosynthesis, superoxide radicals removal, adrenergic receptor signaling pathway, and G-protein coupled acetylcholine receptor signaling pathway. Besides, ASM cell apoptosis and proliferation, arachidonic acid metabolism, interleukin (IL)-17 signaling pathway, and estrogen signaling pathway were also of great importance in clarifying the mechanisms of MXGSD on asthma. Asthmatics are increasingly being reported to also suffer from hypertension, hyperlipidemia, diabetes mellitus, heart disease and other comorbidities, which affects each condition and brings greater challenges to asthma treatment64,65,66. As shown by previous studies67,68,69, we also verified that MXSGD could treat asthma and regulate blood pressure, atherosclerosis, and diabetic complications simultaneously from the network pharmacology level. Asthma drugs can cause systemic changes but control asthma, but may exert a therapeutic or side effect on other diseases. Conversely, drugs regulating systemic changes, such as hormones and angiotensin converting enzyme inhibitors, can also affect asthma. Thus, we deduce that asthma may be a reaction of systemic neuro-immuno-inflammatory diseases in the respiratory system. MXGSD-asthma PPI network To dive deeper into the core pharmacological mechanisms of MXGSD in treating asthma, we first employed a topological method to evaluate the core network. PPI network is beneficial to understanding the role of various proteins in complex diseases, like asthma70. Therefore, we constructed MXGSD (Fig. 3A) and asthma-related targets (Fig. 3B) network with PPI databases, merged the two PPI network to obtain an interactive PPI network of MXGSD and asthma (Fig. 3C), before identifying the core PPI network according to the topology analysis screening criteria “DC ≥ 2 × median DC” (Fig. 3D). Eventually, we obtained 138 MXGSD core targets on asthma treatment, which satisfied the six screening criteria concurrently (Fig. 3E). Figure 3 Figure 3 Construction of MXGSD-asthma PPI network. (A) MXGSD putative targets PPI network with 6265 nodes and 154 870 edges. (B) Asthma-related targets PPI network with 7405 nodes and 173 108 edges. (C) Interactive PPI network of MXGSD and asthma with 4885 nodes and 132 764 edges. (D) First central network evaluation with a core subset of 1194 nodes and 54 626 edges based on the median degree of 33. (E) Second central network evaluation with a core subset of 138 nodes and 3273 edges based on “‘BC’ > 471.658, ‘CC’ > 0.507, ‘DC’ > 214, ‘EC’ > 0.018, ‘LAC’ > 17.129, and ‘NC’ > 18.728”. Blue diamonds, pink diamonds, and yellow diamonds stand for other human proteins, component targets, and selected targets, respectively. Full size image Enrichment analysis of the 138 key targets To clarify the biological functions of the 138 key targets, we classified the final central PPI network into five clusters (Fig. 4A), and performed GO (Fig. 4B) and KEGG pathway enrichment analysis individually (Fig. 4C). Based on these GO terms data, we found that (i) gene expression, silence and replication; (ii) DNA/RNA damage repair and transcriptional regulation; (iii) protein classification, modification, synthesis and stability; (iv) signal transduction and cascade activation of inflammatory immune response; (v) DNA/RNA positive and negative regulation were enriched in their corresponding clusters, suggesting that MXGSD could treat asthma from a genetic perspective with multiple synergies. Figure 4 Figure 4 Enrichment analysis of the 138 core targets. (A) Clusters of the final central PPI network. Blue diamonds, pink diamonds, and yellow diamonds stand for other human proteins, component targets, and selected targets, respectively. (B) GO and (C) KEGG pathway enrichment analysis for every cluster. Full size image Specifically, we found that many diseases such as systemic lupus erythematosus, hepatitis, leukemia, cancer, and other related biological molecules such as estrogen, prolactin, oxytocin, GnRH, aldosterone, renin, thyroid hormone, insulin, glucagon, gastric acid could also indirectly affect the development of asthma, providing strong evidence for our hypothesis that MXGSD could treat asthma through systemic neuro-immuno-inflammatory regulation from a genetic perspective. In addition, based on the analysis of KEGG signaling pathway data, we believed that the key signaling pathways such as neurotrophin signaling pathway, estrogen signaling pathway, PI3K-Akt signaling pathway, and ErbB signaling pathway, the top 4 KEGG pathways in these clusters, might be the core pharmacological mechanism of MXGSD for asthma. All aforementioned analysis could shed light on a novel strategy for drug development on asthma. Discussion Asthma is a common complex respiratory disease with high morbidity and mortality. To date, pharmaceutical drugs are used mainly to control its related symptoms. Comparatively, TCM herbs not only control the symptoms, but may also resolve the underlying cause. MXGSD, through its thousand-year usage has demonstrated its practical effectiveness on asthma71,72,73, but the pharmacological mechanisms have not been uncovered systematically and comprehensively. In this study, we adopted network pharmacology to further explore the mechanisms of MXGSD on asthma. Three aspects aroused our attention: first, 52 common targets for asthma were enriched in pathological changes of airway inflammation and remodeling, as well as other diseases. Second, cluster analysis exhibited a refined detail of MXGSD on asthma compared to the common-target analysis. Thirdly, neurotrophin signaling pathway, estrogen signaling pathway, PI3K-Akt signaling pathway, and ErbB signaling pathway were considered as the key sections of MXGSD on asthma treatment from gene level. (The diagram of the study design is shown in Fig. 5). Figure 5 Figure 5 Diagram of the study design. Full size image Subsequently, we retrieved the four key signaling pathways of MXGSD associated with asthma treatment. Latest research shows that neurotrophic factors can be synthesized and released by a variety of immune and inflammatory cells, which mediate airway inflammation through increase of Th2 cells, eosinophils activation, induction of mast cell proliferation and differentiation, promotion of inflammatory mediators release, and immune response intensification74. Besides, neurotrophic factors stimulate airway epithelium sensory afferent nerve to synthesize neuropeptides75 and promote the differentiation of neural stem cells into cholinergic neurons76,77,78,79,80, which in return mediate airway inflammation, hyper-responsiveness, and remodeling. Combined with the regulation of MXGSD on other diseases as mentioned above, we deduce that asthma is a manifestation of the systemic neuro-immuno-inflammatory response in the respiratory system, which is also in line with the holistic comprehension of the physiological and pathological state of the human body in TCM. Asthma is a polygenic genetic predisposing disease. The polymorphism of human genetic genes determines the body’s sensitivity and response to the diseases and drugs, as well as the diversity of clinical manifestations. This is similar to the concept of Zheng (pattern) in TCM – the summary of pathological properties at a certain stage in the development of disease, which is a functional state of the body responding to the pathogenic factors. Hereby, we speculate one of the theoretical bases of TCM is multi-gene in nature. In this study, we confirmed that MXGSD is effective for asthma from the levels of gene repair and expression to signal transduction and cascade activation in pathways. Meanwhile, some researchers emphasize on the relationships between polymorphisms of human genes and TCM pattern types/therapeutic effect from the perspective of clinical trials and experimental tests81,82,83. Therefore, multi-gene analysis may be a new research approach to verify the effectiveness of TCM herbs. However, there are still some limitations. First, our collection of bioactive components and targets was not comprehensive. It can be improved by new detections using liquid chromatography, mass spectrometry, two-dimensional liquid chromatography or quadrupole-time-of-flight mass spectrometry. Second, our network pharmacology study lacked animal experiments and clinical trials to verify the hypothesis. In the future, we endeavor to verify the relationship between TCM pattern types and gene polymorphisms from animal experiments and clinical trials. Conclusions In conclusion, we believe that asthma is a manifestation of the systemic neuro-immuno-inflammatory response in the respiratory system by network pharmacology. MXGSD not only relieves asthma, but also regulates the functional state of other systems in the human body from the gene level. Four signaling pathways of neurotrophin, estrogen, PI3K-Akt, and ErbB play vital roles in the treatment of asthma by MXGSD, providing a new direction for drug development on asthma. Additional information Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. References 1. Pavord, I. D. et al. After asthma: redefining airways diseases. Lancet. 391, 350–400, https://doi.org/10.1016/S0140-6736(17)30879-6 (2018). Article PubMed Google Scholar 2. Bateman, E. D. et al. Global strategy for asthma management and prevention: GINA executive summary. Eur Respir J. 31, 143–178, https://doi.org/10.1183/09031936.00138707 (2008). CAS Article PubMed Google Scholar 3. Chen, Z. et al. Contrasting male and female trends in tobacco-attributed mortality in China: evidence from successive nationwide prospective cohort studies. Lancet. 386, 1447–1456 (2015). Article Google Scholar 4. Guan, W. J., Zheng, X. Y., Chung, K. F. & Zhong, N. S. Impact of air pollution on the burden of chronic respiratory diseases in China: time for urgent action. Lancet. 388, 1939–1951, https://doi.org/10.1016/S0140-6736(16)31597-5 (2016). Article PubMed Google Scholar 5. Zein, J. G. et al. Asthma is more severe in older adults. PLoS One. 10, e0133490, https://doi.org/10.1371/journal.pone.0133490. eCollection2015 (2015). Article Google Scholar 6. Reddel, H. K. et al. A summary of the new GINA strategy: a roadmap to asthma control. Eur Respir J. 46, 622–639, https://doi.org/10.1183/13993003.00853-2015 (2015). CAS Article PubMed PubMed Central Google Scholar 7. Ahmed, R., Robinson, R. & Mortimer, K. The epidemiology of noncommunicable respiratory disease in sub-Saharan Africa, the Middle East, and North Africa. Malawi Med J. 29, 203–211 (2017). Article Google Scholar 8. Lang, D. M. Severe asthma: epidemiology burden of illness and heterogeneity. Allergy Asthma Proc. 36, 418–424, https://doi.org/10.2500/aap.2015.36.3908 (2015). Article PubMed Google Scholar 9. Barnes, P. J. Severe asthma: advances in current management and future therapy. J Allergy Clin Immunol. 129, 48–59, https://doi.org/10.1016/j.jaci.2011.11.006 (2012). CAS Article PubMed Google Scholar 10. Himes, B. E. et al. Prediction of chronic obstructive pulmonary disease (COPD) in asthma patients using electronic medical records. J Am Med Inform Assoc. 16, 371–379, https://doi.org/10.1197/jamia.M2846 (2009). Article PubMed PubMed Central Google Scholar 11. Gullach, A. J. et al. Sudden death in young persons with uncontrolled asthma-a nationwide cohort study in Denmark. BMC Pulm Med. 15, 35, https://doi.org/10.1186/s12890-015-0033-z (2015). Article PubMed PubMed Central Google Scholar 12. Tattersall, M. C. et al. Asthma predicts cardiovascular disease events: the multi-ethnic study of atherosclerosis. Arterioscler Thromb Vasc Biol. 35, 1520–1525, https://doi.org/10.1161/ATVBAHA.115.305452 (2015). CAS Article PubMed PubMed Central Google Scholar 13. Huang, H. L. et al. Bronchial asthma is associated with increased risk of chronic kidney disease. BMC Pulm Med. 14, 80, https://doi.org/10.1186/1471-2466-14-80 (2014). CAS Article PubMed PubMed Central Google Scholar 14. Liu, D. W. et al. Persistent asthma increases the risk of chronic kidney disease: a retrospective cohort study of 2354 patients with asthma. Chin Med J (Engl). 126, 4093–4099 (2013). ADS PubMed Google Scholar 15. Corren, J. Asthma phenotypes and endotypes: an evolving paradigm for classification. Discov Med. 15, 243–249 (2013). PubMed Google Scholar 16. Su, N. et al. An epidemiological survey of current asthma control status in China. Zhonghua Nei Ke Za Zhi. 53, 601–606 (2014). PubMed Google Scholar 17. Papi, A., Brightling, C., Pedersen, S. E. & Reddel, H. K. Asthma. Lancet. 391, 783–800, https://doi.org/10.1016/S0140-6736(17)33311-1. (2018). Article PubMed Google Scholar 18. Horak, F. et al. Diagnosis and management of asthma - Statement on the 2015 GINA Guidelines. Wien Klin Wochenschr. 128, 541–554, https://doi.org/10.1007/s00508-016-1019-4 (2016). CAS Article PubMed PubMed Central Google Scholar 19. Garber, K. Biochemistry: A radical treatment. Nature. 489, S4–S6, https://doi.org/10.1038/489S4a (2012). ADS CAS Article PubMed Google Scholar 20. Morales, D. R. et al. NSAID-exacerbated respiratory disease: a meta-analysis evaluating prevalence, mean provocative dose of aspirin and increased asthma morbidity. Allergy. 70, 828–835, https://doi.org/10.1111/all.12629 (2015). CAS Article PubMed Google Scholar 21. Pike, K. C., Levy, M. L., Moreiras, J. & Fleming, L. Managing problematic severe asthma: beyond the guidelines. Arch Dis Child.13, https://doi.org/10.1136/archdischild-2016-311368 (2017). Article Google Scholar 22. Kaixian, C. Precision Medicine and Innovative Development of Traditional Chinese Medicine (TCM). Modernization of Traditional Chinese Medicine and Materia Medica-World Science and Technology. 19, 7–18 (2017). Google Scholar 23. Wang, Q., Zhao, S. J., Feng, Q. & Fu, Y. L. Analysis on dosage of contemporary medical cases of Ma Xing Shi Gan Tang. Federation of Chinese medicine societies in the world and the second dose effect research committee meeting international party dose effect relationship and reasonable application seminar. Beijing, China: 56–58 (2014). 24. Wang, H. M., Lin, S. K., Yeh, C. H. & Lai, J. N. Prescription pattern of Chinese herbal products for adult-onset asthma in Taiwan: a population-based study. Ann Allergy Asthma Immunol. 112, 465–470, https://doi.org/10.1016/j.anai.2014.02.012 (2014). Article PubMed Google Scholar 25. Zhao, S. J., Ma, Y. H., Fu, Y. L. & Zhang, L. Effect observation of Maxingshigan Tang and Shengjiang San in treating pediatric bronchial asthma. Journal of Pediatrics of Traditional Chinese Medicine. 1, 15–18 (2015). Google Scholar 26. Zhou, T. T. & Yang, X. N. Curative Observation of Using Singulair, Atomization Inhalation Budesonide Suspension Liquid and Modified Maxing Shigan Decoction Combination Therapy in the Treatment of Infantile Bronchial Asthma. Journal of Sichuan of Traditional Chinese Medicine. 2, 86–88 (2017). Google Scholar 27. Zhao, S. J., Ma, Y. H., Fu, Y. L. & Zhang, L. Systematic review and its dose effect analysis on randomized control trials of Maxing Shigan Decoction in treatment of pneumonia in children. China Journal of Traditional Chinese Medicine and Pharmacy. 28, 361–367 (2013). ADS Google Scholar 28. Yu, L., Pan, J. W. & Ye, L. P. Expression of STAT4 in asthma rats treated with Chinese medicine Maxingshigan Decoction. Zhejiang Medical Journal. 21, 1883–1886 + 1942 (2017). Google Scholar 29. Yu, L., Zhu, L. J., Zhang, Y. J. & Ye, L. P. Regulation of the Expression of STAT6 and Mucin Protein in Asthma Rats’ Airway Epithelial with Maxingshigan Decoction. Journal of Medical Research. 5, 40–44 (2014). CAS Google Scholar 30. Huang, F., Tong, X. Y., Zhang, R. H. & Cai, Y. Primary study on the mechanism of Maxingshigan decoction on the Th1/Th2 response in a murine model of asthma. Journal of Chinese Medicinal Materials. 31, 1519–1522 (2008). PubMed Google Scholar 31. Han, F. Q., Sun, X. W. & Wang, X. H. Effect of Maxing Shigan Decoction Boiling Method on the Smooth Muscle Cell Proliferation and the Lack of acute Oxygen in the Airway of Asthmatic Model Mice. Lishizhen Medicine and Materia Medica. Research. 25, 1885–1886 (2014). Google Scholar 32. Sun, X. W., Wang, X. H., Zhang, Z. T. & Liu, H. Z. The Effects of Different Preparation Methods of Maxing Shigan Decoction on MUC5AC and HIF-1α in the Asthmatic Mice Model. Nat Prod Res Dev. 25, 1507–1509 + 1575 (2013). Google Scholar 33. Zeng, L. & Yang, K. Exploring the pharmacological mechanism of Yanghe Decoction on HER2-positive breast cancer by a network pharmacology approach. J Ethnopharmacol. 199, 68–85, https://doi.org/10.1016/j.jep.2017.01.045 (2017). CAS Article PubMed Google Scholar 34. Zeng, L., Yang, K. & Ge, J. Uncovering the Pharmacological Mechanism of Astragalus Salvia Compound on Pregnancy-Induced Hypertension Syndrome by a Network Pharmacology Approach. Sci Rep. 7, 16849, https://doi.org/10.1038/s41598-017-17139-x (2017). ADS CAS Article PubMed PubMed Central Google Scholar 35. Lyu, M. et al. Network pharmacology exploration reveals endothelial inflammation as a common mechanism for stroke and coronary artery disease treatment of Danhong injection. Sci Rep. 7, 15427, https://doi.org/10.1038/s41598-017-14692-3 (2017). ADS CAS Article PubMed PubMed Central Google Scholar 36. Berger, S. I. & Iyengar, R. Network Analyses in Systems Pharmacology. Bioinformatics. 25, 2466–2472, https://doi.org/10.1093/bioinformatics/btp465 (2009). CAS Article PubMed PubMed Central Google Scholar 37. Ru, J. et al. TCMSP: a database of systems pharmacology for drug discovery from herbal medicines. J Cheminformatics. 6, 13, https://doi.org/10.1186/1758-2946-6-13 (2014). CAS Article Google Scholar 38. Chen, C. Y. TCM Database@Taiwan: The World’s Largest Traditional Chinese Medicine Database for Drug Screening in Silico. PLoS One. 6, e15939, https://doi.org/10.1371/journal.pone.0015939 (2011). ADS CAS Article PubMed PubMed Central Google Scholar 39. Amberger, J. S. & Hamosh, A. Searching Online Mendelian Inheritance in Man (OMIM): A Knowledgebase of Human Genes and Genetic Phenotypes. Curr Protoc Bioinformatics. 58, 1.2.1–1.2.12, https://doi.org/10.1002/cpbi.27 (2017). Article Google Scholar 40. Li, Y. H. et al. Therapeutic target database update 2018: enriched resource for facilitating bench-to-clinic research of targeted therapeutics. Nucleic Acids Res. 46, D1121–D1127, https://doi.org/10.1093/nar/gkx1076 (2018). Article PubMed Google Scholar 41. Minoshima, S., Ohtsubo, M. & Shimizu, N. Databases for mutations in human diseases. Nihon Rinsho. 68, 150–158 (2010). PubMed Google Scholar 42. Whirl-Carrillo, M. et al. Pharmacogenomics knowledge for personalized medicine. Clin Pharmacol Ther. 92, 414–417, https://doi.org/10.1038/clpt.2012.96 (2012). CAS Article PubMed PubMed Central Google Scholar 43. Wang, S., Wang, H. & Lu, Y. Tianfoshen oral liquid: a CFDA approved clinical Traditional Chinese Medicine, normalizes major cellular pathways disordered during colorectal carcinogenesis. Oncotarget. 8, 14549–14569, https://doi.org/10.18632/oncotarget.14675 (2017). Article PubMed PubMed Central Google Scholar 44. Li, J. et al. Systems pharmacology-based dissection of mechanisms of Chinese medicinal formula Bufei Yishen as an effective treatment for chronic obstructive pulmonary disease. 5, 15290, https://doi.org/10.1038/srep15290 (2015). 45. Wu, C. W. et al. Application of drug-target prediction technology in network pharmacology of Traditional Chinese Medicine. Zhongguo Zhong Yao Za Zhi. 41, 377–382, https://doi.org/10.4268/cjcmm20160303 (2016). Article PubMed Google Scholar 46. Yu, H. et al. A systematic prediction of multiple drug-target interactions from chemical, genomic, and pharmacological data. PLoS One. 7, e37608, https://doi.org/10.1371/journal.pone.0037608 (2012). ADS CAS Article PubMed PubMed Central Google Scholar 47. Bosque, G. et al. Topology analysis and visualization of Potyvirus protein-protein interaction network. BMC Syst Biol. 8, 129, https://doi.org/10.1186/s12918-014-0129-8 (2014). CAS Article PubMed PubMed Central Google Scholar 48. De, L. R. J. & Fontanillo, C. Protein-protein interactions essentials: key concepts to building and analyzing interactome networks. PLoS Comput Biol. 6, e1000807, https://doi.org/10.1371/journal.pcbi.1000807 (2010). CAS Article Google Scholar 49. Martin, A. et al. BisoGenet: A New Tool for Gene Network Building, Visualization and Analysis. BMC Bioinformatics. 11, 91, https://doi.org/10.1186/1471-2105-11-91 (2010). CAS Article PubMed PubMed Central Google Scholar 50. Tang, Y. et al. CytoNCA: A cytoscape plugin for centrality analysis and evaluation of protein interaction networks. Biosystems 127, 67–72, https://doi.org/10.1016/j.biosystems.2014.11.005 (2015). CAS Article PubMed Google Scholar 51. Zhang, Y. et al. Identification of GRB2 and GAB1 coexpression as an unfavorable prognostic factor for hepatocellular carcinoma by a combination of expression profile and network analysis. PLoS One. 8, e85170, https://doi.org/10.1371/journal.pone.0085170 (2013). ADS CAS Article PubMed PubMed Central Google Scholar 52. Zheng, R. Q. & Li, M. A WEB-based platform for predicting essential proteins. Chinese Journal of Bioinformatics. 12, 84-89, https://doi.org/10.3969/j.issn.1672-5565.2014.02.02 (2014). 53. Gu, F., Hall, P. & Miles, N. J. Performance evaluation for composites based on recycled polypropylene using principal component analysis and cluster analysis. Journal of Cleaner Production. 41, 343–353 (2016). Article Google Scholar 54. Zhang, X. X. Biological mechanism of the correlation between QiXuXueYu Zheng and the related diseases via network module analysis. Beijing. China, China academy of Chinese Medical Sciences. master: 26–28 (2016). 55. Chen, J. et al. Integrating GO and KEGG terms to characterize and predict acute myeloid leukemia-related genes. Hematology. 20, 336–342, https://doi.org/10.1179/1607845414Y.0000000209 (2015). CAS Article PubMed Google Scholar 56. Li, C. N. et al. Research on Chemical Constituents and Compatibility of Chinese Compound Formula. Liaoning. Journal of Traditional Chinese Medicine. 41, 2419–2422 (2014). CAS Google Scholar 57. Zhong, W. F. et al. Qualitative and quantitative characterization of secondary metabolites and carbohydrates in Bai-Hu-Tang using ultraperformance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry and ultraperformance liquid chromatography coupled with photodiode array detector. J Food Drug Anal. 25, 946–959, https://doi.org/10.1016/j.jfda.2016.12.007 (2017). CAS Article PubMed Google Scholar 58. Tian, T. Y. et al. Analysis of Inorganic Elements in Gypsum Fibrosum from Different Origins Based on ICP-MS. Chinese Archives of Traditional Chinese Medicine. 4, 1041–1043 (2017). Google Scholar 59. Li, S. & Zhang, B. Traditional Chinese Medicine network pharmacology: theory, methodology and application. Chin J Nat Med. 11, 110–120, https://doi.org/10.1016/S1875-5364(13)60037-0 (2013). ADS Article PubMed Google Scholar 60. Smoot, M. E. et al. Cytoscape 2.8: new features for data integration and network visualization. Bioinformatics. 27, 431–432, https://doi.org/10.1093/bioinformatics/btq675 (2011). ADS CAS Article PubMed Google Scholar 61. Luo, X. et al. Polygonum aviculare L. extract and quercetin attenuate contraction in airway smooth muscle. Sci Rep. 8, 3114, https://doi.org/10.1038/s41598-018-20409-x (2018). ADS CAS Article PubMed PubMed Central Google Scholar 62. Gupta, K. et al. Reversion of Asthmatic Complications and Mast Cell Signalling Pathways in BALB/c Mice Model Using Quercetin Nanocrystals. J Biomed Nanotechnol. 12, 717–731 (2016). CAS Article Google Scholar 63. Oliveira, T. T. et al. Potential therapeutic effect of Allium cepa L. and quercetin in a murine model of Blomia tropicalis induced asthma. Daru 23, 18, https://doi.org/10.1186/s40199-015-0098-5 (2015). CAS Article PubMed PubMed Central Google Scholar 64. Lahousse, L., Verhamme, K. M., Stricker, B. H. & Brusselle, G. G. Cardiac effects of current treatments of chronic obstructive pulmonary disease. Lancet Respir Med. 4, 149–164, https://doi.org/10.1016/S2213-2600(15)00518-4 (2016). CAS Article PubMed Google Scholar 65. Ferguson, S. et al. Factors associated with systemic hypertension in asthma. Lung. 192, 675–683, https://doi.org/10.1007/s00408-014-9600-y (2014). CAS Article PubMed PubMed Central Google Scholar 66. Xu, X., Mishra, G. D., Dobson, A. J. & Jones, M. Progression of diabetes, heart disease, and stroke multimorbidity in middle-aged women: A 20-year cohort study. PLoS Med. 15, e1002516, https://doi.org/10.1371/journal.pmed.1002516 (2018). Article PubMed PubMed Central Google Scholar 67. Yu, L. L. The Clinical observation of Weijingtang and Maxingshigantang treat Stroke-associated Pneumonia (phlegm-hot in the lung). Hubei, China, Hubei University of Chinese Medicine. Master: 4–24 (2016). 68. Feng, X. Z. Experience in Treating Diabetes with Pulmonary Infection. Journal of Traditional Chinese Medicine. 52, 969–971 (2011). Google Scholar 69. Zhi, X. Y. Traditional Chinese Medicine diagnosis and treatment of type 2 diabetes in Tianjin urban population. Zhong Xi Yi Jie He Xue Bao. 7, 823–826 (2009). Article Google Scholar 70. Zhang, Y. et al. Identification of GRB2 and GAB1 coexpression as an unfavorable prognostic factor for hepatocellular carcinoma by a combination of expression pro le and network analysis. PLoS One. 8, e85170, https://doi.org/10.1371/journal.pone.0085170 (2013). ADS CAS Article PubMed PubMed Central Google Scholar 71. Peng, C. W. The Study of the combination of the Traditional Chinese treatment and the Western Medicine Treatment in Treating the Hot Type of Bronchial Asthma. Journal of Practical Traditional Chinese Internal Medicine. 11, 139–140 + 161 (2015). Google Scholar 72. Song, Y. Q., Kong, L. H. & Zhang, L. Clinical observation of Maxingshigan decoction combined with Suting pills on febrile asthma children. Chinese. Journal of Practical Medicine. 43, 116–118 (2016). Google Scholar 73. Yu, Z. B. A clinical study on treating bronchial asthma in TCM. Clinical Journal of Chinese Medicine. 6, 38–40 (2014). Google Scholar 74. Barrios, J. & Ai, X. Neurotrophins in Asthma. Curr Allergy Asthma Rep. 18, 10, https://doi.org/10.1007/s11882-018-0765-y (2018). CAS Article PubMed PubMed Central Google Scholar 75. Freeman, M. R. et al. Brain-derived neurotrophic factor and airway fibrosis in asthma. Am J Physiol Lung Cell Mol Physiol. 313, L360–L370, https://doi.org/10.1152/ajplung.00580.2016 (2017). Article PubMed PubMed Central Google Scholar 76. Chen, S. Y. & Fang, X. B. The Signaling Pathway of Nerve Growth Factor Inducing Survival and Differentiation of Neurous and its Relationship with Asthma. Progress of Anatomical. Sciences. 13, 388–391 (2007). CAS Google Scholar 77. Yan, Y. H. et al. Neurotrophin-3 promotes proliferation and cholinergic neuronal differentiation of bone marrow- derived neural stem cells via notch signaling pathway. Life Sci 166, 131–138, https://doi.org/10.1016/j.lfs.2016.10.004 (2016). CAS Article PubMed Google Scholar 78. Bandoła, J. et al. Neurotrophin Receptor p75NTR Regulates Immune Function of Plasmacytoid Dendritic Cells. Front Immunol. 8, 981, https://doi.org/10.3389/fimmu.2017.00981 (2017). CAS Article PubMed PubMed Central Google Scholar 79. Aravamudan, B., Thompson, M. A., Pabelick, C. M. & Prakash, Y. S. Mechanisms of BDNF regulation in asthmatic airway smooth muscle. Am J Physiol Lung Cell Mol Physiol. 311, L270–279, https://doi.org/10.1152/ajplung.00414.2015 (2016). Article PubMed PubMed Central Google Scholar 80. Thompson, M. A. et al. cAMP-mediated secretion of brain-derived neurotrophic factor in developing airway smooth muscle. Biochim Biophys Acta. 1853, 2506–2514, https://doi.org/10.1016/j.bbamcr.2015.06.008 (2015). CAS Article PubMed PubMed Central Google Scholar 81. Shi, H. X. & Fu, W. N. Anti-gene Therapy and TCM Syndrome Differentiation. Modern Jorunal of Integrated Traditional Chinese and Western. Medicine. 5, 563 (2004). Google Scholar 82. Zhu, C. L., Pan, Y. & Yue, W. W. Study on the Essence of TCM Syndrome Based on Individualized Treatment of Tumors. Chinese Journal of Information on Traditional Chinese Medicine. 18, 7–8 (2011). Google Scholar 83. Ling, C. Q. et al. The roles of Traditional Chinese Medicine in gene therapy. J Integr Med. 12, 67–75, https://doi.org/10.1016/S2095-4964(14)60019-4 (2014). Article PubMed Google Scholar Download references Acknowledgements The authors would like to acknowledge the Classical Prescription Basic Research Team of the Beijing University of Chinese Medicine. In addition, we thank Elsevier Language Services for providing language assistance and for proofreading the manuscript. This work was supported by the Research Development Foundation of Beijing University of Chinese Medicine (No. 2016-ZXFZJJ-116). Author information Author notes Wenjie Song and Shenglou Ni contributed equally. Affiliations School of Traditional Chinese Medicine, Beijing University of Chinese Medicine, Beijing, 100029, China Wenjie Song & Yanling Fu Periodicals Publishing Center, Beijing University of Chinese Medicine, Beijing, 100029, China Shenglou Ni School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing, 102488, China Yun Wang Contributions Y.L.F. prepared academic plans for all the studies, revised the final manuscript and was responsible for the funding. W.J.S. designed this study, conducted all the experiments, searched the databases, analyzed the data, created the illustrations and drafted the manuscript. S.L.N. participated in the study design and revised the manuscript. Y.W. provided valuable suggestions for data analysis and figures design. All authors reviewed the final manuscript. Competing Interests The authors declare no competing interests. Corresponding author Correspondence to Yanling Fu. Electronic supplementary material Dataset 1 Dataset 3 Dataset 4 Dataset 5 Dataset 6 Dataset 7 Dataset 8 Dataset 9 Dataset 10 Dataset 11 Dataset 12 Rights and permissions Creative Commons BY Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. About this article Publication history Received 16 April 2018 Accepted 10 November 2018 Published 26 November 2018 DOI https://doi.org/10.1038/s41598-018-35791-9