This work is carried out to evaluate the clinical efficacy of Sanzi Yangqin decoction (SZYQD) treating chronic obstructive pulmonary disease (COPD) and to analyze its mechanism. The clinical efficacy of SZYQD treating COPD was evaluated by meta-analysis, and its mechanism was analyzed by network pharmacology. Molecular docking validation of the main active compounds and the core targets was performed by AutoDock vina software. A cigarette smoke (CS) and LPS-induced COPD model in ICR mice was constructed to confirm the effects of luteolin on COPD. Results showed that SZYQD has a greater benefit on the total effect (OR = 3.85, 95% CI [3.07, 4.83],
Chronic obstructive pulmonary disease (COPD), caused mainly by cigarette smoking, is characterized by chronic airway inflammation and persistent airflow limitation and is the fourth leading cause of death in the world [
Traditional Chinese Medicine (TCM) has defended Chinese people’s health for thousands of years ago. TCM plays a significant role in remodeling the airway and reducing airway hyperresponsiveness in COPD [
The flow chart of this whole analysis for this study.
We systematically searched the Cochrane Library, PubMed, Web of Science, Embase, and CNKI from 2005 to 2020 for eligible articles and included them in the meta-analysis. The keywords that were used are as follows: Sanzi Yangqin tang, Sanzi Yangqin decoction, SZYQD, chronic obstructive pulmonary disease, and COPD. Besides, we also performed other searches and checked reference lists of relevant reviews and eligible randomized controlled trials to ensure a comprehensive search.
The inclusion criteria were as follows: (1) randomized, clinical trial; (2) control group for routine western medicine treatment and experimental group for SZYQD based on other medicines or SZYQD alone; (3) basis of disease diagnosis being nomenclature and diagnostic criteria for COPD; (4) clinical efficacy, lung function index of FEV1% and FEV1%/FVC, and blood gas analysis index of the PaO2 and PaCO2 as outcome indicators; and (5) conforming to the ethical and moral treatment standard. Meanwhile, exclusion criteria include the following: (1) nonrandomized trials; (2) experiments on animals; (3) review; (4) no clear diagnostic criteria or not meeting inclusion criteria; (5) lack of required data for meta-analysis; (6) the sample size being less than 40; and (7) no complete evaluation of efficacy.
Two independent reviewers searched the literature according to the titles and abstracts and the established strategy, then extracted data from the included studies, performed duplicate checking, and compared the results carefully. Disagreements were resolved by a third investigator to avoid bias. The name of the first author and year of publication were extracted for identification in our analysis.
We assessed the quality of the literature according to the Cochrane Collaboration’s Bias risk assessment tool. The main assessment areas are as follows: random sequence generation, allocation concealment, the blinding method for patients/researchers and outcomes assessors, incomplete outcome data, selective reporting, and other sources of bias. The results were judged as “low risk”, “high risk”, and “unclear”.
The main chemical compounds of white mustard, radish, and perilla seed in SZYQD were retrieved from TCM Systems Pharmacology Database (TCMSP,
In order to elucidate the action mechanisms of SZYQD on COPD, DAVID 6.8 (
To facilitate the visualization of multiple-target effects of SZYQD and COPD interrelation, compound-target, disease-target-compound, and compound-target-pathway networks were constructed by Cytoscape 3.7.2. In these networks, we used nodes to stand for the components, compounds, targets, and diseases, and the edges between the two nodes represented their interaction.
Molecular docking was carried out between the top 6-degree value of active compounds in the disease-compound-target network and the top 6-degree value of the target genes in the PPI network. The mol2 format structure files of chemical compounds were obtained from the TCMSP database, and the crystal structures of core targets from the RCSB Protein Data Bank (PDB,
Antibodies against
Luteolin (purity > 98% via HPLC, batch number: L107328) was purchased from Lianshuo Biotechnology Co., Ltd. (Shanghai, China). Dexamethasone Tablets (75 mg/tablet, batch number: 191067) were purchased from Zhangzhongjing Pharmacy (Zhengzhou, China). Ix53 inverted fluorescence microscope and Cx31 upright microscope were purchased from OLYMPUS company, Japan. G: BOX multifunctional gel imaging system was purchased from Syngene, UK. The animal respiratory metabolic measurement system was obtained from Sable Systems International, United States.
Seven-week-old female ICR mice were obtained from Henan Provincial Medical Laboratory Animal Center. All mice were housed in individually ventilated cages (lights on 7: 00 AM to 7: 00 PM). Animals were fed standard rodent chow and water. All animal procedures were approved by the Animal Experimentation Ethics Committee of Henan University (permission number HUSAM 2016-288), and all procedures were performed in strict accordance with the Guide for the Care and Use of Laboratory Animals and the Regulation of Animal Protection Committee to minimize suffering and injury. Animals were euthanized via carbon dioxide overdose based on experimental need. Standard rodent chow was purchased from Henan Provincial Medical Laboratory Animal Center (Zhengzhou, China), under License No. SCXK (YU) 2015–0005, Certificate No. 41000100002406.
The animals were divided into six groups (
The BALF collection was carried out as described [
Lung tissue sections were treated with blocked with 5% BSA for 30 min at room temperature and incubated with anti-EGBB2, anti-MMP2, anti-MMP9, anti-PTGS2, anti-APP, anti-EGFR, and secondary antibody (FITC goat anti-mouse IgG). Then, the sections were fixed with antifluorescence quencher, observed, and photographed under the fluorescence microscope. Fluorescence intensity was quantified using ImageJ software (NIH, Bethesda, MD, USA).
Proteins were extracted and separated via 12% sodium dodecyl sulfate-polyacrylamide gel electrophoresis, electroblotted onto nitrocellulose membranes, and probed with antibodies against
Statistical analyses in the meta-analysis were performed using RevMan 5.3. The SMD and corresponding 95% CI were calculated to evaluate the effect of SZYQD on COPD. The heterogeneity was calculated using
The search process is shown in Figure
Flow diagram of the study selection process. Search strategy and flow chart of the screened, excluded, and analyzed articles.
The characteristics of all included studies.
Author and reference | Year | Total sample | Control/treatment | Period of treatment (days) | Drug | Evaluation standard | |
---|---|---|---|---|---|---|---|
Cui et al. [ | 2017 | 100 | 50/50 | 7 d | 1 | 2 | ① |
Deng and Ceng [ | 2017 | 102 | 51/51 | — | 1 | 2 | ① |
Deng and Tan [ | 2020 | 80 | 40/40 | 84 d | 1 | 2 | ①②③ |
Gong and Shen [ | 2014 | 88 | 44/44 | 14 d | 1 | 2 | ①④ |
He et al. [ | 2018 | 94 | 47/47 | 10 d | 1 | 2 | ①②③ |
Huang [ | 2019 | 94 | 47/47 | 14 d | 1 | 2 | ①②③ |
Hui [ | 2019 | 90 | 45/45 | 7 d | 1 | 2 | ①③ |
Lai et al. [ | 2017 | 80 | 40/40 | 14 d | 1 | 2 | ① |
Li [ | 2014 | 88 | 44/44 | 14 d | 1 | 2 | ① |
Li et al. [ | 2019 | 180 | 90/90 | 7 d | 1 | 2 | ①③ |
Li [ | 2020 | 97 | 48/49 | 12 d | 1 | 2 | ① |
Li et al. [ | 2013 | 112 | 56/56 | 28 d | 1 | 2 | ①③ |
Li et al. [ | 2018 | 150 | 75/75 | 15 d | 1 | 2 | ① |
Liu [ | 2015 | 84 | 42/42 | — | 1 | 2 | ① |
Liu [ | 2019 | 80 | 40/40 | 12 d | 1 | 2 | ① |
Lu and Wang [ | 2018 | 160 | 80/80 | 30 d | 1 | 2 | ①④⑤ |
Lu [ | 2019 | 80 | 40/40 | 14 d | 1 | 2 | ①②③ |
Lv et al. [ | 2011 | 100 | 50/50 | 14 d | 1 | 2 | ① |
Pang [ | 2019 | 82 | 41/41 | 7 d | 1 | 2 | ① |
Tian et al. [ | 2005 | 99 | 49/50 | 30 d | 1 | 2 | ① |
Wang [ | 2019 | 96 | 48/48 | 90 d | 1 | 2 | ①②③ |
Wang et al. [ | 2020 | 84 | 42/42 | 14d | 1 | 2 | ①②③⑤ |
Wu [ | 2018 | 252 | 126/126 | — | 1 | 2 | ① |
Xu [ | 2020 | 80 | 40/40 | 14 d | 1 | 2 | ① |
Yan [ | 2018 | 80 | 40/40 | 15 d | 1 | 2 | ①④⑤ |
Yang [ | 2011 | 160 | 72/88 | 14 d | 1 | 2 | ① |
Yang and Shi [ | 2014 | 86 | 40/46 | 14 d | 1 | 2 | ①②③④⑤ |
Yang [ | 2020 | 80 | 40/40 | 90 d | 1 | 2 | ①③ |
Yuan et al. [ | 2014 | 127 | 63/64 | 14 d | 1 | 2 | ①②③ |
Zhang [ | 2016 | 100 | 50/50 | 15 d | 1 | 2 | ①②③ |
Zhang [ | 2011 | 80 | 40/40 | 30 d | 1 | 2 | ① |
Zhang et al. [ | 2013 | 120 | 62/58 | 7 d | 1 | 2 | ①④⑤ |
Zhang [ | 2018 | 80 | 38/42 | 7 d | 1 | 2 | ① |
Zhang and Fang [ | 2006 | 85 | 37/48 | 28 d | 1 | 2 | ①②③ |
Zhou and Huang [ | 2017 | 160 | 80/80 | 30 d | 1 | 2 | ② |
Quality assessment of 35 available studies. (a) Risk of bias graph: review authors’ judgments about each risk of bias item presented as percentages across all included studies. (b) Risk of bias summary: review authors’ judgments about each risk of bias item for each included study.
All 35 studies, including a total of 3730 patients, reported the total effect of SZYQD on COPD. Due to the low heterogeneity (
Sanzi Yangqin decoction has a curative effect on COPD. (a) Comparison of total effect between the experimental group and the control group for COPD. (b, c) Forest plot analyzing lung function of COPD patients with SZYQD: FEV1; FEV1/FVC%. (d, e) Forest plot analyzing blood gas of COPD patients with Sanzi Yangqin decoction: PaO2; PaCO2. (f) Funnel plot analyzing the bias of the total effect in the treatment of COPD by Sanzi Yangqin decoction.
Funnel plots were made to evaluate publication bias. The result indicated that there is no clear bias in the total effect (Figure
The compounds of white mustard, radish, and perilla seed in SZYQD were retrieved from TcmSPTM. According to the standard of OB ≥ 30%, DL > 0.15. A total of 27 active compounds were obtained for SZYQD, including three compounds in white mustard seed, six compounds in radish seed, and eighteen compounds in perilla seed (shown in Supplementary File
Venn diagram of drug-disease intersection targets. The 290 targets of COPD were mapped to the 599 targets of SZYQD to screen out the 104 common targets.
The PPI network constructed by the STRING database and Cytoscape 3.7.2. (a) The number of nodes in the protein interaction network (Top 30). (b) Core targets of Sanzi Yangqin decoction for the treatment of COPD. The top 6 targets as SZYQD’s main therapeutic targets, such as EGFR, MMP9, PTGS2, MMP2, APP, and ERBB2, were associated with the development of COPD.
104 predicted targets were mapped to the DAVID database to systematically analyze their GO and KEGG pathway enrichment. 37 GO biological processes and 35 KEGG pathways were obtained. GO biological processes showed that these predicted targets have a very strong correlation with physiological mechanisms, such as serotonin receptor signaling pathway, protein autophosphorylation, positive regulation of MAP kinase activity, and release of sequestered calcium ion into the cytosol. The main pathways were the cancer pathway, calcium signaling pathway, PI3K-Akt signaling pathway, and Ras signaling pathway. Besides, GO biological processes and the KEGG pathway visualized the enrichment analysis results via the OmicShare tools (Figures
Gene enrichment analyses performed by DAVID and visualized by OmicShare. (a) GO biological process. The results showed that vital targets have a very strong correlation with physiological mechanisms, such as the serotonin receptor signaling pathway, protein autophosphorylation, positive regulation of MAP kinase activity, and release of sequestered calcium ion into the cytosol. (b) KEGG enrichment analysis. The main pathways were the cancer pathway, calcium signaling pathway, PI3K-Akt signaling pathway, and Ras signaling pathway. The vertical axis is the pathname and the horizontal axis is the Rich Factor value. The larger the
Compound-target network (C-T network) was constructed via Cytoscape 3.7.2. The nodes represent compounds and targets. The edges represent their interactions (Figure
C-T network, D-T-C network, and C-T-P network: (a) a compound-target network, and nodes represent component, compounds, and targets; (b) a disease-target-compound network, red nodes represent the drug-disease intersection targets, green nodes represent the compounds, yellow nodes represent the components, while the pink nodes represent disease, and disease is linked with compounds and targets; (c) a compound-target-pathway network, red nodes represent the drug-disease intersection targets, green nodes represent the compounds, yellow nodes represent the components, while purple nodes represent the pathways. And then, each edge symbolizes the interaction between them.
The crystal structures of EGFR (5y9t), MMP9 (5th9), MMP2 (3ayu), APP (3ktm), ERBB2 (3wlw), and PTGS2 (5kir) were obtained from the RCSB Protein Data Bank (PDB). The crystal structure of each protein was selected based on the best resolution available. The active compounds of SZYQD, such as sinoacutine, exceparl M-OL, phthalic acid, butyl isohexyl ester, luteolin, beta-sitosterol, and stigmasterol, were docked with the targets of EGFR, MMP9, PTGS2, MMP2, APP, and ERBB2. The results showed that sinoacutine, exceparl M-OL, phthalic acid, butyl isohexyl ester, luteolin, beta-sitosterol, and stigmasterol had a good binding ability with EGFR, MMP9, PTGS2, MMP2, APP, and ERBB2 (docking score > 4.25) (Table
Molecular docking of main active compounds of SZYQD and core targets.
Target name | PDB ID | Compound | Energy (kcal/mol) |
---|---|---|---|
EGFR | 5y9t | Beta-sitosterol | −5.28 |
EGFR | 5y9t | Exceparl M-OL | −5.24 |
EGFR | 5y9t | Luteolin | −7.16 |
EGFR | 5y9t | Phthalic acid, butyl isohexyl ester | −5.19 |
EGFR | 5y9t | Sinoacutine | −5.26 |
EGFR | 5y9t | Stigmasterol | −6.27 |
MMP9 | 5th9 | Beta-sitosterol | −5.28 |
MMP9 | 5th9 | Exceparl M-OL | −6.00 |
MMP9 | 5th9 | Luteolin | −8.4 |
MMP9 | 5th9 | Phthalic acid, butyl isohexyl ester | −5.84 |
MMP9 | 5th9 | Sinoacutine | −5.82 |
MMP9 | 5th9 | Stigmasterol | −6.02 |
MMP2 | 3ayu | Beta-sitosterol | −5.12 |
MMP2 | 3ayu | Exceparl M-OL | −5.48 |
MMP2 | 3ayu | Luteolin | −7.21 |
MMP2 | 3ayu | Phthalic acid, butyl isohexyl ester | −6.24 |
MMP2 | 3ayu | Sinoacutine | −5.82 |
MMP2 | 3ayu | Stigmasterol | −6.25 |
APP | 3ktm | Beta-sitosterol | −5.23 |
APP | 3ktm | Exceparl M-OL | −5.71 |
APP | 3ktm | Luteolin | −7.00 |
APP | 3ktm | Phthalic acid, butyl isohexyl ester | −5.30 |
APP | 3ktm | Sinoacutine | −6.77 |
APP | 3ktm | Stigmasterol | −5.34 |
ERBB2 | 3wlw | Beta-sitosterol | −5.87 |
ERBB2 | 3wlw | Exceparl M-OL | −5.53 |
ERBB2 | 3wlw | Luteolin | −7.10 |
ERBB2 | 3wlw | Phthalic acid, butyl isohexyl ester | −5.33 |
ERBB2 | 3wlw | Sinoacutine | −5.02 |
ERBB2 | 3wlw | Stigmasterol | −5.68 |
PTGS2 | 5kir | Beta-sitosterol | −6.12 |
PTGS2 | 5kir | Exceparl M-OL | −5.89 |
PTGS2 | 5kir | Luteolin | −7.50 |
PTGS2 | 5kir | Phthalic acid, butyl isohexyl ester | −5.35 |
PTGS2 | 5kir | Sinoacutine | −5.21 |
PTGS2 | 5kir | Stigmasterol | −6.82 |
Pattern diagram of molecular docking. (a) Beta-sitosterol and EGFR. (b) Exceparl M-OL and MMP2. (c) Luteolin and MMP9. (d) Sinoacutine and APP.
The tidal volume (TV) was used for detecting lung function. As shown in Figure
Effects of luteolin on lung function and alveolar architecture in mice with COPD. (a) Tidal volume (TV) in lung function. (b) The whole lung in the naked eye. (c) HE staining of lung tissues. (d) The area of involved lesions in HE staining. The data presents mean ± SD (
To evaluate the effects of luteolin on inflammatory cells, cytokine levels, and ROS in BALF, the percentage of inflammatory cells (white blood cells, neutrophils, and lymphocytes), concentrations of cytokines (IL-6, TGF-
Effects of luteolin on inflammatory cells, cytokine levels, and ROS in BALF of mice with COPD. (a) The percentage of white blood cells in BALF. (b) The percentage of neutrophil lymphocytes in BALF. (c) The percentage of lymphocytes in BALF. (d) IL-6 level in BALF. (e) TGF-
Resident lung fibroblasts are activated in chronic airway inflammation, accompanied by increased expression of
Effects of luteolin on the expressions of
Western blot analyses were performed to evaluate COPD-related targets (EGFR, MMP9, PTGS2, MMP2, APP, and ERBB2) involved in the therapeutic effects of luteolin on COPD. Outcomes presented showed a significant increase in the expression of COPD-related targets in the CS and LPS group compared with the control group. However, luteolin treatment markedly inhibited the expression of COPD-related targets compared with the CS and LPS group (Figures
Effects of luteolin on COPD-related targets. (a) Core target-associated markers examined by western blot. (b) The relative protein expressions of core targets. (c) Core target-associated markers examined by immunofluorescence; (d) Fluorescence intensity of core targets. Data were expressed as mean ± SD (
TCM has focused on the treatment of COPD for thousands of years [
On the other hand, in a large number of TCM have complex compounds, the active compounds are not clear in clinical and pharmacological studies, the specific targets have not fully been identified, and the mechanism of action has not been effectively elaborated. Network pharmacology illustrates the intricate interactions among genes, proteins, and metabolites related to diseases and drugs from a network perspective, which has become a powerful tool in elucidating complex and holistic mechanisms of TCM [
Luteolin, an active flavonoid compound isolated from
Although luteolin has a broad application prospect in the treatment of lung diseases, inflammation, and oxidative stress process, there are few reports about its treatment of COPD. Thus, we used CS and LPS to establish a COPD mice model, exploring the effects of luteolin on COPD induced by CS and LPS. Results presented a reduction of tidal volume and lung injury under CS and LPS stimulation; meanwhile, treatment with luteolin prominently improved this condition. Additionally, we found that the increase in the number of inflammatory cells in the BAL fluid after CS and LPS stimulation was significantly attenuated by luteolin. Luteolin also reduced the production of IL-6, TGF-
SZYQD has a curative effect on COPD, and its mechanism is related to 27 compounds, 104 targets, and 35 pathways. Meanwhile, luteolin is a candidate compound for COPD treatment by regulating EGFR, MMP9, PTGS2, MMP2, APP, and ERBB2. The strategy of integrating classical pharmacology with systems pharmacology analysis has the potential to provide a better strategy for the understanding of the TCM mechanism.
All data used to support the findings of this study are included within the article.
The authors declare no conflicts of interest.
Mengqi Wang and Gangjun Du conceived and designed the experiments. Mengqi Wang, Wenwen Gu, Fuguang Kui, Fan Gao, Yuji Niu, Wenwen Li, Yaru Zhang, Lijuan Guo, and Shengnan Geng performed the experiments. Mengqi Wang, Gangjun Du, and Shengnan Geng analyzed the data. Shengnan Geng and Gangjun Du contributed reagents/materials/analysis tools. Mengqi Wang and Gangjun Du wrote the paper and plotted the results.
This study was supported by the Joint Foundation of the National Natural Science Foundation of China and Henan Province of China (No. U200410711) and Henan Youth Natural Science Foundation (No. 212300410109).
Supplementary File 1: information of 27 active compounds of Sanzi Yangqin decoction and degree value of corresponding compounds. Supplementary File 2: a component-compound-target network of white mustard seed (A), radish seed (B), and perilla seed (C). The red nodes represent the targets, the green nodes represent the compounds, while the yellow nodes represent the components and those compounds are linked with the corresponding targets.