Jie-Geng-Tang (JGT), a classic and famous traditional Chinese medicine (TCM) prescription composed of
Acute lung injury (ALI) is characterized by hypoxemic respiratory insufficiency from noncardiogenic pulmonary edema caused by increased pulmonary vascular permeability. ALI and its more severe form (acute respiratory distress syndrome) comprise a uniform response of the lung to infectious, inflammatory, or chemical insults and therefore are commonly associated with systemic illness [
An increasing amount of evidence has demonstrated that LPS can trigger the most potent microbial initiators of inflammatory responses by activating numerous inflammatory cells to release proinflammatory factors, such as TNF-
Zhang Zhongjing square Jie-Geng-Tang (JGT) is composed of
Network analysis technology is widely used to study the mechanisms underlying the actions of traditional Chinese medicine (TCM), including chemome, genome, and proteomics approaches. These techniques provide an opportunity to clarify the relationships among herbs, compounds, targets, and diseases [
In the current study, chemomics-integrated proteomics was introduced to explore the bioactive compounds and the potential anti-inflammatory mechanism of JGT for the treatment of LPS-induced ALI in mice. To screen the bioactive components in this remedy, we developed a dual-luciferase reporter assay-guided UPLC-Q/TOF system for NF-
LC/MS grade acetonitrile was obtained from Merck (Darmstadt, Germany). LC grade formic acid was obtained from Acros Organics (Geel, Belgium), and HPLC water was prepared using the Milli-Q system (Millipore, Bedford, MA, USA). Mouse TNF-
PG (batch number 20100121) and GU (batch number 20090401) were purchased from Qirui Pharmaceutical Company in Anguo and identified by Professor Tiejun Zhang from the Tianjin Institute of Pharmaceutical Research. Based on the “Treatise on Exogenous Febrile Diseases,” sixty grams of PG and GU was boiled at a 1 : 2 ratio in 2 L of water until a 1.3 L solution was obtained. The solution was filtered, evaporated to dryness (13.8977 g), and stored at 4°C. The concentrate was dissolved in water to a 10 mg/mL concentration and filtered through a 0.22
A Waters Acquity UPLC System (Waters Co., Milford, MA, USA) equipped with a photodiode array detector was used. UV detection was achieved in the range of 190 to 400 nm. An Acquity BEH C18 column (2.1 × 100 mm, 1.7
Mass spectrometry was performed on a Waters Q-TOF Premier instrument with an electrospray ionization system (Waters MS Technologies, Manchester, UK). The ESI-MS spectra were acquired in both the negative and the positive ion modes. The capillary voltage was set to 2.5 kV for the negative mode and 3.0 kV for the positive mode, and the sample cone voltage was set to 30 V. The nebulization gas was set to 600 L/h at a temperature of 350°C, the cone gas was set to 50 L/h, and the source temperature was 110°C. The Q-TOF Premier acquisition rate was 0.1 s with a 0.02 s interscan delay. The instrument was operated with the first resolving quadrupole in a wide pass mode (50–2,500 Da).
Human embryonic kidney (HEK) 293 cells obtained from the American Type Culture Collection (Rockville, MD, USA) were grown in Dulbecco’s modified Eagle’s medium (Gibco BRL) containing 10% fetal bovine serum (Gibco BRL), 100 U/mL of penicillin, and 0.1 mg/mL of streptomycin. The cells were grown to confluence in 96-well plates at 37°C in a humidified incubator with 5% CO2.
HEK 293 cells were cotransfected with the NF-
After the samples were stimulated, the HEK 293 cells were lysed and assayed for luciferase activity using a dual-luciferase reporter assay system according to the manufacturer’s instructions. The relative luciferase activity was obtained by normalizing the firefly luciferase activity against the activity of the internal Renilla luciferase control (Modulus
Male BALB/c mice weighing 18–22 g were purchased from Vital River Company (Beijing, China) and housed in a unidirectional airflow room under controlled temperature (20–24°C), relative humidity (40–60%), and a 12 h light/dark cycle. Filtered tap water and commercial rodent chow were available
Thirty-six mice were randomly divided into six groups, including an uninfected control (Con) group and five LPS-infected groups as follows: model (Mod) group, Dex group (5 mg/kg, served as positive control), and the JGT groups (0.45 g/kg, 1.35 g/kg, or 4.05 g/kg). Dex and the three JGT doses were orally administered for one week prior to the LPS challenge, whereas the mice from the Con and Mod groups received an equal volume of 0.9% saline instead of the drugs. To induce ALI, the mice were intranasally given 0.5 mg/kg of LPS. The mice were killed 24 h after the LPS challenge. Bronchoalveolar lavage fluid (BALF) was collected three times through a tracheal cannula with 0.5 mL of autoclaved phosphate buffered saline (pH 7.2). Lung tissue samples were harvested at the same time.
Cytokines (TNF-
A histopathological examination (HE) was performed on mice not subjected to BALF collection. The lungs were fixed with 10% buffered formalin, embedded in paraffin, and sliced. After hematoxylin and eosin staining, pathological changes in the lung tissues were observed under a light microscope (Olympus CKX41, Japan). The degree of microscopic injury was evaluated based on the neutrophil infiltration and alveolar density using MATLAB software system (MathWorks, 2013b, USA), which could quantify the severity of inflammation after converting the picture to a grayscale image. An edge detection algorithm for grayscale images was applied to average each image into 100 rectangular units. The intensity of each rectangle of the lung parenchyma was cumulative and was divided by the corresponding pixels. The intensity of each part was normalized and emerged as a percentage of the gray value. The alveolar density, which is an index used to depict the severity of lung injury, was calculated for four sections of the overall integrated image from each group.
The PharmMapper database (
Lung samples from three groups (Con, Mod, and JGT-H) (
The raw data files acquired from the Orbitrap were converted into MGF files using Proteome. Protein identification was performed using the Mascot search engine (Matrix Science, London, UK; version 2.3.02). A protein that contained at least two unique peptides was required for the protein quantitation. The quantitative protein ratios were weighed and normalized by the median ratio in Mascot. The screened proteomes of the three samples were compared in pairs using a pairwise comparison, including Con
AutoDock 4.0 was used to screen the candidate molecules obtained from JGT against target selectivity. The crystal structures of the chosen targets were obtained from the Protein Data Bank (PDB ID: 2UZI, 4ACD, 3EQD, 3OCS, 4KZC, 3KMM, 2R7B, and 3QKK) and optimized using SYBYL X2.0. The target structures were preprocessed and a grid box was generated prior to docking. At this stage, the target structures were prepared by deleting all water molecules and adding all hydrogen atoms. Gasteiger charges were used for the docking. Then, PDBQT files of the targets and ligands were prepared using AutoDock Tools.
The centers of the grids were placed onto the center of ligand mass. A genetic algorithm (GA) was used to simulate ligand-receptor binding. The number of GA runs was 30. The step size parameters of quaternion and torsion were set to 30. A total of 30 independent runs were performed for each compound. The docking parameters were assigned following the strategy proposed in the parameter test section; default values were used for all other parameters.
The free energy of binding calculation was performed using MM/PBSA. The average free energy of the complex, receptor, or ligand was composed of the mechanical energy, energy of solvation, and entropic energy of the system over the trajectory. The single trajectory approach was applied to estimate the energies. This approach extracted the thermodynamic data from a single trajectory of the protein-ligand complex. Because significant binding occurred between the molecules and targets, this error calculation could reduce the effect of incomplete sampling.
Statistical analysis was performed using the SPSS software and the data were presented as the standard error of the mean. Significance was determined by one-way analysis of variance followed by Tukey’s multiple comparison test. Significant differences between the means were determined using Student’s
The optimal UPLC-Q/TOF conditions were applied for the analysis of the JGT extract. The UV (Figure
UPLC/Q-TOF-MS and dual-bioactivity analysis of the JGT extract. (a) UPLC/UV chromatograms of the JGT extract. (b and c) TIC chromatograms in positive ESI mode and negative ESI mode, respectively. (d) Bioactivity chromatograms obtained
UPLC-DAD/Q-TOF-MS identification of the bioactive constituents in JGT.
Peak number |
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Identification | Mode | MS ( |
Error/ppm | MS/MS | Monoisotopic mass | Composition | Source |
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1 | 4.2 | 2-(2-Hydroxyphenyl)-succinic acid | Neg. | 209.0509 | 1.96 | 209 [M − H]−; 165 [M − H − CO2]−; 129 [M − H − CO2 − 2H2O]−; |
210.0528 | C10H10O5 | GU |
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2 | 7.32 | Liquiritin apioside | Pos. | 551.1744 | −3.63 | 551 [M + H]+; 419 [M + H − api]+; 257 [M + H − api − glc]+ | 550.1686 | C26H30O13 | GU |
Neg. | 549.1657 | 8.9 | 549 [M − H]−; 417 [M − H − api]−; 255 [M − H − api − glc]− | ||||||
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3 | 7.57 | Liquiritin | Pos. | 419.1335 | 1.67 | 419 [M + H]+; 257 [M + H − glc]+ | 418.1264 | C21H22O9 | GU |
Neg. | 417.1212 | 6.23 | 417 [M − H]−; 255 [M − H − glc]− | ||||||
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4 | 8.36 | Platycogenic acid A | Neg. | 533.314 | 4.69 | 533 [M − H]− | 534.3193 | C30H46O8 | PG |
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5 | 8.97 | Polygalacic acid | Pos. | 505.3437 | 8.72 | 505 [M + H] | 504.3451 | C30H48O6 | PG |
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6 | 11.55 | Licorice-saponin J2 | Pos. | 825.427 | 0.24 | 825 [M + H]+; 649 [M + H − glu]+; 455 [M + H − glu − Glu]+ | 824.4194 | C42H64O16 | GU |
Neg. | 823.418 | 7.77 | 823 [M − H]−; 351 [2 × C6H8O6 − H]−; 191 [C6H8O6 − H]− | ||||||
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6′ | 11.95 | Platycodin D | Pos. | 1225.5932 |
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1225 [M + H]+ | 1224.5775 | C57H92O28 | PG |
Neg. | 1223.5737 | 3.27 | 1223 [M − H]−; 681 [M − C21H34O16]− | ||||||
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7 | 13.27 | 22- |
Pos. | 881.4104 |
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881 [M + H]+; 705 [M + H − glu]+; 511 [M + H − glu − Glu]+ | 880.4092 | C44H64O18 | GU |
Neg. | 879.4095 | 9.10 | 879 [M − H]−; 351 [2 × C6H8O6 − H]− | ||||||
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8 | 14.64 | Licorice-saponin G2 | Pos. | 839.4102 | 4.41 | 839 [M + H]+; 663 [M + H − glu]+; 469 [M + H − glu − Glu]+ | 838.3987 | C42H62O17 | GU |
Neg. | 837.3927 | 2.15 | 837 [M − H]−; 819 [M − H − H2O]−; 351 [2 × C6H8O6 − H]− | ||||||
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9 | 15.07 | 22- |
Neg. | 863.4142 | 2.43 | 863 [M − H]−; 351 [2 × C6H8O6 − H]− | 864.4144 | C44H64O17 | GU |
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10 | 15.55 | Glycyrrhizic acid | Pos. | 823.4043 |
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823 [M + H]+; 647 [M + H − glu]+; 453 [M + H − glu − Glu]+ | 822.4038 | C42H62O16 | GU |
Neg. | 821.3983 | 2.8 | 821 [M − H]−; 351 [2 × C6H8O6 − H]− |
glc: glucose-H2O; Glu: glucuronide; glu: glucuronide-H2O; api: apiose-H2O.
Among the potential active ingredients, eight compounds belonged to GU and three belonged to PG. GU primarily contained polyphenolic compounds (1–3) and pentacyclic triterpenoids (6–10), whereas PG contained terpenes (4, 5, and 6′) [
Chemical structures of the bioactive compounds in JGT.
To evaluate the prophylactic administration effects of JGT on ALI, we evaluated histological changes among the six groups in the LPS- or non-LPS-treated mice. The cross-sectional images of lung tissues and the local amplified images are shown in Figure
Effects of JGT prophylactic administration on acute lung inflammation induced by LPS. (a) HE staining of lung cross sections. The pictures were taken by light microscopy and partially enlarged with 10x magnification. The pathological classification of lung injury shown by the inflammatory infiltration was expressed as the alveolar density (b) and the average gray values of the images (c). Effects of JGT on (d) TNF-
To evaluate the cytokine (TNF-
To evaluate the different proteins included in the three groups of samples, samples from the three groups were processed by cluster analysis to assess similar protein expression patterns that typically shared similar functions. The data values were standardized, the Euclidean distances between the data were calculated, and the quantitative protein and experimental conditions were evaluated for the hierarchical clustering analysis using the SIMC-P software. The heat map of the protein arrays based on hierarchical clustering is depicted in Figure
Proteomics analysis using mouse lung tissues induced by LPS. (a) Heat map of the iTRAQ differentially expressed protein analysis. (b) Protein interaction analysis by the bioinformatics tool String 9.1.
The bioinformatics tools String 9.1 and KEGG were applied to study the target protein interactions. Three functional proteomics categories were used based on the different proteins (immunoregulation and anti-inflammation, ribosome, and muscle contraction, resp.). The protein interaction analysis is shown in Figure
To study the relationship between the compounds and the predicted pathways and targets, we applied the PharmMapper database, KEGG database, and Cytoscape software to integrate the network pharmacology. Eventually, 28 predicted targets and 17 primary pathways related to the inflammatory response were found through the compound-target-pathway network (Figure
The relationship between the bioactive ingredients and predicted targets and pathways through network pharmacology and AutoDock analysis.
LPS-induced ALI was characterized by the release of a variety of proinflammatory mediators. Monocyte activation leads to the release of various cytokines. These cytokines (especially TNF-
Based on the PharmMapper and KEGG database analyses, the anti-inflammatory ingredients primarily acted on the PI3K/Akt and ERK/MAPK signaling pathways. NF-
Network anti-inflammatory mechanism of JGT on LPS-induced ALI.
Most active compounds in PG and GU are pentacyclic triterpenoids; this type of compound inhibits IKK-mediated activation of the NF-
GU showed a remarkable inhibitory effect on the ERK/MAPK pathway according to the docking results and literature [
In the iTRAQ analysis, PTPRC, VCAM1, ICAM1, and ITGB2 expression was reduced. Notably, these four proteins participate in the PI3K/Akt and ERK/MAPK signaling pathways. PTPRC (also known as CD45) plays a role in the protein tyrosine phosphatase- (PTPase-) dependent signaling cascade that results in the activation of Ras and leads to a Rafl/MEK/MAPK module [
In summary, the present study provided experimental evidence that JGT exerted ameliorating effects on LPS-induced ALI. JGT also suppressed the expression of inflammatory mediators and inhibited the activation of NF-
Jie-Geng-Tang
Traditional Chinese medicine
Acute lung injury
Isobaric tags for relative and absolute quantitation
Lipopolysaccharide
Dexamethasone
Human embryonic kidney
Control
Model
Bronchoalveolar lavage fluid
Histopathological examination
Genetic algorithm.
Animal treatment and maintenance were performed in accordance with the Principle of Laboratory Animal Care (NIH Publication number 85-23, revised 1985). The Animal Ethics Committee of Nankai University approved the experimental protocol.
The authors declare that they have no competing interests.
Gang Bai and Yuanyuan Hou conceived and designed the experiments. Jin Tao, Yan Nie, and Jie Gao performed the experiments. Guoyu Ding, Xiaoyao Ma, and Min Jiang analyzed the data. Jin Tao and Yan Nie wrote the paper. All authors read and approved the final paper.
This work was supported by the National Natural Science Foundation of China (Grants nos. 81374046, 81373506, and 81473403) and the State Key Program of National Natural Science of China (Grant no. 81430095).