Systematic characterization of the components and molecular mechanisms of Jinshui Huanxian granules using UPLC-Orbitrap Fusion MS integrated with network pharmacology

Jinshui Huanxian granules (JSHX) is a clinical Chinese medicine formula used for treating pulmonary fibrosis (PF). However, the effective components and molecular mechanisms of JSHX are still unclear. In this study, a combination approach using ultra-high performance liquid chromatography-Orbitrap Fusion mass spectrometry (UPLC-Orbitrap Fusion MS) integrated with network pharmacology was followed to identify the components of JSHX and the underlying molecular mechanisms against PF. UPLC-Orbitrap Fusion MS was used to identify the components present in JSHX. On the basis of the identified components, we performed target prediction using the SwissTargetPrediction database, protein–protein interaction (PPI) analysis using STRING database, and Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis using Metascape and constructed a component-target-pathway network using Cytoscape 3.7.2. Molecular docking technology was used to verify the affinity between the core components and targets. Finally, the pharmacological activities of three potentially bioactive components were validated in transforming growth factor β1 (TGF-β1)-induced A549 cell fibrosis model. As a result, we identified 266 components, including 56 flavonoids, 52 saponins, 31 alkaloids, 10 coumarins, 12 terpenoids and 105 other components. Of these, 90 validated components were predicted to act on 172 PF-related targets and they exhibited therapeutic effects against PF via regulation of cell migration, regulation of the mitogen-activated protein kinase (MAPK) cascade, reduction of oxidative stress, and anti-inflammatory activity. Molecular docking showed that the core components could spontaneously bind to receptor proteins with a strong binding force. In vitro, compared to model group, hesperetin, ruscogenin and liquiritin significantly inhibited the increase of α-smooth muscle actin (α-SMA) and fibronectin (FN) and the decrease of e-cadherin (E-cad) in TGF-β1-induced A549 cells. This study is the first to show, using UPLC-Orbitrap Fusion MS combined with network pharmacology and experimental validation, that JSHX might exert therapeutic actions against PF by suppressing the expression of key factors in PF. The findings provide a deeper understanding of the chemical profiling and pharmacological activities of JSHX and a reference for further scientific research and clinical use of JSHX in PF treatment.

. The whole framework based on an integration strategy of UPLC-Orbitrap Fusion MS integrated with network pharmacology, molecular docking and experiment in vitro.
Qualitative analysis of JSHX. Sample preparation. Five herbs (PG, RRP, OR, TF, GR) were decocted twice with 12 times the amount of water for 1 h and filtered, and the filtrate was concentrated and reserved. Five herbs (FTB, MC, EF, GS, CRP) were weighed and added in a round-bottom flask with 10 times the amount of 70% ethanol, reflux-heated for 1 h twice and filtered. The filtrate was concentrated and ethanol removed, and this filtrate was then combined with filtrate 1. The mixture was concentrated to thick paste with a relative density of 1. 18-1.22, dried at 60 °C under reduced pressure, and crushed into a fine powder. An appropriate amount of dextrin was added and mixed well, 80% ethanol was used as a wetting agent to make grains, and the grains were dried at 60 °C to obtain JSHX. The plants research in this study complies with the Chinese Pharmacopoeia 2020 edition.
JSHX (2 g) was accurately weighted and added in a 100 mL round-bottom flask with 25 mL of methanol, the mixture was weighed, reflux-heated for 1 h, cooled to room temperature, weighed again to make up for the lost weight, and filtered, the sample solutions were obtained.
Standard solution preparation. Accurately weigh an appropriate amount of each reference standards into a 10 mL volumetric flask, methanol was added to dissolve and dilute to the mark, single-component reference stock solutions were obtained and stored at 4 °C before use. Then, the appropriate amount of stock solution was added to a 25-ml volumetric flask, and methanol was added to reach the volumetric mark, the mixed reference solution was obtained. The solutions were filtered through 0.22 μm microporous membranes and stored at 4 °C before use.
High-resolution MS detection and analysis were performed on an UPLC-Orbitrap Fusion MS (Thermo Scientific) equipped with an ESI source. The acquisition parameters were set as follows: Vaporizer temperature: 275 °C; Ion Trasfer Tube temperature: 300 °C; Carrier gas (N2); Sheath gas flow rate is 35 arb; Aux gas flow rate is 5 arb; the spray voltage was set at 3.5 (positive ion mode) and 2.5 (negative ion mode) kV; the collision energy was set at 35-55 eV. The sample was analyzed in both positive and negative ion Full MS/dd-MS2 modes with the first-level full scan (resolution: 50,000) and the second-level scan (resolution: 60,000), and the mass range was recorded from m/z 120-1200.
Identification of components. JSHX was identified by the optimized UPLC-Orbitrap Fusion MS method. The possible chemical composition (with an error of less than 5 ppm) was determined using Xcalibur based on map www.nature.com/scientificreports/ data, precise molecular weight and resulting fragment ions. The data was analyzed using Compound Discoverer software to integrate ion peak information, attribution information, ChemSpider, mzCloud, and other databases of characteristic fragments integrated with existing chemical composition information reports. The structure of components was inferred using the cracking prediction of MassFrontier and its cracking rule.

Network pharmacology. Target prediction. The structures of validated components present in JSHX
were generated using the PubChem database (https:// pubch em. ncbi. nlm. nih. gov/) and saved in SDF format. These SDF documents were uploaded to the SwissTargetPrediction database (http:// www. swiss targe tpred iction. ch/) for target prediction. For more accurate prediction of the target gene of each component, relevant parameters were set (probability ≥ 0.1). Furthermore, the biological targets related to PF were selected from the Gen-eCards database (https:// www. genec ards. org/) using "Pulmonary Fibrosis" as the keywords. Then, the intersection of the predicted targets from JSHX and the biological targets of PF was taken and the overlapping targets were screened out as potential targets.
Construction of PPI network. The potential targets identified were added to the STRING database. The screening condition used was "Homo sapiens, " and the other parameters were set by default. Cytoscape (version 3.7.2; https:// cytos cape. org/) was used to construct a protein-protein interaction (PPI) network. A network analyzer in Cytoscape was used to analyze relevant topological parameters. Using three parameters, degree, betweenness centrality (Bc), and closeness centrality (Cc), a topology analysis of the PPI network was performed to determine hub genes for further analysis.
GO and KEGG pathway enrichment analyses. The potential targets identified were uploaded into the Metascape database (https:// metas cape. org/ gp/ index. html) for GO and KEGG pathway enrichment analysis to obtain the information of pathways [18][19][20] . The Bioinformatics Data analysis and Visualization online platform (http:// www. bio-infor matics. com. cn/) was adopted to perform GO and KEGG pathway analysis and visualize the bar chart in this study. During this procedure, the significance level was set as p ≤ 0.01, and the organism was selected as "Homo sapiens. " Component-target-pathway network construction. To further clarify the relationship among components, targets and pathways, a component-target-pathway network was constructed using Cytoscape 3.7.2. The core components nodes were obtained based on the three parameters: degree, Bc and Cc. www.nature.com/scientificreports/ the lysate was collected, the supernatant was collected by centrifugation, and levels of E-cad, α-SMA, FN in the supernatant were determined by ELISA.
Statistical analysis. Data were expressed as mean ± standard deviation (SD). Statistical Differences in multiple groups were performed by analysis of variance (ANOVA). P-values < 0.05 was considered statistically significant.

Results and discussions
Identification of components of JSHX. A total of 266 components were identified in JSHX: 56 flavonoids, 52 saponins, 31 alkaloids, 10 coumarins, 12 terpenoids and 105 other components including quinones, esters, and organic acids, etc. Of the 266 components, 37 components were unambiguously identified via comparison with reference standards. The primary and secondary information was compared with the database for confirmation. The total ion chromatograms (TICs) of positive and negative ions were shown in Fig. 2, and detailed information about the 266 peaks identified was listed in Supplementary Table S1. As the identified components were mainly flavonoids, saponins and alkaloids, their characteristics were summarized. During MS information scanning, flavonoids mainly underwent deglycosylation and dehydration, characterized by the loss of glucose (162), rhamnose (146), xylose (132) and (18)  Saponins. Saponins mainly originate from PG, OR in JSHX, include triterpenoid saponins and steroidal saponins 22 . Triterpenoid saponins can be divided into three categories according to structure: ginsenoside diol (A), ginsenoside triol (B), and oleanolic acid (C). In saponins, glycosidic bonds are easily broken and they can lose glycosyl groups to generate aglycon ions. By removing multiple glycosyl groups-glu (162), rha (146), and ara (132) to form characteristic ions, A, B and C finally form the characteristic parent nucleus at m/z 459.3843, m/z 459.3792 and m/z 455.3530, respectively 23 190,191,192,194,195,197,198,199,200,202,206,208,209 Table S2).
In addition, 5440 PF-related genes were collected from the GeneCards database (Supplementary Table S2). To ensure the precision of target collection, based on the parameter of "Relevance score, " the index above the third median value was selected as the key index, and 680 PF-related targets were obtained. After taking the intersec- www.nature.com/scientificreports/ tion of 942 predicted targets and 680 PF-related targets, 172 targets were obtained which means that they may be the key targets of JSHX in PF treatment (Fig. 7), and 90 identified components corresponding to 172 targets were found through reverse search (Supplementary Table S2).  www.nature.com/scientificreports/ After inputting these common targets into the STRING database, we obtained a PPI network comprising 172 nodes and 3388 edges. Using the three parameters degree, Bc, and Cc, the index above the median value was selected as the key index, the threshold value of screening was degree ≥ 33, Bc ≥ 0.001225, and Cc ≥ 0.546326, 68 hub targets were obtained (Fig. 7). Nodes in the top 20 degree were selected (Fig. 7), the results showed that Interleukin-6 (IL6), vascular endothelial growth factor A (VEGFA), glyceraldehyde-3-phosphate dehydrogenase (GAPDH), AKT Serine/Threonine Kinase 1 (AKT1), Tumor necrosis factor (TNF) were the most vital targets of the PPI network, which may be the key targets of JSHX in PF treatment.
GO and KEGG pathway enrichment analysis. By using the Metascape database, 2625 GO terms and 167 KEGG pathways with P ≤ 0.01 were enriched (Supplementary Table S2).
Gene ontology enrichment analysis consisted of three parts: biological process (BP), molecular function (MF), and cellular component (CC). The top 20 terms for BP, CC and MF are shown as a bar plot in Fig. 8, the height of the bar represents the count, which means the number of genes observed in the category. The enrichment results showed that 2466 enrichment terms are related to BP, which cover positive regulation of cell migration, response to oxidative stress, positive regulation of kinase activity, regulation of MAPK cascade, and positive regulation of transferase activity, etc. In addition, 96 enrichment results were related to MF, including protein kinase binding, peptidase activity, and transcription factor binding, etc. A total of 63 CC items were also obtained, and the most enriched terms included membrane microdomain, side of membrane, extracellular matrix and perinuclear region of cytoplasm, etc.
The top 20 enriched KEGG pathways presented in Fig. 8 were crucially involved in the pathological process of PF. The length and color of the bar in the histogram were decided by the number of associated genes and the p-values. As shown in Fig. 8, the hub genes were highly related to human disease for cancer, infectious diseases, metabolic diseases, immune system and signal transduction, including the pathways in cancer, AGE-RAGE Results of molecular docking. The top 10 core components with corresponding five core targets were simulated by molecular docking, and the docking results were analyzed. The basic information about ligands and www.nature.com/scientificreports/ proteins is shown in Table 3. The results of molecular docking analysis showed that the binding free energy (ΔG in kcal/mol) of components for binding to core targets was negative ( Table 4), indicating that the ligand mol- www.nature.com/scientificreports/ ecules could spontaneously bind to receptor proteins. Furthermore, the binding energy was less than − 5.0 kJ/ mol, which further proved the strong binding ability. The binding energies of the core components and targets are shown in Fig. 10. Both liquiritin and hesperetin had strong binding ability with five core targets. Visualization of the docking results was performed using Discovery Studio 4.5 Client software (Fig. 11). Liquiritin formed hydrogen bonds with ABG841 and CYS775 residues on EGFR; THR347 residues on ESR1; GLU127 residues on AKT1; ASN34, SER49, CYS47, TYR130 and GLN461 residues on PTGS2; GLU310 and GLN275 residues on SRC. Hesperetin formed hydrogen bonds with MET793 residues on EGFR; MET343, THR347 and ARG394 residues on ESR1; GLU170 and GLU127 residues on AKT1; TYR130, CYS47, ASN39 and GLN461 residues on PTGS2; and MET341 residues on SRC.

Experimental validation.
Studies have shown alveolar epithelial cells A549 are one of the mediators for PF detection. Transforming growth factor β1 (TGF β1) promotes pulmonary fibrosis, which mediates epithelial mesenchymal transformation of human alveolar epithelial cells, resulting in extracellular matrix deposition 26 . It was found that human lung cancer A549 cells were type II alveolar epithelial cells, which became a common www.nature.com/scientificreports/ in vitro model to study the mechanism of pulmonary fibrosis after induction with TGF-β1 27 . More and more evidences indicate that autophagy, which maintains intracellular homeostasis, is closely related to the occurrence and development of pulmonary fibrosis 28 . Therefore, we selected TGF-β1 induced A549 cells as the in vitro model. According to the network pharmacology and molecular docking analysis, three bioactive components including ruscogenin, liquiritin, hesperetin had the better molecular docking results. To further evaluate the results obtained by systematic pharmacologic analyses, these components were selected from JSHX to examine the potential effects of anti-inflammatory and inhibiting the differentiation of fibroblasts by using TGF-β1 (7.5 ng/ml)-stimulated A549 cells. We employed ELISA assay for E-cad, α-SMA and FN to confirm the predicted anti-PF effects of three bioactive components. Firstly, we determined the effects of different doses of ruscogenin, liquiritin, hesperetin on the viability of A549 cells using MTT assay. As shown in Fig. 12, ruscogenin (2.5, 5, 10, 20 μg/ml), liquiritin (1, 2, 3, 4, 5 μg/ml), hesperetin (4, 8, 16, 32 μg/ml) had high cell viability (> 85%). Therefore, these concentrations were selected for subsequent experiments. As shown in Table 5, compared with the control group, the contents of α-SMA and FN    www.nature.com/scientificreports/ in TGF-β1-induced cells increased significantly, and the content of E-cad decreased significantly. With different concentrations of ruscogenin, liquiritin, hesperetin treatment, the content of α-SMA and FN decreased to varying degrees, the content of E-cad increased when compared with the model group. As shown in Fig. 12, regarding the α-SMA content, each dose group showed a significant downward trend; For FN, only the high-dose group showed a significant downward trend; Similarly, the E-cad content of high dose group showed a rising trend.

Discussion
In this study, we analyzed the chemical components of JSHX and explore its molecular mechanisms against PF using integrative strategy, including chemical components analysis, network pharmacology prediction, molecular docking, and experiment in vitro. Firstly, the total ion chromatograms of JSHX were obtained by UPLC-Orbitrap Fusion MS. Different mobile phase systems (methanol-water, acetonitrile-water, acetonitrile-0.1% formic acid water, methanol-0.1% formic acid water) and different elution gradients were investigated to optimize reliable and effective chromatography conditions. In addition, due to the different response modes of various components in the formula, positive and negative ion-scanning modes were selected for simultaneous detection line monitoring. A total of 266 chemical constituents (193 in ESI+ and 73 in ESI−) were identified or tentatively characterized by matching the MS raw data to mzCloud, Chemspider and other built-in databases using the Compound  Network pharmacology can reveal the relationships between targets/pathways and chemical components, and establish a "component-target-pathway" network to clarify the molecular mechanisms of drug therapy for disease. In the present study, 90 of the 266 identified components of JSHX were considered to have potential key anti-PF effects by acting on 172 related targets. To explore the key bioactive components and underlying mechanisms of JSHX against PF, a multidimensional component-target-pathway network was further constructed and visualized using Cytoscape 3.7.2. Twenty-four key bioactive components were screened based on three topological parameters, which may improve the pathological status of patients with PF by regulating fifty-seven corresponding targets, including twelve flavonoids, one terpenoid, one saponin, one coumarin, one alkaloid, and six other components, which proved that JSHX had multicomponent synergistic effect in the treatment of PF. Studies have shown that TGF-β can promote the proliferation of lung fibroblasts and collagen synthesis, which is the key cytokine leading to fibrosis, so we chose TGF-β as PF inducer 29,30 . As the predicted components, ruscogenin, liquiritin and hesperetin exerted good anti-PF effects based on TGF-β-stimulated A549 cells.
Among these bioactive components, flavonoids have anti-inflammatory and antioxidant activities, which are more consistent with PF treatment 31,32 . Luteolin, and kaempferol were reported to exert therapeutic effects on PF by inhibiting the proliferation and inflammation of the lung fibroblasts in rat 33,34 . Hesperetin play a protective role in PF, which can inhibit inflammatory response by reducing the levels of TGF-β1, TNF-α, and IL-1β 35 . In addition, OMF, panaxytriol, ruscogenin, and brevisamide have relatively high degree values, and can be used as the main active components of JSHX against PF. Among them, ruscogenin, the main saponins in JSHX, can significantly inhibit TGF-β1-induced increase of FN and COL-I in PF model cell 17 . OMF is a secondary metabolite produced by penicillium pinophilum, which can induce the apoptotic pathway and inhibit cell motility and so may have potential as a naturally derived antitumor drug 36 . Panaxytriol shows potential anti-inflammatory activity by inhibiting the mRNA expression of proinflammatory cytokines such as TNF-α, IL-1b, and IL-6 37 . In addition, Panaxytriol can inhibit the proliferation of lung cancer cells and induce apoptosis by downregulating ERK1/2 and mTORC1 pathways 38 . Studies have shown that brevisamide can improve microcirculation disturbance in the lung, promote oxygen utilization, eliminate oxygen free radicals, resist oxidation, and inhibit apoptosis 39 . The mechanisms of these active ingredients cover most aspects of the pathological process of PF, and the target information involved is basically consistent with our predicted results, indicating that the comprehensive pharmacological strategy has certain predictive accuracy. Among the predicted potential targets, EGFR, ESR1, AKT1, PTGS2, SRC, IL6, and VEGFA had high frequency, and were involved in several biological processes, including positive regulation of cell migration, positive regulation of kinase activity, response to oxidative stress, regulation of MAPK cascade, regulation of cell adhesion, which were closely related to the pathogenesis of PF.
KEGG enrichment analysis showed that the potential targets were highly related to human disease for cancer, infectious diseases, metabolic diseases, immune system and signal transduction, including the pathways in cancer, endocrine resistance, EGFR tyrosine kinase inhibitor resistance, HIF-1 signaling pathway, TNF signaling pathway, PI3K-Akt signaling pathway, and VEGFA signaling pathway, etc. Of these, the PI3K-Akt signaling pathway is the key pathway, that participates in the pathophysiological process of apoptosis, proliferation and differentiation 40 , and affects the overall process of PF by activating downstream mTOR and HIF-1α, CTGF, VEGF and other factors to regulate ROS production and metabolism, extracellular matrix deposition, lung fibroblast production, and collagen production [41][42][43] . PI3K is an important part of the signal transduction process of the growth factor receptor superfamily. Activated PI3K can cause downstream protein phosphorylation and promote the www.nature.com/scientificreports/ mesenchymal transformation and extracellular matrix deposition of alveolar epithelial cells to form pulmonary fibrosis 44 . Akt, a direct target protein downstream of PI3K, can participate in the regulation of cell proliferation and metabolism, and promote fibrosis related gene transcription and protein synthesis 45 . Activated Akt activates the downstream HIF-1α signaling pathway, inhibiting alveolar surfactant production and normal repair of alveolar epithelial cells 46 . In addition, it can also activate NF-κB to initiate downstream inflammatory responses that produce inflammatory factors such as TNF-α and IL-6, thereby exacerbating fibrosis 47 . JSHX may exert anti-PF effects by inhibiting lung tissue inflammatory responses and apoptosis through regulating the expression of related genes in the PI3K-Akt signaling pathway. The HIF-1α signaling pathway is involved in apoptosis, proliferation, metabolism, growth and transformation, membrane transport, secretion and chemotaxis, and plays an important role in the pathogenesis of inflammation, tumors, metabolism, and cardiovascular diseases 48 . HIF-1α can mediate the transformation of bronchial and alveolar epithelial stromal cells and participate in the occurrence of PF 49 . The results showed that the JSHX had a certain intervention effect on the formation of PF. Molecular docking technology was used to verify the binding of core components and targets related with inflammation and oxidative stress. The binding free energy of components with the core targets was less than − 5.0 kJ/mol, which indicated that the ligand molecules could spontaneously bind to receptor proteins with a strong binding force. Based on the network pharmacology and molecular docking results, we test the effect of three bioactive components (ruscogenin, liquiritin and hesperetin) presented on differentiation of fibroblast in TGF-β1-induced A549 cells. Experiment in vitro verified that the bioactive components inhibited the increase of α-SMA and FN, and the decrease of E-cad, and relieved pathological manifestation of PF. The results indicated that JSHX may play a role in PF treatment through anti-inflammatory, antioxidant and suppressing differentiation of fibroblasts into myofibroblasts synergistic effects.

Conclusions
A valid, sensible and accessible UPLC-Orbitrap Fusion MS method was established for the chemical ingredient analysis of JSHX. Based on the identified components, network pharmacology and molecule docking were used to screen the effective components of JSHX and clarify their underlying mechanisms for PF treatment, and were further validated by experiments. The inhibitory effects of 3-O-methylfunicone, panaxytriol, ruscogenin, liquiritin and hesperetin displayed on the PI3K-Akt, HIF-1 and TNF signaling pathways might be the mechanisms of action of JSHX against PF. These findings provide an experimental basis for the scientific connotation and clinical application of JSHX against PF. However, the consistency of the components predicted by network pharmacology with those that ultimately enter the organismal circulation cannot be determined, and deeper mechanisms still need to be further demonstrated by more animal experiments, which will be the focus of our future research.