Construction of a Chemical Database of LS

TCMID (http://www.megabionet.org/tcmid), TCM Database@Taiwan (http://tcm.cmu.edu.tw), and Chemistry Database (http://www.chemcpd.csdb.cn/scdb) were searched to collect the names, structures, CAS numbers, and classifications of compounds in LS, thus constructing an in-house chemical database. Then, literature, books, the Encyclopedia of Chinese Medicine, and the PubChem database (https://pubchem.ncbi.nlm.nih.gov) were reviewed to confirm, merge, correct, and supplement the chemical information in our database.

Construction of a Database With Known Anti-Hepatic Fibrosis Compounds in LS

We searched the PubMed database (https://pubmed.ncbi.nlm.nih.gov) with keywords, including the lipophilic compound names in LS in conjunction with liver fibrosis, hepatic fibrosis, or hepatic stellate cells. Compounds with anti-liver-fibrosis activity were screened and summarized.

Construction of a Lipophilic Potential Active Compound Interaction Network Based on Molecular Similarity Calculation

Open Babel was applied to convert the SMILES format of compounds in the chemical database to SDF format (O’Boyle et al., 2011). Then the Rdkit package (https://www.rdkit.org/) in Python (https://www.python.org/) was used to calculate the Tanimoto similarity between compounds with anti-liver-fibrosis activity and compounds in LS based on the ECFP4 circular topological fingerprints. The pandas package was used to save the calculated value to an Excel table. Previous researchers conducted retrospective analysis on the data in ChEML and ZINC, suggesting that the tanimoto similarity thresholds recommended for ECFP4 fingerprints should be set to 0.4 (Muegge and Mukherjee, 2016), meaning compounds with similarity greater than 0.4 were more likely to have the same activity. In order to improve the accuracy, the similarity threshold in this study was set to 0.5. A potential active compound–compound interaction network was constructed with Cytoscape 3.7.2, where compounds with a Tanimoto similarity greater than 0.5 were linked with edges.

Prediction and Enrichment Analysis of Potential Target Proteins

SwissTargetPrediction (http://www.swisstargetprediction.ch) was used to predict the potential target proteins of the compounds, which compares a query molecule with a library of 280,000 active compounds containing more than 2,000 targets. Then, a comprehensive cross-validation analysis was used to rank the predicted target proteins and analyze the accuracy of the target prediction. According to the distribution of target prediction related scores, we selected proteins with an average score greater than 0.1 as potential targets. In addition, because more than 99% of compounds in LS have more than two similar compounds (with a similarity threshold of 0.5), we set the number of target protein-related compounds to greater than two to improve the accuracy of the target prediction. Then, the STRING (https://string-db.org/) database was used to enrich and analyze the pathways and diseases related to the potential target proteins. Pathways or diseases with a false discovery rate of less than 0.05 and the number of related target proteins greater than two were considered to be statistically significant.

Validation of the Interaction Between the Potential Active Compounds and the Potential Target Proteins

For direct verification, the SMILES representations of compounds were used as inputs to search the ChEMBL database (https://www.ebi.ac.uk/chembl/), and the known target proteins of these compounds were summarized. For indirect verification, the transcriptome data (GSE85871) describing the reference cell line MCF7 treated with tanshinone IIA and oleanolic acid from GEO DataSets (https://www.ncbi.nlm.nih.gov/gds/) were downloaded. It is worth noting that only the above two compounds of the LS have related transcriptome data. The GEO2R tool in GEO DataSets was used to screen the differential genes regulated by tanshinone IIA and oleanolic acid. Genes that differed significantly between the control and compound treated group (p < 0.05) were declared differentially expressed. An additional criterion requiring genes to have a two-fold average intensity difference of between the control and compound-treated group was also applied.

Extraction of LS

Two hundred and fifty grams of LS root was crushed into a coarse powder. The lipophilic constituents were extracted via three rounds of ethanol-based heat reflux extraction at 70°C. After filtering and merging the filtrate, ethanol was reclaimed under a reduced pressure to concentrate it into extractum with a relative density of 1.35 (60°C), which was washed with hot water until it is colorless, freeze-dried and crushed it into a fine powder to afford the extraction of LS (1.5 g).

Quality Control of LS Extracts

3 mg of powdered sample was weighed accurately in the eppendorf tube, and 500 μl of ethanol was added. The tube was sealed and placed in the ultrasound system. Once the powder dissolved, centrifuge at 5,000 rpm for 3 min. Two hundred microliter solution was taken and filtered with a 0.22 μm filter membrane, during which the continuous filtrate was collected. Chromatography was performed on Agilent 1290 Infinity UPLC system. A C18 column (2.1 mm × 150 mm, 1.8 μm, Waters, Milford, MA) was used for the separation. The column temperature was set at 25°C. The mobile phase consisted of 0.1% formic acid (A) and acetonitrile (B), using a gradient elution of 35%–60% B at 0–20 min, 60%–80% B at 20–25 min, 80%–95% B at 25–26 min, 95% B at 26–30 min. The flow rate was 0.4 ml•min⁻¹ and the injection volume was 2 μl. The mass spectra were obtained by an Agilent 6530 Accurate-Mass Q-TOF mass spectrometer connected to the UPLC system via an ESI interface. The mass spectrometer was operated in positive ion mode and negative ion mode both with a capillary voltage of 3.5 kV, drying gas flow of 5 L/min, and a gas temperature of 325°C. The nebulizer pressure was set at 30 psig. The fragmentor voltage was set at 135 V and skimmer voltage was set at 65 V. Data were collected in centroid mode and the mass range was set at m/z 100–1,500 using extended dynamic range. The collision energy (CE) was optimized for the target derivatives from 10 to 30 eV. Then, identification of LS based on the acquired TIC chromatogram was conducted. The formulas were proposed based on the mass spectra and other rules, such as the general rule for the number of nitrogen atoms, the double bond equivalent (DBE) index and the ‘show isotopic’ function.

Animal Experiments

C57BL/6 male mice (7 weeks old, 18–20 g) were obtained from Shanghai Slac Laboratory Animal Company (Shanghai, China). In the experiments, mice were randomly assigned to five groups (control, model, LS low-dose (LS-L), LS high-dose (LS-H) and LS-safety groups). In the liver fibrosis model, they were administered with carbon tetrachloride (CCl₄) (5% olive oil dilution, 10 ml/kg) through intraperitoneal injection by twice weekly for nine consecutive weeks. The control (0.5% CMC-Na), model (0.5% CMC-Na), LS-L (18 mg/kg) and LS-H (180 mg/kg) group were administered via oral gavage every day for nine consecutive weeks. The LS-safety group was gavage with LS (180 mg/kg/day) for nine consecutive weeks to assess its safety. All animal experiments were conducted in the Animal Experiment Center of Second Military Medical University in accordance with the standard operating procedures.

Serum Biochemical and Cytokine Analysis

After 24 h following the final injection, the peripheral blood was obtained from every mouse through eyeball enucleation. After 1 h of incubation at room temperature, the blood samples were centrifuged for 10 min at 3,000 rpm and 4°C for separation. The serum levels of laminin (LN; Langdun, cat no. BPE20195), hyaluronic acid (HA; Langdun, cat no. BPE20516), AST (Leidu, cat no. S03030), ALT (Leidu, cat no. S03040) were determined by serum ELISA kits, according to the manufacturer protocols. The optical density was read at specific wavelengths using the BioTek Synergy instrument.

Examination of Hydroxyproline (Hyp) in Liver

Ice-cold lysis buffer was used to prepare tissue homogenates. The mixture was centrifuged for 15 min at 13,000 rpm and 4°C to collect the supernatant fractions, which were stored at −20°C for the quantification of protein levels. Then ELISA kits were used to determine the tissue content of Hyp (Langdun cat no. BPE20231).

Histomorphology Assay

Hepatic tissues were processed with 4% paraformaldehyde fixation before embedding were embedded by paraffin, slicing into 5-µm sections. Hematoxylin-eosin (HE) staining was used to observe the inflammatory cell infiltration of livers. Liver fibrosis was estimated by Sirius Red staining. Liver fibrosis was graded using the Metavir fibrosis scoring.

Cell Proliferation Assay

The HSC-T6 and LX-2 cells were seeded in 96-well plates with 8 × 10³ cells per well and cultivated for 12 h. Then cells were exposed to LS at various concentrations for 24 h. In addition to the administration groups, a control group (without treatment) and a blank group (without cells) were also set up. The cell viability was evaluated by CCK-8. The absorbance was detected using a Bio-Rad microplate reader (SynergyTM 4, BioTek, United States) at 450 nm. The percentage inhibition of cytotoxicity was calculated as follows: [(OD_{administration group}-OD_{blank group})/(OD_{control group}-OD_{blank group})] × 100%. All experiments were repeated three times.

Flow Cytometry Analysis

The HSC-T6 and LX-2 cells were cultured in DMEM containing 10% FBS and 1% penicillin/ streptomycin at 37°C in a humidified atmosphere (5% CO₂). Cells in the exponential growth phase were seeded in a 6-well plate (6 × 10⁵ cells per well) and grown overnight. After being treated with LS at different concentrations for 24 h, cells (both floating and adherent) were harvested and washed twice in PBS. Then the cells were resuspended in PBS and stained with Annexin V/FITC and propidium iodide for 15 min in the dark at room temperature. The cells were analyzed using a FACSCalibur instrument (Becton Dickinson, Mountain View, CA, United States).

Western Blot Analysis

The HSC-T6 and LX-2 cells were seeded in a 6-well plate (6 × 10⁵ cells per well) and grown overnight. After being treated with gradient concentrations of LS for 2 h and subsequent 10 ng/ml recombinant human TGFβ1 for 22 h, the cells were collected and washed with PBS. The total protein was extracted in NP-40 buffer on ice and then centrifuged at 12,000 r/min at 4°C for 20 min to remove insoluble materials. The total protein was quantitated by bicinchoninic acid protein assay kit and retained for subsequent analysis. The total protein was electrophoresed by a 4%–20% SDS-PAGE gradient gel and electrotransferred to a polyvinylidene fluoride membrane. The membranes were blocked with BlockPRO^TM 1 Min Protein-Free Blocking Buffer at room temperature for 15 min and then immunoblotted overnight at 4°C with the following primary antibodies: STAT3 (1:2,000), P-STAT3 (1:1,000), α-SMA (1:10,000), JAK1 (1:500), P-JAK1 (1:1,000), TGFβ1 (1:1,000) and GADPH (1:10,000). After being washed in Tris-Buffered Saline and Tween 20 for 5 min three times, the membranes were subsequently incubated with IRDye® 800CW Goat anti-Rabbit IgG Secondary Antibody (1:3,000) for 1 h at room temperature. The membranes were washed in TBST three times for 5 min each again. Finally, protein gray-scale bands were scanned and analyzed by an Odyssey infrared imaging system (LI-COR, United States), GADPH was used as an internal control.

Known Anti-Hepatic Fibrosis Lipophilic Compounds in Salvia miltiorrhiza

We collected a total of 138 lipophilic compounds in Salvia miltiorrhiza, with no repetitive structures, including 102 diterpenoids, 16 triterpenoids, six steroids, and 14 other compounds (Supplementary Table S1). As there were few reports on the liver-protecting activity of volatile oil in Salvia miltiorrhiza, we did not summarize this kind of lipophilic compound. At present, eight lipophilic compounds in Salvia miltiorrhiza are reported to have anti-liver-fibrosis effects (in vivo or in vitro): three of them were the subject of target-related studies, while the other compounds only involved pathway-related studies. Among the eight lipophilic compounds in Salvia miltiorrhiza, only tanshinone IIA has been involved in more than 10 liver-fibrosis-related studies.

Active Compounds Prediction Through the Construction of a Potential Active Compound Interaction Network

The screening process of active compounds in LS was hierarchically descripted in Supplementary Figure S1. A potential active compound–compound interaction network was constructed, which contained 71 nodes and 100 edges (Figure 2). In this network, the compounds were divided into four categories: including diterpenoids, triterpenoids, steroids and others. Based on eight known active ingredients in the center (red nodes, Figure 2), this network identified 63 potential active compounds (green nodes) from 130 lipophilic constituents of unknown activity. Among the 63 potential active compounds, 50 compounds were diterpenoids, nine compounds were triterpenoids, three compounds were steroids, and only one compound belongs to the others category.

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FIGURE 2

The lipophilic potential active compounds interaction network. The red circles represent compounds that have been clearly reported to have anti-liver fibrosis effects at the animal or cellular level. The green circles represent potential active compounds with a structural similarity greater than 0.5 to the corresponding red nodes. The larger the node, the more related similar compounds. The compound-compound interactions are linked by edges.

The Target Prediction and Validation of LS for the Treatment of Hepatic Fibrosis

A total of 342 potential target proteins were obtained by SwissTargetPrediction, of which 196 potential target proteins were enzymes, 51 potential target proteins were G-protein-coupled receptors, 19 potential target proteins were nuclear receptors, 18 potential target proteins were ion channels, and 58 were other kinds of proteins (Supplementary Table S2). Specifically, there were 53 diterpenoids targeting 298 potential proteins; 12 triterpenoids targeting 129 potential proteins; four steroids targeting 120 potential proteins; and two other compounds targeting 79 potential proteins.

We then we searched the ChEMBL and GEO datasets to determine the validated interaction between the potential active compounds and the potential target proteins (Supplementary Table S3). The results showed that 41 potential active compound-target interactions were reported in the ChEMBL database, and 94 differential genes corresponding to the predicted target protein were regulated by tanshinone IIA and oleanolic acid.

Enriched Diseases and Pathways of the Potential Target Proteins

The enrichment analysis results showed that the potential target proteins were mainly involved in 182 statistically significant pathways or diseases. At present, the enrichment-related data have the following three characteristics. First, there were numerous potential targets, related pathways and diseases regulated by the 71 potential active compounds (Figure 3). Second, we only retained the compound–target interaction pairs with Kd or IC₅₀ values less than 10 μM collected from ChEMBL; thus, these compound–target interactions are credible. Third, since transcriptomics is a high-throughput tool, the reliability of differential genes regulated by the potential active lipophilic compounds in Salvia miltiorrhiza is limited. In order to identify biologically significant pathways or diseases and obtain more accurate results, pathways, or diseases with more than 10 potential target proteins, more than one known target protein and more than 10% known differential genes were selected (Figure 3).

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FIGURE 3

(A) Enriched diseases related to the potential target proteins. (B) Enriched pathways related to the potential target proteins.

As shown in Figure 3A, the potential target proteins are closely related to hepatitis B, hepatitis C, and hepatocellular carcinoma (orange bars). Hepatitis B and hepatitis C are the causes of liver fibrosis, and hepatocellular carcinoma is the result of liver fibrosis. These indicate that the potential target proteins are closely related to liver fibrosis, which proved the reliability of our target identification methods. Since there is no entry containing liver fibrosis in the KEGG database, it cannot be enriched by the enrichment method applied in this study.

In addition, the target pathway enrichment analysis results showed that the LS may exert its anti-liver-fibrosis effect by regulating 22 pathways (Figure 3B). Among them, the pathway containing the largest number of known targets is the JAK/STAT signaling pathway. Therefore, the LS may treat liver fibrosis primarily by regulating this pathway.

JAK1/STAT3 Signaling Pathway was Representatively Selected for Further Experimental Validation

As seen in Figure 3B, the JAK/STAT signaling pathway contains a total of 19 potential target proteins (Supplementary Table S4), of which fours proteins are known targets for the LS and six proteins are known differential genes regulated by LS. The relationships between LS and the target proteins or differential genes in the JAK/STAT signaling pathway are shown in Table 1. In order to screen out the most potential target protein in the JAK/STAT signaling pathway for subsequent experimental verification, we searched the PubMed database and determined the known interactions between the target proteins and liver fibrosis. First, target proteins (PTPN2 and PIM1) that have not been clearly reported to be related to liver fibrosis were excluded. Then, target proteins (PTPN11, PTPN6, PIK3CB, PIK3CD, and EGFR) that had contradictory compound-target protein disease interactions were excluded. For example, the ChEMBL database results show that dihydrotanshinone I, tanshinone I and tanshinone IIA are inhibitors of PTPN11, and their IC₅₀ is less than 10 μM. The literature also shows that PTPN11 inhibitors can alleviate CCl4-induced liver fibrosis (Kostallari et al., 2018), but transcriptome data show that tanshinone IIA can upregulate PTPN11. Due to this contradiction, the relationship between LS and PTPN11 requires further verification In addition, the ChEMBL database shows that dihydrotanshinone I, tanshinone I, and tanshinone IIA are inhibitors of PTPN6, and their IC₅₀ is less than 10 μM. However, the literature reports that PTPN6 agonist could ameliorate liver fibrosis by upregulating PTPN6 (Su et al., 2017). Thus, PTPN6 was excluded due to this contradiction.

Both STAT3 and AKT2 have been reported in the literature to be closely related to liver fibrosis, and inhibiting STAT3 or AKT2 can treat liver fibrosis (Su et al., 2015; Reyes-Gordillo et al., 2019). In addition, cryptotanshinone, tanshinone IIA and oleanic acid have been reported to be inhibitors of STAT3 and AKT2 (Table 1). Thus we speculate that STAT3 and AKT2 in the JAK/STAT signaling pathway are the main potential target proteins of LS. As STAT3 has higher target-prediction-related scores and number of target protein-related compounds than AKT2 (Supplementary Table S4), we selected STAT3 for subsequent experimental verification.

Apart from the four known target proteins and six known differential gene proteins regulated by LS, there are 10 other potential target proteins (PIK3CA, JAK3, JAK1, JAK2, MTOR, CCND1, MCL1, CREBBP, PDGFRA and TYK2) that have not been reported to be related to LS. Among these 10 proteins, PIK3CA, JAK3, JAK, and JAK2, which have more than 10 related compounds are regarded as high potential target proteins.

A previous study reported that STAT3 directly participated in the activation and transdifferentiation of HSC in response to TGFβ and subsequent hepatic fibrosis (Tang et al., 2017), it also showed that the JAK1/STAT3 signaling pathway played an important role in HSC activation and liver fibrosis (Tang et al., 2017). Therefore, we further validated whether the JAK1/STAT3 signaling pathway was involved in the LS-mediated anti-liver-fibrosis process.