Target Prediction

Potential target proteins of the FZHY formula were identified using two methods. Firstly, since approved drugs of a similar structure may have common target proteins and similar curative effects, we used the drug similarity search tool in ChEMBL⁷ to identify compounds similar to the active components of the FZHY formula. Only compounds with a high similarity score (≥0.95) as compared with the structures of active components in FZHY formula were picked out, in order to obtain more accurate results. The therapeutic target proteins of these similar compounds were also collected in ChEMBL; those implicated in liver fibrosis were hypothesized to be targeted by the related active component of the FZHY formula. Secondly, molecular docking, which is very useful in rational drug design, can be used not only to predict the binding sites and pose(s) of drug candidates with their target proteins, but also to evaluate their binding affinities (Kitchen et al., 2004). We used libdock in Discovery Studio 3.0⁸ to predict possible relationships between the target proteins of liver fibrosis and the active components of FZHY. The co-crystallized ligand binding with the target protein was regarded as a positive control. The dock scores of the positive control with corresponding proteins were regarded as cutoff point. If the dock score is higher than the cutoff point, the protein will be recognized as a potential target protein of the compounds. Based on these results, an active component-target protein interaction (CTPI) network can be constructed and displayed using Cytoscape 3.5.1 (Smoot et al., 2011). A component and a related potential target protein are linked with an edge; and components or target proteins are represented by nodes.

Gene Expression Profiles

Gene expression microarray data (GSE68001) of primary human quiescent HSCs and in vitro activated HSCs were obtained from the National Center for Biotechnology Information Gene Expression Omnibus⁹, a public functional genomics data repository.

Network Construction and Analysis

Based on the acquired identities of the potential target proteins and differential genes relating to the activation of HSCs, a protein-protein interaction (PPI) network was built by importing the gene names of above proteins and genes to the public database STRING (version 10.5¹⁰). The minimum required interaction score was set at 0.9, to improve the accuracy of the results. Cytoscape 3.5.1 (Smoot et al., 2011) were used as a tool to visualize the PPI network. Subsequently, PPI combined with CTPI was used to build a new compound-target protein-differential genes (CTPG) network. This network analysis process can identify target proteins which can connect compounds in FZHY formula with differential genes. To facilitate scientific interpretation of identified potential targets, a STRING database was used to perform several analyses such as gene ontology (GO) enrichment analysis and pathway enrichment analysis.

Quality Control of FZHY Formula

The FZHY formula was purchased from Yifeng Pharmacy (Shanghai, China), and extracted by sonication in methanol for 60 min. UPLC-Q-TOF/MS was used to identify the active components of FZHY. Mass spectra was acquired in both negative and positive modes, and non-target compound identification was further conducted based on obtained fingerprints. Formulae were proposed based on the mass spectra and other rules, such as the general rule of the number of nitrogen atoms, double bond equivalent (DBE) index and ‘show isotopic’ function. And the compound was finally confirmed by the comparison with the authentic compound.

Materials and Reagents

Salvianolic acid B, dihydrotanshinone I, salvianolic acid A, tanshinone II-A, amygdalin, adenosine, cordycepin, schizandrin, schisandrin B, schisantherin A, gypenoside XLIX and kaempferol were purchased from EFEBIO (Shanghai, China¹¹), and their structural information is shown in Supplementary Figure S1. 15-deoxy-Δ12,14-prostaglandin J2 (15d-PGJ2) was used as a positive control (Jin et al., 2016), and obtained from Abcam (Cambridge, United Kingdom). The structures of the above chemicals were unambiguously identified by ¹H NMR and MS spectra, and their purity was demonstrated to be 98% by HPLC-UV. Cell Counting Kit-8 (CCK8) detection kit was obtained from Beyotime (Shanghai, China). LX-2 cells and T6 cells were purchased from Cell Resource Center of Fudan IBS (Shanghai, China). These cells were incubated in Dulbecco minimal essential medium (Sigma, United States) with 10% fetal bovine serum (GIBCO, United States) and penicillin, streptomycin (GIBCO, United States) under a humidified atmosphere with 5% CO₂ at 37°C. The medium was renewed every 2 days.

Cell Proliferation Assay

A total of 5 × 10³ cells were planted in 96 well plates and cultivated for 24 h. Then, cells were treated with various concentrations of compounds (6.25, 12.5, 25, 50, 100, 200 μM) and 15d-PGJ2 (100 μM) for 24 h. Cell viability was tested by CCK-8; the absorbance was directly detected using a Bio-Rad microplate reader (Synergy^TM 4, BioTek, United States) at 450 nm. All experiments were repeated three times.

Compound-Target Protein Network Construction and Analysis

We used the above data to construct a CPTI (Figure ​Figure22), which contains 119 nodes (40 active compounds and 79 potential targets) and 172 edges. In this network, the rectangles and ellipses represent the active compounds and their target proteins, respectively. There are two red rectangles in the middle of the blue ellipse, which represent the common components of several herbs in FZHY formula. Specifically, β-sitosterol was present in S. miltiorrhiza, S. Persicae, C. sinensis, G. pentaphyllum, and S. chinensis; and rutin was an ingredient of both S. miltiorrhiza and S. chinensis β-sitosterol and rutin may be the key active compounds of FZHY formula. As shown in Figure ​Figure22, there are more active compounds in S. miltiorrhiza than in the other five herbs, and the active compounds in S. miltiorrhiza targeted the largest number of proteins compared with the active compounds in other five herbs. Thus, we infer that S. miltiorrhiza is the principal ingredient of FZHY, which is consistent with previous work (Zhao C.Q. et al., 2006; Yang et al., 2015).

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FIGURE 2

The Component-Target protein network. The rectangles represent the 45 candidate compounds in Fuzheng Huayu (FZHY), the cyan, red, green, yellow, purple, pink rectangles refers to active compounds in Pinus massoniana Lamb. (Song Hua Fen), Prunus persica (L.) Batsch (Tao Ren), Salvia miltiorrhiza Bunge (Dan Shen), Schisandra chinensis (Turcz.) Baill. (Wu Wei Zi), Cordyceps sinensis (BerK.) Sacc. (Dong Chong Xia Cao), Gynostemma pentaphyllum (Thunb.) Makino (Jiao Gu Lan), respectively. The red rectangles in the center of network represent the common components of several herbs. The blue circles represent the gene names of target proteins of the six herbs found by text mining and molecular docking.

STRING (version 10.5, see foot note text 10) is a database which aims to collect and integrate all functional interactions between expressed proteins by consolidating known and predicted protein-protein association data for a large number of organisms. This platform can also be used to conduct functional enrichments of user inputs. In this study, we performed the gene ontology enrichment analysis and pathway enrichment analysis of potential target proteins with the functions in STRING. The meaningful pathways (Supplementary Table S4), biological processes (Supplementary Table S3), cellular components (Supplementary Table S5) and molecular functions (Supplementary Table S6) were selected with a p-value < 0.05. Figure ​Figure3A3A depicts the enriched molecular functions of the target protein, which are mainly associated with binding and steroid hormone receptor activity. The binding activities are primarily connected with protein binding and enzyme binding. As to the cellular components’ distribution, the target proteins were mainly distributed in nuclear part, organelle part and organelle lumen (Figure ​Figure3A3A). The main biological processes of the target proteins are also summarized in Figure ​Figure3A3A, which shows that the active ingredients in FZHY formula could exert an anti-liver fibrosis effect through cellular response to endogenous stimulus, positive regulation of macromolecule biosynthetic process, response to endogenous stimulus, regulation of cell death, positive regulation of nucleobase-containing compound metabolic process, transcription initiation from RNA polymerase II promoter, positive regulation of macromolecule metabolic process, cellular response to hormone stimulus, positive regulation of cellular biosynthetic process, and positive regulation of transcription. Among these processes, FZHY formula had been reported to cure liver fibrosis by positive regulation of macromolecule metabolic process (Gao et al., 2016) and positive regulation of macromolecule biosynthetic process (Liu et al., 2006; Cheng et al., 2013). In addition, cellular responses to endogenous stimulus (Huang et al., 2016) and regulation of cell death (Zhong et al., 2017) have been reported to be closely related to liver fibrosis, further experiments are needed to validate the interaction between FZHY formula and these two biological processes.

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FIGURE 3

(A) The enrichment analysis in biological processes, cellular components and molecular functions of 79 identified target proteins by STRING database. (B) The Target protein-Pathway network. Blue nodes refer to target proteins. The pink nodes represent pathways, and the color is consistent with pathways related target protein numbers, the deeper the pink, the more number of proteins.

In order to identify the significant pathways that the target proteins are involved in, we made a pathway enrichment analysis of target proteins by STRING database (Figure ​Figure3B3B). Logically, the pathway that contains more target proteins is more important than the pathway that contains fewer target proteins. Hence, a target protein-pathway network (Figure ​Figure3B3B) was constructed to indicate the most important pathways. The results showed that the FZHY formula exerted its protective effects against liver fibrosis primarily by regulating 15 pathways (Figure ​Figure3B3B), of which the TGF-beta signaling pathway has been reported (Wang et al., 2012). In addition, although lots of references indicated that liver fibrosis was closely related to the other 14 pathways, such as hypoxia inducible factor-1 (HIF-1) signaling pathway (Zhao et al., 2014), the forkhead box O (FoxO) signaling pathway (Wu et al., 2015), the ErbB signaling pathway (Scheving et al., 2016), the Chemokine signaling pathway (Marra and Tacke, 2014), the thyroid hormone signaling pathway (Bai et al., 2014), the estrogen signaling pathway (Deng and He, 2007), the Ras signaling pathway (Abbas et al., 2011), the phosphatidylinositol-3-kinase (PI3K-Akt) signaling pathway (Zhang D.Q. et al., 2016), the adenosine 5′-monophosphate (AMP)-activated protein kinase (AMPK) signaling pathway (Bai et al., 2014), the mammalian target of rapamycin (mTOR) signaling pathway (Li et al., 2015), the PPAR signaling pathway (Jiang et al., 2015), the mitogen-activated protein kinase (MAPK) signaling pathway (Park et al., 2015), the toll-like receptor signaling pathway (Aoyama et al., 2010), and the Wnt signaling pathway (Miao et al., 2013); further experiments are needed to validate these findings.

Taken together, FZHY formula may exert an anti-liver fibrosis effect primarily by regulating 10 biological processes and 15 pathways. The multiple active components in FZHY formula target multiple proteins in the biological network to regulate and restore the network equilibrium, thereby mitigating the development of liver fibrosis.

Construction and Analysis of Target Protein-Disease Network

Figure ​Figure44 depicts the classification of the target-related diseases, and indicates that FZHY formula can show a protective effect against pancreatic cancer, prostate cancer, colorectal cancer, glioma, inflammatory bowel disease, hepatitis B, hepatitis C, melanoma, chronic myeloid leukemia, and chagas disease. The utility of FZHY for the treatment of hepatitis B (Tang et al., 2012), hepatitis C (Zhang B. et al., 2014), liver cirrhosis (Chen et al., 2016), and steatohepatitis (Jia et al., 2012) has been previously reported; however, we were unable to find reports pertaining to its efficacy against cancer, inflammatory bowel disease, chronic myeloid leukemia and chagas disease.

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FIGURE 4

Component-Target protein-Disease network. The Top rectangles in the network represent active components of six herbs in FZHY, red ellipse nodes in the Middle of the network denote target proteins of active compounds, the Bottom rectangles refer to diseases correlated with above target proteins.

Compound-Target Protein-Differential Gene Protein Network Analysis

In order to improve the accuracy of target identification, a compound-target protein-differential gene protein network (CTPG) was established through the integration of CTPI and PPI (Figure ​Figure55). This network consists of chemical components, target proteins and differential gene proteins, including 179 nodes and 31862 edges. The components acting on target proteins cause the up- or down-regulation of related differential gene proteins. The interactions between target proteins and differential gene proteins were obtained from the STRING database with high confidence (>0.9). The active components were considered as the initial nodes, and the target proteins and differential gene proteins were as the terminal nodes. Figure ​Figure55 depicts 28 components interacted with 31 target proteins and 122 differential gene proteins. The 31 target proteins in this network are more likely to be the true targets of the active components in FZHY compared to the 79 target proteins identified from network pharmacology. This deduction has been proved by reference–briefly, a total of 79 potential target proteins were identified by network pharmacology, but only 40 of these were validated to be related to liver fibrosis. Of the 31 target proteins identified by network analysis, 25 (Supplementary Table S8) are in agreement with existing research results. Target proteins related to liver fibrosis validated by references are summarized in Supplementary Table S7.

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FIGURE 5

Compound-Target protein-Differential gene protein network. This network was built by linking 28 active components (blue rectangles) in FZHY formula, 31 target proteins (purple ellipses) and 122 different gene proteins (pink ellipses).

We randomly selected signal transducer and activator of transcription 1 (STAT1) and PPARG identified by network analysis as examples to explain the potential synergistic mechanism of the active components in FZHY for alleviating liver fibrosis through inhibition of HSCs activation (Figure ​Figure55). In addition, heparin-binding epidermal growth factor-like growth factor (HBEGF) was also selected representatively from the other 15 liver fibrosis related target proteins predicted only by network pharmacology.

Previous researches showed that the activation of STAT1 could attenuate liver fibrosis through inhibiting HSCs activation and proliferation (Thomas et al., 2004; Jeong et al., 2006), and promoting apoptosis by inhibiting anti-apoptotic genes or inducing pro-apoptotic genes in different tissues (Stephanou and Latchman, 2003). As STAT1 was identified as a target of the active components in FZHY formula (Figure ​Figure55), one possible explanation for the liver fibrosis-related medicinal properties of FZHY formula is that it inhibits the activation and proliferation of HSCs through activation of STAT1.

Peroxisome proliferator activator receptor gamma was proposed as a crucial factor for the inhibition of HSCs activation (Hazra et al., 2004; Tsukamoto, 2005; Yang et al., 2006). The level of PPARG was higher in quiescent HSCs, compared to activated HSCs; and its abundance, expression, and transcriptional activity were all suppressed during HSCs activation in vivo and in vitro (Galli et al., 2000; Miyahara et al., 2000; She et al., 2005). That up-regulation of PPARG expression could suppress the HSCs activation (Zhai et al., 2015), a pivotal event in the pathology of liver fibrosis (Liu et al., 2017), was also shown. Thus, increasing PPARG expression levels in HSCs should be an effective means of liver fibrosis prevention (Galli et al., 2000, 2002; Xu et al., 2003). Furthermore, some PPARG agonists, such as troglitazone, thiazolidinediones, saroglitazar, KR62776, GW7845, 6-octadecynoic acid, and 15d-PGJ2, have been proven to attenuate liver fibrosis by cell-based or animal experiments (Marra et al., 2000; Galli et al., 2002; Zhao C. et al., 2006; Bae et al., 2010; Ohtera et al., 2013; Jain et al., 2017). PPARG agonists can therefore be concluded to be effective treatments for liver fibrosis by inhibiting the HSC activation; and PPARG is a promising therapeutic target for antifibrotic chemotherapy (Zhang F. et al., 2013). Thus, the active components in FZHY formula may inhibit HSCs viability by targeting PPARG. Interestingly, one component of FZHY formula, ergosterol, was shown to cure liver fibrosis by upregulation of the expression of PPARG in HSC-T6 cells (Tai et al., 2016). In addition, rosmarinic acid and baicalin (both also present in FZHY formula) were identified to impart an antifibrotic effect by epigenetic expression repress of PPARG (Yang et al., 2012; Tai et al., 2016).

Heparin-binding epidermal growth factor, which was identified by network pharmacology and not of network analysis, has also been reported to serve as a fibrosis inducer (Guo et al., 2017). Also, other studies indicated that HBEGF inhibitor could cure liver fibrosis by efficiently inhibiting HSCs activation (Zhang D. et al., 2014). Thus, we inferred that the active components in FZHY formula may inhibit HSCs activation to cure liver fibrosis through obstruction of HBEGF. This deduction not only demonstrates the reliability of the network pharmacology method, but also exemplifies the limitation of the network analysis approach, the precision of which needs improvement.

In summary, the active components of FZHY counter liver fibrosis by simultaneously targeting STAT1, PPARG and HBEGF, to prevent HSCs activation, proliferation and migration.

The authors would like to acknowledge the technical supports to Dr. Xiang Li from School of Pharmacy in Second Military Medical University.