To clarify the active components of SYD in CIM | DDDT

Introduction

Cancer is a broad group of diseases that involve unregulated cell growth and is internationally acknowledged as one of the main causes of human death and heavily imperiling human life.^1,2 Chemotherapeutic drugs are crucial tools in malignancy treatment and are frequently accompanied by serious side effects.^3,4 Myelosuppression is one of the serious side effects, which may even cause serious mortality problems, making alleviating Chemotherapy-induced myelosuppression (CIM) an important issue in clinical cancer treatments.⁵ Currently, the treatment of CIM is mainly symptomatic, including biological agents (colony-stimulating factor, et al) and blood products. However, these drugs may lead to vascular events and lung injury, and even promote cancer development and metastasis.⁶ Therefore, the development of a safe and effective drug to treat CIM is imperative.

The long-term clinical use of Traditional Chinese medicine (TCM) has led to the accumulation of valuable experience in the treatment of CIM.^7–9 Shengyu Decoction (SYD) is a classical prescription in ancient China, which was first mentioned in Mai Yin Zheng Zhi, a famous medical monograph of the Yuan Dynasty. SYD is believed to treat myelosuppression by replenishing qi and the blood. SYD is composed of six herbs: Radix Rehmanniae Praeparata (shudihuang, SDH), Ginseng (renshen, RS), Radix Paeoniae Alba (baishao, BS), Rhizoma Chuanxiong (Chuanxiong, CX), Radix Angelicae Sinensis (danggui, DG), and Radix Astragali (huangqi, HQ). Studies have revealed that iridoid and phenylpropanoid glycosides are characteristic components of SDH; BS is abundant in monoterpenes; DG and CX are rich in organic acids and essential oils; RS is distinguished by the presence of ginsenosides; And HQ is distinguished by the presence of flavonoids. These extracts and compounds have a broad range of pharmacological activities, such as hemopoietic, immunoregulatory, anti-inflammatory, antibacterial, antiviral, antioxidant, and antitumor activities.^10–13 However, the primary effective substances of SYD against CIM and their regulatory mechanisms remain to be explored.

The multiple constituents of SYD are the advantages of its efficacy, but they also seriously hinder the in-depth understanding of its active substances and the underlying mechanisms for treating CIM. Network pharmacology provides an available tool for elucidating the network synergy of multi-component, multi-pathway, and multi-target between TCM and specific human diseases, which was widely used to elucidate the pharmacological mechanisms of TCM.^14,15 The molecular docking technology plays an important role in the prediction of docking modes and binding affinities between drug ligands and macromolecule receptors, and it is benefit for predicting the active ingredients of TCM.¹⁶ Therefore, network pharmacology and molecular docking are frequently used complementarily in pharmacological studies of TCM, and their predicted results are then verified experimentally.^17–19 In network pharmacology research, the construction of drug-disease networks usually based on relevant databases. However, the actual composition of TCM is not equal to the sum of the total herbal ingredients in the components database, and only absorbable ingredients can be considered effective ingredients.²⁰ At present, LC-QTOF/MS is widely applied to the multi-component determination of complex samples, including the detection of TCM compounds and other fields.^21,22 Therefore, the combination of network pharmacology and LC-QTOF/MS can overcome these drawbacks and improve the credibility of the prediction results.

This study aimed to clarify the active constituents and underlying mechanisms of SYD in the treatment of CIM using network pharmacology and molecular docking, followed by experimental validation. Therefore, an LC-QTOF/MS method was established to determine the absorbable components of SYD for use in network pharmacology research. Next, a “compound-target-pathway” network was constructed by network pharmacology to find the key targets and related signaling pathways for treating CIM, and molecular docking was used to screen effective components. Subsequently, the effectiveness of the screened compounds was evaluated. Finally, we validated that the active component of SYD exerts proliferative and anti-apoptotic effects by activating the PI3K-AKT signaling pathway. This study identified the effective ingredients of SYD and the mechanism of action of the components against CIM in vitro, and provided a pharmacological basis for its treatment.

Materials and Methods

LC-QTOF/MS Analysis of SYD Solution and Medicated Rat Serum

Preparation of SYD extract: First, SYD (160 g) was pulverized into fine powders and soaked in 10 times the amount of water for 30 minutes and then boiled for 1 hour. Second, filtered the extraction solution with gauze, then add 8 times and 5 times the amount of water to the filter residue and boil for 1 hour, separately. Third, filtered and combined the extraction solution. Finally, concentrated the extraction solution under reduced pressure to 80mL(2.0 g/mL).

Preparation of SYD-medicated rat serum. Male Wistar rats weighing 220 ± 20 g were obtained from Shenyang Pharmaceutical University Experimental Animal Center. All animal experiments (executed based on the Guidelines for Animal Experimentation of China Medical University) were approved by the Institutional Animal Care and Use Committee of the CMU. Blood samples were collected from the ophthalmic venous plexuses. In our experiments, the rats were randomly divided into 2 groups (n= 6 per group): SYD-containing group and blank group. In the SYD group, rats were gavaged SYD extract (11 g/kg/d). Meanwhile, the same volume of distilled water was gavaged to blank group. All rats were gavaged only once. Rat blood samples were collected 0.5, 1.5, and 3 h after dosing. All serum samples were stored at −80°C until analysis.

Chromatographic conditions: LC-QTOF/MS analysis was performed using an Agilent 1260 HPLC System (Agilent, USA) equipped with a Triple TOF™ 5600+ mass spectrometer (AB SCIEX, USA). HPLC separation was performed on a ZORBAX Eclipse Plus C18 column (150 mm × 4.6 mm, 3.5 μm). A gradient elution program was employed, using 0.1% formic acid in both water (A) and acetonitrile (B) at a flow rate of 0.5 mL/min at 35°C. The injection volume was 4 μL. The gradient program was shown as follows: 5% B at 0–2 min, 5% −15% B at 2–6 min, 15%-25% B at 6–9 min, 25%-30% B at 9–14 min, 30%-33% B at 14–18 min, 33%-40% B at 18–20 min, 40%-95% B at 20–25 min, maintained 95% B at 25-30min, and then maintained 5% B at 30.1-40min.

QTOF/MS conditions: A quadrupole time-of-flight tandem mass spectrometer (QTOF/MS, QTRAP^TM 5600+, AB Sciex, USA) was connected to the LC system via an electrospray ionization (ESI) interface. The ionization mode of ESI source adopted positive and negative ionization mode. The optimized conditions were as follows: ion source voltage, 5500V in positive mode and −4500V in negative mode; Declustering potential, 100V in positive mode and −80V in negative mode; collision energy, 30V in positive mode and −30V in negative mode; ion source temperature, 450 °C; The ion source gas1(N₂) and gas2 (N₂) flows were set at 50 psi; curtain gas 30psi.

The operation flow was as follows: Appropriate amount of SYD extract was diluted with methanol to 0.1 g/mL, then filtrated through 0.22 μm microporous membrane for LC-QTOF/MS analysis. To analyze SYD-medicated rat serum, 1 mL of rat serum was added to 3 mL of methanol and vortexed for 3 min to precipitate protein. The sample was centrifuged at 12,000 rpm for 5 min and the supernatant was dried under nitrogen gas at 30 °C. The residue was reconstituted in 100 μL methanol, vortexed for 3 min, and centrifuged at 12,000 rpm for 5 min. The supernatant (4 μL) was injected into the LC-QTOF/MS system and the control blank serum sample was subjected to the same process.

Screening and Target Prediction of SYD Absorbable Ingredients

A total of 33 absorbed compounds of SYD in rat serum were identified using LC-QTOF/MS. Network pharmacology studies have focused on 33 absorbed components. Putative targets of the absorbed compounds in SYD were obtained from the TCMSP and Swiss Target Prediction (taking the top five targets with the score value) databases.

Collection of Gene Targets for Myelosuppression

Myelosuppression-related targets were obtained by searching the keywords “myelosuppression” and “bone marrow suppression” in the OMIM (https://omim.org/) and GeneCards (https://www.genecards.org/) databases, respectively. Gene names were calibrated using UniProt database (https://www.uniprot.org/). Finally, we merged the data and removed duplicate content.

Collection of Potential Targets for SYD Treatment of Myelosuppression

Venny 2.1 (https://bioinfogp.cnb.csic.es/tools/venny/index.html) was used to plot a Venn diagram of the interaction between the targets of SYD and the disease targets of myelosuppression. The common targets of both were used as potential targets for the SYD treatment of myelosuppression.

Construction of Component-Target Network

For visualization, the potential active compounds of SYD and their potential effector targets were entered into Cytoscape 3.8.2 software to draw the “SYD-compound-target” network analysis diagram.

Construction of Protein-Protein Interaction (PPI) Network and Screening of Core Targets

The common gene targets of SYD and myelosuppression were uploaded to the STRING database (https://string-db.org/), in which the biological species were selected as “homosapiens” and all other parameters were default values. PPI network results were imported into Cytoscape 3.8.2. MCODE and Cytohubba plug-ins were used to identify core targets.

Enrichment Analysis

GO enrichment analysis, including biological process (BP), cellular component (CC), and molecular function (MF), and KEGG pathway enrichment analysis were performed on key targets using the DAVID Database (https://david.ncifcrf.gov/). The top 10 items in the GO analysis and the TOP 20 items in the KEGG analysis were selected and visualized. According to Fisher’s test, statistical significance was set at p

Molecular Docking

SMILES files of small-molecule compounds were downloaded from the PubChem database and converted to mol2 format using Discovery Studio 2020 software. The 3D structures of the core targets were obtained from the Protein Data Bank database (PDB) (http://www.rcsb.org/).

First, the iGEMDOCK molecular docking software was used to quickly screen compounds with a good binding capacity with six core proteins. Select “standard docking” for docking mode, and all other parameters are set to default values. A total binding energy of less than −90 kJ/mol was considered as the criterion for stable binding, and the components that had good binding with all six core proteins were initially identified as the key components.

Then, the selected key components with core proteins were accurately docked and visualized using AutodockTool 1.5.6 and PyMol software. OpenBabel 3.1.1 were used to convert the SDF format of key components to mol2 format, and the mol2 format of the ligands was input into AutoDockTools 1.5.6, to add polar hydrogen atoms and Gasteiger charges and output them in pdbqt format. Proteins were de-liganded, dehydrated, and hydrogenated in AutoDockTools 1.5.6, and output as files in the pdbqt format. The pdbqt files of the ligands and receptors were imported into the AutoDockTools 1.5.6. When the docking box was constructed, the receptor proteins were centered, the docking boxes completely covered the receptor proteins, and the ligands were located outside the docking box. Molecular docking was performed using AutoDockTools 1.5.6, and a total binding energy less than −0 kJ/mol was used as the criterion for stable binding. The components that had good binding with all six core proteins were considered as effective components. The results of molecular docking were visualized using PyMOL software.

Reagents and Cell Culture

Calycosin-glucoside and ferulic acid were purchased from the National Institutes for Food and Drug Control (Beijing, China), while 5-FU, LY294002, albiflorin, and paeoniflorin were purchased from MCE (Shanghai, China).

The Mouse bone marrow stromal cell line (OP9) (Shanghai Institute of Biological Sciences, Chinese Academy of Science, Shanghai, China) was maintained in DMEM (BI, USA) with 10% FBS (BI, USA) in a 5% CO₂ humidified 37°C incubator. Fresh medium was added to the cells every 2–3 days.

Validation of Active Ingredients

Cells were divided into control, model, and treatment groups. The treatment groups were further divided into four groups: ferulic acid, calycosin-7-glucoside, albiflorin, and paeoniflorin groups. The control group was cultured as a routine for 24 h, and the model group was treated with 25 μg/mL 5FU for 24 h. Treatment groups were cultured with 25 μg/mL 5FU and different compounds at different concentrations for 24 h: ferulic acid (10, 20, 40, 60 μmol/mL), calycosin-7-glucoside (10, 20, 40, 60 μmol/mL), albiflorin (10, 40, 80, 160 μmol/mL), and paeoniflorin (5, 10, 20, and 40 μmol/mL). Cell proliferation assays were performed using Cell Counting Kit-8 (CCK8).

Validation of the Mechanism of Action Based on the PI3K-Akt Pathway

Cell grouping and treatments. Cells were divided into five groups: control group, 5FU group, albiflorin group, albiflorin+LY294002 group, and LY294002 group. Control group was cultured as routine; all cells except the control group were cultured with 5FU (25 μg/mL) and different drugs for 24 h: albiflorin group was cultured with albiflorin (40 μmol/mL); albiflorin+LY294002 group was cultured with albiflorin (40 μmol/mL) and LY294002 (50 μM); LY294002 group was cultured with LY294002 (50 μM).

Flow cytometry assays. Cell apoptosis and cell cycle analyses were performed using flow cytometry. Cell pellets from different groups were collected separately, digested with 0.25% trypsin for 2 min, and washed with PBS buffer at 37°C. Apoptosis of OP9 cells was evaluated using an Annexin V-FITC/PI Apoptosis Kit (Annoron, Beijing, China). OP9 cells were resuspended in 200 μL binding buffer and double-stained with Annexin V-FITC and PI (Thermo Fisher, USA) for 15 min in the dark. For cell cycle analysis, cells were fixed with 70% ice-cold ethanol overnight at −20°C, then added 500 μL cycle reagent (Millipore, USA) and incubated for 30 min at room temperature. Subsequently, OP9 cells were analyzed using a flow cytometer (EC201827C1; Luminex, USA) and FlowJo software v.7.6 (FlowJo LLC, USA).

Western blotting. Total protein was extracted from OP9 cells using RIPA buffer (Beyotime Biotech, Shanghai, China) supplemented with 0.05% phosphatase inhibitors (Beyotime Biotech). The Membranes were blocked by TBST with 5% non-fat dry milk and incubated overnight at 4°C with anti-PI3K, p-PI3K, Akt, p-Akt, p21, cyclin D1, Bax, Bcl-2, GAPDH (Cell Signaling Technology, Beverly, Massachusetts, USA). Blots were visualized using ECL reagents (Millipore, Bedford, MA, USA) and the bands were scanned and quantified using ImageJ software. The relative expression levels of target proteins were normalized to those of GAPDH and β-actin.

Statistical Analysis

Statistical analyses were performed using the SPSS software (version 20.0; SPSS, Chicago, USA). For continuous data, data normality was tested by Q-Q plots and the homogeneity of variance was performed by Levene’s test. Normally distributed variables were presented as mean ± standard deviation (Mean ± SD), one-way analysis of variance (ANOVA) and Tukey’s post hoc test were used if variance was equal, and the Welch ANOVA and Games-Howell hoc test were used if variance was not equal to compare the groups. Non-normally distributed data were presented as median (M) and inter-quartile range (IQR) and were analyzed by Kruskal–Wallis test and Bonferroni’s post hoc test. p

Results

LC-QTOF/MS Analysis of SYD Solution and Medicated Rat Serum

Ingredients in SYD solution: First, a reference database including the chemical name, structural formula, relative molecular mass, and characteristic fragments was constructed for SYD. Information was collected from the PubMed, SciFinder databases, and other researchers. In addition, unique neutral-loss molecules and/or the characteristic fragment ions of each type of component were obtained. Second, two specific data processing methods of PeakView^® V. 2.2 Workstation, neutral loss filtration (NLF) and sub ion filtration (PIF), were used to identify the components in SYD, which two further helped to find and predict the compounds with specific neutral loss molecules and characteristic fragment ions effectively. Classified the possible components in SYD based on the constructed chemical information database. After obtained the unique neutral loss molecules and/or characteristic fragment ions to each type of component, NLF and PIF screening were performed. Finally, the structure of the compound was predicted based on the obtained effective information. In total, 148 compounds were identified. The total ion flow chromatography (TIC) under positive and negative ion modes and the identified 148 compounds are shown in Supplementary Figure 1 and Supplementary Table 1, respectively. The proposed fragmentation patterns of the typical compounds are shown in Supplementary Figure 2.

Ingredients in SYD-medicated rat serum: The components detected in the serum samples were compared with those in SYD solution. If the chromatographic and mass spectrometric characteristics of compounds in the serum are consistent with those of compounds in the SYD solution, they are considered potential medicinal ingredients. Finally, 33 components were absorbed as the prototypes. Detailed information on the identified 33 compounds is shown in Supplementary Table 2 and the TIC are shown in Supplementary Figure 3.

Collection of component targets for SYD and disease targets for myelosuppression.

A total of 33 absorbable compounds were identified using LC-QTOF/MS and retrieved from the TCSMP and Swiss Target Prediction databases. Of these, 5 compounds were not present in the above databases. After removing duplicate contents, 113 compound-related targets were identified. A total of 2946 myelosuppression targets were identified by searching and screening the GeneCards and OMIM databases. A total of 62 intersection targets for components and diseases were drawn using Venny 2.1.0. The intersecting targets were considered as potential targets for SYD treatment of CIM in the next step of the analysis (Figure 1).

The 62 intersection targets and the corresponding 28 compounds were imported into Cytoscape 3.8.2 software to construct an active ingredient-target network for image visualization and analysis. The network contained 113 nodes and 248 edges (Figure 2). Among them, one compound can interact with multiple targets, and different compounds can act on the same target simultaneously. This reflects the multi-component and multi-target effects of SYD.

Figure 2 The Chinese medicine-compound-target-disease network.

GO and KEGG Enrichment Analysis

Through GO enrichment analysis, the potential BP, CC, and MF of the 62 target genes were identified. These Results suggest that SYD may be involved in the biological processes of negative regulation of apoptosis and the positive regulation of cell proliferation in the treatment of myelosuppression. The potential signaling pathways of the 62 target genes were identified using KEGG enrichment analysis. A total of 111 significantly enriched signaling pathways were identified, and the top 20 significantly enriched signaling pathways are shown in Figure 3. The results showed that the main signaling pathways related to myelosuppression were enriched in the PI3K-Akt, HIF-1, VEGF, and estrogen pathways.

Figure 3 The top 10 pathways for GO analysis (A) and the top 20 pathways for KEGG analysis (B) of the targets of SYD.

Construction of the PPI Network and Cluster Analysis

As shown in Figure 4A, the PPI network consisted of 58 nodes (4 targets were not involved) and 374 edges. The “edges” represent the interrelationship between the targets and indicate the degree of association. Therefore, the larger the node, the higher is the degree of association between the corresponding target and other targets. Cluster analysis was conducted using the MCODE plug-in of Cytoscape 3.8.2, to obtain subnetworks with the highest scores, in which all 18 genes were considered to play an important role in the PPI network (Figure 4B). The Cytohubba plug-in was used to identify hub genes. As shown in Figure 5, the darker the color of the gene in the figure, the higher is the degree of association between other genes. Genes with high scores in all the six algorithms were identified as hub genes. Finally, TNF, IL-6, VEGFA, SRC, HRAS, and STAT3 were identified as hub genes and used as potential core targets for the next analysis.

Figure 4 The PPI network of interaction targets (A) and subnetworks with the highest score (B).

Figure 5 The hub genes screened by Cytohubba plug-in in six algorithms. (A) The algorithm of Closeness; (B) The algorithm of Degree; (C) The algorithm of EPC; (D) The algorithm of MCC; (E) The algorithm of MNC; (F) The algorithm of radiality.

Molecular Docking Results

The 28 compounds in the SYD-active ingredient-target network were molecularly docked with six core targets using iGEMDock v2.1. The lower the binding energy, the more stable is the conformation of the ingredient that binding to the target. The results indicated that 10 compounds, including Campneoside II, darendoside B, leucosceptoside A, purpureaside C, verbascoside, ferulic acid, calycosin-7-glucoside, albiflorin, paeoniflorin, and oxypaeoniflorin, had relatively high binding potential to the core targets.

Subsequently, AutoDockTool 1.5.6 software was used to further screen the active ingredients of the 10 compounds mentioned above, and the results are shown in Supplementary Table 3. The results showed that ferulic acid, calycosin-7-glucoside, albiflorin, paeoniflorin, and oxypaeoniflorin had lower affinities, demonstrating that they have a higher possibility of binding to core targets, suggesting that they may be the active ingredients of SYD in the treatment of myelosuppression. Representative molecular docking mode diagrams are presented in Supplementary Figure 4.

In vitro Experiments Results

Combined with the results of molecular docking and related literature, calycosin-glucoside, albiflorin, ferulic acid, and paeoniflorin were preliminarily selected as the active ingredients. The CCK8 method was used to detect the effects of the four predicted components on the cell proliferation activity of OP9 cells. Based on the CCK8 results, albiflorin with the strongest activity was selected for the next study. Furthermore, Western blot and flow cytometry were used to verify the regulatory effects of albiflorin on the PI3K-Akt signaling pathway in OP9 cells.

Active ingredients alleviated the inhibitory effect of 5-FU on bone marrow stromal cells (BMSCs) proliferation.

As shown in Figure 6, compared to the control group, the proliferation of OP9 cells was significantly decreased after treated with 5-FU. Compared to the 5-FU group, after the co-cultured compounds reached a certain concentration, all four compounds obviously increased the proliferation activity of OP9 cells (p