Total-cell protein lysates and Western blot materials were prepared as follows. Then, 48 h after drug treatment, the cells were rinsed with lysed ice-cold PBS and lysed in RIPA buffer (150 mmol/L NaCl, 50 mmol/L Tris–HCl, pH 8.0, 1% deoxycholate, 0.1% SDS, and 1% Triton X-100) containing protease and phosphatase inhibitors (Roche, United Kingdom) for at least 30 min and then centrifuged at 14,000 × g for 10 min at 4◦C. The concentration of total protein for each sample was measured by DCTM protein assay kit (Bio-Rad). Then, equal amounts of total protein of each sample were resuspended in loading buffer and denatured for 5 min in 100◦C. The total protein (30 µg) of each sample was separated by 10% SDS-PAGE and then transferred to PVDF membranes (Millipore, United States). The protein membranes were blocked by 5% nonfat milk in 1× TBST for 1 h at room temperature. The samples were incubated with different primary antibodies [phospho-C-RAF, phospho-AKT (Ser473), phospho-ERK (Thr202/Thy204), total-C-RAF, total-ERK, total-AKT, total-p70s6k, phosphop70s6k, total-mTOR, phospho-mTOR, phospho-PI3K, total-PI3K, caspase-3, PARP, and GAPDH] at 4◦C overnight. The above-mentioned primary antibodies were diluted in 1:1,000. Protein membranes were subsequently incubated with secondary fluorescent antibodies for 2 h and then washed in 1× TBST three times for 5 min each time. All secondary antibodies (anti-rabbit or anti-mouse) were diluted in 1:10,000. All membranes were analyzed by an LI-COR Odyssey scanner (Belfast, United States).

#### Colony Formation Assay

H460 and A549 cells were planted into six-well plates (500 cells/well), respectively. After attachment overnight, the cells were treated with various concentrations (0, 5, 7.5, 10, and 20 µM) of krukovine, and the medium was changed every 3 days. When colony formation was visible, the medium was discharged. The colonies were washed with ice-cold PBS gently, fixed in 4% paraformaldehyde (PFA) for 15 min, and then stained with 0.5% crystal violet (20% methanol, 0.5% crystal violet, and 1% PFA in

#### REFERENCES

ddH2O) for 30 min. After the extra crystal violet was washed away and dried off, the colonies were photographed and analyzed.

#### Scratch Wound Healing Assay

H460 and A549 cells were planted into six-well plates (500 cells/well), respectively. After attachment overnight, they should reach ∼70% confluence as a monolayer, then, the confluent monolayer was scratched with a 200 µL sterile pipette tip. After scratching, gently wash the well twice with medium to remove the detached cells. The cells were treated with various concentrations (0, 5, 7.5, 10, and 20 µM) of krukovine. After growing for additional 24 h, wash the cells twice with 1× PBS, take photos for the monolayer on a microscope in the same configurations.

#### Statistical Analysis

Statistical analysis was carried out by GraphPad Prism 5.0 software. The results were presented as (mean ± SEM) of three individual experiments. ANOVA or Student's t-test followed by Bonferroni's test was used to compare all pairs of columns. p-Values <0.05 were set as statistically significant.

### AUTHOR CONTRIBUTIONS

LLi, EL, and XY conceived the study, participated in the design and coordination of the whole study, and helped in critically revising the draft for important intellectual content. HL carried out the cell culture studies, molecular biology experiments, data collection, statistical analysis, and manuscript drafting. YW participated in the data collection and performed the statistical analysis. ZJ and FD participated in the molecular biology experiments. YL and LLu helped in critically revising the draft for important intellectual content. All authors have checked and approved the final manuscript and agreed to be accountable for all aspects of the work by ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

### FUNDING

This work was supported by Macao Science and Technology Development Fund (Project Nos. 046/2016/A2, 086/2015/A3, 005/2014/AMJ, and FDCT-16-010-SKL).

**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2018 Lai, Wang, Duan, Li, Jiang, Luo, Liu, Leung and Yao. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

fphar-09-00958 August 20, 2018 Time: 19:30 # 9

# Cyclocarya paliurus Leaves Tea Improves Dyslipidemia in Diabetic Mice: A Lipidomics-Based Network Pharmacology Study

Lixiang Zhai1 , Zi-wan Ning1 , Tao Huang1 , Bo Wen1,3, Cheng-hui Liao3 , Cheng-yuan Lin1 , Ling Zhao1 , Hai-tao Xiao2 \* and Zhao-xiang Bian1,3 \*

1 School of Chinese Medicine, Hong Kong Baptist University, Kowloon, Hong Kong, 2 School of Pharmaceutical Sciences, Health Science Center, Shenzhen University, Shenzhen, China, 3 Shenzhen Research Institute and Continuing Education, Hong Kong Baptist University, Shenzhen, China

#### Edited by:

Yuanjia Hu, University of Macau, Macau

#### Reviewed by:

Xiao Yu Tian, The Chinese University of Hong Kong, Hong Kong Rene Cardenas, Universidad Nacional Autónoma de México, Mexico

\*Correspondence:

Hai-tao Xiao<EMAIL_ADDRESS>Zhao-xiang Bian<EMAIL_ADDRESS>
#### Specialty section:

This article was submitted to Ethnopharmacology, a section of the journal Frontiers in Pharmacology

Received: 11 April 2018 Accepted: 06 August 2018 Published: 28 August 2018

#### Citation:

Zhai L, Ning Z-w, Huang T, Wen B, Liao C-h, Lin C-y, Zhao L, Xiao H-t and Bian Z-x (2018) Cyclocarya paliurus Leaves Tea Improves Dyslipidemia in Diabetic Mice: A Lipidomics-Based Network Pharmacology Study. Front. Pharmacol. 9:973. doi: 10.3389/fphar.2018.00973 Hyperlipidemia and hepatic steatosis afflict over 75% of patients with type 2 diabetes, causing diabetic dyslipidemia. Cyclocarya paliurus (CP) leaf is a herbal tea which has long been consumed by the Chinese population, particularly people suffering from obesity and diabetes. CP appears to exhibit a hypolipidemic effect in lipid loaded mice (Kurihara et al., 2003), although the detailed mechanisms and active ingredients for this hypolipidemic effect have not yet been answered. In this study, we investigated the beneficial effects of CP and predicted the mechanisms by utilizing lipidomics, serumpharmacochemistry and network pharmacology approaches. Our results revealed that serum and hepatic levels of total triglyceride (TG), total cholesterol (T-CHO), low-density lipoproteins (LDL) and high-density lipoproteins (HDL), as well as 30 lipids including cholesterol ester (CE), diglyceride (DG), phosphatidylethanolamine (PE), phosphatidylcholine (PC), and sphingomyelin (SM) in CP-treated mice were improved in comparison with untreated diabetic mice. In parallel, 14 phytochemical compounds of CP were determined in mice serum after CP administration. Mechanistically, the network pharmacology analysis revealed the predicted targets of CP's active ingredients ALOX12, APP, BCL2, CYP2C9, PTPN1 and linked lipidome targets PLD2, PLA2G(s), and PI3K(s) families could be responsible for the CP effects on diabetic dyslipidemia. In conclusion, this study revealed the beneficial effects of CP on diabetic dyslipidemia are achieved by reducing accumulation of hepatic lipid droplets and regulating circulatory lipids in diabetic mice, possibly through PI3K signaling and MAPK signaling pathways.

### INTRODUCTION

Hyperlipidemia and hepatic steatosis are frequently found in the metabolic syndrome and type 2 diabetes (Jung and Choi, 2014; Perry et al., 2014). Hyperlipidemia is characterized as high T-CHO, TG, LDL, and low HDL levels (Richhariya et al., 2015), whilst hepatic steatosis is represented by high TG, T-CHO, AST, and ALT levels (Stern and Castera, 2017). Emerging evidence suggest that dyslipidemia is a significant risk factor for the development of type 2 diabetes (Andriankaja and Joshipura, 2014; Association, 2015), and pharmacological lipid-lowering therapy is effective to alleviate the complications of type 2 diabetes including hyperlipidemia, hepatic steatosis, coronary heart disease, etc. (Zafrir and Jain, 2014; Sattar et al., 2016; Spence et al., 2016). Cyclocarya paliurus (Batal.) Iljinskaja (Juglandaceae) is a native medicinal plant grown in the southwest of China. The leaves of C. paliurus (CP) have been used as a herbal tea in China for its special flavor and the benefits to the obese and diabetic Chinese populations. The bioactive components isolated from CP are flavonoids, triterpenoids, organic acids, and polysaccharides, these components contribute to its versatile biological properties including antihyperglycemia, antihyperlipidemia, and antihypertension (Jiang et al., 2015; Yoshitomi et al., 2017). Previous studies have investigated the anti-diabetic function and mechanism of CP leaves on STZ and high-fat diet-induced type 2 diabetic mice, and its potent hypoglycemic effect has been verified on diabetic mice (Wang Q. et al., 2013; Ma et al., 2015). Moreover, CP has also been reported with hypolipidemic effects in vivo (Yao et al., 2015; Lin et al., 2016; Yang et al., 2016). However, the mechanism of bioactive constituents of CP on diabetic dyslipidemia remain unknown. Therefore, our objective is to study the effects of CP on lipid disorders in diabetes and elucidate its mechanism-of-actions.

It is acknowlegded that the majority of herbal medicines, including TCMs are orally delivered drugs of polypharmacy. Their active components are firstly absorbed into the bloodstream and then selectively and simultaneously interact with multiple targets at the root causes of the disease (Zhao et al., 2015). Serum pharmacochemistry is a rapid and reliable method using the UPLC-MS technique to track the components absorbed into the bloodstream and has been widely used to reveal the efficacy of TCMs (Yan et al., 2017). Moerover, metabolomics is an approach to analyze the metabolites, the intermediate products of metabolic reactions of living systems. This technology contains many subclasses based on the chemical characteristics of metabolites, namely lipidomics, amino metabolomics, and sugar metabolomics, etc. It has been widely used in the evaluation of the therapeutic effects and elucidation of the therapeutic mechanisms of herbal products (Wang X. et al., 2017). Metabolomics profiling can reveal whole metabolic profile changes of living systems in response to endogenous or exogenous stimuli such as drug treatment (Beger et al., 2016). Because any effects of herbal products are mediated by their constituents in a complex biological system, metabolomics can help us to analyze their action network comprehensively. Network pharmacology is a bioinformatics strategy to understand drug action and mechanisms by mapping drug-target-disease networks from the biological level (Li et al., 2014). To date, accumulating evidence suggest that network pharmacology approach is a powerful tool to study the molecular mechanisms of the complex components found in medicinal herbs.

Considering CP as a herbal tea with multiple components and diabetic dyslipidemia as a cluster of lipid abnormalities, we focused on the investigation of the beneficial effect of CP and its mechanisms against diabetic dyslipidemia using lipidomics, serum pharmacochemistry, and network pharmacology approaches.

#### MATERIALS AND METHODS

#### Chemicals and Materials Regents and Standards

UPLC grade organic solvent was purchased from Merck (Darmstadt, Germany). Deionized water was obtained from a milli-Q system (Millipore, Billerica, MA, United States). Formic acid, ammonium formate, and phosphoric acid were obtained from Sigma (St. Louis, MO, United States). Lipids standards including a lipids mixture of TG, CE, DG, PE, PC, and SM, etc, were purchased from Avanti Polar Lipid (Alabaster, AL, United States). Strptomycin and glibenclamide was purchased from Sigma (St. Louis, MO, United States).

#### Plant Material Preparation

fphar-09-00973 August 27, 2018 Time: 10:45 # 3

The leaves of Cyclocarya paliurus (Batal.) Iljinskaja were collected and authenticated by Prof. Hu-Biao Chen from School of Chinese Medicine, Hong Kong Baptist University, and voucher specimens (No. CP20151201) was stored in our Research Laboratory. For the preparation of the extract of C. paliurus leaves (CP extract), the dried leaves of C. paliurus (5 kg) were soaked in boiled water (1:10 m/v) for 2 h twice. The solution was concentrated and dried under vacuum freezer to obtain crude extract. The crude extract was then extracted by 70% EtOH for 2 h (1:10 m/v) in an ultrasound bath. The refined solution was concentrated and dried under vacuum freezer again and the refined extract was stored in 4◦C fridge until use. CP solution was prepared with 0.5% sodium carboxymethyl cellulose (CMC-Na) solution for animal oral administration and MeOH for phytochemical analysis.

## Animal Studies

#### Animal Handling and Diets

Eight-week-old C57/BL6J mice were purchased from Laboratory Animal Services Centre, Chinese University of Hong Kong and raised in the Laboratory Animal Services Center, School of Chinese Medicine, Hong Kong Baptist University. The mice were raised in a 12 h light/dark cycle, temperature controlled (22◦C) standard animal room provided with diet and water ad libitum. All experimental protocols were approved by the Animal Ethics Committees of Hong Kong Baptist University, in accordance with "Institutional Guidelines and Animal Ordinance" (Department of Health, Hong Kong Special Administrative Region) (Registration No. LIUYE/15-16/01-CLNC). Body weight, food consumption, and blood glucose level were monitored each week. Blood glucose level was determined by OMRON glucometer (Beijing, China) using blood samples collected from the tail vein.

#### Animal Groups

The diabetes mice model was induced by high-fat diet (adjusted Calories Diet, 42% from fat) (No. 881372) (Harlan Laboratories, Inc., Indianapolis, IN, United States) for 4 weeks and intraperitoneal (i.p) injection with STZ (25 mg/kg) three times in following 3 days. The mice with fasting glucose level higher than 11 mmol/L were considered as diabetic mice, and the diabetic mice with consecutive 7-day hyperglycemia (11 mmol/L or greater) were used for the experiment. An equal volume of vehicle was injected into the control mice. The diabetic mice were then divided into the diabetic group (model group), the CP treatment group (CP group) and glibenclamide treatment group (positive group). For the CP treatment group, the mice were orally administrated 2 g/kg/day CP until end of the experiment according to previous experimental data (**Supplementary Figure S1**). The normal mice were divided into two groups: vehicle control group (0.5% CMC-Na solution treated, named as blank group) and CP-treated control group (CP treated, named as control group). CP-treated control group mice were orally administered 2 g/kg/day CP extract and blank group mice were orally administered same volume 0.5% CMC-Na solution as CP solution. Glibenclamide was given at 15 mg/kg/day in 0.5% CNC-Na solution to mice in positive group according to the previous study (Xiao et al., 2017).

#### Blood Sample Collection and Preparation

For serum pharmacochemistry study, mice were orally administered CP solution and anesthesia by 3% chloral hydrate through intraperitoneal injection after 10 mins. About 1 mL blood was collected in heparin-tube. Plasma was obtained after 3,000 rpm centrifuge for 30 min at room temperature. A total of 900 µL MeOH was added to 100 µL plasma and centrifuged at 14,000 for 10 min at 4◦C to precipitate protein. A total of 800 µL supernatant was dried under vacuum concentrator within 30 min and dissolved in 200 µL 70% methanol for LC-MS analysis.

For lipidomics study, about 1 mL blood was collected from mice under anesthesia, serum was obtained after centrifuging at 3,000 rpm for 30 min at room temperature. A total of 50 µL serum was used for lipidomics study. Briefly, 250 µL Folch solvent with the internal standard was added in 50 µL serum and vortexed vigorously. Two phases were formed after centrifugation at 5,000 rpm for 15 min. The bottom layer was dried under vacuum concentrator and dissolved in 200 µL of ACN/IPA/H2O (65:30:5 v/v/v) for analysis.

Another 100 µL serum was used for clinical index analysis including ALT, AST, ALP, CREA and BUN using a biochemical analyzer (Hitachi 902 Automatic Analyzer; Hitachi, Japan). TG, TCHO, LDL, and HDL were analyzed using assay kits purchased from Nanjing Jiancheng Bioengineering Institute (Nanjing, China) according to the manufacturers' instructions.

#### Hepatic Histopathology Analysis and Biochemical Analysis

At the end of the study, the mice were sacrificed, and liver tissue was collected for H&E staining to analyses histology changes. Liver tissue from sacrificed mice was soaked in 10% formalin solution (prepared by 1× phosphate-buffered saline, PBS) and fixed for 12 h. Samples were then made into paraffin sections as specimens and stained with hematoxylin and eosin (H&E) according to manufacturer's protocol (Mayer's Hematoxylin Solution, Sigma-Aldrich). The sections were observed and captured under microscopy. About other 50 mg liver tissues were homogenized in iced 1× PBS solution (1:10, m/v). The homogenized solution was used for TG and TCHO analysis following the protocol of Nanjing Jiancheng Bioengineering Institute (Nanjing, China).

#### Phytochemical and Metabolomics Study Phytochemical Analysis

The chromatographic analysis was performed on an Agilent 1290 UPLC system equipped with an autosampler, binary gradient pump, and PDA detector. The system was operated at 30◦C and a water ACQUITY UPLC HSS T3 column (150 mm × 2.1 mm,

1.7 µm) was used. The injection volume was 5 µL and the mobile phase flow rate was 0.4 mL/min. Solvents that constituted the mobile phase were (A) 0.2% aqueous acetic acid and (B) acetonitrile. The elution conditions were as follows: 0–5 min, linear gradient 2–5% B; 5–10 min, linear gradient 5–10% B; 10– 15 min, linear gradient 10–25% B; 15–25 min, linear gradient 25– 40% B; 25–28 min, linear gradient 40–90% B. Peaks were detected at 254 nm. The compounds of CP extracts were characterized by retention time, MS/MS information and related chemical standards in positive and negative modes.

#### Lipidomics Study

The liquid chromatogram was performed on an Agilent 1290 UPLC system. A waters ACQUITY UPLC HSS C18 column (2.1 mm × 100 mm, 1.8 µm particle size) was used for separation. The column temperature was maintained at 40◦C and autosampler temperature was maintained at 8◦C. The separation was performed with mobile phase A and B within 20 min per sample. Phase A consists of 60:40 water/ACN in 10 mM ammonium formate and 0.1% formic acid, and phase B is made by 90:10 IPA/ACN with 10 mM ammonium formate and 0.1% formic acid. The linear gradient was as follow: 32% B (0–1.5 min); 32–45% B (1.5–4 min); 45–52% B (4–5 min); 52–58% B (5– 8 min); 58–66% B (8–11 min); 66–70% B (11–14 min); 70–75% B (14–18 min); 75–97% B (18–21 min); maintain 97% B (21– 25 min); decrease to 32% B (25–30 min); maintained at 32% B (Gregory et al., 2013). The injection volume is 2 µL for the positive mode and 6 µL for the negative mode.

XCMS package in R was used to convert chromatograms intensity into raw data for multivariate statistical analysis using metaboanalyst (Huan et al., 2017). For lipid identification, the frame m/z values were used to search information on LIPID MAPS, Human Metabolome Database (HMDB) and Metlin database. The matches were confirmed by lipids' exact tandem mass (MS/MS) and retention time based on our internal standards (SPLASHTM Lipidomix R Mass Spec Standard, Avanti Polar Lipids, United States).

### Bioinformatics and Statistical Analysis

Potential Targets Prediction and Linking Analysis The detected chemical components of CP in plasma after oral

administration were imported into our in-house target prediction

tool "MOST" (Huang et al., 2017) to construct the target prediction database. Targets from the prediction database were then used to determine the relevance with diabetic dyslipidemia with literature review, and the relevant genes were considered as CP targets for diabetic dyslipidemia. The lipids data were imported into Cytoscape to generate a metabolites-reactionenzyme-gene database. The genes of significantly changed lipids in CP treatment group were considered as lipid targets of CP. The predicted targets and lipids targets of CP were combined together to upload to STRING (Szklarczyk et al., 2014) for PPIs analysis to link the action of predicted targets with lipids targets of CP.

#### Target-Diabetic Dyslipidemia Link Analysis

All determined and predicted targets were searched on PubMed using key words: [targets name] AND [diabetes] AND [lipidemia]. The targets which were studied on lipid disorders in diabetes were also included.

#### Statistical Analysis

GraphPad was used for statistical analysis of the biochemical data. Statistically significant differences (p < 0.05) in mean values were calculated by Student's t-test or one-way ANOVA. The lipidomics data was processed by R package with XCMS. Metaboanalyst (Xia et al., 2015) was used for multivariate statistical analysis. Cytoscape was used trace associated gene-enzyme using KEGG database.

#### RESULTS

#### CP Attenuated Diabetic Dyslipidemia in Diabetic Mice Induced by High Fat Diet and STZ

As shown in **Table 1**, after high fat diet and STZ treatment, blood glucose levels of mice were significantly elevated whereas their body weight was significantly demoted compared with control and blank group, indicating that the diabetic mice model was well established. In contrast, those clinical signs were significantly rescued after CP treatment for 5 weeks. Since both hyperlipidemia and hepatic steatosis are characterized as

#### TABLE 1 | Blood glucose (mM) levels and body weight changes on week 0, 3, and 5.

The effects of CP extract on blood glucose levels and body weight of diabetic mice. All data are presented as means ± SD. Diabetic model group vs. control group: #p < 0.05, ##p < 0.01 and ###p < 0.001. CP or glibenclamide (positive) treatment group vs. diabetic model group: ∗p < 0.05, ∗∗p < 0.01, ∗∗∗p < 0.001.

compared to that of control and blank group, serum TG, TCHO, and LDL levels, as well as hepatic AST, ALT, TG, and TCHO levels of the model group were significantly increased, whereas serum HDL levels were significantly decreased, indicating that

fphar-09-00973 August 27, 2018 Time: 10:45 # 5

the hyperlipidemia and hepatic steatosis were developed in the model group. In CP or glibenclamide-treated groups, those altered lipids-associated indexes were significantly alleviated. In addition, histological examination of hepatic sections also revealed that there were a large number of vesicles of fat accumulating within hepatocytes of diabetic mice, which was significantly reduced after CP or glibenclamide treatment. These results suggested that CP extract has a great potential to attenuate diabetic dyslipidemia and hepatic steatosis induced by high fat diet and STZ in mice.

### CP Improved Circulating Lipid Metabolism in Diabetic Dyslipidemia

To study the CP effect on lipid metabolism in diabetic mice, we performed a lipidomics study on mice serum. Fatty acyls (fatty acids), glycerolipids (DG and TG), glycerophospholipids (PC, PE, and PI), and sphingolipids (SM) were then used for multivariate statistical analysis as shown in **Supplementary Table S1.1**. As the PCA and PLS-DA are two effective multivariate analysis methods for large-scale data matrix, the combining data were analyzed using PCA and PLS-DA approaches. As shown in **Figures 2A,B**, PCA and PLS-DA score showed a clear classification between model group and control group, demonstrating that circulating lipids profiles are significantly different in the model group. As well, the CP-treated group or glibenclamide treated group also had its own distinctive classification, bearing some overlap. Considering the overlap between model group and CP-treated group, we further narrowed down the profiles of lipids in these two groups using OPLS-DA method. Notably, CE, DG, TG, PC, and SM were significantly distinguished between these two groups (**Figures 2C,D**), And then, we applied the hierarchal clustering analysis to segregate the lipid metabolites into three distinct groups (shown in **Figure 2E**). A significant number of lipids were up-regulated in model group in comparison with control or blank groups. After CP or glibenclamide treatment, the levels of these lipids were greatly down-regulated. The results suggest that CP or glibenclamide could significantly regulate lipid metabolism in diabetes. Interestingly, PCA and PLS-DA analysis result showed that the score plot of CP-treated group and glibenclamide-treated group were not separated, and the same result was observed in heat map of hierarchal clustering analysis (**Figures 2B,E**), suggesting CP and glibenclamide may share the similar metabolic pathways to improve the diabetic dyslipidemia.

Subsequently, we analyzed the changes of lipids between control (blank) groups and model groups, and found that 47 lipids were elevated and 53 lipids were reduced in model group. After CP treatment, 30 lipids including CE, DG, TG, PC, and SM species were significantly either up-regulated or down-regulated (p < 0.05) (**Figure 3**). After importing those CP-regulated lipids to Cytoscape, a network pathway was built based on KEGG database and revealed that metabolic pathways including arachidonic acid metabolism, bile acid biosynthesis, de novo fatty acid biosynthesis, glycerophospholipid metabolism, glycosphingolipid metabolism, linoleate metabolism and saturated fatty acids beta-oxidation are involved in the regulating function of CP in lipid metabolism (As shown in **Supplementary Figure S5**).

#### CP Possess Multiple Components-Multiple Targets-Multiple Pathways Properties for Diabetic Dyslipidemia

Since absorption into the bloodstream is one of the prerequisites for drug efficacy, we performed a pharmacochemistry study to detect the CP components in blood stream after oral administration in mice. As shown in **Table 2** and **Supplementary Figure S2**, after oral administration of CP to normal C57/BL6 mice, 13 compounds in total: quinic acid, neochlorogenic acid, chlorogenic acid, 4-hydroxybenzoic acid, gallic acid, quercetin-3-glucuronide, kaempferol, loganin 7-pentoside, astragalin, kaempferol-3-rhamnoside, quercetin, quadranoside IV, and asiatic acid were detected. Subsequently, we used in-house tools "MOST: most-similar ligand-based approach to target prediction" (Huang et al., 2017) to predict the protein targets of major potential active components of CP identified in bloodstream as shown in **Supplementary Table S1.2**. Sixty-nine

targets were matched for flavonoids, 59 targets were matched for organic acids, and 2 targets were matched for saponins. There targets were searched by related key words online to determine the relevance of diabetic dyslipidemia. Five targets were selected after reference searching. Finally, we linked the lipids targets with predicted targets using STRING (Szklarczyk et al., 2014) to build interaction networks of predicted targets and lipids targets via PPI. Among these compounds, the main predicted targets of CP were ALOX12, APP, BCL2, CYP2C9, and PTPN1 while the predicted-targets linked lipids targets via PPI analysis were PLA2G(s) and PI3K(s) families as shown in **Supplementary Figure S3**.

We then conducted reference searching to review the experiment study on these targets as shown in **Supplementary Table S2**. The ALOX, BCL-2, CYP 2C9, PLA2G(s) and PI3K(s) families, PLD2 and PTEN were determined as quercetin and kaempferol targets on diabetic dyslipidemia (Guo et al., 2017). PTPN1, PI3K(s) family and PLD2 were determined as targets of saponins such as quadranoside IV and asiatic acid (Ramachandran and Saravanan, 2015), whilst BCL-2, PI3K(s) family, PLD2 and PTEN were determined as action targets of gallic acid and 4-hydrobenzoic acids (**Figure 4**).

### DISCUSSION

Hyperlipidemia is the most common form of dyslipidemia, refers to the abnormally elevated levels of any/all lipids or lipoproteins in the blood, frequently happened in long-term type II diabetes patients (Dixit et al., 2014). Recent studies have indicated that diabetic dyslipidemia may not only be the consequence but also the cause of disturbed glucose metabolism (Parhofer, 2015). Recently, CP was reported to improve insulin sensitivity (Jiang et al., 2014), attenuates inflammation (Wang Z. et al., 2017), and control hyperglycemic and hyperlipidemic abnormalities (Xu et al., 2017) both in vitro and in vivo, although the hypolipidemic mechanism has not been elucidated yet. In this study, we confirmed CP alleviated lipid dysfunction in diabetes, particularly diabetic dyslipidemia, as revealed by the clinical index, histological analysis, and lipidomics analysis. Mechanistically, we employed a network pharmacology approach to determine that CP's hypolipidemic effect involvement in PI3K and MAPK signaling pathways.

Lipidomics is a powerful tool to investigate lipid profiles changes. Previous studies indicated that circulating lipidomes were correlated with hyperlipidemia and hepatic steatosis

targets based on CP constituents. The PPI network of predicted targets-linked lipid targets. The lipids regulated by CP in diabetic dyslipidemia.

(Wouters et al., 2008), therefore making lipids profiles an adequate indicator of CP effects in this study. Through the lipidomics study, it was shown that STZ and high-fat diet induced type 2 diabetic mice had lipid disorders due to lipid metabolism changes. Among the changed lipids, the majority lipids classes were DG, TG, CE, PC, SM, FA, PI, and PE. We found that CE, DG, TG, SM, PC, PI, and LPE were increased in the model group, while PE was decreased in the model group. CE is a dietary lipid involved in fat digestion and absorption as well as cholesterol metabolism. It directly affects LDL as well as HDL levels, and can be hydrolyzed by pancreatic enzymes to produce cholesterol and free fatty acids (Rosenson et al., 2016). DG and TG represent the main lipid components of fat deposits, which accumulated largely in lipid droplets of hepatocytes when in hepatic steatosis (Szczepaniak et al., 2005). Our results showed that CE, DG, and TG were downregulated after CP treatment which further indiacates the reduxction in fat accumulation in lipid droplets. PC (lecithin) are one class of glycerophospholipids with choline as a head group, which constitute major component of biological membranes, associating with the health benefits such as liver repair and lipolysis (Payne et al., 2014). Lipid absorption and utilization, particularly TG, is closely related to the PC content and its associated enzymes. Besides, PC can protect against steatosis in mice (Niebergall et al., 2011). Our results displayed that CP could reduce the palmitic acids levels and adjust PC content to improve lipid utilization and protect hepatic steatosis. PE is directly associated with diabetes and dyslipidemia and PC/PE ratio is closely correlated with the accumulation of hepatic TG content (Bradley et al., 2015; Ling et al., 2017). Our results showed that the levels of PE were elevated in CP-treated diabetic mice, indicating CP can reduce TG accumulation. SM contains either PC or PE as its head group. As lipid rafts, SM takes part in lipid microdomains and inhibition of SM synthesis would increase the ceramide level, a mediator of non-alcoholic fatty liver diseases (Kasumov et al., 2015). Our results showed that PC could increase SM content by reducing ceramide levels, possibly to alleviate hepatic steatosis. Our results indicate that CP has a great potential in the improvement of circulating lipid metabolism in diabetic dyslipidemia.

The beneficial function of CP on diabetic dyslipidemia can be deduced by its lipid regulating effect on circulatory system through multiple metabolic pathways. Therefore, it is important to identify the CP components and elucidate their biological activities to further define its use as a herbal product. Moreover, identifying the targets of CP's active components can lead to greater understanding of CP's mechanisms of action against diabetic dyslipidemia. By using lipidomics-based network pharmacology approach, we determined several CP targets for diabetic dyslipidemia based on its potential active components. Besides of ACP1, ALDH2, BCL2, and CA2 targets, the ALOX15, CYP2C9, and PTPN1 targets can interact with lipid targets PLD2, PTEN, PLA2G(s), and PI3K(s) family targets. BCL2, PTPN1, and PI3K alters expression of PI3K signaling pathway (Rahmani et al., 2013; Sugiyama et al., 2017). ALDH2, ALOX15, and BCL2 are involved in MAPK signaling pathway (De Chiara et al., 2006; Zhang et al., 2011; Zhao et al., 2011), and activation of MAPK can influence the expression of CYP2C9 (Bachleda et al., 2009). PLD2, and its product phosphatidic acid, can also activate MAPK (Grab et al., 2004). Therefore, the signaling pathway for its pharmacological mechanism may be related with PI3K and MAPK signaling pathway (Ma et al., 2015; Wu et al., 2017; Xiao et al., 2017). The multiple components of CP targeting multiple targets through multiple metabolic pathways to improve diabetic dyslipidemia in type 2 diabetic mice.

To our knowledge, we have provided a novel metabolomicsbased network pharmacology approach to combine and link experimental targets and predicted targets together based on the bioactive components of herbal products. For a network pharmacology study, particularly on herbal medicine or herbal formula, the metabolomics-based network pharmacology strategy can integrate more comprehensive targets for targets prediction and linking their interactions. Integrating the analysis of systemic metabolic profiles based on metabolomics study with computational prediction based on serum pharmacochemistry information can result in more precise pharmacological mechanism associated targets prediction (Huang et al., 2018). It is also the first study to link lipids targets from lipidomics study with predicted targets from computational tools through PPI to improve the network pharmacology analysis. Up to now, there are some questions that still need to be answered: First, some of PPI between predicted targets and lipid targets have not been validated experimentally. Further experimental work can be performed to uncover the relationship between targets-targets. Next, the potential active compounds we identified in this study have been shown to be effective for anti-diabetic studies on animals, the systemic evaluation of combinatorial use on these compounds have not been conducted yet. Additional in vitro and in vivo studies in future will help to uncover the multiple pharmacological mechanisms found in herbal medicines.

### CONCLUSION

In this study, we report CP attenuated diabetic dyslipidemia and hepatic streatosis in high fat diet and STZ-induced diabetic mice. The lipidomics study revealed CP improves circulatory lipids disorder, and the serum pharmacochemistry study revealed quinic acid, neochlorogenic acid, chlorogenic acid, 4-hydroxybenzoic acid, gallic acid, quercetin-3-glucuronide, kaempferol, loganin 7-pentoside, astragalin, kaempferol-3 rhamnoside, quercetin, quadranoside IV, and asiatic acid are potential active components of CP. Combining lipidomics and bioinformatics analysis, ALOX12, APP, BCL2, CYP2C9, and PTPN1 were predicted as direct targets of CP, whilst PLD2, PTEN and PLA2G(s) and PI3K(s) families were predicted as lipids linked targets of CP in diabetic hyperlipidemia. In conclusion, the CP was shown to be a multi-component and multi-targets herbal product with potent lipid regulation properties in dyslipidemia.

### AUTHOR CONTRIBUTIONS

Z-xB and H-tX designed the study and revised the manuscript. Ht-X, BW, C-hL, LxZ, and Z-wN performed the animal

experiment. LxZ performed the clinical index analysis, lipidomics analysis, bioinformatics analysis, and wrote the manuscript. Z-wN performed phytochemicals analysis of CP. TH performed bioinformatics analysis and revised the manuscript. LZ and C-yL provided technical support and advices toward study.

#### FUNDING

This work was kindly funded by grants from Kong Baptist University Research Grant (No. LIUYE/15-16/01-CLNC) and Shenzhen Science and Technology Innovation Committee Grant (No. JCYJ20170413170320959).

#### REFERENCES

### ACKNOWLEDGMENTS

We would like to thank Mr. Chan Chi Leung and Mr. Ho Hing Man for providing technical support for metabolomics study and Ms. Wang Jianying for providing some lipids reference standards. We also thank Dr. Anthony Booker for writing improvement.

#### SUPPLEMENTARY MATERIAL

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fphar. 2018.00973/full#supplementary-material

PI3K inhibition-induced apoptosis in human myeloid leukemia cells through a GSK3- and Bim-dependent mechanism. Cancer Res. 73, 1340–1351. doi: 10. 1158/0008-5472.CAN-12-1365

**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2018 Zhai, Ning, Huang, Wen, Liao, Lin, Zhao, Xiao and Bian. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

# Network Pharmacology-Based Validation of Caveolin-1 as a Key Mediator of Ai Du Qing Inhibition of Drug Resistance in Breast Cancer

Neng Wang1 , Bowen Yang1,2, Xiaotong Zhang1 , Shengqi Wang1,2, Yifeng Zheng1,2 , Xiong Li1,2, Shan Liu1 , Hao Pan1 , Yingwei Li1,3, Zhujuan Huang1 , Fengxue Zhang1 \* and Zhiyu Wang1,2 \*

1 The Research Center of Basic Integrative Medicine, Guangdong Provincial Academy of Chinese Medical Sciences, Guangzhou University of Chinese Medicine, Guangzhou, China, 2 Integrative Research Laboratory of Breast Cancer, Discipline of Integrated Chinese and Western Medicine, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China, 3 Tropical Medicine Institute, Guangzhou University of Chinese Medicine, Guangzhou, China

#### Edited by:

Shi-Bing Su, Shanghai University of Traditional Chinese Medicine, China

#### Reviewed by:

Li-Wha Wu, National Cheng Kung University, Taiwan Juntaro Matsuzaki, National Cancer Center, Japan Chuanbin Yang, Hong Kong Baptist University, Hong Kong

#### \*Correspondence:

Fengxue Zhang<EMAIL_ADDRESS>Zhiyu Wang<EMAIL_ADDRESS>
#### Specialty section:

This article was submitted to Ethnopharmacology, a section of the journal Frontiers in Pharmacology

Received: 16 April 2018 Accepted: 10 September 2018 Published: 02 October 2018

#### Citation:

Wang N, Yang B, Zhang X, Wang S, Zheng Y, Li X, Liu S, Pan H, Li Y, Huang Z, Zhang F and Wang Z (2018) Network Pharmacology-Based Validation of Caveolin-1 as a Key Mediator of Ai Du Qing Inhibition of Drug Resistance in Breast Cancer. Front. Pharmacol. 9:1106. doi: 10.3389/fphar.2018.01106 Chinese formulas have been paid increasing attention in cancer multidisciplinary therapy due to their multi-targets and multi-substances property. Here, we aim to investigate the anti-breast cancer and chemosensitizing function of Ai Du Qing (ADQ) formula made up of Hedyotis diffusa, Curcuma zedoaria (Christm.) Rosc., Astragalus membranaceus (Fisch.) Bunge, and Glycyrrhiza uralensis Fisch. Our findings revealed that ADQ significantly inhibited cell proliferation in both parental and chemo-resistant breast cancer cells, but with little cytotoxcity effects on the normal cells. Besides, ADQ was found to facilitate the G2/M arresting and apoptosis induction effects of paclitaxel. Network pharmacology and bioinformatics analysis further demonstrated that ADQ yielded 132 candidate compounds and 297 potential targets, and shared 22 putative targets associating with breast cancer chemoresponse. Enrichment analysis and experimental validation demonstrated that ADQ might improve breast cancer chemosensitivity via inhibiting caveolin-1, which further triggered expression changes of cell cycle-related proteins p21/cyclinB1 and apoptosis-associated proteins PARP1, BAX and Bcl-2. Besides, ADQ enhanced in vivo paclitaxel chemosensitivity on breast cancer. Our study not only uncovers the novel function and mechanisms of ADQ in chemosensitizing breast cancer at least partly via targeting caveolin-1, but also sheds novel light in utilizing network pharmacology in Chinese Medicine research.

Keywords: breast cancer chemosensitivity, network pharmacology, bioinformatics analysis, Ai Du Qing, caveolin-1

**Abbreviations:** ADQ, Ai Du Qing; CAV1, caveolin-1; DAD, diode array detection; DAPI, 40 ,6-diamidino-2-phenylindole; DAVID, Database for Annotation, Visualization and Integrated Discovery; DEGs, differentially expressed genes; DL, drug-likeness; GEO, Gene Expression Omnibus; GO, Gene Ontology; H&E staining, hematoxylin-eosin staining; HPLC, high-performance liquid chromatography; HUMECs, the primary human mammary epithelial cells; HUVECs, the human umbilical vein endothelial cells; IC50, the half maximal inhibitory concentration; IHC, immunohistochemistry; KEGG, Kyoto Encyclopedia of Genes and Genomes; NCBI, National Center for Biotechnology Information; OB, oral bioavailability; PARP1, Poly (ADP-ribose) polymerase 1; PI, propidium iodide; PPI, protein–protein interaction; TCM, traditional Chinese medicine; TCMID, Traditional Chinese Medicine Integrated Database; TCMSP, Traditional Chinese Medicine Systems Pharmacology; TUNEL, terminal dexynucleotidyl transferase(TdT)-mediated dUTP nick end labeling.

## INTRODUCTION

fphar-09-01106 September 28, 2018 Time: 16:12 # 2

Breast cancer is the most common female malignancy and one of the leading causes of cancer-related deaths, with 252,710 new cases of invasive breast cancer and 40,610 breast cancer-related deaths diagnosed in women in the United States in 2017 (DeSantis et al., 2017). Chemotherapy is one of the main therapeutic approaches for treating this disease, but it remains non-selectively toxic to normal tissues (Li et al., 2005). Paclitaxel (taxol) is the first-line treatment for metastatic breast cancer. The activity of paclitaxel is primarily due to its inhibitory effects on microtubule assembly, which leads to arrest of the mitotic phase of the cell cycle and subsequent apoptosis. Nevertheless, paclitaxel application is still limited due to its systemic toxicity and acquired resistance (Janczar et al., 2017; Stage et al., 2018). Thus, there is an urgent need for novel and safe chemosensitizing strategies. Currently, increasing attention has been paid to the synergistic effects of natural phytochemicals in enhancing the chemoresponse and relieving cytotoxic effects (Wang et al., 2014).

Traditional Chinese Medicine (TCM) has attracted worldwide attention from clinicians and researchers due to its multi-target and multi-substance characteristics and good safety profile (Gupta et al., 2013). In contrast to Western medicine, TCM prescriptions are composed of several herbs and are called formulas. Originating from Yellow Emperor's Classic theory (Chinese name Huang Di Nei Jing), the golden principle of formula composition should be based on Jun-Chen-Zuo-Shi (Lu, 1985; Fan et al., 2006). The ADQ formula was created by Prof. Zhiyu Wang based on the Jun-Chen-Zuo-Shi principle and long-term clinical experience. The ADQ formula is mainly composed of four herbs: Jun (Emperor) herb Hedyotis diffusa (Chinese name Bai Hua She Cao, BHSSC), Chen (Minister) herb Curcuma zedoaria (Christm.) Rosc. (Chinese name E Zhu, EZ), Zuo (adjuvant) herb Astragalus membranaceus (Fisch.) Bunge (Chinese name Huang Qi, HQ), and Shi (courier) herb Glycyrrhiza uralensis Fisch (Chinese name Gan Cao, GC). Each herb in the formula can inhibit cancer growth via cell cycle arrest, apoptosis induction, and immune regulation (Fu et al., 2014a; Gao et al., 2014; Wang et al., 2014, 2015b; Feng et al., 2017). In addition, each herb is a drug that is frequently prescribed to cancer patients according to statistical analyses of the medication rules of national TCM masters (Song et al., 2015). However, the multi-target and multi-substance properties of this formula have made it very challenging to explore its underlying mechanisms.

In the past decade, multi-omic technologies including genomics, transcriptomic, proteomics, metabolomics, and serum pharmacokinetics have been developed for the high-throughput screening and identification of targets involved in TCM formulas (Li et al., 2014). However, these traditional approaches are expensive and require multidisciplinary collaboration and complex analytical procedures (Xu et al., 2017). With the development of bioinformatics, systems biology is emerging as a more holistic approach for integrating compound–target interactions from a molecular to system level. One of the most significant applications of systems biology is to use network pharmacology to understand the complex mechanism of actions of TCM formulas. Following ingredient collection and screening, pharmacokinetic evaluation (absorption, distribution, and metabolism), target prediction, and network analysis (Liu et al., 2013), it is becoming faster and easier to present an entire drug-target interaction network and determine the involved core molecule and pathways. In addition, by intersecting with a disease target database, it is more efficient to elucidate how formulas intervene with critical targets that facilitate disease occurrence and progression (Ru et al., 2014).

The current study was designed to determine the preclinical efficacy of ADQ against breast cancer in vitro and in vivo. To this end, we investigated the anti-cancer and chemosensitizing functions of ADQ extracts in this disease. The results showed that ADQ effectively and safely enhanced breast cancer chemosensitivity in vitro and in vivo. To determine the mechanism of action, we constructed a "drug–target–disease" network among ADQ components (drug), ADQ targets (target), and genes in breast cancer chemoresistance (disease). The results of the network analysis and biological experimental findings suggested that ADQ mainly targets CAV1 to induce chemosensitizing effects. The results of this study not only provide scientific evidence to support the application of ADQ formula in the treatment of breast cancer but also highlight the novel role of network pharmacology in the modernization of TCM.

### MATERIALS AND METHODS

#### Preparation and Quality Control of ADQ

For ADQ preparation, BHSSC, EZ, HQ, and GC were mixed at a 1:1:1:1 ratio and then subjected to a grinding machine. The mixture was extracted with 95% alcohol by reflux extraction for 1 h and repeated three times. Then the supernatants were concentrated by rotary evaporation and evaporated to dryness in a water bath to obtain raw ethanol extract powder. The production ratio was calculated as 7.2–9.6%. For quality control analysis, the Agilent 1260 System (Agilent, Palo Alto, CA, United States) with DAD was applied for HPLC analysis. The Agilent C18 Column (5 µm, 250 mm × 4.6 mm) with the SecurityGuard Cartridge System (Phenomenex, Sacramento, CA, United States) was applied for HPLC analysis. The mobile phases consisted of acetonitrile (A) and 0.05% (v/v) phosphoric acid (B) using a gradient program of 15% A in 0–23 min, 15–38% A in 23–40 min, 38% A in 40–50 min, 38–61% A in 50–60 min, and 61% in 60–75 min. The flow rate was 1.0 mL/min and the column temperature was set to 30◦C The DAD detector was set at 216, 236, 260, 276, and 308 nm. P-coumaric acid, calycosin-7-glucoside, liquiritin, glycyrrhizic acid, and curcumol were prepared and diluted with methanol for the preparation of standard solutions. A volume of 10 µL of these solutions was analyzed with HPLC, and the calibration curves were finally established. For the preparation of sample solutions, 0.1 g ADQ was dissolved in 20 mL methanol. After sonicating for 60 min, the sample solution was filtrated through a 0.2 µm membrane filter for HPLC analysis.

### Cell Culture

fphar-09-01106 September 28, 2018 Time: 16:12 # 3

The human breast cancer cell lines MDA-MB-231 and MCF-7 were purchased from the American Type Culture Collection (Manassas, VA, United States). MDA-MB-231/T and MCF-7/T cells were derived from parental cells by gradually increasing paclitaxel (taxol) treatments for 6 months in the laboratory. MDA-MB-231, MCF-7, MDA-MB-231/T, and MCF-7/T cells were maintained in Dulbecco's Modified Eagle Medium (DMEM) containing 10% fetal bovine serum (FBS) and 1% penicillin and streptomycin (Gibco Life Technologies, Lofer, Austria). Primary human mammary epithelial cells (HUMECs) and its Ready Medium (Catalog No. 12752010) were purchased from Gibco, and human umbilical vein endothelial cells (HUVECs) were purchased from the National Infrastructure of Cell Line Resource1 . The cells were maintained in DMEM supplemented with 10% FBS, 1% penicillin, 1% streptomycin (Gibco), 40 U/L insulin (Sigma, St. Louis, MO, United States), 40 U/mL heparin (Sigma), and 1% non-essential amino acids (Cyagen Biosciences, Santa Clara, CA, United States).

### Cell Number and Colony Formation Assay

After the indicated drug treatment, cell numbers were counted using trypan blue exclusion on a Cellometer Mini device (Nexcelom, Boston, MA, United States). Experiments were performed in triplicate. For the colony formation assay, cells at a density of 1 × 103 cells/well were seeded into 6-well plates. After cell attachment, paclitaxel or ADQ was added to the wells alone or in combination for 4 h. Then the cells were cultured with fresh medium for 2 weeks. The colonies were fixed in 4% paraformaldehyde, stained with Coomassie Blue, photographed, and counted under a microscope.

#### Flow Cytometry Analysis

For the drug efflux assay, MDA-MB-231 or MCF-7 cells were pretreated with ADQ for 24 h, followed by incubation with epirubicin for 60 min at 37◦C. After dug exposure and washing, the cells were released in drug-free medium for 90 min and harvested for flow cytometry analysis. For cell cycle analysis, cells were fixed in ice-cold 70% ethanol at −20◦C overnight. Then cells were washed with phosphate-buffered saline, stained with 50 mg/mL propidium iodide (Sigma), and dissolved in 100 mg/L RNase A (Sigma). For apoptosis analysis, the cells were stained with the Annexin V-FITC Apoptosis Staining/Detection Kit (BD Biosciences, San Jose, CA, United States). All flow cytometry analyses were conducted with FACSAria SORP (BD Biosciences) and analyzed by Modifit LT or FlowJo software.

#### Immunofluorescence Analysis and Hoechst 33258 Staining

For measurements of phosphorylated histone p-H2AX expression, cells were incubated with 4% paraformaldehyde and 0.2% triton X-100 for 10 min. Following blocking in goat serum for 60 min, the samples were co-incubated with primary antibodies against p-H2AX (ABclonal Technology, Boston, MA, United States) at 4◦C overnight and subsequently labeled with fluorescence-conjugated secondary antibodies for 2 h at room temperature. Then DAPI was applied for nuclear staining, and the signals were detected by fluorescence microscopy (TS2R; Nikon, Tokyo, Japan). With regard to Hoechst 33528 detection, cells were seeded at 60–70% confluency in 6-well plates, and then treated with the indicated drug for 48 h. Then Hoechst 33258 staining was conducted according to the manufacturer's instructions.

#### Establishment of the Herb–Ingredient–Target Interaction

The chemical ingredients were collected from TCM databases including the TCMSP Database2 the TCMID3 , and the BATMAN-TCM4 . The ingredients were screened according to drug likeness (DL) and OB values, and the ingredients were retained if DL ≥ 0.18 and OB ≥ 30, a criterion suggested by the TCMSP database. The ingredient–target networks were constructed for these herbs using Cytoscape software (version 3.2.1).

#### Gene Ontology and Pathway Enrichment Analysis

Gene expression data were retrieved from the NCBI GEO database5 , and then analyzed with the GEO2R online analysis tool6 . The dataset GSE41112 includes 24 tumors of breast cancer patients with chemotherapy and 37 without chemotherapy, and the GSE87455 dataset includes human breast cancer samples with "no treatment" (n = 122) and "chemo only" (n = 83) groups. The DEGs were screened with P ≤ 0.05 and fold control (FC) ≥ 1.5 criteria, delivered to the Search Tool for the Retrieval of Interacting Genes/Protines (STRING) database to evaluate the PPI information, and also submitted to the DAVID7 for enrichment analysis. The significant enrichment analysis of DEGs was assessed based on the GO and KEGG8 .

### Plasmids and Small Interfering RNA Construction and Transfection

The pcDNA 3.1(+)-CAV1 was provided by Vigene Company (Jinan, China) and transfected into cells using Lipofectamine 2000 (Invitrogen, Carlsbad, CA, United States). After 24 h, the transfected cells were passaged and selected for 2 weeks with 10 µg/mL puromycin (Invitrogen). Pooled populations of positive cells were used for subsequent experiments. Negative control cell lines were generated by transfecting cells with scrambled plasmids. The small interfering RNAs (siRNAs) targeting CAV1 or scrambled siRNAs were purchased from

1http://www.cellresource.cn

2http://lsp.nwu.edu.cn/tcmsp.php

3https://academic.oup.com/nar/article/41/D1/D1089/1057998

4http://bionet.ncpsb.org/batman-tcm/

5http://www.ncbi.nlm.nih.gov/geo

6http://www.ncbi.nlm.nih.gov/geo/geo2r/

7http://david.abcc.ncifcrf.gov/

8http://www.genome.jp/kegg/kegg2.html

Invitrogen (Carlsbad) and transfected using the X-tremeGENE siRNA transfection reagent (Roche Diagnostics, Indianapolis, IN, United States) according to the manufacturer's instructions.

ADQ at the indicated concentrations (0–140 µg/mL) for 24, 48, and 72 h.

#### Western Blotting

To determine the protein concentration, cells were lysed in RIPA buffer (Sigma) containing a protease inhibitor mixture (Roche Diagnostics). The protein concentration was measured with the bicinchoninic acid assay (Thermo Fisher Scientific, Bonn, Germany). Quantified protein lysates (15 µg) were subjected to sodium dodecyl sulfate polyacrylamide gel electrophoresis and resolved on 12% polyacrylamide gels. Then the proteins were transferred onto a PVDF membrane (GE Healthcare, Freiburg, Germany). The membrane was probed with primary antibodies including CAV1, p21, cyclin B1, cleaved poly (ADP-ribose) polymerase (PARP), Bcl2-associated X protein (BAX), B-cell lymphoma 2 (Bcl-2), p53, and phosphorylated p53 (p-p52, ser 15), and β-actin (Cell Signaling Technology, Beverly, MA, United States) at 4◦C overnight. After washing three times with Tris-buffered saline and 0.05% Tween-20, the membrane was incubated with secondary anti-rabbit or anti-mouse antibodies for 2 h at room temperature. The signals were visualized using the ECL Advance Western Blotting Detection Reagent (GE Healthcare) and quantified with FlowJo software.

and MCF-7 cells treated with ADQ (50 µg/mL) and/or paclitaxel (50 ng/mL), as well as (D) in the MDA-MB-231/T and MCF-7/T cells treated with ADQ (50 µg/mL) for 48 h.

### Breast Cancer Mice Models and Drug Treatment

All animal procedures were performed in accordance with institutional guidelines for the care and use of laboratory animals approved by the Animal Care and Use Committee of Guangzhou University of Chinese Medicine and the National Institutes of Health guide for the care and use of laboratory animals.  In this study, 9-week-old MMTV-PyMT mice were randomly divided into four groups, and treated with saline (Ctrl group), 10 mg/kg paclitaxel (paclitaxel group), 100 mg/kg ADQ (ADQ group), or 10 mg/kg paclitaxel plus 100 mg/kg ADQ (paclitaxel + ADQ group) for 26 days (n = 6 mice, total of 60 glands), respectively. Paclitaxel was given by intraperitoneal injection at 10 mg/kg every 3 days, and ADQ was given by oral gavage at 100 mg/kg once a day. The body weight and tumor volumes were recorded throughout the whole experimental period.

### Hematoxylin and Eosin Staining, Immunohistochemistry, and Terminal Deoxynucleotidyl Transferase dUTP Nick End Labeling

Hematoxylin and eosin staining and IHC were conducted according to the protocol provided by Wang et al. (2017a). The terminal dexynucleotidyl transferase dUTP nick end labeling (TUNEL) assay was conducted according to the manufacturer's instructions (Catalog No. KGA7051; KeyGen Biotech, Nanjing, China).

#### Statistical Analysis

The data are shown as the mean ± standard deviation. The two-tailed Student's t-test or one-way analysis of variance

was used to determine the significance of the data between groups. P-values less than 0.05 were considered statistical significant.

### RESULTS

#### ADQ Markedly Inhibits Growth in Paclitaxel-Sensitive and Paclitaxel-Resistant Human Breast Cancer Cells

For quality control of ADQ, the chromatographic fingerprinting of ADQ was conducted and quantitative analysis of p-coumaric acid (peak 1), calycosin-7-glucoside (peak 2), liquiritin (peak 3), glycyrrhizic acid (peak 4), and curcumol (peak 5) in ADQ was compared among different batches (**Supplementary Figure S1**). Based on an established HPLC method, good linearity of five compounds was achieved with a correlation coefficient of R 2 ≥ 0.9995 (**Supplementary Table S1**). The retention times of these compounds were determined to be 19.8, 23.9, 25.8, 50.7, and 69.5 min, respectively. The contents of the five compounds in the ADQ samples were also determined (**Supplementary Table S2**). We evaluated the influence of ADQ on the proliferation of breast cancer cell lines including MDA-MB-231 and MCF-7, as well as their derived paclitaxel-resistant cell lines MDA-MB-231/T and MCF-7/T. Significant inhibition of growth was observed in both parental and resistant cells at 24 h (**Figure 1A**),

48 h (**Figure 1B**), and 72 h (**Figure 1C**). IC50 of ADQ was shown in **Figure 1D** for the indicated cell lines. Specifically, the IC50 values of ADQ at 48 h for MDA-MB-231, MDA-MB-231/T, MCF-7, and MCF-7/T were 49.809, 57.789, 65.799, and 70.964 µg/mL, respectively, suggesting that ADQ had similar suppressive effects on both sensitive and resistant breast cancer cells. To determine the cytotoxic effects of ADQ on normal cells, we also investigated its effects on HUMECs and HUVECs, and found that ADQ did not have cytotoxic inhibitory effects on both types of normal cells (**Figures 1E,F**).

### ADQ Significantly Enhances the Chemosensitivity of Breast Cancer Cells

To determine the synergistic activities of ADQ with paclitaxel in breast cancer, MDA-MB-231 and MCF-7 cells were treated with ADQ and paclitaxel for 48 h. As presented in **Figure 1** and **Supplementary Figure S2**, the tested concentrations of taxol and ADQ were set according to their IC50 at 48 h. ADQ significantly enhanced paclitaxel-induced death in MDA-MB-231 and MCF-7 cells. Interestingly, ADQ administration at 50 µg/mL also caused a clear reduction in the number of resistant breast cancer cells (**Figure 2A**). We also evaluated the long-term inhibitory effects of ADQ on the colony formation capabilities of breast cancer cells with or without paclitaxel. The results showed that the combination group exhibited a substantial reduction in the colony numbers of parental MDA-MB-231 and MCF-7 cells (**Figure 2B**). In addition, the growth of paclitaxel-resistant MDA-MB-231/T and MCF-7/T cells was greatly suppressed in the presence of ADQ (**Figure 2C**). The drug efflux assay revealed that ADQ could enhance epirubicin influx into breast cancer cells, as shown by the increased fluorescence intensity of epirubicin in ADQ-treated cells (**Figure 2D**).

These results indicated that ADQ could chemosensitize both paclitaxel-sensitive and paclitaxel-resistant human breast cancer cells.

### ADQ Induced Breast Cancer Cell Cycle Arrest at the G2/M Checkpoint

Uncontrolled cell mitosis represents one of the hallmarks of cancer. Thus, we used PI staining to examine the effects of ADQ on the cell cycle distribution of both paclitaxel-sensitive and paclitaxel-resistant cells. As labeled in **Figure 3A**, the flow cytometry results revealed that paclitaxel or ADQ alone could induce G2/M checkpoint arrest in both breast cancer cell lines. In addition, ADQ and paclitaxel combination increased G2/M arrest by 26% in MDA-MB-231 cells and by 66% in MCF-7 cells (**Figure 3A**). Furthermore, the G2/M population of MDA-MB-231/T and MCF-7/T cells was also arrested by ADQ with a 56 and 63% increase, respectively (**Figure 3B**). Previous studies have shown that perturbation of the G2/M transition was largely due to DNA damage, and p-H2AX could be a marker for monitoring DNA damage (Sancar et al., 2004). The immunofluorescence results showed that p-H2AX intensity was significantly elevated following paclitaxel or ADQ treatment in both breast cancer cell lines. Consistent with the cell cycle findings, ADQ synergistically interacted with paclitaxel to enhance p-H2AX expression in both breast cancer cell lines (**Figure 3C**). Furthermore, the expression of p-H2AX in MDA-MB-231/T and MCF-7/T cells was also significantly enhanced following ADQ administration, indicating that the chemosensitizing effects of ADQ might be attributed to DNA damage-induced G2/M checkpoint arrest (**Figure 3D**).

### ADQ Augmented Paclitaxel-Induced Apoptosis in Breast Cancer Cells

Apoptosis is another important mechanism that causes cell death induced by chemotherapy drugs (Johnstone et al., 2002; Li et al., 2010). To determine if ADQ could synergistically aggravate paclitaxel-induced apoptosis, Annexin V/PI staining was applied to detect the apoptotic events. As shown in **Figure 4A**, the percentage of early and late apoptotic events in MDA-MB-231 and MCF-7 cells reached about 20 and 25%, respectively, after exposure to 50 ng/mL paclitaxel. Interestingly, when ADQ was administrated with paclitaxel concurrently, the percentage of MDA-MB-231 and MCF-7 apoptotic cells was increased to approximately 60% and 51%, respectively. Notably, ADQ was also capable of inducing apoptosis in paclitaxel-resistant breast cancer cells. The percentage of apoptotic cells in MDA-MB-231/T reached 45% following ADQ treatment after 48 h, and MCF-7/T cells reached 30% (**Figures 4B–D**). Based on flow cytometry results, Hoechst 33258 staining was utilized to observe the morphological changes of apoptotic cells by fluorescence imaging (Zhang et al., 2010). In paclitaxel-sensitive breast cancer cells, ADQ

synergistically enhanced Hoechst 33258 staining intensity induced by paclitaxel in both MDA-MB-231 and MCF-7 cells. In paclitaxel-resistant cell models, typical morphological characteristics of apoptosis, such as chromatin condensation and cell pyknosis, were more easily observed following ADQ treatment (**Figure 4E**). Together, these findings confirmed that ADQ promoted paclitaxel-induced apoptosis in breast cancer cells.

#### Network Pharmacology Analysis of ADQ

ADQ consists of four herbs including BHSSC, EZ, HQ, and GC. To establish the ingredient–target network of ADQ, we screened candidate compounds for (OB ≥ 30%) and DL (DL ≥ 0.1) in each herb. There were 12 compounds in BHSSC targeting 225 genes (**Figure 5A**), 14 compounds in EZ targeting 41 genes (**Figure 5B**), 21 compounds in HQ targeting 222 genes (**Figure 5C**), and 94 compounds in GC targeting 241 genes (**Figure 5D**). The "candidate active compounds" are listed in **Supplementary Table S3**. Overall, our results showed that ADQ yielded 132 candidate compounds and 297 potential targets after eliminating all duplicates (**Figure 6**). Specifically, the network included 429 nodes and 2874 ingredient–target interactions, of which 132 candidate compounds had a median of 10 target correlations, suggesting the existence of complex correlations among different compounds and targets.

### Establishment of the Compound–Target–Disease Network of ADQ

To determine the pharmacological mechanisms of ADQ against chemoresistance, the DEGs of breast cancer patients before and after chemotherapy were extracted from GSE41112 and GSE87455 microarray sets. A total of 3286 genes in GSE5764 and 4877 genes in GSE87455 were identified using the GEO2R analysis tool (P ≤ 0.05, FC ≥ 1.5). The Venn diagram analysis showed that ADQ shared 22 putative targets with the two datasets (**Figure 7A**). These hub genes included CAV1, HIF1A, CCNB1, BAX, BCL2, PARP1, ERBB3, MCL1, PRKCA, CDCA7, CDKN1A,

CDKN2A, TOP1, TOP2A, SELE, ATP5B, RUNX2, BIRC5, ATF2, RUNX1T1, MT2A, and EIF5B (**Figure 7B**), among which CAV1 was the key target with the largest node size according to "Degree" in the node size mapping (**Figure 7C**). We further extracted the 22 significant targets to construct the PPI containing 22 nodes and 71 edges based on the STRING database, and the PPI enrichment P-value of these hub genes was 2.22 × 10−16 , indicating that they were at least partially biologically connected (**Figure 7D**). All of these findings suggested that CAV1 might be one of the the most likely mechanisms affecting ADQ regulation network.

Thus, we continued to identify candidate targets by setting all human genes as background, and using GO and pathway enrichment analysis. GO classified function into categories of cellular components, molecular function, and biological process. The top three enrichments in the cellular components category were organelle lumen, nuclear lumen, and membrane-enclosed lumen (**Figure 8A**); in the molecular function category were enzyme binding, protein binding, and transcription factor binding (**Figure 8B**); and in the biological process category were cell death, cell proliferation, and cell differentiation (**Figure 8C**). In **Figure 8D**, KEGG analysis (P < 0.05) indicated that

multiple cancer-related pathways were significantly involved in the mechanisms of ADQ including focal adhesion, apoptosis, cell cycle, p53, HIF-1, ErbB, phosphoinositide 3-kinase (PI3K)/Akt, Janus kinase/signal transducer and activator of transcription, mammalian target of rapamycin, NF-kappa B, and tumor necrosis factor signaling pathways. Notably, It was various targets in p53 signaling were tightly associated with ADQ pharmacological action (P = 0.00003896097, red rectangle, **Figure 8D**). P53 signaling can be stimulated by a number of stress signals including DNA damage, oxidative stress, and activated oncogenes, consequently leading to cell cycle arrest and apoptosis (Mello and Attardi, 2017). Meanwhile, accumulating evidence has demonstrated that CAV1, the leading hub target of ADQ, is correlated with various stressors including chemotherapy, radiotherapy, fluid shear, oxidative stress, and ultraviolet exposure (Wang et al., 2015c). This novel stress response protein plays significant roles in modulating cell survival, proliferation, and apoptosis (Wang et al., 2017d). In **Figure 9**, we postulated that CAV1 might be activated in response to ADQ treatment, subsequently influencing the cell cycle regulatory proteins p21 and cyclin B1, and apoptotic markers such as BAX.

#### Validation of CAV1 as a Major Chemosensitizing Target of ADQ in Breast Cancer

We continued to validate whether the chemosensitizing activity of ADQ was CAV1-dependent. Among the multiple breast cancer cell lines tested, ZR75–1, SKBR3, and MCF7 had no or low CAV1 expression, whereas MDA-MB-231 and MDA-MB-436 had strong CAV1 expression (Shi et al., 2015). In this study, we detected the expression of CAV1 among a series of breast cancer and normal mammary cell lines. The results showed that CAV1 expression was significantly downregulated in the MDA-MB-231 and MCF-7 breast cancer cells, and was enhanced in their taxol-resistant counterparts (**Supplementary Figure S3**). This phenomenon was consistent with the oncogenic and tumor suppressor roles of CAV1 in breast cancer development (Wang et al., 2015c). To directly examine whether CAV1 was critical for ADQ action, we elevated CAV1 levels by transfecting recombinant CAV1 plasmid in CAV1-deficient MCF-7 cells, and decreased its expression with siCAV1 in CAV1-expressing MDA-MB-231 cells. CAV1 expression in the gene-modified cells was confirmed by western blot analysis (**Supplementary Figure S4A**). Then we determined if CAV1 overexpression abrogated the anti-cancer and chemosensitizing effects induced by ADQ in breast cancer cells. As shown in **Figure 10A**, ADQ synergistically interacted with paclitaxel to suppress MCF-7 cell proliferation, whereas CAV1 overexpression obviously abrogated the synergistic effects of ADQ with paclitaxel in suppressing breast cancer growth (p ≤ 0.01). To determine if CAV1 signaling was involved in ADQ chemosensitizing activity, the indicated proteins were analyzed (**Figure 10B**). Western blot analysis revealed that ADQ alone could inhibit CAV1 expression, accompanied by the increased expression of p21 and decreased expression of cyclin B1. In addition, the expression of cleaved PARP1 and BAX were enhanced by ADQ, whereas Bcl-2 expression was decreased following ADQ treatment. Notably, CAV1 overexpression resulted in decreased p21 and increased cyclin B1 expression. Furthermore, CAV1 overexpression inhibited apoptosis, as shown by the decreased expression of cleaved PARP1 and BAX and increased Bcl-2 expression. Cell cycle analysis further revealed that CAV1

cytometry. The length of each cell cycle phase was calculated; (D) Representative apoptosis analysis indicated CAV1 overexpression reduced the apoptotic events induced by paclitaxel plus with ADQ treatment from 47.890 to 36.780% using flow cytometry (∗P < 0.05 v.s. control, values represented as the mean ± SD, n = 3).

overexpression relieved the G2/M arresting effects of ADQ from 68.08 to 57.08% in MCF-7 cells (**Figure 10C**). CAV1 overexpression also significantly reduced the apoptotic events induced by paclitaxel with ADQ treatment from 47.89 to 36.78% (**Figure 10D**). These data showed that ADQ inhibited CAV1 to improve breast cancer chemosensitivity.

In CAV1high MDA-MB-231 cells, CAV1 knockdown did not significantly aggravate the inhibitory ability of ADQ in the presence of paclitaxel, suggesting that ADQ might target CAV1 to chemosensitize breast cancer (**Figure 11A**). Similarly, siCAV1 did not significantly change the expression levels of p21, cyclin B1, PARP1, BAX, and Bcl-2 when administrated together with paclitaxel and ADQ (**Figure 11B**), nor were significant changes observed in cell cycle distribution and apoptotic cells (**Figures 11C,D**). Furthermore, MDA-MB-231/T and MCF-7/T showed strong CAV1 expression compared with their parental cell lines (**Supplementary Figure S4B**). In paclitaxelresistant breast cancer cell models, both CAV1 silencing and ADQ treatment resulted in the significant suppression of cell proliferation, but CAV1 silencing did not increase the effects of ADQ, namely inhibition of proliferation, cell cycle arrest, or apoptosis induction in MDA-MB-231/T and MCF-7/T cells (**Figures 12A–D**). These findings demonstrated that ADQ could target CAV1 to induce cell cycle arrest and apoptosis, thereby chemosensitizing paclitaxel-resistant breast cancer cells.

 Therefore, it is an appropriate preclinical model for investigating the chemosensitivity effects of ADQ with paclitaxel (**Figure 13A**). The results of the in vivo experiments revealed that ADQ alone could significantly inhibit breast tumor growth (∗P = 0.0444, **Figure 13C**) and reduce tumor volume (∗P = 0.0104, **Figure 13D**), while with little influence

on mouse body weight (**Figure 13B**). Meanwhile, ADQ alone caused an increase in the apoptotic ratio and a decrease in ki67 expression, further demonstrating its tumor suppressive functions (**Figure 13E**). The results also revealed that the combination of paclitaxel and ADQ exerted stronger inhibitory effects on overall tumor growth than paclitaxel alone (**Figure 13C**). Notably, ADQ did not lead to significant weight loss throughout the experiment, whereas a slight reduction of body weight was found in the paclitaxel-treated mice (**Figure 13B**). Furthermore, both H&E staining and the TUNEL assay revealed that the synergistic use of paclitaxel and ADQ resulted in significant increases of apoptosis in tumor tissues, accompanied by the decreased expression of Ki67 and CAV1 (**Figure 13E**). These results indicate that ADQ is a potential adjuvant drug for breast cancer treatment with good safety.

 In this study, we investigated the anti-cancer and chemosensitizing functions of ADQ extracts in breast cancer. Our findings revealed that ADQ significantly inhibited the growth of breast cancer cells by cell cycle arrest and apoptosis induction. In particular, ADQ did not cause cytotoxic effects in normal cells. Similar findings have also been shown for other herbs including Huangqi, Ginseng, Banzhilian, Huachansu injection, TJ-48, Shenqi fuzheng injection, and Kanglaite injection (Qi et al., 2015). Notably, the four-herb formula PHY906 was developed as an adjuvant therapy with chemosensitizing effects. Currently, three phase I and one phase II clinical trial on PHY906 have been completed in the United States. In these clinical studies, PHY906 administration led to the reduction of chemotherapy-associated side effects in patients with metastatic colorectal cancer (PHY906+CPT-11/5FU/LV) and advanced pancreatic cancer (PHY906+capecitabine) (Lam et al., 2015). Omics studies have demonstrated that PHY906 can inhibit colon cancer growth by modulating cell apoptosis by intervening interferon-gamma production and responses to steroid hormone stimulus. Our study also demonstrated that ADQ could increase breast cancer chemosensitivity to paclitaxel by increasing breast cancer cell apoptosis and cell cycle arrest at the G2/M checkpoint. In addition, in vivo findings further confirmed the anti-breast

cancer and chemosensitizing effects of ADQ with good safety. Therefore, it is of great value to explore the effects and mechanisms of ADQ in breast cancer.

The development of network pharmacology has shifted our traditional view from the "one drug, one target" model to the "drug–target network" (Hopkins, 2008). Bioinformatics analysis has further optimized the high-throughput screening strategy to validate candidate targets associated with diseases (Eichler, 2012). A target fishing approach has been applied by a number of TCM studies. For example, Wang et al. (2017b) identified tumor-associated macrophages/C-X-C motif chemokine ligand 1 as key modulators of XIAOPI formula in the prevention of breast cancer metastasis based on network pharmacology analysis and cytokine array screening. Chemoinformatics, bioinformatics, and network biology were applied together to predict the active compounds of Tianfoshen oral liquid and to validate its therapeutic targets against colorectal cancer (Wang et al., 2017c). In this study, network analysis revealed a total of 132 active compounds and 297 genes in the ADQ formula, and bioinformatics analysis further identified 22 genes closely correlated with the chemosensitizing activities of ADQ through Venn diagram analysis with breast cancer chemoresponsive genes extracted from GSE41112 and GSE87455. These results suggested that ADQ efficacy was through the synergistic effect of multi-compounds, multi-targets, and multi-pathways. Among these hub molecules, CAV1 was the key target node with the largest "Degree," indicating that it may be one of the most likely mechanisms affecting the ADQ regulation network. Meanwhile, pathway enrichment analysis demonstrated that CAV1 acted as a key upstream node influencing cell cycle, apoptosis, and p53 signaling. Interestingly, CAV1 has also been considered an important stress-responsive molecule by recent studies and our previous report (Lavie et al., 1998; Yang et al., 1998; Glait et al., 2006; Wang et al., 2015c). Therefore, it was selected for subsequent validation. Interestingly, a lot of work has demonstrated that CAV1 is necessary for p53 activation. For example, Volonte et al. (2009) validated that CAV1 expression is required for the activation of ATM-p53-p21 pathway, and Bartholomew et al. (2009) indicated that CAV1 is a novel binding protein for mouse double minute 2 homolog, thereby preventing p53 proteasome degradation and stabilizing its cellular expression. Our study demonstrated that p53 expression in both MDA-MB-231 and MCF-7 cells was downregulated compared with primary mammary epithelial cells, accompanied by CAV1 reduction (**Supplementary Figure S5**). However, although ADQ administration led to CAV1 inhibition, the expression of p53 and p-p53 (ser15) did not significantly change, indicating that ADQ-induced p21 and apoptosis activation might not be related to p53 (**Supplementary Figure S6**). With regard to the genetic status of p53, MDA-MB-231 was mutated and MCF-7

FIGURE 13 | ADQ enhanced in vivo paclitaxel chemosensitivity on breast cancer. 9-week-old MMTV-PyMT mice were randomly divided into four groups, and were treated with vehicle (Ctrl group), 10 mg/kg paclitaxel (Paclitaxel group), 100 mg/kg ADQ (ADQ group), or 10mg/kg paclitaxel plus 100 mg/kg ADQ (Paclitaxel + ADQ group) according to designated treatment schedule. (A) Representative images of tumors dissected, (B) Body weight, and (C,D) Tumor volumes, and (E) H&E staining, TUNEL detection, and IHC detection of Ki67 and CAV1 expressions from the indicated groups (n = 6 mice, total of 60 glands, ∗P < 0.05, ∗∗P < 0.01, ∗∗∗P < 0.0001 v.s. control).

was wild type. Because the degradation speed of mutated p53 was significantly reduced, p53 expression was highly elevated in MDA-MB-231 cells compared with MCF-7 cells (**Supplementary Figure S5**); however, ADQ had little effect on p53 expression in both breast cancer cells (**Supplementary Figure S6**), indicating that the susceptibility of MDA-MB-231 or MCF-7 cells to ADQ was not related to p53 genetic status. Overall, network pharmacology has provided us with a highly efficient strategy to identify key targets and their complex correlation in the development of TCM formulas.

CAV1 plays dual roles in the progression of breast, lung, cervical, gastric, glioma liver, and prostate cancers (Wang et al., 2017d). During tumor initiation, its loss not only triggers tumor-survival signals including PI3K/Akt and MAPK, but also leads to the inactivation of tumor suppressor genes such as BRCA1 and PTEN (Glait et al., 2006). By contrast, accumulating evidence has suggested that CAV1 overexpression correlates with cancer drug resistance, metastasis, the survival of cancer stem cells, and advanced carcinoma (Wang et al., 2015c).  Moreover, clinical investigations have revealed that CAV1 expression is positively correlated with chemotherapy response in gastric cancer (Yuan et al., 2013) and non-small lung cancer (Brodie et al., 2014). Therefore targeting CAV1 is a promising strategy for overcoming cancer drug resistance. Interestingly, our study demonstrated that ADQ could inhibit CAV1 to improve breast cancer chemosensitivity. In addition, CAV1-overexpressing MDA-MB-231 cells were more susceptible to ADQ than low CAV1 expressing MCF-7 cells. Consistent with our findings, previous studies have revealed that multiple active compounds in ADQ exerted anti-cancer effects partly by mediating CAV1 expression. For example, it was shown that quercetin reversed tamoxifen resistance in breast cancer cells, and its metabolites likely suppressed CAV1 expression (Wang et al., 2015a; Kamada et al., 2016). Calycosin glycoside regulates nitric oxide/CAV1/matrix metalloproteinase signaling (Fu et al., 2014b). The CAV1-mediated anti-cancer effects of ADQ in this study were possibly due to the complex interaction and synergistic/neutralizing effects among the involved compounds. However, ADQ also exhibited a significant inhibitory effects on MCF-7 cell proliferation, indicating that there may be other molecular targets responsible for the effects of ADQ. Because the phytochemicals in Chinese formula are too complex to analyze, it is unlikely that CAV1 is the only target of ADQ. Our results also demonstrated that CAV1 expression was highly elevated in paclitaxel-resistant breast cancer cells, and ADQ chemosensitized breast cancer cells by inhibiting CAV1, consistent with the oncogenic role of CAV1. This phenomenon might be explained by the property of the stressrelated function of CAV1, which plays a key role in protecting cells from hazardous stimuli. During cancer initiation, malignant transformation may be accelerated due to CAV1 loss, which would sensitize normal cells to oncogenic events. In contrast, when cancer progresses and is treated, CAV1 expression may be upregulated to protect cancer cells from escaping death by speeding aerobic glycolysis, increasing stem cell populations, or overexpressing ATP-binding cassette transporters (Wang et al., 2015c).

### CONCLUSION

The results of our study demonstrated that ADQ improved the cancer chemoresponse, and that CAV1 was at least in part responsible for these chemosensitizing effects. Our work has great implications for the discovery of breast cancer therapeutic targets by integrating bioinformatics and network pharmacology followed by experimental validation. This study not only provides experimental evidence and molecular mechanisms that may facilitate the safe and effective therapeutic use of herbal medicines for breast cancer, and may lead to CAV1-based therapeutic strategies for mammary malignancies. However, preclinical studies are needed to confirm its chemosensitizing effects and active ingredients.

## AUTHOR CONTRIBUTIONS

NW and ZW conducted the design of the experiments and wrote the manuscript. FZ contributed to the revised manuscript. NW conducted network pharmacology analysis and bioinformatics analysis. BY, XL, and XZ contributed to the drug preparation and quality control of ADQ. SW, YZ, and SL carried out cell culture and molecular biology experiments. ZH, HP, and YL conducted flow cytometry analysis.

## FUNDING

This work was supported by the National Natural Science Foundation of China (81703764, 81573651, 81873306, and 81703749), Guangdong Science and Technology Department (2016A030306025), Pearl River S&T Nova Program of Guangzhou<PHONE_NUMBER>98), Combined Scientific Project Funded by Guangdong Provincial Science and Technology Agency and Guangdong Provincial Academy of Traditional Chinese Medicine (2014A020221047), Guangdong High-level University Construction Project (A1-AFD018161Z1510, A1- AFD018171Z11102, and A1-AFD018171Z11101), Guangdong High-level Personnel of Special Support Program (A1-3002- 16-111-003), Guangdong traditional Chinese medicine bureau project (20181132 and 20182044), the Post-doctoral Science Foundation of China (2017M612644, 2017M622669, and 2018T110861), the Ph.D. Start-up Fund of Natural Science Foundation of Guangdong Province (2017A030310213) and Guangdong provincial key project (2016kzdxm032), and the Specific Research Fund for TCM Science and Technology of Guangdong Provincial Hospital of Chinese Medicine (YN2018MJ07 and YN2018QJ08).

#### ACKNOWLEDGMENTS

fphar-09-01106 September 28, 2018 Time: 16:12 # 18

We thank LetPub (www.letpub.com) for the linguistic assistance.

#### SUPPLEMENTARY MATERIAL

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fphar. 2018.01106/full#supplementary-material

FIGURE S1 | The chromatographic fingerprints of ADQ. HPLC chromatograms on 216 nm detection for (A) standard compounds, (B) ADQ (1: p-Coumaric acid; 2: Calycosin-7-glucoside; 3: Liquiritin; 4: Glycyrrhizic acid; 5: Curcumol).

FIGURE S2 | The IC50s of paclitaxel for MDA-MB-231, MDA-MB-231/T, MCF-7, and MCF-7/T cells. The parental/resistant cells of (A) MDA-MB-231 and (B) MCF-7 were treated with paclitaxel at the indicated concentrations (0–200 ng/mL) for 48 h.

### REFERENCES

FIGURE S3 | The expressions of CAV1 were determined by western blot among MDA-MB-231, MDA-MB-231/T, MCF-7, MCF-7/T, HUMECs, and HUVECs.

FIGURE S4 | The expressions of CAV1 using western blotting analysis. (A) MCF-7 cells were transfected with the recombinant plasmid of CAV1, and MDA-MB-231 cells were transfected with siCAV1 for 48 h. The CAV1 levels were then confirmed by western blot analysis; (B) The expressions of CAV1 on the indicated parental breast cancer cells and the paired paclitaxel-resistant cells (∗∗P < 0.01 v.s. control, values represented as the mean ± SD, n = 3).

FIGURE S5 | The expressions of CAV1, p53 and p-p53 (ser15) were determined by western blot among MDA-MB-231, MCF-7, and HUMECs (∗∗P < 0.01 v.s. control, values represented as the mean ± SD, n = 3).

FIGURE S6 | The expressions of CAV1, p53 and p-p53 (ser15) were determined by western blot with or without ADQ in MDA-MB-231 and MCF-7 (∗∗P < 0.01 v.s. control, values represented as the mean ± SD, n = 3).

TABLE S1 | The establishment of calibration curves for HPLC analysis.

TABLE S2 | The contents of five components in ADQ.

TABLE S3 | Information on candidate active compounds from BHSSC, EZ, HQ and GC herbs of ADQ decoration.

breast cancer stem cells through WIF1 demethylation. Oncotarget 6, 9854–9876. doi: 10.18632/oncotarget.3396

**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2018 Wang, Yang, Zhang, Wang, Zheng, Li, Liu, Pan, Li, Huang, Zhang and Wang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

# The Yin-Yang Property of Chinese Medicinal Herbs Relates to Chemical Composition but Not Anti-Oxidative Activity: An Illustration Using Spleen-Meridian Herbs

Yun Huang1,2, Ping Yao2 , Ka Wing Leung1,2, Huaiyou Wang1,2, Xiang Peng Kong1,2 , Long Wang2† , Tina Ting Xia Dong1,2, Yicun Chen2,3 and Karl Wah Keung Tsim1,2 \*

1 Shenzhen Key Laboratory of Edible and Medicinal Bioresources, Shenzhen Research Institute, Shenzhen, China, 2 Division of Life Science and Center for Chinese Medicine, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong, 3 Department of Pharmacology, Shantou University Medical College, Shantou, China

#### Edited by:

Yuanjia Hu, University of Macau, Macau

#### Reviewed by:

Wei Song, Peking Union Medical College Hospital (CAMS), China Rongbiao Pi, Sun Yat-sen University, China

> \*Correspondence: Karl Wah Keung Tsim<EMAIL_ADDRESS>
#### †Present address:

Long Wang, Department of Applied Mathematics and Statistics, Johns Hopkins University, Baltimore, MD, United States

#### Specialty section:

This article was submitted to Ethnopharmacology, a section of the journal Frontiers in Pharmacology

Received: 03 August 2018 Accepted: 24 October 2018 Published: 15 November 2018

#### Citation:

Huang Y, Yao P, Leung KW, Wang H, Kong XP, Wang L, Dong TTX, Chen Y and Tsim KWK (2018) The Yin-Yang Property of Chinese Medicinal Herbs Relates to Chemical Composition but Not Anti-Oxidative Activity: An Illustration Using Spleen-Meridian Herbs. Front. Pharmacol. 9:1304. doi: 10.3389/fphar.2018.01304 "Yin-Yang" and "Five Elements" theories are the basis theories of Traditional Chinese Medicine (TCM). To probe and clarify the theoretical basis of these ancient Chinese wisdoms, extensive efforts have been taken, however, without a full success. In the classification of TCM herbs, hot, cold and neutral herbs are believed to possess distinct profile of chemical compositions of which the compounds should have different polarity and mass: this view provides a new perspective for further illustration. To understand the chemical properties of TCMs in the classification of "Yin-Yang" and "Five Elements," 15 commonly used herbs attributed to spleen-meridian were selected for analyses. Chemically standardized water extracts, 50% ethanol extracts and 90% ethanol extracts were prepared and subjected to different analytic measurements. Principle component analysis (PCA) of full spectrum of HPLC, NMR and LC-MS of the extracts were established. The results revealed that the LC-MS profile showed a strong correlation with the "Yin-Yang" classification criterion. The Yang-stimulating herbs generally contain more compounds with lower molecular weight and less polar property. Additionally, a comprehensive anti-oxidative profiles of selected herbs were developed, and the results showed that its correlation with cold and hot properties of TCM, however, was rather low. Taken together, the "Yin-Yang" nature of TCM is closely related to the physical properties of the ingredients, such as polarity and molecular mass; while such classification has little correlation with anti-oxidative property. Therefore, the present results provide a new direction in probing the basic principle of TCM classification.

Keywords: Yin-Yang classification criterion, cold and hot properties, principle component analysis, chemical composition, anti-oxidative activity

## INTRODUCTION

In the theoretical basis of TCM clinical application, "Yin-Yang" and "Five Elements" theories could further differentiate into secondary classification principles, e.g., four natures and five flavors, meridian tropism, floating and sinking (Li et al., 2008; Fu et al., 2009, 2015; Mohd et al., 2013; Qiu, 2015). Having a long history of clinical experience, these TCM theories are aiming to merge

**80**

with each other and achieve unity gradually. As the "Yin-Yang" attribution is not only specified to the description of patient symptoms ("Zheng" or syndrome), but also defines the nature of herbal medicines. Thus, the exact relationship between these two identification systems of TCM, i.e., "Yin-Yang" versus chemical composition, remains as a puzzle (Zhou et al., 2009; Ma et al., 2010; Li, 2012). In order to maintain a homeostasis in our body, the herbs with cold nature, also known as Yin-stimulating herbs, were generally used to treat hot diseases like inflammation, while the herbs with nature of warm and hot, belonging to Yang-stimulating herbs, were good for various types of deficiency (Fu et al., 2017a). In recent years, the works on chemical properties of TCMs has made a significant progress, and new ideas have been proposed, e.g., molecular drug hypothesis (Fu et al., 2017b), biodynamics (Yang et al., 2017), genetic hypothesis (Rezapour-Firouzi, 2017), transient receptor potential channel hypothesis (Bishnoi et al., 2018). Indeed, many of these newly proposed methods have been adopted and improved, including micro-calorimetry (Gao et al., 2014), mathematical modeling (Ramirez-Rodrigues et al., 2011), biophoton detection (Han et al., 2011; Zhao and Han, 2013). These methods are mainly derived from the collation of ancient books (Wang et al., 2010), pharmacological effects (Liang et al., 1988; Xu et al., 2011; Xiong et al., 2011), structure of components (Wang and Zhou, 2006) and thermodynamics (Yang et al., 2010). However, these studies are still lacking a co-relationship of the current TCM theory with the chemical composition of different classes of herbs.

Having highly developed analytical techniques and various novel chemometrics methods today, the study on relationship between TCM herb properties with their chemical composition has become possible. The widely employed analytical techniques, including HPLC, NMR and LC-MS, enabled researchers to establish robust methods to qualitative and quantitative determination of almost all kinds of compounds in biological matrices (Kuang et al., 2012). According to clinical application and experimental basis, the major ingredients corresponding to property and taste of TCM herbs are expected to be different. For example, volatile oil and polysaccharide are major bioactive components of pungent and sweet herbs; while alkaloid, glycoside and phenolic acid are generally abundant in bitter and acid herbs (Ung et al., 2007). In addressing these problems being encountered in TCM research, the omics techniques play a key role due to its advantage in solving information-rich puzzles and providing comprehensive and holistic results (Wiklund et al., 2008). Among the most commonly used multivariate statistical and visualization tools, principal component analysis (PCA) is a bilinear decomposition method that reduces original data to a few principal components and reserves the features contributing to variance (Kuang et al., 2012).

According to the concept of "Yin-Yang" nature of herbal medicine, the property of Chinese medicinal herbs was mainly summarized based on the efficacy and cognitive experience gained from patients' response. Some pharmacological studies supported the notion that cold and hot properties of TCM herbs were closely related to excitability of nervous system and endocrine (Li et al., 2008; Fu et al., 2009), mitochondrial ATP generation, and immunomodulatory function (Ko et al., 2004, 2006; Ko and Leung, 2007). On the other hand, the potential relationship between "Yin-Yang" properties and redox system of herbs have been proposed (Ou et al., 2003). Nevertheless, the physical meaning behind the criteria of "Yin-Yang" classification is still missing (Szeto and Benzie, 2006; Wong et al., 2006). Moreover, previous research has mainly focused on chemically based of anti-oxidative activity in TCM herbs; however, the relationship of anti-oxidative property with chemical nature of herbs is still missing. PCA analysis of full spectrum of HPLC, NMR and LC-MS of different classes of herbs were conducted to provide a multi-angle and multi-dimensional perspectives of the connection between the "Yin-Yang" properties and their chemical composition. In addition, the possible relationship between "Yin-Yang" nature and anti-oxidant of various herbs was clarified.

### MATERIALS AND METHODS

#### Chemicals and Preparation of Herbal Extracts

HPLC-grade acetonitrile and ethanol were from Merck (Darmstadt, Germany). Ultra-pure water was obtained from a Milli-Q purification system (Millipore, Molsheim, France). Deuterium oxide (D2O), 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetra-zolium bromide (MTT), gallic acid, 1,1-Diphenyl-2-picrylhydrazyl radical 2,2-diphenyl-1-(2,4,6 trinitrophenyl) hydrazyl (DPPH), tert-butyl hydroperoxide (tBHP), vitamin C, 20 ,70 -dichlorodihydrofluorescein diacetate (DCFH-DA), β-galactosidase, dimethyl sulfoxide (DMSO) and Hank's Balanced Salt Solution (HBSS) were purchased from Sigma-Aldrich (St. Louis, MO, United States). Lipofectamine 3,000 was bought from Invitrogen (Waltham, MA, United States) and Triton X-100 was from BDH Tech (Marshalltown, IA, United States). The pARE-Luc DNA construct was obtained from Promega (Madison, WI, United States).

Three batches of 15 spleen-meridian herbs (**Table 1**) and a batch of Quinquefolium Radix, Rehmanniae Radix and Rehmanniae Radix Praeparata were collected from their production sites or pharmacy. The collected samples were first authenticated by Dr. Tina Dong and stored in dry and clean container before further analysis. The authentication of these herbs was according to Hong Kong Materia Medica Standards including morphology, microscopy, chemical fingerprint and chemical assay. The voucher specimens were stored in Centre for Chinese Medicine R&D at the university.

A unified extraction method was developed. Water extract, 50% ethanol extract and 90% ethanol extract of each herb were prepared to obtain extracts having different polarities. The specific extraction procedure was as following. First, the herbal samples were milled and passed through a No. 2 sieve and mixed well. Then, 4 g of the powdered sample was extracted in 100 mL distilled water, 50% ethanol and 90% ethanol, respectively, for 2 h by reflux, and the herbs were extracted twice. For the second extraction, the residue from the first extraction was filtered, and the same extraction condition was applied onto the filtered

TABLE 1 | The pharmacological action, origin and extracting yields of the tested 15 spleen-meridian herbs.

(Continued)

fphar-09-01304 November 13, 2018 Time: 14:49 # 4

aW, water extract; E50, 50% ethanol extract; E90, 90% ethanol extract. bPharmacological effects were based on the Chinese Pharmacopoeia. The bold words are to emphasize the main pharmacological effects of the TCM.

residue. Then, the extracts were combined, dried under vacuum and stored at 4◦C.

The preparation of wine-treated Angelica Sinensis Radix was according to previous research (Zhan et al., 2011). To be specific, 30 g of the dried roots were sliced and sprayed with 3 mL of 15% ethanol and then processed in an oven at 80◦C for 90 min with 3 flipping. Then 4 g of processed Angelica Sinensis Radix was weighed and prepared as the extraction procedure mentioned above.

### HPLC Fingerprint Analysis of Herbal Extracts

The fingerprint analysis of herbal extracts was performed on an Agilent HPLC 1200 series system (Agilent, Waldbronn, Germany), which was equipped with a degasser, a binary pump, an auto sampler, a thermostated column compartment and a DAD. The samples were separated on a Waters Symmetry C18 column (4.5 mm × 250 mm, 5 µm i.d.) after filtered with a guard column. The mobile phase was composed of acetonitrile (A) and water (B) according to the pre-set gradient program: 0–50 min, linear gradient 0–70% (A); 50–55 min, linear gradient 70–100% (A); 55–60 min, isocratic elution. A pre-balance period of 5 min was used between each run. The injection volume was 10 µL, and the flow rate was set at 1 mL/min. To get the fingerprints of herbal extracts, the wavelength of UV detector was set to 254 nm with full spectral scanning from 190 to 400 nm.

#### NMR Spectrum Profiling

The1H NMR experiments were conducted to the extracts in revealing high abundance chemicals. Fifty mg of the dried extract was dissolved in 400 µL of D2O. All particulate materials were removed by centrifugation at 13,000 × g for 1 min, and the supernatant was transferred to a standard 5-mm NMR tube. NMR spectra were acquired on a Varian 300 MHz NMR spectrometer, operating at 300.13 MHz 1H NMR frequency at 298 K. Gradient shimming was used to improve the magnetic field homogeneity prior to all acquisition. 1H NMR spectra of the samples were acquired using a 1D CPMG pulse sequence (RD-90◦ -t1-90◦ -tm- 90◦ -acquire) to generate a spectrum with a reduced residual solvent peak. The experiment time for each sample was around 10 min.

### LC-MS Spectrum Profiling

fphar-09-01304 November 13, 2018 Time: 14:49 # 5

The LC-MS profiles of extracts were established on the Xevo G2-XS R Quadrupole Time-of-Flight (Q-TOF) Mass Spectrometer coupled with an integrated ACQUITY UPLC R I-Class System. The analysis column is ACQUITY UPLC R BEH C18 column (2.1 mm × 50 mm, 1.7 µm). The mobile phase condition was recorded as follows: acetonitrile (A) and water (B), 0–0.5 min, isocratic gradient 5% (A); 0.5-8 min, linear gradient 5–50% (A); 8–12 min, linear gradient 50–90% (A); 12–14 min, linear gradient 90–100% (A); 14–16 min, isocratic gradient 100% (A). The flow rate was set at 0.4 mL/min with injection volume of 5 µL. The column temperature was 25◦C. The effluent was further analyzed by Xevo G2-XS R Quadrupole Time-of-Flight (Q-TOF) Mass Spectrometer with an ESI ion source in positive mode. The acquisition range was 50–1200 Da with 0.1 s scan rate. These analytic settings were aiming to reveal most of the chemicals within the herbal extracts.

### Cell Culture

RAW264.7 cell, a mouse blood macrophage cell line, was obtained from American Type Culture Collection (ATCC, Manassas, VA, United States). Cells were cultured in Dulbecco's modified Eagles medium supplemented with 100 U/mL penicillin, 100 µg/mL of streptomycin and 10% heat in-active fetal bovine serum in a humidified CO2 (5%) incubator at 37◦C. When the cells reached 80% confluence, they were harvested from plate with a scraper (Corning Incorporated, Corning, NY, United States).

### Cell Viability

Cell viability was measured by MTT assay. Cells were seeded in 96-well plates at a density of 1 × 104 cells per well. After 24 h drug treatment, cells in each well were incubated with 10 µL MTT (5 mg/mL) at a final concentration of 0.5 mg/mL for 2 h at 37◦C. After the solution was removed, DMSO was used to re-suspend the purple precipitate inside the cells, and the absorbance was detected at 570 nm. The cell viability was calculated as percentage of absorbance value of control (without drug treatment) while the value of control was 100%.

## Folin-Ciocalteu Assay

Total phenolic content of herbal extracts was measured with Folin-Ciocalteu assay. To be specific, 20 µL of each extract together with 40 µL 10% (v/v) Folin-Ciocalteu reagent was added into each well of 96-well microplate. Then, 160 µL Na2CO3 (700 mM) was added into each well. The assay plates were incubated at room temperature in dark for 2 h and then the absorbance at 765 nm were recorded. Here, gallic acid (Sigma-Aldrich, > 98%) was used as the reference compound, and the total phenolic contents of each extract were expressed as the value compared with gallic acid.

## DPPH Radical Scavenging Assay

Free radical scavenging activity of herbal extracts was measured with DPPH radical scavenging assay. Briefly, 50 µL of each extract with different concentrations (0–8 mg/mL) was mixed with 150 µL DPPH solution in each well of 96-well microplate. After standing for 10 min, the absorbance at 517 nm was recorded. The DPPH free radical scavenging activity was calculated as an inhibition percentage based on the following equation: Inhibition (%) = [(A0 - A1)/A0] × 100, where A0 is the absorbance of the control, and A1 is the absorbance of the RA sample aliquot. Here, gallic acid (0–100 µM) was used as a positive control.

### tBHP-Induced Oxidative Stress Assay

The dose of tBHP (150 µM; Sigma-Aldrich) and positive control (vitamin C, 1 mM) were optimized with MTT assay as previously reported (Huang et al., 2018a,b). Similar to the cell viability assay, the cells were cultured in 96-well plate first. After drug treatments for 24 h, tBHP (150 µM) were added into the wells for 3 h before MTT at a final concentration of 0.5 mg/mL was added. After the solution was removed, the purple precipitate inside the cells was re-suspended in DMSO and then measured at 570 nm absorbance.

### ROS Formation Assay

The measurement of ROS content in cell cultures was performed by using DCFH-DA, an oxidation-sensitive dye. Cultured RAW264.7 cells (1 × 104 cells/well) in a 96-well plate were pre-treated with herbal extracts or standard compounds for 24 h, and the cells were labeled with 100 µM DCFH-DA (Sigma-Aldrich) in HBSS (Sigma-Aldrich) for 1 h at 37◦C. After washing three times with HBSS, the cells were then treated with 150 µM tBHP for 1 h at 37◦C. Then the amount of intracellular tBHP-induced ROS formation was detected by fluorometric measurement with excitation at 485 nm and emission at 530 nm.

### Assay for Anti-oxidant Response Element

To reveal the transcriptional activation of anti-oxidant response element (ARE), the pARE-Luc DNA construct, containing four copies of ARE (50 -TGACnnnGCA-30 ) that drives transcription of the luciferase reporter gene luc2P (Photinus pyralis), was transfected into cultured RAW264.7 cells by Lipofectamine 3000 (Invitrogen) according to the manufacturer's instructions. Transfected RAW264.7 cells were treated with various concentrations of ginseng extracts for 1 day. Then, the medium was aspirated, and the cultures were lysed by a buffer containing 100 mM potassium phosphate buffer (pH 7.8), 0.2% Triton X-100 and 1 mM DTT at 4◦C. After centrifugation at 16,100 × g for 5 min, the supernatant was collected and used to perform luciferase assay. The activity was normalized as absorbance (up to 560 nm) per mg of protein.

### Statistical Analysis

HPLC profile processing were conducted using Agilent MassHunter workstation software version B.01.00. The 1H

Huang et al. Factors Related to Herbal Property

NMR spectra obtained from each sample were phased, baseline-corrected, calibrated and integrated using MestReNova 6.1.1 software. LC-MS profile were analyzed with Waters MassLynx V4.1 SCN923. The data were then formatted in XML for importing into PCA software SIMCA-P+ version 12.0 (Umetrics, Sweden). All data were expressed as the mean ± SEM for n = 3–5, unless otherwise specified. Statistical tests were performed by one-way ANOVA with multiple comparisons using Dunnett's test. Differences were considered significant at p < 0.05.

#### RESULTS

### Chemical Composition of Herbal Extracts

By reflux, the water extracts and ethanol extracts of individual herbs were prepared with the consideration of their common usage and historical preparation of a herbal decoction in TCM. The origin of herbs and its extracting yields are shown in **Table 1**. The extracting yield of Codonopsis Radix, Astragali Radix, Jujubae Fructus, Ginseng Radix, Nelumbinis Semen and Coicis Semen suggested that the water-soluble ingredients in these herbs were relatively abundant; while Angelicae Sinensis Radix, Amomi Fructus, Aucklandiae Radix and Areca Catechu contained less water-soluble ingredients (**Figure 1**). Among the tested herbs, Codonopsis Radix, Angelicae Sinensis Radix and Astragali Radix possessed the highest yield of extraction up to ∼25% per dried weight. In contrast, the yields of water and ethanol extracts of Amomi Fructus, Poria Cocos and Coicis Semen were less than 3%.

The HPLC fingerprints of water and ethanol extracts showed that there were plenty of trace compounds (**Figure 2**). By comparing the HPLC profiles, the 90% ethanol extracts in general contained higher amounts of trace compounds, recognized by the number of peaks, as compared to that of water and 50% ethanol extracts. The distribution of peaks in HPLC profiles showed apparent migration in accord to decreasing polarity of extracting solvent, i.e., more peaks at the end of run in ethanol extracts (**Figure 2**). The 1H NMR experiment was conducted to reveal those high abundance compounds in aforementioned

extracts to herbal powders. All values are in Mean ± SD, n = 3. The detailed yield and origin of each batch were shown in Table 1.

herbal extracts. Similar to HPLC fingerprint, the NMR profile showed close similarity between herbs extracted with the same solvents, and generally the 90% ethanol extracts showed higher content of chemical composition, as compared with that of other extracts (**Figure 3**).

Macrocephalae Rhizoma; c, Angelicae Sinensis Radix; d, Astragali Radix; e, Jujubae Fructus; f, Ginseng Radix; g, Amomi Fructus; h, Aucklandiae Radix; i, Areca Catechu; j, Dioscoreae Ahizoma; k, Lablab Semen Album; l, Poria Cocos; m, Nelumbinis Semen; n, Coicis Semen; o, Crataegi Fructus.

High resolution mass spectrometry was established in analyzing the herbal extracts. The LC-MS profiles could be displayed in two forms: total ion chromatogram (TIC) and mass spectrum. **Figure 4** shows a typical profile of 90% ethanol extract of Ginseng Radix. The distribution of trace ingredients with different polarity and molecular mass were shown. A list of mass spectrum with intensity was obtained for the herbal extracts. The LC-MS profiles of 15 spleen-meridian

herbs were established, and the data processing was performed on Waters MassLynx V4.1 SCN923. With MassLynx, each chromatographic peak was identified by a mass-to-charge ratio (m/z) and retention time, as illustrated in **Supplementary Figure 1**. As shown in **Table 2**, 65 compounds in 15 herbs were identified with unique relative molecular mass of [M + H]+, [M + Na]+ or [2M + H]+ within acceptable experimental error, which therefore could be used for the purpose of chemical authentication.

Cocos; m, Nelumbinis Semen; n, Coicis Semen; o, Crataegi Fructus.

FIGURE 4 | A typical total ion chromatogram (TIC) and mass spectrum. The 90% ethanol extract of Ginseng was served as an example. (A) Total ion chromatogram (TIC) and (B) mass spectrum of the Ginseng extracts are shown. The X-axis of TIC is time (min); while the X-axis of spectrum is mass (Da). Both profiles were obtained with a Xevo G2-XS R Quadrupole Time-of-Flight (Q-TOF) Mass Spectrometer coupled with an integrated ACQUITY UPLC R I-Class System. The mobile phase was composed of acetonitrile and water, and the effluent was analyzed with an ESI ion source in positive mode. The acquisition range was set from 50 to 1200 Da with 0.1 s scan rate to reveal most of the chemicals within the herbal extracts. The extracts from 3 batches of herbs were subjected to analysis, a representative profile was shown.

### Principal Component Analysis of Chemical Composition Profiles

To determine the similarities and differences among the herbal extracts, PCA analyses of chemical profiles deriving from HPLC, NMR and LC-MS were conducted. PCA is a chemometrics method to determine the possible relationship of different samples. The general procedure of PCA on full spectrum was shown in **Figure 5**. The spectrums were firstly divided into bins according to the resolution of analytic methods, i.e., 9,000 bins for HPLC, 1,200 bins for NMR, 10,800 bins for TIC and 230,000 bins for mass spectrum, with analytic softwares, e.g., Agilent MassHunter workstation software version B.01.00 for HPLC, Waters MassLynx V4.1 SCN923 for LC-MS and MestReNova 6.1.1 for NMR. The intensity of each bin was extracted and arranged. The obtained excel or txt files were further imported into PCA software (SIMCA-P+ version 14.1) for chemometrics analysis.

According to the TCM theory, the selected 15 spleen-meridian herbs could not only be divided into three different groups

#### TABLE 2 | The compounds identified from the herbal extracts.

fphar-09-01304 November 13, 2018 Time: 14:49 # 8

(Continued)

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aMass-to-charge ratio (m/z) is [M + Na]+.

according to their "Yin-Yang" nature, and which also could be divided into four groups according to their pharmacological activities (**Table 1**). In clinical application, these herbs nourish the blood, calm the heart and promote the digestion. By characterizing the relationship between the distributions of herbal extracts in score scatter plots with their identified pharmacological activities, the correlation between chemical composition basis and known pharmacology could be clarified. Although the score scatter plots of HPLC fingerprints (**Figure 6A**) and NMR profiles (**Figure 6B**) possessed acceptable first principal component (58.9%, 50.2%) and second principal component (14.1%, 13.2%), the distribution of herbal extracts showed no apparent regular pattern among different groups. The results indicated that HPLC and NMR profiles might not able to distinguish the herbal extracts with different pharmacological properties.

The LC-MS profiles, including TIC and mass spectrum obtained from UPLC/Q-TOF, were further analyzed with the same procedures as that of HPLC and NMR profiles. Both score scatter plots possessed acceptable first principal component and second principal component, especially in the scenario of two principal components of mass spectrum accounting for 97.7% of total variance (**Figures 7A,B**). These results therefore could be an outcome of ultra-high resolution of UPLC/Q-TOF. The two PCA scoring plots showed similar distribution according to their identified pharmacological activities, indicating that the polarity of herbal compounds might have positive correlation with their mass, at least to certain degree. Moreover, the herbal extracts having similar pharmacological activities showed a clustering effect. In the clustering analysis, the blood nourishing herbs could be distinguished obviously from other herbs (**Figure 7**). Thus, the plots showed a strong correlation between distribution of herbal extracts with their pharmacological activities, which was in line with the hypothesis that the bioactivities of herbal extracts should be strongly related to their chemical composition.

The relationship between "Yin-Yang" nature and distribution of the herbal extracts in scoring plots were determined. The distribution of Yin, Yang and neutral herbs were significantly identified to be different, in particular the right part of score scatter plots contained only the Yang-stimulating herbs (**Figures 8A,C**). To clarify the chemical basis of distinct distribution, the variables in loading scatter plots could be divided into three clustering groups, according to their retention time and molecular mass. From the distribution of herbal extracts in score scatter plot, the contents of compounds eluted between 6 and 12 min showed greater impacts in the "Yin-Yang" distinction of herbal extracts (**Figure 8B**). Similarly, the lighter molecules, i.e., less than 900 g/mol, were distributed in marginal positions in the loading scatter plots (**Figure 8D**), indicating that these compounds might have obvious influence

to the herbs' properties. Moreover, it could be deduced that the Yang-stimulating herbs were shown to contain more compounds, i.e., the compounds with higher polarity and lower molecular mass. These results not only illustrated the chemical basis in classify "Yin-Yang" nature of TCM, and which also suggested the polarity and molecular mass of ingredients in the herbs could be two major influencing factors.

To determine applicability of present analytic methods, three pair-herbs having typical cold and hot properties, as recorded in TCM practice, were subjected into our developed PCA analysis. Ginseng Radix and Rehmanniae Radix Praeparata are considered as hot and warm herbs aiming to tonify qi and to nourish blood; while Quinquefolium Radix and Rehmanniae Radix are cold herbs used to clear heat aiming to promote fluid production (Yu et al., 2001; Zhang et al., 2008). Angelica Sinensis Radix is a typical herb in invigorating blood in TCM therapy. However, the treatment of Angelica Sinensis Radix with wine reduces the amount of volatile oil, and this treatment could directly minimize the hot property of Angelica Sinensis Radix (Zhan et al., 2011). These three paired-herbs have the same or similar origin, and however, they have opposite functions. From the PCA score scatter plots of TIC and spectrum, the distribution of herbal extracts with hot or warm properties (i.e., Ginseng Radix and Rehmanniae Radix Praeparata) were significantly different from the herbs having cold or cool properties (i.e., Quinquefolium Radix and Rehmanniae Radix) (**Figures 9A,B**). As expected, the polarity and molecular mass of major components in the extracts of pair-herbs were different, accordingly. Although the difference between Angelica Sinensis Radix and wine-treated Angelica Sinensis Radix in pharmacological properties was not as obvious as other paired-herbs, as mentioned above, the distribution of the two herbal extracts could also be distinguished significantly in the score scatter plots (**Figures 9A,B**). The result strongly supported the precision applicability of the present established analytical model.

#### Anti-oxidative Profile of Spleen-Meridian Herbs

To elucidate the connection between the "Yin-Yang" nature and redox system, a series of in vitro assays were conducted to measure the anti-oxidative properties of herbal extracts. Folin-Ciocalteu assay was used to determine total phenolic compounds in herbal extracts. Phenolic compounds, highly abundant in plant, generally have unstable electrons in the phenolic hydroxyl groups, and which protect cells from being oxidized by directly neutralizing free radicals or decomposing peroxides. The content of total phenolic compounds in the selected herbs were rather similar (**Figure 10** and **Table 3**). In exception, the water and 50% ethanol extracts of Lablab Semen Album contained much less of total phenolic content (**Figure 10**). In contrast, Arecae Pericarpium and Crataegi Fructus possessed relative higher contents, as compared with

other herbs. The 50% ethanol extracts of herbs contained higher amounts of phenolic compounds in general, except in the scenario of Aucklandiae Radix and Nelumbinis Semen (**Figure 10**).

The free radical scavenging activity of the polyphenolic compounds in herbal extracts was measured with DPPH radical scavenging assay. DPPH, having stable free radicals, was used to detect radical-scavenging ability of herbal extracts. Here, the EC50 of each herbal extract was determined (**Figure 10**). The DPPH radical scavenging activity of most extracts were similar (**Figure 10** and **Table 3**). Among all the herbs, Lablab Semen Album showed the largest value of EC50, indicating the weakest anti-oxidant. In contrary, Arecae Pericarpium and Crataegi Fructus showed stronger DPPH radical scavenging activity: the outcome was consistent with the result of Folin-Ciocalteu assay (**Figure 10**).

To reveal the protective effect, the herbal extract was applied onto the tBHP-treated RAW264.7 macrophages. RAW264.7 murine cell was chosen because of its high breeding speed and stability. A stress inducer, tBHP, was chosen to damage

the macrophage, which induced cell death in a dose-dependent manner. Vitamin C served as a positive control (Huang et al., 2018a,b). A series of concentrations was employed, and the 15 herbs were divided into four groups, according to their efficiency (**Supplementary Figure 2**). After the optimization of dose of herbal extracts with MTT assay, the protective effects of the extracts to tBHP-induced cell cytotoxicity were determined. As shown in **Supplementary Figure 3**, the extracts protected the cells from oxidative damage in a dose-dependent manner. The protective effects and EC50 of the herbal extracts were summarized in **Figure 10** and **Table 3**. The extracts of Angelicae Sinensis Radix and Ginseng Radix possessed the best protection effects to more than 50%, as compared to the control. In contrast, Aucklandiae Radix, Nelumbinis Semen and Coicis Semen provided significantly lower protective effects at around 20% (**Figure 10**).

The formation of ROS is one of the vital causes in inducing cell death. By determining the inhibition effects of herbal extracts to tBHP-induced ROS formation, the protective effect of herbal

FIGURE 8 | PCA analyses of LC-MS profiles of signals from the 15 spleen-meridian herbs. (A) Scoring plots of TIC. (B) Loading scatter plots of TIC. (C) Scoring plots of spectrum. (D) Loading scatter plots of spectrum. The distribution of each herbal extract in score scatter plots was calculated according to its correlation with the major components. The classification of herbal extracts was according to the "Yin-Yang" nature of herbs, i.e., Yin, neutral and Yang herbs, according to TCM practice. The results showed very obvious discrimination between herbal extracts with different properties. Each dot in the loading scatter plots represents a variable. Dots in (B) represent time intervals for 0.1 s, and dots in (D) represent mass intervals of 0.005 Da.

scatter plots was calculated according to its correlation with the major components.

extracts was further compared and analyzed. As shown in **Table 3**, the inhibition effects to ROS formation were positively related with the protective effects to cell viability. The 50% ethanol extracts of Atractylodis Macrocephalae Rhizoma, Angelicae Sinensis Radix, Jujubae Fructus possessed the best effect having over 50% of inhibition, as compared to the control (**Figure 10**). Moreover, the luciferase-reporter construct (i.e., pARE-Luc) containing four ARE DNA regulatory elements deriving from the promoters of anti-oxidative genes, tagged upstream of a luciferase gene, was transfected into cultured RAW264.7 cells.

∗p < 0.05; ∗∗p < 0.01; ∗∗∗p < 0.001.

The activation of pARE-Luc, triggered by herbal extracts, showed the same pattern as the result of ROS formation. Again, the 50% ethanol extracts of Atractylodis Macrocephalae Rhizoma, Angelicae Sinensis Radix, Jujubae Fructus exhibited the best inhibition effects of over 50% (**Figure 10** and **Table 3**).

### Correlation Between Yin-Yang Attributes With Anti-oxidative Profile

After establishment of anti-oxidative profiles of spleen-meridian herbs, the correlation of this activity with the "Yin-Yang" classification criterion was determined. By comparing the specific anti-oxidative data of herbal extracts with the mean value of 45 extracts, the statistical tests were performed by one-way ANOVA with multiple comparisons using Dunnett's test (**Figure 10** and **Table 3**). According to the comparison results, the differences between species were relatively higher than that from different herbal extracts. Moreover, the correlation between the anti-oxidative profiles of the spleen-meridian herbal extracts with the "Yin-Yang" classification criterion was rather weak in the tested parameters. Poria Cocos is a plain medicine used to fortify spleen, while Crataegi Fructus is classified as Yin herb due to its acid flavor. These herbs contain abundant phenolic compounds

 | Thecorrelation between anti-oxidant profiles of the spleen-meridian herbs with the "Yin-Yang" classification

criterion.

TABLE

3

ethanol extract; E90, 90% ethanol extract. dValues are expressed as Mean ± SD, where n = 4, each with triplicate samples. Statistical comparison

GAE/g; mean value of DPPH radical scavenging

formation is 30.5%; mean value of

 EC50 is 1.38 mg/mL; mean value of tBHP protection effect is 37.9%; mean value of tBHP protection effect EC50 is 0.65 mg/mL; mean value of inhibition effects to ROS

pARE-luciferase

 activity increase is 29.6%). ∗p < 0.05; ∗∗p < 0.01; ∗∗∗p < 0.001.

 was made with mean value (mean value of total phenolics is 12.87 mg

fphar-09-01304 November 13, 2018 Time: 14:49 # 14

and possess appreciable anti-oxidative activity, resulting in better protective effects to oxidative stressed cells than that from Yang herbs, e.g., Amomi Fructus, Aucklandiae Radix (**Figure 10**).

Furthermore, the PCA analysis of anti-oxidative profiles was conducted. As shown in **Figure 11**, the two principal components of the mass spectrum accounted for 86.9% of total variance, which is enough to support the credibility of the results. The distribution of Yang, neutral and Yin herbs could not be distinguished in the score scatter plot, indicating there are no significant difference between anti-oxidative profiles of these herbs (**Figure 11A**). This insignificant distinction could also be predicted from the loading scatter plot (**Figure 11B**). Having the parameters of tBHP-protected effect (%), ROS formation (% of inhibition) and pARE-luciferase activity (% of increase) at similar locations, there should be insufficient variables to distinguish the samples. Thus, the anti-oxidative properties of herbal extract were not equivalent to its "Yin-Yang" properties of herbal medicine.

### DISCUSSION

The concept of four natures and five flavors of TCM herb is accumulated from long-term medical practice, and this theory plays a key role in clinical medication. To clarify the modern physiological meaning of the "Yin-Yang" properties, 15 spleen-meridian herbs were selected for the illustration of such classification. In accordance to TCM clinical practice, we have chosen different types of herbs that are known to have certain pharmacological functions: (i) herbs to fortify spleen and promote digestion, e.g., Aucklandiae Radix and Crataegi Fructus; (ii) herbs to keep digestion system in normal state by resolving dampness and treating diarrhea, e.g., Atractylodis Macrocephalae Rhizoma, Amomi Fructus, Areca Catechu, Dioscoreae Ahizoma, Lablab Semen Album, Nelumbinis Semen and Coicis Semen; (iii) herbs to replenish Qi and nourish blood, e.g., Codonopsis Radix, Angelicae Sinensis Radix, Astragali Radix, Jujubae Fructus and Ginseng Radix; and (iv) herbs to calm the heart and soothe the nerves, e.g., Poria Cocos and Nelumbinis Semen. Apart from the classification according to their pharmacological activity, these herbs could also be further divided into three groups, Yin, Yang and neutral herbs. Thus, the herbs chosen in present study is trying to cover the most commonly used TCM in treating spleen-related unwell.

Identifying chemical composition in herbal extract is the key to probe the properties of TCM herbs. However, the natural products always accompany with a huge amount of chemical variables, which greatly shake the credibility of any outcome (Kuang et al., 2012). Here, we employed standard methods, i.e., HPLC fingerprinting, NMR profiling and LC-MS profiling, as to reduce the variables in herbal extracts. Generally, HPLC fingerprinting is adopted to show all compounds with ultraviolet absorption; 1H-NMR profiling is able to display all compounds with active hydrogen; while LC-MS profiling is capable to show trace compounds (Zhao et al., 2005; Crockford et al., 2006). To obtain information of the components as much as possible, the detection wavelength of HPLC was set from 190 to 400 nm, and