Anti-Cancer Effects of Emodin on HepG2 Cells as Revealed by 1H

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Anti-cancer effects of emodin on HepG2 cells as revealed by 1H-NMR based metabolic profiling Yue-Xiao Xing, Ming-Hui Li, Liang Tao, Ling-Yu Ruan, Wei Hong, Cheng Chen, Wen-Long Zhao, Han Xu, Jian-Feng Chen, and Jun-song Wang J. Proteome Res., Just Accepted Manuscript • DOI: 10.1021/acs.jproteome.8b00029 • Publication Date (Web): 20 Apr 2018 Downloaded from http://pubs.acs.org on April 21, 2018

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Journal of Proteome Research

Anti-cancer effects of emodin on HepG2 cells as revealed by 1H-NMR based metabolic profiling

Yue-Xiao Xing a,#, Ming-Hui Li

a,#

, Liang Tao a, Ling-Yu Ruan a, Wei Hong a, Cheng

Chen a, Wen-Long Zhao a, Han Xu a, Jian-Feng Chen a, Jun-Song Wang a,*

a

Center for Molecular Metabolism, School of Environmental and Biological

Engineering, Nanjing University of Science and Technology, 200 Xiao Ling Wei Street, Nanjing 210094, People’s Republic of China.

*

Corresponding author:

Email: [email protected]. Telephone: +86 25 8430 3216.

#

These authors contributed equally to this work.

1

ACS Paragon Plus Environment

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ABSTRACT Hepatic carcinoma is one of the most common cancers in the world with a high incidence. Emodin is an anthraquinone derived from Polygonum multiflorum Thunb, possessing anti-cancer activity. The purpose of this study is to investigate the anti-cancer effect of different dosages of emodin on HepG2 cells using 1H-NMR based metabolic approach complemented with qRT-PCR and flow cytometry to identify potential markers and discover the targets to explore the underlying mechanism. Emodin can dose-dependently inhibit the growth of HepG2 cells, perturb cell cycle progression, down-regulate the expression of genes and proteins related to glycolysis and trigger intracellular ROS generation. Orthogonal signal correction partial least-squares discriminant analysis (OSC-PLS-DA) and correlation network analysis of the 1H NMR data showed significant changes in many endogenous metabolites after emodin exposure concerning oxidative stress and disturbances in amino acid and energy metabolism. These findings are helpful to understand the anti-cancer mechanism of emodin and provide a theoretical basis for its future application and development.

KEYWORDS: emodin; anti-cancer; HepG2; metabolomics; glycolysis; energy metabolism; ROS

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INTRODUCTION Hepatocellular carcinoma (HCC) is the third most common cancer leading to death worldwide 1, with the high incidence rates especially in western and central Africa, and eastern and southeastern Asia. Hepatocellular carcinoma is usually diagnosed at an advanced stage, and the average survival time is only 1-4 months without treatment after diagnosis 2. Surgeries such as liver transplantation and surgical resection are the treatment of choice for early stage patients, but are not effective for those of advanced HCC 1. The therapeutic effects of traditional chemotherapeutic agents were still unsatisfactory because of their side-effects and frequent development of drug resistance 3. Therefore, it is very important to find new anti-tumor agents with good efficacy and low toxicity. In recent years, traditional Chinese medicine (TCM) has been increasingly adopted in HCC prevention and treatment due to its safety 4, 5

. The anthraquinone, emodin, is one of the main active components in

the roots and rhizomes of Polygonum multiflorum Thunb, a famous herb that has been used to treat various diseases for many centuries in China. Emodin exhibited variety of activities such as anti-inflammatory 6, anti-bacterial

7

and immunomodulation 8. Besides, emodin has growth

inhibitory and cytotoxic effects against several types of tumor cells

9, 10

.

Therefore, studies and applications of emodin in cancer treatment become 3



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the hot topic in recent years. However, most of these studies focused on apoptosis related genes and proteins, which were more like the consequences of drugs other than the reasons. In addition, few genes and proteins could hardly clarify the mechanism of drugs, especially those of natural origin, as most of which functioned through complicated mechanism with the involvement of many genes, proteins and metabolites. Metabolites are reactants and products of biochemical processes, which was regulated by gene and proteins. Metabolomics, the collections of all metabolites, could sensitively reveal what actually happened by detecting metabolic responses to stimulation or disturbance

11

.

1

H

NMR-based metabolomics, for its inherent advantages of non-bias and throughput nature and richness in structural information has already been successfully applied to pharmacological and cancer metabolism studies 12. In this study, a 1H NMR-based metabolic approach was used to study the anti-cancer effects of emodin on HepG2 cells and the underlying mechanisms. Metabolomics approach complemented with flow cytometry, quantitative real-time RT-PCR and western blot analysis revealed that emodin induces apoptosis, oxidative stress and an inhibition of glycolysis, showcasing the feasibility of metabolomics to explore the mode of action of natural products. MATERIALS AND METHODS 4


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Materials Emodin (CAS No. 518-82-1) with a purity of 98% was purchased from

BBI

Life

Sciences

Corporation.

DMSO

and

sodium

3-(trimethylsilyl)-propionic acid (TSP) were obtained from Sigma (St. Louis, MO, USA). Deuterium oxide (D2O, 99.9%) was purchased from Sea Sky Bio Technology Co., Ltd. (Beijing, China). Acetonitrile (HPLC-grade) was purchased from Merck (Darmstadt, Germany). Trypsin and BCA protein assay kit were purchased from Solarbio science & technology co., ltd. (Beijing, China). Cell cycle and apoptosis analysis kit and the protease inhibitor cocktail were purchased from Beyotime Biotechnology co., ltd. (Shanghai, China). PKM2-specific rabbit polyAb, LDHA-specific rabbit polyAb, Hexokinase II rabbit polyAb, β-actin mouse mAb, anti-rabbit IgG antibody and anti-mouse IgG antibody were purchased from Proteintech Group. Cell lysis buffer was purchased from Cell Signaling Technology (Danvers, MA, USA). Signalfire ECL reagent was purchased from Cell Signaling Technology. Ultrapure water was used for all solution preparation. Other chemical reagents were purchased from Nanjing Chemical Reagent Co., Ltd. (Nanjing, China). Cell Culture HepG2 cells were kind gifts of Dr. Chao Zhang from China Pharmaceutical University (Nanjing, People’s Republic of China). Cells were cultured in culture dish (Corning) supplemented with high-glucose 5



DMEM (Dulbecco's modified Eagle's medium, Gibco) containing 10% fetal bovine serum (Capricorn) and 1% antibiotics (penicillin and streptomycin, Gibco). The culture medium were refreshed every two days. Cells were maintained as monolayer cultures in an incubator flushed continuously at 37 ℃ with 5% CO2. The cells were harvest for metabolomics experiments using cell scrapers. MTT Assay of Cell Viability HepG2 in DMEM supplemented with 10% FBS were seeded at a density of 5×103 cells/well in 96-well plate. After 24 h, the medium was substituted with fresh medium containing various emodin solutions in DMSO (0.1%, v/v) to final concentrations of 12.5, 25, 50, 100, 200, and 400 µM, respectively, and cells were cultured for another 24 h. Cells cultured in medium without emodin but 0.1% DMSO served as a control, and medium without cells served as the blank. All the measurements were using five replicates for each treatment. At the end of the drug treatment for 24 h, 10 µL of MTT solution (5 mg/mL in PBS) was fed to each well of the culture plate, then incubated for 4 h at 37 ℃. The formazan crystal formed in the well. After discarding the medium, the formazan crystal was solubilized with 150 µL DMSO to measure optical density (OD) values at 570 nm using a Sunrise micro plate absorbance reader (TECAN, Austria). Cell viability of emodin were calculated using following equation, and the cell vitality curve of emodin was depicted using 6


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Graphpad Prism (version 6.0, Intuitive Software for Science, San Diego, CA, USA) accordingly. Cell viability (%) = (ODdosed-ODblank) / (ODcontrol-ODblank) × 100 Metabolite Extraction Protocol of HepG2 Cells for 1H NMR Analysis Cells were seeded in 100-mm-diameter culture dish in high-glucose DMEM, triplicate dishes as one sample for NMR analysis (about 2 × 107 cell per sample). After 24 h, medium was replaced by fresh medium containing 0.1% DMSO (Control group) and three emodin treated groups at the concentrations of 10, 20 and 40 µM corresponding to the low, medium and high dosages. Cells were incubated for 24 h and then washed with PBS twice. Afterwards, 2 mL ice cold solution of 50% acetonitrile/water (v/v) was added per dish and cells were scraped from the bottom of the plate with a cell scraper. Cell suspension was transferred into a fresh 10 mL eppendorf tube, resuspended and homogenized to extract the intracellular metabolites for 5 min, and then centrifuged at 12,000 rpm for 10 min at 4 ℃. The supernatant was transferred into fresh Eppendorf tubes, frozen at -80 ℃ overnight and then lyophilized until dryness on a vacuum concentrator. The dried samples were dissolved in 550 µL 99.8% D2O phosphate buffer (0.2 M Na2HPO4 and 0.2 M NaH2PO4, pH 7.0, containing 0.05% TSP). After vortex and centrifugation, the supernatant was then transferred to a 5 mm NMR tube for 1H NMR analysis. 7



1

H NMR Spectroscopy All the 1H NMR spectra of cell samples were recorded at 298 K on a

Bruker Avance III 500 MHz spectrometer (Bruker GmbH, Karlsruhe, Germany) with a Bruker 5 mm probe, using a modified transverse relaxation edited Call-Purcell-Meiboom-Gill (CPMG) sequence (90 (τ-180-τ) n-acquisition) with a total spin-echo delay (2nτ) of 40 ms to suppress the signals of macromolecules. 1H NMR spectra were measured with 128 scans collected into 32 K data points, a spectral width of 10 000 Hz and acquisition time of 3.28 s. The spectra were fourier transformed after multiplying the FIDs by an exponential weighting function corresponding to a line-broadening of 0.5 Hz. NMR Data Processing and Analysis All the 1H NMR spectra were phase and baseline corrected using Topspin 2.1 software (Bruker GmbH, Karlsruhe, Germany) with chemical shift referencing to TSP at 0.00 ppm. The 1H NMR spectra were converted into ASCII files using MestReC (3.7.4, Mestrelab Research SL), which were then imported into “R” (http://cran.r-project.org/). The signals of water and its affected neighboring regions between 4.33 and 5.50 ppm were excluded before analysis. To account for variations of the overall concentrations of samples, the NMR data were binned using an adaptive binning approach based on the code implemented in Matlab with an average of 0.015 ppm for each bin. To remove or minimize the effects 8


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of unequal mass of the samples, probability quotient normalization was applied, which calculates a most probable discount factor based on the distribution of the quotients of the amplitudes of a test spectrum by those of a reference spectrum 13. The binned segments were subsequently mean centered and pareto-scaled prior to multivariate statistical analysis to attenuate the effects of dominant variables and noise while amplify weak signals to the largest possible 14. The univariate and multivariate analysis were made on the integral area of metabolites between groups using “R” software and ANOVA-simultaneous component analysis (ASCA) was applied to deal with the temporal and/or design structure of complex multivariate datasets. Unsupervised Principal Component Analysis (PCA) and supervised orthogonal signal correction partial least-squares discriminant analysis (OSC-PLS-DA) were performed for multivariate statistical analysis, which can maximize the discrimination between classes by filtering out irrelevant effects. Repeated two-fold cross-validation method and permutation test (n = 2,000) were applied to assess the validity of OSC-PLS-DA models with R2 (the total explained variation) and Q2 (the predictive capacity). The color-coded loadings plot and S-plot were constructed to reveal the variables that contributed to the separation of groups and to mark the differential metabolites. The fold changes of metabolites and the 9



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corresponding p-values were calculated and corrected by Benjamini and Hochberg method, which are shown in a colored Table 1. Table 1. Identified intracellular metabolites from different groups with log2 (FC)a and p-valuesb Metabolites Valine Leucine Isoleucine Ethanol Lactate Alanine Acetate N-Acetylaspartate Glutamate Glutamine Glutathione Aspartate Sarcosine Creatine Choline Trimethylamine N-oxide Taurine Glycine UDP-glucose NAD+ NADP+ Orotic acid Fumarate Tyrosine Phenylalanine Formate Oxypurinol a

LD vs. CON log2(FC) -0.436 -0.221 -0.225 -0.878 -1.612 -1.016 -0.535 -0.442 1.247 0.169 -0.386 0.343 1.740 -0.492 1.086 -1.003 0.091 -1.155 0.325 0.992 0.911 0.580 0.770 0.180 0.293 0.413 0.695

p

* ** *

*

** * * * ** ** * **

**

MD vs. CON log2(FC) -0.136 -0.030 -0.054 -1.032 -1.116 -1.418 -0.353 -0.612 1.517 -0.526 -0.702 0.335 1.764 -0.803 0.840 -0.800 0.017 -1.034 0.094 0.814 0.704 0.385 0.937 0.203 0.365 0.047 0.464

p

* * * * * * ** * ** * * * *** ** ***

HD vs. CON log2(FC) 0.219 0.282 0.257 -0.760 -0.659 -1.683 -0.621 -0.268 0.863 -0.531 -0.827 0.617 2.055 -0.525 0.755 -1.568 0.295 -1.155 -0.191 1.276 1.045 0.734 1.392 0.605 0.795 0.513 0.560

p

** *

* ** ** ** * * ** ** *** *** ** *** * * *

Color coded according to the logarithmic transformation of fold change (FC),

log2(FC), red represents the increased and blue the decreased in the emodin treated groups.

10


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b

P-values corrected by Benjamini–Hochberg methods were calculated based on a

parametric Student’s t-test or a nonparametric Mann–Whitney test. *p < 0.05, **p < 0.01, ***p < 0.001. Metabolite color bar

Correlation Network Analysis Pearson’s correlation networks between metabolites were calculated using R software. The metabolites nodes were color coded based on their fold changes: reds and blues indicate their increases and decreases in emodin-treated groups versus the control group, respectively. The colored edges represented correlations between metabolites surpassing a given threshold: the widths were scaled based on the absolute values of correlation coefficients and the colors were in warm or cold denoting positive and negative correlations, respectively. Quantitative Real-Time RT-PCR HepG2 cells with/without the presence of emodin were lysed with 1 mL of Trizol reagent (Invitrogen, Carlsbad, CA, USA), and then processed according to the manufacturer's protocol to obtain total cellular RNA with SYBR Green PCR Core Reagent Kit. Data were normalized to the expression of β-actin based on the cycle threshold value. Transcript levels were calculated by the 2-∆∆CT method 15. The sequences of primers used for quantitative real-time PCR are listed in Table 2.

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Table 2. Primers used for qRT-PCR Gene

Forward primer (5′-3′)

Reverse primer

(5′-3′)

HKII

TCGCATCTGCTTGCCTACTTC

CTCTCCGTGTTCTGTCCCATC

PKM2

CCACTTGCAATTATTTGAGGAA

GTGAGCAGACCTGCCAGACT

LDHA

TGGTTGAGAGTGCTTATG

GCCTAAGATTCTTCATTATACT

β-Actin

GCGTGACATTAAGGAGAAG

GAAGGAAGGCTGGAAGAG

Western Blot Analysis HepG2 cells were seeded into 6-well culture plates at concentrations of 1×106 cells/mL and incubated with CON, LD, MD and HD groups for 24 h. Harvested with trypsin, cells were treated with a 1× cell lysis buﬀer with the protease inhibitor cocktail to extract the total proteins. Protein concentration was measured using the BCA assay kit. Then, aliquots of the proteins from the total cell lysates (40 µg/lane) were separated via sodium dodecyl

sulfate polyacrylamide gel electrophoresis and

wet-transferred to a PVDF membrane. After blocking for 1.5 h with 1×TBST buffer containing 5% skimmed milk powder at room temperature, the antibodies (HKII, LDHA and PKM2) were incubated in the membrane at 4 ℃ overnight with β-actin as the internal standard. All the antibodies were diluted at 1:1,000 in TBST buffer with 5% skimmed milk powder. The membranes were washed with 1× TBST three times, 10 min each, and further incubated with either HRP-conjugated anti-mouse IgG (1:2,000 dilution) or HRP-conjugated anti-rabbit IgG (1:2,000 12


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dilution) for one hour at room temperature. With the membranes washed as above, HRP was developed with the ECL substrate for 5 min and then its activity was detected under a chemiluminescence imaging system (Clinx Science Instruments Co., Ltd, Shanghai, China). Determination of Intracellular ROS Generation The intracellular ROS generation in HepG2 cells was detected by 2’, 7’-dichlorofluorescin diacetate (DCFH-DA) probe. The HepG2 cells with the density of 1× 106 cells/well were treated with emodin (control and exposed groups) for 24 h. After treatment, cells were washed with PBS and incubated with fresh FBS-free medium containing 10 µM DCFH-DA at 37℃ for 30 min. After incubation, cells were harvest by trypsinization, washed with PBS twice and suspended in PBS for flow cytometry analysis. The fluorescence of cells was recorded with excitation/emission wavelength at 500/525 nm. Cell Cycle and Apoptosis Analysis For cell cycle analysis, the cell DNA was stained with propidium iodide (PI) using cell cycle and apoptosis analysis kit. The HepG2 cells with the density of 1× 106 cells/well were harvested by trypsinization after treatment of emodin (0 µM as control group and 10 µM, 20 µM, 40 µM as exposed group) for 24 h, washed with ice-cold PBS twice and fixed with 70% cold ethanol at 4 ℃ overnight. The fixed cells were collected after centrifugation, washed with ice-cold PBS twice to remove 13



residual ethanol, suspended with ice-cold PBS, stained with PI in the presence of 2 % RNAase and then incubated in the dark at 37 ℃ for 30 min before flow cytometry analysis. Wound Healing Assay on HepG2 Cells Cells were inoculated in 12-well plate, and after their growth reaching 80% fusion, 1-mm scarifications at an interval of 5 mm were made on the surface of cells with a 10-µL pipette tip, and the debris was removed by washing with PBS twice. Cells were then incubated with CON, LD, MD and HD groups using fresh medium containing 1% antibiotics but without fetal bovine serum for 24 h. After incubation, the width of cell scarifications were measured under 100× microscope: the narrower of the scarifications, the stronger ability of migration. RESULTS Emodin Inhibits HepG2 Cells Growth To investigate the effect of emodin on cell growth or viability of HepG2, we treated HepG2 cells with different concentration of emodin for 24, 48, 72 h and measured the cell viability by the MTT assay 16. The cell viability treated with emodin was decreased in a dose- and time-dependent manner significantly (Figure 1). The IC50 value was determined to be 79.01, 51.39 and 33.13 µM at 24, 48 and 72 h, respectively. For NMR based metabolomics study, cell mass harvested must be enough. Therefore, we investigated scaled-up culture of emodin. 14


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Corresponding to c.a. half of IC50 at each time point, 40, 25 and 16 µM emodin were added to cells scaled-up cultured in 100 mm culture dish for 24, 48 and 72 h respectively. With the increase of culture time, a lot of dead and non-attached cells were observed and the culture medium became muddy at 48 and 72 h, which were not observed at 24 h. Finally, cells were cultured for 24 h in the presence of 10, 20 and 40 µM emodin in 100 mm culture dish for metabolomics experiment and in well plate for others.

Figure 1. Effect of emodin on HepG2 cell growth. Cell were treated with different concentration of emodin for 24, 48 and 72 h. Data were represented as mean ± SD.

Metabolites Identification Metabolites were assigned according to the previous literature and the public metabolomics databases such as Human Metabolome Database (HMDB, http://www.hmdb.ca) and Madison-Qingdao Metabolomics Consortium

Database

(MMCD,

http://mmcd.nmrfam.wisc.edu).

Chenomx NMR suite 8.1 (Chenomx Inc., Edmonton, Canada) and statistical total correlation spectroscopy (STOSCY) technique which was 15



calculated and drawn using R software were also applied to assist metabolites identification. The typical 500 MHz 1H NMR spectra with peak assignments for cellular extracts of HepG2 cells are shown in Figure S-1. A total of 27 metabolites were assigned. Multivariate Analyses of 1H NMR Spectra The PCA score plot (Figure S-2) for 1H NMR data of HepG2 cells showed a clear separation between CON and emodin-treated groups along PC1, with the dosed groups overlapped with each other. To get a better separation, orthogonal signal correction-partial least squares discriminant analysis (OSC-PLS-DA) was performed to explore the differences in metabolomics among the four groups. In the OSC-PLS-DA score plot (Figure 2A), control and the dosed groups were well separated along PC1; the three dosage groups were dose-dependently separated along PC2. NMR data for CON and HD group were further subjected to OSC-PLS-DA analysis to acquire better understanding of the anti-cancer effect of emodin. The validity of the model was assessed according to cross validation results (Figure S-3).

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Figure 2. OSC-PLS-DA analysis of NMR data of cellular extracts of HepG2 cells. OSC-PLS-DA analysis of metabolomics profiles from CON, HD, MD and LD groups: score plot (A), s-plot (B) and loadings plot (C-D). OSC-PLS-DA analysis of metabolomics profiles between CON and HD group: score plot (E), s-plot (F) and loadings plot (G-H). In loadings plot, the color bar corresponds to the weight of the

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corresponding variable contributing to grouping: significant in red or insignificant in blue. Upward and downward peaks in the loading plots indicate relatively decreases and increases of metabolites in the emodin-treated groups.

In the score plot (Figure 2E), CON group and HD group were well separated, demonstrating significant metabolic differences among the two groups. OSC-PLS-DA loadings plot and S-plots identified the metabolites contributing to the grouping in the score plots. In the loadings plot color-coded according to the absolute correlation coefficient of the model (Figures 2G-H), the warm color peaks had more differentiating ability than those with the cool color. In the S-plot (Figure 2F), the significant metabolites are those far away from the center. From the loadings plot and S-plot, marked increases of aspartate, sarcosine, choline, NAD+, NADP+, orotic acid, fumarate, tyrosine, phenylalanine, formate, oxypurinol and decreases of alanine, acetate, glutamine, glutathione, creatine, trimethylamine N-oxide, glycine were observed in HD group. Inhibited Glycolysis Pathway by Emodin The metabolomics analysis showed that lactate was significantly decreased after exposure to emodin (Table 1). Lactate is the marker of glycolysis, indicating that glycolysis alterations might have a close association with the anti-cancer eﬀects of emodin. So the role of emodin playing on glycolysis pathway was further examined. The mRNA levels of hexokinase II (HKII), pyruvate kinase isoform M2 (PKM2) and lactate 18


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dehydrogenase-A (LDHA) in emodin treated cells were all decreased in a concentration-dependent manner (Figure 3A). To demonstrate further the effect of emodin on protein expression, western blot analysis was used to evaluate protein expression of HepG2 cells cultured with CON, LD, MD and HD groups. As shown in Figure 3C, levels of glycolysis related proteins were significantly decreased compared with their levels in CON group. The results demonstrated that emodin indeed inhibited glycolysis of HepG2 cells.

Figure 3. Decreased expressions of glycolysis related genes and proteins after treatment with various concentrations of emodin for 24 h. (A) The gene expression levels of HKII, PKM2, and LDHA in HepG2 cells measured via qRT-PCR with β-actin as an internal control. (B and C) The protein expressions of HKII, PKM2, and LDHA in HepG2 cells, determined by western blotting, normalized with β-actin. *p < 0.05, **p < 0.01, ***p < 0.001.

Flow Cytometry Analysis In the flow cytometer, the particles and cells can pass through the beam scatter light. The forward scatter (FSC) and side scatter (SSC) 19



modes correlated with the cell size and granularity, respectively. Usually, cellular debris and dead cells have lower forward scatter than living cells and in addition, dead cells have higher side scatter. Therefore, percentages of cellular debris and dead cells could be estimated by flow cytometry analysis. Events were gated according to their FSC/SSC profile (Figures 4A-B) to exclude debris and dead cells and then single living cells were further analyzed. Emodin dose-dependently induced either cell cycle arrest (Figure 4A) or ROS generation (Figure 4B): events encircled in red representing the percentage of living cells. These results are consistent with the marked accumulation of sub-G1 fraction (Figure 4C), demonstrating that emodin inhibited the growth of HepG2 cells. Notably, the percentage of living cells between the cell cycle and ROS generation is quite different, even in the CON group, which might due to different pretreatment before flow cytometry analysis. Before cell cycle analysis, cells need to be fixed with 70% ethanol, which is essential step for PI to enter cells efficiently, ensuring DNA stained quantitatively.

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Figure 4. Flow cytometry analysis of emodin induced cell cycle (A) arrest and ROS (B) generation in HepG2 cells, detected in forward scatter and side scatter mode. The SSC and FSC values are in logarithmic form. The colored dots indicate the cell events and the red circle represents the percentage of living single cells. (C) Flow cytometry analysis of the cell cycle distribution of emodin-treated HepG2 cells. (D) Emodin triggers the ROS generation in HepG2 cells. Cells were treated with different concentrations of emodin (0, 10, 20, and 40 µM corresponding to CON, LD, MD and HD group) for 24 h.

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Emodin Inhibited Cell Migration of HepG2 Cells Effects of emodin on cell migration of HepG2 cells were investigated by wound healing assay. As showing in Figure S-4, the scarification in the CON group was narrower than the other three emodin dosed groups, showing the inhibitory effects of emodin on cell migration. Among them, HD group showed the strongest inhibition with the widest scarification, which combined with the flow cytometry results, demonstrated the potential cytotoxicity of high dosage emodin on HepG2 cells. DISCUSSION The above results indicated that emodin inhibited the growth of HepG2 cells, exhibited cytotoxicity, and induced cell cycle arrest, apoptosis and ROS generation. Their relationships with emodin induced metabolic changes were still unclear. In this study, 1H NMR-based metabolomics approach was adopted to explore biomarkers and the affected metabolic pathways after the administration of emodin. The multivariate statistical analysis and correlation networks analysis between the HD group and CON group revealed disturbed metabolites concerning energy metabolism, amino acid metabolism and oxidative stress. Energy Metabolism In the presence of oxygen, normal cells primarily utilize glucose by aerobic respiration in the mitochondria through tricarboxylic acid (TCA) 22


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cycle. However, cancer cells tend to convert most glucose to small organic molecules such as lactate and ethanol via glycolysis regardless of whether oxygen is present or not, i.e. Warburg effect

17

. The increased

glucose uptake and excess generation of lactate often appear during rapid cellular proliferation of tumors 18. In our study, both levels of lactate and ethanol were obviously decreased after emodin treatment, suggested an inhibited glycolysis. Hexokinase II (HKII), pyruvate kinase isoform M2 (PKM2) and lactate dehydrogenase-A (LDHA) are the rate-limiting enzymes in glycolysis. For verification, the expressions of glycolysis related genes and proteins (HKII, PKM2 and LDHA) were measured via qRT-PCR and western blot: their expressions were indeed inhibited by the treatment of emodin. Glycolysis is the main energy production means of tumor cells, and thus is vital for their growth, proliferation and survival. Down-regulation of glycolysis is also an important strategy to inhibit the growth and induce death of tumor cells

19

, which should also be one of the mechanisms

underlying the antitumor effects of emodin. In addition, the extracellular pH of the cancer cells is usually acidic, and the high concentration of lactate can maintain the extracellular pH and facilitate the invasion and metastasis of cancer cells in vitro 20. Invasion and metastasis are the basic features of cancer cells, which involved in destruction of extracellular matrix and make it easier for cancer cells to infiltrate normal adjacent 23



tissues 21. Wound healing assay result confirmed that emodin inhibited the migration of HepG2 cells, especially in high dosage. Taken together, emodin inhibited glycolysis pathway, destroyed the balance of extracellular pH, thereby reducing cell migration. In addition to glycolysis, cancer cells also rely on glutamine as energy raw materials as glutamine can be transferred into TCA cycle, fueling their growth and proliferation 22. Increased levels of fumurate and aspartate in emodin treated groups reflected accelerated conversion from glutamine to TCA cycle intermediates to support insufficient energy requirement due to inhibited glycolysis by emodin. Sarcosine is an intermediate metabolite of choline pathway and is rapidly degraded to glycine. In our study, the level of sarcosine was significantly lower in HD group than in control group, associated with the increased level of choline and decreased level of glycine. These results reflected a dysregulation of the sarcosine pathway by emodin. Sarcosine could also be obtained from creatine, generating NAD+ which was increased in our study. The decreased level of creatine reflected energy buffering and transportation: facilitated hydrolysis of creatine to sarcosine by creatinase, generating ATP for energy supply. Amino Acid Metabolism The level of glutamate was significantly increased and the level of glutamine was decreased after emodin treatment, which indicated the 24


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enhanced glutaminase activity that converted glutamine to glutamate. Glutamate is a precursor of many amino acids. The levels of its product amino acids, alanine and glycine were markedly decreased. Centering the network (Figure 5) of the emodin-treated groups, glutamate exhibited obvious negative correlations with many decreased amino acids, suggesting a severe disturbance in amino acid metabolism.

Figure 5. Metabolic correlation network analysis for control and HD groups. Metabolites with coefficients of Pearson’s correlations are connected by solid lines that are color-coded according to the values of coefficients, the warm and cold colors represent positive and negative correlations, respectively. The width of each line is scaled based on its absolute value. The names of the metabolites shown in red and blue indicate that they were significantly increased and decreased in HD group.

The three amino acids alanine, aspartate and glycine have significant roles in central carbon metabolism. Alanine was significantly decreased while aspartate was markedly increased in HD group. Aspartate is a 25



Page 26 of 41

source of nitrogen that is essential for several biosynthetic processes, in particular for the synthesis of pyrimidine. Orotic acid as the intermediate in the synthesis of pyrimidine was obviously increased in HD group. Studies showed that the overproduction of orotic acid could inhibited pyrimidine

synthesis

and

also

reduce

intracellular

levels

of

5-phosphoribosyl 1-pyrophosphate which is an essential substrate for orotate phosphoribosyl-transferase, which was unique to pyrimidine pathway

23

. Glycine was decreased significantly in HD group. As the

precursor of glutathione, an important natural antioxidant, its decrease helped to replenish glutathione consumption to counteract emodin induced oxidative damage. In addition, the decrease of glycine might also due to disruption of the HIF-1 signaling formation pathway 24. Majority of solid tumors are living under a relative hypoxic condition, and HIF-1α promotes the genes that allow cancer cells to survive and proliferation in the hypoxic environment. Increased tumor HIF-1 has been associated with increased angiogenesis and aggressive tumor growth

25

. Recently,

studies demonstrated that emodin in combination with other anti-cancer drugs down-regulated the transcription factor of HIF-1 and the inhibitory effects depended on the redox state

26

. In our study, ROS showed a

dose-dependent increase after treatment of emodin. The generation of ROS induces decreased expression of HIF-1 27. Targeting of HIF-1 is now considered to be a pivotal and efficient strategy for cancer treatment. 26


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Emodin induced increased generation of ROS which suppressed the expression of HIF-1, thus inhibiting the growth of HepG2 cells under hypoxia. Phenylalanine and tyrosine were significantly increased in HD group. Phenylalanine showed positive correlations with many other amino acids such as tyrosine, leucine and valine, also increased in HD group. The increase of phenylalanine could inhibit the expression of PKM2 28. PKM2 is the rate-limiting enzyme of glycolysis, and the expression of PKM2 was indeed decreased in emodin treated group. Oxidative Stress Cellular redox status is essential for the growth and function of cells 29

. Oxidative stress occurred when the levels of cellular ROS outweigh

the inherent antioxidant defenses, leading to lipid peroxidation, DNA damage and abnormal expression of the proteins

30

. Our results showed

that emodin dose-dependently increased the level of ROS. Oxidative stress has been one of the most important mechanisms of emodin induced toxicity 31. Emodin is a reactive oxygen species-producing agent

32

with

the molecular structure similar to mitochondrial ubiquinone which have been reported to be able to induce ROS production 33. The p53 protein, the tumor suppressor gene for growth, is an important sensor of cellular stress. Studies demonstrated that emodin inhibited cancer cellular

27



Page 28 of 41

proliferation by up-regulating p53 levels both in protein and gene levels 34

. TMAO, the product of choline metabolism, was significantly

declined in HD group, which accompanied with the increased choline level, indicating choline metabolism disorder induced by emodin. Choline and its derivatives are important constituents in phospholipid metabolism of cell membranes and identified as markers of cell proliferation. Cell membranes are rich in phospholipids and polyunsaturated fatty acids. The bonds in membrane are sensitive to free radicals, inducing the lipid peroxidation. Free radicals could also attach the enzymes in membrane, resulting in the change of the membrane composition and structure. 35 As the main component of phospholipid, the significant increase of choline suggested severe cell membrane disruption

36

. The broken of cell

structure integrity lead to the collapse of mitochondrial membrane potential (MMP) and the release of cytochrome c from the mitochondria into the cytosol. Emodin was reported to induce the loss of MMP and the release of cytochrome c to cytosol 37. Cytochrome c is an electron transfer correlated with the mitochondrial and the release of cytochrome c from mitochondrial is an indicator of mitochondrial dysfunction and induction of apoptosis under the oxidative stress

38

. Apoptosis is a process of

programmed cell death that occurs in multicellular organisms. Cell initiates intracellular apoptosis signaling in response to oxidative stress, 28


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inducing cell suicide. In our study, the percentage of sub-G1 phase was increased significantly in emodin treated HepG2 cells, suggesting increased apoptosis due to emodin induced oxidative stress. Cells also developed a defense system to scavenge ROS and keep redox balance. Glutathione, a major cellular antioxidant, can fight against ROS to maintain such balance 39. In many cancer cells, the level of GSH is increased due to the anoxic growth condition of most tumor cells. Decreased GSH level was found in emodin treated cells, which is good for triggering ROS-mediated apoptotic signaling and sensitizing cancer cells for chemotherapy 40. CONCLUSION NMR based metabolomics and molecular biological methods were combined to study the toxicity and cell growth inhibitory effects of emodin. Emodin inhibited glycolysis, limiting energy supply and the growth of HepG2 cells; emodin also increases ROS production, leading to increased mitochondrial damage and apoptotic cell death. The results warranted further study on the potential of emodin as an alternative option for cancer therapy. ASSOCIATED CONTENT Supporting Information Figure S-1. The typical 500MHz 1H NMR spectra for cellular extracts of HepG2 cells with the metabolites labeled. 29



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Figure S-2. The PCA score plot of 1H NMR spectra of cellular extracts at the four groups. Figure S-3. OSC-PLS-DA scatter plots of statistical validation obtained by 2,000 times permutation test for the HepG2 cells between CON and HD group. Figure S-4. Effect of emodin on HepG2 cells migration. CONFLICTS OF INTEREST There are no conflicts of interest to declare. ACKNOWLEDGEMENTS This research work was supported by the National Natural Science Foundation of China (No. 81773857). REFERENCES (1) Ciocco, A. Clinical development and future direction for the treatment of hepatocellular carcinoma. J. Exp. Clin. Med. 2010, 2, 93-103. (2) Chiun, H.; Shen, Y. C.; Cheng, C. C.; Hu, F. C.; Cheng, A. L. Geographic difference in survival outcome for advanced hepatocellular carcinoma: implications on future clinical trial design. Contemp. Clin. Trials. 2010, 31, 55-61. (3) Li, X.; Wu, M.; Pan, L.; Shi, J. Tumor vascular-targeted co-delivery of anti-angiogenesis

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(for TOC only)

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Figure 1. Effect of emodin on HepG2 cell growth. Cell were treated with different concentration of emodin for 24, 48 and 72 h. Data were represented as mean ± SD. 104x70mm (300 x 300 DPI)



Figure 2. OSC-PLS-DA analysis of NMR data of cellular extracts of HepG2 cells. OSC-PLS-DA analysis of metabolomics profiles from CON, HD, MD and LD groups: score plot (A), s-plot (B) and loadings plot (C-D). OSC-PLS-DA analysis of metabolomics profiles between CON and HD group: score plot (E), s-plot (F) and loadings plot (G-H). In loadings plot, the color bar corresponds to the weight of the corresponding variable contributing to grouping: significant in red or insignificant in blue. Upward and downward peaks in the loading plots indicate relatively decreases and increases of metabolites in the emodin-treated groups. 102x130mm (300 x 300 DPI)


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Figure 3. Decreased expressions of glycolysis related genes and proteins after treatment with various concentrations of emodin for 24 h. (A) The gene expression levels of HKII, PKM2, and LDHA in HepG2 cells measured via qRT-PCR with β-actin as an internal control. (B and C) The protein expressions of HKII, PKM2, and LDHA in HepG2 cells, determined by western blotting, normalized with β-actin. *p < 0.05, **p < 0.01, ***p < 0.001. 148x46mm (300 x 300 DPI)



Figure 4. Flow cytometry analysis of emodin induced cell cycle (A) arrest and ROS (B) generation in HepG2 cells, detected in forward scatter and side scatter mode. The SSC and FSC values are in logarithmic form. The colored dots indicate the cell events and the red circle represents the percentage of living single cells. (C) Flow cytometry analysis of the cell cycle distribution of emodin-treated HepG2 cells. (D) Emodin triggers the ROS generation in HepG2 cells. Cells were treated with different concentrations of emodin (0, 10, 20, and 40 µM corresponding to CON, LD, MD and HD group) for 24 h. 150x154mm (300 x 300 DPI)


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Figure 5. Metabolic correlation network analysis for control and HD groups. Metabolites with coefficients of Pearson’s correlations are connected by solid lines that are color-coded according to the values of coefficients, the warm and cold colors represent positive and negative correlations, respectively. The width of each line is scaled based on its absolute value. The names of the metabolites shown in red and blue indicate that they were significantly increased and decreased in HD group. 177x86mm (300 x 300 DPI)


Anti-Cancer Effects of Emodin on HepG2 Cells as Revealed by 1H

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