Abstract
In this study, the effects of different concentrations of
chrysophanol-8-O-β-D-glucoside (C-8-O-β-D-glu) on L-02 liver cells were
analyzed by high content analysis (HCA) and metabonomics to explore the
potential mechanism involved. The results showed that low
concentrations (12 and 24 μM) of C-8-O-β-D-glu increased the cells
viability significantly, while high concentration (96 μM) showed
significant cytotoxicity on L-02 cells. HCA was applied to analyze the
changes of nuclei and mitochondria after the cells being exposed to
C-8-O-β-D-glu for 24 h. The results showed high concentration (96 μM)
of C-8-O-β-D-glu significantly reduced the number of living cells,
increased average nucleus area, DNA content and mitochondrial membrane
potential (MMP). Then non-target metabonomics was carried out to
identify potential biomarkers and metabolic pathways of L-02 cells
impacted by C-8-O-β-D-glu. Eleven important potential biomarkers
associated with four metabolic pathways were identified in this
analysis. Dysregulation of alanine, aspartate and glutamate metabolism
were observed in both LCG and HCG. In addition, low concentration (24
μM) of C-8-O-β-D-glu would impact arginine and proline metabolism. High
concentration (96 μM) of C-8-O-β-D-glu would impact phenylalanine
metabolism and beta-alanine metabolism. Alanine, aspartate and
glutamate metabolism, arginine and proline metabolism, phenylalanine
metabolism, beta-alanine metabolism were involved in different effects
of C-8-O-β-D-glu on L-02 cells.
Keywords: chrysophanol-8-O-β-D-glucopyranoside, L-02 cell,
metabonomics, high content analysis, amino acid metabolism
Introduction
Anthraquinones are widely distributed in nature as the secondary
metabolic products of various plants. Most anthraquinones are
derivatives of tricyclic aromatic organic compound 9,10-anthracenedione
([33]Duval et al., 2016) and can be classified into free anthraquinones
and glycosylated anthraquinones according to chemical structure. Both
them have been proved to possess a broad range of pharmacological
effects such as protecting liver ([34]Alisi et al., 2012), antioxidant
([35]Brkanac et al., 2015; [36]Zhao et al., 2016), anticancer ([37]Chen
et al., 2015; [38]Cui et al., 2016), anti-inflammatory ([39]Chen et
al., 2016), antileukemic ([40]Ghoneim et al., 2013), anticoagulant
([41]Seo et al., 2012), anti-diabetic ([42]Lee and Sohn, 2008),
antibacterial ([43]Chen and Zhu, 2013) and protecting kidney ([44]Tan
et al., 2004) etc.
However, anthraquinones are reported to induce liver damage ([45]Yu et
al., 2011; [46]Wang et al., 2017) in recent years. Many researches have
been carried out to investigate the hepatotoxicity of free
anthraquinones, and have elucidated toxicity mechanisms involving
inflammation, oxidative stress, mitochondrial dysfunction, the
expression of metabolic enzymes and hepatocyte apoptosis ([47]Lai et
al., 2009; [48]Qu et al., 2013). But the hepatotoxicity of glycosylated
anthraquinones has not received much attention until now.
As one of the glycosylated anthraquinones,
chrysophanol-8-O-β-D-glucoside (C-8-O-β-D-glu, Figure [49]1) exhibits
various activities including anti-aging, neuroprotective ([50]Chen et
al., 2008), antiplatelet and anticoagulant activities ([51]Seo et al.,
2012). However, the effect of C-8-O-β-D-glu on liver has not been
investigated. Pharmacokinetics study has proved C-8-O-β-D-glu could be
absorbed into blood in intestine and transported to liver ([52]Zhao et
al., 2011). Therefore, C-8-O-β-D-glu might directly act on liver cells
to affect the physiological state of liver which was verified in our
laboratory.
FIGURE 1.
FIGURE 1
[53]Open in a new tab
Chemical structure of Chrysophanol-8-O-β-D-glucopyranoside.
In present study, HCA was applied on L-02 cell line to provide cell
morphological information and quantify specific fluorescent targets,
and metabonomics was used to qualitatively and quantitatively analyze
the variations of differential metabolites and the disturbed metabolic
pathways to further clarify the varied influences of C-8-O-β-D-glu with
different concentrations on liver cells.
Materials and Methods
Chemicals and Drugs
Methanol (HPLC grade) was purchased from Wokai Chemical Technology Co.,
Ltd. (Shanghai, China). Acetonitrile (HPLC grade) was obtained from
Merck Chemicals (Shanghai, China). Formic acid (HPLC grade) was
purchased from TCI Chemical Industry Development Co., Ltd. (Shanghai,
China). Other reagents and chemicals were of analytical grade and
purchased from Kelong Chemical Reagent Factory (Chengdu, China).
C-8-O-β-D-glu (purity above 98%) was purchased from Chengdu
Chroma-Biotechnology Co., Ltd. (Chengdu, China), and was dissolved in
DMSO (MP Biomedicals LLC, France) at the concentration of 96 mM as
stock solution.
Cell Culture
The entire research was carried out on human liver cell line L-02 cells
purchased from KeyGEN BioTECH (Nanjing, China). The cells were
cultivated in DMEM medium (GIBCO, United States) supplemented with 10%
FBS (GIBCO, United States) and antibiotics (100 U/ml of penicillin and
0.1 mg/ml of streptomycin). They were incubated at 37°C, 5% CO[2] in
LabLine CO[2] Incubator (Thermo, United States) and subcultured every 2
days.
C-8-O-β-D-Glu Stability Analysis
C-8-O-β-D-glu stock solution was diluted to 96 μM with cell culture
medium. After being incubated for 0, 6, 12, 24, and 48 h, 10 μL sample
were analyzed (n = 3) by HPLC. Agilent 1260 Infinity HPLC system
(Agilent, United States) was applied to conduct the analysis on a
Zorbax Eclipse Plus C[18] column (4.6 × 250 mm, 5 μm, Agilent, United
States) at 30°C. The analyte was eluted by 0.1% phosphoric acid water:
methanol (20:80) at 1 ml/min for 10 min.
MTT Assay
Exponentially growing cells were plated in 96-well plate (Costar,
United States) at the density of 6 × 10^3 per well and grew in
incubator for 24 h. At the same time, the culture medium with 0.1% DMSO
were added into wells without cells to zero the OD value. The adhered
cells were treated with different concentrations of C-8-O-β-D-glu (0,
12, 24, 48, and 96 μM) prepared in DMEM medium supplemented with 0.1%
DMSO and cultured for 24 h. Then the supernatants were carefully
removed, and 20% 3-(4, 5-dimethylthiazol-2-yl) 2, 5-
diphenyltetrazolium bromide (MTT) were added. After 4 h, MTT-formazan
crystals were dissolved by 150 μL DMSO. The absorbance of the solution
was measured at 570 nm (n = 6). The influence of different
concentrations on cells viability was calculated by the percentage of
viable cells between drug experimental groups and the CG.
High Content Analysis
Exponentially growing cells were plated in 96-well plate at the density
of 6 × 10^3 per well and grew in incubator for 24 h. Then the cells
were treated with different concentrations of C-8-O-β-D-glu (0, 24, 48,
and 96 μM) prepared in DMEM medium supplemented with 0.1% DMSO for
another 24 h. After that, the medium was removed and the cells were
washed with PBS. Then cells were stained by 50 μL freshly prepared
Rho123, 10 μM (Beyotime, China), per well. After 30 min incubation
without light, the dye was removed. Cells were washed with PBS and then
exposed to Bisbenzimide H 33342(10 μM, Sigma, United States) for 15 min
in incubator for imagination.
Cells were imaged under High Content Screening ImageXpress^® Micro
(Molecular Devices, United States). The detection conditions were set
as follows: the first channel wavelength was 350 nm/460 nm irradiation
for Bisbenzimide H 33342 labeled nuclei. The second channel wavelength
was 507 nm/530 nm irradiation for Rho123 labeled mitochondria. Five
images were captured per well for image analysis performed with
MetaMorph image processing. Cells number was directly counted by the
software. Average nucleus area, DNA content and MMP were calculated
based on the data recorded.
[MATH: Average nucleus
area=wavelength 1 stained area/total cells :MATH]
[MATH: DNA content=wavelength 1
integrated intensity/total cells :MATH]
[MATH: MMP=wavelength 2 stained
integrated intensity/total cells :MATH]
Metabonomics
Cell Sample Collection and Preparation
Exponentially growing L-02 cells were plated in 175 cm^2 culture flasks
(Costar, United States) at the density of 5 × 10^6 per flask and grew
in incubator for 24 h. After that, the drug experimental groups were
treated with C-8-O-β-D-glu of different concentrations (24, 48, and 96
μM) for another 24 h. CG was treated with the culture medium with 0.1%
DMSO as the same way. Then the supernatants were removed, and the
flasks were washed with PBS twice. The cells were digested into
suspension by 0.25% trypsin. 1 × 10^7 cells were looked as one volume
and five volume PBS (4°C) was added to resuspend the cells which was
centrifuged at 200 g for 4 min. After repeating the process three
times, the cells were quenched by liquid nitrogen after removing the
supernatants.
The cells were resuspended in 500 μL methanol (-80°C) for 30 s. 60 μL
of 0.2 mg/mL nonadecylic acid in methanol and 60 μL of 10 mM d4-alanine
in methanol as internal quantitative standards were added into the
cells. After 30 s vortex, the mixture was snap-frozen in liquid
nitrogen. The frozen-quenched cells were thawed, vortexed for 30 s and
centrifuged at 800 g for 1 min. The supernatant was transferred to a
microcentrifuge tube on dry ice and the cell pellet was resuspended in
methanol (-80°C). The above step was repeated and the cells were
vortexed for 30 s and pelleted by centrifugation. The supernatant was
pooled with the previous methanol fraction and the cell pellet was
resuspended in 250 μL ice cold Milli-Q water. The freeze-thaw cycle was
repeated for the last time and then the cells were vortexed for 30 s
and pelleted by centrifugation at 15000 g for 1 min. The supernatant
was removed and pooled with the previously pooled methanol extracts,
and any remaining cell debris was removed by centrifugation at 15000 g
for 1 min. The supernatant was removed to a fresh tube and blow-dried
by vacuum concentration at 30°C. The samples were dissolved with 300 μL
methanol aqueous solution (1:1, 4°C), and filtered by 0.22 μm membrane
before UPLC – MS/MS detection.
Twenty microliter sample was collected from each sample to prepare
quality control (QC) samples which were injected before and after the
injection of the test samples.
Chromatography and Mass Spectrometry
Chromatographic separation was processed in a Thermo UHPLC Ultimate
3000 system equipped with an ACQUITY UPLC^® HSS T3 (150 × 2.1 mm, 1.8
μm, Waters) column at 40°C. The temperature of the autosampler was set
at 4°C. Gradient elution of analytes was performed with 0.1% formic
acid in water (A) and 0.1% formic acid in acetonitrile (B) at the flow
rate of 0.25 mL/min: 0∼1 min, 2% B; 1∼9.5 min, 2∼50% B; 9.5∼14 min,
50∼98% B; 14∼15 min, 98% B; 15∼15.5 min, 98∼2% B; 15.5∼17 min, 2%. 3 μL
sample was injected for analysis after equilibration.
ESI-MS^n experiments were executed on a Thermo Q Exactive Plus mass
spectrometer with the spray voltage of 3.5 and -3.5 kV in positive and
negative modes, respectively. Sheath gas and auxiliary gas were set at
30 and 10 arbitrary units, respectively. The capillary temperature was
325°C. The Orbitrap analyzer scanned over a mass range of m/z 70–1000
for full scan at a mass resolution of 70000. Data dependent acquisition
(DDA) MS/MS experiments were performed with HCD scan. The normalized
collision energy was 30 eV. Dynamic exclusion was implemented with an
exclusive duration of 10 s.
Data Processing and Pattern Recognition Analysis
All raw UPLC-MS/MS data were converted into mzXML format (xcms input
file format) by Proteowizard software (v3.0.8789) ([54]Smith et al.,
2006). The XCMS package of R (v3.3.2) was used for the peaks
identification, peaks filtration and peaks alignment. The main
parameters were bw = 5, ppm = 15, peakwidth = c(10, 120), mzwid =
0.015, mzdiff = 0.01, method = “centWave.” After that, the data matrix
including mass to charge ratio (m/z), retention time and intensity
information was derived and exported to excel for subsequent analysis.
In order to compare the data of different magnitude, batch
normalization of peak area was applied.
The data set of all samples, consisting of retention time and
normalized peak area of metabolites, was imported into Simca-P v13.0
software (Umetrics AB, Umea, Sweden) and R language tools package for
multivariate statistical analysis. The data was pre-processed by unit
variance scaling and mean-centered method. Then, the data were
processed by PCA, partial least squares-discriminant analysis (PLS-DA)
and OPLS-DA to discriminate CG and drug experimental groups.
Variables with VIP exceeding 1 showed a higher influence on the
classification. Therefore, the metabolite biomarkers were screened with
VIP value (based on OPLS-DA) ≥1 and q-value (p-value corrected for
multiple hypothesis testing by using FDR correction) ≤0.05.
Metabolite Biomarker Identification and Metabolic Pathway Analysis
All metabolite biomarkers were identified based on exact molecular
weight, retention time and MS/MS information at first. Then, several
databases including Human Metabolome Database (HMDB^[55]1),
Metlin^[56]2, massbank^[57]3, LipidMaps^[58]4, and mzclound^[59]5 were
used for further confirmation. To further determine the metabolic
patterns of differential metabolites in each group, the dataset was
scaled by heat-map package in R(v3.3.2). The samples and metabolites
were bidirectionally clustered. To determine the relevant metabolic
pathways, the metabolic pathway analysis of potential biomarkers was
performed using Kyoto Encyclopedia of Genes and Genomes (KEGG^[60]6)
database. And possible metabolic pathways were identified by metabolic
pathways enrichment and topology analysis through MetPA^[61]7 database.
Statistical Analysis
Data were analyzed by SPSS 21.0 for Windows (SPSS Inc.). Results were
represented as mean ± SD and evaluated using the two-tailed unpaired
student’s t-test or one-way analysis of variance. The p < 0.05 was
considered to be significant and p < 0.01 to be very significant.
Results
C-8-O-β-D-Glu Stability Analysis
The stability of C-8-O-β-D-glu (96 μM) in cell culture medium was
analyzed within 48 h. The concentration of C-8-O-β-D-glu incubated for
0, 6, 12, 24, and 48 h were shown in Table [62]1. The RSD (<2%) of drug
concentration at each time point indicated that C-8-O-β-D-glu was
stable in cell culture medium which guaranteed the study was focused on
C-8-O-β-D-glu instead of other chemicals.
Table 1.
Contents of C-8-O-β-D-glu in cell culture medium after being incubated
for 0, 6, 12, 24, and 48 h. Each value represented the mean ± SD (n =
3).
Time (h) 0 6 12 24 48
Content (μM) 95.25 ± 0.38 91.43 ± 2.28 94.08 ± 1.49 92.95 ± 1.77 93.19
± 1.39
RSD 1.5%
[63]Open in a new tab
MTT Assay
To investigate the effects of C-8-O-β-D-glu on L-02 cells viability and
select the concentrations for the follow-up studies, MTT assay was
performed at first. In CG, the same FBS and antibiotics were added to
eliminate the blank interference. As shown in Figure [64]2, 12 and 24
μM C-8-O-β-D-glu significantly increased the cells viability (p <
0.05), while 48 μM exert no influence. However, when concentration
increased to 96 μM, significant inhibition of the viability of L-02
cells after 24 h was observed (p < 0.05). It indicated that lower
concentrations of C-8-O-β-D-glu increased cells viability, but high
concentration led to liver injury. Consequently, 24, 48, and 96 μM were
selected as the low, medium and high concentrations of C-8-O-β-D-glu,
respectively in the follow-up studies.
FIGURE 2.
[65]FIGURE 2
[66]Open in a new tab
The viability of L-02 cells affected by different concentrations of
C-8-O-β-D-glu determined by MTT assay. Each value represented the mean
± SD (n = 6). One-way analysis of variance (ANOVA) was used to
calculate significant difference. ^∗P < 0.05, compared with the CG.
High Content Analysis
To further explore the effects of C-8-O-β-D-glu on L-02 cells, HCA was
applied to examine the changes of nuclei and mitochondria after the
cells being exposed to the drug for 24 h. The representative cell
staining images of the four groups were shown in Figure [67]3, which
showed an increase of cells number in LCG and a decrease in HCG when
compared with CG. Based on staining on nuclei by Bisbenzimide H 33342,
the number of L-02 cells was counted. The results were in accord with
the MTT assay (Figure [68]4A) that HCG showed significant cytotoxicity
compared with CG (p < 0.01). It was also observed that the average
nucleus area and average DNA content of L-02 cells in HCG were
significantly increased (Figures [69]4B,C) (p < 0.01). As for
mitochondria, the MMP was calculated from the intensity of Rho123
staining divided by cells number. As shown in Figure [70]4D, the MMP of
HCG was significantly higher than that of CG (p < 0.01). However, no
statistical difference of cells number, average nucleus area, average
DNA content and MMP can be found among LCG, MCG and CG. These results
above suggested that the hepatotoxic mechanisms of C-8-O-β-D-glu at
high concentration might be related to the changes in nuclei and
mitochondria.
FIGURE 3.
[71]FIGURE 3
[72]Open in a new tab
The representative cell staining images of the four groups. Blue
fluorescence indicated that the nuclei were stained by Bisbenzimide H
33342. Green fluorescence indicated that mitochondria were stained by
Rho123.
FIGURE 4.
[73]FIGURE 4
[74]Open in a new tab
The cells number (A), average nucleus area (B), average DNA content (C)
and MMP (D) of the four groups. ANOVA was used to calculate significant
difference. ^∗P < 0.05 and ^∗∗P < 0.01, compared with the CG.
UPLC-MS/MS Fingerprinting
All cell samples were analyzed by UPLC-MS/MS in both positive and
negative ionization modes, and the representative BPC (Figure [75]5),
of four groups were obtained under the optimal conditions. The
chromatographic peaks considered to be the representative chemical
fingerprints of endogenous metabolites were detected within 18 min with
some remarkable differences observed among the four groups.
FIGURE 5.
[76]FIGURE 5
[77]Open in a new tab
Typical BPC of the cells sample from the four groups in the ESI
negative (A) and positive ion modes (B).
Multivariate Statistical Analysis
The QC samples were used to correct the deviations of analysis results
from mixed samples and the errors caused by analytical instrument. PCA
score plots (Figures [78]6A,B) showed that the QC samples gathered
together in both positive and negative mode, which indicated that the
test samples and instrument were stable in long-term run.
FIGURE 6.
[79]FIGURE 6
[80]Open in a new tab
The score plot of QC samples and test samples from PCA in negative mode
(A) and positive mode (B), respectively. The score plot of the CG, LCG,
MCG, and HCG from PCA in negative mode (C) and positive mode (D).
To obtain the difference of metabolic components among four group
samples, multivariate statistical analysis method was used to screen
the metabolites of each sample. PCA score plot (Figures [81]6C,D)
showed a great separation trend of CG and drug experimental groups (R2X
= 0.215, Q2 = 0.048 in negative mode; R2X = 0.223, Q2 = 0.025 in
positive mode). Supervised analysis including PLS-DA and OPLS-DA was
applied to identify the differences and outliers between CG and drug
experimental groups. The cross test parameters R2X, R2Y, and Q2 values
of PLS-DA model were 0.312, 0.989, and 0.805 in negative mode and
0.371, 0.994, and 0.841 in positive mode, which suggested that fitness
and prediction of the model were good. Moreover, the permutation test
was applied to evaluate whether the model was overfitting. The results
shown in Figures [82]7C,D indicated that the model was valid. As shown
in PLS-DA score plot (Figures [83]7A,B), every group was clearly
separated from each other in both positive and negative modes, which
indicated that there were significant metabolite differences among the
groups. It also suggested that the effects of C-8-O-β-D-glu on L-02
cells were related to the concentration. OPLS-DA was carried out to
further confirm the potential differential metabolites of drug
experimental groups compared with CG, respectively. The OPLS-DA score
plot was shown in Figures [84]7E–[85]G. The VIP values of the
metabolites were calculated based on this to screen the potential
biomarkers.
FIGURE 7.
[86]FIGURE 7
[87]Open in a new tab
The score plot of the CG, LCG, MCG and HCG from PLS-DA in negative mode
(A) and positive mode (B), respectively. 100-permutation test of PLS-DA
model in negative mode (C) and positive mode (D). The score plot of CG
vs. LCG (E), CG vs. MCG (F), CG vs. HCG (G) from OPLS-DA in negative
mode and positive mode, respectively.
Screening and Identification of the Potential Biomarkers, and Metabolic
Pathway Analysis
VIP values reflected the influence of each variable, and a larger
distance indicated a more important projection. Therefore, all the
metabolites were selected according to the VIP values from OPLS-DA
firstly. The corrected p-values (q-values) between CG and drug
experimental groups were also applied to screen the differential
metabolites. Ultimately, 42 endogenous metabolites contributing most to
the separation of drug experimental groups from CG were selected, which
might account for the effects of C-8-O-β-D-glu on L-02 cells. These
metabolites were identified according to MS/MS information combined
with online database information. There were 18 and 5 identified
differential metabolites between CG and LCG or MCG, respectively. As
for biomarkers of hepatotoxicity of C-8-O-β-D-glu, 26 metabolites were
identified as differential metabolites between HCG and CG. Changes of
all the differential metabolites between CG and drug experimental
groups were shown in Figure [88]8, a heat map.
FIGURE 8.
[89]FIGURE 8
[90]Open in a new tab
Heat-map of differential metabolites of CG compared with LCG (A), MCG
(B), and HCG (C), respectively. Rows: differential metabolites;
columns: samples. The color of each small square represents the level
of metabolite expression. Red: highest; green: lowest; black; mean.
The pathways influenced by the changes of above metabolites were
identified by metabolic pathway enrichment and topology analysis
through MetPA database (Figure [91]9). The -log(p) value from the
pathway enrichment analysis and the pathway impact value from the
pathway topology analysis were calculated by MetaboAnalyst 4.0. The
higher the -log(p) value and the pathway impact value, the more
important the pathway. Four crucial pathways of C-8-O-β-D-glu on L-02
cells were finally identified based on the pathway impact and -log(p)
value, which were summarized in Table [92]2 suggesting that different
concentrations of C-8-O-β-D-glu would affect L-02 cells viability
through the impacts on the pathways of alanine, aspartate and glutamate
metabolism, arginine and proline metabolism, phenylalanine metabolism,
beta-alanine metabolism in different ways.
FIGURE 9.
[93]FIGURE 9
[94]Open in a new tab
Pathway analysis on differential metabolites of LCG vs. CG (A), MCG vs.
CG (B), and HCG vs. CG (C). The circles marked with name of the
pathways were the identified important pathways.
Table 2.
Metabolic pathways associated to varied influences of
chrysophanol-8-O-β-D-glucoside on L-02 cell.
Metabolic pathways Groups Total Hits -LOG(p) Impact
Alanine, aspartate, and glutamate metabolism CG vs. LCG 24 3 8.1277
0.26496
CG vs. HCG 3 6.6046 0.26496
Arginine and proline metabolism CG vs. LCG 77 4 7.1824 0.1182
Phenylalanine metabolism CG vs. HCG 45 4 7.2241 0.14126
beta-Alanine metabolism CG vs. HCG 28 2 3.5614 0.25694
[95]Open in a new tab
Remarked dysregulation of alanine, aspartate and glutamate metabolism
were observed in both LCG and HCG. In addition, low concentration of
C-8-O-β-D-glu would impact arginine and proline metabolism. High
concentration of C-8-O-β-D-glu would impact phenylalanine metabolism
and beta-alanine metabolism. There was no important pathway involved in
MCG vs. CG. Based on these metabolic pathways, relevant important
differential metabolites were selected and summarized in Table [96]3.
As can be seen in Figure [97]10, relative intensities of most
differential metabolites varied in a dose-dependent manner. For
example, contents of L-aspartic acid, (N(omega)-L-arginino)succinic
acid, L-proline, N-phosphocreatine, trans-2-coumaric acid and
L-tyrosine decreased with increasing dose, while the content of
N(6)-(1,2-dicarboxyethyl)-AMP increased with increasing dose.
Table 3.
Identification results and change trends of important differential
metabolites.
No. Deduced metabolites Elemental composition mz rtmin VIP p-value
q-value KEGG ESI mode Related pathway Change Trend
__________________________________________________________________
LCG MCG HCG
1 L-aspartic acid C4H7NO4 132.03 1.41 1.53 0.000 0.000 [98]C00049 –
Alanine, aspartate and glutamate metabolism, beta-Alanine metabolism,
Arginine and proline metabolism ↑ ↑ ↓
2 (N(omega)-L-arginino)succinic acid C10H18N4O6 291.13 1.50 1.09 0.019
0.030 [99]C03406 + Alanine, aspartate and glutamate metabolism,
Arginine and proline metabolism ↑ – ↓
3 N(6)-(1,2-dicarboxyethyl)-AMP C14H18N5O11P 462.07 4.94 1.10 0.021
0.032 [100]C03794 – Alanine, aspartate and glutamate metabolism ↓ – –
4 N-acetyl-L-aspartic acid C6H9NO5 176.05 2.59 1.56 0.018 0.048
[101]C01042 + – – ↓
5 L-proline C5H9NO2 116.07 1.66 1.08 0.031 0.040 [102]C00148 + Arginine
and proline metabolism ↑ – ↓
6 N-phosphocreatine C4H10N3O5P 210.03 1.50 1.58 0.000 0.000 [103]C02305
– Arginine and proline metabolism ↑ – –
7 L-phenylalanine C9H11NO2 164.07 5.47 1.86 0.000 0.000 [104]C00079 –
Phenylalanine metabolism – – ↓
8 D-phenylalanine C9H11NO2 166.09 5.47 1.60 0.000 0.000 [105]C02265 + –
– ↓
9 trans-2-coumaric acid C9H8O3 165.05 3.76 1.27 0.009 0.018 [106]C01772
+ – – ↓
10 L-tyrosine C9H11NO3 182.08 3.76 1.36 0.003 0.008 [107]C00082 +
Phenylalanine metabolism ↓ – ↓
11 beta-Alanine C3H7NO2 90.06 1.45 1.64 0.011 0.039 [108]C00099 +
beta-Alanine metabolism – – ↓
[109]Open in a new tab
The shown change trends have significant differences in comparison with
CG.
FIGURE 10.
[110]FIGURE 10
[111]Open in a new tab
Relative intensities of the 11 important differential metabolites. ^∗q
< 0.05 and ^∗∗q < 0.01, significant differences compared to the control
group.
Discussion
A lot of studies about free anthraquinones inducing liver injury have
been published. But little attention has been put on the effect of
glycosylated anthraquinones on liver. In pre-screening experiment, we
found that the effects of C-8-O-β-D-glu on liver cells viability were
opposite with different concentrations. Thus, in this study, we
performed the metabonomics and HCA research to further explore its
mechanism. Before the experiments, the contents of C-8-O-β-D-glu in
cell culture medium at different time were tested to ensure the
stability during the whole experiment process.
Different Concentrations of C-8-O-β-D-Glu Exposure Impacted L-02 Cells
Viability and Cell State
Because of the poor solubility of C-8-O-β-D-glu, the concentration
wasn’t high enough to detect the IC[50] of C-8-O-β-D-glu. So the
concentration with maximum solubility (96 μM) was used as the high
dosage in MTT assay.
The results of MTT assay suggested that C-8-O-β-D-glu in low
concentrations (12 and 24 μM) increased L-02 cells viability. However,
HCA results didn’t show statistical difference in LCG and MCG compared
to CG. It suggested that C-8-O-β-D-glu at lower concentrations did not
affect the cells number or cells status of the nuclei and mitochondria
of L-02 cells, which was inconsistent with MTT assay. Although the
results of MTT assay implied a rise of mitochondrial activity in LCG,
MMP analyzed by HCA remained unchanged. It indicated the mitochondria
in LCG were not significantly affected. Since there was an upward trend
in cells number of LCG (1925 ± 283 in LCG, 1594 ± 163 in CG) measured
by HCA, the difference between the cells viability and cells number
might be caused by the difference between the two experimental methods.
The results of MTT assay and HCA experiment both indicated that high
concentration (96 μM) of C-8-O-β-D-glu inhibited cells growth.
Moreover, significant changes in cell nuclei and mitochondria were
found in HCG. Normally, the cells injured by toxic substances showed
nucleus shrinkage caused by cell apoptosis and necrosis. However, both
the average nucleus area and DNA content were increased in HCG which
needed further study. Normal MMP is mainly dependent on the proton pump
located in the gap between mitochondrial matrix and mitochondrial
membrane so that different protons between the matrix and the membrane
maintain a certain electrochemical gradient. MMP reflects the
properties of electron transport chain and changes in pathological
conditions. Respiratory chain of mitochondria is the main site of ROS
production ([112]Brand and Nicholls, 2011). Under normal condition,
mitochondria produce a small amount of ROS to maintain the normal
physiological functions of cells. However, the reduced proton reflux
increases MMP in pathological condition. Then electrons are leaked
which resulted in producing a large number of ROS ([113]Su et al.,
2016). Finally, excessive ROS causes oxidative damage to cells
([114]Ray et al., 2012). Thus, we conjectured that the increase of MMP
level in HCG resulted in L-02 cells damage by affecting the production
of ROS.
Cell Metabolism Involved in Varied Influences of C-8-O-β-D-Glu With Different
Concentrations on L-02 Cells
Changes of intracellular metabolites reflect abnormalities of cells’
physiological state. The extent of important metabolites change has a
relationship with C-8-O-β-D-glu concentration. More importantly, the
contents of several metabolites in LCG and HCG changed in different
trends compared to CG, including L-aspartic acid,
(N(omega)-L-arginino)succinic acid and L-proline. These metabolites
might be more relevant to the different effects of C-8-O-β-D-glu on
L-02 cells.
In present research, three concentrations (24, 48, and 96 μM) of
C-8-O-β-D-glu affected different metabolic pathways in varying degrees
to induce different effects on L-02 cells viability. The schematic
diagram of these pathways was shown in Figure [115]11. The metabolism
of amino acids occurs mainly in liver. Therefore, physiological changes
of liver are usually accompanied with the abnormality of amino acid
metabolism. Previous metabonomics studies have shown that
anthraquinones would induce liver injury by disordering amino acid
metabolism, including tryptophan metabolism ([116]Zhang et al., 2016),
phenylalanine metabolism, alanine, aspartate and glutamate metabolism
([117]Li et al., 2017), L-threonine and serine metabolism ([118]Zhang
et al., 2015). In this research, we found amino acid metabolism was
involved in most identified pathways.
FIGURE 11.
[119]FIGURE 11
[120]Open in a new tab
Schematic diagram of the metabolic pathway related to the effects of
different concentrations of C-8-O-β-D-glu on L-02 cells. The
metabolites in red represented the potential biomarkers identified in
this research. The arrows next to the metabolites represented their
change trends in each drug group. The substances in the green box
represented the enzymes needed for the reaction.
Low concentration of C-8-O-β-D-glu might promote L-02 cells viability
by regulating the expression of alanine, aspartate and glutamate
metabolism, arginine and proline metabolism. High concentration of
C-8-O-β-D-glu also affected alanine, aspartate and glutamate
metabolism, but to the contrary trend. Thus, differential metabolites
including L-aspartic acid, (N(omega)-L-arginino)succinic acid,
N(6)-(1,2-dicarboxyethyl)-AMP and N-acetyl-L-aspartic acid could be
considered as potential biomarkers of C-8-O-β-D-glu affecting liver
cells viability. In addition, arginine and proline metabolism was
significantly changed in LCG. High concentrations of C-8-O-β-D-glu
disrupted phenylalanine metabolism and beta-alanine metabolism. The
results indicated that the mentioned metabolites were closely related
to promoting or toxic effects of C-8-O-β-D-glu. In MCG, there was no
significant change in cellular metabolic pathway. This was consistent
with MTT results that medium concentration had little effect on cell
viability.
L-aspartic acid is an important non-essential amino acid which
participates in many biochemical processes including urea cycle,
gluconeogenesis, and malate-aspartate shuttle etc. Rising content of
L-aspartic acid in LCG indicated a gain in energy generation via these
processes. N-phosphocreatine and L-proline were also up-regulated in
LCG. N-phosphocreatine is an important energy source for cells.
Promotion of these processes related to energy generation will result
in an increase in L-02 cells viability.
The content of L-aspartic acid, as well as its by-products
(N(omega)-L-arginino)succinic acid and N-acetyl-L-aspartic acid were
decreased in HCG. One important physiological function of L-aspartic
acid is providing an ammonia molecule for the urea cycle to remove
excess ammonia ([121]Mew et al., 2011). Thus, the decrease in
L-aspartic acid content will cause ammonia accumulation and damage to
the liver. (N(omega)-L-arginino)succinic acid is synthesized from
L-aspartic acid by the loss of ammonia. The decline of
(N(omega)-L-arginino)succinic acid in HCG conformed to the decrease of
L-aspartic acid which can be produced from oxaloacetate via
transamination by the function of aspartate aminotransferase (AST). AST
is also one of the most commonly used indicators for diagnosis of liver
disease. When the hepatocyte is damaged, the permeability of the cell
membrane increases, and the AST outflows from the cell
([122]Otto-Ślusarczyk et al., 2016). The decrease in L-aspartic might
be caused by the outflow of AST. Based on these facts, we speculated
that high concentration of C-8-O-β-D-glu induced L-02 cells damage via
reducing AST in cells, inhibiting the production of L-aspartic acid,
increasing the amount of ammonia and finally aggravating the liver
injury. In addition, L-proline in HCG was significantly reduced. It has
been reported that L-proline can protect cells by directly cleaning up
ROS, protecting and up-regulating the antioxidant enzymes, and
maintaining the key redox molecules ([123]Krishnan et al., 2008;
[124]Szabados and Savouré, 2010; [125]Natarajan et al., 2012).
According to our previous speculation about the result of HCA, high
concentration of C-8-O-β-D-glu induced L-02 cells damage might be
related to oxidative stress. Therefore, the decreasing of L-proline
probably aggravated the damage caused by oxidative stress.
This study revealed that the different effects of C-8-O-β-D-glu on
liver L-02 cell with varying concentrations, which prompts us to pay
more attention to dosage applied in clinic. But the dosage-relationship
of effect or toxicity remained obsure which will be investigated in the
further experiment.
Conclusion
This study explored the different effects of three concentrations of
C-8-O-β-D-glu on L-02 liver cells that high concentration induced L-02
cells damage, while low concentration promoted the cells viability. The
HCA results indicated that high concentration of C-8-O-β-D-glu can
significantly reduce the cells number, increase average nucleus area,
DNA content and MMP. The results of metabonomics analysis indicated
that metabolic profiles of each group were clearly separated. Eleven
important differential metabolites associated with four pathways
including alanine, aspartate and glutamate metabolism, arginine and
proline metabolism, phenylalanine metabolism, pantothenate and
beta-alanine metabolism were identified in this study. Based on these
results, it is concluded that different concentrations of C-8-O-β-D-glu
could impact L-02 cells via disturbing metabolism in different ways.
Author Contributions
ML and YL conducted the experiments. ML and YL wrote the manuscript and
prepared the figures. XG, YQ, and YZ conducted the sample collection
and data analysis. CP conceived the study.
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.
Abbreviations
BPC
Base peak chromatogram
C-8-O-β-D-glu
Chrysophanol-8-O-β-D-glucopyranoside
CG
Control group
DMEM
Dulbecco’s modified eagle medium
DMSO
Dimethyl sulfoxide
FBS
Fetal bovine serum
FDR
False discovery rate
HCA
High content analysis
HCG
High concentration group
IC50
50% inhibitory concentration
LCG
Low concentration group
MCG
Medium concentration group
MMP
Mitochondrial membrane potential
MTT
3-(4,5-dimethylthiazol-2-yl) 2,5- diphenyltetrazolium bromide
OPLS-DA
Orthogonal projection to latent structure discriminate analysis
PBS
Phosphate buffer saline
PCA
Principal component analysis
PLS-DA
Partial least squares discriminant analysis
Rho123
Rhodamine 123
ROS
Reactive oxygen species
UPLC-MS/MS
Ultra-performance liquid chromatography – mass spectrometry/mass
spectrometry
VIP
Variable importance in the projection
Funding. The study was supported by National Natural Science Foundation
of China (81573583 and 81630101), Sichuan Provincial Science and
Technology Department of Youth Science, and Technology Innovation
Research Team Program (2017TD0001).
^1
[126]http://www.hmdb.ca
^2
[127]http://metlin.scripps.edu
^3
[128]http://www.massbank.jp/
^4
[129]http://www.lipidmaps.org
^5
[130]https://www.mzcloud.org
^6
[131]http://www.kegg.jp/
^7
[132]http://www.metaboanalyst.ca
References