Abstract
Compound Kushen Injection (CKI) is a Traditional Chinese Medicine (TCM)
preparation that has been clinically used in China to treat various
types of solid tumours. Although several studies have revealed that CKI
can inhibit the proliferation of hepatocellular carcinoma (HCC) cell
lines, the active compounds, potential targets and pathways involved in
these effects have not been systematically investigated. Here, we
proposed a novel idea of “main active compound-based network
pharmacology” to explore the anti-cancer mechanism of CKI. Our results
showed that CKI significantly suppressed the proliferation and
migration of SMMC-7721 cells. Four main active compounds of CKI
(matrine, oxymatrine, sophoridine and N-methylcytisine) were confirmed
by the integration of ultra-performance liquid chromatography/mass
spectrometry (UPLC-MS) with cell proliferation assays. The potential
targets and pathways involved in the anti-HCC effects of CKI were
predicted by a network pharmacology approach, and some of the crucial
proteins and pathways were further validated by western blotting and
metabolomics approaches. Our results indicated that CKI exerted
anti-HCC effects via the key targets MMP2, MYC, CASP3, and REG1A and
the key pathways of glycometabolism and amino acid metabolism. These
results provide insights into the mechanism of CKI by combining
quantitative analysis of components, network pharmacology and
experimental validation.
Introduction
Hepatocellular carcinoma (HCC) is the 3rd leading cause of
cancer-related death worldwide, and its incidence is increasing^[34]1.
Approximately three-quarters of HCC cases are attributed to chronic HBV
and HCV infections^[35]2. In recent years, substantial evidence has
shown that genetic alterations^[36]3,[37]4 and metabolic
disorders^[38]5,[39]6 also play critical roles in the pathogenesis of
HCC. Most HCC patients are diagnosed at an advanced stage with few
therapeutic measures. Trans-arterial chemoembolization (TACE),
radiotherapy and chemotherapy are the current treatment modalities for
HCC, and sorafenib is the only drug that has been approved by the
FDA^[40]7.
Compound Kushen Injection (CKI) is derived from two herbs, Radix
sophorae flavescentis and Rhizoma smilacis glabrae. CKI has been
clinically used in China for over 15 years for the treatment of many
types of solid tumours, especially for cancer-related pains^[41]8. CKI
combined with TACE treatment could elevate the therapeutic efficacy of
unresectable HCC^[42]9. CKI may deliver anti-HCC effects through
multiple compounds acting on multiple targets and pathways. Qu et al.
used functional genomics to identify the anti-cancer mechanisms of CKI
in the MCF-7 cell line. They found that CKI exerted anti-cancer effects
likely through the regulation of the cell cycle, cell apoptosis,
lncRNAs and other pathways^[43]10. However, the candidate mechanisms
underlying the anti-HCC effects of CKI are still unknown.
Network pharmacology, first proposed by Andrew L Hopkins^[44]11, has
greatly promoted the mechanistic study of Traditional Chinese
Medicine^[45]12–[46]14. This approach has advantages in interpreting
the synergistic effects of Traditional Chinese Medicine (TCM) with
multiple components and multiple targets. In most studies, network
pharmacology considers drug-like ingredients in herb databases, while
the contents of the ingredients are often neglected. Thus, the primary
ingredients and targets predicted by network pharmacology may deviate
from the truth.
In the current study, network pharmacology analysis was performed
focusing on the main active compounds of CKI. The workflow is
illustrated in Fig. [47]1 as follows: (1) the anti-HCC effects of CKI
were evaluated in SMMC-7721 cells; (2) the ingredients of CKI were
quantitatively analysed by UPLC-MS; (3) the effects of the primary
compounds (top 5 in content) of CKI on cell proliferation were measured
to identify the main active compounds; (4) the 4 main active compounds
were used for network pharmacology analysis to predict the potential
targets and pathways of CKI against HCC; (5) the key targets and
pathway were validated by experiments.
Figure 1.
[48]Figure 1
[49]Open in a new tab
A schematic diagram of the integrative strategy combining quantitative
analysis of components, network analysis and experimental validation
for investigation of the mechanisms of Compound Kushen Injection (CKI)
against HCC.
Results
CKI inhibits the proliferation and migration of SMMC-7721 cells
The effects of CKI on the proliferation and migration of SMMC-7721
cells were determined. MTT assays showed that 2, 4 and 8 mg/mL CKI
dramatically inhibited the proliferation of SMMC-7721 cells at 24, 48,
and 72 h in a time-dependent manner (Fig. [50]2A). Wound-healing and
transwell assays showed that 1 and 2 mg/mL significantly suppressed the
migration of SMMC-7721 cells (Fig. [51]3). These results suggest that
CKI displayed significant inhibitory activities on the proliferation
and migration of SMMC-7721 cells.
Figure 2.
[52]Figure 2
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Effects of CKI and its components on proliferation of SMMC-7721 cells.
(A) CKI, (B) matrine, (C) oxymatrine, (D) sophoridine, (E)
N-methylcytisine, (F) oxysophocarpine. The cell viabilities under
different treatments were measured using MTT. Data are represented as
mean ± SEM (n = 6). *p < 0.05, **p < 0.01, ***p < 0.001 versus control
group.
Figure 3.
[54]Figure 3
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CKI can inhibit the migration of SMMC-7721 cells. (A) Wound-healing
assays show that CKI (1 and 2 mg/mL) significantly inhibits the
migration of SMMC-7721 cells at 12, 24 and 36 h. (B) Transwell
migration assays show that CKI (1 and 2 mg/mL) significantly inhibits
the migration of SMMC-7721 cells at 36 h. *p < 0.05, ***p < 0.001
versus untreated cells.
Identification of chemical ingredients in CKI by UPLC-MS
UPLC-MS is a rapid, reliable and accurate technique to identify the
chemical ingredients in TCM. In the present study, 22 ingredients of
CKI were identified by UPLC-MS (Fig. [56]4, Table [57]1). The top five
compounds by content were matrine (6.07 mg/mL), oxymatrine
(5.46 mg/mL), oxysophocarpine (3.89 mg/mL), sophoridine (2.23 mg/mL)
and N-methylcytisine (1.14 mg/mL). These 5 compounds were further
identified by comparing the retention times and accurate masses with
those of standard substances (Supplementary Figure [58]S1–[59]S5).
Other compounds were determined by comparing the retention time and
mass spectra with those of authentic substances and the reported data
in the literature.
Figure 4.
Figure 4
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Positive total ion current chromatography (TIC) of CKI.
Table 1.
Identified Compounds in CKI by UPLC-MS.
No. t[R](min) compounds Molecular formula [M + H]^+ Fragment ions
1 2.28 oxymamanine C[15]H[23]O[3]N[2] 279.1702 177.1386, 261.1595,
557.3329
2 2.54 9β-hydroxylamprolobine N-oxide C[15]H[25]O[4]N[2] 297.1808
279.1700, 593.3537
3 4.29 9β-hydroxylamprolobine C[15]H[25]O[3]N[2] 281.1859 148.1120,
263.1751, 561.3642
4 4.56 5α, 9α-hydroxymatrine C[15]H[25]O[3]N[2] 281.1859 148.1114,
245.1646, 263.1750, 561.3648
5 4.99 9α -hydroxymatrine C[15]H[25]O[2]N[2] 265.1910 177.1379,
247.1801, 529.3748
6 5.52 oxysophoranol C[15]H[25]O[3]N[2] 281.1859 148.1121, 245.1645,
263.1753, 561.3643
7 6.05 no identified 283.2015 152.1433
8 6.22 oxymatrine C[15]H[25]O[2]N[2] 265.1910 148.1119, 150.1275,
247.1801 [M + H[2]O]^+, 529.3748 [2M + H]^+
9 6.53 oxysophocarpine C[15]H[23]O[2]N[2] 263.1753 150.1275, 245.1647
[M + H[2]O]^+, 525.3426 [2M + H]^+
10 7.27 mamanine C[15]H[23]O[2]N[2] 263.1753 150.1275, 231.1496,
245.1645 [M + H[2]O]^+, 525.3434 [2M + H]^+
11 7.82 sophoranol C[15]H[25]O[2]N[2] 265.1910 148.1117, 150.1279,
247.1802 [M + H[2]O]^+
12 8.09 oxysophoridine C[15]H[25]O[2]N[2] 265.1909 164.1607, 247.1808
[M + H[2]O]^+
13 8.52 baptifoline C[15]H[21]O[2]N[2] 261.1598 114.0915,243.1488
14 8.25 N-methylcyticine C[12]H[17]ON[2] 205.1335 146.0601
15 9.09 9α-hydroxysophocarpine C[15]H[23]O[3]N[2] 279.1703 261.1595
[M + H[2]O]^+
16 9.58 (-)-oxylehmannine C[15]H[23]O[2]N[2] 263.1754 245.1647
17 9.47 sophoridine C[15]H[25]ON[2] 249.1960 150.1280
18 10.12 14β-hydrsophoridine C[15]H[25]O[2]N[2] 265.1910 247.1805
19 10.22 (-)-acetylbaptifoline C[17]H[24]O[3]N[2] 303.1702 243.1491
20 10.39 Sophocarpine C[15]H[22]ON[2] 247.1805 148.1121, 150.1278
21 10.59 isomatrine C[15]H[25]ON[2] 249.1961 150.1279
22 10.82 matrine C[15]H[25]ON[2] 249.1960 148.1120, 150.6241, 134.5847,
231.1852 [M + H[2]O]^+
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Identification of the main active compounds in CKI
MTT assay was used to screen the active compounds against HCC from the
top-5 compounds. Our results showed that matrine, oxymatrine,
sophoridine and N-methylcytisine, each at 4 mg/mL significantly
inhibited the proliferation of SMMC-7721 cells at 24 h (Fig. [62]2B–E).
These 4 compounds were considered the main active compounds of CKI.
However, even at 4 mg/ml, oxysophocarpine showed no obvious effect on
the proliferation of SMMC-7721 cells after 24 h of treatment
(Fig. [63]2F).
Network construction of the anti-HCC targets of CKI
In our previous study^[64]13, 566 genes that were significant to HCC
were collected from the OncoDB.HCC^[65]15 and Liverome
databases^[66]16. The validated targets and predicted targets of the
main active compounds of CKI were collected and then mapped to these
genes to obtain shared genes, which were predicted to be the candidate
targets of CKI. Forty-eight targets of CKI were obtained, including 7
validated targets (AR, CASP3, CD44, HPSE, ICAM-1, MMP2, and MYC) and 41
predicted targets; 384 proteins associated with the 48 targets of CKI
were acquired from the STRING database. The protein-protein interaction
network of CKI for the anti-HCC effects consisted of 432 proteins
through 1540 interactions with an average degree of 7.13 (Fig. [67]5).
Figure 5.
[68]Figure 5
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The network of CKI targets-interacted proteins. The yellow nodes
represent validated targets, the red nodes represent predicted targets
and the blue nodes represent associated proteins of targets.
Network topological analysis
NetworkAnalyzer was used to calculate the average shortest path lengths
and betweenness centrality of the 48 targets of CKI, and the results
are shown in Table [70]2. The topological parameters such as shortest
path length and betweenness centrality were usually used for the
analysis of key nodes in the network. Nodes with a small average
shortest path length and large betweenness centrality value were
considered as important proteins in the network^[71]17. The R value was
used to evaluate the importance of the 48 targets as equation ([72]1).
From the predicted targets of the main active compounds, CASP3, MYC,
and MMP2 were predicted as crucial targets of CKI from the validated
targets, and QDPR, GABRE, and REG1A were predicted as crucial targets
of CKI.
Table 2.
48 targets of CKI with average shortest path length and betweenness
centrality.
Swiss prot Genes/proteins Description validated or predicted Average
shortest path length Betweenness centrality R
[73]P09417 QDPR quinoid dihydropteridine reductase predicted 1.00
0.80000 0.0000
[74]P78334 GABRE Gamma-aminobutyric-acid receptor subunit epsilon
precursor predicted 1.00 0.73889 0.0000
[75]P05451 REG1A Lithostathine 1 alpha precursor predicted 1.00 0.70370
0.0000
[76]P00918 CA2 carbonic anhydrase II predicted 1.00 0.26688 0.0000
[77]P00736 C1R complement component 1, r subcomponent predicted 1.00
0.14741 0.0000
[78]P11717 IGF2R insulin-like growth factor 2 receptor predicted 1.65
0.33684 0.0613
[79]P43681 CHRNA4 Neuronal acetylcholine receptor subunit alpha-4
predicted 1.79 0.51600 0.0746
[80]P42574 CASP3 Caspase-3 validated 1.90 0.04377 0.0849
[81]P23141 CES1 carboxylesterase 1 predicted 2.13 0.45833 0.1060
[82]P36544 CHRNA7 Neuronal acetylcholine receptor subunit alpha-7
predicted 2.29 0.09934 0.1217
[83]P35228 NOS2 Nitric oxide synthase predicted 2.98 0.05616 0.1865
[84]Q92597 MYC Myc proto-oncogene protein validated 3.07 0.04787 0.1951
[85]P05089 ARG1 Arginase-1 predicted 3.23 0.10864 0.2099
[86]P00966 ASS1 argininosuccinate synthase 1 predicted 3.37 0.03375
0.2231
[87]P09211 GSTP1 Glutathione S-transferase P predicted 3.45 0.10380
0.2251
[88]P04181 OAT Ornithine aminotransferase predicted 3.38 0.00546 0.2261
[89]P32929 CTH cystathionase predicted 3.40 0.01890 0.2279
[90]P35520 CBS Cystathionine beta-synthase predicted 3.41 0.00541
0.2310
[91]P08253 MMP2 Matrix metalloproteinase-2 validated 3.46 0.00064
0.2341
[92]P14210 HGF Hepatocyte growth factor precursor predicted 3.47
0.00046 0.2373
[93]P30041 PRDX6 peroxiredoxin 6 predicted 3.48 0.00605 0.2400
[94]P04075 ALDOA aldolase A predicted 3.52 0.00072 0.2403
[95]P11216 PYGB phosphorylase, glycogen; brain predicted 3.54 0.00500
0.2407
[96]Q5JWF2 GNAS Guanine nucleotide-binding protein G(s) subunit alpha
isoforms XLas predicted 3.54 0.00253 0.2418
[97]P83916 CBX1 chromobox homolog 1 predicted 3.55 0.00130 0.2425
[98]P11388 TOP2A topoisomerase (DNA) II alpha 170 kDa predicted 3.55
0.00104 0.2432
[99]P28482 MAPK1 Mitogen-activated protein kinase 1 predicted 3.64
0.02154 0.2486
[100]P03372 ESR1 Estrogen receptor predicted 3.63 0.00146 0.2503
[101]P10275 AR Androgen receptor validated 3.65 0.00819 0.2505
[102]P35354 PTGS2 Prostaglandin G/H synthase 2 precursor predicted 3.67
0.00602 0.2524
[103]P61769 B2M beta-2-microglobulin predicted 3.97 0.11074 0.2751
[104]P13196 ALAS1 aminolevulinate, delta-, synthase 1 predicted 3.92
0.03940 0.2796
[105]P01116 KRAS GTPase KRas predicted 4.15 0.00012 0.2983
[106]P80188 LCN2 Neutrophil gelatinase-associated lipocalin precursor
predicted 4.16 0.00751 0.2985
[107]P21549 AGXT alanine-glyoxylate aminotransferase predicted 4.17
0.02412 0.3024
[108]O95954 FTCD formiminotransferase cyclodeaminase predicted 4.20
0.00607 0.3059
[109]P61626 LYZ lysozyme predicted 4.25 0.07924 0.3166
[110]P09038 FGF2 Heparin-binding growth factor 2 precursor predicted
4.35 0.00683 0.3184
[111]P05230 FGF1 Heparin-binding growth factor 1 precursor predicted
4.38 0.01212 0.3249
[112]Q9Y251 HPSE Heparanase precursor validated 4.39 0.00001 0.3266
[113]P11498 PC pyruvate carboxylase predicted 4.45 0.01814 0.3524
[114]P02671 FGA fibrinogen alpha chain predicted 4.74 0.04801 0.3790
[115]P05362 ICAM1 Intercellular adhesion molecule 1 precursor validated
5.02 0.05416 0.3813
[116]P16070 CD44 CD44 antigen precursor validated 5.04 0.00918 0.3886
[117]Q9UJM8 HAO1 hydroxyacid oxidase (glycolate oxidase) 1 predicted
5.12 0.01655 0.4330
[118]P00915 CA1 Carbonic anhydrase 1 predicted 5.59 0.05388 0.4817
[119]P01008 SERPINC1 serpin peptidase inhibitor, clade C
(antithrombin), member 1 predicted 6.11 0.01214 0.5011
[120]P05091 ALDH2 aldehyde dehydrogenase 2 family (mitochondrial)
predicted 6.31 0.00301 0.8195
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Pathway enrichment analysis
Reactome was used to explore the potential pathways affected by CKI
through analysis of the 48 targets. The pathways were ranked by their
nominal p values with a cut-off of 0.001 (Table [122]3). Seven pathways
were enriched: Metabolism of amino acids and derivatives, Metabolism,
FRS2-mediated cascade, FGFR1b ligand binding and activation, Amyloids,
Reversible hydration of carbon dioxide and SHC-mediated cascade.
Table 3.
Pathway enrichment for the targets of CKI.
Reactome Pathway P-value FDR HitGenes
Metabolism of amino acids and derivatives 0 0.002252408 AGXT, QDPR,
CTH, OAT, ASS1, ARG1, FTCD, CBS
Metabolism 0.0001 0.019183984 PTGS2, AGXT, ALAS1, CD44, HPSE, QDPR,
CTH, GNAS, CA2, CA1, OAT, GSTP1
FRS2-mediated cascade 0.0002 0.026828354 FGF1, FGF2, MAPK1, KRAS
FGFR1b ligand binding and activation 0.0005 0.046185577 FGF1, FGF2
Amyloids 0.0007 0.046185577 B2M, LYZ, FGA
Reversible hydration of carbon dioxide 0.0008 0.046185577 CA2, CA1
SHC-mediated cascade 0.001 0.046185577 FGF1, FGF2, KRAS
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Experimental validation of key targets
To delineate the anti-HCC mechanisms of CKI, some of the crucial
proteins predicted by network pharmacology were experimentally
validated in SMMC-7721 cells in response to CKI treatment. As shown in
Fig. [124]6, CKI significantly inhibited the expression of MMP2, MYC,
and REG1A and significantly increased the expression of caspase 3 in a
dose-dependent manner.
Figure 6.
[125]Figure 6
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The effects of CKI on protein levels of CASP3 (A), MYC (B), MMP2 (C)
and REG1A (D) in SMMC-7721 cells. β-Actin was used as loading control.
n = 3,
[MATH: x¯ :MATH]
± SEM. *p < 0.05, **p < 0.01 versus untreated cells.
Experimental validation of the metabolism pathway
Metabolomics was used to detect the metabolism pathways affected by
CKI. The typical ^1H-NMR spectra of cell and medium are present in
Supplementary Figure [127]S6. Resonance assignments (Supplementary
Table [128]S1) were performed based on the chemical shifts of standard
compounds from the Chenomx NMR suite, the Human Metabolome Database
(HMDB)^[129]18, and Biological Magnetic Resonance Data Bank
(BMRB)^[130]19, as well as the literature data^[131]20,[132]21. The NMR
spectra of cell and medium were dominated by peaks from amino acids,
organic acids, choline-containing metabolites, and amine metabolites,
in addition to some other metabolites.
To obtain more details of the metabolic differences after treatment
with CKI, all the NMR data were subjected to multivariate data
analysis. A partial least squares discriminant analysis (PLS-DA) model
was further constructed and validated using the response of the
permutation test through 200 permutations in which all R^2 and Q^2
values were lower than the original ones deemed to be of great
predictive ability and reliability. The good PLS-DA models (cell model
parameters: R^2X = 0.632, Q^2 = 0.979; medium model parameters: R^2X =
0.741, Q^2 = 0.993) indicate excellent predictive powers. Potential
biomarkers associated with CKI treatment were further identified by
OPLS-DA. The corresponding S-plot, VIP values and t-tests were used to
test the statistical significance of the altered metabolites and to
find metabolites contributing to the separation (Fig. [133]7). Compared
with the control group, 16 differential metabolites were confirmed in
cells, including higher levels of leucine, valine, acetate, glutamine,
glycerol, β-glucose, tyrosine and phenylalanine and lower levels of
glutamate, glutathione, creatine, GPC^c, glycine, 1,3-dihydroxyacetone,
adenosine monophosphate and hypoxanthine. In the medium 10 differential
metabolites were confirmed, including higher levels of pyruvate,
succinate, pyroglutamate, glycine, threonine, glycerol and alanine and
lower levels of leucine, valine and lactate. CKI could significantly
regulate the contents of different metabolites and attenuate the
metabolic disorders in hepatoma cells (Table [134]4).
Figure 7.
[135]Figure 7
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OPLS scores plot (A), corresponding validation plot (B) and S-plot (C)
derived from SMMC-7721 cells; OPLS scores plot (D), corresponding
validation plot (E) and S-plot (F) derived from medium.
Table 4.
The differential metabolites in SMMC-7721 cell and medium after
treatment of CKI. *p < 0.05, **p < 0.01, ***p < 0.001 versus untreated
cells.
No Metabolites CKI cell fold change CKI medium fold change
1 Leucine ↑** 4.00 ↓*** 1.97
2 Valine ↑** 5.39 ↓*** 1.60
3 Acetate ↑*** 4.81
4 Lactate ↓*** 5.25
5 Pyruvate ↑*** 4.91
6 Succinate ↑*** 2.17
7 Pyroglutamate ↑*** 2.29
8 Glutamate ↓*** 2.98
9 Glutamine ↑** 1.52
10 Glutathione ↓*** 4.10
11 Creatine ↓** 2.61
12 GPC^c ↓** 2.57
13 Glycine ↓*** 2.91 ↑*** 1.67
14 Threonine ↑*** 12.76
15 1,3-Dihydroxyacetone ↓** 2.01
16 Glycerol ↑*** 2.31 ↑** 2.08
17 Alanine ↑*** 1.32
18 Adenosine monophosphate ↓* 1.52
19 β-Glucose ↑** 11.05
20 Tyrosine ↑*** 3.69
21 Phenylalanine ↑*** 3.34
22 Hypoxanthine ↓*** 3.18
[137]Open in a new tab
Metabolites with significant abundance changes between the CKI
treatment and control groups were subjected to pathway analysis using
MetaboAnalyst 3.0 and the KEGG database ([138]www.genome.jp/kegg/)
(Fig. [139]8). The most relevant metabolic pathways regulated by CKI
included Pyruvate metabolism; D-Glutamine and D-glutamate metabolism;
Glycine, serine and threonine metabolism; Alanine, aspartate and
glutamate metabolism; Glutathione metabolism; and Glycerolipid
metabolism. To gain additional insights into the relationship between
metabolites, the differential biomarkers were mapped to KEGG IDs and
the metabolite network was constructed by MetScape (Fig. [140]9).
Figure 8.
Figure 8
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MetPA analysis of metabolic pathway.
Figure 9.
[142]Figure 9
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The network of potential biomarkers of CKI for the anti-HCC effect. The
figure was constructed using MetScape and the nodes represent
metabolites and edges represent biochemical reactions.
Effects of CKI on the levels of pyruvate and glutamate
Pyruvate participates in pyruvate metabolism; glycine, serine and
threonine metabolism; alanine, aspartate and glutamate metabolism.
Glutamate is involved in D-Glutamine and D-glutamate metabolism. Thus,
representative metabolites pyruvate and glutamate were selected to
identify the variation between the CKI treatment and control groups.
CKI significantly increased the content of pyruvate in the medium but
decreased the content of glutamate in the cell (Fig. [144]10).
Figure 10.
[145]Figure 10
[146]Open in a new tab
Effects of CKI on the contents of glutamate (A) and pyruvate (B) of
SMMC-7721 cells. n = 3,
[MATH: x¯ :MATH]
± SEM. *p < 0.05, ***p < 0.001 versus untreated cells.
Compound-target-metabolite network of CKI
The compound-target-metabolite network was constructed for the 4 main
active compounds and 4 experimentally validated targets. As shown in
Fig. [147]11, matrine acted on the key targets of CASP3, MMP2, and MYC;
oxymatrine acted on the key targets of MMP2 and REG1A; sophoridine
acted on the key targets of MMP2 and REG1A; and N-methylcytisine acted
on the key targets of MMP2. By acting on these targets and interacting
targets, CKI could regulate the metabolites and metabolic pathways.
Figure 11.
[148]Figure 11
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Compound-target-metabolite network. The nodes of active compounds,
targets, pathway genes, differential metabolites, and metabolism
pathway were colored in yellow, red, blue, purple, and green,
respectively.
Discussion
CKI has been approved by the State Food and Drug Administration of
China for over 15 years and is widely known for its pain relief roles
in cancer. Increasing evidence has shown that CKI combined with
radiotherapy significantly improved the clinical efficacies of acute
leukaemia^[150]22, non-small cell lung cancer^[151]23, and HCC^[152]9.
Particularly, the combination treatment of TACE and CKI can improve the
1-and 2-year survival rates in patients with unresectable HCC. Our
results suggested that CKI significantly suppressed the proliferation
and migration of SMMC-7721 cells. However, the mechanisms of anti-HCC
effects of CKI needed further investigation.
Network pharmacology embraces some aspects of biological networks, such
as connectivity, redundancy and pleiotropy^[153]24, allowing it to
provide insights into biological systems^[154]25. Network pharmacology
is a useful approach to investigate the mechanisms of TCM. Generally,
in previous studies, all ingredients of herbs were collected from herb
databases, such as TCMSP^[155]26, TCM database@ Taiwan^[156]27, and
TCMID^[157]28. In some cases, the ingredients were filtered according
to ADMET properties or drug-likeness value^[158]13,[159]29. However,
few studies have focused on the main active compounds of TCM via
network pharmacology analysis.
Here, we proposed a novel idea of “main active compound-based network
pharmacology”. The concept was consistent with the method proposed by
Li. et al.^[160]30,[161]31, which was based on the combination of
chemical and therapeutic properties with network pharmacology. Li’s
approach used computational methods to predict the role and mechanism
of herbal formulae. The method could easily integrate newly found
ingredients and then provide a more comprehensive understanding of the
herbal formula. However, in our approach, network pharmacology analysis
was performed based on the main active compounds, which filters out
some ineffective compounds and focuses on the high content and active
compounds. Our approach provides a novel strategy to achieve an
accurate and systematic exploration of the mechanisms of TCM.
In this study, 22 compounds of CKI were identified by UPLC-MS, and the
top 5 compounds by content (matrine, oxymatrine, sophoridine,
N-methylcytisine and oxysophocarpine) were identified by comparison
with standard substances. Subsequent assays showed that 4 compounds
(matrine, oxymatrine, sophoridine, and N-methylcytisine) at the maximum
of 4 mg/kg could markedly inhibit the proliferation of SMMC-7721 cells.
These 4 compounds were considered main active compounds of CKI. The
mechanisms of the 4 main active compounds predicted by network
pharmacology were speculated to be the mechanisms of CKI.
By integration of the validated targets and predicted targets of the 4
compounds, the key targets of CKI were predicted according to network
parameters including the average shortest path length and betweenness
centrality. Western blotting confirmed that CKI significantly
up-regulated CASP3 expression but down-regulated the expression of
MMP2, MYC and REG1A. Caspase proteins contain cysteine residues at
their active site and cleave their substrate at positions next to the
aspartate residue^[162]32. Caspase-3, a principal enzyme in the
apoptotic cascade, is often used to detect apoptotic activity. Matrix
metalloproteinase 2 (MMP2) has been implicated in the development and
morphogenesis of tumours^[163]33. Increased expression of MMP2 has been
shown to promote the invasion and metastasis of tumour cells^[164]34.
Additionally, Zhao et al. found that the down-regulation of MYC protein
in HepG2 cells significantly inhibited the migration, invasion and
proliferation of HepG2 cells, suggesting that MYC might be a potential
therapeutic target for HCC^[165]35. REG1A has been reported to be
expressed in various human cancers, and it plays crucial roles in the
tumourigenesis of HCC^[166]36,[167]37. REG1A has also been shown to act
as a factor to reduce epithelial apoptosis in inflammation^[168]38.
Pathway enrichment analysis uncovered the novel anti-HCC mechanisms of
CKI, such as the regulation of amino acid metabolism and FRS2-mediated
cascade. Metabolic disorders are involved in the pathogenesis of
HCC^[169]5,[170]6. Therefore, metabolomics was used to detect the
differential metabolites and metabolic pathways regulated by CKI.
Metabolomics provides a valuable platform for the investigation of the
metabolic perturbations in HCC cells. In the current study, ^1H-NMR
metabolomics approach was used to investigate the effects of CKI on
metabolic disorders in SMMC-7721 cells. Twenty-two differential
metabolites were identified after treatment with CKI, including 16
differential metabolites in cells and 10 differential metabolites in
medium. Moreover, these 22 metabolites were mainly mapped to 6
metabolic pathways, which are important for identifying and analysing
metabolites in biochemical reaction networks^[171]21,[172]39.
Pyruvate, lactate and acetate participate in pyruvate metabolism.
Tumour cells require more glucose than normal cells to support their
rapid proliferation and expansion in the body^[173]40. Glycolysis, a
universal property of malignant cells, induces acidification of the
tumour environment, favouring the development of a more aggressive and
invasive phenotype^[174]41. The Warburg effect is characterized by the
capacity for using the glycolytic pathway even under aerobic
conditions, indicating tumour cell-specific aerobic glycolysis^[175]42.
Therefore, effective control of glycolytic levels is closely related to
the fate of cancer cells^[176]43,[177]44. The increase in the pyruvate
level after the administration of CKI was possibly due to the increased
rates of apoptosis, as well as the increase in aerobic glycolysis in
apoptotic cancer cells^[178]45. The increase in lactate may be
associated with increased lactate dehydrogenase activity, which is
enhanced in various cancers. At the same time, the expression of
lactate dehydrogenase in tumours increases, and a large amount of
pyruvate is converted into lactate, resulting in an increased level of
lactate^[179]46. In our study, after treatment with CKI, the lactate
level was significantly decreased, a finding that was consistent with
the previous result that antitumour drugs could decrease the lactate
level^[180]47.
Glutamate and glutamine are involved in D-Glutamine and D-glutamate
metabolism. During hepatocarcinogenesis, energy consumption grows
because of cell proliferation and survival, resulting in a high uptake
of glutamate^[181]48. Thus, glutamate levels are increased in HCC
cells, whereas CKI can significantly decrease intracellular glutamate
levels. The immune system is the main anti-tumour defence system in the
body, and glutamine is used to maintain the basic functions of the
immune system in the body. In addition, glutamine protects cells,
tissues and organs from free radical damage^[182]49. Elevated levels of
glutamine after the administration of CKI suggest that CKI may improve
immune dysfunction and free radical damage.
We further reconstructed a metabolic network related to anti-HCC
through MetScape based on the metabolites belonging to six metabolic
pathways that were regulated by CKI. In the network, the effect of CKI
on anti-HCC has many characteristics of multi-link and multi-level
comprehensive effects. The integrative strategy presented in this study
can be used as a powerful tool to understand the mechanisms of TCM.
In the current study, an integrative strategy combining the
quantitative analysis of components, network analysis and experimental
validation was used to explore the possible targets and pathways of CKI
against HCC. After proving the efficacy of CKI on HCC, four main active
compounds (matrine, oxymatrine, sophoridine and N-methylcytisine),
instead of the whole ingredients of CKI were used for network
pharmacology analysis. Through validating the experimental processes,
the key validated targets (i.e., MMP2, MYC, CASP3 and REG1A) and key
pathways (i.e., metabolism-associated pathways) were identified as
mechanisms of the anti-HCC effects of CKI. Additionally, the results
provide a scientific basis for the elucidation of the mechanisms of CKI
against HCC.
Materials and Methods
Reagents and materials
CKI (total alkaloid concentration of 20.8 mg/mL) was purchased from
Shanxi Zhendong Pharmaceutical Co.Ltd. (Shanxi, China), and
5-fluorouracil was ordered from Beijing Solarbio Science Technology Co.
Ltd. (Beijing, China). Matrine, oxymatrine, oxysophocarpine and
sophoridine were obtained from the National Institute for the Control
of Pharmaceutical and Biological Products (Beijing, China; Batch
numbers: 110805-200508, 110780-201508, 111652-200301, 110784-201405),
and N-methylcytisine was purchased from J&K Scientific Ltd. (Beijing,
China). High glucose DMEM, foetal bovine serum (FBS), 0.25% trypsin,
3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) and
dimethyl sulphoxide (DMSO) were obtained from Sangon Biotech Co.
(Shanghai, China). Sodium 3-trimethlysilyl [2, 2, 3, 3-d[4]] propionate
(TSP) was purchased from Cambridge Isotope Laboratories Inc. (Andover,
MA, USA). D[2]O was obtained from Norell (Landisville, Pennsylvania,
USA). Primary antibodies against β-actin, MMP2 and Myc were purchased
from ProteinTech (Chicago, IL, USA). The caspase-3 antibody was
obtained from Cell Signalling Technology, Inc. (Beverly, MA, USA). The
REG1A antibody was obtained from Abcam Inc. (Cambridge, MA, USA).
Cell culture and treatments
Human hepatoma SMMC-7721 cells were kindly donated by Professor
Xiongzhi Wu (Tianjin Medical University Cancer Institute and Hospital,
China). SMMC-7721 cells were maintained in DMEM culture medium
supplemented with 10% FBS, 100 units/ml penicillin G, and 100 μg/ml
streptomycin. All cells were cultured at 37 °C in a humidified
atmosphere containing 5% CO[2]. Cells in the exponential phase of
growth were used for all experiments. When SMMC-7721 cells reached 80%
confluency, the cells were then continuously exposed to 1 mg/mL,
2 mg/mL, 4 mg/mL or 8 mg/mL CKI. Subsequently, the cells were exposed
to 0.5 mg/mL, 1 mg/mL, or 2 mg/mL and 4 mg/mL matrine, oxymatrine,
oxysophocarpine, sophoridine or N-methylcytisine, respectively.
Cell viability assay
Cell viability of SMMC-7721 cells was evaluated using the MTT assay.
Cells were seeded on 96-well plates with a density of 5 × 10^4 cells/mL
in 100 µL of medium for 24 h and then were exposed to different
concentrations of agents for 24, 48 or 72 h. Following incubation,
10 μL of MTT (5 mg/ml) was added to each well. After 4 h of incubation
at 37 °C, the culture medium was removed and 100 μL of dimethyl
sulphoxide (DMSO, Sangon Biotech, Shanghai, China) was added to
dissolve the formazan crystals. The absorbance was measured at 570 nm
using a microplate reader (Infinite M200 Pro, Tecan, Switzerland), and
the cell viability was expressed as a percentage of the value of the
untreated group.
Wound-Healing Assay
SMMC-7721 cells were seeded in six-well plates at a density of 5 × 10^4
cells/mL. The centre of each well was scratched with a sterile 10-µL
pipette tip. After washing with phosphate-buffered saline, different
concentrations of CKI (0, 1, and 2 mg/ml) were added to the wells and
then were incubated for 12, 24 or 36 h. Micrograph images were taken
with a microscope at the indicated time points to observe the extent of
wound closures.
Transwell Assay
The Transwell assay was used to evaluate the migration inhibitory
activities of CKI against SMMC-7721 cells. The assay was performed
using a Boyden chamber with an inserted micropore membrane (6.5 mm in
diameter, 8.0 μm pore size) in 24-well plates (Corning Inc., Corning,
NY, USA). Next, 2 × 10^5 cells in 200 μL of serum-free DMEM
supplemented with 0.1% FBS were placed in the upper part of each
chamber, whereas the lower compartments were filled with 600 μL of CKI
(0, 1, and 2 mg/mL, 10% FBS). After 36 h of incubation, non-migrating
cells on the top of the membrane were removed. Thereafter, the migrated
cells on the bottom of the membrane were fixed, stained with 0.1%
crystal violet, and observed under an inverted microscope at 100×
magnification for three independent experiments.
Sample Preparation for UHPLC
One millilitre of CKI was diluted to 10 mL with water and was filtered
through a micropore membrane (0.22 mm; Jinteng Corp., Tianjin, China)
before use.
For the quantitative determination of the five constituents in CKI
samples, an accurately weighed amount of each reference substance was
mixed and dissolved in 10 mL of methanol to obtain a stock solution
with a concentration of 0.502 mg/mL for matrine, 0.494 mg/mL for
oxymatrine, 0.501 mg/mL for oxysophocarpine, 0.503 mg/mL for
sophoridine, and 0.499 mg/mL for N-methylcytisine.
Ingredient identification
Ultra-performance liquid chromatography in tandem with mass
spectrometry (UPLC-MS) (Thermo Fisher Scientific, Runcorn, Cheshire,
UK) analysis was used to assess the main ingredients in CKI. UHPLC was
conducted in tandem with mass spectrometry using a Thermo fisher U3000
UHPLC and Thermo Scientific Q Exactive mass spectrometer with an ESI
source and the following parameters: mobile phase (A) acetonitrile:
0.01 mol/L ammonium acetate (pH = 8.0) = 3:2 and (B) 0.01 mol/L
ammonium acetate (pH = 8.0); injection volume 5 µL; column temperature
35 °C, using a gradient elution mode. Run times were from 0 to 12 min
up to 8% B and from 11 to 20 min up to 27% B. The UHPLC system
consisted of an Acquity UPLC HSS T3 column (2.1 × 100 mm, 1.8 µm)
(waters, USA) with a 0.3 mL/min flow rate.
Target fishing for CKI
In our previous research^[183]13, HCC-related genes were collected from
the liver cancer databases OncoDB.HCC^[184]15
([185]http://oncodb.hcc.ibms.sinica.edu.tw) and Liverome^[186]16
([187]http://liverome.kobic.re.kr/index.php). The validated targets of
the 4 compounds were extracted from the Herbal Ingredients’ Targets
(HIT) Database^[188]50 ([189]http://lifecenter.sgst.cn/hit/). The
predicted targets of CKI were obtained using ChemMapper^[190]51
([191]http://lilab.ecust.edu.cn/chemmapper/), an online tool for
predicting targets based on 3D similarity. These targets were mapped to
HCC-related genes to obtain the candidate targets of CKI.
Network construction and analysis
The associated proteins of the targets of CKI were obtained from the
String^[192]52 ([193]http://string-db.org/) database. Cytoscape^[194]53
was applied to eliminate duplicate interactions and to construct a
protein-protein interaction network. Parameters such as Average
shortest path length and Betweenness centrality were calculated by
NetworkAnalyzer^[195]54. R value was used to determine the ranks of the
48 targets by the following formula:
[MATH: R=Xi−X<
/mrow>i(min)X
i(max)−Xi(min)×50%+1Xj−1Xj(min)1
Xj(max)−1Xj(min)×50%
:MATH]
1
where X[i] is the average shortest path length, X[j] is the betweenness
centrality, and R is an indicator to evaluate the importance of a
target.
Pathway analysis
Cytoscape plugin Reactome^[196]55 was used to enrich the possible
pathways involved in the anti-HCC effect of CKI.
Western blot analyses
SMMC-7721 cells (5 × 10^4 cells/mL) were seeded on 90 × 20-mm dishes.
After treatment, the SMMC-7721 cells were scraped off and washed twice
with cold PBS. The cells were solubilized by RIPA lysis buffer
(Beyotime, China) containing 1% phenyl methylsulphonylfluoride (PMSF,
Beyotime, China) for 30 min on ice. Whole-cell lysates were clarified
by centrifuging at 12 000 rpm for 15 min at 4 °C, and the supernatants
were collected. Protein concentrations were determined by the BCA
protein assay. Equal amounts of protein (50 μg) were separated by
electrophoresis on 12% sodium dodecyl sulphate polyacrylamide gels and
were transferred onto PVDF membranes. These membranes were soaked in 5%
skimmed milk dissolved with TBST buffer (Tris Buffer Saline
supplemented with 0.1% Tween-20) for 2 h to block nonspecific binding
sites. The membranes were then incubated overnight at 4 °C with the
primary antibodies (MMP2^[197]56,[198]57, MYC^[199]58,[200]59, Caspase3
and REG1A). After washing with TBST, the membranes were incubated for
2 h at room temperature with fluorescent secondary antibodies. After
rewashing with TBST, the membranes were scanned using a fluorescent
scanner (Odyssey CLX, Gene Company Limited, USA).
Cell collection for NMR analysis
All experiments included six independent replicates. Cells were
harvested by scraping and then were rinsed with 4 mL of PBS after
treatment with 4 mg/mL CKI for 24 h. The mixture was centrifuged at
1000 r/min for 5 min. Next, the supernatant was discarded and the cell
pellet was rinsed with 4 mL of PBS. The precipitate was then collected,
immediately frozen in liquid nitrogen, and stored at −80 °C. To isolate
extracellular metabolites, 10 mL of extracellular medium was pipetted
from cells. The samples were subsequently centrifuged at 1000 r/min for
10 min. The collected supernatant, which was used as the extracellular
fraction, was immediately frozen in liquid nitrogen and stored at
−80 °C.
Sample preparation for NMR analysis
The cells and culture broth were removed from −80 °C and thawed at 4 °C
according to the literature^[201]60 with minor adjustment. The
extracellular medium was prepared for freeze-drying by taking 2 mL of
the medium. Cell extraction for repeated freeze-thaw and ultrasonic
disruption was conducted according to the following procedure. After
repeated freeze-thawing 5 times, the cell pellets were kept on ice for
5 min before being re-suspended in 1 mL of ice-cold methanol/water
(1/2, v/v), and ultrasonic disruption for 5 min on the ice (sonicate
5 s, stop 9 s). The supernatant was collected after centrifugation at
13000 r/min for 10 min at 4 °C, and 1 mL of methanol aqueous solution
was added to the precipitate. The above ultrasonic sieving was repeated
and the supernatant was collected two times in 5-mL EP tubes for
lyophilization.
The lyophilized powder of cells and fluids of cells were dissolved in
600 µL of phosphate buffer (0.1 M, KH[2]PO[4]/Na[2]HPO[4], pH 7.4)
containing 0.005% and 0.02% TSP, respectively, as well as 10% D[2]O.
After centrifugation (13,000 r/min, 4 °C, 10 min), 600 µL of
supernatant was transferred into a 5-mm NMR tube for analysis.
^1H-NMR Measurement
The ^1H-NMR spectrawere recorded at 298 K using a Bruker 600-MHz AVANCE
III NMR spectrometer (Bruker Biospin, Germany) and the noesygppr1d
pulse sequence for water suppression. The ^1H-NMR spectrum for each
sample consisted of 64 scans requiring 5 min of acquisition time with
the following parameters: spectral width 12,345.7 Hz; spectral size
65,536 points; relaxation delay of 1.0 s; acquisition time of 2.654 s.
All spectra were manually phased and baseline corrected using
MestReNova software (Mestrelab Research, Santiago de Compostella,
Spain). Chemical shifts were referenced to TSP at δ 0.00. Regions
distorted by residual water (δ 4.5~5.0) were excluded in the subsequent
analysis. Each spectrum was then segmented at 0.01-ppm intervals across
the chemical shift 0.50~9.00; each data point was normalized to the sum
of its row and then was exported as a text file for further
multivariate statistical analysis.
Multivariate pattern recognition analysis
The normalized integral values were then subjected to multivariate data
analysis using SIMCA-P 13.0 software (Umetrics, Sweden). Partial
least-squares-discrimination analysis (PLS-DA) was performed to
distribute and separate different groups in a supervised manner. Next,
the PLS-DA model was validated by the response values of the
permutation test in which the class membership was randomly shuffled
200 times. Additionally, another supervised pattern recognition
approach—orthogonal projection to latent structures discriminant
analysis (OPLS-DA)—was then performed to improve the classification of
the different groups, as well as to screen the biomarkers. The
corresponding loading, where each point represents a single NMR
spectral region segment, was used to identify which spectral variables
contributed to the separation of the samples on the scores plot.
Variable importance in the projection (VIP) values and coefficients
were also applied to screen the important variables.
Metabolic pathway analysis
The potential metabolic pathway was analysed by using MetPA. Potential
biological roles were evaluated using the MetaboAnalyst enrichment
analysis tool. Metscape, the metabolic network analysis and
visualization tool (http://metscape.ncibi.org./)^[202]61, was used to
generate the compound network associated with each of the differential
metabolites.
Content determination of representative metabolites
The contents of pyruvate (PA) and glutamate (Glu) were determined
according to the manufacturer’s protocols (Komin, Suzhou, China), which
were based on extraction with a specific extract and then using a
colour reagent for colour development.
Construction a compound-target-metabolite network
According to the network constructed by MetScape, 174 key genes
involved in differential metabolites were obtained. Subsequently, the
potential interactions between the 174 genes and 48 targets of CKI were
obtained from the String database. A bio-network of
compound-target-metabolite was constructed using Cytoscape software.
Statistical analysis
Quantitative data were presented as means ± standard error of mean
(SEM) from three or more independent repetitions. Student’s t test was
used to test the differences between two groups, and one-way ANOVA
followed by Dunnett post hoc test was used for statistical analysis to
determine significant differences of three or more groups. All
preprocessed NMR data were imported into the software package SIMCA-P
13.0 (Umetrics, Sweden) for multivariate data analysis. P < 0.05 was
deemed to indicate statistical significance.
Electronic supplementary material
[203]Supplementary Information^ (10.5MB, doc)
Acknowledgements