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Biochimica et Biophysica Acta 1830 (2013) 2779–2789
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Biochimica et Biophysica Acta
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Effects of celastrol on human cervical cancer cells as revealed by ion-trap gaschromatography–mass spectrometry based metabolic profiling
Yongsheng Hu a,b,c,1, Yunpeng Qi a,b,1, Hua Liu a,b, Guorong Fan a,b,⁎, Yifeng Chai a,b,⁎⁎a Department of Pharmaceutical Analysis, School of Pharmacy, Second Military Medical University, Shanghai 200433, Chinab Shanghai Key Laboratory for Pharmaceutical Metabolite Research, Second Military Medical University, Shanghai 200433, Chinac Department of Pharmacy, the 118th Hospital of PLA, Wenzhou 325000, China
⁎ Correspondence to: G. Fan, Department of PharmaceutiSecond Military Medical University, Shanghai 200433, Chin⁎⁎ Correspondence to: Y. Chai, Department of PharmaceutiSecond Military Medical University, Shanghai 200433, Chin
E-mail addresses: [email protected] (Y. Hu), [email protected] (H. Liu), [email protected](Y. Chai).
1 These authors contributed equally to this work.
0304-4165/$ – see front matter © 2012 Elsevier B.V. Allhttp://dx.doi.org/10.1016/j.bbagen.2012.10.024
a b s t r a c t
a r t i c l e i n f oArticle history:
Received 19 April 2012Received in revised form 4 October 2012Accepted 29 October 2012Available online 8 November 2012Keywords:CelastrolHuman cervical cancer cellApoptosisGas chromatography–mass spectrometryMetabolic profilingMetabolomics
Background: Celastrol, a quinine methide triterpene extracted from a Chinese medicine (Trypterygiumwilfordii Hook F.), has the potential to become an anticancer drug with promising prospects. Cell culturemetabolomics has been a powerful method to study metabolic profiles in cell line after drug treatment,which can be used for discovery of drug targets and investigation of drug effects.Methods: We analyzed the metabolic modifications induced by celastrol treatment in human cervical cancer cells,using an ion-trap gas chromatography–mass spectrometry basedmetabolomics combinedwithmultivariate statis-tical analysis,which allows simultaneous screeningofmultiple characteristicmetabolic pathways related to celastroltreatment. Three representative apoptosis-inducing cytotoxic agents, namely cisplatin, doxorubicin hydrochlorideand paclitaxel, were selected as positive control drugs to validate reasonableness and accuracy of our metabolomicinvestigation on celastrol.Results: Anti-proliferation and apoptotic effects of celastrol were demonstrated by CCK-8 assay, Annexin-V/PIstaining method, mitochondrial membrane potential (ΔΨm) assay and caspase-3 assay. Several significant
metabolites involved in energy, amino acid and nucleic acid metabolism in HeLa cells induced bycelastrol and positive drugs were reported. Our method is proved to be effective and robust to providenew evidence of pharmacological mechanism of celastrol.Conclusions: The metabolic alterations induced by drug treatment showed the impaired physiologicalactivity of HeLa cells, which also indicated anti-proliferative and apoptotic effects of celastrol andthese positive drugs.General significance: GC/MS-based metabolomic approach applied to cell culture could give valuable informa-tion on the systemic effects of celastrol in vitro and help us to further study its anticancer mechanism.© 2012 Elsevier B.V. All rights reserved.
1. Introduction
Traditional medicine represents a cornucopia of plant-derivedremedies to discover novel lead molecules for the development ofnew drugs. Celastrol (structure see Fig. 1A), a quinine methidetriterpene extracted from a typical Chinese medicine (Trypterygiumwilfordii Hook F.), has been extensively investigated as a promisingdrug for the treatment of autoimmune diseases, asthma, chronic in-flammation, and neurodegenerative disease [1,2]. In 2006, celastrol isreported for the first time to be a natural proteasome inhibitor and
cal Analysis, School of Pharmacy,a. Tel./fax: +86 21 81871260.cal Analysis, School of Pharmacy,a. Tel./fax: +86 21 [email protected] (Y. Qi),(G. Fan), [email protected]
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has exhibited a great potential for cancer prevention and treatment[3]. From then on, investigation on therapeutic efficacy of celastrolagainst various cancer cells has become a hot spot [4–9]. Celastrol caninhibit the proliferation of wide variety of human tumor cells, andprevent their malignant tissue invasion and block angiogenesis[3,4,10,11]. When used in combination therapy, it can also sensitizeresistant melanoma cell to temozolomide treatment, and potentiateradiotherapy in prostate cancer cells [12,13]. These studies show thatcelastrol has the potential to become an anticancer drugwith promisingprospects.
Several molecular targets of celastrol have been characterized,including heat shock protein (HSP), reactive oxygen species (ROS),vascular endothelial growth receptor (VEGFR), nuclear factor-κB(NF-κB) and so on [14,15]. Interestingly, many of them are centeredon the function of IκB kinase enzyme (IKK) complex and NF-κB system[1,11], which is the key regulator in cancer disease [16,17]. However,the NF-κB system is highly integrated with other signaling pathwaysvia a variety of protein kinases [18,19], which makes it difficult toexplain the mechanism of celastrol's therapeutic effects. Hence, although
Fig. 1. Chemical structures of celastrol (A), cisplatin (B), doxorubicin hydrochloride (C) and paclitaxel (D).
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investigations focusing on celastrol's effects on specific cellular pathwayshave revealed a number of targets in a diverse array of in vitro models[14,15], there is still lack of a thorough insight into its anticancer effectsfrom a global view.
Metabolomics seeks to characterize the metabolic profile of abiological system. Since the panel of metabolites with relativelylow molecular weight are downstream products of biomolecularprocesses, their identity and concentration in living biological systemcan provide biochemical signatures for globally tracking the physiologicaleffects, and exploring the drug effects [20–22]. Particularly, becausecancer cells have several specific metabolic features, such as highenzyme activities, high phosphometabolite levels and high energymetabolism (for an overview see http://www.metabolic-database.com/html/tumor_metabolome_overview.html), cell culture metabolomicshas been instrumental in finding further susceptible biomarkers forcancer diagnosis or drug treatment [23–26].
Apoptosis is an important phenomenon in cancer therapy andrepresents a common mechanism of drug effect [27]. Therefore, in-vestigation on biomarkers indicative of early apoptosis is crucial intheranostics of cancer therapy [28]. Importantly, celastrol has beenreported to induce apoptosis in many cancer cells. However, the meta-bolic intervention of celastrol on cancer cells has not been revealed,whereas this is clearly very meaningful for exploring its mechanism ofaction in preventing and treating cancer. These considerationsprompted us to study the metabolic modifications induced by celastroltreatment in cancer cells.
Comparing to LC-MS and NMR, GC–MS remains a good choice formetabolomic study, since it has been proved of high selectivity andreproducibility with relatively low cost, and a number of structuredatabases are available [29]. The present study aimed to design afast, robust and reliable GC–MS analysis system for metabolite mea-surements in cancer cells, for the purpose of providing a global viewof celastrol's effects. We report for the first time several metabolitesindicative for early apoptotic processes in HeLa cells culture inducedby celastrol using ion-trap gas chromatography–mass spectrometry.Meanwhile, in order to validate reasonableness and accuracy of ourmetabolomic investigation on celastrol, we selected three representa-tive apoptosis-inducing cytotoxic agents, namely cisplatin, doxorubicinhydrochloride and paclitaxel (structures see Fig. 1B to D) as positivecontrol drugs. In our study, cell fate and apoptosis were determined
by CCK-8 assay, Annexin-V/PI staining method, mitochondrial mem-brane potential assay and caspase-3 assay.
2. Materials and methods
2.1. Reagents
Dulbecco's modified Eagle's medium (DMEM) was purchasedfrom HyClone Thermo scientific (Beijing, China). Fetal bovine serum(FBS) was obtained from GIBCO BRL (Grand Island, NY, US). Celastrol,cisplatin, doxorubicin hydrochloride and paclitaxel were all purchasedfrom Sigma-Aldrich (St. Louis, MO, US). Celastrol and positive controldrugs were prepared from stock solutions in dimethyl sulfoxide(DMSO). The stock solutions were kept frozen in aliquot at −20 °Cand thawed immediately prior to each experiment. Methoxylaminehydrochloride, N-methyl-N-(trimethylsilyl)-trifluoracetamide (MSTFA),pyridine, trimethyl-chlorosilane (TMCS) and ribitol (used as internalstandard) were purchased from Sigma-Aldrich (St Louis, MO, US).
2.2. Cell culture
Human cervical cancer HeLa cells line was purchased from ChineseAcademy of Sciences (Shanghai, China). Cells were routinely culturedin DMEM, supplemented with 10% fetal bovine serum (FBS), penicillin(100 U/mL), streptomycin (0.1 mg/mL) and were maintained in ahumidified atmosphere of 5% CO2 at 37 °C. Cells were passagedevery 3–4 days. For drug treatment, appropriate amounts of celastroland positive control drugs were added to culture medium to achievethe appropriate concentrations and then incubated for the indicatedtime periods.
2.3. CCK-8 assay to determine cell viability
To evaluate the percentage of viable cells after different treatments,the Cell Counting Kit-8 (CCK-8) assay (Beyotime Biotech, China) wasperformed. Cells were seeded in 96-well plates at a density of 2000cells/well. On the next day, cells were incubated with different concen-trations of celastrol. After appropriate incubation time, 10 μL CCK-8wasadded to eachwell. After another 1 h of incubation at 37 °C, absorbancewas measured at 480 nm (A480) with the Synergy™ 4 Mulit-Detection
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Microplate Reader (BioTek, US). The percentage of viable cellswas calculated by the formula: the percentage of viable cells(%)=100−[(A480, control−A480, drug)/A480, control]×100.
2.4. Apoptosis analysis by flow cytometry
Cell apoptosis was examined by the Annexin-V/PI method. HeLacells were seeded at a density of 1×105 cells/well into a 6-well plate.After being treated with celastrol (2.5, 5, and 10 μM) and incubatedfor 24 h, the HeLa cells were collected and apoptosis was examined byusing an Annexin V-FITC Apoptosis Detection Kit (KeyGEN, Nanjing,China), which detects phosphatidylserine exposed on the outer surfaceof the cell membrane. The cells were harvested with trypsin (withoutEDTA) and washed in phosphate-buffered saline (PBS, pH 7.4). Aftercentrifugation at 2000 rpm, the supernatant was removed and thenthe cells were suspended in stain containing Annexin V-FITC andpropidium iodide (PI). This wasmixedwell and incubated at room tem-perature for 15 min in the dark. The cells were analyzed byMACSQuant™ flow cytometry (Miltenyi Biotec, Germany) within 1 h of staining.Apoptotic cells were defined as annexin V-FITC-positive cells.
2.5. Mitochondria membrane potential (ΔΨm) assay
The ΔΨm was determined by using the mitochondrial membranepotential assay kit with JC-1 (Beyotime Biotech, China). JC-1 is capableof selectively entering mitochondria, where it forms monomers andemits green fluorescence when ΔΨm is relatively low. At a high ΔΨm,JC-1 aggregates and gives red fluorescence [30]. Briefly, HeLa cellswere seeded in 6-well plates. After celastrol treatment, cells were col-lected, and then suspended in 1 mL staining dye (culture medium:JC-1 working dye=1:1) and incubated at 37 °C for 20 min, 5% CO2.After this, cells were washed twice with cold JC-1 staining buffer, andexamined with the FACSCalibur flow cytometry (BD, New York, USA).The depolarization of ΔΨm was represented by the percent of R2(percent of JC-1 monomer).
2.6. Caspase-3 assay to detect apoptosis
For detection of apoptosis cells were grown in culture dish(10 cm). The enzymatic activity of caspase-3 was determined usingthe Caspase-3 Colorimetric Assay Kit (KeyGEN Biotech, China). Aftertreatment of 5 or 10 μM celastrol for 6, 12 and 24 h, the cells werecollected to measure the caspase-3 activity according tomanufacturer'sinstructions. Cell extracts were incubatedwith 5 μL caspase-3 substrateat 37 °C for 4 h. The reactionwasmeasured at 405 nm in the Synergy™4 Mulit-Detection Microplate Reader. Celastrol treated samples werenormalized to the caspase activity of the untreated sample, which wasset to 1.0. Fold of increases in caspase activities were presented.
2.7. Cell quenching
For metabolite measurements, HeLa cells were cultured in culturedish (10 cm) to approximately 70% confluence, and then incubatedwith celastrol and positive control drugs for 12 h. After the culturemedium was removed from the culture dish, cells were rapidlywashed twicewith 37 °C PBS. The residual PBSwas removed by vacuum.Cells were then quenched using 1.5 mL −80 °C HPLC grade methanol(Sigma-Aldrich, St. Louis, MO). Next, cells were quickly detached fromthe culture dish using a cell lifter (Fisher Scientific, Suwanee, GA). Themethanol solution containing the quenched cells was pipetted into a2 mL centrifuge tube and frozen in liquid nitrogen until extraction. Inaddition, five parallel dishes of cells were trypsinized and counted, sub-sequent metabolite measurements were normalized to cell count.
2.8. Preparation of intracellular extracts for GC–MS analysis
The tube containing the quenched cells were thawed in an ice bathfor 10 min, spiked with the internal standard (5 μL of 1 mg/mL ofribitol), vortexed vigorously, and incubated on ice for 10 min. Afterthat, the tube was frozen in liquid nitrogen for 10 min, thawed in icebath for 10 min, and briefly vortexed. The tube was centrifuged at4 °C and 12,000 rpm for 5 min and the supernatant was transferred toa new tube. The cell pellet was re-extracted twice with 500 μL cold80% methanol (20% water). The combined extracts was transferredinto a GC vial and evaporated to dryness under N2 stream at roomtemperature. The derivatization was performed using methoxyaminepyridine (75 μL; 15 mg/mL) at 60 °C for 1 h, followed by MSTFA(75 μL) with 1% TMCS as catalyst at 60 °C for 1 h.
2.9. GC–MS analysis
The derivatized samples for GC–MS were analyzed on a ThermoScientific ITQ 1100™ GC/MSn (ThermoFisher Electron Corporation,USA). A 1.0 μL of sample solution was injected with splitless modeto TR-5MS column, 30 m×0.25 mm ID×0.25 μm film thickness(ThermoFisher Electron Corporation, USA), with helium as the carriergas at a flow of 0.6 mL/min. The initial oven temperature was set at50 °C, ramped to 300 °C by 8 °C/min, and held for 10 min. The tem-peratures of injector, ion source and transfer line were all set at280 °C. The electron energy was 70 eV. The mass spectrometer wasoperated in full scan mode from 35 to 600 m/z with a scan time of0.5 s. The solvent delay was set at 5 min. Identification of the metab-olites in the GC–MS spectra was performed by searching the NIST(National Institute of Standards and Technology) database installedin the ITQ 1100™ GC–MSn system. To ensure the stability of theGC–MS system, an equal volume of each sample was pooled togetherto generate a pooled quality control (QC) sample [31,32]. This QCsample was processed in the same way as the samples and thenanalyzed randomly through the analytical batch.
2.10. Data processing and multivariate statistical analysis
The acquired GC–MS data was first converted into CDF format.XCMSOnline (https://xcmsonline.scripps.edu/) was used for nonlinearalignment of the data in the time domain and automatic integrationand extraction of the peak intensities, using default GC/Single Quadparameters. The XCMS output data containing 4378 ion peakswas preprocessed using the Microsoft Excel software (Microsoft,Redmond,WA), where the IS peaks, and impurity peaks from columnbleeds and derivatization procedures were excluded, and the vari-ables presenting in at least 80% of either group were extracted.Then, the remaining ion features were normalized to the internalstandard (m/z 217.1, the most abundance fragment ion for thesilylation derivative of ribitol). Next, the most abundant fragmention with the same retention time (the time bin is 0.01 min) wasremained and the other ions were excluded [33]. The processeddata matrix with intensities of 353 ion peaks was further subjectedto statistical analysis.
Pattern recognition methods including PLS-DA (partial leastsquares-discriminate analysis) and PCA (Principal Component Analysis)(using SIMCA-P, version 11, Umetrics) and Heatmap (using MetATT,http://metatt.metabolomics.ca/MetATT/) were established to investi-gate the intracellular metabolic profiles of the negative control,celastrol and positive control treated groups. The data weremean-centered and unit variance (UV)-scaled before multivariatestatistical analysis. The discriminating metabolites were obtainedusing a statistically significant threshold of variable influence on pro-jection (VIP) values obtained from the PLS-DA model and two-tailedStudent's t test (P value) on the normalized raw data at univariateanalysis level, where the metabolites with VIP values larger than
Fig. 2. HeLa cells were seeded in 96-well plate and treated with 2.5 μM, 5 μM and10 μM celastrol for the indicated time points. The cell proliferation was determinedby the CCK-8 assay. Data were presented as the percentage of proliferation relativeto DMSO-treated control. All measurements were performed in triplicate. Significantdifferences were compared with the control at ** pb0.01 by Student's t-test.
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1.0 and P values less than 0.05 were selected. Fold changes werecalculated as the average mass response (area) ratio between twogroups.
Fig. 3. Effect of celastrol on apoptosis ofHeLa cells. HeLa cells treatedwithDMSOor celastrol forcytometry. TheHeLa cellswere treated for 24 hwith celastrol 2.5 μM(B), 5 μM(C) and 10 μM(cells increased in a dose dependent manner as shown in the figure.
3. Results and discussion
3.1. Effects of celastrol treatment on cell viability, apoptosis, mitochondria,ΔΨm and caspase-3 expression
At first, the effects of celastrol on proliferation, apoptosis, ΔΨm andcaspase-3 expression of HeLa cells were investigated, in order to evalu-ate the efficacy of celastrol as well as choose the best conditions for themetabolomic analysis.
To test whether celastrol inhibits HeLa cells growth, we incubatedthe cells in various concentrations of celastrol for 24, 48, and 72 h,and then performed CCK-8 assay. As shown in Fig. 2, celastrolinhibited the growth of HeLa cells in a time- and dose-dependentmanner (with increasing concentrations from 2.5 to 10 μM) andshowed significant inhibition at concentrations of 5 and 10 μM aftercelastrol treatments for 24, 48 and 72 h (pb0.05).
The ability of celastrol to induce apoptosis in HeLa cells wasassessed using the Annexin-V/PI method. Celastrol induced a sig-nificant concentration-dependent apoptosis in HeLa cells (Fig. 3).The apoptosis rates were 4.28% in the control group, 7.22%,37.3%, and 34.8% in the celastrol (2.5, 5 and 10 μM) treated groups,respectively.
24 hwere co-stainedwith annexin V–FITC and PI and then examined for apoptosis byflowD). Control cellswere treatedwithDMSO (A). The celastrol-induced apoptosis rate of HeLa
Fig. 4.Mitochondrial dysfunction induced by celastrol treatment. HeLa cells were treated for 24 h with celastrol 2.5 μM (B), 5 μM (C) and 10 μM (D). Control cells were treated withDMSO (A), and then stained with JC-1 dye, incubated with cells for 20 min at 37 °C, 5% CO2 and examined by flow cytometry at the emission wavelength of 530 nm (green, R2). Thedepolarization of ΔΨm was represented by the percentage of R2 (percentage of JC-1 monomer).
Fig. 5. Effect of celastrol on activation of caspase-3. HeLa cells were treated withcelastrol for 6, 12 and 24 h. Data values were expressed as mean±SD of triplicate de-terminations. Significant differences were compared with the control at ** pb0.01 and*** pb0.001 by Student's t-test.
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Rapid loss of ΔΨm is believed to be the hallmarks of mitochon-drial dysfunction, which may induce the activation of caspaseand lead to apoptotic cell death. To determine whether or notapoptotic cell death was triggered by mitochondrial dysfunction,mitochondrial membrane potential assay was performed. Asshown in Fig. 4, celastrol induced a significant concentration-dependent depolarization of ΔΨm in HeLa cells. The percentagesof R2 (JC-1 monomer) were 0.99% in the control group, 4.64%,28.58%, and 87.12% in the celastrol (2.5, 5 and 10 μM) treated groups,respectively.
To estimate the extent of apoptosis induced in the HeLa celllines by celastrol treatment, caspase-3 activity was analyzed.Values were normalized to the caspase-3 activity of control group.As shown in Fig. 5, the enzymatic activity of the caspase-3 signifi-cantly increased after celastrol treatment. The HeLa cells showed 1.5and 3.5 fold of increases in caspase-3 enzyme activity as comparedto untreated controls after 6 h celastrol (5 and 10 μM) treatment(Pb0.001).
Fig. 6. The typical total ions current chromatograms (TICs) of HeLa cells intracellular extract. (A) Negative control group; (B) celastrol treated group; (C) cisplatin treated group;(D) doxorubicin hydrochloride treated group; and (E) paclitaxel treated group.
Fig. 7. Score plot from PLS-DA model. (A) Score plot of Celastrol treated group (blue triangle) and negative control group (red square); (B) Score plot of cisplatin treated group(green open triangle) and negative control group (red square); (C) score plot of doxorubicin hydrochloride treated group (violet star) and negative control group (red square);(D) score plot of paclitaxel treated group (brown open inverted triangle) and negative control group (red square). The PLS discriminant model was validated and found to be pre-dictive (A: R2X=0.822, R2Y=0.99 andQ2=0.987; B: R2X=0.749, R2Y=0.992 and Q2=0.981; C: R2X=0.801, R2Y=0.994 andQ2=0.993; D: R2X=0.819, R2Y=0.998 andQ2=0.984).(For interpretation of the references to color in this figure legend, the reader is referred to the web of this article.)
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Table 1Summary of the significantmetabolites revealed in this study. Fold change was calculatedas the average mass response (area) of celastrol or positive drugs treated group to thenegative control group. Abbreviation: Cel (celastrol treated group), Cis (cisplatintreated group), Dox (doxorubicin hydrochloride treated group), Pac (paclitaxel treatedgroup) and NA (not significant).
Metabolites R.T.(min)
Folder changesof metabolites
Pathway names
Cel Cis Dox Pac
Urea 6.02 NA NA 7.13 3.23 Pyrimidine metabolismD-Lactic acid 7.66 6.52 5.10 6.30 3.75 Pyruvate metabolismL-Leucine 9.47 NA 0.36 0.66 0.66 Valine, leucine and
isoleucine biosynthesisL-Isoleucine 9.86 NA 0.41 0.70 0.72 Valine, leucine and
isoleucine biosynthesisL-Valine 10.30 NA 0.38 0.50 0.60 Valine, leucine and
isoleucine biosynthesisD-Mannose 11.37 NA 1.79 4.03 3.21 Fructose and mannose
metabolismL-Threonine 11.77 NA 0.37 NA NA Valine, leucine and
isoleucine biosynthesisGlycine 11.88 0.64 0.45 0.23 0.26 Glycine, serine and
threonine metabolismSerine 12.71 0.48 0.33 0.61 0.52 Glycine, serine and
threonine metabolismMalic acid 14.78 0.60 0.17 0.37 0.53 Citrate cycle (TCA cycle)L-Aspartic acid 15.30 NA 0.24 NA 0.56 Alanine, aspartate and
glutamate metabolismL-Cysteine 15.85 0.45 NA NA NA Cysteine and
methioninemetabolism
L-Alanine 16.07 NA NA 0.53 0.45 Alanine, aspartate andglutamate metabolism
α-Ketoglutarate 16.25 0.56 0.12 0.60 0.29 Citrate cycle (TCA cycle)L-Glutamine 16.77 NA 0.46 0.43 0.34 Alanine, aspartate and
glutamate metabolism1,4-Butanediamine 18.36 3.51 1.99 1.66 2.24 Glutathione
metabolismOrnithine 18.71 NA 2.16 4.39 3.08 Glutathione
metabolismIsocitric acid 19.44 0.50 0.24 0.31 0.43 Citrate cycle (TCA cycle)D-Fructose 19.93 2.81 3.31 6.18 2.93 Fructose and mannose
metabolismHexadecanoic acid 22.55 NA 0.59 0.71 0.57 Fatty acid biosynthesisOctadecanoic acid 24.79 0.32 0.64 0.73 0.62 Fatty acid biosynthesisInosine 26.12 5.21 1.40 6.08 3.39 Inosinemonophosphate
biosynthesisGuanosine 29.80 2.97 2.09 6.37 3.94 Nucleic acid
metabolismD-Ribofuranose 30.29 0.43 NA NA NA Pentose phosphate
pathwayα-D-Galactopyranoside 34.40 0.46 NA NA NA Unknown
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3.2. Dose selection, cell quenching and metabolites extraction
Based on the results in Section 3.1, in this investigation,weused a con-centration of 5 μMcelastrol treatment in confluent cultures for 12 h; thisdose could causemostly cell apoptosis but only little necrosis. The dosesof positive control drugs were referred to previous studies (20 μM cis-platin, 4.8 μM doxorubicin hydrochloride and 0.5 μM paclitaxel)[34–36].
The goal of metabolomics is to analyze all or, at least, as many aspossible different metabolites without selectivity for any particularmolecular type and/or characteristic. Cell quenching and metaboliteextraction are the two crucial steps during the cell culture metabolomicsample preparation. Over the past few years, several optimized protocolshave been developed for the preparation of intracellular metabolites[37–39]. According to these protocols, we used cold methanol to quenchthe cells and directly harvest the cells using a cell lifter to avoid thechange of metabolites. After that, in order to extract the metabolites ascomplete as possible, we used methanol–water (80:20) mixture toextract metabolites from frozen-thawed cells [38]. Our method isproved to be rapid, effective and robust compared to the conventionalmethod.
3.3. Metabolic profiling of celastrol treated HeLa cells
Typical total ion current chromatograms (TICs) of cell extracts wereshown in Fig. 6. For the QC samples, the relative standard deviation(RSD) ranged from 0.04% to 0.52% for the retention times and rangedfrom 1.58% to 4.96% for the peak areas, which demonstrate the robust-ness of the method.
In order to explore clustering of the negative control and thecelastrol treated group, PLS-DA, a method derived from PLS analysiswhere the Y matrix was set as a dummy descriptor, was used. Thegoodness of the fit and prediction ability of the model were validated(R2X=0.822, R2Y=0.99 and Q2=0.987). As could be observed inthe PLS-DA score plot (Fig. 7A), separation between these two groupswas clearly seen, indicating that biochemical perturbation significantlyhappened due to celastrol treatment. According to the loading plot ofthis PLS-DA model, and using the above-stated statistically significantthreshold, 14metabolites with VIP-values greater than 1.0 and P valuesless than 0.05 were finally revealed to be significant in differentiatingthe celastrol treated and negative control groups, in which D-lacticacid, D-fructose, 1,4-butanediamine, inosine and guanosine wereelevated in celastrol treated group, and other metabolites includingserine, L-cysteine, glycine, malic acid, α-ketoglutarate, isocitric acid,octadecanoic acid, D-ribofuranose and α-D-galactopyranoside werereduced in this group (Table 1). The alteration in levels of these me-tabolites in celastrol treated group manifested the characteristics ofmetabolic profile in cell response to celastrol's apoptotic effect,which will be explained later.
3.4. Biological explanation of the marker metabolites in celastrol treatedgroup
The enzymes of the citrate cycle are mostly located in the mito-chondrial matrix, hence mitochondrial dysfunction can reduce the effi-ciency of citrate cycle [40]. Our study indicated that celastrol led tomitochondrial dysfunction as it induces rapid loss of ΔΨm in HeLa cells.Accordingly, we observed the decline of a couple of key intermediatesin citrate cycle (isocitric acid,α-ketoglutarate andmalic acid) in celastroltreated group, which also implied the reduced citrate cycle flux (for anoverview see http://www.nutritionreview.org/library/krebs.php).
Decrease in oxidative metabolism (citrate cycle) could be com-pensated by increased glycolysis from supplement of pyruvate [41].In term of the cancer cells, the well-known Warburg effect has re-vealed that they have increased aerobic glycolysis producing morelactate [42]. In our study, fluctuation of several metabolites in glycolysis
coincides well with the above established knowledge. As the main in-termediates of glycolysis, fructose in celastrol treated group is foundto increase significantly by 2.81 times compared to the negative controlgroup, which implied up-regulated glycolysis (for an overview seehttp://themedicalbiochemistrypage.org/non-glucose-sugar-metabolism.php). Lactic acid is another important substance as end product ofglycolysis. The dramatically increase of lactic acid (reaching 6.52 timescompared to the negative control group) may indicated an augmentedconsumption of pyruvate, its precursor. All together, the increase offructose and lactic acid in celastrol treated group implied that celastrolmay aggravate Warburg effect of cancer cells, and the insufficiency ofpyruvate may partly lead to the reduced citrate cycle [43].
In this study, levels of serine, cysteine and glycine decreases incelastrol treated group. Among them glycine and serine are involved inglycine, serine and threonine metabolism [44,45] and cysteine is in-volved in cysteine and methionine metabolism [46,47]. The down-regulation of these amino acidmetabolismsmay result from the reducedenergy supply, which can also down-regulate protein biosynthesis be-cause of shortage of the metabolism pool [48].
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For inosine and guanosine, 5.21 and 2.97 fold increase was foundin celastrol treated group compared to negative control group, re-spectively. Inosine and guanosine are key nucleic acid metabolites in-volved in nucleic acid metabolism, cellular energy metabolism andprotein biosynthesis. The reduced nucleic acid metabolism, cellularenergy metabolism and amino acid metabolism may lead to reducedconsumption of inosine and guanosine. Meanwhile, inosine can im-prove activity of a variety of enzymes, in particular, coenzyme Aand pyruvate oxidase, which can up-regulate glycolysis of the cellsunder anaerobic conditions [49,50]. Therefore, the increased inosineprobably enhances glycolysis.
As shown in Fig. 8, the changes of metabolic profiles in thecelastrol treated HeLa cells reflect the cell's physiological state.Celastrol can induce apoptosis in HeLa cells by loss of ΔΨm andcaspase-3 activation. Mitochondrial dysfunction reduced the efficiencyof citrate cycle, and glycolysis is increased to compensate reducedcitrate cycle. Concomitantly, the synthesis of amino acids, proteinbiosynthesis and nucleic acid metabolism may be hampered withthe lower energy supply. These metabolic alterations induced bycelastrol treatment showed the impaired HeLa cell's physiologicalactivity, and can also provide new insights into celastrol's actionmechanisms of anti-proliferation and apoptotic effects.
3.5. Validation of the metabolomic assessment on celastrol using somerepresentative cytotoxic agents
In order to validate the reasonability and accuracy of the presentmetabolomic assessment on celastrol, we proceeded to investigatethe effects of three widely used representative cytotoxic agents,
Fig. 8. Schematic overview of metabolic perturbation induced by celastrol and positive drugs(negative control group), Cel (celastrol), Cis (cisplatin), Dox (doxorubicin hydrochloride) a
namely cisplatin, doxorubicin hydrochloride and paclitaxel on cervicalcancer HeLa cells, which have all been reported to induce apoptosis inHeLa cell [34–36]. Among them, cisplatin is known to cause both DNAdamage and apoptosis by binding the cis-[Pt(NH3)2] unit to DNA [51].Doxorubicin is an antibiotic agent that inhibits DNA topoisomeraseand can also induceDNAdamage and apoptosis [52]. Paclitaxel, currentlythe most successful microtubule-targeted chemotherapeutic agent, cancause both mitotic arrest and apoptotic cell death [53]. Three PLS-DAmodels were established to characterize the metabolic profiles of HeLacells treated by each of these positive drugs. As shown in the PLS-DAscore plots (Fig. 7B to D), obvious separation between the negative con-trol and the positive drug treated groupswas seen, indicating that all thedrugs intervened metabolic profiles of HeLa cells. Using the screeningthreshold again (VIP>1.0 and Pb0.05), we subsequently found severalspecific metabolites associated with activity of the corresponding posi-tive drugs (Table 1). These metabolites, together with the 14 significantmetabolites revealed in Section 3.4, are capable of expanding ametabolicnetwork that is associated with the apoptosis-inducing effects of thesedrugs, and may possibly extend and strengthen our understanding oncelastrol's mechanism of action.
To facilitate observing and comparing the metabolic characteristicsof all these groups, a heatmap exhibiting levels of all the significantmetabolites listed in Table 1 was then constructed (Fig. 9). As shownin Fig. 9, quite a few metabolites have similar trends in celastrol andpositive drug treated groups. For example, the down-regulation ofseveral amino acids (glycine and serine) and the citrate cycle interme-diates (isocitric acid, α-ketoglutarate and malic acid), and the buildupof intermediates in glycolysis (fructose and lactic acid), glutathionemetabolism (1,4-butanediamine) and two synthesis precursors of
in HeLa cells. Column value in histograms is expressed as mean±SD. Abbreviation: NCnd Pac (paclitaxel).
Fig. 9. Heatmap of significant metabolites detected in HeLa cells intracellular extract metabolomics analysis. Rows: metabolites; columns: sample. Label: NC (negative controlgroup), Cel (celastrol treated group), Cis (cisplatin treated group), Dox (doxorubicin hydrochloride treated group) and Pac (paclitaxel treated group). Color key indicates metaboliteexpression value, blue: lowest, red: highest. (For interpretation of the references to color in this figure legend, the reader is referred to the web of this article.)
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nucleic acid (inosine and guanosine). These findings showed thatsimilar to the positive control drugs, celastrol destructed HeLa cell'sphysiological activity through affecting the above related pathways(Fig. 8).
More specifically, glycine level decreased in all the positive controlgroups as well as celastrol treated group. According to a recent study[54], glycine uptake and glycine mitochondrial biosynthesis are antago-nized in HeLa cells by the cytotoxic drugs. Subsequently, proliferation ofHeLa cells could be impaired due to shortage of themetabolism pool as asubstrate for synthesis of proteins, nucleic acids and other substances[48]. From the heatmap (Fig. 9), we also found serine level decreasedafter celastrol and positive drugs treatments. A recent study indicatedthat targeting serine synthesis pathwaymay be therapeutically valuablein breast cancers with elevated phosphoglycerate dehydrogenase(PHGDH) expression or PHGDH amplification [55]. From our observa-tion on serine, we supposed that serine synthesis pathway may alsobe a target of these anticancer drugs in HeLa cells. It remains to be inves-tigated, though, whether PHGDH, a key enzyme in serine synthesispathway is the target of celastrol. Contents of inosine and guanosineincreased in all the drug treated HeLa cells. As is known, inosine andguanosine are employed to produce DNA and RNA. As DNA-targeteddrugs [51–53], cisplatin, doxorubicin and paclitaxel could hamperDNA and RNA synthesis,which possibly resulted in the excess of inosineand guanosine. This suggested that celastrol may also play its role byaffecting nucleotide synthesis, similar to the positive control drugs.Moreover, similar trends of themetabolites in citrate cycle and glycolysis
in the drug treatment groups indicated that all of them influence thesepathways.
Interestingly, cysteine level in celastrol treated group decreasedsignificantly whereas it remains unchanged or slightly increased inthe positive drug treated groups. Previous studies have proved thatcelastrol contains electrophilic sites within the rings of quinonemethide structure in positions C2 (ring A) and C6 (ring B), enabling itsreaction with the nucleophilic thiol groups of cysteine residues toform covalent Michael adducts. This seems to be the major mechanismby which celastrol affects the functions of a series of proteins andexhibits a wide variety of pharmacological activities [14,15,56]. As thecysteine level decreased only in celastrol treated HeLa cells, wesuspected that this may result from the covalent binding of thiolgroup in cysteine to celastrol, being an new evidence of the speculativepharmacological mechanism of celastrol [57].
Although celastrol and the three positive drugs were proved toimpair HeLa cell's physiological activity and induce apoptosis inHeLa cells, they act via different mechanisms [3,51–53]. Hence, aftertreatment on HeLa cells, these drugs are likely to induce various meta-bolic characteristics. Therefore, we finally used the data of the significantmetabolites in all the groups to construct an unsupervised PCAmodel, toobserve the clustering of groups treated with drugs of different mecha-nisms of action. As shown in the PCA score plot (Fig. 10), separationamong the negative control group, celastrol treated group and positivecontrol groups was clearly seen. The negative control group was locatedin bottom left of the figure, and all the drug treated groups were located
Fig. 10. Score plot from PCA model of significant metabolites revealed in this study. Negative control group (red square), celastrol treated group (blue triangle), cisplatin treatedgroup (green open triangle), doxorubicin hydrochloride treated group (violet star) and paclitaxel treated group (brown open inverted triangle). The PCA model was validatedand found to be predictive (R2X=0.864 and Q2=0.708). (For interpretation of the references to color in this figure legend, the reader is referred to the web of this article.)
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in the right or up of thefigure, which shows differentmetabolic profile ofHeLa cells after treatment of these four cytotoxic agents. In all, character-ization of metabolic perturbations signatures in HeLa cells due to drugtreatment may help to elucidate drug effects and provide new insightsinto drug action mechanism.
4. Conclusion
In this study, we applied a GC/MS-based metabolomic approach toinvestigate the metabolic system affected by celastrol treatment inHeLa cells, and for the first time several metabolites indicative forearly apoptotic processes in HeLa cells culture induced by celastrolwere reported. Three representative apoptosis-inducing cytotoxicagents were selected as positive control drugs to validate reasonable-ness and accuracy of our metabolomic investigation on celastrol. Wefound that celastrol and positive control drugs affected citrate cycle,glycolysis, amino acid metabolism and protein biosynthesis of HeLacells. Moreover, from our results celastrol was likely to bind to cysteine,which provided new evidence of the speculative pharmacological mech-anism of celastrol. In conclusion, metabolomic approach applied to cellculturemetabolomics could give valuable information on the systemic ef-fects of celastrol treatment and help us to further study the anticancermechanism of celastrol.
Acknowledgements
This work was supported by Platform on Research of MetabolismTechnology of Traditional Chinese Medicine funded by Science &Technology Department of Shanghai, China (09DZ1975100) and theNational Science & Technology Major Special Project for “Major NewDrugs Innovation and Development” of China (2009ZX09301-011).Our special thanks go to Professor Junping Zhang (Department ofBiochemical Pharmacy, Second Military Medical University) for hishelpful advice.
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