Article pubs.acs.org/jpr
Metabolomics−Proteomics Combined Approach Identifies Differential Metabolism-Associated Molecular Events between Senescence and Apoptosis Mengqiu Wu,†,§ Hui Ye,†,§ Chang Shao,† Xiao Zheng,‡ Qingran Li,† Lin Wang,† Min Zhao,† Gaoyuan Lu,‡ Baoqiang Chen,‡ Jun Zhang,† Yun Wang,† Guangji Wang,*,† and Haiping Hao*,†,‡ †
Key Laboratory of Drug Metabolism and Pharmacokinetics, State Key Laboratory of Natural Medicines and ‡School of Pharmacy, China Pharmaceutical University, Tongjiaxiang #24, Nanjing 210009, China S Supporting Information *
ABSTRACT: Apoptosis and senescence are two types of cell fates in response to chemotherapy. Besides canonical pathways that mediate cell fates, cancer cell metabolism has been revealed as a crucial factor affecting cell fate decisions and thus represents a new target for antitumor therapy. Therefore, a comprehensive description of metabolic pathways underlying cell senescence and apoptosis in response to chemotherapy is highly demanded for therapeutic exploitation of both processes. Herein we employed a metabolomics−proteomics combined approach to identify metabolism-associated molecular events that mediate cellular responses to senescence and apoptosis using doxorubicin-treated human breast cancer cells MCF7 as models. Such biomics approach revealed that tricarboxylic acid cycle, pentose phosphate pathway, and nucleotide synthesis pathways were significantly upregulated in the senescent model, whereas fatty acid synthesis was reduced. In apoptotic cells, an overall reduced activity of major metabolic pathways was observed except for the arginine and proline pathway. Combinatorially, these data show the utility of biomics in exploring biochemical mechanism-based differences between apoptosis and senescence and reveal an unprecedented finding of the metabolic events that were induced for survival by facilitating ROS elimination and DNA damage repair in senescent cells, while they were downregulated in apoptotic cells when DNA damage was irreparable. KEYWORDS: metabolomics, proteomics, biomics, premature senescence, apoptosis, DNA damage, metabolism, pentose phosphate pathway, G6PDH
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INTRODUCTION
quality of life for cancer patients and thus represents an attractive alternative in cancer intervention.6,7 Senescence and apoptosis are two alternative cell fates in response to damage caused by anticancer drugs.8 The severity of induced damage appears to influence the cell fate decision.9−12 However, the mechanisms regarding how cells orchestrate fine-tuned signaling networks in response to differential extents of stress and thereby choose between senescence and apoptosis remain unclear. Recently it is reported that the p53 network determines cell fates based on the types and intensities of damage by delicately modulating the
Current chemotherapeutic strategy for anticancer treatment is to rapidly eliminate dividing cancer cells by causing extensive DNA damage and promoting cellular apoptosis with high dosage of drugs.1 Nevertheless, such pro-apoptosis therapy strategies often cause delayed side effects, such as recurrence, secondary cancers, and normal tissue damage2 and thus have encountered failures in treating various types of cancers, such as sarcomas, breast, prostate, pancreas, and lung cancers.3,4 Apart from apoptosis, premature senescence has been identified as another therapy-responsive program that impacts the outcome of cancer therapy.5 In comparison with pro-apoptosis strategy, pro-senescence therapy could minimize toxicity and enhance © 2017 American Chemical Society
Received: February 26, 2017 Published: May 3, 2017 2250
DOI: 10.1021/acs.jproteome.7b00111 J. Proteome Res. 2017, 16, 2250−2261
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Journal of Proteome Research
penicillin, and 0.1 mg/mL streptomycin (Invitrogen, Carlsbad, CA) with 5% CO2. Cells at early passages (within 15 passages) were used in all experiments to avoid complications of replicative senescence. Cells treated with 10 μM of Dox for 1 h (hr) or 8 h were used as models for senescence and apoptosis, respectively, whereas cells treated with 0.1% DMSO were used as control. All of the treated cells were then rinsed with fresh culture medium, followed by culturing in fresh RPMI-1640 medium.
expression levels, kinetics, and post-transcriptional modifications of p53.13−15 Other cell cycle events-related molecules, such as transforming growth factor β (TGFβ), cMyc, Forkhead box protein O (FOXO), and phosphatase and tensin homologue deleted on chromosome ten (PTEN) are also important players in the induction and determination of cell fates between senescence and apoptosis.16,17 Apart from the canonical signaling pathways, increasing evidence supports the idea that metabolic changes also regulate cell-fate decisions.18−23 For example, glucose metabolism was impaired during apoptosis. This leads to the loss of energy production, the dismantling of cells, and ultimately cell death.18,19 Unlike apoptosis, several key metabolic pathways remained active during senescence. For instance, both replicative and γ-ray-induced senescence was accompanied by upregulation of glycolysis, gluconeogenesis, and pentose phosphate pathway (PPP).24,25 In turn, the inhibition of glycolysis attenuated radiation-induced senescence.22 More recently, two enzymes that bridge tricarboxylic acid (TCA) cycle with the glycolysis pathway, malic enzymes ME1 and ME2, were demonstrated to lead to strong induction of senescence rather than apoptosis.21 Because accumulating evidence points to a crucial role for metabolism in determination of cell fates, knowledge of metabolic changes during senescence and apoptosis could help reveal the pathway perturbations underlying the two distinct processes induced by chemotherapy. Nonetheless, little information is currently available regarding the profiles of chemotherapy-induced senescence. In addition, although several metabolic markers have been identified in different cell lines under apoptotic condition,26−29 a comprehensive description and comparison of metabolic profiles during chemotherapy-induced cellular senescence and apoptosis is lacking. Herein we employed a biomics approach that combines highthroughput stable isotope dimethylation labeling quantitative proteomics and high-resolution mass spectrometry (HRMS)based metabolomics to investigate cellular networks and metabolic pathways perturbed during senescence and apoptosis using doxorubicin (Dox)-treated human breast cancer cells MCF7 as models. Using this approach, the metabolic and signaling pathways altered in senescent and apoptotic cells were analyzed and compared. TCA cycle, PPP, nucleotide synthesis and the arginine and proline pathway all exhibited different trends during senescence and apoptosis. Specifically, we selected G6PDH, a rate-limiting enzyme of PPP that was shown to be up-regulated in the senescent model yet displayed no significant changes in the apoptotic model according to our biomics data, and showed that the silencing of G6PDH is capable of modulating cell fates from senescence to apoptosis. These data demonstrated the feasibility of our approach with an integrated system focus and, more importantly, provided comprehensive and in-depth information for deciphering metabolic responses to chemotherapy and ultimately facilitating the rational use of anticancer drugs in the eradication of cancer.
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Flow Cytometry Analysis of Cell Cycle and Apoptosis
The distribution of control and Dox-treated MCF7 cells in different cell cycle phases and apoptosis was determined by propidium iodide (PI) staining of DNA content. The apoptotic rate of cells was also determined by annexin V-allophycocyanin (APC)/4′,6-diamidino-2-phenylindole (DAPI) staining. Data were acquired on a BD FACSVerse (Becton and Dickinson, Franklin, NJ). Senescence-Associated β-Galactosidase Staining
Cells were stained for presence of β-galactosidase using senescence-associated-β-galactosidase (SA-β-gal) staining kit (Cell Signaling Technology, Danvers, MA) according to manufacturer’s instructions. The images of SA-β-gal positive senescent cells were captured with Leica QWin software (Leica Microsystems, Bensheim, Germany). The rates of senescence were calculated based on at least 300 cells from three independent experiments under each condition. Quantitative Reverse-Transcriptase PCR
Total RNA isolation, reverse transcription, and quantitative real-time PCR reactions were carried out following standard protocols as specified by the manufacturer. Detailed procedures and sequences of primers are available in the Supporting Information (SI). Metabolomics Sample Preparation and LC−MS/MS Analysis
Sample preparation for metabolomics experiments was performed as described previously with modification.30 In brief, differentially treated MCF7 cells were extracted using 80% methanol (v/v) solution containing 1.5 μg/mL 1,2-13C2glutamine (Cambridge Isotope Laboratories, Tewksbury, MA) as an internal standard (IS). The extracted samples were then vortexed, centrifuged, concentrated, and reconstituted before analysis on an HPLC−MS/MS. A 20 μL aliquot of the samples was injected onto a LC-30A Shimadzu LC system (Shimadzu, Japan) using an XBridge Amide HPLC column (3.5 μm, 4.6 mm i.d., 100 mm) (Waters, Milford, MA). The eluent was then introduced via electrospray (ESI) ion source into a TripleTOF 5600 system (SCIEX, Framingham, MA). More details regarding sample preparation and LC−MS/MS setup can be found in the SI. Metabolomics Data Analysis
Peak areas were extracted from raw data to generate a data matrix. After normalizing the peak areas of metabolites to the protein concentrations and IS, principal component analysis (PCA) was performed with software SIMCA-P (Umetrics, Kinnelon, NJ). The Student’s t test was subsequently conducted on the acquired metabolomics data at 24 h, and p < 0.05 was considered significant. Molecular identification of the significantly changed metabolites was achieved by matching the acquired precursor and fragment ions against standard metabolome databases including MassBank (http://www.
EXPERIMENTAL SECTION
Cell Culture and Treatment
Human breast cancer cells MCF7 were obtained from the Institute of Hematology and Blood Diseases Hospital (Tianjin, China) and cultured at 37 °C in RPMI-1640 medium supplemented with 10% (v/v) fetal bovine serum, 100 U/mL 2251
DOI: 10.1021/acs.jproteome.7b00111 J. Proteome Res. 2017, 16, 2250−2261
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Journal of Proteome Research Western Blot Analysis
massbank.jp/index.html), METLIN (http://metlin.scripps. edu/index.php), and human metabolome database (http:// www.hmdb.ca/). A mass error of 10 ppm was allowed for precursor ions matching and 20 ppm for fragment ion matching. The metabolites assigned by database matching were further confirmed by comparison with available standards. Details of the identified metabolites are summarized in Table S1. Metabolic pathway enrichment analysis was carried out using Pathway Enrichment Analysis (http://www. metaboanalyst.ca/faces/ModuleView.xhtml).
Cells were lysed by NP-40 in the presence of protease inhibitors (Sigma-Aldrich, St. Louis, MO). The cell lysates were then separated on 10% SDS-PAGE (Bis-Tris Midi Gel, Invitrogen) and transferred onto polyvinylidene difluoride (PVDF) membranes. The blots were incubated and analyzed by immunoblotting with primary antibodies, followed by incubation with fluorescently tagged secondary antibodies. The resulting immunoblotted bands were subsequently detected using an enhanced chemoluminescence reagent (Bio-Rad, CA) on a ChemiDoc XRS+ System (Bio-Rad). Antibodies of phosphor-histone H2AX (p-H2AX) (Ser139) and β-actin were purchased from Cell Signaling Technology (Beverly, MA), and antibody of G6PDH was obtained from Abcam (Cambridge, MA). The protein expression levels were normalized to the levels of β-actin.
Quantitative Proteomics Sample Preparation and LC−MS/MS Analysis
Differentially treated MCF7 cells were pelleted by Sorvall Biofuge Stratos Centrifuge (Thermo Fisher Scientific, MA) at 2000 rpm for 5 min. The cell pellets were then solubilized using 8 M urea-based buffer with 1% (v/v) protease and phosphatase inhibitors. The lysates were ultrasonicated and centrifuged. A 100 μg aliquot of protein was reduced, alkylated, and digested with sequencing-grade trypsin (Promega, WI) overnight. The digested samples were quenched and concentrated by SpeedVac concentrator (Thermo Fisher Scientific). Dried peptides were redissolved in 0.1% formic acid (FA). Subsequently, stable isotope dimethylation labeling was performed. The senescence and apoptosis groups were both labeled with D-formaldehyde as “heavy”-labeled groups, whereas the control groups were labeled with formaldehyde as “light”labeled group. The senescence and apoptosis groups were then mixed with the control group, respectively. The senescence/ control and apoptosis/control samples were dried in a SpeedVac (Thermo Fisher Scientific), followed by redissolving in 0.1% FA in water. An Orbitrap Fusion mass spectrometer (Thermo Fisher Scientific) coupled to an online EasynLC 1000 nano-HPLC system (Thermo Fisher Scientific) was employed for LC−MS/MS data acquisition. The detailed LC−MS/MS conditions are provided in the SI.
Intracellular Reactive Oxygen Species Assay
MCF7 cells were cultured in 24-well plates and treated with 10 μM Dox for 1 or 8 h. After treatment, intracellular reactive oxygen species (ROS) level of each sample was determined by calculating the amount of the cell-permeable fluorogenic probe 2′,7′-dichlorodihydrofluorescein diacetate (DCFH-DA) converted to its fluorescent derivative 2′,7′-dichlorodihydrofluorescein (DCF) using an intracellular ROS assay kit (Beyotime, Jiangsu, China). The fluorescence intensity of each sample was analyzed with a Synergy-H1 fluorimeter (Bio-Tek Instruments) using excitation and emission wavelengths of 480 and 530 nm, respectively. RNA Interference
Scrambled small interfering RNA (siRNA) and siRNA targeting G6PDH (sequences, ACCAAGAAGCCGGGCAUGUUCUUCA and UGAAGAACAUGCCCGGCUUCUUGGU) were purchased from Invitrogen. MCF7 cells were cultured in six-well plates and transfected using RNAiMAX transfection reagent (Invitrogen) according to the manufacturer’s instructions.
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Quantitative Proteomics Data Analysis
Peptide and protein identification were performed by the Proteome Discoverer software version 2.1 (Thermo Fisher Scientific). MS/MS spectra of each sample were searched for Homo sapiens species against the UniProt/Swissprot database (release-2017_01, 20 081 protein entries). The search parameters were set as follows: a precursor mass tolerance of 15 ppm; a fragment mass tolerance of 0.06 Da; two missed cleavages were allowed; static modifications of cysteine carbamidomethyl (C) and peptide N-terminus and lysine dimethyl or dimethyl: 2H (4); a variable modification of methionine oxidation (M); the false discovery rate (FDR) was estimated and protein identifications with FDR< 1% were considered acceptable. A minimum of two unique peptides was used for relative quantification. The Student’s t test was subsequently conducted on the quantified proteins, and protein changes that delivered p < 0.05 were considered significant. Gene ontology analysis was subsequently performed by importing the proteins that displayed significant changes with ratio higher than 1.20 or lower than 0.83 fold to Protein Analysis through Evolutionary Relationships (PANTHER) (http://www.pantherdb.org/). Raw data have been deposited to the iProX system (http:// www.iprox.org/index) with the identifier IPX0000883000 and can be accessed with user ID mengqiuwu and password 900904.
RESULTS AND DISCUSSION
Establishment of Premature Senescence and Apoptosis Models Using MCF7 Cells
Dox is a routinely prescribed anticancer agent that causes DNA damage31,32 and a typical inducer of both apoptosis and premature senescence when applied at different exposure levels.10,11,33 Herein, human breast cancer cells MCF7 were exposed to 10 μM Dox for 1 or 8 h and cultured after the removal of drug (Figure 1A). We found that the 8 h treatment induced a relatively high apoptotic rate (Sub G1 peak) of 23.71% in MCF7 cells (Figure 1B), which accompanied marked cell shrinkage (Figure 1C), a morphological marker of apoptosis.34 In contrast, the 1 h treatment delivered a low apoptotic rate of cells (Figure 1B). Nevertheless, a remarkable percentage (∼80%) of cell senescence was invoked by the 1 h treatment, as shown by SA-β-gal staining (Figure 1C,D). Moreover, the SA-β-gal positive cells displayed flat morphology (Figure 1C), a phenotype of cellular senescence.35 In line with the biochemical and morphological evidence, we further examined the transcription levels of p53 target genes involved in apoptosis (APAF1 and BAX) and senescence (PML and YPEL3) because Dox-mediated cytotoxicity is mainly dependent on the p53 pathway.36,37 As shown in Figure 1E,F, the transcription levels of senescence-associated genes were 2252
DOI: 10.1021/acs.jproteome.7b00111 J. Proteome Res. 2017, 16, 2250−2261
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Journal of Proteome Research
score plot showed that there was a clear time-dependent alteration between the metabolic profiles of senescent cells and control cells at time points 12, 24, and 48 h (Figure 2A) as well as those of apoptotic cells and control cells at time points 2, 12, and 24 h (Figure 2B), respectively. The most significant differences of senescent and apoptotic cells were both observed at 24 h. Consequently, the significantly altered metabolites at 24 h (P < 0.05) in the senescence and apoptosis groups were selected and are summarized in Tables S2 and S3 for following analysis. A total of 67 and 147 metabolite biomarkers were identified, respectively. Pathway enrichment analysis was thus performed using the identified metabolites that underwent significant expression level changes in senescent and apoptotic cells. It was found that several canonical metabolite pathways including purine and pyrimidine metabolism, TCA cycle, PPP, glutathione metabolism, pyruvate metabolism, ketone metabolism, taurine and hypotaurine metabolism, and glycine, serine, and threonine metabolism were affected in both senescent and apoptotic cells (Figure 2C,D). Moreover, alanine, asparate, and glutamate metabolism, arginine and proline metabolism, and propanoate metabolism were exclusively disturbed by cellular apoptosis (Figure 2D). Such differences indicated that in the face of a severe genotoxic challenge, cellular metabolism was more extensively disturbed in apoptotic cells compared with senescent cells. It is noteworthy that although several pathways changed in response to apoptosis and senescence overlapped, the trends were reversed (Figure 2E−G). Quantitative Proteomics Revealed Disturbed Pathways in Senescent and Apoptotic Cells
Figure 1. Establishment of premature-senescence and apoptosis models using MCF7 cells. (A) Cells were incubated with 10 μM doxorubicin for 1 or 8 h, followed by incubation in drug-free medium. (B) Cells treated for 1 or 8 h were subjected to cytometry analysis using nuclear PI staining. Control cells were cultured without Dox for 48 h. (C) Cells were incubated with 10 μM Dox for 1 or 8 h and allowed for recovery until 48 h. The cellular senescence rate was examined by SA-β-gal staining. Senescent cells (blue staining) were observed by bright-field microscopy. (D) Percentage of SA-β-gal positive cells was quantified. Cells were incubated with 10 μM doxorubicin for 1−24 h and allowed for recovery until 48 h. Data are represented as mean ± SEM (n = 3); p value was calculated by unpaired Student’s t test; * denotes p < 0.05 and ** denotes p < 0.01. Different exposure duration of Dox selectively transactivated different sets of p53 downstream target genes. PML and YPEL3 were grouped into senescence related genes (E) and BAX and APAF1 were grouped as apoptosis-related genes (F). The expression levels of mRNA were normalized to β-Actin. Data are represented as mean ± SEM (n = 3). NC denotes negative control, S denotes senescence, and A denotes apoptosis.
After gathering a holistic view of the metabolic reprogramming of senescent and apoptotic MCF7 cells, we next sought to identify the specific proteins disturbed during cellular senescence and apoptosis. Stable isotope dimethylation labeling-based quantitative mass spectrometric analysis was employed to investigate the differentially expressed proteins and thus provide insight into the disturbed pathways. Figure 3A,B illustrates a representative protein glucose-6-phosphate isomerase (GPI), a glycolytic enzyme that converts glucose-6phosphate to fructose-6-phosphate, being identified and detected to be up-regulated in senescent cells compared with control cells via dimethylation labeling. Using this approach, 733 and 540 proteins were quantitated in senescent and apoptotic cells, respectively. The relatively low amount of proteins in apoptotic cells can be explained by extensive protein cleavage that often happens during apoptosis.34 Proteins with fold change ≥1.20 and ≤0.83 with P value
