Quantitative Proteomic Approach to Understand Metabolic Adaptation

Article pubs.acs.org/jpr

Quantitative Proteomic Approach to Understand Metabolic Adaptation in Non-Small Cell Lung Cancer Alfonso Martín-Bernabé,†,‡,§,∥ Roldán Cortés,† Sylvia G. Lehmann,‡,§,∥ Michel Seve,‡,§,∥ Marta Cascante,*,† and Sandrine Bourgoin-Voillard*,‡,§,∥ †

Department of Biochemistry and Molecular Biology, IBUB, Faculty of Biology, Universitat de Barcelona and Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), 08007 Barcelona, Spain ‡ Univ. Grenoble Alpes, IAB, Plateforme de Protéomique PROMETHEE, F-38000 Grenoble, France § INSERM, IAB, Plateforme de Protéomique PROMETHEE, F-38000 Grenoble, France ∥ CHU de Grenoble, IAB, Institut de Biologie et de Pathologie, Plateforme de Protéomique PROMETHEE, F-38000 Grenoble, France S Supporting Information *

ABSTRACT: KRAS mutations in non-small cell lung cancer (NSCLC) are a predictor of resistance to EGFR-targeted therapies. Because approaches to target RAS signaling have been unsuccessful, targeting lung cancer metabolism might help to develop a new strategy that could overcome drug resistance in such cancer. In this study, we applied a large screening quantitative proteomic analysis to evidence key enzymes involved in metabolic adaptations in lung cancer. We carried out the proteomic analysis of two KRAS-mutated NSCLC cell lines (A549 and NCI-H460) and a non tumoral bronchial cell line (BEAS-2B) using an iTRAQ (isobaric tags for relative and absolute quantitation) approach combined with twodimensional fractionation (OFFGEL/RP nanoLC) and MALDI−TOF/TOF mass spectrometry analysis. Protein targets identified by our iTRAQ approach were validated by Western blotting analysis. Among 1038 proteins identified and 834 proteins quantified, 49 and 82 proteins were respectively found differently expressed in A549 and NCI-H460 cells compared to the BEAS-2B non tumoral cell line. Regarding the metabolic pathways, enzymes involved in glycolysis (GAPDH/PKM2/LDH-A/LDH-B) and pentose phosphate pathway (PPP) (G6PD/TKT/6PGD) were up-regulated. The up-regulation of enzyme expression in PPP is correlated to their enzyme activity and will be further investigated to confirm those enzymes as promising metabolic targets for the development of new therapeutic treatments or biomarker assay for NSCLC. KEYWORDS: cancer metabolism, NSCLC, drug resistance, iTRAQ quantitative proteomic approach and acquired resistance.3,4 The efficacy of these treatments is known to depend on the EGFR mutation status. Many mutations in the EGFR gene have been reported in NSCLC but only patients whose tumors harbor classical mutations in the tyrosine kinase domainin exon 18 (G719A/C), exon 19 (deletions E749-A750), and exon 21 (L858R and L861Q) have been associated with sensitivity to the EGFR TKIs treatments.3 The efficacy of these treatments is also known to depend on KRAS mutations. KRAS mutation is an important predictor of resistance to EGFR TKI therapy, and patients harboring KRAS mutation are not selected for such targeted therapies.5 Oncogenic KRAS mutations lead to constitutive activity of RAS signaling independent of upstream signals by impairing the function of the RAS GTPase. Consequently,

1. INTRODUCTION Lung cancer is the leading cause of cancer-related deaths, with an estimated 18.2% (1.38 million deaths) of overall cancer deaths worldwide in 2008.1 Lung cancer is broadly classified into two categories: non-small cell lung cancers (NSCLC) and small cell lung cancers (SCLC). NSCLC are the most common type, accounting for about 80% of all lung cancers and is associated with poor prognosis, with less than 15% of patients surviving five years after initial diagnosis.2 This is due to the difficulty of early detection and lack of well-established therapies. Thereby, the development of novel biomarkers for improving early detection capability, as well as new therapeutic targets for effectively overcoming resistance, are research priorities that would enable a drastic improvement in the survival outcomes in NSCLC patients. Among the targeted therapies in lung cancer, the epidermal growth factor receptor (EGFR) is targeted by EGFR tyrosine kinase inhibitors (EGFR TKIs), such as gefitinib (Iressa) and erlotinib (Tarceva). However, EGFR TKI treatments are not fully successful in all NSCLC patients because of both primary © 2014 American Chemical Society

Special Issue: Proteomics of Human Diseases: Pathogenesis, Diagnosis, Prognosis, and Treatment Received: March 31, 2014 Published: July 16, 2014 4695

dx.doi.org/10.1021/pr500327v | J. Proteome Res. 2014, 13, 4695−4704

Journal of Proteome Research

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KRAS proteins are permanently “turned on” and unregulated downstream signaling will not be blocked by EGFR-targeted agents such as TKIs, leading to uncontrolled cellular proliferation. The prevalence of KRAS mutations in the adenocarcinoma histology is 20% to 30%, whereas KRAS mutations are uncommon in lung squamous cell carcinomas.6−8 To date, many attempts targeting the KRAS signaling pathway have been made.9 However, neither direct inhibitors nor downstream signaling pathways specifically targeting the mutant KRAS have been proven clinically effective. The problem is not due to the lack of therapeutic targets but rather to the lack of a comprehensive understanding of the biology underlying the disease. Because cell signaling in cancer is complex and dynamic, effective treatment will require the regulation of many targets to achieve a sustained response. Therefore, a novel therapy capable of inhibiting multiple targets or pathways is likely the best option for treating mutant KRASdriven cancers.9 New strategies to overcome treatment resistance due to oncogenic KRAS are currently investigated and among them, targeting cancer cellular metabolism may represent a promising strategy to improve the response to cancer therapeutics and may sensitize cells to current cancer drugs.10 Clear evidence now supports that the active metabolic reprogramming associated with cancer, which has been increasingly recognized as one of the crucial markers of the disease, is due to the intimate connections between metabolic pathways and all major oncogenes and tumor suppressor genes.11−15 Activated KRAS signaling was initially linked to increased glucose uptake, but recent studies indicate that the role of KRAS in cancer metabolism is more complex.16 Therefore, a better understanding of specific metabolic adaptations in lung cancer cells is expected to open up new avenues for designing novel therapeutic approaches in NSCLC treatment. Current molecular targets in metabolism include a wide spectrum of metabolic pathways such as glycolysis, the tricarboxylic acid cycle (TCA cycle) and associated pathways, pentose phosphate pathway (PPP), glutaminolysis, lipid synthesis, and cofactor synthesis.17 Certainly, targeting metabolic pathways remains a major challenge in the development of effective cancer therapies that specifically target the tumoral cells without compromising healthy cell survival. We used a proteomic approach in order to identify key metabolic enzymes that are associated with the KRAS-driven non-small cell lung cancer phenotype. This strategy will provide an integrated view of the metabolic pathways, creating a comprehensive knowledge of the metabolic processes that are enhanced and are fundamental to support the phenotype of lung cancer cell lines. Thus, this understanding might facilitate the design of more effective diagnosis and treatments. Proteomics approach used for a large screening of proteins may provide important insight into the multiple pathways necessary to support cancer cell metabolism. Among the proteomic approaches, the use of quantitative proteomics, especially “isobaric tags for relative and absolute quantitation” (iTRAQ) method has gained increased popularity.18−20 iTRAQ is well suited for protein biomarker and disease-specific drug target discovery studies, as it enables both identification and quantitation of proteins in efficient sample multiplexing by evidencing the proteins differentially expressed in each sample. In the present study, we used iTRAQ labeling coupled with two-dimensional fractionation (OFFGEL/RP nanoLC) and MALDI−TOF/TOF mass spectrometry analysis as proteomic

approach to quantitatively compare the protein expression level of two lung cancer cells harboring KRAS mutations at codons 34 and 183 (A-549 and NCI-H460, respectively) with a non tumoral bronchial cell line (BEAS-2B). In particular, we focused our attention on the expression regulation of key metabolic enzymes involved in glycolysis, TCA cycle, lipid biosynthesis, and PPP. We believe that these data provide clues for the better understanding of the metabolic pathways, in particular PPP and glycolysis, in KRAS-driven lung tumors. These results may contribute to develop new therapies based on selective metabolic inhibitors against key enzyme targets, as well as to identify novel diagnostic/prognosis protein markers associated with the altered cellular metabolism.

2. MATERIAL AND METHODS a. Cell Culture and Protein Extraction

Human lung adenocarcinoma epithelial cell line A549, large-cell carcinoma epithelial cell line NCI-H460, and normal immortalized bronchiolar epithelial cell line BEAS-2B were obtained from the American Type Culture Collection (Manassas, VA, U. S. A.). NCI-H460 and A549 cell lines were cultured in DMEM (Sigma-Aldrich) supplemented with 10% fetal bovine serum +0.5% penicillin/streptomycin, and BEAS-2B cell lines was cultured in DMEM (Sigma-Aldrich) supplemented with growth factors from the BEGM SingleQuots kit (Lonza). Cells were routinely cultured in 5% CO2 at 37 °C. For total protein extractions, cells were washed twice with phosphate-buffered saline (PBS) and then incubated with lysis buffer (40 mM of HEPES pH 7.4, 100 mM NaCl, 1 mM EDTA, 0.02% Triton X-100, 0.02% sodium deoxycholate) containing protease inhibitors (1× Halt Protease Inhibitor Cocktail; Thermo Scientific) and phosphatase inhibitors (1× Halt Phosphatase Inhibitor Cocktail; Thermo Scientific) for 10 min at 4 °C. Lysis was achieved by short sonication at 4 °C, and lysates were clarified by centrifugation at 12 000 g at 4 °C for 20 min. Protein concentrations were determined using BCA assay kit (Thermo Fisher Scientific, IL, U. S. A.) according to the manufacturer’s instructions. Equal amount of protein sample (100 μg) was used for the iTRAQ comparative analysis. b. Protein Digestion and iTRAQ Labeling

Samples from the three cell line cultures were labeled with iTRAQ reagents in a 4-plex set according to the manufacturer’s instructions (iTRAQ Reagents 4 plex Applications kit; AB Sciex, Framingham, MA, U. S. A.). Briefly, a total of 100 μg of each sample was reduced in 20 mM of TCEP (tris(2carboxyethyl)phosphine) at 37 °C for 1 h, and the cysteineresidues were blocked in 10 mM of MMTS (methylmethanethiosulfonate) at room temperature for 10 min, followed by trypsin digestion at a ratio of 1:10 (trypsin:protein) at 37 °C overnight. Each sample was labeled at room temperature for 1 h with one iTRAQ reagent: iTRAQ reporter ions of m/z 114.1 for BEAS-2B, m/z 116.1 for A549, and m/z 117.1 for NCIH460. Finally, the different samples were pooled and the labeling reaction stopped by evaporation in a vacuum concentrator to obtain a brown pellet. To favor the detection of peptides in low abundance, pooled labeled samples were fractionated in two-dimensions: OFFGEL (isoelectrofocusing system) and RP nanoLC prior to the mass spectrometry analysis. 4696



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c. 2D Fractionation

e. Analysis of iTRAQ Data

Peptide OFFGEL Isoelectrofocusing. The OFFGEL fractionation according to pI was conducted on 3100 OFFGEL Fractionator using OFFGEL Kit linear pH 3−10 (both Agilent Technologies) in a 24-well setup following the manufacturer’s instructions. Briefly, 300 μg of iTRAQ-labeled peptides were dried by vacuum centrifugation and resuspended with 3.6 mL of focusing OFFGEL buffer. The IPG gel strip was rehydrated and 150 μL of sample were loaded in each well. Peptides were focused until 50 kVh was reached with a maximum voltage of 8000 V, 50 μA and 200 W. After fractionation was completed, the 24 OFFGEL fractions were transferred into individual tubes and desalted using C18 ZipTips (Millipore, MA, U. S. A.). The resulting eluates were collected, dried down using a vacuum concentrator, and maintained at −20 °C prior to nanoLC-MS/ MS analysis. Reversed Phase Nanoliquid Chromatography. Further peptide separation was performed on an Ultimate 3000 C18 reversed-phase liquid chromatography (RP-LC) system controlled by Chromeleon v. 6.80 software (Dionex/Thermo Scientific/LC Packings, Amsterdam, The Netherlands) and coupled to a PROBOT MALDI spotting device controlled by the μCarrier 2.0 software (Dionex/Thermo Scientific/LC Packings, Amsterdam, The Netherlands). Just prior to the separation, the vacuum-dried fractions were resuspended in buffer A (98% water, 2% ACN and 0.05% TFA). Before peptide separation, the peptides were trapped on a trapping column (C18, 3 μm, 100 Å pore size; LC Packing) in 2% ACN and 0.05% TFA at a flow rate of 20 μL/min for 5 min. Then, trapped peptides were separated by reversed phase chromatography (Acclaim PepMap300 75 μm, 15 cm, nanoViper C18, 3 μm, 100 Å pore size; Thermo Scientific) operated on a nanoHPLC (Ultimate 3000, Dionex/Thermo Scientific) with a binary gradient of buffer A (2% ACN and 0.05% TFA) and buffer B (80% ACN and 0.04% TFA) at a flow rate of 0.3 μL/min. The entire run lasted 60 min and the nanoLC gradient was set up as follows: 5−35 min, 8−42% B; 35−40 min, 42− 58% B; 40−50 min, 58−90% B and 50−60 min, 90% B. Fractions from eluted solution were collected for 15 s and spotted on a MALDI sample plate (AB Sciex, Les Ulis, France) resulting in 200 spots per fraction. The α-cyano-4-hydroxycinnamic acid matrix (HCCA, 2 mg/mL in 70% ACN and 0.1% TFA) was continuously added to the column eluted solution at a flow rate of 0.9 μL/min, and therefore, integrated in each spot of MALDI sample plate.

MS and MS/MS spectra were used for identification and relative quantitation by using ProteinPilot software v 4.0 with the Paragon Algorithm (AB Sciex, Les Ulis, France). The analysis was performed with the human database of UniProtKB release 2013_02 - February 6, 2013/Swiss-Prot (European Bioinformatics Institute, Hinxton, U. K.). In the present study, we included in our data sets only proteins with an “Unused ProtScore” of ≥3. The search effort was set to “Thorough ID”, and the False Discovery Rate Analysis (FDR) of 1% was applied. For quantification, bias and background correction was applied. Only quantified proteins with at least 2 peptides at the 95% peptide confidence level, an error factor (EF) < 2 and p values ≤0.05 were included in the final set of quantified proteins. Additionally, a fold-change cutoff for all iTRAQ ratios was selected to classify proteins as significantly up- or downregulated. According to volcano plots of the distribution of −ln(p value) versus ln(iTRAQ ratio) (Supporting Information Figure S1), proteins with iTRAQ ratios ≤0.83 were considered to be significantly down-expressed, whereas those ratios ≥1.20 were considered to be significantly overexpressed. For output of our quantitative iTRAQ results, all protein ratios were expressed as tumoral cell lines over the non tumoral one (116:114, A549:BEAS-2B and 117:114, NCI-H460:BEAS-2B) to present relative protein quantification ratios. f. Western Blot Analysis

Equal amounts of protein lysate (30 μg) were separated by SDS-PAGE with 12% polyacrylamide gel and transferred to a nitrocellulose membrane (0.45 μm pore size, Bio-Rad) by electroblotting for 2 h at 45 V using the Mini Trans-Blot Electrophoretic Transfer Cell (Bio-Rad) and transfer buffer (25 mM Tris; 192 mM glycine; 20% ethanol). Membranes were blocked with 5% nonfat milk in PBS-0.1% Tween 20. Primary and secondary antibodies were diluted in the same solution. Primary antibodies used include mouse anti-α-tubulin (SC23948, Santa Cruz Biotechnology) diluted at 1:1000, rabbit anti-PKM2 (SAB 4300662, Sigma-Aldrich) diluted at 1:200, rabbit anti-LDH-A (SAB 1100050, Sigma-Aldrich) diluted at 1:500 and rabbit anti-GAPDH (SAB 2100894, Sigma-Aldrich) diluted at 1:500. HRP labeled antimouse IgG (A8924, SigmaAldrich) and antirabbit IgG (7074, Cell signaling) were used as secondary antibodies at 1:3000 and 1:2000, respectively. Horse radish peroxidase (HRP) activity was assessed with the Clarity Western ECL substrate (Bio-Rad Laboratories, Inc.) on a Chemidoc XRS+ system (Bio-Rad Laboratories, Inc.). The levels of proteins were normalized and relatively quantified according to α-tubulin levels in three independent experiments for PKM2 and LDH-A and five independent experiments for GAPDH. Image analysis and quantification by densitometric scanning were performed by using Image Lab 2.0.1 software (Bio-Rad Laboratories, Inc.).

d. MALDI−TOF/TOF Analysis

MS and MS/MS analysis of nanoLC-off-line spotted peptide samples were performed using the 4800 MALDI−TOF/TOF Analyzer (AB Sciex, Les Ulis, France) controlled by the 4000 Series Explorer software v. 3.5. The mass spectrometer was operated in positive reflector mode. Each spectrum was externally calibrated using the Peptide Calibration Standard II (Bruker Daltonics, Bremen, Germany) and the peptide mass tolerance was set to 50 ppm. MS spectra were acquired in a m/z 700−4000 range. Up to 40 of the most intense ions per spot position characterized by a S/N (signal/noise) ratio higher than 40 were chosen for MS/MS analysis. Selected ions were fragmented by using CID (collision induced dissociation) activation mode in order to obtain the corresponding MS/MS spectrum that is necessary to determine the sequence of these peptides and quantify them.

g. Enzyme Activity Determinations

Activities of PPP enzymes glucose-6-phosphate dehydrogenase (G6PD), 6-phosphogluconate dehydrogenase (6PGD), and transketolase (TKT) were determined from cell culture extracts in two independent experiments performed in triplicate. In order to obtain accurate enzyme activities for G6PD and 6PGD, both 6PGD activity and total dehydrogenase activity (G6PD and 6PGD) were determined separately by measuring the rate of NADH production. G6PD activity was obtained by subtracting the activity of 6PGD from total dehydrogenase activity.21 Cell extracts were prepared and enzyme activities 4697



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Table 1. Metabolic Enzymes Quantified in the iTRAQ Analysis iTRAQ values A-549:BEAS-2Bb protein name Glycolysis Pathway glucose-6-phosphate isomerase 6-phosphofructokinase type C 6-phosphofructokinase, muscle type 6-phosphofructokinase, liver type fructose-bisphosphate aldolase A triosephosphate isomerase glyceraldehyde-3-phosphate dehydrogenase phosphoglycerate kinase 1 phosphoglycerate mutase 1 α-enolase pyruvate kinase isozymes M1/M2 L-lactate dehydrogenase A chain L-lactate dehydrogenase B chain Pentose Phosphate Pathway glucose-6-phosphate 1dehydrogenase 6-phosphogluconate dehydrogenase transketolase transaldolase TCA Cycle malate dehydrogenase citrate synthase fumarate hydratase Lipid Metabolism fatty acid synthase ATP-citrate synthase acetyl-CoA acetyltransferase, cytosolic fatty acid desaturase 2

protein short name

accession number

NCHI-H460:BEAS2Bc

unuseda (ProtScore)

ratiod

p valuee

ratiod

p valuee

PGI PFKP PFKM PFKL ALDOA TPI GAPDH PGK1 PGAM1 ENO1 PKM1/2 LDH-A LDH-B

P06744 Q01813 P08237 P17858 P04075 P60174 P04406 P00558 P18669 P06733 P14618 P00338 P07195

26 16 1 4 39 30 56 28 14 52 89 37 19

1.63 1.65 0.99 0.80 1.15 0.87 1.27 0.95 1.00 0.81 1.77 1.63 1.60

0.20 0.08 0.96 0.83 0.19 0.14 0.03 0.77 0.99 0.07 0.00 0.00 0.02

1.04 1.04 0.86 1.64 1.72 0.60 1.44 1.16 1.27 0.87 0.97 0.85 1.24

0.91 0.88 0.46 0.05 0.00 0.00 0.00 0.59 0.28 0.54 0.54 0.28 0.18

G6PD 6PGD TKT TALD

P11413 P52209 P29401 P37837

61 28 37 13

2.58 2.60 1.41 0.89

0.00 0.02 0.01 0.56

1.90 1.61 0.99 0.98

0.01 0.22 0.96 0.87

MDH CS FDH

P40926 O75390 P07954

10 10 6

1.18 0.90 0.70

0.46 0.60 0.75

1.07 1.23 1.42

0.81 0.36 0.01

FASN ACLY ACAT2 FADS2

P49327 P53396 Q9BWD1 O95864

120 52 8 5

0.70 1.18 0.46 0.88

0.00 0.17 0.25 0.42

0.74 0.73 0.51 0.52

0.01 0.10 0.37 0.01

a

Measure of the protein confidence. bA549 cells, labeled with iTRAQ reagent m/z 116 and BEAS-2B, labeled with iTRAQ reagent m/z 114. cNCIH460 cells, labeled with iTRAQ reagent m/z 117 and BEAS-2B, labeled with iTRAQ reagent m/z 114. dAverage iTRAQ ratio for proteins, corrected with experimental bias. eMeasure of the probability that the average ratio differs from one; the differential expression is significant if p values ≤0.05. Regarding p values, statistically significant iTRAQ ratios (p values ≤0.05) for proteins significantly dysregulated are in bold (proteins were considered to be significantly down-expressed and over-expressed when those iTRAQ ratios were ≤0.83 and ≥1.20, respectively).

iTRAQ-tagged peptides of glucose-6-phosphate dehydrogenase (G6PD) and fatty acid synthase (FASN) were selected to illustrate the protein identification and relative quantification procedure (Supporting Information Figure S2). The complete lists of proteins, including percent coverages, UniProtKB accession numbers, protein names, number of peptides assignments, iTRAQ ratios, and p values for both tumoral cell lines versus non tumoral cell line, are provided in supplemental data (Supporting Information Table S1). Considering the iTRAQ ratio, cutoffs of 1.20 and 0.83 for significantly differently expressed proteins (DEPs) with p values ≤0.05, from the 834 proteins identified, 49 proteins were differentially expressed between A549 and BEAS-2B cell lines (Supporting Information Table S2). From them, 34 proteins were up-regulated and 15 down-regulated. With the above criteria, we identified 82 DEPs between NCI-H460 and BEAS2B cell lines (Supporting Information Table S3), of which 40 were up-regulated and 42 were down-regulated. Among these dysregulated proteins, several metabolic enzymes detected in the iTRAQ analysis (Table 1) were implicated in essential pathways of central metabolism including glycolysis, TCA cycle, PPP, and fatty acid metabolism. Among the target enzymes detected, we identified and quantified almost all of the

were measured on the Roche Cobas Mira Plus chemistry analyzer as previously described.16 Each enzyme activity was normalized by determining the protein concentration using BCA method (Thermo Fisher Scientific, IL, U. S. A.). Enzyme activities are expressed as milliunits per milligram of protein (mU/mg prot).

3. RESULTS a. Quantitative Analysis of Differentially Expressed Proteins

The bioinformatics iTRAQ-based OFFGEL-nanoLC-MALDI− TOF/TOF analyses for the three cell lines resulted in the identification of 1038 proteins using a local FDR of 1%. However, for quantitative analysis we considered only proteins that were identified with at least two peptides at ≥95% peptide confidence level and unused protein score ≥3. By applying these settings, we quantified 834 proteins. A549 cells were labeled with iTRAQ m/z 116 tag, NCI-H460 cells were labeled with iTRAQ m/z 117 tag and non tumoral BEAS-2B cells were labeled with iTRAQ m/z 114 tag. Thus, the ratio 116:114 (A549:BEAS-2B) and 117:114 (NCI-H460:BEAS-2B) indicate the relative protein abundance between tumoral and non tumoral cell samples. Two MALDI−TOF/TOF spectra of two 4698



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glycolytic enzymes (10 out of the 11 enzymes required in the glycolytic pathway), 4 enzymes of the PPP, 4 enzymes involved in fatty acid metabolism, and 3 enzymes of the TCA cycle. The glycolytic enzymes included glucose-6-phosphate isomerase (PGI), phosphofructokinase (PFK), fructose-biphosphate aldolase (ALDOA), triosephosphate isomerase (TPI), glyceraldehyde phosphate dehydrogenase (GAPDH), phosphoglycerate kinase (PGK), phosphoglycerate mutase (PGM), α enolase, pyruvate kinase (PK), and lactate dehydrogenase (LDH). For the glycolysis, the number of enzymes upregulated were 4 (GAPDH, PKM2, LDH-A, and LDH-B) and 2 (GAPDH and ALDOA) in A549 and NCI-H460 cells, respectively. In contrast, only 1 enzyme was down-regulated (TPI). Moreover, we detected the first 2 enzymes of the pentose phosphate pathway (PPP), glucose-6-phosphate dehydrogenase (G6PD), and 6-phosphogluconate dehydrogenase (6PGD) and key enzymes in the nonoxidative PPP branch, transaldolase (TALD) and transketolase (TKT). Three of these enzymes (G6PD, 6PGD, and TKT) were up-regulated in A549 cells, whereas 1 enzyme (G6PD) was up-regulated in NCIH460 cells. In addition, several enzymes involved in fatty acid metabolism such as fatty acid synthase (FASN), ATP-citrate synthase, acetyl-CoA acetyltransferase, and fatty acid desaturase 2 were identified. Among them, fatty acid synthase and fatty acid desaturase 2 were down-regulated in A549 and NCI-H460 cells, respectively. Finally, 3 enzymes that participate in TCA cycle: malate dehydrogenase, citrate synthase, fumarate hydratase were detected but only fumarate hydratase was upregulated in NCI-H460 cells. Western blot analysis of a set of proteins showed by iTRAQ method as significantly up-regulated (p ≤ 0.05), was carried out to validate the iTRAQ ratio obtained regarding the relative protein expression level. Three glycolytic enzymes were selected to evaluate whether the glycolic flux on these NSCLC cell lines was enhanced. As shown in Figure 1, PKM2 and LDH-A were significantly overexpressed in A549 cells compared with BEAS-2B cells (ratio of 1.71 and 1.22, respectively), whereas it was not differently expressed in NCIH460 cells compared with BEAS-2B cells (ratio of 0.86 and 0.92). These results confirmed the iTRAQ ratios observed for PKM2 and LDH-A for A549:BEAS-2B (ratio of 1.77 and 1.63, respectively) and for NCI-H460:BEAS-2B (ratio of 0.97 and 0.85, respectively) (Figure 1). In contrast, GAPDH were slightly overexpressed compared with BEAS-2B in A549 cells (ratio of 1.61, Figure 1) and strongly overexpressed compared with BEAS-2B in NCI-H460 cells (ratio of 1.77, Figure 1). Considering the iTRAQ ratios found for GAPDH (ratio of 1.27 and 1.44 for A-549:BEAS-2B and NCI-H460:BEAS-2B respectively, Figure 1), we verified that the results observed in the iTRAQ and Western blot experiments were concordant. Although the extent of the changes was slightly different, the trend of the change from two different methodologies was the same.

Figure 1. Western blot analysis of the differential expression of targets identified by iTRAQ: PKM2 and GAPDH in tumoral (A549 and NCIH460) versus non tumoral BEAS-2B cell lines. (A) Immunoblotting of α-tubulin, pyruvate kinase M2 (PKM2), glyceraldehyde-3-phosphate dehydrogenase (GAPDH), and lactate dehydrogenase A (LDHA). The lysates were immunoblotted with the protein-specific antibodies to reveal the relative expression levels. (B) Average densitometric analysis of Western blots from two independent experiments. Densitometry values were normalized to α-tubulin, which was used as control because of its stable expression level in all the three cell lines according to the iTRAQ data and Western blot analysis. *Statistically significant iTRAQ ratios (p values ≤0.05) between the two cell lines.

iTRAQ experiment. The activities of the PPP enzymes were the lowest in BEAS-2B cells and the activities of G6PD and 6PGD were the highest in A549 and NCI-H460 cells, respectively, resulting in markedly higher activity ratios of G6PD and 6PGD in A549:BEAS-2B (1.69 and 4.85 mU/mg prot, respectively) and NCI-H460:BEAS-2B (3.21 and 2.66 mU/mg prot, respectively). In addition, the highest TKT activity was observed in A549 cells, followed consecutively by lower activities in the NCI-H460 and BEAS-2B cell lines. Thus, the TKT activity ratio of A549:BEAS-2B and NCI-H460:BEAS-2B were 1.73 and 1.12 mU/mg prot, respectively. These results were also confirmed by the significant high iTRAQ ratio in A549:BEAS-2B (1.41, p ≤ 0.05), whereas no significant change iTRAQ ratio was observed in NCI-H460:BEAS-2B (0.99, p ≤ 0.05).

4. DISCUSSION In our study, using a quantitative proteomic approach based on iTRAQ coupled with two-dimensional fractionation OFFGELnanoLC and tandem MS, we analyzed the differential protein expression of core metabolic pathways in two lung cancer cell lines harboring oncogenic KRAS (A549 and NCI-H460 cells) and a non tumoral bronchial cell line (BEAS-2B cells). To date, there is no study using a quantitative proteomic approach to elucidate the changes in the abundance of key metabolic enzymes in KRAS mutated non-small lung cancer. We identified 49 differently expressed proteins in the A549 adenocarcinoma cell line, of which 34 were up-regulated and 15 down-regulated. Similarly, we identified 82 differently expressed proteins in the NCI-H460 large cell carcinoma line,

b. Enzyme Activity Assays

To further validate our iTRAQ results, we evaluated not only the impact of the protein expression level but also the enzyme activity. For that, we performed enzyme activity assays of the PPP enzymes G6PD, 6PGD, and TKT, whose activities has not been well described in lung cancer, in tumoral cells (A549 and NCI-H460) and non tumoral cells (BEAS-2B). The enzyme activity measurements are presented in Figure 2 and in general were in line with the protein expression level obtained from 4699



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fructose-biphosphate aldolase A (ALDOA) and GAPDH were up-regulated and only triosephosphate isomerase (TPI) was observed as down-regulated in NCI-H460 cells (large cell carcinoma). Glyceraldehyde-3-phospate Dehydrogenase (GAPDH). GAPDH is a key glycolytic enzyme that catalyzes the conversion of glyceraldehyde-3-phosphate to 1,3-bisphosphoglycerate, coupled with the reduction of NADP+ to NADPH. GAPDH is overexpressed in cancer cells, but recent studies have shown also a pro-apoptotic role.22 From a metabolic point of view, GAPDH acts as a switch for cell metabolic pathways enabling the protection of a cell from oxidative stress. High levels of reactive oxygen species (ROS) cause inactivation of GAPDH, which allows the cell to actively employ the PPP to accumulate NADPH by rerouting the cytoplasmic carbohydrate flux from glycolysis to PPP. In our study, GAPDH was up-regulated in both NSLC cell lines analyzed. Recent studies suggest GAPDH as a promising target for therapy of some carcinomas but further research is needed because this enzyme combines glycolysis, pro-apoptotic function and other activities including DNA repair, tRNA transport, iron metabolism, membrane trafficking, histone biosynthesis, and receptor-mediated cell signaling.22 Various agents inhibiting GAPDH have been identified and, among them, 3-bromopyruvate (3-BrPA) can be highlighted as one of the most promising therapeutic agents inducing apoptosis in human NSCLC cell lines.23 Pyruvate Kinase M1/M2 (PKM1/PKM2). PK is the final enzyme of the glycolysis pathway converting phosphoenolpyruvate (PEP) into pyruvate with ATP production. There are four isoforms of PK (L, R, M1, and M2), of which the PKM2 is a key of interest in cancer metabolism and tumor growth. PKM1 is constitutively in a highly active form, whereas PKM2 has two states and it can switch from high active to low active form depending on the cellular needs. Studies have shown that PKM2 is overexpressed in many tumor cells.24 PKM2 isoform is less efficient than PKM1, but PKM2 expression is advantageous in cancer because it slows down the last step of the glycolysis pathway. Furthermore, changes in PKM2 expression are associated with drug resistance in different tumors and silencing PKM2 by using shRNA can effectively improve the efficacy of chemotherapeutic drugs.10 Consequently, PKM2 has been proposed as a potential target for adjuvant lung cancer therapy. Here, the profile of PKM2 expression confirmed by Western blot (Figure 1) reveals that PKM2 is significantly up-regulated in A549 cells. Lactate Dehydrogenase (LDH-A and LDH-B). LDH catalyzes the conversion of pyruvate and NADH to lactate and NAD+ and is important in order to allow glycolysis to continue, especially when mitochondria respiration is compromised. LDH is a tetrameric enzyme composed of combinations of 2 subunits (LDH-A and LDH-B). LDH-A is elevated and activated in many cancers, and plays a crucial role in tumor initiation, maintenance, and progression,25 whereas the significance of LDH-B expression and regulation in tumor development remains unclear.10 In general, the high rates of glycolysis in cancer cells result in high pyruvate production. However, most of the resulting pyruvate does not enter in the TCA cycle for ATP production, but is converted to lactate by LDH-A, which is induced by oncogenes during proliferation.13 Recently, McCleland et al. reported that LDH-B is required for the growth of KRAS-dependent lung cancers and adenocarcinomas. This study identified LDH-B as a regulator of cell

Figure 2. Enzyme activity determinations of the PPP enzymes glucose6-phosphate dehydrogenase (G6PD), 6-phosphogluconate dehydrogenase (6PGD) and transketolase (TKT) in tumoral (A549 and NCIH460) versus non tumoral BEAS-2B cell lines. (A) PPP enzyme activities of G6PD, 6PGD, and TKT were normalized to total protein concentrations. Bars represent mean values ± standard deviations of triplicate measurements in two independent experiments. Enzyme activities are expressed as milliunits per milligram of protein (mU/mg prot). (B) PPP enzyme activities expressed as a ratio using the activity of BEAS-2B of each enzyme analyzed as the denominator. *Statistically significant iTRAQ ratios (p values ≤0.05) between the two cell lines.

of which 40 were up-regulated and 42 were down-regulated. The pattern of protein expression found using the iTRAQ methodology was confirmed by Western blot analysis. Enzyme activity analysis confirmed that the activity of the PPP enzymes (G6PD, 6PGD, and TKT) is correlated to their protein expression level and that their activity is exacerbated in tumoral cell lines harboring KRAS mutations. These results highlighted the key role of those enzymes in the regulation of the altered metabolism of cancer cell lines with KRAS mutations. The link between metabolism and cancer through enhanced aerobic glycolysis (also known as the Warburg effect) is a hallmark in cancer.15,16 However, it remains unclear how cancer cells coordinate glycolysis and biosynthesis to support cancer metabolism and tumor growth. Our study showed notable variation of metabolic enzymes (mostly in glycolysis and more interestingly in PPP) that disrupts the normal metabolic pathway regulation (Figure 3). a. Glycolysis

Among the target enzymes identified by our proteomic approach, we identified several dysregulated glycolytic enzymes in cancer cells suggesting their important role in lung cancer metabolism. We detected the up-regulation of glyceraldehyde3-phospate dehydrogenase (GAPDH), pyruvate kinase M1/M2 (PKM1/PKM2), and lactate dehydrogenase (LDH-A and LDH-B) in the A-549 cell line (adenocarcinoma), whereas no significant changes in the protein expression were found in the rest of the glycolytic enzymes. In contrast, only a few enzymes, 4700



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Figure 3. Dysregulated enzymes identified within central metabolic pathways in KRAS-driven lung cancer cell lines (A549 and NCI-H460). Glycolysis is shaded in gray, PPP is shaded in red, TCA cycle is shaded in blue, and lipid metabolism is shaded in green. Up-arrows represent upregulation and down-arrows represent down-regulation. Identified enzymes are in bold and underlined. iTRAQ ratios are all normalized on BEAS2B. Those iTRAQ ratios are in bold and italic for A549 and NCI-H460 cells, respectively.

has been previously shown to be overexpressed in lung cancer.27 Triosephosphate Isomerase (TPI). TPI is a crucial glycolytic enzyme that converts DHAP and G3P. We observed a significant down-regulation of TPI in NCI-H460 cells. In humans, TPI-deficient homozygotes exhibit markedly reduced TPI enzyme activity and consequently glycolysis is blocked with intracellular accumulation of DHAP. However, TPI deficiencies in heterozygous have approximately 50% normal TPI activity and exhibit no evidence of metabolic block in glycolysis or clinical effects, implying that a reduced enzyme activity level is sufficient to maintain normal metabolic function.28 Thus, this down-regulation is probably unrelated to a loss of metabolic activity and might be explained by moonlighting activity associated with this enzyme. The downregulation of TPI has been reported in several proteomics studies and although its functional role remains unclear, it has been associated with drug resistance.28,29

proliferation in a subset of lung adenocarcinoma harboring KRAS mutations and suggests LDH-B as a novel therapeutic approach for treating lung cancer. In our study, there was not a significant up-regulation of LDH in NCI-H460 cells, but we observed up-regulation of both LDH-A and LDH-B in A549 cells, which is in agreement with previous findings. In addition, several studies suggest that inhibition of LDH-A in cancer cells could stimulate mitochondrial respiration and lead to apoptosis.23,25,26 Therefore, LDH-A has been suggested as a potential target to overcome chemotherapy resistance because increased glycolysis and LDH-A have been associated with drug resistance. Considering that LDH-A inhibition has no significant toxic effect on normal tissue, it is promising to develop novel LDH-A inhibitors.23 Fructose-bisphosphate Aldolase A (ALDOA). In our study, ALDOA, the glycolytic enzyme that catalyzes the reversible conversion of fructose 1,6-bisphosphate into dihydroxyacetone phosphate (DHAP) and glyceraldehyde 3phosphate (G3P), was up-regulated in NCI-H460 cells. This enzyme is also known as lung cancer antigen NY-LU-1 and it 4701



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b. Pentose Phosphate Pathway (PPP)

5. CONCLUSION In light of our results obtained by a large screening study of proteins involved in metabolic pathways, enhanced PPP in combination with glycolysis may represent the best strategy for targeting metabolic pathways in KRAS-driven lung cancers. Because enzymes G6PD, TKT, and 6PGD are strongly upregulated, we can consider them as the most promising targets for the development of new cancer therapies and diagnostic purposes. Significantly, many glycolic enzymes such as GAPDH, PKM2, LDH-A, LDH-B were up-regulated as well, but to a lower extent compared to the PPP enzymes. The combination of these enzymatic targets from glycolysis and PPP may represent the most powerful strategy to exploit lung cancer metabolism in order to find an effective therapy. Nevertheless, assessing the potential value of targeting these up-regulated metabolic enzymes needs further studies such as cell proliferation assays after inhibition of targeted metabolic enzymes and extension of this study in other non tumoral lung cell lines or tissues. Another interesting discovery is the different regulation of the metabolic pathways found in the two histological subtypes with different mutation status analyzed. Although the adenocarcinoma cell line (A549) appears to have preference for both glycolysis and PPP, this metabolic preference was not found in the large cell carcinoma line (NCI-H460). But even if there is evidence of differential metabolic adaptations between histological subtypes and mutation status in KRAS-driven lung cancer, targeting PPP pathway in combination with glycolysis might be useful for developing an effective treatment of KRASdriven lung cancers for both NSCLC histological subtypes (adenocarcinoma and large cell carcinoma). A better understanding of the metabolic reprogramming and how certain enzymes fuel tumorigenesis will definitely help us to identify effective new therapeutic targets as well as the possibility to combine them with other targeted drugs in order to overcome drug resistance.

PPP is another major pathway of glucose metabolism, which is frequently up-regulated in cancer cells.30 Although the PPP is a source of NADH and nucleotide production in cells and may represent a target for cancer therapy and overcoming drug resistance,15,31,32 the implication of this pathway in lung cancer cells regulation is not so widely described. Our investigation evidenced that PPP enzymes may be considered as new targets for KRAS-driven lung adenocarcinoma therapy. Glucose-6-phosphate Dehydrogenase (G6PD). Interestingly, G6PD, which was significantly up-regulated in both cancer cell lines, is the unique and rate-limiting enzyme in the PPP playing roles in cellular redox homeostasis and cell survival.33 Thus, this increase of G6PD might reveal a potential target for non-small cell lung cancer therapy as high levels of this enzyme has been reported in several cancers including lung cancers.15 6-Phosphogluconate Dehydrogenase (6PGD) and Transketolase (TKT). Similarly, 6PGD and TKT were upregulated as well but only in A549 cells. Both proteins have been previously identified as potential targets.34 The nonoxidative PPP is controlled by transketolase enzymes, which are encoded by three human TK genes: TKT, TKTL1, and TKTL2.35 The isoform TKTL1 has been reported overexpressed in cancer cells and is the most likely therapeutical target.36 Recently, inhibition of 6PGD has been suggested as a potential novel strategy to treat glycolyic tumors such as lung cancer.37 Therefore, this finding gives new evidence about the PPP regulation in KRAS-mutated lung adenocarcinoma cells. Moreover, the observed differences between lung adenocarcinoma cell lines can be exploited to design specific therapeutic approaches based on a metabolically defined tumor classification. c. Tricarboxylic Acid Cycle (TCA cycle)

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Recent studies suggest that KRAS oncogene induces mitochondrial dysfunction, which has an impact on energy metabolism and redox status such as TCA cycle.38,39 In our analysis, we showed that fumarate hydratase, which catalyzes the reversible hydration of fumarate to malate, was up-regulated in NCI-H460 cells, whereas none of the proteins detected within the TCA cycle were significantly differently expressed in any of the cell lines. Higher levels of fumarate hydratase mRNA have been reported recently in cisplatin-resistant cells that display also high ROS levels.40 Thus, this up-regulation can be related to a higher cisplatin resistance and raise a therapeutic potential for future therapies.

ASSOCIATED CONTENT

S Supporting Information *

Figure S1: Volcano plots showing the distribution of −ln(p value) versus ln(iTRAQ ratio). Protein significantly downregulated (red area) for p value ≤0.05 and ln(iTRAQ ratio) cutoff of −0.18, that is, iTRAQ ratio cutoff was chosen at 0.83. Protein significantly up-regulated (green area) for p value ≤0.05 and ln(iTRAQ ratio) cutoff of 0.18, that is, iTRAQ ratio cutoff was chosen at 1.20. Figure S2: Representative MS/MS spectra of precursor ions in (A) m/z 1951.9054 and (B) m/z 1757.9307 corresponding to NSYVAGQYDDAASYQR (from G6PD enzyme) and EDGLAQQQTQLNLR (from FASN enzyme) protonated peptides, respectively. Table S1: Protein identification and relative quantitation summary generated by Protein Pilot from iTRAQ-based OFFGEL-LC-MALDI TOF/ TOF analyses. Proteins expression level in A-549 and NCIH460 cell lines are normalized to protein expression level in BEAS-2B cell line (iTRAQ ratio 116/114 and 117/114, respectively). Statistically significant iTRAQ ratios (p values ≤0.05) for proteins up-regulated are highlighted in gray, whereas proteins down-regulated are highlighted in black. Table S2: iTRAQ analysis of proteins up- and down-regulated between A-549 cell line (iTRAQ 116) and BEAS-2B (iTRAQ 114). Statistically significant iTRAQ ratios (p values ≤0.05) for proteins up-regulated are highlighted in gray, whereas proteins

d. Lipid Metabolism

Our analysis also revealed the unexpected slight downregulation in both tumoral cell lines of the lipogenic enzyme fatty-acid synthase (FASN), which is usually overexpressed in many cancers including lung cancer.41 However, a decrease in de novo fatty acid synthesis has been previously reported in KRAS-driven cells, suggesting that RAS pathway activation confers metabolic robustness by increasing the uptake of exogenous lipids.42 Because an increased de novo lipid biosynthesis catalyzed by FASN has been considered as a hallmark of proliferating cancer cells, further confirmations need to be done in order to fully understand the regulation of lipid metabolism in these tumoral cell lines. 4702



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(9) Wang, Y.; Kaiser, C. E.; Frett, B.; Li, H. Targeting Mutant KRAS for Anticancer Therapeutics: A Review of Novel Small Molecule Modulators. J. Med. Chem. 2013, 56, 5219−5230. (10) Zhao, Y.; Butler, E. B.; Tan, M. Targeting cellular metabolism to improve cancer therapeutics. Cell Death Dis. 2013, 4, e532. (11) DeBerardinis, R. J.; Lum, J. J.; Hatzivassiliou, G.; Thompson, C. B. The biology of cancer: metabolic reprogramming fuels cell growth and proliferation. Cell Metab. 2008, 7, 11−20. (12) Hsu, P. P.; Sabatini, D. M. Cancer cell metabolism: Warburg and beyond. Cell 2008, 134, 703−707. (13) Vander Heiden, M. G.; Cantley, L. C.; Thompson, C. B. Understanding the Warburg effect: the metabolic requirements of cell proliferation. Science 2009, 324, 1029−1033. (14) Levine, A. J.; Puzio-Kuter, A. M. The control of the metabolic switch in cancers by oncogenes and tumor suppressor genes. Science 2010, 330, 1340−1344. (15) Vander Heiden, M. G. Targeting cancer metabolism: a therapeutic window opens. Nat. Rev. Drug Discovery 2011, 10, 671− 684. (16) De Atauri, P.; Benito, A.; Vizán, P.; Zanuy, M.; Mangues, R.; Marín, S.; Cascante, M. Carbon metabolism and the sign of control coefficients in metabolic adaptations underlying K-ras transformation. Biochim. Biophys. Acta 2011, 1807, 746−754. (17) Jones, N. P.; Schulze, A. Targeting cancer metabolism–aiming at a tumour’s sweet-spot. Drug Discovery Today 2012, 17, 232−241. (18) Ross, P. L.; Huang, Y. N.; Marchese, J. N.; Williamson, B.; Parker, K.; Hattan, S.; Khainovski, N.; Pillai, S.; Dey, S.; Daniels, S.; et al. Multiplexed protein quantitation in Saccharomyces cerevisiae using amine-reactive isobaric tagging reagents. Mol. Cell Proteomics 2004, 3, 1154−1169. (19) Karp, N. A.; Huber, W.; Sadowski, P. G.; Charles, P. D.; Hester, S. V.; Lilley, K. S. Addressing accuracy and precision issues in iTRAQ quantitation. Mol. Cel. Proteomics 2010, 9, 1885−1897. (20) Unwin, R. D.; Griffiths, J. R.; Whetton, A. D. Simultaneous analysis of relative protein expression levels across multiple samples using iTRAQ isobaric tags with 2D nano LC-MS/MS. Nat. Protoc. 2010, 5, 1574−1582. (21) Tian, W. N.; Braunstein, L. D.; Pang, J.; Stuhlmeier, K. M.; Xi, Q. C.; Tian, X.; Stanton, R. C. Importance of glucose-6-phosphate dehydrogenase activity for cell growth. J. Biol. Chem. 1998, 273, 10609−10617. (22) Krasnov, G. S.; Dmitriev, A. A.; Snezhkina, A. V.; Kudryavtseva, A. V. Deregulation of glycolysis in cancer: glyceraldehyde-3-phosphate dehydrogenase as a therapeutic target. Expert Opin. Ther. Targets 2013, 17, 681−693. (23) Wang, Z.-Y.; Loo, T. Y.; Shen, J.-G.; Wang, N.; Wang, D.-M.; Yang, D.-P.; Mo, S.-L.; Guan, X.-Y.; Chen, J.-P. LDH-A silencing suppresses breast cancer tumorigenicity through induction of oxidative stress mediated mitochondrial pathway apoptosis. Breast Cancer Res. Treat. 2012, 131, 791−800. (24) Cairns, R. A.; Harris, I. S.; Mak, T. W. Regulation of cancer cell metabolism. Nat. Rev. Cancer 2011, 11, 85−95. (25) Le, A.; Cooper, C. R.; Gouw, A. M.; Dinavahi, R.; Maitra, A.; Deck, L. M.; Royer, R. E.; Vander Jagt, D. L.; Semenza, G. L.; Dang, C. V. Inhibition of lactate dehydrogenase A induces oxidative stress and inhibits tumor progression. Proc. Natl. Acad. Sci. U. S. A. 2010, 107, 2037−2042. (26) Fantin, V. R.; St-Pierre, J.; Leder, P. Attenuation of LDH-A expression uncovers a link between glycolysis, mitochondrial physiology, and tumor maintenance. Cancer Cell 2006, 9, 425−434. (27) Güre, A. O.; Altorki, N. K.; Stockert, E.; Scanlan, M. J.; Old, L. J.; Chen, Y. T. Human lung cancer antigens recognized by autologous antibodies: definition of a novel cDNA derived from the tumor suppressor gene locus on chromosome 3p21.3. Cancer Res. 1998, 58, 1034−1041. (28) Ationu, A.; Humphries, A.; Lalloz, M. R.; Arya, R.; Wild, B.; Warrilow, J.; Morgan, J.; Bellingham, A. J.; Layton, D. M. Reversal of metabolic block in glycolysis by enzyme replacement in triosephosphate isomerase-deficient cells. Blood 1999, 94, 3193−3198.

down-regulated are highlighted in black. Table S3: iTRAQ analysis of proteins up- and down-regulated between NCIH460 cell line (iTRAQ 117) and BEAS-2B (iTRAQ 114). Statistically significant iTRAQ ratios (p values ≤0.05) for proteins up-regulated are highlighted in gray, whereas proteins down-regulated are highlighted in black. This material is available free of charge via the Internet at http://pubs.acs.org.

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AUTHOR INFORMATION

Corresponding Authors

*M. Cascante. E-mail: [email protected]. Address: Department of Biochemistry and Molecular Biology, Faculty of Biology, Universitat de Barcelona, Av Diagonal 643 (edifici nou annex p-2), 08028 Barcelona, Spain. *S. Bourgoin-Voillard. E-mail: [email protected]. Address: Promethee Proteomics Platform, Université Grenoble Alpes, INSERM U823 IAB, Institut de Biologie et Pathologie, CHU de Grenoble, Boulevard la Chantourne, F-38000 Grenoble, France. Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS This work was supported by the BioHealth Computing Erasmus Mundus program (512383-1-2010-1-FR), the Ministerio de Ciencia e Innovación of Spain (SAF2011-25726), the Generalitat de Catalunya (2009SGR-1308), the European Commission (FP7) grant METAFLUX (PITN-GA-2010264780) and COSMOS research infrastructures grant (n°312941), and the Icrea Academia award 2010 (granted to M. Cascante). The authors are also grateful to the Biochimie des enzymes et des protéines laboratory (BEP/DBTP, CHU de Grenoble, France) for the support during the western blot analysis.

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REFERENCES

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Quantitative Proteomic Approach to Understand Metabolic Adaptation

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