Article pubs.acs.org/jpr
Mass Spectrometric/Bioinformatic Identification of a Protein Subset That Characterizes the Cellular Activity of Anticancer Peptides Filippo Genovese,*,† Alessandra Gualandi,‡ Laura Taddia,‡ Gaetano Marverti,§ Silvia Pirondi,‡ Chiara Marraccini,‡ Paul Perco,∥ Michela Pelà,⊥ Remo Guerrini,⊥ Maria Rosaria Amoroso,‡ Franca Esposito,# Andrea Martello,‡ Glauco Ponterini,‡ Domenico D’Arca,§ and Maria Paola Costi*,‡ †
C.I.G.S., University of Modena and Reggio Emilia, Via G. Campi 213/A, Modena 41125, Italy Department of Life Sciences, University of Modena and Reggio Emilia, Via Giuseppe Campi 183, Modena 41125, Italy § Department of Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, Via Giuseppe Campi 183, Modena 41125, Italy ∥ Emergentec biodevelopment GmbH, Gersthofer Straße 29-31, Wien 1180, Austria ⊥ Department of Chemical and Pharmaceutical Sciences, University of Ferrara, Via Fossato di Mortara 17-19, Ferrara 44100, Italy # Department of Molecular Medicine and Medical Biotechnology, University of Naples “Federico II”, Via S. Pansini 5, Napoli 80131, Italy ‡
S Supporting Information *
ABSTRACT: The preclinical study of the mechanism of action of anticancer small molecules is challenging due to the complexity of cancer biology and the fragmentary nature of available data. With the aim of identifying a protein subset characterizing the cellular activity of anticancer peptides, we used differential mass spectrometry to identify proteomic changes induced by two peptides, LR and [D-Gln4]LR, that inhibit cell growth and compared them with the changes induced by a known drug, pemetrexed, targeting the same enzyme, thymidylate synthase. The quantification of the proteome of an ovarian cancer cell model treated with LR yielded a differentially expressed protein data set with respect to untreated cells. This core set was expanded by bioinformatic data interpretation, the biologically relevant proteins were selected, and their differential expression was validated on three cis-platinum sensitive and resistant ovarian cancer cell lines. Via clustering of the protein network features, a broader view of the peptides’ cellular activity was obtained. Differences from the mechanism of action of pemetrexed were inferred from different modulation of the selected proteins. The protein subset identification represents a method of general applicability to characterize the cellular activity of preclinical compounds and a tool for monitoring the cellular activity of novel drug candidates. KEYWORDS: human thymidylate synthase peptidic inhibitors, pemetrexed, folate pathway, drug targets, anticancer drugs, ovarian cancer, bioinformatics, label-free quantification
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INTRODUCTION The preclinical study of the mechanism of action of anticancer small molecules is challenging due to the complexity of cancer biology and the fragmentary nature of the biological data available. A more comprehensive approach for determining a broader profile of the cellular activity of anticancer compounds is needed. Proteomic strategies, such as label-free proteome quantification, represent fundamental tools to study the biology of cancer cells with the purpose of characterizing pathological vs healthy cells and provide insight into the molecular mechanisms involved.1−4 In this contribution, we propose to apply differential mass spectrometry (MS) analysis to identify the proteomic changes induced in model cancer cells by small cell-growth inhibitors with respect to those induced by a known clinical drug. Our © XXXX American Chemical Society
case study is focused on anticancer compounds targeting thymidylate synthase (TS). Human TS (hTS) is a known target for anticancer drugs. As a dimer, hTS catalyzes the conversion of 2′-deoxyuridine-5′-monophosphate (dUMP) to 2′-deoxythymidine-5′-monophosphate (dTMP) and requires N5,N10methylenetetrahydrofolate (MTHF) as a cofactor.5 Classical inhibitors of the protein used in clinical therapy are N5,N10methylentetrhydrofolate analogs such as pemetrexed (PMX) and raltitrexed (Figure 1). Their protein binding site and mode Special Issue: Proteomics of Human Diseases: Pathogenesis, Diagnosis, Prognosis, and Treatment Received: April 21, 2014
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μM after 72 h.12 A recently developed derivative of LR, [DGln4]LR, shows the same inhibition profile but is more cytotoxic (70−75% inhibition of OC cell growth at 5 μM).12 As part of a wider effort to clarify the biological effects as well as the intracellular mechanism of action of the LR octapeptide and its derivatives, the present work aimed to design and validate a protein subset that could contribute to the understanding of the cellular behavior of the peptides in comparison with that of PMX. This approach can be regarded as a general method to identify a protein subset that characterizes the cellular activity of a preclinical candidate. A mass-spectrometry (MS)-based semiquantitative blind approach was adopted to determine which molecular events are triggered directly or indirectly by this new class of hTS inhibitors at the level of the cellular proteome. Successive labelfree quantitative proteomic analyses highlighted a set of proteins whose levels were significantly modulated by the peptide lead compared with an untreated control group of epithelial OC cell lines. The global interpretation of the data obtained in this study was driven by bioinformatic analysis and consisted of integrating interaction metadata with pathway and molecular function annotations. The set of proteins was further expanded to include functional neighbors showing strong correlations with our original protein panel and folic acid molecular partners, since the LR peptide has been shown to interact with important enzymes related to folate metabolism.9 This type of approach has previously been successfully applied for the interpretation of large data sets.13,14 Differentially expressed proteins with a strong biological meaning in the context of cancer biology and proteins emerging as highly connected with our protein data set from the bioinformatic analysis, including folate pathway-related critical nodes, were validated through standard Western blot analysis in three different OC cell lines. The same set of proteins was monitored on OC cells treated with a more effective LR analog ([D-Gln4]LR) for validation purposes and with PMX, with the aim of evaluating the effects of this clinical drug on the subset of proteins identified compared with those of the peptides. Our findings support the hypothesis that the identified peptides show a different protein modulation profile compared with the classical TS-targeting drug. This pharmacodynamic behavior reflects a different cellular activity, that is, one that does not result in hTS overexpression. Therefore, these lead compounds are promising starting points for the development of more potent antitumoral agents with lower drug resistance potential with respect to PMX.
Figure 1. Chemical structure of MTHF (the natural cofactor of TS), raltitrexed and pemetrexed (classical anticancer compounds directed to TS active site), and LR peptide and its [D-Gln4] analog (directed to TS dimer interface).
of action have been thoroughly characterized by X-ray crystallography and enzyme kinetic studies (Figure 2C,D).6,7 These compounds are used for treating different cancer types, especially solid cancers such as colorectal cancer, mesothelioma, and lung cancer. PMX is considered a multitarget drug because it interferes with the metabolism of folic acid at different levels; its main targets are TS, DHFR, and GART. OC first line treatment is based on platinum drugs (Pt drugs) that may cause the development of a Pt drug-resistant phenotype with increased concentrations of hTS and other related folate-dependent proteins, such as dihydrofolate reductase (DHFR); therefore, this treatment regimen may trigger the development of cross-resistance to anti-hTS drugs.8 Consequently, OCs that are resistant to Pt drugs may also be less sensitive to anti-hTS drugs.9,10 In an effort to overcome drug resistance induced by these drugs, we have recently identified a new class of peptidic compounds that inhibit hTS with a novel mechanism of action and reduce cancer cell growth.11,12 Among these, the peptide with the best activity profile, LR (LSCQLYQR), inhibits the intracellular enzyme in cisplatin-sensitive and cisplatin-resistant OC cell lines without causing the typical protein overexpression associated with acquired resistance to PMX, thus showing the potential of this innovative hTS inhibition strategy.11 These peptides specifically target the hTS dimer interface and stabilize its di-inactive form as shown in the crystallographic structure deposited in the Protein Data Bank (PDB 1HVY) (Figure 2A,B) and through biophysical studies.11,12 The LR peptide inhibits intracellular hTS activity and reduces the growth rate of various ovarian cancer cells by 50−60% at a concentration of 5
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EXPERIMENTAL SECTION
Cell Culture and Treatment
Three OC lines, CSD-OC (A2780 cisplatin-sensitive derived ovarian cancer cell lines), A2780/CP, and IGROV-1, were cultured in RPMI 1640 medium (Lonza, Lonza Group Ltd., Switzerland), supplemented with 10% FBS and 200 mM Lglutamine. The cells were incubated at 37 °C under 5% CO2 for at least 24 h before treatment with the peptides. LR (LSCQLYQR) and its [D-Gln4] analog (LSCqLYQR) were obtained through canonical solid-phase peptide synthesis on Wang resin using Fmoc/t-Bu chemistry.15 The crude peptides were further purified via standard preparative RPHPLC, yielding a final product purity higher than 99%, as determined by HPLC-UV analysis. B
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Figure 2. Schematic representation of the differences in the ligand binding sites of the human thymidylate synthase (PDB ID 3N5G). (A) Peptidebased inhibitor (in lime) binds at the interface of the two monomers. (B) The flexible loop containing catalytic Cys195 is in inactive confirmation (pointing toward the interface), and the regions interacting with the peptide are highlighted in yellow. (C) On the other hand, raltitrexed (in orange) enters the monomeric active site. (D) The flexible loop is in active confirmation with Cys195 pointing toward the active site. dUMP is shown in line representation in cyan color.
water to a final concentration of 1 μg/μL. A total of 12 μg of protein extract per condition was used in subsequent analyses. Proteins were reduced with DTT (10 mM, 1 μL in 100 mM AMBIC) at 56 °C for 30 min and alkylated with iodoacetamide (55 mM, 1 μL in AMBIC 100 mM) at room temperature in the dark for 1 h. The excess alkylating agent was quenched with 10 μL of 10 mM DTT, which was allowed to react at room temperature for 10 min. After proper dilution of the urea concentration to 2 M with 10 mM AMBIC, the protein mixtures were digested with TPCK-modified sequencing grade trypsin (1:50 w/w enzyme-to-substrate final ratio) at 37 °C overnight. The samples were then acidified with 1 μL of a 5% formic acid (FA) solution and dried in a vacuum evaporator. The peptides were subsequently resuspended in 30 μL of a 1% FA/acetonitrile (98:2) solution spiked with Glu-1-fibrinopeptide B (sequence EGVNDNEEGFFSAR, Sigma-Aldrich) as an internal standard, at a final concentration of 25 nM. Analyses were performed on an ESI-Q-TOF accurate-mass spectrometer (G6520AA, Agilent Technologies), controlled by MassHunter (v. B.02.00) and interfaced with a CHIP-cube to an Agilent 1200 nanopump. Chromatographic separation was performed on a highcapacity loading chip (Agilent Technologies) with a 75 μm I.D., 150 mm, 300 Å C18 column, prior to a desalting step through a 500 nL trap column. The injected sample (2 μL, 0.8 μg) was loaded onto the trap column with a 4 μL/min 0.1% FA/ACN (98:2) phase flow, and after 3 min, the precolumn was switched in-line with the nanoflow pump (400 nL/min, phase A water/ ACN/FA 96.9:3:0.1, phase B ACN/water/FA 94.5:5:0.1), equilibrated in 3% B. The peptides were eluted from the RP
Membrane barrier crossing of the peptides was promoted by the SAINT PhD delivery system (Synvolux Therapeutics, NL), according to the standard transfection protocol; a 0.8 mM stock solution of the peptide was prepared in HBS and stored at −80 °C before use. For each treatment, delivery system−peptide complexes were prepared, adding 20 μL/mL of SAINT PhD to a diluted peptide solution to reach a final LR/[D-Gln4]LR subcytotoxic dose of 5 μM. After the culture medium was aspirated from the cells, the SAINT-PhD/peptide complex was added to the wells and incubated (37 °C, 5% CO2); complete RPMI was added after 4 h. Cells were treated for 48 h, then the culture medium was aspirated, and cells were lysed for protein extraction. A delivery system solution at the same concentration was used as the control treatment. For PMX cell treatment, a 2 mM stock solution was obtained by solubilizing the drug in DMSO and then stored at −20 °C before use. The drug was diluted to a 20 μM final concentration of PMX in complete RPMI medium, in which with the cells were incubated for 48 h. Cells treated with 1% DMSO were used as the control. Proteomic Analysis
Cells were washed twice with ice-cold PBS, and total proteins were extracted directly in the plate by scraping the surface in RIPA lysis buffer (50 mM TRIS-HCl, pH 7.4; 150 mM NaCl; 1% NP-40; 2 mM EDTA; 2 mM PMSF; 5 μg/mL leupeptin; 5 μg/mL pepstatin). Cell lysates were then precipitated with 4 volumes of cold acetone, held overnight at −20 °C, and resuspended with 30 μL of an 8 M urea solution. The protein concentrations, determined via a standard Bradford assay against bovine serum albumin, were normalized with deionized C
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annotation. All of the successive analyses were based on ENSEMBL IDs. Official Gene Symbols were used in all graphical representations. A network of direct protein−protein interactions among all members of the DEP set was generated using data from three protein−protein interaction databases: BioGRID, IntAct, and Reactome. The interaction data from these three sources were consolidated in the omicsNET protein framework, which takes into account direct protein−protein and computationally calculated interactions based on shared pathway information, gene ontology annotations, and common protein domains.21,22 In a second step, we extended the set of DEPs via an interneighbor expansion method, adding proteins to the initial set of DEPs that showed at least two interactions with functional neighbors through querying the BioGRID, IntAct, and Reactome databases. The rationale for this expansion was the goal of identifying proteins that were of relevance due to their strong connectivity to members of the DEP set that were too diluted or suppressed by more abundant ions to be identified via mass spectrometry.23 The MCODE algorithm was further applied to the resulting network to identify highly connected functional complexes for further investigation.24
column through the following gradient: 3−45% B over a period of 75 min, 45−90% B for 10 min, hold in 90% B for 5 min, and switched back to 3% B for 8 min, for a total runtime of 110 min, including a 10 min post-run reconditioning step. Each biological replicate was run three times. Analytical controls (a mixture of baker’s yeast enolase and bovine serum albumin tryptic digests) were run twice a day to monitor chromatographic performance. Centroided MS and MS2 spectra were recorded from 350 to 1700 m/z and 50 to 1700 m/z, respectively, at scan rates of 6 and 3 Hz. The eight most intense multicharged ions were selected for MS2 nitrogen-promoted collision-induced dissociation. The collision energy was calculated according to the following expression: CE(V) = 3.6 × (m/z)/100 − 3. A precursor active exclusion of 0.2 min was set, and the detector was operated at 2 GHz in extended dynamic range mode. Mass spectra were automatically recalibrated with two reference mass ions. Raw data, converted from the vendor’s data format into mzXML using msconvert in the Proteowizard toolbox,16 were searched against the human IPI database (v. 3.82, 92104 entries; the GFP internal standard sequence was appended) with PepArML, v. 1.1. This meta-protein database search engine combines and analyzes the outputs of several types of peptide identification software (Mascot, X!Tandem naive and kscore, InsPecT, MiryMatch, OMSSA, SScore), providing significant increases in the number of peptide-to-spectrum matches and confidence.17 The following search parameters were used: 100 ppm precursor tolerance, 0.2 Da fragment mass error allowed, trypsin with semispecific cleavage, one missed cleavage allowed, carbamidomethyl as a fixed modifier of cysteine residues and methionine oxidation, and lysine and peptide N-terminus carbamylation as variable modifications. The false discovery rate was estimated through an internal PepArML double decoy database search. The PepArML output underwent protein prophet validation for protein inference.18 Only hits with at least two significantly scoring peptides or siblings per protein and a score >0.9 were quantified. Ambiguous protein isoforms with the same score were all taken into account. No “one-hit-wonder” was included.
Bioinformatic Analysis of Folate Pathway Members
To probe the effects of our lead candidate drug on the members of the folate pathway, we consolidated a set of folic acid-related proteins. This set was obtained from the Reactome pathway database25 through extracting all members of the “metabolism of folate and pterines” pathways, as well as querying the Gene Ontology database for terms containing the substring “folate” or “folic acid”. In addition, we included protein targets of the drug PMX, obtained by querying DrugBank.26 Both the differentially expressed proteins in the original data set and proteins in the expanded network were investigated. The obtained list of folate molecular partners is provided as Table SI1, Supporting Information. Western Blot Analysis
Cells were washed twice in ice-cold PBS, lysed in RIPA buffer (20 mM TRIS-HCl, pH 7.5; 150 mM NaCl; 1 mM Na2EDTA; 1 mM EGTA; 1% NP-40; 1% sodium deoxycholate; 1 mM Na3VO4; 1 mM PMSF; Complete Mini Protease inhibitor cocktail (Roche); and Phosphatase Inhibitor Cocktail l and 2 (Sigma)) and then centrifuged at 14000g (rcf) at 4 °C for 30 min to remove debris. The protein concentration in each lysate was determined using the Bradford protein assay reagent (Sigma-Aldrich). Forty micrograms of the cell extracts was subsequently loaded on a polyacrylamide gel after denaturation according to the method of Laemmli.27 After SDS-PAGE, blotting was performed on PVDF membranes (Hybond-P, Amersham). The membranes were blocked in nonfat dry milk (2%) in TBS buffer containing 0.1% Tween-20 at room temperature for 1 h. Primary antibodies were incubated overnight in nonfat dry milk (2%) in PBS buffer containing 0.1% Tween-20. The following antibodies were used in these assays: anti-DHFR (clone A-4, Santa Cruz Biotechnology, 1:500 dilution), anti-GART (clone 4D6-1D5, Abnova, 1:1000 dilution), anti-ATIC (clone F38 P7 H9, Abnova, 1:1000 dilution), anti-TS (clone TS106, Abnova, 1:500 dilution), antiHSP90AA1 (clone 4F10, Abnova, 1:5000 dilution), antiTRAP1 (clone TR-1A, Santa Cruz Biotech, 1:2000 dilution), anti-EIF2S1 (clone [EIF2a], Abcam, 1:500 dilution), anti-βactin (Santa Cruz Biotechnology, Inc., 1:5000 dilution), antiMTHFR (clone 1G12, Abnova, 1:1000 dilution), and anti-
Label-Free Relative Quantification
The PepArML search results were cross-related to the corresponding MS1 profiles (mzXML format) with the quantitative software IDEAL-Q, v. 1.024.19 Pooled search hits were manually validated and quantified on a moverz/RT 2D map. A nonendogenous peptide was used as an internal standard (GFP) for inter-run normalization. The validated peptide XIC area data were exported and elaborated using the DanteR tool.20 Briefly, the peak areas were Log2 transformed and normalized (central tendency), and the corresponding protein intensities were then obtained from the peptides through an analysis of variance-based peptide roll-up of the most abundant 10 peptides per protein. Entries with a Benjamini−Hochberg corrected p-value
