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Docosohaexanoic acid-supplemented PACA44 cell lines andover-activation of Krebs cycle: An integrated proteomic,metabolomic and interactomic overview

Angelo D'Alessandro, Gian Maria D'Amici, Anna Maria Timperio,Nicolò Merendino, Lello Zolla⁎

Department of Ecological and Biological Science, University of Tuscia, Viterbo, Italy

A R T I C L E I N F O A B S T R A C T

Article history:Received 10 March 2011Accepted 6 June 2011Available online 23 June 2011

Recent investigations have pointed out the ability of fatty acids, in particular ofdocosohaexanoic acid (DHA), to induce growth inhibition and apoptosis in the humanPaCa-44 pancreatic cancer cell line through a series of mechanisms which has beenhypothesized to mimic apoptosis. While preliminary evidences indicated the involvementof lipid-targeting oxidative stress in DHA-induced apoptotic processes, mainly through thealteration of the glutathione (GSH) homeostasis and oxidized-glutathione (GSSG) turn-overthrough their extra-cellular extrusion, no further molecular data have been hithertoaccumulated. To this end, we hereby propose simultaneous protein-targeting andmetabolite-oriented analyses, which have been integrated through the auxilium of in silicoelaboration of those protein–protein interaction pathways and enrichment of biological/molecular functions. To determine the most suitable time window for the early onset of theDHA-triggered apoptosis phenomena we performed flow cytometry-based apoptoticassessment at 24, 48 and 72 h. Results indicated that the focus of apoptosis onset rangedfrom 48 to 72 h. From these analyses it emerges that themetabolism of control human PaCa-44 pancreatic cancer cell line mainly leans on glycolytic pathways, while it is promptlyswitched to Kreb's cycle activation (overexpression of Kreb's cycle enzymes in DHA-treatedcells against controls) and modulation of the GSH homeostasis through an increasedproduction of GSSG-reducing NADPH coenzyme via the shift of the glycolytic energy fluxtowards the pentose phosphate pathway.Interestingly, it also emerges a role for structural protein alteration in DHA-treated cells,which might be linked to cytoskeletal alterations occurring during apoptosis.

© 2011 Elsevier B.V. All rights reserved.

Keywords:PACA44Docosohexaenoic acidProteomicsMetabolomicsInteractomics

1. Introduction

Recent developments in cancer research have led the scien-tific community to reconsider the strong commitment ofmetabolic dysfunctions in cancer cell proliferation anddifferentiation [1]. This concept stems from the observationthat, even in the presence of oxygen, highly proliferating cells

tend to generate energy strictly from the glycolytic pathway,through a process that has been named “aerobic glycolysis” oralsoWarburg effect, after the name of the first researcher whodiscovered this basic biochemical behavior [1]. More recently,appreciation of the generality of the Warburg effect has beendocumented in a broad variety of tumors, including pancreaticadenocarcinoma, in which over-expression and over-

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⁎ Corresponding author at: Tuscia University, Largo dell'Università snc, 01100 Viterbo, Italy. Tel.: +39 0761 357 100; fax: +39 0761 357 630.E-mail address: zolla@unitus.it (L. Zolla).

1874-3919/$ – see front matter © 2011 Elsevier B.V. All rights reserved.doi:10.1016/j.jprot.2011.06.006

ava i l ab l e a t www.sc i enced i r ec t . com

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phosporylation of key glycolytic enzymes, such as enolase 3,have been reported [2].

These observations stimulated the broader concept that a‘metabolic transformation’ is required for tumorigenesis and,conversely, that oncosuppressor activity might benefit fromthe activation of the aerobic metabolism passing throughKreb's cycle via a glycolytic bypass, which stems from theconsumption of aminoacidic (such as glutamic acid) or, likelyenough, fatty acid substrates. This mechanism might repre-sent a physiological process which is activated through theparticipation of oncosuppressor genes, such as p53 and itsclosely-related p63 and p73 families [3–5].

Out of the numerous tumor cell lines currently available forexperimentation, human PACA44 cell lines represent an invitro model comparable to human pancreatic ductal carcino-ma and offer the advantage of thorough molecular character-ization, both at the genomics [6] and proteomics level [7].Moreover, the study of pancreatic cancer cells holds somerelevant biomedical pitfalls, since pancreatic cancer repre-sents the fourth leading cause of cancer death in westerncountries.8 Indeed, it is almost associated to fatal outcomes, asit displays a high degree of resistance to conventionalradiotherapy and chemotherapy [8]. In particular, elevatedGSH levels in pancreatic carcinoma have been reported to beassociated with resistance to chemotherapy [9]. In thisrespect, preliminary proteomic profiles of the changes occur-ring upon 5-aza-2′-deoxycytidine (DAC) treatment on PACA44cells have highlighted alterations of the protein expressionpatterns mainly involving metabolism-related functions(42.86%), which implies that inhibition of growth and prolif-eration and induction of apoptosis might be tied to alterationof the metabolic poise in this cancer cell line [7].

In parallel, accumulating evidence has been growinglyattributing a role for n-3 and n-6 dietary poly-unsaturatedfatty acids (PUFAs) in the induction of growth inhibition and/orapoptosis as well as the inhibition and/or reversal of drugresistance in a variety of tumor cells [10–13]. In particular,docosahexaenoic acid (DHA; 22:6 n-3) has been shown to be themost potent inducer of apoptosis inhuman colon cancer cells ina dose-dependent fashion, and lipid peroxidation has beenindicated to be involved in the apoptotic process [14], althoughthe molecular mechanisms at the basis of this phenomenonhave not yet been further elucidated. A role has emerged foralteration of intra-cellular/extra-cellular GSH homeostasis andthus for oxidative stress targeting lipids (accumulation ofmalondialdehyde upon DHA-supplementation) [6], while nodetailedmolecular evidences have been produced so far. To thisend, we decided to perform integrated protein- andmetabolite-oriented analyses, which have been elaborated in silico for thedetermination of the main molecular protein and metabolitepathways, in order to assess whether the pro-apoptoticphenomena which had been observed upon DHA-treatment ofPACA44 cell lines [6] could be better elucidated through adetailed biochemical analysis. As a result, it emerged that DHA-treated pancreatic cancer cells ended up to overexpress Kreb'scycle-related enzymes, which have been related to an increasedgeneration of Kreb's cycle intermediates and diminution ofglycolytic counterparts. On the otherhand, previously-observedalteration of the GSH-homeostasis has been confirmed, bothdirectly and indirectly, the latter through the observation of a

diversion from the main glycolytic pathway towards thepentose phosphate pathway. This shift resulted in accumula-tion of NADPH, an essential coenzyme in oxidized-glutathione(GSSG) reduction to GSH.

2. Materials and methods

2.1. Cell cultures and treatments

The human PaCa-44 pancreatic adenocarcinoma cell line waskindly provided by Prof. A. Scarpa, Department of Pathology,University ofVerona, Italy. Cellsweremaintained inRPMI-1640supplemented with 10% FCS, 2 mM L-glutamine, 100 IU/mlpenicillin and 10 mg/ml streptomycin in a humidified atmo-sphere of 5% CO2 at 37 °C. For the experiments, cells wereseeded onto 24-well cell culture plates and allowed to adherefor 24 h. Then, the medium was replaced with fresh mediumsupplemented with 200 μM of docosahexaenoic acid (DHA,22:6n-3) (Sigma Chemical Co) dissolved in ethanol or ethanolalone. After 48 h of treatments, cells were detached withtrypsin and solubilized in lysis buffer for protein extraction.

2.1.1. Assessment of apoptosisApoptosis was assessed by annexin V-FITC (Bender Medsys-tems) and propidium iodide (PI) as previously described [6].Briefly the cells treated with DHA (200 μM) for 24, 48 and 72 h,were collected, washed in PBS and resuspended in bindingbuffer. 195 μl of cell suspension were incubated with annexinV for 10 min at room temperature, followed by washing withPBS. Cells were therefore resuspended in 190 μl of bindingbuffer with 10 μl of propidium iodide (20 μg/ml) and subse-quent analysis by FACScan (Becton Dickinson) using CellQuest software.

2.2. Proteomics

2.2.1. Two-dimensional electrophoresisA total of 18 2DE gels have been performed during theproteomic analysis of membrane proteins, following thissubdivision: 3 biological replicates×3 technical replica-tes×groups (DHA-treated and controls). For 2DE analysiscellular membrane protein extracts were prepared as previ-ously described, with minor modifications [15].

To remove lipids, proteins were precipitated from a desiredvolume of each sample with a cold mix of tri-n-butylphosphate/acetone/methanol (1:12:1). After incubation at4 °C for 90 min, the precipitate was pelleted by centrifugationat 14,000 g, for 20 min at 4 °C. After washing with the samesolution, the pellet was air-dried and then solubilized in thefocusing solution containing 7 M urea, 2 M thiourea, 2% (w/v)ASB 14, 0.8% (w/v) pH 3–10 carrier ampholyte, 40 mM Tris,5 mM TBP, 10 mM acrylamide, 0.1 mMEDTA (pH 8.5), 2% (v/v)protease inhibitor cocktail (Sigma-Aldrich), and 2 mM PMSF.Before focusing, the sample was incubated in this solution for3 h at room temperature, under strong agitation. To preventover-alkylation, acrylamide was destroyed by adding anequimolar amount of DTE. A total of 250 μL of the resultingprotein solution was then used to rehydrate 13 cm long IPG 3–10 NL (Amersham Biosciences) for 8 h. IEF was carried out on a

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Multiphor II (Amersham Biosciences) with a maximumcurrent setting of 50 μA/strip at 20 °C. The total product timevoltage applied was 40,000 Vh for each strip. For the seconddimension, the IPG strips were equilibrated for 30 min in asolution containing 6 M urea, 2% (w/v) SDS, 20% (v/v) glycerol,and 375 mM Tris-HCl (pH 8.8), with gentle agitation. The IPGstrips were then laid on a 5–16% T gradient SDS-PAGE gel with0.5% (w/v) agarose in the cathode buffer (192 mM glycine, 0.1%w/v SDS and Tris to pH 8.3). The anode buffer was 375 mMTris-HCl, pH 8.8. The electrophoretic run was performed at aconstant current (10 mA for 60 min, followed by 40 mA untilthe run was completed). During the whole run, the temper-ature was set at 11 °C.

Proteins were visualized by a double staining procedure:sensitive Coomassie Brilliant Blue G-250 stain [16].

2.2.2. Image analysisStained gels were digitalized, and image analysis was per-formed using Progenesis SameSpot software v.2.0.2733.19819software package (Nonlinear Dynamics, New Castle UK). Eachgel was analyzed for spot detection and background subtrac-tion. Within-group comparison of protein spot numbers wasdetermined by repeated measures analysis; thus, the arith-metic mean of the total spot number (standard deviation (SD)was considered (see Fig. 1)). Among-group comparisons weredetermined by ANOVA (Analysis of Variance) procedure inorder to classify sets of proteins that showed a statisticallysignificant difference with a confidence level of 0.05. More-over, protein spots matching across all the replica maps wereselected and analyzed by MS/MS.

2.2.3. In-gel digestionSpots from 2-DE maps of biological interest (p<0.05) werecarefully excised from the gel and subjected to in-gel trypsindigestion according to Shevchenko et al. [17] with minormodifications. The gel pieces were swollen in a digestion

buffer containing 50 mM NH4HCO3 and 12.5 ng/mL trypsin(modified porcine trypsin, sequencing grade, Promega, Mad-ison, WI) in an ice bath. After 30 min, the supernatant wasremoved and discarded; then 20 μL of 50 mM NH4HCO3 wereadded to the gel pieces, and digestion was allowed to proceedovernight at 37 °C. The supernatant containing tryptic pep-tides was dried by vacuum centrifugation. Prior to massspectrometric analysis, the peptidemixtures were redissolvedin 10 μL of 5% FA (formic acid).

2.2.4. Nano-HPLC MS/MS identificationMass spectrometric procedures were performed as previouslydescribed [15]. Peptide mixtures were separated using nano-flow-HPLC system (Ultimate; Switchos; Famos; LC Packings,Amsterdam, The Netherlands). A sample volume of 10 μL wasloaded by the autosampler onto a homemade 2 cm fused silicapre-column (75 μm I.D.; 375 μm O.D) Reprosil C18-AQ, 3 μm(Ammerbuch-Entringen, DE) at a flow rate of 2 μL/min. Sequen-tial elution of peptides was accomplished using a flow rate of200 nL/min and a linear gradient from Solution A (2% acetoni-trile; 0.1% formicacid) to50%ofSolutionB (98%acetonitrile; 0.1%formic acid) in 40 min over the precolumn in-line with ahomemade 10–15 cm resolving column (75 μm I.D.; 375 μm O.D.; Reprosil C18-AQ, 3 μm (Dr. Maisch GmbH, Ammerbuch-Entringen, Germany). Peptides were eluted directly into a HighCapacity ion Trap HCTplus (Bruker-Daltonik, Bremen, Germa-ny). Capillary voltage of 1.5–2 kV and a dry gas flow rate of 10 L/min were used at a temperature of 200 °C. The scan range usedwas from 300 to 1800m/z. Protein identification was performedby searching in the National Center for Biotechnology Informa-tion non-redundant database (NCBInr, version 20081128, www.ncbi.nlm.nih.gov) using the Mascot program in-house version2.2 (MatrixScience, London,UK). The followingparameterswereadopted for database searches: complete carbamidomethyla-tion of cysteines and partial oxidation of methionines, peptideMass Tolerance±1.2 Da, Fragment Mass Tolerance±0.9 Da,missed cleavages 2. For positive identification, the score of theresult of (−10×Log(P)) had to be over the significance thresholdlevel (p<0.05). Even though high MASCOT scores are obtainedwith values greater than 60, when proteins were identified byone peptide only a combination of automated database searchand manual interpretation of peptide fragmentation spectrawas used to validate protein assignments. In this manualverification the mass error, the presence of fragment ion seriesand the expected prevalence of C-terminus containing ions (Y-type) in the high mass range were all taken into account.Moreover, replicatemeasurements have confirmed the identityof the protein hits.

2.3. Metabolomics

Metabolomic analysis has been performed as previouslyreported, with minor modifications [18].

Extraction and metabolite quantification method robust-ness, linearity and intra- and inter-day reproducibility havebeen confirmed, as previously reported [18].

2.3.1. Metabolite extractionSamples were extracted from controls or treated samples at48 h from DHA-supplementation following the protocol by

Fig. 1 – PaCa-44 cells were incubatedwith 200 μMDHA for 24,48 and 72 h, followed by immunofluorescence staining usingannexin V-FITC (AV)/iodure propide (IP) and cytofluorimetry;results are expressed as the mean±S.D. of threeexperiments; Ctr, control: ethanol solution alone; DHA: DHAdissolved in ethanol solution. *p-value<0.05 (ANOVA).

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Sana et al. [19] with minor modifications as previouslydescribed [18].

The sample was resuspended by adding 0.15 mL of ice coldultra-pure water (18 MΩ) to lyse cells; the tubes were plungedinto dry ice or a circulating bath at −25 °C for 0.5 min and theninto a water bath at 37 °C for 0.5 min. To each tube was addedfirst 0.6 mL of −20 °C methanol. 0.45 mL of −20 °C chloroformwas added and tubes were then mixed every 5 min for 30 min.Subsequently, 0.15 ml of ice cold pH adjusted ultra-pure water(18 MΩ) was added to each tube and the tubes were centri-fuged at 1000×g for 1 min at 4 °C, before being transferred to−20 ° C for 2–8 h. After thawing, liquid phases were recoveredand an equivalent volume of acetonitrile was added toprecipitate any residual protein. The tubes were then trans-ferred to refrigerator (4 °C) for 20 min, centrifuged at 10,000×gfor 10 min at 4 °C and the supernatants were recovered into a2 ml tube. Collected supernatants were dried as to obtainvisible pellets. Finally, the dried sampleswere re-suspended in1 mL of water, 5% formic acid and transferred to glassautosampler vials for LC/MS analysis.

Finally, the dried samples were re-suspended in 1 mL ofwater, 5% formic acid and transferred to glass autosamplervials for LC/MS analysis.

2.3.2. Rapid resolution reversed-phase HPLCAn Ultimate 3000 Rapid Resolution HPLC system (LC Packings,DIONEX, Sunnyvale, USA) was used to perform metaboliteseparation. The system featured a binary pump and vacuumdegasser, well-plate autosampler with a six-port micro-switching valve, a thermostated column compartment. ADionex Acclaim RSLC 120 C18 column 2.1 mm×150 mm,2.2 μm was used to separate the extracted metabolites.Acetonitrile, formic acid, and HPLC-grade water, purchasedfrom Sigma Aldrich (Milano, Italy).

LC parameters: injection volume, 20 μL; column tempera-ture, 30 °C; and flow rate of 0.2 mL/min. The LC solventgradient and timetable were identical during the whole periodof the analyses. A 0–95% linear gradient of solvent A (0.1%formic acid in water) to B (0.1% formic acid in acetonitrile) wasemployed over 15 min followed by a solvent B hold of 2 min,returning to 100% A in 2 min and a 6-min post-time solvent Ahold.

2.3.3. ESI mass spectrometryMetabolites were directly eluted into a High Capacity ion TrapHCTplus (Bruker-Daltonik, Bremen, Germany). Mass spectrafor metabolite extracted samples were acquired in positiveand negative ion mode, as previously described [18,19]. ESIcapillary voltage was set at 3000 V (+) ion mode. The liquidnebulizer was set to 30 psig and the nitrogen drying gas wasset to a flow rate of 9 L/min. Dry gas temperature wasmaintained at 300 °C. Data was stored in centroid mode.Internal reference ions were used to continuously maintainmass accuracy. Data were acquired at the rate of 5 spectra/swith a stored mass range of m/z 50–1500. Data were collectedusing Bruker Esquire Control (v. 5.3 – build 11) data acquisitionsoftware. In MRM analysis, m/z of interest were isolated,fragmented and monitored (either the parental or fragmentions) throughout the whole RT range. Validation of HPLC on-line MS-eluted metabolites was performed by comparing

transitions fingerprint, upon fragmentation and matchingagainst the standards metabolites through direct infusionwith a syringe pump (infusion rate 4 μl/min). Standard curvecalibration was performed either on precursor and fragmention signals. Only the former were adopted for quantitation (asreported in Supplementary Table 1), as precursor ion signalsguaranteed higher intensity and thus improved limit ofdetection (LOD) and thus quantitation of metabolites ofinterest [18]. However, transitions were monitored in inde-pendent runs to validate each detected metabolite.

Metabolites were directly eluted into a High Capacity ionTrap HCTplus (Bruker-Daltonik, Bremen, Germany). Massspectra for metabolite extracted samples were acquired inpositive and negative ion mode, as previously described[18,19]. ESI capillary voltage was set at 3000 V (+) ion mode.The liquid nebulizer was set to 30 psig and the nitrogen dryinggas was set to a flow rate of 9 L/min. Dry gas temperature wasmaintained at 300 °C. Data was stored in centroid mode.Internal reference ions were used to continuously maintainmass accuracy. Data were acquired at the rate of 5 spectra/swith a stored mass range of m/z 50–1500. Data were collectedusing Bruker Esquire Control (v. 5.3 – build 11) data acquisitionsoftware. In MRM analysis, m/z of interest were isolated,fragmented and monitored (either the parental or fragmentions) throughout the whole RT range. Validation of HPLC on-line MS-eluted metabolites was performed by comparingtransitions fingerprint, upon fragmentation and matchingagainst the standards metabolites through direct infusionwith a syringe pump (infusion rate 4 μl/min). Standard curvecalibration was performed either on precursor and fragmention signals. Only the former were adopted for quantitation (asreported in Supplementary Table 1), as precursor ion signalsguaranteed higher intensity and thus improved limit ofdetection (LOD) and thus quantitation of metabolites ofinterest [18]. However, transitions were monitored in inde-pendent runs to validate each detected metabolite.

2.3.4. Metabolite analysis and data elaborationQuantitative analyses of standard compounds were performedon MRM data against comparison to standard metabolite runs.Each standard compound was weighed and dissolved innanopure water. Calibration curves were calculated as previ-ously reported [18], and are schematized in SupplementaryTable 1. In brief, each standard metabolite was run in triplicate,at incrementaldilutionuntil limit ofdetectionLODwas reached.The LOD for each compound was calculated as the minimumamount injected which gave a detector response higher thanthree times the signal-to-noise ratio (S/N).

Standards (equal to or greater than 98% chemical purity)ATP, phosphogluconic acid (PG), NADH, NADPH, D-fructoseand D-glucose 6-phosphate (G6P/F6P), D-fructose 1,6 bipho-sphate (FDP), glyceraldehyde phosphate (G3P), phosphoenol-pyruvic acid (PEP), L-lactic acid (LA), α-ketoglutarate (KET), L-malic acid (MA), succinic acid (SUCC), L-glutamic acid (GLUT),glutamine (GLTM), glutathione (GSH), oxidized glutathione(GSSG), were purchased from Sigma Aldrich (Milan).

Standards were stored either at −25 °C, 4 °C or roomtemperature, following manufacturer's instructions.

LC/MS data files were processed by Bruker DataAnalysis 4.0(build 234) software. Files from each run were either analyzed

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as .d files or exported as mzXML files, to be further elaboratedfor spectra alignment, peak picking and quantitation withInSilicos Viewer 1.5.4 (Insilicos LLC; Seattle, USA).

Data were further refined (normalization of treated/con-trols) and plottedwith GraphPad Prism 5.0 (GraphPad SoftwareInc.). Data were grouped and Student's t-test was performedinter-group (control vs DHA-treated) for each metabolite tocalculate p-values. Statistical significance was attributed tothose values varying constantly (p-value<0.01) betweencontrol and treated groups with a fold-change variationequal to or above 2. Hierarchical clustering analysis wasperformed to group metabolites showing similar trends inDHA-treated PACA44 cells, upon normalization of eachdetected quantity to the matching control. Data were elabo-rated and plotted with PermutMatrix upon z-score normali-zation of fold-change values for each treated/matched controlsample [20].

2.4. Bioinformatic analysis

2.4.1. Protein-protein interaction analysisMapping of the protein interactors to the experimentallyidentified protein species has been performed through thesoftware STRING 8.3 software. [21] Proteins identified experi-mentally have been updated in the software along withindications of the proteomic analysis and the species underinvestigation (Homo sapiens) in order to exclude false-positiveprotein–protein interactions and functional annotations de-rived from investigations on other species. An internalalgorithm individuates proteins from the submitted list inthe STRING database and maps them as colored nodes. Whitenodes represent predicted interactors upon matching againstthe internal database. Confidence interval was set at 0.750(high confidence), additional white nodes to 10 (so as to reduce

noise) and network depth was kept to the minimum value (1),to exclude as many false positive interactions as possible.Clustering was performed through the internal K-Meansalgorithm (set at 5; average value which identifies the numberof clusters to be individuated with good confidence throughfunctional annotationmining in the internal STRINGdatabase.

2.4.2. Functional enrichment of GO termsDifferentially-expressed proteins have been elaboratedagainst databases to get additional annotations on theirfunctions and consequently to establish some hypothesesconcerning their linkage to the DHA treatment. To this end,functional enrichment of gene ontologies (GOs) has beenperformed exploiting Babelomics tools such as FatiGO, [22–24]in order to indirectly validate observations from protein–protein interaction analyses.

FatiGO takes one list of genes and compares it against therest of the human genome upon conversion of the proteinentries into a list of GO terms, using the corresponding gene-GO association table. Then a Fisher's exact test is used to checkfor significant over-representation of GO terms in the submit-ted dataset against the rest of the genome. GO [25] terms wereenriched through division in three sub-categories: biologicalfunction, molecular function and subcellular localization.

3. Results and discussions

3.1. Assessment of apoptosis upon DHA supplementation

Time course assessment of apoptosis was performed throughflow cytometry with Annexin V and propidium iodide at 24, 48and 72 h upon DHA-supplementation. This routine apoptosisassessment approach allows discriminating between cells

Fig. 2 –Master 2DE gels for control (left side) and DHA-treated (right side) PACA44 cells. Spot numbers of statistically-significantdifferentially-expressed spots (p-value<0.05 ANOVA) upon Progenesis Samespots Analysis are highlighted.

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Tab

le1–MS/MSpe

ptidese

quen

cean

alys

isof

succes

sfully

iden

tified

proteins.

SPOT

Mr,

kDa

theo

r.pI

pred

ict

Mas

cot

ionsc

ore

NCBI

acce

ssion

numbe

rProteinID

m/z

Charg

state

Start-

end

aSe

quen

ce

9855

604

6.62

755.86

02+

14–2

8R.M

FGGPG

TASR

PSSS

R.S

26gi|471

1531

7VIM

[Hom

osa

pien

s]74

8.33

82+

37–5

0R.TYSL

GSA

LRPS

TSR

.S20

714.86

62+

51–6

4R.SLY

ASS

PGGVYATR.S

4791

8.91

12+

451–

466

R.D

GQVIN

ETSQ

HHDDLE

.–20

129

5373

86.33

755.87

32+

14–2

8R.M

FGGPG

TASR

PSSS

R.S

22gi|340

219

Vim

entin

714.87

62+

51–6

4R.SLY

ASS

PGGVYATR.S

5948

5.80

22+

70–7

8R.LRSS

VPG

VR.L

2763

5.83

72+

160–

170

R.Q

VDQLT

NDKAR.V

7777

344

079

6.49

777.88

62+

8–24

R.Q

SSATSS

FGGLG

GGSV

R.F

79gi|242

3469

9Keratin

19[H

omosa

pien

s]53

2.81

32+

91–9

9R.LASY

LDKVR.A

6253

7.30

62+

141–

150

K.IL

GATIENSR

.I48

521.30

92+

151–

159

R.IV

LQID

NAR.L

4251

5.30

52+

189–

197

R.VLD

ELTLA

R.T

5161

4.30

92+

217–

226

K.N

HEE

EIST

LR.G

4660

5.80

22+

265–

274

R.KDAEA

WFT

SR.T

7569

5.35

22+

318–

330

K.A

ALE

DTLA

ETEA

R.F

9268

3.36

72+

371–

381

K.SRLE

QEIATYR.S

2780

4728

35.29

705.81

72+

25–3

6K.EQFL

DGDGW

TSR

.W45

gi|475

7900

Calreticu

linpr

ecursor

[Hom

osa

pien

s]51

2.94

43+

42–5

5K.H

KSD

FGKFV

LSSG

K.F

3710

4.39

82+

56–7

3K.FYGDEE

KDKGLQ

TSQ

DAR.F

5954

2.82

52+

154–

162

K.N

VLINKDIR.C

384

0.40

43+

186–

207

K.ID

NSQ

VES

GSL

EDDW

DFL

PPKK.I

3060

0.95

13+

208–

222

K.IK

DPD

ASK

PEDW

DER

.A28

987.13

53+

223–

248

R.A

KID

DPT

DSK

PEDW

DKPE

HIPDPD

AK.K

2629

469

119

8.34

928.32

3+80

–103

K.GQLT

TDQVFP

YPS

VLN

EEQTQFL

K.E

49gi|565

4111

0Acy

l-Coe

nzy

meA

dehyd

roge

nas

e,ve

rylongch

ain[Rattusno

rvegicus

]41

1.62

2+10

4–11

1K.ELV

GPV

AR.FGlu->py

ro-G

lu(N

-term

E)39

598.2

2+11

2–12

1R.FFE

EVNDPA

K.N

3262

8.21

3+11

2–12

7R.FFE

EVNDPA

KNDSL

EK.V

6399

8.7

3+11

2–13

7R.FFE

EVNDPA

KNDSL

EKVEE

DTLQ

GLK

.E32

606.54

3+12

2–13

7K.N

DSL

EKVEE

DTLQ

GLK

.E38

859.67

3+13

8–16

2K.ELG

AFG

LQVPS

ELGGLG

LSNTQYAR.L

4783

2.32

3+20

5–22

9R.VASG

QALA

AFC

LTEP

SSGSD

VASIR.S

9492

6.39

2+24

8–26

4K.IW

ISNGGLA

DIFTVFA

K.T

101

596.24

2+27

7–28

6K.EKITAFV

VER

.SOxida

tion

(M)

3746

7.65

2+27

9–28

6K.IT

AFV

VER

.S47

614.71

2+28

7–29

8R.SFG

GVTHGLP

EK.K

4145

2.8

3+28

7–29

9R.SFG

GVTHGLP

EKK.M

2670

8.75

2+30

4–31

6K.A

SNTSE

VYFD

GVK.V

7276

5.8

2+31

7–33

1K.VPA

ENVLG

EVGDGFK

.V10

860

7.72

2+34

2–35

3R.FGMAATLA

GTMK.A

6562

1.23

3+36

7–38

2R.TQFG

DKIH

NFG

VIQ

EK.L

3350

4.67

2+42

0–42

8K.IF

GSE

AAW

K.V

4890

8.75

2+42

9–44

4K.VTDEC

IQIM

GGMGFM

K.E

75

(con

tinue

don

next

page)

2143J O U R N A L O F P R O T E O M I C S 7 4 ( 2 0 1 1 ) 2 1 3 8 – 2 1 5 8

Author's personal copy

Tab

le1(con

tinu

ed)

SPOT

Mr,

kDa

theo

r.pI

pred

ict

Mas

cot

ionsc

ore

NCBI

acce

ssion

numbe

rProteinID

m/z

Charg

state

Start-

end

aSe

quen

ce

589.24

2+46

0–46

9R.IF

EGTNDILR.L

8064

9.21

2+47

0–48

0R.LFV

ALQ

GCMDK.G

3750

8.19

2+48

3–49

2K.ELT

GLG

NALK

.NOxida

tion

(M)

3279

332

044

9.09

1.00

1.04

63+

31–5

7R.TPE

IQVQALT

GPN

QGIT

EILM

NRPH

AR.N

51gi|157

8211

53En

oylC

oenzy

meA

hyd

ratase

domain

containing2[Rattusno

rvegicus

]68

6.39

13+

58–7

6R.N

ALG

NVFV

SELL

EALA

QLR

.E39

656.25

32+

106–

117

R.M

SAAEV

GTFV

QR.L

Oxida

tion

(M)

6476

8.34

42+

155–

169

R.IA

ASS

AVMGLIET

TR.G

Oxida

tion

(M)

9559

1.97

43+

228–

244

R.A

LALA

QEILP

QAPIAVR.L

7899

5.96

43+

253–

278

R.GMEV

DIA

SGMAIEHMCYAQNIPTQDR.L

68

854.44

3+6–

32K.SGQSY

LAAGLL

QNQVAVVTGGATGIG

K.A

69gi|189

5923

6Pe

roxiso

mal

tran

s-2-en

oyl-CoA

redu

ctas

e[Rattusno

rvegicus

]49

4.14

62+

55–6

3R.LTAAVDEL

R.A

5577

9.88

43+

101–

122

K.IN

FLVNNAGGQFM

APA

EDITAK.G

5843

3.11

32+

176–

183

R.A

GVYNLT

K.T

3364

8.21

52+

184–

195

K.TMALT

WASS

GVR.I

6777

2.90

73+

271–

291

R.N

FTIPDHDNW

PVGAGDSS

FIK.K

61

833.29

3+9–

30R.W

YFG

GLA

SCGAACCTHPL

DLL

K.V

53gi|191

7378

8So

lute

carrierfamily

25(m

itoc

hon

drial

carrier;

dica

rbox

ylatetran

spor

ter),

mem

ber10

[Rattusno

rvegicus

]60

5.23

82+

31–4

0K.VHLQ

TQQEV

K.L

3169

5.70

52+

41–5

2K.LRMTGMALQ

VVR.TOxida

tion

(M)

3855

3.17

2+43

–52

R.M

TGMALQ

VVR.T

8592

9.32

92+

53–6

9R.TDGFL

ALY

NGLS

ASL

CR.Q

8650

8.12

2+70

–77

R.Q

MTYSL

TR.FOxida

tion

(M)

6151

6.13

22+

78–8

5R.FAIY

ETMR.D

4561

9.68

92+

91–1

01K.D

SQGPL

PFYSK

.V49

771.65

53+

102–

125

K.VLL

GGISGLT

GGFV

GTPA

DLV

NVR.M

7374

6.73

92+

126–

137

R.M

QNDMKLP

LSQR.R

2Oxida

tion

(M)

4473

2.73

82+

138–

149

R.RNYSH

ALD

GLY

R.V

4865

5.19

62+

139–

149

R.N

YSH

ALD

GLY

R.V

6662

8.17

42+

158–

169

K.KLF

SGATMASS

R.G

3356

4.15

2+15

9–16

9K.LFS

GATMASS

R.G

6557

0.48

43+

170–

185

R.GALV

TVGQLS

CYDQAK.Q

Oxida

tion

(M)

6876

2.27

73+

226–

245

R.LMNSK

GEY

QGVFH

CAVET

AK.L

Oxida

tion

(M)

45

848.29

72+

231–

245

K.GEY

QGVFH

CAVET

AK.L

5264

8.62

12+

262–

277

R.LVPH

TVLT

FMFL

EQLR

.K48

Mitoc

hon

drialo

rnithinetran

spor

ter1

isofor

m1[M

usmus

culus]

691.36

22+

262–

278

R.LVPH

TVLT

FMFL

EQLR

K.H

Oxida

tion

(M)

4170

9.14

32+

134–

145

R.LQTMYEM

ETSG

K.I

45gi|334

2890

9

2144 J O U R N A L O F P R O T E O M I C S 7 4 ( 2 0 1 1 ) 2 1 3 8 – 2 1 5 8

Author's personal copy

701.77

62+

146–

158

K.IA

ASQ

NTVW

SVVK.E

5989

5.83

92+

276–

291

R.A

FPANGALF

LAYEY

SR.K

3866

547

683

8.96

554.71

32+

59–6

8K.KMNLG

VGAYR.D

48gi|698

0972

aspa

rtateam

inotransferas

e2[Rattu

snor

vegicu

s]49

8.63

72+

60–6

8K.M

NLG

VGAYR.D

6481

3.30

63+

60–8

1K.M

NLG

VGAYRDDNGKPY

VLP

SVR.

KOxida

tion

(M)

39

730.74

12+

69–8

1R.D

DNGKPY

VLP

SVR.K

3797

6.89

12+

91–1

07K.N

LDKEY

LPIG

GLA

DFC

K.A

6874

1.75

92+

95–1

07K.EYLP

IGGLA

DFC

K.A

6076

5.74

52+

108–

122

K.A

SAEL

ALG

ENSE

VLK

.S62

725.26

32+

126–

139

R.FVTVQTISGTGALR

.V10

443

9.11

82+

140–

147

R.VGASF

LQR.F

6557

7.66

12+

171–

180

R.D

AGMQLQ

GYR.Y

6382

3.79

12+

186–

200

K.TCGFD

FSGALE

DISK.I

9347

0.08

72+

280–

287

K.N

MGLY

GER

.V45

490.64

62+

288–

296

R.VGAFT

VVCK.D

6654

1.84

02+

288–

302

R.VGAFT

VVCKDAEE

AK.R

6159

3.87

82+

288–

303

R.VGAFT

VVCKDAEE

AKR.V

7077

4.68

32+

326–

337

R.IA

ATILTSP

DLR

.K74

774.68

32+

408–

430

R.IS

VAGVTSG

NVGYLA

HAIH

QVTK.–

5065

1.55

43+

91–1

07K.N

LDKEY

LPIG

GLA

DFC

K.A

48gi|698

0972

aspa

rtateam

inotransferas

emitoc

hon

drial[Rattu

snor

vegicu

s]76

5.78

32+

108–

122

K.A

SAEL

ALG

ENSE

VLK

.S68

725.31

2+12

6–13

9R.FVTVQTISGTGALR

.V95

439.12

32+

140–

147

R.VGASF

LQR.F

6382

3.72

22+

186–

200

K.TCGFD

FSGALE

DISK.I

100

490.64

92+

288–

296

R.VGAFT

VVCK.D

6354

1.80

83+

288–

302

R.VGAFT

VVCKDAEE

AK.R

4563

5.74

52+

326–

337

R.IA

ATILTSP

DLR

.K66

467.27

43+

326–

338

R.IA

ATILTSP

DLR

K.Q

3237

072

750

7.04

659.27

33+

215–

235

R.LANFG

GLA

VGLG

FGALA

EVAK.K

49gi|615

572

Chap

eron

eac

tivity

ofbc

1co

mplex

-like,

mitoc

hon

drialp

recu

rsor

[Rattusno

rvegicus

]91

7.31

42+

247–

263

K.KAVLD

SSPF

LSEA

NAER

.I91

569.17

23+

248–

263

K.A

VLD

SSPF

LSEA

NAER

.I85

704.61

43+

278–

296

K.LGQMLS

IQDDAFINPH

LAK.I

7752

6.63

52+

303–

311

R.Q

SADFM

PLK.Q

3666

1.73

22+

316–

326

K.TLN

NDLG

PHW

R.D

4188

2.98

13+

327–

349

R.D

KLE

YFE

ERPF

AAASIGQVHLA

R.L

7567

7.74

72+

518–

528

R.SFT

DLY

IQVIR.A

7059

6.25

92+

588–

597

K.IH

NLIPIMLK

.H34

776.29

2+60

0–61

2R.LIPPP

EETYSL

HR.K

3862

2.21

2+61

3–62

3R.KMGGSF

LICSK

.L45

558.15

62+

614–

623

K.M

GGSF

LICSK

.L68

770.71

32+

632–

643

K.A

MFE

EAYSN

YCR.M

5737

257

574

7.96

456.43

82+

110–

117

R.GIEIPEV

R.L

35gi|407

8646

9Dihyd

rolip

oamidede

hyd

roge

nas

epr

ecursor

[Rattusno

rvegicus

]56

4.21

2+13

3–14

3K.A

LTGGIA

HLF

K.Q

5989

6.30

2+16

0–17

7K.N

QVTATTADGST

QVIG

TK.N

6454

9.79

42+

284–

300

K.KSD

GKID

VSV

EAASG

GK.A

67

(con

tinue

don

next

page)

2145J O U R N A L O F P R O T E O M I C S 7 4 ( 2 0 1 1 ) 2 1 3 8 – 2 1 5 8

Author's personal copy

Tab

le1(con

tinu

ed)

SPOT

Mr,

kDa

theo

r.pI

pred

ict

Mas

cot

ionsc

ore

NCBI

acce

ssion

numbe

rProtein

IDm/z

Charg

state

Start-

end

aSe

quen

ce

507.13

33+

285–

300

K.SDGKID

VSV

EAASG

GK.A

5656

6.66

2+28

9–30

0K.ID

VSV

EAASG

GK.A

8366

0.56

43+

347–

365

K.IP

NIFAIG

DVVAGPM

LAHK.A

Oxida

tion

(M)

5177

6.15

22+

405–

417

K.SEE

QLK

EEGVEF

K.V

5973

9.78

22+

496–

509

R.EANLA

ASF

GKPINF.–

5436

966

992

7.84

540.19

2+51

–59

K.A

LVNQLH

ER.A

70gi|588

6592

6Methylcroton

oyl-Coe

nzy

meA

carbox

ylas

e2(beta)

[Rattusno

rvegicus

]91

5.92

2+10

8–12

5K.LYGEE

EVPA

GGIITGIG

R.V

5585

4.78

2+12

6–14

1R.VSG

VEC

MIV

ANDATVK.G

Oxida

tion

(M)

7052

1.65

2+15

6–16

4R.A

QEIALQ

NR.L

6287

2.82

2+16

5–18

0R.LPC

IYLV

DSG

GANLP

R.Q

7352

1.18

3+18

1–19

3R.Q

ADTFP

DRDHFG

R.I

3565

1.25

2+19

4–20

4R.IF

YNQAIM

SSK.N

9466

2.35

2+23

6–24

8R.Q

GTIFLA

GPP

LVK.A

Gln

->py

ro-G

lu(N

-term

Q)

56

686.66

3+24

9–26

8K.A

ATGEE

VSA

EDLG

GADLH

CR.R

100

719.92

3+27

0–28

8R.SGVTDHYALD

DHHALH

LTR.K

6369

2.28

2+34

9–36

1K.A

LYGDTLV

TGFA

R.I

9875

2.26

2+38

3–39

4K.KGAHFV

QLC

CQR.N

5168

8.22

2+38

4–39

4K.GAHFV

QLC

CQR.N

3649

8.13

2+41

2–42

0K.D

YEA

EGIA

K.D

4868

3.69

2+41

2–42

4K.D

YEA

EGIA

KDGAK.M

4246

8.6

2+42

5–43

3K.M

VAAVSC

AK.V

3688

7.45

2+47

0–48

7R.IS

VMGGEQ

AATVLA

TVAR.D

103

692.64

3+49

3–51

1R.EGKQFS

SAEE

AALK

EPIIK.R

4459

0.64

2+49

6–50

6K.Q

FSSA

EEAALK

.E32

581.80

3+49

6–51

1K.Q

FSSA

EEAALK

EPIIK.RGln

->py

ro-G

lu(N

-term

Q)

39

633.92

2+49

6–51

2K.Q

FSSA

EEAALK

EPIIKR.F

Gln->py

ro-G

lu(N

-term

Q)

58

639.63

2+49

6–51

2K.Q

FSSA

EEAALK

EPIIKR.F

4585

2.76

2+51

2–52

5K.RFE

EEGNPY

YSS

AR.L

5377

4.72

2+51

3–52

5R.FEE

EGNPY

YSS

AR.L

6175

7.80

2+52

6–53

8R.LW

DDGIIDPV

DTR.L

8342

8.10

2+55

6–56

2R.TDFG

IFR.M

4050

1.65

2+55

6–56

3R.TDFG

IFRM.-Oxida

tion

(M)

49

875.96

53+

53–7

5K.D

VGILALE

VYFP

AQYVDQTDLE

K.F

66gi|128

3643

9Hyd

roxy

methylglutaryl-CoA

synth

ase,

mitoc

hon

drialp

recu

rsor

[Mus

mus

culus]

439.63

92+

76–8

3K.FNNVEA

GK.Y

5979

1.26

3+93

–112

R.M

GFC

SVQED

INSL

CLT

VVQR.LOxida

tion

(M)

57

609.22

2+12

7–13

7R.LEV

GTET

IIDK.S

77

2146 J O U R N A L O F P R O T E O M I C S 7 4 ( 2 0 1 1 ) 2 1 3 8 – 2 1 5 8

Author's personal copy

548.22

52+

222–

231

K.A

PLVLE

QGLR

.G64

953.62

3+23

2–25

6R.GTHMEN

AYDFY

KPN

LASE

YPL

VDGK.L

6652

6.71

52+

257–

264

K.LSIQCYLR

.A52

753.72

62+

315–

327

R.LMFN

DFL

SSSS

DK.Q

7664

8.25

82+

340–

350

R.GLK

LEET

YTNK.D

6358

4.89

3+34

0–35

4R.GLK

LEET

YTNKDVDK.A

5649

9.13

32+

343–

350

K.LEE

TYTNK.D

5394

2.8

2+40

7–42

4R.IG

AFS

YGSG

LAASF

FSFR

.V10

850

0.63

42+

428–

437

K.D

ASP

GSP

LEK.L

4352

2.63

62+

438–

447

K.LVSS

VSD

LPK.R

7081

4.18

52+

455–

467

R.M

SPEE

FTEIMNQR.E

Oxida

tion

(M)

901.19

8.91

92+

474–

494

K.VNFS

PPGDTSN

LFPG

TW

YLE

R.V

5045

958

361

8.63

831.77

52+

1–15

K.Q

KTGTAEM

SSILEE

R.IG

ln->py

ro-G

lu(N

-term

Q)

76gi|672

9934

ChainA,rat

liver

F1-A

tpas

e

712.24

22+

3–15

K.TGTAEM

SSILEE

R.I

8578

8.26

12+

16–3

0R.IL

GADTSV

DLE

ETGR.V

115

500.62

22+

31–4

0R.VLS

IGDGIA

R.V

6184

2.12

92+

46–6

0R.N

VQAEE

MVEF

SSGLK

.G10

51.06

0.85

2+61

–80

K.GMSL

NLE

PDNVGVVVFG

NDK.L

8158

9.58

43+

90–1

06K.RTGAIV

DVPV

GDEL

LGR.V

5780

5.80

72+

91–1

06R.TGAIV

DVPV

GDEL

LGR.V

7558

6.17

12+

107–

118

R.VVDALG

NAID

GK.G

6984

9.33

62+

107–

124

R.VVDALG

NAID

GKGPV

GSK

.I46

687.77

02+

140–

151

R.IS

VREP

MQTGIK

.AOxida

tion

(M)

4951

3.65

92+

152–

161

K.A

VDSL

VPIGR.G

5065

8.76

22+

176–

187

K.TSIAID

TIINQK.R

7446

3.08

32+

189–

196

R.FNDGTDEK

.K39

664.21

82+

199–

209

K.LYCIY

VAIG

QK.R

5543

2.62

92+

220–

227

R.LTDADAMK.Y

3664

4.23

22+

263–

273

K.H

ALIIY

DDLS

K.Q

6776

8.28

32+

292–

304

R.EAYPG

DVFY

LHSR

.L79

719.78

32+

360–

373

K.GIRPA

INVGLS

VSR

.V39

541.89

02+

385–

398

K.Q

VAGTMKLE

LAQYR.E

Oxida

tion

(M)

3277

4.29

02+

399–

420

R.EVAAFA

QFG

SDLD

AATQQLL

SR.G

Glu->py

ro-G

lu(N

-term

E)72

764.67

52+

430–

450

K.Q

GQYSP

MAIEEQ

VAVIY

AGVR.G

Oxida

tion

(M)

575.16

42+

451–

460

7657

5.16

42+

451–

460

R.GYLD

KLE

PSK.I

790.28

32+

464–

484

K.FES

AFL

SHVVSQ

HQSL

LGNIR.T

3978

9.34

32+

497–

510

K.LKEIVTNFL

AGFE

P.58

659.69

82+

499–

510

K.EIV

TNFL

AGFE

P.-Glu->py

ro-G

lu(N

-term

E)58 59

784

3538

47.34

676.24

92+

29–4

0R.VLD

ASW

YSP

GTR.Q

40gi|570

69Rhod

anes

e[Rattusno

rvegicus

]83

2.25

12+

50–6

3R.H

VPG

ASF

FDIEEC

R.D

8252

5.15

12+

121–

130

R.YLG

TQPE

PDAVGLD

SGHIR.G

6172

6.78

52+

162–

173

K.TYEQ

VLE

NLQ

SK.R

5867

5.87

23+

186–

204

R.YLG

TQPE

PDAVGLD

SGHIR.G

61

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2147J O U R N A L O F P R O T E O M I C S 7 4 ( 2 0 1 1 ) 2 1 3 8 – 2 1 5 8

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Tab

le1(con

tinu

ed)

SPOT

Mr,

kDa

theo

r.pI

pred

ict

Mas

cot

ionsc

ore

NCBI

acce

ssion

numbe

rProteinID

m/z

Charg

state

Start-

end

aSe

quen

ce

1.01

5.84

42+

205–

222

R.GSV

NVPF

MNFL

TED

GFE

K.SOxida

tion

(M)

6192

0.30

43+

205–

228

R.GSV

NVPF

MNFL

TED

GFE

KSP

EELR

.AOxida

tion

(M)

38

686.73

52+

236–

247

K.VDLS

QPL

IATCR.KOxida

tion

(M)

44

805

3255

56.78

713.29

12+

15–2

7K.N

SSVGLIQLN

RPK

.A46

gi|239

2291

ChainA,2

-Enoy

l-Coa

Hyd

ratase

,data

colle

cted

at10

0K,P

h6.5Ra

ttus

norvegicus

432.12

2+64

–72

K.A

FAAGADIK

.E50

769.22

82+

64–7

7K.A

FAAGADIK

EMQNR.TOxida

tion

(M)

5955

3.10

12+

78–8

6R.TFQ

DCYSG

K.F

411.05

5.92

82+

129–

149

K.A

QFG

QPE

ILLG

TIPGAGGTQR.L

5267

7.72

72+

157–

168

K.SLA

MEM

VLT

GDR.I2

Oxida

tion

(M)

9065

9.55

63+

183–

199

K.IF

PVET

LVEE

AIQ

CAEK

.I68

878.73

82+

213–

228

K.ESV

NAAFE

MTLT

EGNK.LOxida

tion

(M)

7170

9.92

13+

213–

231

K.ESV

NAAFE

MTLT

EGNKLE

K.KOxida

tion

(M)

3966

8.20

62+

233–

243

K.LFY

STFA

TDDR.R

4849

8.16

82+

245–

253

R.EGMSA

FVEK

.RGlu->py

ro-G

lu(N

-term

E)53

396.08

62+

13–1

9R.VID

FAVK.I

37gi|519

4841

2Elec

tron

-transfer-flavo

protein,

beta

polype

ptide[Rattusno

rvegicus

]63

5.59

63+

36–5

1K.H

SMNPF

CEIAVEE

AVR.LOxida

tion

(M)

5065

3.13

3+60

–76

K.EIIAVSC

GPP

QCQET

IR.T

5071

9.20

93+

86–1

06R.GIH

VEV

PGAEA

ENLG

PLQVAR.V

5257

2.20

32+

115–

124

K.EKVDLL

FLGK.Q

Glu->

pyro

-Glu

(N-term

E)55

452.67

72+

117–

124

K.VDLL

FLGK.Q

4655

1.71

22+

165–

174

R.EID

GGLE

TIR.L

4764

8.29

92+

175–

186

R.LKLP

AVVTADLR

.L32

527.67

2+17

7–18

6K.LPA

VVTADLR

.L62

538.20

52+

211–

221

K.A

GDLG

VDLT

SK.V

7655

2.68

62+

239–

248

K.VET

TED

LVAK.L

3981

526

228

7.85

977.35

32+

46–6

1R.LLP

PEDNPL

WQYLL

SR.S

7267

2.12

72+

130–

141

R.VVTCEV

DAEP

PK.L

52gi|334

6899

9Catec

hol-O

-methyltran

sferas

edo

main

containing1[M

usmus

culus]

555.68

3+19

8–21

2R.CLQ

LLRPG

GVLA

VLR

.V66

ChainG,rat

liver

F1–A

tpas

e65

0.73

92+

40–5

1R.VYGTGSL

ALY

EK.A

86gi|672

9936

548.70

62+

63–7

2K.H

LIIG

VSS

DR.G

5260

0.23

2+73

–84

R.GLC

GAIH

SSVAK.Q

5873

3.20

12+

85–9

8K.Q

MKNDMAALT

AAGK.EOxida

tion

(M)

532.11

52+

88–9

8K.N

DMAALT

AAGK.E

791.07

4.50

21+

99–1

08K.EVMIV

GIG

EK.I

4765

4.71

52+

116–

126

R.THSD

QFL

VSF

K.D

4612

16.22

3+13

1–16

3R.

KPP

TFG

DASV

IALE

LLNSG

YEF

DEG

SIIFNQFK

.S46

2148 J O U R N A L O F P R O T E O M I C S 7 4 ( 2 0 1 1 ) 2 1 3 8 – 2 1 5 8

Author's personal copy

869.82

62+

235–

249

K.N

ASD

MID

KLT

LTFN

R.T

7264

9.24

32+

258–

270

K.ELIEIISGAAALD

.-Glu->py

ro-G

lu(N

-term

E)77

657.75

32+

258–

270

K.ELIEIISGAAALD

8679

036

494

8,87

589.19

82+

61–7

3K.VAFITGGGTGLG

K.A

48gi|377

4845

62,4-dien

oylC

oAredu

ctas

e1,

mitoc

hon

drial[Ra

ttus

norvegicus

]95

6.89

72+

74–9

1K.A

MTTFL

SSLG

AQCVIA

SR.N

4371

8.58

93+

116–

133

R.CDVRDPD

MVHNTVLE

LIK.V

4054

7.25

83+

120–

133

R.D

PDMVHNTVLE

LIK.V

Oxida

tion

(M)

3775

5.57

23+

134–

155

K.VAGHPD

VVIN

NAAGNFISP

SER.L

4266

8.25

63+

299–

316

R.FDGGEE

VFL

SGEF

NSL

KK.V

6760

5.91

3+31

6–33

0K.KVTKEE

WDVIEGLIR.K

5184

3.98

42+

317–

330

K.VTKEE

WDVIEGLIR.K

7667

9.81

52+

320–

330

K.EEW

DVIEGLIR.K

5161

946

244

8.52

402.61

2+6–

13R.GVFIVAAK.R

42gi|184

2686

63-ke

toac

yl-C

oAth

iolase

,mitoc

hon

drial

[Rattusno

rvegicus

]56

2.23

2+15

–25

R.TPF

GAYGGLL

K.D

4772

9.20

62+

26–3

8K.D

FTATDLT

EFAAR.A

8691

2.36

23+

46–7

1K.VPP

ETID

SVIV

GNVMQSS

SDAAYLA

R.H

7371

4.29

92+

77–9

0R.VGVPT

ETGALT

LNR.L

8870

6.62

3+91

–109

R.LCGSG

FQSIVSG

CQEICSK

.D50

763.24

53+

110–

130

K.D

AEV

VLC

GGTES

MSQ

SPYSV

R.N

5957

5.89

23+

144–

158

K.LED

TLW

AGLT

DQHVK.L

5468

9.75

12+

159–

171

K.LPM

GMTAEN

LAAK.Y

6799

1.86

62+

192–

209

K.A

ANEA

GYFN

EEMAPIEV

K.T

6587

4.72

3+24

2–26

9K.EGTVTAGNASG

MSD

GAGVVIIASE

DA.

KOxida

tion

100

760.68

42+

313–

332

K.D

MDLIDVNEA

FAPQ

FLAVQK.S

Oxida

tion

(M)

61

904.38

82+

341–

360

K.TNVSG

GAIA

LGHPL

GGSG

SR.I

8459

4.77

92+

361–

370

R.IT

AHLV

HEL

R.R

48

963.96

42+

42–5

989

gi|557

4154

4Ubiqu

inol

cytoch

romecredu

ctas

eco

repr

otein2pr

ecursor

[Rattusno

rvegicus

]58

1.60

93+

70–8

3R.YEN

YNYLG

TSH

LLR.L

6350

0.72

52+

147–

154

R.RW

EVAALR

.S46

657.79

42+

172–

182

R.IIEN

LHDVAYK.N

6078

3.74

82+

183–

195

K.N

ALA

NPL

YCPD

YR.M

4648

0.87

83+

217–

230

R.M

ALV

GLG

VSH

SILK

.EOxida

tion

(M)

6060

9.75

12+

231–

240

K.EVAEQ

FLNIR.G

6371

0.28

2+30

1–31

4R.GNNTTSL

LSQSV

AK.G

8976

5.35

52+

359–

374

K.A

VAQGNLS

SADVQAAK.N

7590

3.32

32+

436–

452

K.SMTASG

NLG

HTPF

LDEL

.Oxida

tion

(M)

64gi|839

2836

acetyl-C

oenzy

meA

acetyltran

sferas

e1pr

ecursor

[Rattu

snor

vegicu

s]83

7.89

12+

47–6

3R.TPIGSF

LGSL

ASQ

PATK.L

7960

7.24

32+

64–7

5R.TPIGSF

LGSL

ASQ

PATK.L

7051

2.65

92+

179–

187

K.LGTIA

IQGAIEK.A

4378

0.23

62+

206–

218

K.D

GLT

DVYNK.I

3671

6.84

2+22

8–24

0R.EEQ

DKYAIG

SYTR.S

68

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2149J O U R N A L O F P R O T E O M I C S 7 4 ( 2 0 1 1 ) 2 1 3 8 – 2 1 5 8

Author's personal copy

Tab

le1(con

tinu

ed)

SPOT

Mr,

kDa

theo

r.pI

pred

ict

Mas

cot

ion

scor

eNCBI

acce

ssion

numbe

rProteinID

m/z

Charg

state

Start-

end

aSe

quen

ce

895.85

32+

241–

255

K.FANEITPITISVK.G

6497

8.35

63+

271–

300

K.GKPD

VVVKED

EEYKR.V

4781

5.02

63+

309–

332

K.

ENGTVTAANAST

LNDGAAAVVLM

TAEA

AQR.

L

72

644.61

13+

371–

390

R.IA

AFA

DAAVDPIDFP

LAPA

YAVPK

.V36

K.VNVHGGAVSL

GHPIGMSG

AR.I

642

4749

28.64

806.27

92+

35–4

8R.RTPL

YDFH

LAHGGK.M

58gi|620

7873

7Aminom

ethyltran

sferas

e[Rattusno

rvegicus

]72

8.23

92+

36–4

8R.TPL

YDFH

LAHGGK.M

7481

2.85

72+

49–6

2K.M

VAFA

GW

SLPV

QYR.D

7562

5.55

23+

74–8

8R.RHCSL

FDVSH

MLQ

TK.IOxida

tion

(M)

5456

8.52

63+

75–8

8R.H

CSL

FDVSH

MLQ

TK.I

5058

8.70

32+

150–

159

R.D

KDLA

LMQDK.V

4871

1.59

33+

201–

219

R.KLP

FMTSA

VMEV

FGVSG

CR.V

Oxida

tion

(M)

42

1002

.35

2+20

2–21

9K.LPF

MTSA

VMEV

FGVSG

CR.V

Oxida

tion

(M)

120

900.30

13+

223–

249

R.CGYTGED

GVEISV

PAAGAVHLA

TALL

K.N

8854

6.19

92+

297–

306

R.RVAMDFP

GAK.I

5147

6.11

32+

298–

306

R.VAMDFP

GAK.I

4767

9.76

42+

320–

331

R.RVGLICEG

APM

R.A

4360

1.72

52+

321–

331

R.VGLICEG

APM

R.A

6386

5.04

43+

332–

357

R.A

HSP

ILNTEG

AVIG

TVTSG

CPS

PSLK

.K62

622.18

42+

358–

368

K.KNVAMGYVAFK

.Y54

558.20

82+

359–

368

K.N

VAMGYVAFK

.Y54

759.29

92+

369–

381

K.YSR

PGTQLL

VEV

R.R

4441

8.11

82+

392–

398

K.M

PFVPT

K.YOxida

tion

(M)

3965

1.60

33+

91–1

07K.N

LDKEY

LPIG

GLA

DFC

K.A

5573

2.78

82+

95–1

07K.EYLP

IGGLA

DFC

K.A

Glu->py

ro-G

lu(N

-term

E)56

gi|698

0972

Asp

artate

aminotransferas

e2[Rattusno

rvegicus

]76

5.79

72+

108–

122

K.A

SAEL

ALG

ENSE

VLK

.S57

725.31

52+

126–

139

R.FVTVQTISGTGALR

.VOxida

tion

(M)

8957

7.66

62+

171–

180

R.D

AGMQLQ

GYR.Y

6282

3.80

42+

186–

200

K.TCGFD

FSGALE

DISK.I

6746

0.65

42+

288–

296

R.VGAFT

VVCK.D

6363

5.77

2+32

6–33

7R.IA

ATILTSP

DLR

.K74

718.20

12+

145–

157

K.LLD

ELSP

DTAPH

K.Y

53gi|149

0644

67Fe

rroc

helatas

e(predicted

),isofor

mCRA_b

[Rattusno

rvegicus

]85

3.83

92+

304–

319

K.VGPV

PWLG

PQTDEA

IK.G

3778

978

002

5.42

614.83

543+

32–4

2R.VEIIA

NDQGNR.I

49gi|647

0150

BiPpr

otein[H

omosa

pien

s]78

3.91

442+

43–5

6R.IT

PSYVAFT

PEGER

.L37

839.42

852+

64–7

8K.N

QLT

SNPE

NTVFD

AK.R

2271

5.86

9361

7.33

282+

84–9

5R.TW

NDPS

VQQDIK

.F36

2150 J O U R N A L O F P R O T E O M I C S 7 4 ( 2 0 1 1 ) 2 1 3 8 – 2 1 5 8

Author's personal copy

which are early in the apoptotic process (which will stain withthe annexin V (AV)-FITC alone) and necrotic cells (which willbe stained by both propidium iodide (IP) and AV-FITC).

As a result, we could observe that the percentage ofnecrotic cells did not increase upon DHA supplementationover the time span monitored during the analysis (Fig. 1), asthe fraction of AV+/IP+ did not vary consistently from controlsto treated cells in each tested period.

On the other hand, the percentage of early apoptotic cellsincreased constantly at 48 h from DHA-supplementation toreach extremely significant levels at 72 h, when the greatmajority of the cells resulted to be AV+/IP− (63.2±2.8%). Theseresults are in agreement with recent reports from literature onthis cell line [6], and indicated that any further experimentalapproach to understand the molecular phenomena occurringupon DHA-supplementation and leading to apoptosis shouldbe explored in the 48 h to 72 h time window, when earlyapoptotic onset reached a climax (Fig. 1).

3.2. Proteomic and bioinformatic analyses

Preliminary proteomic analyses were performed at 48 h uponsupplementation of DHA to culture media of PACA44 cells inorder to determine the main molecular events taking place atthe protein level at the early onset of apoptosis (Fig. 1).

Two-dimensional electrophoresis of DHA-treated versusuntreated controls led to the individuation of 21 over-expressed spots in the former group (Fig. 2), out of which wemanaged to identify 26 distinct proteins. Table 1 summarizesprotein spots and relatively identified proteins, along withindications of the gene identifiers, the spot number, m/z,sequence coverage details and amino acid sequences ofdetected peptides, theoretical MW and pI.

In silico elaboration of the data helped to identify threemain groups of protein-protein interactors (Fig. 3): i) the firstone accounting for a limited number of nodes which aremainly characterized by structural and chaperonine-like pro-teins (keratin 19 – KRT19, vimentin - VIM, calreticulin – CALR,HSP90AA1, GRP58); the second one ii) including a minor groupof low-interacting nodes mainly representing solute carriers,which is coherent with the proteomic analysis targeting themembrane fractions; and the third one iii) which enclosedprotein nodes spanning across the whole map, which aremainly tied to metabolism. In this group, dihydrolipoamidedehydrogenase (DLD) represented the highest degree nodes(higher number of connection with other nodes in the map)and was characterized by the highest betweenness centralityvalue (i.e. it maps in the very heart of the map), two criteriawhich indicate its likely relevant role among the differentiallyexpressed proteins upon DHA-treatment. DLD is a compo-nent of the glycine cleavage system aswell as of the α-ketoaciddehydrogenase complexes [26]. Its main direct interactors inFig. 3 included i) aminomethyltransferase (AMT), which takespart in the glycine cleavage system as it catalyzes thedegradation of glycine; and ii) ATP synthase subunit beta(ATP5B),whichpromotesATPsynthesis fromADPand inorganicphosphate in mitochondrial membranes [27].

It is noteworthy to stress that growth inhibition andinduction of apoptosis has been already observed in DHA-treated PACA44 cells [6] and hereby confirmed (Fig. 1) espe-

cially in the time window spanning from 48 to 72 h from DHAsupplementation. However, the molecular events related tothe onset of DHA supplementation-induced apoptosis havenot yet so far been clarified. In this respect, alteration ofstructural proteins might relate to cell cytoskeleton remodel-ing taking place during apoptotic phenomena, in which CALRmembrane exposure, in particular, seems to be particularlyinvolved through triggering of immunogenic cell death signals[28]. Nonetheless, the molecular mechanisms triggering pro-apoptotic cascades in PACA44 cells upon DHA-treatment havenot yet been elucidated.

Hereby, 2-DE and interactomic mapping of proteins over-expressed upon DHA-treatment suggested that energy pro-duction through mitochondrial pathways seemed to be over-activated in treated cells, as a wide series of nodes related tomitochondrial metabolism clustered together at the centerof the interactomic map (Fig. 3). This observation wasconsistent with reports on the alterations of the expressionprofiles of metabolism-involved proteins upon administrationof chemotherapeutic specific drugs to PACA44 cells, such asDAC [7].

To confirm and validate this preliminary rough functionalclassification with STRING 8.3, thorough functional GO termenrichment for biological and molecular function, along withsub-cellular localization has been performed with FatiGO/Babelomics (Table 2). Top molecular function of proteins over-expressed upon DHA-treatment indicated a significant enrich-ment of ontologies linked to oxidoreductase activity (CALR,AIFM1,PRODH2,ACOX1,PECR,DLD,DECR1 - GO:0016491), in par-ticular in relation to the oxidoreductases acting on the CH-CHgroup of donors (GO:0016627), along with cofactor binding(GO:0048037). These molecular functions are suggestive of alinkage to β-oxidation of fatty acids (DECR1, for example, is anauxiliary enzyme of beta-oxidation, which participates in themetabolism of unsaturated fatty enoyl-CoA esters havingdouble bonds in both even- and odd-numbered positions;DECR1 overexpression has been linked to the arrest ofmammary tumor growth development [29]) and Kreb's cycleactivation, which indeed occur (organic acid metabolic process(GO:0006082); carboxylic acid metabolic process (GO:0019752);fatty acid metabolic process (GO:0006631); electron transportchain (GO:0022900)) in top six statistically significant ontologiesupon enrichment of GO terms for biological functions (Table 2).Looking into further details, over-expressed proteins reinforcedthe conclusion that oxidative metabolism over-activationappears to be the most relevant difference occurring at theprotein level upon DHA-treatment of PACA44 cells. Examplesinclude: i) ACAT1 (GO:0019866), playing a major role in ketonebody metabolism; ii) ACOX (GO:0006631), which catalyzes thedesaturation of very long chain acyl-CoAs to 2-trans-enoyl-CoAs [30], and iii) DCI (GO:0006631); iv) ETFB (GO:0022900),whichtransfers the electrons to the main mitochondrial respiratorychain via ETF-ubiquinone oxidoreductase: its chemically-in-duced re-activation in pancreatic tumors has been shown torelate to induction of cell death mechanisms [31]; v) ECHDH, vi)MCCC2 (GO:0006631) or vii) PECR (GO:0006631), the latterparticipating in chain elongation of fatty acids (under a p53-dependent modulation of expression [32]).

It is worthwhile to underline that enrichment for subcel-lular compartment localization indicated that highest score

2151J O U R N A L O F P R O T E O M I C S 7 4 ( 2 0 1 1 ) 2 1 3 8 – 2 1 5 8

Author's personal copy

ontologies referred to proteins localizing at the mitochondrialpart (GO:0044429) or organelle inner membrane (GO:0019866).This might mean that, as the proteomic analysis had beenthought to enrich the membrane protein fraction, it alsoended up including and promoting determination of differen-tial expression patterns of mitochondrial membrane proteinsas well, which is relevant to the present study.

Finally, shock stress response proteins with chaperoneactivity have been individuated as well (chaperone activity ofbc1 complex-like; 90 kDa heat shock protein – HSP90; CALR;protein disulfide isomerase – GRP58), which might be inter-preted as a cell attempt to face the increased oxidative stresswhich stems from a metabolic switch from glycolysis toaerobic metabolism [3,4]. Modulation of chaperone moleculessuch as HSPD1, HSPA9B, GRP58 had already been reported in aproteomic investigation assessing the effects of DAC chemo-therapeutic to PACA44 cell lines [7].

Activation of aerobic metabolism and increased likelihoodof ROS production might not only be strictly tied to directinduction of apoptosis. Indeed, ROS signaling is a widespreaddiffused and widely accepted concept that involves activationof ROS-mediated cascades to produce nuclear effects in a“save or sacrifice” fashion, with the mediation of chaperone-like proteins and nuclear transcription factors, especially inplants [33]. Coherently, in the present model DHA-supple-mentation is accompanied by over-expression of both aerobicmetabolism related enzymes and chaperone-like proteins, aswell as proteins which are known to act at the transcriptionallevel, such as the tumor suppression protein acidic (leucine-

rich) nuclear phosphoprotein 32 (ANP32) [34]. The pathwaysdownstream ANP32 signaling might either justify the attemptof the cell to rescue itself from increased stress or rather pavethe way for further explanation of the domino events at thenuclear level leading to growth inhibition and apoptosis uponsupplementation of the PUFA.

3.3. Metabolomics

Since proteomic analyses and bioinformatic elaboration ofdata pinpointed at a likely solid correlation of PACA44 DHA-treatment with over-expression of metabolism-related pro-teins, a targeted metabolomic analysis was performed toinvestigate whether the main metabolic pathways werealtered and to calculate the extent of the observed variations.

Results for sixteen main metabolic intermediates ofglycolysis, Kreb's cycle, pentose phosphate pathway andGSH-GSSG homeostasis are reported in Fig. 4.A, as fold-changevariations of absolute values normalized against matchedcontrols. A preliminary overview indicates that i) α-ketoglu-tarate (KET), NADPH (a detail of mass spectra for treated cellsversus untreated controls is provided in Fig. 4.C); ii) malate(MA), phosphogluconic acid (PG), and succinate (SUCC), both i)significantly (p<0.01 t-test from control vs DHA-treated andfold-change ≥2) or ii) slightly accumulated upon DHA-treat-ment, respectively. Conversely, glutamine (GLTM), GSH, GSSG,phosphoenol pyruvate (PEP) and lactate (LA) (Fig. 4.B) dimin-ished in DHA-treated cells.

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Table 2 – Gene ontology term enrichment.

Significantly (p-value<0.05) up-regulated ontologies (vs the rest of the human genome)

#Index Term #1 vs #2 p value Pivotal proteins

Molecular function1 Oxidoreductase activity (GO:0016491) 25.93 vs 3.59 3.73849 10−5 CALR,AIFM1,PRODH2,ACOX1,PECR,DLD,DECR12 Cofactor binding (GO:0048037) 18.52 vs 1.25 2.05117 10−5 AIFM1,ACOX1,GOT2,DLD,DECR13 Oxidoreductase activity, acting on the CH-CH

group of donors (GO:0016627)11.11 vs 0.41 1.94135 10−4 ACOX1,PECR, DECR1

Biological function1 Organic acid metabolic process (GO:0006082) 44.44 vs 2.83 3.38499 10−12 PRODH2,ACOX1,GOT2,PECR,DLD,MCCC2,SLC25A10,

TST,DCI,SLC25A15,DECR1,AMT2 Carboxylic acid metabolic process (GO:0019752) 40.74 vs 2.81 8.09788 10−11 PRODH2,ACOX1,GOT2,PECR,DLD,MCCC2,SLC25A10,

DCI,SLC25A15,DECR1,AMT3 Oxidation reduction (GO:0055114) 29.63 vs 2.84 5.98594 10−7 AIFM1,FDX1,PRODH2,ACOX1,PECR,DLD,ETFB,DECR14 Generation of precursor metabolites

and energy (GO:0006091)22.22 vs 1.68 5.19121 10−6 FDX1,ACOX1,DLD,ATP5B,ETFB,FECH

5 Fatty acid metabolic process (GO:0006631) 18.52 vs 0.98 6.43642 10−6 ACOX1,PECR,MCCC2,DCI,DECR16 Electron transport chain (GO:0022900) 11.11 vs 0.55 4.52226 10−4 FDX1,DLD,ETFB

Cellular component1 Mitochondrial part (GO:0044429) 55.56 vs 2.48 1.273 10−17 AIFM1,FDX1,PRODH2,GOT2,DLD,MCCC2,SLC25A10,

ATP5B,HMGCS2,TST,DCI,ETFB,SLC25A15,ACAT1,FECH2 Organelle inner membrane (GO:0019866) 37.04 vs 1.38 2.01968 10−12 PRODH2,GOT2,SLC25A10,ATP5B,HMGCS2,TST,DCI,

SLC25A15,ACAT1,FECH

Fig. 3 – Protein–protein interaction analysis of the differentially-expressed proteins through STRING software. Three mainnetworks can be individuated. The first one spans across thewholemap and includesmetabolism-related proteins (blue square).The second one occupies the bottom-right portion of themap and includes structural and chaperonine-like proteins (red square).The third one in the right portion of the map, including low-interacting nodes representing solute carrier proteins (black square).Protein node list:ACAT1 Acetyl-CoA acetyltransferaseACOX1 Acyl-coenzyme A oxidase 1, peroxisomalAIFM1 Apoptosis-inducing factor 1AMT AminomethyltransferaseATP5B ATP synthase subunit betaCABC1 Chaperone-activity of bc1 complex-likeCALR Calreticulin precursor (CRP55)COMTD1 Catechol-O-methyltransferase domain-containing protein 1DCI 3,2-trans-enoyl-CoA isomeraseDECR1 2,4-dienoyl-CoA reductase, mitochondrial precursorDLD Dihydrolipoyl dehydrogenase, mitochondrial precursorECHDC3 enoyl Coenzyme A hydratase domain containing 3ETFB Electron transfer flavoprotein subunit beta (Beta-ETF)FDX1 Adrenodoxin, mitochondrial precursor (Ferredoxin- 1)FECH Ferrochelatase, mitochondrial precursorGOT2 Aspartate aminotransferaseHMGCS2 Hydroxymethylglutaryl-CoA synthaseKRT19 Keratin, type I cytoskeletal 19 (Cytokeratin-19)MCCC2 Methylcrotonoyl-CoA carboxylase beta chainPECR Peroxisomal trans-2-enoyl-CoA reductasePOT1 Protection of telomeres 1 (hPot1)PRODH Proline oxidaseSLC25A10 Mitochondrial dicarboxylate carrier (solute carrier family 25 member 10)SLC25A15 Mitochondrial ornithine transporter 1 (Solute carrier family 25 member 15)TST Thiosulfate sulfurtransferaseVIM Vimentin.

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In order to determine whether metabolite trends could begrouped as to individuate pathways which could be likelymodulated by DHA supplementation, hierarchical clusteringanalysis was performed through PermutMatrix software. Fold-change variation (treated/matched control) data were nor-malized (z-score normalization of column data) and plotted asin Fig. 5. Metabolites showing similar trends (either increasingor decreasing their relative concentrations upon treatment)mapped together or in close branches in the hierarchical tree(furthest neighbor algorithm). Negative values in the center ofthe tree (green to black transition) do not necessarily relate toabsolute decreased expression, as data are plotted upon z-score normalization in order to increase their spread range, asto ease automatic regrouping through the software algorithm.

From top to bottom of the tree (Fig. 5), metabolites could beregrouped as follows:

i) GLTM, PEP, LA, NADH;ii) GSSG, GSH;

iii) Glutamate (GLUT), ATP, F6P/G6P, glyeraldehyde 3-phos-phate (G3P), fructose 1,6 biphosphate (FDP);

iv) SUCC, MA, PG;v) KET, NADPH.

Hierarchical clustering analysis yielded that DHA supple-mentation induced metabolite changes that could beregrouped in five main subgroups, which roughly followedthe actual physiological connections of these biomolecules inmetabolic pathways in living cells. In order to ease visualiza-tion of these data, we decided to plot detected metabolitevariations by looking at the rough variations within the mainpathways covered from the analysis, including glycolysis,penthose phosphate pathways, Krebs cycle and glutamatehomeostasis (Fig. 6). Increases, decreases or insignificantvariations were graphed as red, blue or black squares,respectively. At a first glance, it emerged that DHA-treatmentended up to depress glycolysis while turning on Kreb's cycle.This result is in line with proteomic observations and

Fig. 4 – A - Fold-change variation of a set of metabolites from the main metabolic pathways (glycolysis, Kreb's cycle, pentosephosphate pathway and glutathione homeostasis). Data fromDHA-treated cells were normalized againstmatched controls andresults plotted to GraphPad Prism 5.1 software (GraphPad Software, San Diego California USA). Gaped line = normalizedcontrols; * = statistically significant fold-change variation =2 (p<0.01); ** = highly statistically significant fold-change variation ≥2(p<0.01); *** = highest statistically significant fold-change variation ≥3 (p<0.01). B and C – Details of MRM runs for lactic acid(89 m/z negative ion mode) and α-ketoglutaric acid (147 m/z positive ion mode). Blue lines indicate signal intensities forDHA-treated cell samples while black lines represent controls.

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confirms the solidity of the bioinformatic analysis. Under aphysiological perspective, these observations indicate thatcontrol pancreatic cell lines tend to produce energy throughglycolysis even in the presence of oxygen, in agreement withthe widespread diffusion of the Warburg effect and withprevious observations about glycolytic enzymes (enolase)over-expression and hyper-phosphorylation in this tumorcell line [2]. On the other hand, DHA supplementation appearsto trigger a metabolic switch in these pancreatic cancer cellsfrom glycolysis towards Kreb's cycle, which results in theactivation of oxidative metabolism and the increased likeli-hood of oxidative stress burden on treated cells, which indeedwe observed to over-express stress-related proteins (seeprevious paragraph). In parallel, pentose phosphate pathway(phosphogluconic acid and NADPH) appeared to be tuned upby DHA supplementation. This is relevant in the light of thepentose phosphate-dependent production of NADPH, anessential coenzyme in the reduction processes of severalanti-oxidant enzymes and biomolecules, including peroxire-doxins, and GSSG to GSH, which also takes part in themodulation of ROS and RNS generation at the root of immunecell respiratory burst [35].

On the other hand, a depression of GSH-homeostasis wasobserved, as all themetabolites analyzed in this pathwaywere

found to be down-regulated (GLTM, GLUT, GSH and GSSG). It isworthwhile to underline that the observed significant GSH andGSSG decreases in the intracellular compartment of DHA-supplemented PACA44 cells are in direct agreement withprevious data from literature, in which a model had beenproposed for GSH and GSSG extrusion to the extracellularenvironment through active transport, which could be blockedthrough cysthionine treatment resulting in cell rescue fromapoptosis [6,36]. However, GSH/GSSG ratio remained almostunaltered (Fig. 7) in DHA-supplemented cells in comparison tocontrols, which is again in good agreement with data fromliterature [6]. The present and previous observations on theGSH/GSSG ratio remaining constant are rather counterintui-tive and not yet explained, since DHA-treated PACA44 cellshave been demonstrated to suffer from increased oxidativestress (DHA-dose dependent accumulation of MDA in thesame model [6]), and thus the rate of oxidized intermediatesin comparison to their respective reduced counterpartsshould be unbalanced to favor the former or, in otherwords, GSH/GSSG ratios in DHA-supplemented cells shouldbe lower than in matched controls. Our data clash with thishypothesis and confirm previous observations [6]. The key tounderstand this fine tuning might stem from a series ofconsiderations (Fig. 8), which can be resumed as follows.

Fig. 5 – Hierarchical cluster analysis of metabolite concentrations graphed through PermutMatrix software. Fold-changevariations (induced/controls) have been normalized (z-score normalization) and grouped (furthest neighbor algorithm)depending on detected MS count values for each metabolite upon treatment with DHA and normalization to the untreatedcontrols.

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Decrease in GLTM levels have been observed in treated cellsalthough it had been equally supplemented in culture mediaof both DHA-treated and control PACA44 cells. This is

suggestive of the likely metabolization of GLTM in the formergroup. One key step of GLTM metabolism involves GLTM/GLUT inter-conversion by a specific class of enzymes,glutamine synthetases [37]. However, GLUT was observed todecrease as well, though less significantly. Indeed, GLUT canencounter two main fates, namely it can be either used bycells to build up GSH, a tripeptide consisting of GLUT, glycineand cysteine; or alternatively, GLUT might be metabolized toKET thus directly entering the Kreb's cycle. Hereby observedincreases in KET levels seem to support the hypothesis ofGLUTmetabolization via the tricarboxylic acid cycle, althoughKET might also accumulate as a downstream metabolite offatty acid consumption. GSH levels decrease, which meansthat, at least, GLUT-exploitation to build up new GSH is not amechanism as much as fast as it is the already documentedextrusion of GSH and GSSG from the intracellular environ-ment to the culture medium. However, GSH is expected to bealmost fully oxidized to GSSG under oxidative stress condi-tions, such as in the case of the present model [6].Nonetheless, GSH/GSSG ratios are almost constant in DHA-supplemented cells. This could be explained by NADPH-dependent reduction of GSSG to GSH. In agreement with thishypothesis, extremely significant accumulation of NADPHwas observed upon DHA supplementation and, overall, thepentose phosphate pathway—in which NADPH is generated—is switched on (PG also increases).

In conclusion, this implies that the observed stability of theGSH/GSSG ratios even in the oxidative environment of DHA-supplemented PACA44 cells might derive from a continuousprocess of GSSG reduction via NADPH.

Taken together, these considerations confirm a central rolefor GSH-homeostasis in this cell line upon DHA-supplementa-tion, not only through modulation of cell extrusion of GSH andGSSG, as previously demonstrated [6,36], but also through thecontinuous reduction of the GSH/GSSG battery via NADPH,through cell metabolism diversion towards the pentose phos-phate pathway as to counterbalance Kreb's cycle over-activa-tion. Furthermore, NADPH-system over-activation might besomehow related to an increased “respiratory burst”-likemetabolic modulation, which might trigger ROS accumulationin DHA-supplemented cells as in immune cells [35].

4. Conclusion

DHA supplementation to PACA44 cells was known to inhibitgrowth progression and induce apoptosis, through a mecha-nism involving lipid-targeting oxidative stress (MDA accumu-lation) [6].

To determine the most suitable time window to monitorearly onset of apoptosis we performed flow-cytometry-basedapoptosis assessment at 24, 48 and 72 h. Apoptosis onsetstarted at 48 h as to reach a climax at 72 h, when the fate of thegreat majority of the cells resulted to be destined to apoptosis.Since the molecular mechanisms underlying these phenom-ena had not hitherto been better elucidated, in the presentstudy, we addressed the changes taking place at the proteinlevel upon 48 h from supplementation of DHA to PACA44 cells.In silico analysis of these data allowed us to individuate afunctional enrichment of those proteins/enzymes involved in

Fig. 6 – An overview of the main metabolic pathwaysinvestigated in this study. Numbers indicate metabolitesinvolved in glycolysis (1–5), TCA cycle (6–8), nucleotides(9–10) or glutamate homeostasis (13–16), as in the legendbelow the figure. DHA-treatment provoked fluctuation of awide series of metabolites (upon normalization againstmatched controls). Black squares indicate no substantialalterations, blue squares a decrease, while red squares anincrease in metabolite detected levels. 1 = F6P; 2 = FDP;3 = G3P; 4 = PEP; 5 = LA; 6 = KET; 7 = MA; 8 = SUCC; 9 = ATP;10 = NADH; 11 = NADPH; 12 = PG; 13 = LUT; 14 = GLTM;15 = GSH; 16 = GSSG.

Fig. 7 – Fold-change ratio of glutathione (GSH)/oxidizedglutathione (GSSG) in control (black columns) andDHA-treated (white columns) PACA44 cells, as it emerged frommetabolomic analysis. Standard deviations are indicated.

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over-activation of oxidative metabolism via the Kreb's cycle.These observations were confirmed through targeted meta-bolomic experiments, from which it emerged that tricarbox-ylic acid cycle was turned on and glycolysis was switchedtowards pentose phosphate pathway at 48 h from the additionof DHA to the culture medium. This implies an increase in thelikelihood of ROS generation, which is consistent withprevious results in the same model [6]. The GSH/GSSG ratioremaining constant despite the pro-oxidant environment andthe confirmation of the previously reported GSH and GSSGdecrease in treated cells against matched controls [6],prompted considerations about the existence of a finelytuned modulation of metabolic intermediates which areinvolved in GSH-homeostasis. This domino of proteomic andmetabolomic events, leading to Kreb's cycle activation,glycolysis depression and switching towards the pentosephosphate pathway, might represent the primary stepstowards growth inhibition and activation of apoptosis whichhas already been documented in this tumor cell line uponDHA-supplementation [6]. In this view, ROS might mediatespecific signaling targeting the nucleus and modulate cellability to face stress or rather trigger apoptotic cascades,which have already been reported through cell sorter-basedanalyses [6] and hereby indirectly confirmed through thealteration of several cytoskeleton and cell cycle-arrest-relatedmetabolic proteins (e.g. PECR, DECR1), remodeling of structur-al proteins (VIM, KRT19), individuation of tumor suppressorproteins (ANP32) and immunogenic cell death-inducingmarkers (CALR). However, the increased expression of heatshock proteins with chaperone-like activity highlights the

stress from which PACA44 seems to suffer upon DHAsupplementation, in like fashion to the stress the same cellline (PACA44) undergoes in response to chemotherapeuticdrug treatment [7]. A hint of the likely commitment of nucleartargeting signals might be represented by the observed over-expression of the ANP32 transcription factor protein: itsdownstream targets deserve further in-depth investigationsin the future.

In conclusion, this integrated proteomic, metabolomic andinteractomic/functional multi-approach overview helped togain and confirm preliminary insights into the complexphenomena occurring at the molecular level in PACA44 cellsupon supplementation with DHA, and offer the opportunity topropose the same approach to break through the complexityof a wide series of analogous biological matrices/models.

Supplementary materials related to this article can befound online at doi:10.1016/j.jprot.2011.06.006.

Acknowledgments

The Authors would like to thank Dr. Gevi Federica, PhD andZolla Valerio, M.Sc. for constructive critics on metabolomicsapproaches and careful reading of the manuscript.

This study was supported by the “Nutrigenomicamediter-ranea: dalla nutrizione molecolare alla valorizzazione deiprodotti tipici della dieta mediterranea - NUME" project,funded by the Italian Ministry of Agricultural, Food andForestry Policies (Ministero delle Politiche Agricole, Alimentarie Forestali).

Fig. 8 – Glutathione homeostasis in DHA-treated cells. Trends are referred for DHA-treated cells upon normalization againstmatched controls. Black lines inter-connect metabolites which are directly linked in metabolic cascades. The description in theright explains the glutathione homeostasis model suggested in the text.

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R E F E R E N C E S

[1] Warburg O. On the origin of cancer cells. Science 1956;123:309–14.

[2] Tomaino B, Cappello P, Capello M, Fredolini C, Sperduti I,Migliorini P, et al. Circulating autoantibodies tophosphorylated α-enolase are a hallmark of pancreaticcancer. J Proteome Res 2011;10(1):105–12.

[3] Suzuki S, Tanaka T, Poyurovsky MV, Nagano H, Mayama T,Ohkubo S, et al. Phosphate-activated glutaminase (GLS2), ap53-inducible regulator of glutamine metabolism andreactive oxygen species. Proc Natl Acad Sci USA 2010;107(16):7461–6.

[4] HuW, Zhang C, Wu R, Sun Y, Levine A, Feng Z. Glutaminase 2,a novel p53 target gene regulating energy metabolism andantioxidant function. Proc Natl Acad Sci USA 2010;107(16):7455–60.

[5] Melino G, Lu X, Gasco M, Crook T, Knight RA. Functionalregulation of p73 and p63: development and cancer. TrendsBiochem Sci 2003;28(12):663–70.

[6] Merendino N, Loppi B, D'Aquino M, Molinari R, Pessina G,Romano C, et al. Docosahexaenoic acid induces apoptosis inthe human PaCa-44 pancreatic cancer cell line by activereduced glutathione extrusion and lipid peroxidation. NutrCancer 2005;52(2):225–33.

[7] Cecconi D, Astner H, Donadelli M, Palmieri M, Missaglia E,Hamdan M, et al. Proteomic analysis of pancreatic ductalcarcinoma cells treated with 5-aza-2′-deoxycytidine.Electrophoresis 2003;24:4291–303.

[8] Matsuno S, Egawa S, Arai K. Trends in treatment forpancreatic cancer. Hepatobiliary Pancreat Surg 2001;8:544–8.

[9] Schnelldorfer T, Gansauge S, Gansauge F, Schlosser S, BegerHG, Nussler AK. Glutathione depletion causes cell growthinhibition and enhanced apoptosis in pancreatic cancer cells.Cancer 2000;89:1440–7.

[10] Beguin ME, Ells G, Das UN, Horrobin DF. Differential killing ofhuman carcinoma cells supplemented with n-3 and n-6polyunsaturated fatty acids. J Natl Cancer Inst 1986;77:1053–62.

[11] Reddy BC, Burill C, Rigotty J. Effect of diets high in omega-3and omega-6 fatty acids on initiation and postinitiationstages of colon carcinogenesis. Cancer Res 1991;51 487–51.

[12] Grammatikos SI, Subbaiah PV, Victor TA, Miller WM. n-3 andn-6 fatty acid processing and growth effects in neoplastic andnon-cancerous human mammary epithelial cell lines. Br JCancer 1994;70:219–27.

[13] Das UN. Reversal of tumor cell drug resistance by essentialfatty acids. Lipids Cancer 1999;34:S103.

[14] Chen ZY, Istfan NW. Docosahexaenoic acid is a potentinducer of apoptosis in HT-29 colon cancer cells.Prostaglandins Leukot Essent Fatty Acids 2000;63:301–8.

[15] D'Amici GM, Rinalducci S, Zolla L. Proteomic analysis of RBCmembrane protein degradation during blood storage.J Proteome Res 2007;6:3242–55.

[16] Neuhoff V, Arold N, Taube D, Ehrhardt W. Improved stainingof proteins in polyacrylamide gels including isoelectricfocusing gels with clear background at nanogram sensitivityusing Coomassie Brilliant Blue G-250 and R-250.Electrophoresis 1988;9:255–62.

[17] Shevchenko A,WilmM, VormO,MannM.Mass spectrometricsequencing of proteins silver-stained polyacrylamide gels.Anal Chem 1996;68:850–8.

[18] D'Alessandro A, Gevi F, Zolla L. A robust high resolutionreversed-phase HPLC strategy to investigate variousmetabolic species in different biological models. Mol Biosyst2011;7(4):1024–32.

[19] Sana TR, Waddell K, Fischer SM. A sample extraction andchromatographic strategy for increasing LC/MS detectioncoverage of the erythrocyte metabolome. J Chromatogr BAnalyt Technol Biomed Life Sci 2008;871(2):314–21.

[20] Caraux G, Pinloche S. PermutMatrix: a graphical environmentto arrange gene expression profiles in optimal linear order.Bioinformatics 2005;21:1280–1.

[21] Jensen JL, Kuhn M, Stark M, Chaffron S, Creevey C, Muller J,et al. STRING 8—a global view on proteins and their functionalinteractions in 630 organisms. Nucleic Acids Res 2009;37:412–6.

[22] Al-Shahrour F, Minguez P, Tárraga J, Medina I, Alloza E,Montaner D, et al. FatiGO +: a functional profiling tool forgenomic data. Integration of functional annotation,regulatory motifs and interaction data with microarrayexperiments. Nucleic Acids Res 2007;35:91–6.

[23] Al-Shahrour F, Minguez P, Vaquerizas JM, Conde L, Dopazo J.BABELOMICS: a suite of web tools for functional annotationand analysis of groups of genes in high-throughputexperiments. Nucleic Acids Res 2005;33:460–4.

[24] Al-Shahrour F, Díaz-Uriarte R, Dopazo J. FatiGO: a web tool forfinding significant associations of gene ontology terms withgroups of genes. Bioinformatics 2004;20:578–80.

[25] Medina I, Carbonell J, Pulido L, Madeira SC, Goetz S, Conesa A,et al. Babelomics: an integrative platform for the analysis oftranscriptomics, proteomics and genomic data withadvanced functional profiling. Nucleic Acids Res 2010;38:210–3.

[26] Johnson MT, Yang HS, Patel MS. Targeting E3 component ofalpha-keto acid dehydrogenase complexes. Methods Enzymol2000;324:465–76.

[27] Zhang X, Niwa H, Rappas M. Mechanisms of ATPases—amulti-disciplinary approach". Curr Protein Pept Sci 2004;5(2):89–105.

[28] Panaretakis T, Kepp O, Brockmeier U, Tesniere A, BjorklundAC, Chapman DC, et al. Mechanisms of pre-apoptoticcalreticulin exposure in immunogenic cell death. EMBO J2009;28(5):578–90.

[29] Ursini-Siegel J, Rajput AB, Lu H, Sanguin-Gendreau V, Zuo D,Papavasiliou V, et al. Elevated expression of DecR1 impairsErbB2/Neu-induced mammary tumor development. Mol CellBiol 2007;27(18):6361–71.

[30] Reddy JK, Hashimoto T. Peroxisomal beta-oxidation andperoxisome proliferator-activated receptor alpha: an adaptivemetabolic system. Annu Rev Nutr 2001;21:193–230.

[31] Hinke SA, Martens GA, Cai Y, Finsi J, Heimberg H, Pipeleers D,et al. Methyl succinate antagonises biguanide-inducedAMPK-activation and death of pancreatic beta-cells throughrestoration of mitochondrial electron transfer. Br J Pharmacol2007;150(8):1031–43.

[32] Braastad CD, Leguia M, Hendrickson EA. Ku86 autoantigenrelated protein-1 transcription initiates from a CpG island andis induced by p53 through a nearby p53 response element.Nucleic Acids Res 2002;30(8):1713–24.

[33] Timperio AM, Egidi MG, Zolla L. Proteomics applied on plantabiotic stresses: role of heat shock proteins (HSP). J Proteomics2008;71:391–411.

[34] Huyton T, Wolberger C. The crystal structure of the tumorsuppressor protein pp 32 (Anp32a): structural insights intoAnp32 family of proteins. Protein Sci 2007;16(7):1308–15.

[35] Muntané J, la Mata MD. Nitric oxide and cancer. World JHepatol 2010;2(9):337–44.

[36] Newsholme P, Procopio J, Lima MM, Pithon-Cur TC, Curi R.Glutamine and glutamate—their central role in cellmetabolism and function. Cell Biochem Funct 2003;21(1):1–9.

[37] Ghibelli L, Fanelli C, Rotilio G, Lafavia E, Coppola S, Colussi C,et al. Rescue of cells from apoptosis by inhibition of activeGSH extrusion. FASEB J 1998;12:479–86.

2158 J O U R N A L O F P R O T E O M I C S 7 4 ( 2 0 1 1 ) 2 1 3 8 – 2 1 5 8