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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-
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
⁎ 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
(con
tinue
don
next
page)
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
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
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
(con
tinue
don
next
page)
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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