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 ) 1 2 1 8 – 1 2 2 9
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A workflow for peptide-based proteomics in a poorlysequenced plant: A case study on the plasma membraneproteome of banana
A. Vertommena, A.L.B. Møllerb, J.H.G. Cordewenerc,d, R. Swennena, B. Panisa, C. Finnieb,A.H.P. Americac,d, S.C. Carpentiera,c,⁎aLaboratory of Tropical Crop Improvement, Department of Biosystems, K.U. Leuven, Kasteelpark Arenberg 13, PO box 2455,B-3001 Heverlee, BelgiumbEnzyme and Protein Chemistry, Department of Systems Biology, Technical University of Denmark, Building 224,DK-2800 Kgs Lyngby, DenmarkcPlant Research International, Wageningen UR, PO box 16 6700AA Wageningen, The NetherlandsdNetherlands Proteomics Centre, PO box 80082, 3508 TB, Utrecht, The Netherlands
Article history:Received 14 December 2010Accepted 7 February 2011Available online 19 February 2011
Membraneproteins are an interesting class of proteins because of their functional importance.Unfortunately their analysis is hampered by low abundance and poor solubility in aqueousmedia. Since shotgunmethods arehigh-throughput andpartly overcometheseproblems, theyare preferred for membrane proteomics. However, their application in non-model plantsdemands special precautions to prevent false positive identification of proteins.In the current paper, a workflow for membrane proteomics in banana, a poorly sequencedplant, is proposed. The main steps of this workflow are (i) optimization of the peptideseparation, (ii) performing de novo sequencing to allow a sequence homology search and (iii)visualization of identified peptide–protein associations using Cytoscape to removeredundancy and wrongly assigned peptides, based on species-specific information. Byapplying this workflow, integral plasma membrane proteins from banana leaves weresuccessfully identified.
Two approaches are generally distinguished in the field ofproteome analysis. In the first approach proteins are separated,quantified, digested and the resulting peptides are identifiedusing mass spectrometry. In a second approach, often referredto as shotgun proteomics, protein digestion precedes the
ssisted sample preparatipressure (performance) liqlix.cal Crop Improvement, De
.kuleuven.be (S.C. Carpen
er B.V. All rights reserved
separationandquantificationof peptides. Yates and co-workerswere one of the pioneers to explore the use of two-dimensionalliquid chromatography coupled to electrospray tandem massspectrometry (theMudpit approach) to realize automated high-throughput proteomics [1]. A hybrid form where separation ofproteins by gel electrophoresis is followed by the separation ofpeptides in a second dimension is called geLC.
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Successful membrane proteome studies generally include apeptide-based separation, since it is a high-throughput methodand because problemswith the solubility ofmembrane proteinscan be partly circumvented by focusing on the water solublepeptides (for a review see [2,3]). On the other hand, proteomestudies on poorly sequenced plant species preferentially use aprotein-based approach since all peptide-based strategies havethe disadvantage to lose connectivity between peptides derivedfrom the same protein [4]. Membrane proteomics in poorlysequencedplants suchasbanana is thusconflictedby the choicebetweenapeptide- andprotein-based approach. The first optionis associated with a higher risk of identifying false positives,while all the existing gel-based techniques for membraneproteomics are characterized by a low resolving power evenwhen a very stringent enrichment method [5] is applied.
Whether a protein- or peptide-based approach is applied, ageneral challenge in membrane proteomics is the low abun-dance of most membrane proteins compared to water solubleproteins. Thisproblemissolvedbyusinganenrichedmembranefraction as starting material. Plasma membranes are veryattractive study objects because they are located at the interfacebetween the cell and its environment. Therefore, proteinsassociated with these membranes carry out some importantfunctions such as mediating the exchange of signals, nutrientsand chemicals and sensing changes in environmental condi-tions and generating the appropiate response. Most plantplasma membrane proteome studies have been performed oncompletely sequenced species suchasArabidopsis [6], rice [7] andpoplar [8]or onplants forwhicha largenumberof ESTsequencesexist such as barley (appr. 445,000 entries) [9,10]. In this study, apeptide-based approach for plasma membrane proteomics in apoorly sequenced plant is proposed, by further optimizing theworkflows of Grossmann et al. [11] and Shevchenko et al. [12,13].Proteomics on poorly sequenced plants relies on cross-speciesidentification. To increase the chance of identifying conservedpeptides, it is important to ensure good peptide separation. Inthis study this was achieved by using a RP–RP UPLC systemcoupled to a high accuracy mass spectrometer as described byGilar and colleagues [14]. The obtained mass spectra wereinitially searched against a small banana specific in-housedatabase (50,571 entries). Subsequently, de novo sequencing wasautomatically applied to the unassigned spectra. This de novoapproach was a crucial step since it allowed a homology searchof sequences instead of a search based onm/z values. An in silicoinnovation to theworkflowsofGrossmannandShevchenkowasthe visualization of the peptides that were assigned to specificproteinsusingCytoscape (Americaet al. unpublished results). Bya simultaneousvisualizationof thesignificanthits of thebananaspecific search and the hits of the cross-species search, theconfidence of the banana specific hits increased. Moreover,some peptides wrongly assigned to cross-species hits could beremoved.Cytoscape also allowed the visualizationof theproteininference, a common problem in a cross-species approach. Toverify the obtained identifications and to deliver a proof ofprinciple for a confident protein reconstruction and identifica-tion, the PM enriched fraction was also analyzed through one-dimensional SDS-PAGE.
The proposed workflow proved to be a succesful methodfor peptide-based membrane proteomics in banana, a repre-sentative of poorly sequenced plants.
2. Materials and methods
2.1. Two phase partitioning
Banana leaves (20 g, one year old greenhouse plants, cultivarCachaco, ITC0643) were crushed and homogenized in a 75 mLice cold homogenization buffer, containing 100 mM Tris–HCl(pH 8.3), 5 mM EDTA.Na2, 85 mM KCl, 2% DTT, 0.33 M sucroseand complete EDTA-free protease inhibitor cocktail (Roche)using a cooled kitchen coffee blender. Sample heating wasprevented by applying short blending strokes. The homoge-nate was filtered through 4 layers of miracloth filter (Merck(Calbiochem), Darmstadt, Germany) and cell debris and nucleiwere removed by centrifugation at 6500 g for 15 min at 4 °C.Membrane vesicleswere prepared through ultracentrifugationat 150,000 g for 1 h (4 °C). They were resuspended in 10 mL of0.33 M sucrose, 0.1 mM EDTA, 1% DTT, protease inhibitorcocktail and5mMpotassiumphosphate (Applichem,Darmstadt,Germany) buffer (pH 7.8). Plasma membranes wereseparated from the other membranes by aqueous two-phasepartitioning as described by Larsson and Widell with slightmodifications [15]. Membrane vesicles (in a total volume of 9 g)were added to the phase systems containing 6.4% (w/w)dextranT500 (Pharmacosmos,Holbaek, Denmark), 6.4% (w/w)PEG 3350, 0.33 M sucrose, 5 mMpotassium-phosphate (pH 7.8)and 5 mM KCl (Acros, Geel, Belgium), with a final weight of36 g. Phase separation was enhanced by centrifugation at1800 g for 5 min (4 °C). The upper phases were re-extractedthree times. The final upper phase was diluted two times inthe resuspension buffer before ultracentrifugation at 100,000 gat 4 °C for 1 h. Soluble contaminants were removed by washingthe obtained pellets with 7 mL Brij58 buffer (150 mM KCl, 1 mMEDTA, 0.01% Brij58, 25 mM sodium acetate pH 4.0) by shakingduring 30 min on ice. After a new centrifugation round(200,000 g, 45 min, 4 °C), pellets were resuspended in 4% SDS,1% DTT and 100 mM Tris–HCl (pH 6.8) and stored at −80 °C.
2.2. The protein-based approach
Sampleswere vortexed for 30min at room temperature and theproteinconcentrationwasdeterminedusing themicro-Bradfordprotocol as described by Zuo [16]. Prior to loading, sampleswere alkylated using 20 μM iodoacteamide, heated at 37 °C for30 min and for a short time centrifuged. Twenty micrograms ofproteins were added to the loading buffer yielding a final bufferconcentration of 3% SDS, 75mMTris–HCl (pH 6.8), 15% glycerol,3.5 Murea, 1%DTTand0.05%bromophenol blueand loadedona12.5% acrylamide gel with a 4% stacking gel (18×24 cm, 1 mmthick). Gels were run overnight at 2 W/gel at 12 °C. For allseparations the Laemmli protocol [17] was used. Gels werestainedwithG-250ColloidalCoomassie [18]andgel imageswerecaptured with the labscan 5 software (GE Healthcare). After theCoomassie blue staining and image analysis, stained bandswere manually picked. In-gel digestion with trypsin andanalysis of the tryptic peptides by MALDI TOF-TOF (MALDI4800TOF/TOFTMAnalyzer fromAppliedBiosystems/MDSSCIEX)was performed at the “Centre de Recherche Public GabrielLipmann” in Luxembourg. For digestion, the Ettan dalt spothandling workstation (GE Heathcare) was used. Gel pieces were
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cut into small pieces (1 mm3), washed and desalted in 50mMammoniumbicarbonate in 50% (v/v)methanol and subsequent-ly in 75% (v/v) ACN. After adding 8 μL of a Trypsin Gold solution(5 ng/mL) in20mMammoniumbicarbonate (Promega,Madison,WI, USA) to the dried gel plugs, samples were incubated at37 °C for six hours. After extraction and drying, the resultingpeptides were dissolved in 3 μL of a 50% ACN solution contain-ing 0.1% TFA and 0.7 μL of each well was spotted on disposableMALDI-TOF target plates. Spotted peptides were mixed with0.7 μL of α-cyano-4-hydroxycinnamic acid (7 mg/L, 50% ACN/0.1%TFA) andallowed to air-dry.Mass spectrometrical analyses(MS and MS/MS) were carried out using the peptide masscalibration kit according to the manufactures instructions(Applied Biosystems). An in-house Mascot platform was usedfor searching against the NCBI Viridiplantae as well as the in-house Musa database. The searching parameters allowed 2missed cleavages, a tolerance of 0.50 Da on MS/MS fragmentsand 50 ppmonprecursormass, aswell as the carbamidomethy-lation on cysteine as a fixed modification. Oxidation ofmethionine was allowed as variable modification. Proteinidentification was considered significant when the proteinscore exceeded the MOWSE score (p<0.05) and at least onepeptide showed a significant ion score (p<0.05). Estimation offalse positive rates was made by searching a decoy databasewith the same criteria.
2.3. The peptide-based approach using RP–RP (UPLC)separation
Samples were prepared according to the filter assisted samplepreparation protocol (FASP) protocol of Wisniewski with slightmodifications [19]. The pellet of the PM fraction was solubilizedin 500 μL buffer containing 4% SDS, 1% DTT and 100 mM Tris–HCl (pH 6.8). One hundred μg of protein sample wasmixed with400 μL of 8 M urea in 0.1 M Tris/HCl (pH 8.5) (i.e. buffer UA) andadded to a Microcon YM-30 filter (Millipore, Billerica, MA, USA).The filter device was subsequently centrifuged at 14,000 g for15min. The filter was washed with 200 μL of the buffer UA andcentrifuged again. Sampleswere incubatedwith 50mMDTT for15min at room temperature and centrifuged at 14,000 g for15min. Subsequently, 100 μL of 50 mM iodoacetamide in ureabuffer UA was added. After 30min, filter devices were centri-fuged (14,000 g, 15 min). Two hundred μL of a buffer containing8 M urea in 0.1 M Tris/HCl (pH 8.0) (i.e. buffer UB) was added andcentrifuged at 14,000 g for 15min. This stepwas repeated twice.After these three washes Lys-C (in UB buffer) was added in anenzyme:protein ratio of 1:50. Proteins were mixed with theenzyme and incubated overnight at room temperature. Thenext day, 120 μL of 40 mMammonium bicarbonate with trypsin(enzyme:protein ratio of 1:100) was added and peptides weremixed with the enzyme at room temperature. Samples wereincubated in a wet chamber at 37 °C for 4 h. Filter units werecentrifuged at 14,000 g for 20min. After centrifugation, 50 μL of500 mM NaCl was added and filter devices were centrifuged at14,000 g for 20min. The peptide concentration was determinedthrough measurement at A280 nm (nanodrop, Thermo Scien-tific, Waltham, MA, USA). Trifluoroacetic acid (0.5% finalconcentration) was added to acidify and samples were desaltedusing solid phase extraction. Columns (Supelco Inc, Bellefonte,PA,USA)werewashedwith1 mL95%ACNandequilibratedwith
1 mL 2% ACN, 0.1% TFA. Subsequently, the tryptic digest wasadded to the columns. After a wash with 1 mL 2% ACN, 0.1%TFA, peptides were eluted with 1 mL 84% ACN, 0.1% TFA.Solvents were evaporated using a speedvac and peptides weredissolved in 40 μL 0.1 M ammonium formate (AF). Two dimen-sional LC-MS/MS experiments were performed using the 2-DnanoAcquity UPLC system online coupled to a Synapt HDMS Q-TOF MS instrument (Waters, Milford, MA, USA). Two runs wereanalyzed. For each run, 4 μL sample (~2.5 μg) was injected(partial loop method) on the first RP (bridge C18, 5 μm) column(300 μm×50 mm, Waters). Elution from this column wasstepwise under high pH and ultrahigh pressure at 2 μL/min.Samples were eluted in fractions of 12, 15, 18, 20, 25, 35 and 65%ACN in20mMammoniumformate (pH10). These fractionswereoptimized to ensure an equal distribution of peptides over thedifferent fractions in MSE mode. Eluates were trapped on a C18(Symmetry 5 μm) trap column (180 μm×20mm). Peptides wereeluted inanexcess of 0.1%FA inwater at a flow rate of 20 μL/minto reach a ten fold dilution before loading on the analyticalcolumn (75 μm×250 mm, BEH 130 C18 (1.7 μm)). Peptides wereeluted from this column at 0.2 μL/min using 0.1% formic acid inwater as eluent A and 0.1% formic acid in ACN as eluent B. Theseparationwas carried out using 5%B for 1 min, 10%B for 2 min,10–40% B over 62min and 40–85% B over 9 min. After 6 min ofrinsing with 85% B and a linear gradient back to 5% B over 2 minthe column was re-equilibrated at initial conditions. The curvesets the rate at which the solvent is to change to the newproportions and/or flow rates. Curves are specified by a numberwith available choices from1 to 11. Curve 1 immediately goes tospecified conditions; curve 2 to 5 are convex; curve 6 is linear;curve 7 to 10 are concave and curve 11 maintains the startcondition until the next step. All gradient slopes were linear(curve 6), exept for the 10–40% B step of the 12% ACN fractions(concavecurve7)and for the fractions35%and65%ACN (convexcurve 5).
The analytical column temperature was maintained at35 °C by a built-in columnheater. Mass spectrometric analyseswere performed in positive mode using ESI with a NanoLock-Spray source. Eluates were immediately sprayed into a Q-TOFdevice (Waters). As lock mass, [Glu1]fibrinopeptide B (1 pmol/L) was delivered from a syringe pump (Harvard Apparatus,USA) to the reference sprayer of the NanoLockSpray source ata flow rate of 0.2 μL/min. The lock mass channel was sampledevery 30 s. For MS/MS, the three most intensive multiplycharged ions eluting from the column were selected forfragmentation. The eluting peptide ions were detected in theMS survey scan (0.6 s) from a m/z of 300 to 1400. A dynamicexclusion window was set at 60 s.
Obtained peak lists were searched against the Musa specificdatabase using Proteinlynx Global Server (PLGS 2.4, Waters).During the databank search a peptide tolerance of 20 ppm, afragment tolerance of 0.05 Da and one trypticmiscleavage wereallowed. Carbamidomethylation of C was set as fixed modifica-tion and deamination of Q and N as variable modifications. Forthe “automod” query one non-specific cleavage and oneaminoacid substitution or modification were allowed perpeptide. Proteins identified with at least one peptide with aladder score above 50 were considered significant. Unassignedspectra (ladder score less than 50) after both the databank andautomod query were used for automatic de novo sequencing
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using theProteinlynx software. The sequences that had a ladderscore greater than 50, were used in combination with thesequences obtained from the Musa database to BLAST againstthe NCBI and Swiss-Prot Viridiplantae databases via MS Homol-ogy (freely available at thewebsite of theUniversity ofCalifornia(http://www.ucsf.edu/)). Peptides assigned to keratin or trypsinwere not taken into account. The number of allowed amino acidsubstitutions for the MS Homology search was determined as“the length of the peptide divided by 5”. Protein identificationswere considered significant when at least one peptide had aMSHomology score of 40.
2.4. Data analysis and informatics
After theMSHomology search, the identified peptideswere firstranked according to the retrieved peptide and subsequently tothe protein score. This list was filtered for redundancy. Tovisualize the protein–peptide associations Cytoscape (freelyavailable at http://www.cytoscape.org/download.php) was uti-lized. By combining a visualization of the results of the MSHomology search and the Musa searches, wrongly associatedpeptideswere recognizedand removed and someMusaproteinsthat were classified as unknown could be annotated. As such, alist of identified non-redundant protein homologs was created.From these proteins the Arabidopsis homolog was determinedthrough a BLAST search against Arabidopsis. This search wasperformed to retrieve information from the Plant Proteomedatabase regarding the number of TMH, the GRAVY score, Mr
and pI and functional MapMann annotation. The Arabidopsisgene identifiers (Agi) also allowed using GoMiner (http://discover.nci.nih.gov/gominer/) [20] to detect all proteins with aGO annotation for the cellular component plasma membrane.To characterize the proteins of which no Arabidopsis homologcould be detected, the different tools of the Expasy server wereapplied. ProtParam was used for the determination of GRAVYscore, Mr and pI, TMHMM for the identification of transmem-brane regions and TargetP to predict the protein location. Tovisualize protein redundancy, protein sequences were theoret-ically cleaved with trypsin, using the MS Digest tool on http://prospector.ucsf.edu. The obtained tryptic peptides (at least 5amino acids long) of different protein isoforms were subse-quently visualized in Cytoscape.
The complete workflow applied for the identification andcharacterization of the integral PM proteins in banana isdemonstrated on Fig. 1.
3. Results and discussion
Amembrane proteome study generally starts with the isolationof the membrane of interest. Since the plasma membrane islocated at the interface of the cell and the extracellularenvironment, the proteins associated with this membraneplay an important role in the response to several environmentaltypes of stress.
Although it is generally assumed that an almost completelysequenced genome is required to perform shotgun proteomicsin plants [21], this peptide-based approach is preferred formembrane proteomics [2,22]. Shotgun proteomics is not only ahigh-throughput method and a gel-based technique with
sufficient resolving power for the separation of membraneproteins is not available [23]. Moreover, some of the solubilityproblems ofmembrane proteins are circumvented in a peptide-based approach since the peptides from hydrophilic regionsoutside the transmembrane regions can be analyzed.
Grossmann et al. already demonstrated that shotgunproteomics on poorly sequenced plants is feasiblewhen takingsufficient precautions [11]. Also Shevchenko and his collea-gues proposed a similar workflow [13,24]. These workflows arebased on (i) de novo sequencing of high quality spectra whichare obtained by removing trypsin, keratin and backgroundspectra and (ii) error tolerant homology searches of theobtained peptide sequences. The workflow proposed in thisstudy is based on these recommendations but new elementswere added (sample preparation see 3.1 and protein identifi-cation see 3.3).
3.1. Protein solubilization, digestion and peptideseparation
A first new element included the steps of protein solubiliza-tion (solubilization in SDS), digestion in high urea with Lys-Cfollowed by trypsin digestion in lower urea and peptideseparation via optimized RP–RP gradients. For protein solubi-lization the sample preparation protocol of Wisniewski et al.[19] was applied. This protocol has the advantage that the highsolubilizing power of SDS can be used for removal of mem-brane proteins from their lipid environment while reducingthe risk on protein aggregation. Subsequently, the detergent isreplaced by urea by means of a filter device. The presence ofhigh urea concentrations in the replacing buffer ensures thatthe SDS is removed and that the majority of solubilized mem-brane proteins stay in solution. Although these high concen-trations are not compatible with trypsin cleavage, they doallow cleavage with Lys-C, leaving the possibility of a sub-sequent trypsin digestion. The peptides resulting from theLys-C digestion have a lower risk on aggregation compared tothe intact proteins so the urea concentration can be lowered to2 M; a concentration compatible with trypsin cleavage.Trypsin was preferred as the digesting enzyme because ofthe formation of highly ionizable peptides. A drawback of thisapproach is that small proteins could be lost during the bufferswap since they are not retained by the 30 kDa filter. Alsointegral membrane proteins with short soluble domains andno tryptic sites in the transmembrane domains will not beanalyzed.
The obtained tryptic peptides were separated by a RP–RP(UPLC) approach as described by Gilar and colleagues [14]. Thisapproachhas several advantages compared to the classic SCX–RP approach; (i) after optimization the number of peptides thatelute in several consecutive fractions is limited, (ii) the peptidelosses in the first dimension are much smaller and (iii) themobile phases are salt free [14]. Moreover, the theoretical peakcapacity is higher. By coupling this system to a Q-TOF,characterized by a high resolution andmass accuracy, spectraof high quality were obtained. Prior optimization is of utmostimportance to ensure the best possible peptide separation andan increased number of identified non-redundant peptides.Indeed, themore peptides per protein are identified, the higherthe confidence of the final protein identification becomes. The
Fig. 1 – The workflow applied for peptide-based proteomics in a poorly sequenced plant such as banana (Musa spp).
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optimization included the reduction of the number of m/zvalues per retention time by adapting the concentration ofacetonitrile in the first dimension and the ACN gradient slopein the second dimension. The slope was linear (curve 6) for the15, 18, 20 and 25% ACN, concave (curve 7) for the 12% ACNfraction (see Fig. 2) and convex (curve 5) for the fractions of 35%and 65% ACN. Due to this optimization less than 10% of thetotal peptides with a ladder score above 50 were identified inmore than one fraction.
3.2. Peptide identification
Because the genetic distance between banana and sequencedplants is quite large, usingm/z values for a cross-species search
Fig. 2 –Optimization of the distribution ofm/z values per retentionMSE mode for the 12% ACN fraction. A: curve 6, B: curve 7.
does not result in a high number of confident identifications.Therefore the spectrawere initially searchedagainst thebananaspecific database complemented with trypsin and keratinsequence information. Since the banana database is small andlargely based on partial genomic information, mainly fromESTs, most entries do not contain complete protein sequences.Consequently, only a limited number ofm/z values couldmatchto a given entry. However, the main advantage of this searchwas that it allowed the identificationofbananaspecific peptideswith high confidence.
Subsequently, automatic de novo sequencing was performedon the remaining unassigned spectra. The initial search on thebanana bank ensured that the number of spectra to be searchedwasnot too large.Moreover, becauseof the initial searchagainst
time by adjusting the ACN concentration and by analyzing in
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the trypsin and keratin sequences, it was avoided that thesespectra were used for de novo sequencing. According toShevchenko these sequences might hit a very large number oftotally unrelated proteins. Trypsin for example would hit to amultitude of serine proteases [13].
The application of de novo sequencing greatly improved thenumber of identified peptides compared to the Musa specificsearch. On average, 67% of the peptides were identifiedthrough de novo sequencing. Investing time in a good peptideseparation and mass spectrometric analysis resulting in highquality spectra is thus of utmost importance to increase thenumber of identified peptides from poorly sequenced species.
3.3. Protein identification
3.3.1. MS Homology searchThe purpose of most proteomic experiments is not the identifi-cationof peptides, but the identificationof theproteins present inthe sample before digestion. Thus, the peptide sequences neededto be assigned to their corresponding protein (Fig. 1).
The peptide sequences obtained by de novo sequencing andthe banana specific search were used in a homology based
Protein separationC
Tryptic dige
Protein A,ArabidopsisProtein B,Arabidopsis
Protein A, Arabidopsis:4 peptides in common
Prot
Protein C, Arabidopsis
Protein A, Arab
Species specific search
A
Possiblehits
Most likely ID
Protein separationC
Tryptic dig
Protein A,Species 1
Protein B,Species 1
ProteProteProte
Protein A, Mus
x
xx
x
x
Protein A,Species 2
xProtein A,Species 3
Protein A, Species 1 or 3:3 peptides in common
Cross species search
x Protein C,Species 1
No A
1 A
ProProProPro
xPossiblehits
Most likelyID’s
B
xx
x
Fig. 3 –Upper part (A) Protein identification in amodel species. In aprotein are still “connected” during mass spectrometric analysiswhich the most peptides are assigned receives the highest proteapproach (right) the connectivity is lostwhichmakes protein idensequenced species is dependent on cross species searches. Thispossible to give a protein description (i.e., protein A) since the exaThe x indicates an AA substitution in comparison to the protein
sequence search using the MSHomology BLAST tool. Searchingwith sequences instead ofm/z values enabled an error-tolerantsearch. The number of allowed amino acid substitutions wasdependent on the lengthof the searchedpeptides (i.e. the lengthof the peptide divided by 5). A main challenge in this homologydriven approach is the peptide inference problem [25], meaningthat one peptide sequence is present in more than one entry inthe protein sequence database. This is illustrated in Fig. 3. Onepeptide sequence was searched against a pool of peptidesequences derived from all proteins which are entered in theSwiss-Prot and NCBI Viridiplantae databases. In these databasesthe same protein description is entered several times becauseprotein homologs occur in several species. Moreover, sinceamino acid substitutions were allowed, the number of proteinsto which this one peptide hits becomes even larger (see Fig. 3B).To remove this redundancy in our results, the hit with thehighest peptide and protein score was retained. In a protein-based approach the redundancy problem is smaller sinceseveral different peptides of the same protein all have tomatch to one protein which decreases the number of possiblehits (Fig. 3, left side). Therefore, the proteins from the PMenriched fractionwere also separated through one dimensional
Nstion - Separation
ein A or B Protein A or B Protein A, B or CProtein A or C
Peptide separation
idopsis
Nestion - Separation
in A, sp 1in B, sp 1in A, sp 3
Peptide separation
a
x
x x
A substitution
A substitution
tein A, sp 1tein B, sp 1tein A, sp 2tein A, sp 3
Protein A, sp 1Protein A, sp 2Protein A, sp 3Protein C, sp 1
Protein A, sp 1Protein B, sp 1Protein A, sp 2Protein A, sp 3
Protein A, sp 1Protein B, sp 1Protein A, sp 2Protein A, sp 3Protein C, sp 1
Protein A, sp 1Protein A, sp 3Protein C, sp 1
Protein B, sp 1Protein A, sp 3
Protein A, sp 1Protein A, sp 2Protein C, sp 1
protein-based approach (left) peptides belonging to the samewhich facilitates the protein identification. The protein toin score and is the most likely ID. In the peptide-basedtification harder. Lower part (B) Protein identification in poorlyincreases the number of hits obtained. In general it is onlyct ID (protein A, banana (Musa)) is not present in the database.of the model species.
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SDS-PAGE. The fourteen most intensely stained bands wereexcised (Fig. 1 supplementary materials) and proteins wereidentified byMALDI-TOF/TOF. Thepeptides thatwere identifiedin this protein-based approachwere instrumental to estimate asignificance threshold for protein identification after the MSHomology search (i.e. at least one peptideMSHomology score of40). Below this threshold some peptides were assigned toproteins to which they did not belong. Using this thresholdscore, all peptides assigned to one protein homolog in theprotein-based approach were assigned to the same proteinhomolog in the MS Homology search (Supplementary Table S1)which is indicative for a reliable protein identification.However,only 570 of the 1283 identified (in theMusa search, after de novosequencing and in the gel-based approach) peptide sequences(44%) could be assigned to a protein in the cross-species searchwith sufficient significance. This indicates that a major part ofthe identified banana peptides show too many dissimilaritieswith known peptide sequences. This is confirmed by looking atthe peptides identified during theMusa specific searches. Of the420 non-redundant identifiedMusapeptides 132 (31%) could notbe assigned to a protein homolog during the MS Homologysearch.
Fig. 4 –A. Cytoscape is used to visualize the interaction between prepresent protein descriptions (white: MS Homology search; blueassignment of a peptide to a specific protein description. B. Illusindicate ladder scores; red≤50, orange 50–65, green≥65.
3.3.2. The use of Cytoscape to increase the confidence of theidentification and to visualize protein redundancyA problem encountered in a peptide-based approach is theassignment of peptides to proteins to which they may notbelong. This mainly occurs in the cross-species approachwhere amino acid substitutions are allowed. A visualizationprogram such as Cytoscape can facilitate the detection ofthese peptides and visualize the confidence of a proteinidentification. Using this program, peptides (white circles onFig. 4) that are assigned to a specific protein (squares) areconnected with it by a line. The color of the line is indicativefor the peptide score (ladder score). As such all peptidesassigned to one protein description form a cluster (Figs. 4and 5). In case one peptide is assigned to two proteindescriptions, it can easily be detected by a line that connectstwo of these clusters. This is illustrated in Fig. 4B. Results fromthe Musa and MS Homology searches were simultaneouslyvisualized in one Cytoscape view. The peptide with sequenceQAIVGDLR was assigned to the 14-3-3 protein via the MSHomology search while it had also been assigned to theMusa000004293 via the Musa specific search (banana specifichit via a non-error tolerant search against the Musadb, ladder
eptides and the proteins to which they are assigned. Squares: Musa ID's), circles peptide sequences and lines thetration of a wrongly assigned peptide. Colors of the lines
Fig. 5 – The use of Cytoscape allows to link EST sequences and assigning banana specific peptides to known protein homologs.The white circles represent the peptides that were identified and assigned (green lines) to the different subunits of the 40Ssubunit (green square in themiddle). By the combined visualization of theMusa hits (blue squares) and the protein descriptionsobtained from theViridiplantae homology searches (white squares), the yellow peptide could be assigned (yellow dotted line) tothe 12S subunit (yellow square).
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score 68). BLASTP analysis and the Cytoscape view show thatMusa000004293 is a hypersensitive induced reaction protein(HIR). As the peptide was assigned to Musa000004293 withhigh confidence and since the MS Homology peptide score of14-3-3 was low, the peptide was considered as false positiveand was manually removed from the 14-3-3 protein list.
The Cytoscape view also enabled the confidence of bananaspecific identifications to be increased. When several MusaID's were associated with one protein homolog, the certaintythat this protein homolog indeed occured in the plasmamembrane enriched fraction increased. Moreover, some Musaspecific peptides could be assigned to a cross-species proteinhomolog by visualization of the peptide–protein associations.For example, the ALCADHINVHLVTVPSAK peptide was notassigned to a protein homolog but as it was associated with aMusa ID that contained peptides of the protein S12 subunit ofthe 40S ribosomal protein (green lines), it could be assigned tothe S12 subunit cluster (yellow dotted line, Fig. 5).
Another interesting application of Cytoscape is thedetection of proteotypic peptides and the visualization ofprotein redundancy. An obvious prerequisite for this appli-cation is the knowledge of the complete protein sequence.This is demonstrated by the visualization of the differentaquaporin PIP1 isoforms. Fig. 6 shows the different PIP1
Arabidopsis isoforms (green squares) that are theoreticallycleaved by trypsin. The proteotypic peptides can easily berecognized as the white circles that are solely connected withone isoform. Also the peptides shared between the differentisoforms can easily be recognized using Cytoscape (differentcolors on Fig. 6).
In caseswherenocomplete sequence information isavailable,neither the number of different isoforms nor the proteotypicpeptidescanbedeterminedexactly. Fig. 7 showsavisualizationofthedifferentMusa IDs (blue) that contain apart of a PIP1 sequencewhich was theoretically cleaved by trypsin. By combining the insilico data of the Musa and Arabidopsis PIP1 digests, it becomesclear that most Musa peptides (in gray) are not conserved in theArabidopsis PIP1 isoforms. The Musa ID's can only be groupedaccording to thenumber of peptides theyhave in commonwith agiven Arabidopsis homolog. For example, the Musa sequences atthe bottomof the picture all theoretically share one short peptidesequence (SWSFYR; in blue) that is specific for the PIP1.5Arabidopsis isoform. Since this short peptide is the only proteo-typic peptide shared between Arabidopsis PIP1.5 and some MusaID's, it is dangereous to consider the respectiveMusa ID's as PIP1.5isoforms. It proves the difficulties that are associated with cross-species identification and shows the necessity of allowing aminoacidmodifications. This Cytoscape illustration nicely showswhy
Fig. 6 – Cytoscape view of the in silico tryptic peptides of the five different Arabidopsis PIP1 proteins (green squares). The yellowcircles represent peptides that are shared by the five isoforms, the pink circles are shared between isoform one and two, thedark green circles between isoforms 1, 2, 3 and 4 and the mustard circle between isoforms 1, 3, 4 and 5. The white circlesrepresent the proteotypic peptides.
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the identification of protein isoforms is troublesome in a cross-species approach and why only a protein decription of thehomologs can be listed in this study.
3.4. Classification of the identified proteins
Based on the Cytoscape analyses, a list with non-redundantprotein homologs was created (Table S1, supplementarymaterials). To classify the identified proteins we preferred todefine the closest Arabidopsis homolog of the identifiedproteins. Though Arabidopsis is phylogenetically not theclosest sequenced plant species related toMusa, this approachwas preferred since Arabidopsis is the best annotated plant. Itallowed using specific tools that have been developed to listand search the characteristics of Arabidopsis proteins such asthe Plant Proteome database and GoMiner. Proteins of whichno homolog was found in Arabidopsiswere classified using thedifferent programs as explained in the Materials and methodssection and as shown on Fig. 1.
As such, indications about possible functions and otherprotein properties of the Musa proteins were retrieved.However, the real Musa characteristics might differ from theones listed in Table S1.
Plasma membrane proteins, unlike chloroplastic andmitochondrial proteins, cannot be recognized based on aspecific sequence. Therefore, we used the GO (Gene Ontol-ogy) annotation for the cellular component plasma mem-brane as a criterion to define a protein as being PMassociated. Taking different ribosomal proteins subunitsand three classes of receptor like kinases into account, intotal 79 non-redundant proteins were associated with theplasma membrane (Table S2).
It is worth mentioning that several other proteins, such asfructose–bisphosphate aldolase, calreticulin and carbonicanhydrase, have also been reported to be PM associated.Therefore, identified proteins that are also present in the listof proteins published by Ephritikhine [26] are also indicated inTable S1 (column M). These authors compared several of themost important PM proteome publications and created a tablewith proteins identified in these studies which can be foundon http://www.grenoble.prabi.fr/.
Since contaminants from other cellular fractions wereidentified as well, the two-phase partitioning could be furtheroptimized. However, since the main focus of this study wasdeveloping a peptide-based approach for identification ofmembrane proteins, the purity of the fraction was considered
Fig. 7 – Cytoscape view of the in silico tryptic peptides of the five different Arabidopsis PIP1 proteins (green squares) incombination with the in silico tryptic peptidesMusa ID's (blue circles). Gray circles representMusa peptides that were not foundin the Arabidopsis sequence. Colored lines are connected to peptides that have also been experimentally identified. The colorsrepresent the significance of the ladder score in analogy with Fig. 4; red≤50, orange 50–65, green≥65. The white peptides arespecific for the Arabidopsis isoforms (proteotypic). The blue peptides represent proteotypic Arabidopsis proteins that were alsofound in the Musa sequences. The other colored peptides are shared between isoforms of Arabidopsis (see Fig. 6).
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sufficient. Therefore the number of confidently identifiedintegral plasma membrane proteins was determined.
In total 19 of the 79 identified plasma membrane proteins ofTable S2 were predicted to contain at least one transmembrane
domain (Table 1). Eight of them contained more than 3transmembrane domains. Compared to other studies, thisnumber appears to be low, but this is explained by the fact thatonly protein homologs are listed. In Table 1 the possible
Table 1 – Integral membrane proteins in the PM fraction of banana leaves, listed according to their function. Proteins wereidentified as described in the Materials and methods. The number of predicted transmembrane helices (TMH), the totalnumber of peptides (Pept), the number of non redundant peptides (NR) and the number of NR above the significance treshold(>T) identified per protein, the possible isoforms based on the sequences of the identified peptides (Iso seq.) or the number ofArabidopsis gene identifiers (Iso Agi) per identified protein are shown.
Function Protein description TMH Pept NR >T Iso seq Agi
Plasma membrane ATPase 10 42 28 20 3 7Transporters ABC transporter B family member 12 2 2 2 1 1
Vesicle-associated membrane protein 1 2 2 1 1 1Putative lipid transfer protein 1 2 2 2 1 1Secretory carrier membrane protein 4 1 1 1 1 1
Other Senescence-associated protein 1 1 1 1 1 1Enolase 1 4 3 1 2 1
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occurrence of isoforms is indicated by listing the number ofdifferent Arabidopsis gene identifiers (Agi) associated with oneprotein description and by listing the different variants of aspecific peptide sequence. As such, by using the proposedworkflow integral plasma membrane proteins with up to 16transmembrane helices could be successfully identified using apeptide-based approach.
4. Conclusions
In this paper a new workflow for the analysis of membraneproteins from poorly sequenced plants using a peptide-basedapproach is proposed. This workflow builds further on therecommendations made by Grossmann [11] and Shevchenko[12,13]. Important elements of this workflow are (i) optimiza-tion of the peptide separation to ensure the identification ofthe highest possible number of peptides (ii) a database searchagainst a species specific database (iii) de novo sequencing onthe remaining spectra (iv) a cross-species homology search ofthe obtained peptide sequences fromboth the database searchand de novo sequencing and (v) visualization of peptide–protein associations to remove redundancy and wrongly-assigned peptides to increase the confidence of identificationsby combining species-specific and cross-species results and todiscover possible isoforms. Using this workflow, integralplasma membrane proteins from banana leaves could besuccesfully identified. In view of the protein inferenceproblem the next challenge is to perform reliable quantitativestudies.
Supplementary materials related to this article can befound online at 10.1016/j.jprot.2011.02.008.
Acknowledgements
The Belgian Fund for Scientific Research (FWO-Flanders) isgreatly acknowledged for providing a post-doctoral andmobilitygrant to Dr. S.C. Carpentier. Also the grant from the EuropeanCooperation inScience and technology (COST) to support a shortterm scientific mission of Dr. A. Vertommen is highly appreci-ated.Additional fundingwas obtained from theDanish researchcouncils for production and technological sciences (FTP) and fornatural sciences (FNU). We are also grateful to the laboratory ofgene technology (Kasteelpark Arenberg 30) and the laboratoryformolecularandcellular signal transmission for theuseof theirultracentrifuge and to the Centre de Recherche Public GabrielLipmann in Luxembourg for themass spectrometric analysis ofthe gel samples.
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