Profiling the Secretome of the Marine Bacterium Pseudoalteromonas

Gene sequences from the in-house database were analyzed using a .... Most of the proteins fall within the pI range of 4−10 and molecular weight of u...
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Profiling the Secretome of the Marine Bacterium Pseudoalteromonas tunicata Using Amine-Specific Isobaric Tagging (iTRAQ) Flavia F. Evans,†,‡ Mark J. Raftery,§ Suhelen Egan,†,‡ and Staffan Kjelleberg*,†,‡ School of Biotechnology and Biomolecular Sciences, Centre for Marine Biofouling and Bio-Innovation, and Bioanalytical Mass Spectrometry Facility, University of New South Wales, NSW 2052, Australia Received August 16, 2006

The eukaryote-associated marine bacterium Pseudoalteromonas tunicata produces a range of targetspecific compounds that inhibit different types of marine organisms including invertebrate larvae and algal spores, as well as a broad spectrum of fungi, protozoa, and bacteria. The ability to produce such bioactive compounds is correlated to the expression of a yellow and a purple pigment in P. tunicata. To investigate the regulation and biosynthesis of the pigments and bioactive compounds, the expressed secretome of the pigmented wild-type P. tunicata and a nonpigmented mutant (wmpD-) defective in the type-II secretion pathway were compared. Secreted proteins were digested with trypsin, labeled using amine-specific isobaric tagging reagents (iTRAQ), and identified using two-dimensional SCX and nano C18 RP liquid-chromatography coupled with tandem mass spectrometry (LC/LC-MS/MS). The iTRAQ labeling experiments enabled accurate measurement of the proteins identified in this work. A sequence-base prediction of P. tunicata secretome was also obtained and compared to the expressed proteome to determine the role of the type-II secretion pathway in this bacterium. Our results suggest that this secretion pathway has a role in iron transport and acquisition in P. tunicata. Keywords: secretome • iTRAQ • LC-MS/MS • GSP • type-II secretion • Pseudoalteromonas sp.

Introduction Protein secretion is essential to various cellular processes in bacteria including nutrient uptake, cell-to-cell communication, and elimination of potential competitors in the environment by the release of proteins with antimicrobial activity and other virulence factors.1,2 Secretome is a term used to describe the entire complement of secreted proteins of the proteome that includes extracellular and outer membrane, as well as proteins exported to the periplasm in Gram-negative bacteria. The pigmented marine bacterium Pseudoalteromonas tunicata produces a range of extracellular compounds that are active against invertebrate larvae, algal spores, diatoms, fungi, heterotrophic flagellates, and bacteria.3,4 The ability to synthesize different bioactive compounds as secondary metabolites is beneficial for the survival of P. tunicata in the marine environment. A recent study has shown that P. tunicata is an aggressive competitor in mixed species communities due to the activity of an antibacterial protein (AlpP).5 The synthesis of bioactive compounds in this bacterium is correlated to pigmentation, as previously described in experiments with a transposon * To whom correspondence should be addressed: Prof Staffan Kjelleberg, Center for Marine Biofouling and Bio-Innovation, University of New South Wales, NSW, 2052, Australia. Tel, +61 2 9385-2102; e-mail, s.kjelleberg@ unsw.edu.au; fax, +61 2 9385-1779. † School of Biotechnology and Biomolecular Sciences, University of New South Wales. ‡ Centre for Marine Biofouling and Bio-Innovation, University of New South Wales. § Bioanalytical Mass Spectrometry Facility, University of New South Wales. 10.1021/pr060416x CCC: $37.00

 2007 American Chemical Society

mutagenesis library. One of the transposon mutants generated by Egan et al.6 lacks the ability to produce both yellow and purple pigments, resulting in a white phenotype (wmpD-). The wmpD- mutant is disrupted in a gene that encodes a protein belonging to the type-II secretion machinery. Interestingly, in addition to the pigments, the wmpD- mutant has also lost the ability to produce the bioactive compounds against all target organisms. As a result of this observation, the authors suggested that the type-II secretion pathway may be responsible for the transport of extracellular enzymes required to obtain precursors for the pigments and other bioactive compounds produced by P. tunicata. In Gram-negative bacteria, the type-II secretion pathway is a two-step process where proteins are first exported into the periplasmic space via a translocase and subsequently transported across the outer membrane by the secreton or type-II apparatus.7 To utilize this pathway, proteins carry an Nterminal signal peptide to enable the translocation across the inner membrane by the Sec apparatus or the twin-arginine translocation (Tat) pathway.8 The type-II secretion is often called the main terminal branch of the General Secretory Pathway (GSP)9 because of its dependence on the Sec translocon. This secretion pathway can be associated with virulence in plant and human pathogenic bacteria by means of transporting toxins and hydrolytic enzymes to the extracellular environment.10 However, the type-II secretion has also been shown to transport the outer membrane-attached lipoprotein pullulanase in Klebsiella oxytoca.9 Some known examples of Journal of Proteome Research 2007, 6, 967-975

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research articles extracellular enzymes transported by the type-II secretion pathway are the cholera toxin in Vibrio cholerae,11 aerolysin in Aeromonas hydrophila,12 as well as cellulases in Erwinia carotovora and Erwinia chrysanthemi.13 Previous studies applied proteomics through the use of twodimensional gel electrophoresis (2-D gels) to analyze the secreted proteins in various bacteria.14-16 More recently, gelfree approaches that allow the identification and relative quantification of proteins from different samples have gained popularity in proteomics.17 The recently developed isobaric tagging for relative and absolute quantitation (iTRAQ) coupled with tandem mass-spectrometry (MS/MS) method employs chemical labeling of the primary amines of amino acids, which enables the quantification of every suitable peptide in a protein sample.18,19 This is an important improvement in relation to the isotope-coded affinity tags (ICAT) strategy, in which only peptides containing cysteine residues are labeled.20,21 Further advantages of the iTRAQ method are the possibility of multiplexing the analysis of up to four samples in a single experiment, and that the mass spectra of peptides generated are relatively simple.22 With this strategy, isobaric-derivatized peptides generate specific reporter ions for each iTRAQ tag in the fragmentation spectra following liquid chromatography and MS/MS. Because the peptide fragmentation in the low-mass region (from m/z 114 to 117) is normally free of other common fragment ions, an accurate quantification of differentially expressed proteins can be achieved by comparing the intensities of the reporter ions in the MS/MS spectra. In this study, we investigated the secretome of P. tunicata wild-type (wt) in comparison to the white mutant (wmpD-) to characterize the role of the type-II secretion pathway for the production of the pigments and bioactive compounds. The availability of P. tunicata’s draft genome sequenced as part of the Gordon and Betty Moore Marine Microbial Sequencing Project enabled the prediction of part of the secreted proteome in this bacterium by searching for N-terminal signal peptides in the protein sequences. Then, we employed the newly developed iTRAQ approach for the simultaneous identification and relative quantification of the expressed secretome of P. tunicata. While examining differences in the extracellular protein profile of P. tunicata wt in comparison to wmpD-, we found that a range of cytosolic proteins and an ABC-transporter were significantly differentially expressed in the two strains. An extensive analysis of all the proteins identified indicated that P. tunicata secretome is represented by a wide range of proteins with no described function to date (hypothetical proteins) and that the type-II secretion pathway may have a role in iron uptake.

Experimental Procedures Computational Analyses and Proteome Predictions. The draft genome data was based on the JGI (Joint Genome Institute) assembly of September 2005. P. tunicata genome was sequenced to 8× level of coverage with the completion of 98.77% of the entire genome (44 982 425 base pairs).23 P. tunicata gene scaffolds were converted to GenBank files using ARTEMIS to visualize gene groups. Gene sequences from the in-house database were analyzed using a browser created using BIOPERL modules. Automated data processing involved the comparative analyses of protein sequences using BLAST, RPSBLAST, and Pfam databases. Genes from the draft genome were assigned to metabolic pathways according to KEGG Pathway Database (http://www.genome.jp/kegg/). Simulated 2D-gel of 968

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P. tunicata proteome was generated using the JVirGel tool (http://www.jvirgel.de).24 For the prediction of signal peptides, the whole proteome of P. tunicata was analyzed using the SignalP v.3.0 software25 (http://www.cbs.dtu.dk/services/SignalP/) with the settings for Gram-negative bacteria. This software employs a combination of neural networks methods with Hidden Markov Models. Protein sequences were also tested for the presence of membrane spanning domains using the TMHMM server v.2.0 (http://www.cbs.dtu.dk/services/ TMHMM-2.0/) because transmembrane (TM) helices can be erroneously interpreted as signal peptides. Membrane spanning domains are usually less hydrophobic and longer than Sec signals, and as a result, SignalP does not detect such motifs with high accuracy, which increases the chance of false-positive predictions for signal peptides. Sequences with at least one TM helix containing more than 10 amino acids in the first 60 amino acids of a transmembrane helix were computed. Proteins with a tat cleavage site were predicted using the TatP v.1.026 software (http://www.cbs.dtu.dk/services/TatP/). Tat cleavage sites were considered positive when the neural network scores were positive and a tat motif was found in the sequence. Growth Conditions and Sample Preparation. P. tunicata wild-type (D2Wt) and wmpD- mutant (D2W3) were grown as a preculture in Bacto Marine Broth 2216 (Difco) overnight. To maintain the transposon insertion, kanamycin was added at a concentration of 85 µg mL-1 for the cultivation of D2W3. Then, 10 mL of each culture was inoculated into 1 L of Marine Broth 2216 and incubated at room temperature (RT) under agitation for approximately 16 h until early stationary phase of growth was reached. The cell numbers of D2Wt and D2W3 were 1.2 × 109 and 1.7 × 109 cfu/mL in the first experiment; and 3.8 × 109 and 5.0 × 109 cfu/mL in second experiment. This growth phase was chosen because it is when D2Wt expresses the pigments and other bioactive compounds.27,28 The protocol for the preparation of culture supernatant proteins was modified from Mattow et al.29 and is described as follows: P. tunicata cells were harvested at 8000g for 15 min at 16 °C, and the culture supernatant was collected and filtered through 0.2 µm filters (Sartorius). A total of 100 µL of a mixture of protease inhibitor cocktail (Sigma) was added to the remaining supernatant, and sodium deoxycholate was added to a final concentration of 0.015%. The mixture was incubated under agitation at room temperature for 10 min. Subsequently, trichloroacetic acid was added to a final concentration of 10%, and the solution was incubated at 4 °C for 1 h. The precipitated supernatant proteins were harvested by centrifugation at 4000g for 15 min at 4 °C. The protein pellet was collected and washed twice with cold acetone and collected by centrifugation at 6000g for 10 min in each wash step. The remaining pellet was resuspended in MilliQ water supplemented with 0.05% of SDS for the subsequent iTRAQ labeling. Protein concentration was determined using the BCA protein assay reagents (Sigma) according to the manufacturer’s instructions. Individual cultures of D2Wt and D2W3 were grown in duplicate under the conditions described above for iTRAQ labeling. iTRAQ Labeling. Supernatant proteins (100 µg) from D2Wt and D2W3 were labeled according to the manufacturer’s instructions (Applied Biosystems) with one important modification that improved the detection by mass spectrometry using our QStar pulsar i hybrid tandem mass spectrometer (Applied Biosystems, Foster City CA). Samples were labeled as shown in Figure 1, except the manufacturer’s “dissolution buffer” was replaced with Na2CO3 (1 M) and cysteines were alkylated with

Profiling P. tunicata’s Secretome Using Amine-Specific iTRAQ

research articles using Stage Tips (Proxeon, Denmark) and lyophilized. Peptides were dissolved in formic acid (0.1%, 25 µL) and loaded onto a SCX micro trap (1 × 8 mm, Michrom Bioresources, Auburn, CA). Peptides were eluted sequentially using 5, 10, 15, 20, 25, 30, 40, 50, 75, 150, 300, and 1000 mM ammonium acetate (20 µL). The unbound load fraction and each salt step were concentrated and desalted onto a micro C18 precolumn (500 µm × 2 mm, Michrom Bioresources) with H2O/CH3CN (98:2, 0.1% formic acid, buffer A) at 20 µL/min. After a 10 min wash, the precolumn was switched (Switchos) into line with a fritless analytical column (75 µm × ∼12 cm) containing C18 reverse phase packing material (Bakerbond, 5 µm, 300 Å) (Gatlin). Peptides were eluted using a linear gradient of buffer A to H2O/ CH3CN (40:60, 0.1% formic acid-buffer B) at 200 nL/min over 60 min. High voltage (2300 V) was applied through a low volume tee (Upchurch Scientific) at the column inlet and the outlet positioned ∼1 cm from the orifice of an API QStar Pulsar i hybrid tandem mass spectrometer (Applied Biosystems, Foster City, CA). Positive ions were generated by electrospray, and the QStar operated in information-dependent acquisition mode (IDA). A TOF MS survey scan was acquired (m/z 350-1700, 0.75 s), and the 2 largest multiply charged ions (counts >20, charge state g2 and e4) sequentially selected by Q1 for MSMS analysis. Nitrogen was used as collision gas, and an optimum collision energy was automatically chosen (based on charge state and mass). Tandem mass spectra were accumulated for 2 s (m/z 65-2000). Offline fractions were loaded (20 µL) directly onto the C18 trap column as explained above.

Figure 1. Diagram showing the workflow of the iTRAQ experiment. Supernatant (CSN) from P. tunicata wild-type (D2Wt) and white mutant wmpD- (D2W3) were labeled with different iTRAQ reagents after protein digestion. Protein digests were combined, and 2D chromatography of labeled peptides was accomplished by either an online or an offline approach with sample fractionation using strong cation exchange (SCX) chromatography prior to LC-MS/MS. Fractionated sample (a) and single fraction sample (b) were run through 2D-liquid chromatography coupled with MS/MS in duplicate to generate 2 pair-runs. Each pair-run was combined, and the experiment was repeated with another set of D2Wt and D2W3 cultures. MS/MS spectra from 4 combined runs were analyzed to compare the two methods of peptide separation prior to the runs.

iodoacetamide. Only sufficient Na2CO3 (typically 50 µL) was added to maintain the pH ∼8.1 throughout the reaction, and care was taken to reduce the salt content to ∼10 mM to allow successful strong cation exchange (SCX) chromatography. The labeled peptide mixture was separated in two distinct ways: (a) by a single step online cation exchange chromatography (see below), and (b) by sample fractionation using offline SCX chromatography to lower the complexity of the mixture (Figure 1). For fractionation, labeled sample of combined D2Wt and D2W3 was loaded onto a SCX column (1 × 50 mm) packed with Poros S10. The column was equilibrated with buffer A (10 mM NaH2PO4, pH 3.0) and eluted with a linear gradient of buffer B (10 mM NaH2PO4 and 1 M NaCl, pH 3.0) at 50 µL/min over 25 min. The flow rate was increased to 100 µL/min, the first fraction was collected after 8 min elution, and the following fractions were collected every min. Two-Dimensional Liquid Chromatography and Mass Spectrometry. iTRAQ samples were separated by automated online strong cation exchange (SCX) and nano C18 LC using an Ultimate HPLC, Switchos and Famos autosampler system (LCPackings, Amsterdam, The Netherlands). Samples were desalted

Peptide mixture fractions separated by online and offline cation exchange chromatography were run in duplicated LCESI MS/MS experiments. Figure 1 shows the workflow of the experiment with duplicated samples over 8 separate mass spectrometry runs. For the analysis, MS/MS spectra results from each duplicated experiment with fractionated and singlefraction samples were combined. Data Analysis. The data analysis for the iTRAQ reagent experiments were performed with the software packages ProQuant 1.0 followed by ProGroup 1.0.2 (Applied Biosystems). For the ProQuant analysis, the cutoff confidence setting was 75%. A mass deviation of 0.2 Da for precursor and 0.15 Da for fragment ions was permitted with the searches against the P. tunicata database. The ProGroup software applies a set of rules to ensure that each MS spectrum is used to support the identification of only one protein. The accuracy of protein identification was increased because sequences are detected in the context of other putatively identified sequences in the same experiment. ProGroup reports were generated for further quantification analysis with protein confidence level set up for 95% confidence (or “ProtScore” of 1.3). Mascot Distiller (Matrix Science, London, U.K.) generated data suitable for submission to the database search program Mascot (Version 2.1, Matrix Science). Proteins identified from only one peptide were manually inspected using the Mascot algorithm. A total of 23 789 MS/MS spectra were searched against the P. tunicata database and a shuffled-database30 with the following searching parameters: only fully tryptic peptides were included and peptides with score 38) were identified by searchJournal of Proteome Research • Vol. 6, No. 3, 2007 969

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Figure 2. (A) MS/MS spectrum of peptide MLILTR showing a series of y- and b-type ions and precursor ion at 445.76 m/z; (B) the reporter ion region shows fragment ions 114.10 and 117.11 derived from iTRAQ reagents.

ing the true database, and 4 high scoring peptides (Mowse score >38) were identified by searching the shuffled database. The false-positive rate was estimated using the expression %false ) 2[nshuf/(nshuf + ntrue)].31 To determine the differential expression of proteins between D2Wt and D2W3, the mean ratio of identified proteins was calculated. The P-value available in the ProGroup report was inspected, and the cutoff of 5% (or p < 0.05) of level of significance was chosen as a criterion for the analysis of the differentially expressed proteins. Standard deviation and 95% confidence interval were also calculated for each protein ratio within the significant range of entries.

Results Predicted Secretome. From a total of 4378 open reading frames available in the draft genome of P. tunicata (JGI, September 2005), 370 proteins accounting for 8.5% of the whole proteome available in the genome draft were found to have a predicted signal peptide using the SignalP software.25 Because signal peptides can be mistaken for a TM helix or, conversely, TM helices can be interpreted as a signal peptide, a transmembrane prediction tool was also run. As a result of this prediction, 61 out of the 370 sequences were positive for TM helices. Therefore, 7% of the whole proteome is predicted to have a signal peptide and have no transmembrane domains. Six proteins were found to have the tat motif signal peptide using the TatP software. Protein Identification after iTRAQ Labeling. MS/MS spectra were searched against the P. tunicata database using ProQuant, and the results were summarized by the ProGroup software. In total, 182 iTRAQ-labeled proteins were identified with >95% confidence (Supporting Information Table S1). Additionally, 78% of the proteins were identified from two peptides or more. All proteins identified were validated by repeating searches using Mascot, including entries identified from a single peptide with the exception of one protein (gene no. 11487). However, the identification of this protein was validated by manual inspection of the MS/MS ion spectra (Figure 2), where a series 970

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Figure 3. Distribution of all proteins identified after iTRAQ labeling and tandem mass spectrometry into different functional categories according to the KEGG database.

of sequence-specific b- and y-type ions was observed. In this work, the proportion of false-positives derived from the Mascot searches was estimated to be lower than 1.5% based on the calculation proposed by Peng et al.31 Proteins identified were separated into functional categories according to the KEGG database (Figure 3). It is noteworthy that more than 23% of all proteins found were hypothetical (unknown category). The use of a primary separation technique prior to LC-MS/MS to separate and concentrate a highly complex peptide mixture improves the mass spectrometry analysis. In total, 49 unique proteins were identified using the offline cation exchange chromatography, whereas 59 unique proteins were identified without the offline separation step (Figure 4). In this work, 2D chromatography using the online approach resulted in a comparable amount of MS/MS data to the offline approach due to the prefractionation of the samples prior to iTRAQ labeling, which resulted in a less complex peptide mixture. To assess the coverage of iTRAQ-labeled secreted proteins, a plot of predicted pI versus molecular weight was generated

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Profiling P. tunicata’s Secretome Using Amine-Specific iTRAQ

Table 1. Proteins Identified after iTRAQ Labeling and NanoLC Tandem Mass Spectrometry That Have a Predicted N-Terminal Signal Peptide

contig/gene no.a

Figure 4. A total of 182 proteins was identified after iTRAQ labeling and tandem mass spectrometry with stationary-phase grown P. tunicata cells. The complex peptide mixture was separated by a combination of online-SCX (A) and offline-SCX (B) chromatography. A number of proteins were uniquely identified through each separation method indicating that both methods are complementary. The identification of 74 proteins overlapped using both techniques.

function/similarity

Cellular Processes and Signalling 1098687001752_19072 Oar-like OM protein, OmpA family 1098687001749_14201 Outer membrane protein OmpH Environmental Information and Processing 1098687001724_01491 Alkaline phosphatase 1098687001750_15353 Phosphate-binding protein 1098687001747_13811 TonB biopolymer transport component 1098687001752_17557 TonB biopolymer transport component 1098687001752_17557 TonB biopolymer transport component Folding, Sorting, and Degradation 1098687001727_03741 Periplasmic trypsin-like serine protease 1098687001755_21421 Probable disulfide isomerase 1098687001755_21426 Thiol:disulfide interchange protein DsbA Genetic Information Processing 1098687001735_09510 ComE operon protein Unclassified Metabolism 1098687001727_03121 Putative cytochrome c5 1098687001732_07805 Predicted O-methyltransferase 1098687001727_03151 Cytochrome c4 1098687001731_06229 Secreted metalloprotease Unknown Metabolism 1098687001729_03949 Hypothetical protein 1098687001752_19192 Hypothetical protein 1098687001733_08880 Hypothetical protein 1098687001741_12409 Conserved secreted protein 1098687001721_12519 Invasin domain protein 1098687001741_12464 Peptidase, putative 1098687001729_04884 Hypothetical protein 1098687001739_11067 Hypothetical protein 1098687001749_15156 Hypothetical protein 1098687001748_14011 Hypothetical protein 1098687001749_15151 Hypothetical protein 1098687001749_15161 Hypothetical protein 1098687001733_08330 Hypothetical protein 1098687001730_05144 Conserved secreted protein 1098687001754_20972 Hypothetical protein

no. of unique peptidesb 6 1c 26 16 6 4 3 11 6 5 1c 2 2 1c 1c 26 16 14 7 5 4 4 4 4 3 3 3 3 2 2

a Contig and gene numbers are based on the P. tunicata in-house database. b Number of unique peptides that matched to each protein. All peptide matches have 95-99% confidence based on analysis using ProGroup. c Protein also identified using Mascot.

Figure 5. Simulated 2D-gel of the P. tunicata secreted proteome. Predicted MW and pI for the predicted secretome (gray, closed circles) and expressed secretome of 30 proteins (black, closed circles) that have a predicted signal peptide.

for each of the 376 sequences that were predicted to have a sec or tat N-terminal signal peptide. Most of the proteins fall within the pI range of 4-10 and molecular weight of up to 125 kDa (Figure 5). Thirty proteins out of the predicted secreted sequences were expressed by P. tunicata under laboratory conditions. While this number represents secreted proteins, it does not reflect the entire expressed extracellular proteome, as predictions were restricted to proteins that are secreted via sec- or tat-dependent pathways (including the type-II secretion). An example of an expressed extracellular protein that is excluded from the prediction is flagellin type B (gene no. 15021), which is exported out of the cells via the flagellar machinery independently of the sec-translocon.32 Not surprisingly, most P. tunicata proteins with a signal peptide identified by iTRAQ belong to the unknown category of proteins (Table 1), including a protein that contains an invasin domain (gene no. 12519). Differential Expression of Proteins Identified Using iTRAQ. The relative quantification of proteins identified with iTRAQ was achieved during MS/MS by estimating the abundance of reporter ion peaks that corresponded to D2Wt (m/z 114) and D2W3 (m/z 117) samples (Figure 2). Approximately 47% of the

Figure 6. Differential expression of all iTRAQ labeled proteins in natural log scale. Zero (0) relative abundance indicates expression ratio (D2W3/D2Wt) of 1. Relative abundance higher than 0 indicates up-regulation of D2W3 proteins; and relative abundance lower than 0 indicates down-regulation of D2W3 proteins. Proteins that were not differentially expressed significantly are represented by the bars with DE -0.3 e 0 e 0.3.

proteins identified had expression ratios close to 1.0 in both D2Wt and D2W3 culture supernatants (Figure 6). Table 2 shows several proteins across different role categories for which the relative abundance between D2W3/D2Wt varied by at least 2-fold in one or more MS/MS experiments. A number of Journal of Proteome Research • Vol. 6, No. 3, 2007 971

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Table 2. Differentially Expressed Proteins with P-Value < 0.05 Showing Relative Fold Change contig/genea 1098687001730_05234 1098687001730_05239 1098687001729_04829 1098687001752_19187 1098687001724_01496 1098687001724_01491 1098687001747_13811 1098687001752_17557 1098687001607_02077 1098687001755_21046 1098687001724_01231 1098687001749_14241 1098687001739_11487 1098687001752_19192 1098687001752_19202 1098687001731_06589 1098687001749_15161

function/similarity

In(D2W3:D2Wt) ( 95% CIb

DE (D2W3:D2Wt)c

Carbohydrate Metabolism 0.42 ( 0.05 2.7-fold up-regulated 0.37 ( 0.06 2.5-fold up-regulated 0.35 ( 0.31 2.5-fold up-regulated Nucleotide Metabolism 2,3-phosphodiesterase -0.50 ( 0.29 3.7-fold down-regulated Environmental Information Processing Alkaline phosphatase -0.45 ( 0.10 2.7-fold down-regulated Alkaline phosphatase -0.48 ( 0.10 3.1-fold down-regulated TonB-dependent transport component 0.82 ( 0.21 6.6-fold up-regulated TonB-dependent transport component 0.73 ( 0.03 5.3-fold up-regulated ABC transporter, ATP-binding protein 1.11 12.8-fold up-regulated Genetic Information Processing Chaperonin 60 kDa subunit -0.32 ( 0.02 2.3-fold down-regulated Ribosomal protein S3 0.50 ( 0.37 3.1-fold up-regulated Elongation factor Ts 0.64 ( 0.08 4.4-fold up-regulated Carbon storage regulator -0.59 ( 0.43 4.8-fold down-regulated Unknown Metabolism Hypothetical protein -0.59 ( 0.11 3.7-fold down-regulated Hypothetical protein -1.24 ( 0.25 18.5-fold down-regulated Hypothetical protein -0.90 ( 0.16 10.2-fold down-regulated Hypothetical protein -0.76 ( 0.81 5.8-fold down-regulated Succinyl-CoA synthetase subunit beta Succinyl-CoA synthase, alpha subunit Malate dehydrogenase

no. peptidesd

signal peptidee

no. runsf

15 13 6

4 4 2

18

4

28 26 6 3 2

Sec Sec Sec

26 2 8 1# 16 8 5 3

4 4 2 2 1 2 2 2 2

Sec

Sec

4 3 2 2

a Contig and gene numbers are based on the P. tunicata in-house database. b ln(D2W3:D2Wt), natural log of the average ratio of the relative quantification of peptide ions from wmpD- mutant and P. tunicata wild-type samples. c DE, differential expression of wmpD- mutant in relation to P. tunicata wild-type cells. Only proteins higher than 2-fold up- or down-regulated are shown in the table. d Number of unique peptides matched to each protein. e Sequences that have an N-terminal signal peptide. f Number of MS/MS runs where protein was found; #ion spectra used to identify this protein was manually inspected.

differentially expressed proteins do not have a described function to date, and only one of them is significantly similar to other proteins in the NCBInr database after BLAST searches. The 3.7-fold down-regulated hypothetical protein (gene no. 19192) in D2W3 matched to the acetyl-CoA biotin carboxy carrier protein from Saccharophagus degradans (55% identity and 69% similarity over 724 aa; gi_90020426). Two hypothetical proteins (gene nos. 19202 and 06589) were 18.5 and 10.2 times down-regulated in the D2W3. Other iTRAQ proteins that were down-regulated in D2W3 were a hypothetical protein (gene no. 15161) with 5.8-fold difference and an alkaline phosphatase (gene no. 01491) with 3.1-fold difference. Several proteins were up-regulated in D2W3, including two putative TonB biopolymer transport component proteins with 6.6-fold difference (gene no.13811) and 5.3-fold difference (gene no. 17557), respectively. The ABC-transporter protein (gene no. 02077) was up-regulated 12.8 times in D2W3. This protein matched to homologues from several prokaryotic groups, including to Pseudoalteromonas haloplanktis (78% identity and 87% similarity over 638aa; gi_76876718).

Discussion Extracellular Proteome of P. tunicata. Eukaryote-associated bacteria such as P. tunicata have developed different strategies to compete with other microorganisms by producing a range of extracellular molecules with inhibitory activity. This study shows that the percentage of secreted proteins in P. tunicata (8.5%) is in accordance with predictions in other bacteria, for which reports have demonstrated that the export of secdependent proteins can account for 4-11% of the proteome of common plant pathogens.2 Escherichia coli can translocate over 250 proteins across the inner membrane,33 which accounts for about 6% of the genome of E. coli K-12. Similarly, 7% of the Bacillus subtilis proteome is believed to be transported across the plasma membrane according to signal peptide predictions.34 The Tat translocon has emerged as an alternative means of protein export, and it has been reported to be involved in protein secretion via the type-II pathway.35 The key 972

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attribute of the Tat system is the ability to translocate large folded proteins across the inner membrane. P. tunicata has six proteins bearing the twin-arginine motif (tat), which accounts for 0.14% of the draft genome. The Gram-positive bacterium B. subtilis seems to have only five proteins with a Tat-signal peptide,26 while in other bacteria, this translocation machinery appears to be responsible for the transport of 0.050.87% of all proteins encoded in the genome.2 iTRAQ Analysis. An extensive number of proteins identified using isobaric tagging approach (iTRAQ) are predicted to be localized in the cytoplasm, due to their function in various cell metabolic pathways. These proteins have most likely been released into the medium by cell lysis. Mid-exponential phase cultures of D2Wt and D2W3 were also employed for iTRAQ labeling in an attempt to reduce the number of cytoplasmic proteins detected in the supernatant. However, analysis of the results obtained by harvesting the cells earlier in the growth curve also revealed a large number of cytosolic proteins in the extracellular fraction of both D2Wt and D2W3 cultures (data not shown). While it is assumed that the highest levels of protein secretion take place from mid-exponential up to stationary growth phase,1,15 typically secondary metabolites appear during the transition from late-exponential to stationary growth phase,36 and this is the case for the bioactive compounds produced by P. tunicata.6,27 Recently, a study to monitor the extracellular proteomes of six strains of Pseudomonas aeruginosa during growth revealed that prolonged incubation of up to 2.5 days led to a complex protein pattern.14 Although this pattern was, to some extent, due to proteins secreted at the late-exponential phase, contaminating cytosolic proteins were increasingly detected. To investigate the role of the type-II secretion pathway in the regulation of the secondary metabolites found in P. tunicata, we employed cultures grown until early stationary phase to enable the identification of the highest possible number of proteins potentially related to secondary metabolite production in the supernatant. A likely

Profiling P. tunicata’s Secretome Using Amine-Specific iTRAQ

explanation for the high number of cytosolic proteins detected in this study is the production of the autotoxic antibacterial protein (AlpP) that targets cells in logarithmic growth phase.27,37 Surprisingly, the AlpP was not detected, which may be due to limitations inherent in MS/MS methods such as difficulties with detection of low-abundance proteins due to poor digestion and/or separation by electrospray mass spectrometry. However, we cannot discard the possibility that this antibacterial protein is not transported out to the culture supernatant as previously suggested.5,37 In the iTRAQ strategy, all peptides in the samples are targeted for labeling, which results in the labeling of multiple peptides of the same protein. This greatly increases the sequence coverage during identification of proteins by MS/MS and increased confidence in ratio reports when compared to other stable isotope labeling methods, such as the ICAT.21 In the study, more than 78% of all proteins were identified from at least two peptide matches using ProGroup. This correlates to a recent study on protein expression of E. coli, showing that more than 65% of the iTRAQ proteins were identified with at least two high confidence peptide matches.18 Sample separation by offline strong cation exchange chromatography to reduce peptide complexity prior to LC-MS/MS has been employed in several studies of whole-cell lysates.19,17 The main advantage of the offline over the online approach is the improved separation of peptides in a linear gradient of salt as opposed to the step-gradient adopted in the online approach. In addition, a higher number of fractions can be collected offline.31 In this work, a comparison of the results obtained by offline and online separation approaches of iTRAQ-labeled peptides suggest that the two methods are complementary (Figure 4). The iTRAQ strategy enabled the quantification of a range of differentially expressed proteins that were linked to different metabolic categories. Twenty-seven of all proteins that were significantly quantified (p < 0.05) were at least 2-fold up- or down-regulated in D2W3 in comparison to D2Wt. Ribosomal and translational proteins were up-regulated in the D2W3 in comparison to the D2Wt, probably due to the addition of kanamycin to the medium to maintain the transposon insertion. This antibiotic inhibits protein synthesis by binding to the 30S ribosomal subunit, which may lead to an accumulation of translational proteins in the cytoplasm. Interestingly, the carbon storage regulator protein (gene no. 11487) was downregulated in the D2W3. This protein is a component of the global regulatory system Csr that is responsible for the repression of a series of stationary-phase genes.38 This can explain the up-regulation of proteins involved in carbohydrate metabolism, such as malate dehydrogenase (gene no. 04829) and succinyl-CoA synthetase (gene nos. 05234 and 05239). Most of the predicted secreted proteins are part of the environmental information processing category, including the nonspecific phosphomonoesterase alkaline phosphatase (gene no. 01491). This enzyme is required for the utilization of certain classes of dissolved organic phosphorus (DOP) in the ocean and is part of the phosphate regulatory system (pho regulon) in several bacteria.39 The pho regulon can control the expression of pigments and secondary metabolites, including antibiotics in different prokaryotes.40 The down-regulation of two alkaline phosphatases in D2W3 in comparison to D2Wt suggests that the phosphate metabolism is repressed in D2W3 at stationary-growth phase. It is not known whether the reduced expression of genes involved in phosphate metabolism is linked to the lack of pigments and bioactive compounds in the white

research articles mutant D2W3. The two most extreme cases of differential expression between D2W3 and D2Wt are proteins that belong to the unknown metabolism category (gene nos. 19202 and 06589). This category represents 23% of all proteins identified with iTRAQ, and it reflects the potential for finding novel proteins and secondary metabolites in P. tunicata. Type-II Secretion and Pigment Production by P. tunicata. Secreted proteins that have a role in environmental information processing have the potential to explain the link between the production of bioactive compounds and protein secretion via the type-II pathway in P. tunicata. Of particular interest is the differential expression of TonB-related proteins between D2Wt and D2W3. Two putative TonB system biopolymer transport proteins (gene nos. 13811 and 17557) were found to be upregulated in D2W3 in comparison to D2Wt. TonB-dependent receptors are responsible for the transport of large extracellular molecules into the bacterial cells, such as vitamin B12 and iron carriers (siderophores).41 Previous work suggests that iron metabolism is important for the biosynthesis of the pigments and inhibitory compounds in P. tunicata.42 Stelzer et al. investigated a white mutant disrupted in a global regulator, WmpR28 and demonstrated that WmpR controls the expression of genes involved in iron acquisition and uptake. Interestingly, an ABC-transporter protein (gene no. 02077) was 12.8-fold upregulated in D2W3. ABC transporters are generally involved in transporting various molecules that range from small molecules (ions, carbohydrates, amino acids, and antibiotics) to macromolecules (polysaccharides and proteins).43 The P. tunicata ABC-transporter protein matches to the ATPase component of ABC-transporters with duplicated ATPase domains of several other bacteria. Whether an ABC system imports or exports molecules depends upon the presence or absence of a periplasmic binding protein associated to the coding sequences for the ABC and transmembrane domains.44 ABC importers are also involved in transporting ferri-siderophore complexes across the periplasmic space and cytoplasmic membrane back into the cells.45 The interpretation of the data presented here, together with recent studies of iron regulation in P. tunicata,46 indicates that this ABC-transporter may be also involved in iron transport in P. tunicata. The results presented here suggest that the type-II secretion pathway is involved in iron acquisition in P. tunicata. Two possible explanations for this link could be considered: (1) Type-II secretion pathway is involved in siderophore transport; or (2) Type-II secretion pathway is required for siderophore uptake by transporting at least one TonB-dependent outer membrane receptor that binds to ferri-siderophore complexes. The first explanation is unlikely to take place in P. tunicata, as siderophores, which are low molecular weight molecules, are generally transported out of the cells by ABC transporters.45 However, there is supporting evidence to suggest that the typeII secretion pathway can transport TonB-dependent receptors out to the outer membrane in P. tunicata. For example, the gamma-proteobacterium Shewanella putrefaciens requires the type-II secretion pathway for transport of a heme-containing outer membrane protein involved in Fe(III) reduction.47 Given that the ferric uptake regulator (Fur) works as a repressor of iron acquisition genes under high iron levels in several bacteria,48 a likely explanation is that D2W3 lacks the ability to transport one or more specific TonB-dependent ferric siderophore receptors to the outer membrane. As the ability to uptake iron is impaired, Fur would in turn de-repress other TonB-dependent proteins to compensate for the iron deficiency Journal of Proteome Research • Vol. 6, No. 3, 2007 973

research articles in the cells, as suggested by the results presented in the current study (the up-regulation of 2 putative TonB receptors and an ABC-transporter). Iron deficiency in P. tunicata negatively affects growth and pigmentation in this bacterium, with pigment production reduced by half under low iron conditions.46 Iron may be critical for the biosynthesis of the pigments in P. tunicata; for example, the protein LppA that is involved in the biosynthesis of the yellow pigment contains an ironsulfur cluster.6

Conclusions The iTRAQ strategy coupled with LC/LC-MS/MS enabled us to successfully study the differential expression of secreted proteins of P. tunicata. Our work described part of P. tunicata secretome, representing a key step to characterize the expression of the novel bioactive compounds produced by this prokaryote. Comparison between P. tunicata wild-type and wmpD- mutant (D2W3) showed that the type-II secretion pathway has a role in iron acquisition in this bacterium. Moreover, the down-regulation of proteins involved in phosphate metabolism in the wmpD- mutant suggests that there is a correlation between pigment production and phosphate regulation in the wild-type. The results presented here are important to further understand the regulation of pigments and bioactive compounds in P. tunicata and, more importantly, to learn more about the strategies used by P. tunicata to eliminate competitors and survive as a surface-associated bacterium in the marine environment.

Acknowledgment. We thank Neil F. W. Saunders for setting up and maintaining P. tunicata’s genome browser. This work was supported by the Australian Research Council, Centre for Marine Biofouling and Bio-Innovation, and by the Faculty of Medicine, University of New South Wales. Supporting Information Available: Table listing all proteins identified with the iTRAQ labeling method. This material is available free of charge via the Internet at http:// pubs.acs.org. References (1) Antelmann, H.; Tjalsma, H.; Voigt, B.; Ohlmeier, S.; Bron, S.; Dijl, J. M. v.; Hecker, M. A proteomic view on genome-based signal peptide predictions. Genome Res. 2006, 11, 1484-1502. (2) Preston, G. M.; Studholme, D. J.; Caldelari, I. Profiling the secretomes of plant pathogenic Proteobacteria. FEMS Microbiol. Rev. 2005, 29, 331-360. (3) Holmstro¨m, C.; James, S.; Neilan, B. A.; White, D. C.; Kjelleberg, S. Pseudoalteromonas tunicata sp. nov., a bacterium that produces antifouling agents. Int. J. Syst. Evol. Microbiol. 1998, 48, 1205-1212. (4) Holmstro¨m, C.; Egan, S.; Franks, A.; McCloy, S.; Kjelleberg, S. Antifouling activities expressed by marine surface associated Pseudoalteromonas species. FEMS Microbiol. Ecol. 2002, 41, 4758. (5) Rao, D.; Webb, J. S.; Kjelleberg, S. Competitive interactions in mixed-species biofilms containing the marine bacterium Pseudoalteromonas tunicata. Appl. Environ. Microbiol. 2005, 71 (4), 1729-1736. (6) Egan, S.; James, S.; Holmstro¨m, C.; Kjelleberg, S. Correlation between pigmentation and antifouling compounds produced by Pseudoalteromonas tunicata. Environ. Microbiol. 2002, 4 (8), 433442. (7) Desvaux, M.; Parham, N. J.; Scott-Tucker, A.; Henderson, I. R. The general secretory pathway: a general misnomer? Trends Microbiol. 2004, 12 (7), 306-309. (8) Sandkvist, M. Biology of type II secretion. Mol. Microbiol. 2001, 40 (2), 271-283.

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