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1608 DOI 10.1002/pmic.200701017 Proteomics 2008, 8, 1608–1623 RESEARCH ARTICLE Detailed proteome analysis of growing cells of the planctomycete Rhodopirellula baltica SH1T Cao Xuan Hieu1, Birgit Voigt1, 2, Dirk Albrecht1, Dörte Becher1, Thierry Lombardot3, Frank Oliver Glöckner3, Rudolf Amann3, Michael Hecker1, 2 and Thomas Schweder2, 4 1 Institute for Microbiology, Ernst-Moritz-Arndt-University, Greifswald, Germany Institute of Marine Biotechnology, Greifswald, Germany 3 Microbial Genomics Group, Max Planck Institute for Marine Microbiology, Bremen & Jacobs University Bremen, Bremen, Germany 4 Institute for Pharmacy, Ernst-Moritz-Arndt-University, Greifswald, Germany 2 Rhodopirellula baltica SH1T, which was isolated from the water column of the Kieler Bight, a bay in the southwestern Baltic Sea, is a marine aerobic, heterotrophic representative of the ubiquitous bacterial phylum Planctomycetes. We analyzed the R. baltica proteome by applying different preanalytical protein as well as peptide separation techniques (1-D and 2-DE, HPLC separation) prior to MS. That way, we could identify a total of 1115 nonredundant proteins from the intracellular proteome and from different cell wall protein fractions. With the contribution of 709 novel proteins resulting from this study, the current comprehensive R. baltica proteomic dataset consists of 1267 unique proteins (accounting for 17.3% of the total putative protein-coding ORFs), including 261 proteins with a predicted signal peptide. The identified proteins were functionally categorized using Clusters of Orthologous Groups (COGs), and their potential cellular locations were predicted by bioinformatic tools. A unique protein family that contains several YTV domains and is rich in cysteine and proline was found to be a component of the R. baltica proteinaceous cell wall. Based on this comprehensive proteome analysis a global schema of the major metabolic pathways of growing R. baltica cells was deduced. Received: November 8, 2007 Revised: December 12, 2007 Accepted: December 12, 2007 Keywords: Master gel / Rhodopirellula baltica 1 Introduction Rhodopirellula baltica SH1T (previously described as Pirellula sp. strain 1) is a marine bacterium belonging to the ubiquitous bacterial phylum Planctomycetes [1]. Members of the Planctomycetes are abundant in the marine environment and Correspondence: Professor Thomas Schweder, Institut für Marine Biotechnologie, W.-Rathenau-Straße 49, D-17489 Greifswald, Germany E-mail: [email protected] Fax: 149-03834-864238 Abbreviations: CWP, cell wall protein; EBP, enhancer-binding protein; FAH, forkhead-associated domain © 2008 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim play important roles in the global carbon and nitrogen cycles. This group of bacteria influence the carbon exchange process between the geosphere and the atmosphere and thus the global climate by converting organic material, such as “marine snow”, into carbon dioxide [2, 3]. Marine snow consists of aggregates mainly formed of dead zooplankton, phytoplankton, or protists. The undegraded part of these aggregates slowly sediments from the upper layers of the water column to the seafloor. Because of its role in the marine environment and several unique characteristics such as a proteinaceous cell wall, budding cell division, and the existence of intracellular compartments, R. baltica was selected as the first Planctomycetes member for genome sequencing in the Real Environmental Genomics consortium (http://www.regx.de). The single cirwww.proteomics-journal.com Proteomics 2008, 8, 1608–1623 cular chromosome of R. baltica SH1 consists of more than seven million nucleotides, making it one of the largest marine bacterial genomes sequenced so far with a total of 7325 putative ORFs [4]. The availability of the genome sequence has fuelled the rapid development of functional genomics studies of this bacterium via bioinformatics [5–9] and proteomic approaches [10–12]. Previous proteomic studies of R. baltica SH1 were based on 2-DE master maps of soluble proteins in the pH range of 4–7 which covers only a part of the proteome. In this study, we increased substantially the number of identified proteins in a broader pH range from 3 to 10. The master gel was further expanded by using narrow range pH gradient gels in order to increase resolution and allow better spot identification in overcrowded gel regions. An extensive intracellular proteome mapping of R. baltica could be also achieved by using either a gel-free approach or a combination of 1-D SDS-PAGE and nHPLC separation. Finally, the extracellular and the cell wall proteome were analyzed. Ultimately, we identified 709 novel proteins contributing to a nonredundant proteome dataset of 1267 proteins in total which cover 17.3% of the predicted ORFs in R. baltica SH1. Obviously, such a complex proteome dataset is not only an experimental evaluation of the genome annotation, but provides an important comprehensive resource for further physiological studies [13]. 2 Materials and methods 2.1 Growth conditions and preparation of soluble protein extracts and extracellular protein fractions R. baltica SH1T (DSM 10527) was grown aerobically in defined mineral medium [14] with ammonium chloride (1 mM) as sole nitrogen source and glucose (10 mM) as organic carbon source. Harvesting of cells was performed during the exponential growth phase at an OD600 of 0.3 by centrifugation. The proteins of the supernatant were precipitated for 16 h at 47C with 10% TCA. Precipitated extracellular proteins were washed with ethanol and dissolved in the 8 M urea, 2 M thiourea solution for IEF. The cell pellets resulting from centrifugation were washed twice with 10 mM Tris-HCl, 1 mM EDTA pH 7.5, 1 mM PMSF, and mechanically disrupted using the PlusOne® grinding kit (Amersham Biosciences) as described previously [11]. After removal of cell debris by centrifugation (100 0006g, 1 h, 47C), the protein concentration of the soluble protein fraction was determined with the RotiNanoquant Kit (Roth). 2.2 Preparation of cell wall protein (CWP) fractions Cells harvested during exponential growth were disrupted by two passages through a French press (Minicell) at 6.2 MPa and further sonication at 54 W for 5 min in order to achieve © 2008 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim Microbiology 1609 complete cell disruption. After centrifugation for 10 min at 80006g, the pellets were boiled in 10% SDS for 30 min using the same approach as in previously published chemical studies [15]. In further cell wall studies, R. baltica cell envelopes were also treated with different solutions containing either 10 or 2% SDS and 10 mM DTT, or 10% Triton X100 (after washing the cell envelopes two times with 2% Triton X-100 solution) at room temperature for 30 min. The supernatants containing the detergents were diluted and the proteins precipitated with 10% TCA before gel electrophoresis. All protein pellets were washed several times with TE buffer containing PMSF before adding 26SDS-PAGE sample buffer (0.15 M Tris (pH 6.8), 1.2% SDS, 30% glycerol, 15% b-mercaptoethanol, and bromophenol blue) and separated by 1-D SDS-PAGE. 2.3 Protein gel electrophoresis, gel staining, and image acquisition Gels for 1-D SDS-PAGE comprising 12.5% acrylamide were prepared according to Laemmli [16] with a vertical gel apparatus (BioRad Laboratories or Hoefer Scientific Instruments). 2-D PAGE with 500 mg protein was performed by using commercially available 18 cm IPG strips (Amersham Biosciences) in the pH ranges 4–7, 4.5–5.5, 5.5–6.7, and 3–10 as previously described [17]. As recommended by the manufacturers IPG strips were rehydrated in a protein solution containing 8 M urea, 2 M thiourea, 1% w/v CHAPS, 20 mM DTT, and 0.5% v/v Pharmalyte 3–10 (Amersham Biosciences) for at least 10 h. IEF was done in a Multiphor II unit (Amersham Biosciences). In the second dimension SDS-PAGE gels of 12.5% acrylamide and 2.6% bisacrylamide were used. All gels were stained with colloidal CBB G250 (Sigma). Gel images were acquired with an X–finity ultra scanner (QuatoGraphic) and quantitative analysis of the images was done using the Delta 2D® software version 3.2 (Decodon) based on standard position computing and image fusion functions. 2.4 Analysis of peptides and identification of proteins 2.4.1 Spot excision, trypsin digestion, and MALDITOF-MS Nearly all recognizable protein spots from the 2-D gels and all visible bands from 1-D gels were transferred into microtiter plates. In-gel trypsin digestion of the proteins and spotting of the resulting peptides onto MALDI targets were automatically performed in the Ettan Spot Handling Workstation (Amersham Biosciences). Peptide masses were measured by MALDI-TOF-MS in the 4700 Proteomics Analyzer (Applied Biosystems). The standard methods and parameters used to create the “peak list” from raw data were described previously [18]. The published ORF set of R. baltica (BX119912) was used as the database for peptide mass fingerprint searching with the GPS Explorer™ software version www.proteomics-journal.com 1610 C. X. Hieu et al. 3.5 (Applied Biosystems) using the integrated Mascot® Search Engine version 2.0 (Matrix Science). Under standard thresholds (mass error tolerance: 50 ppm; fixed modifications: Cys-carbamidomethylation; variable modifications: oxidation; one tolerated missed cleavage), proteins with a MOWSE score of at least 51 and sequence coverage of at least 30% were considered as significantly identified (p,0.05). Subsequent MS/MS analysis of the strongest peaks was usually performed as a confirmation. 2.4.2 Nano-HPLC-ESI MS/MS Protein samples of R. baltica SH1 were either directly digested with trypsin or separated on a 1-D SDS-PAGE prior to trypsin digestion. The resulting tryptic peptides were separated by a multidimensional high-pressure LC in a Dionex Ultimate™ system (LC Packings) and either measured in an LTQ FTICR mass spectrometer (Thermo Electron) or in an ESI Qstar Pulsar mass spectrometer (Applied Biosystems) as previously descibed [17]. Only peptides in a mass range from 300 to 1700 Da were fragmented and MS/MS ion spectra were recorded. The R. baltica theoretical protein database was searched by the SEQUEST algorithm version 27.12 (Thermo Electron) with a typical peptide mass tolerance of 6120 ppm and a fragment-ion mass tolerance of 60.01 Da for the appropriate unrestricted-mass species proteins with one missed cleavage of trypsin allowed. Oxidation of methionine was considered as modification. 2.5 Signal peptide predictions Presence and location of signal peptide cleavage sites in each theoretical amino acid sequence from the R. baltica SH1 genome were predicted by using the SignalP 3.0 server (http:// www.cbs.dtu.dk/services/SignalP/). Proteins with a SignalP score.0.75 from both predictions methods, based on neural network and hidden Markov model algorithms, were considered as potentially translocated [19]. 3 Results and discussion 3.1 The intracellular proteome of growing R. baltica cells Recently, several R. baltica SH1T intracellular protein sets were published that consist either of constitutively expressed proteins [11] or of proteins expressed depending on the growth conditions (growth phases [12], different carbon sources [10]). All these studies used the standardized approach with a combination of 2-D pH 4–7 gels and MALDI-TOF-MS to analyze the soluble proteome of this marine bacterium. One of the goals of this study was to provide a more comprehensive proteome dataset of the intracellular soluble R. baltica protein fraction. From more than 1000 different © 2008 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim Proteomics 2008, 8, 1608–1623 protein spots present in the gels covering the pH range of 4– 7, 716 proteins were identified by PMF and MS/MS (Fig. S1 and Table S1 in Supporting Information). In comparison to the previously published R. baltica master gel [11], there were 330 novel proteins (about 40% of the proteins identified in the pH 4–7 region). Some of them are among the most abundant proteins in the gel, including GroES, Grp chaperone, ribosomal proteins RplJ, RplL, RpmC, and the unknown proteins RB4405, RB4438, RB4474, RB6428, RB6430, RB8580, RB10934, RB10956, RB11176, and RB12297. Sixty percent of the 200 most abundant proteins on the master gel (contributing to 64.6% of the total spot volume) were encoded by genes that were predicted to be highly expressed (PHX) according to codon usage adaptation [20]. Most of these abundant proteins, like in other growing bacterial cells, belong to the chaperone and ribosomal protein families (Fig. 1, Table 1). Other functional protein groups being highly abundant in the intracellular proteome are involved in carbohydrate metabolism (particular glycolysis and the pentose phosphate pathway) and transport processes. The results indicate that, although R. baltica is a slowly growing bacterium (doubling time between 10 and 14 h), exponentially growing R. baltica cells express the same protein families to a high extent that were found to be highly expressed under exponential growth conditions in other bacteria [18, 21]. With the introduction of narrow range IPG strips in the first dimension, the depth of the proteome analysis, i.e., the number of proteins that could be resolved, was increased substantially. The separation of proteins in the pH range 4.5– 5.5 (containing the highest density of protein spots) facilitated the identification of 467 different proteins and 103 of them were not previously identified in our master gel. Some spots that are a mixture of different proteins could be separated in this zoom-in pH range, allowing us to gain quantitative information about these proteins (Fig. 2). For example, the protein spot containing GroES and RpsF at the 10 kDa molecular mass region of the pH 4–7 gel was separated into two single protein spots in the pH 4.5–5.5 gels. From other zoom gels (pH range 5–6 and 5.5–6.7) additional 65 novel proteins were identified. Due to the overlapping separation of different proteins in standard pH 4–7 gels, we suggest that the analysis of the intracellular proteome with zoom gels in the pH range 4.5–5.5 would significantly improve the results in subsequent physiological studies of R. baltica. 3.2 Analysis of the intracellular proteome by gel-free LC MS/MS and a combined 1-D SDS-PAGE and LC MS/MS approach We used 1-D SDS-PAGE with subsequent LC for protein/ peptide separation prior to MS in order to further analyze the intracellular proteome of R. baltica. Based on this approach, 465 proteins were identified and 153 (32.9%) of them were not detected in 2-D gels (Fig. 3, and Table S1 in Supporting www.proteomics-journal.com Microbiology Proteomics 2008, 8, 1608–1623 1611 Figure 1. The 200 most abundant proteins in the master 2-D gel of intracellular R. baltica proteins in a pH gradient 4–7 stained with colloidal CBB. R. baltica cells were grown at 247C in minimal medium and harvested in the exponential growth phase (OD600 = 0.3). Information). This result suggests that 1-D electrophoresis combined with nHPLC can be used as an alternative way to identify proteins that cannot be reached by 2-D gel studies. This approach was also used to study cell wall-associated proteins (see Section 3.4). Alternatively we used a gel-free system with multidimensional LC followed by MS. With 329 MS/MS ion spectra recorded, we identified 250 proteins whose individual ion score was above the 95% confidence threshold (p,0.05; Table S1 in Supporting Information). One hundred forty-five (58%) proteins could not be accessed by gel-based approaches (Fig. 3). The gel-free and 1-D-based proteome analyses lead especially to the identification of ribosomal proteins and nucleic acid binding proteins (Hup, PilB_1, UvrA_3, RB6500, and RB6669). Those proteins were probably not detected in the © 2008 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim 2-D gels because they either have an alkaline pI (Hup, PilB_1) or a molecular mass above 200 kDa (UvrA_3, RB6500, RB6669). The combination of HPLC and ESI MS/ MS thereby proved to be a complementary method to discover additional proteins [17, 22]. 3.3 Extracellular proteome In order to test whether R. baltica utilizes nutrients from marine snow particles by means of extracellular enzymes, we analyzed the extracellular proteome by precipitating proteins from the culture medium. However, there was a very low protein amount secreted by R. baltica. Only 13 proteins (ArsA_9, Prc, CypH, RB365, RB1555, RB2647, RB2830, RB3405, RB6789, RB9101, RB10078, RB10581, and RB12461) with a predicted signal peptide were identified www.proteomics-journal.com 1612 C. X. Hieu et al. Proteomics 2008, 8, 1608–1623 Table 1. The 200 most abundant intracellular R. baltica proteins identified in the master gels Accession Protein number name Function pI Mr 5.72 57.1 5.25 6.20 4.69 4.54 5.64 4.72 4.43 5.08 49.3 51.8 36.7 26.6 36.7 41.3 45.9 54.0 1 1 1 1 1 1 1 1 0.19 0.85 0.82 0.28 1.73 0.92 0.83 0.48 4.99 4.56 6.25 4.71 4.88 5.15 6.10 73.4 41.4 39.5 42.2 40.7 39.4 97.0 1 1 1 1 1 0.36 0.42 0.18 0.51 0.23 0.69 0.32 5.16 4.71 101.5 48.5 1 1 0.25 0.32 4.95 4.62 4.74 4.78 4.84 5.13 50.0 96.7 79.8 29.9 44.1 73.9 1 1 0.20 0.23 0.38 0.40 0.34 0.23 4.50 72.0 0.68 4.87 5.62 5.40 5.53 4.79 66.9 50.5 39.8 50.6 51.4 0.25 0.20 0.20 0.88 0.51 4.58 53.1 0.23 1.1.5.2 C1 compound metabolism RB4131 YjjN Zinc-type alcohol dehydrogenase (EC 1.1.1.1) 5.24 37.3 0.16 1.1.5.3 Metabolism of other carbohydrates RB12361 Predicted ribokinase family sugar kinase (EC 2.7.1.-) 4.38 37.4 0.20 1.2 Amino acids and Proteins 1.2.1 Amino acid metabolism 1.2.1.1 Arginine, glutamate and proline metabolism RB5179 CarA Carbamoyl-phosphate synthase, small chain (EC 6.3.5.5) 5.26 42.2 1 Intermediary metabolism 1.1 C-compound and carbohydrate metabolism 1.1.1 Glycolysis and phosphogluconate pathways RB399 Pgi Glucose-6-phosphate isomerase (EC 5.3.1.9) (synonyms: Gpi, Pgi, Phi) RB10591 Pfk PPi-phosphofructokinase (EC 2.7.1.90) RB7572 PfkA 6-Phosphofructokinase, pyrophosphate-dependent (EC 2.7.1.11) RB6690 Fba Fructose-1,6-bisphosphate aldolase (EC 4.1.2.13) RB7095 TpiA Triosephosphate isomerase (EC 5.3.1.1) (TIM) RB2627 GapA Glyceraldehyde 3-phosphate dehydrogenase (EC 1.2.1.12) RB10500 Pgk Phosphoglycerate kinase (EC 2.7.2.3) RB12381 Eno Enolase (EC 4.2.1.11) RB2817 Gnd 6-phosphogluconate dehydrogenase, decarboxylating (EC 1.1.1.44) RB12921 Tkt Transketolase (EC 2.2.1.1) RB3193 Transaldolase (EC 2.2.1.2) RB1220 GalM_1 Probable aldose 1-epimerase (EC 5.1.3.3) RB6537 GalM_2 Aldose 1-epimerase (EC 5.1.3.3) RB10002 Gcd Glucose dehydrogenase Gcd RB8731 KdgK 2-Keto-3-deoxygluconate kinase (EC 2.7.1.45) RB1998 PpdK Pyruvate,phosphate dikinase (EC 2.7.9.1) 1.1.2 Tricarboxylic acid cycle RB3424 Pyruvate dehydrogenase E1 component (EC 1.2.4.1) RB3423 AceF Pyruvate dehydrogenase, E2 component, dihydrolipoamide acetyltransferase (EC 2.3.1.12) RB1231 Lpd Dihydrolipoamide dehydrogenase (EC 1.8.1.4) RB2114 CitB Aconitate hydratase (EC 4.2.1.3) RB1593 Icd Isocitrate dehydrogenase (EC 1.1.1.42) RB10619 SucD Putative succinyl-CoA synthetase alpha subunit (EC 6.2.1.5) RB10617 SucC Succinyl-CoA synthetase (beta subunit) (EC 6.2.1.5) RB10554 SdhA Succinate dehydrogenase subunit A (EC 1.3.99.1) 1.1.3 Glycogen synthesis and degradation RB548 GlgB_1 1,4-Alpha-glucan branching enzyme (EC 2.4.1.18) 1.1.4 Sugar and polysaccharide Degradation RB6061 Pmm Phosphomannomutase (EC 5.4.2.8) RB9832 Phosphomannomutase (Pmm) RB2518 GDP-mannose 4,6 dehydratase (EC 4.2.1.47) RB2658 XylA Xylose isomerase (EC 5.3.1.5) RB10127 Protein up-regulated by thyroid hormone-putative PQQ-dependent glucose dehydrogenase (EC 1.1.99.17) 1.1.5 Other related pathways 1.1.5.1 Amine and polyamine degradation RB6977 UDP-N-acetylhexosamine pyrophosphorylase (EC 2.7.7.-) (EC 2.7.7.23) © 2008 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim SP score PHX % Vol. 0.16 0.99 1 1 1 1 1 0.96 1 0.33 www.proteomics-journal.com Proteomics 2008, 8, 1608–1623 Microbiology 1613 SP score PHX % Vol. 1 0.22 Table 1. Continued Accession Protein number name Function pI Mr RB12113 CarB 4.60 118.1 RB6930 RB10968 RB7247 RB5653 GdhA GabD GlnII GltD Carbamoyl-phosphate synthase (glutamine-hydrolyzing) large chain (EC 6.3.5.5) Glutamate dehydrogenase A (EC 1.4.1.3) Succinate-semialdehyde dehydrogenase (EC 1.2.1.16) Glutamine synthetase (EC 6.3.1.2) (glutamate-ammonia ligase) NADH-glutamate synthase small chain (EC 1.4.1.13) 6.37 4.38 4.81 4.93 45.7 50.2 40.4 55.0 1 1 0.18 0.50 0.80 0.19 1.2.1.2 Phenylalanine, tyrosine and tryptophan metabolism RB6821 AspC_1 Aspartate aminotransferase (EC 2.6.1.1) 4.88 46.6 1 0.20 1.2.1.3 Lysine and methionine metabolism RB9188 LysA_2 Diaminopimelate decarboxylase (EC 4.1.1.20) RB8221 O-acetylhomoserine sulfhydrylase (EC 4.2.99.10) RB5444 MetK S-adenosylmethionine synthetase (EC 2.5.1.6) (MAT) 4.71 6.32 4.76 47.2 47.7 42.4 1 1 0.40 0.44 0.43 1.2.1.4 Serine, glycine and threonine metabolism RB6248 SerA_1 Phosphoglycerate dehydrogenase (EC 1.1.1.95) RB6246 SerC Phosphoserine aminotransferase (EC 2.6.1.52) (PSAT) RB6215 GlyA Serine hydroxymethyltransferase (EC 2.1.2.1) (SHMT) RB6932 CysK Cysteine synthase (O-acetylserine sulfhydrylase) (EC 4.2.99.8) 5.09 5.24 5.98 5.02 57.7 41.8 45.4 33.7 1 1 1 0.23 0.23 0.20 0.18 1.2.1.5 Valine, leucine and isoleucine metabolism RB9869 IlvC Ketol-acid reductoisomerase (EC 1.1.1.86) RB8126 IlvE_2 Putative branched-chain amino acid aminotransferase (EC 2.6.1.42) RB9871 IlvN Probable acetolactate synthase small subunit (EC 4.1.3.18) RB12905 Acetolactate synthase III (EC 4.1.3.18) 4.74 4.74 36.5 31.0 1 1 0.97 0.25 5.05 6.04 21.3 67.0 1 1 0.27 0.39 1.2.2 Protein/peptide processing 1.2.2.1 Proteases/peptidases RB12148 Prc Periplasmic tail-specific proteinase (EC 3.4.21.-) RB1359 Probable serine protease do-like DEGP (EC 3.4.21.-) RB4394 Proteinase I RB7590 Probable proteinase (EC 3.4.-.-) RB9029 Probable zinc metalloproteinase (EC 3.4.24.71) RB9674 Putative Xaa-Pro dipeptidyl-peptidase (EC 3.4.14.11) (X-PDAP) 4.62 6.54 4.41 4.72 4.89 5.02 79.2 66.7 20.6 110.9 85.7 70.0 1 1.00 0.34 0.50 0.31 0.35 0.42 0.17 1.2.2.2 Protein modification RB8649 CypH Peptidyl-prolyl cis-trans isomerase (EC 5.2.1.8) RB10129 Mip_2 Macrophage infectivity potentiator (EC 5.2.1.8) 7.84 4.89 21.7 26.2 1.00 1.00 5.64 4.63 1.3 Nucleotides 1.3.1 Purine metabolism RB8343 PurB Adenylosuccinate lyase (EC 4.3.2.2) RB10113 PurH Bifunctional purine biosynthesis protein purH (EC 2.1.2.3) (EC 3.5.4.10) RB5395 PurL Phosphoribosylformylglycinamidine synthase II (EC 6.3.5.3) RB8374 GuaA_1 GMP synthase [glutamine-hydrolyzing] (EC 6.3.5.2) (glutamine amidotransferase) (GMP synthetase) RB11832 Ndk Nucleoside diphosphate kinase (NDK) (EC 2.7.4.6) 0.45 0.60 54.0 56.2 1 1 0.30 0.18 4.38 4.94 108.2 64.6 1 1 0.19 0.19 1 0.26 4.70 17.1 6.00 61.9 1.4 Lipids, fatty acids and isoprenoids RB8550 AccC Biotin carboxylase (EC 6.3.4.14) RB320 FabF_1 3-oxoacyl-[acyl-carrier-protein] synthase (EC 2.3.1.41) 5.34 5.14 49.3 45.1 5.86 55.2 © 2008 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim 1 1 1 1.3.2 Pyrimidine metabolism RB12061 PyrG CTP synthase (EC 6.3.4.2) 1.5 Vitamins, cofactors and prosthetic groups RB7020 HemD Uroporphyrinogen III synthase/methyltransferase (EC 2.1.1.107) (EC 4.2.1.75) 1.00 0.28 1 0.21 0.23 0.19 www.proteomics-journal.com 1614 C. X. Hieu et al. Proteomics 2008, 8, 1608–1623 Table 1. Continued Accession Protein number name 1.6 Sulfatases RB9498 ArsA_8 RB11502 1.7 Others RB4404 RB11008 Function pI Mr SP score PHX % Vol. Arylsulfatase (EC 3.1.6.1) Alkyl sulfatase or beta-lactamase (EC 3.1.6.-) 6.42 6.38 58.4 48.6 1.00 1.00 1 1.06 0.27 Hypothetical oxidoreductase yhxD-putative dehydrogenase of the short-chain dehydrogenase family (EC 1.-.-.-) Similar to cycloartenol synthase 4.85 32.8 5.17 42.9 4.74 4.84 5.32 54.2 52.2 51.0 4.28 33.0 4.64 4.57 4.11 4.50 4.29 4.15 35.1 35.5 11.6 49.6 54.2 44.6 5.16 4.92 5.08 8.30 4.98 4.29 5.00 4.57 4.60 6.86 4.39 95.8 25.1 21.8 38.3 34.9 17.8 30.1 22.1 18.6 24.9 18.0 1 1 1 1 1 0.33 0.38 0.49 0.26 0.20 0.17 1.10 0.64 0.75 0.97 0.91 2 Cellular processes 2.1 Electron transport chain and ATP synthases RB10215 AtpA_2 Protein ATP synthase alpha chain (EC 3.6.3.14) RB10217 AtpB_3 H1-transporting ATP synthase beta chain (EC 3.6.1.34) RB1831 NqrA Sodium-translocating NADH:ubiquinone oxidoreductase subunit nqrA (EC 1.6.5.-) RB1833 NqrC Sodium-translocating NADH dehydrogenase (ubiquinone) subunit nqrC (EC 1.6.5.-) RB4399 Quinone oxidoreductase (EC 1.6.5.5) RB11985 Quinone oxidoreductase (EC 1.6.5.5) RB12160 TrxA_2 Thioredoxin 1 RB6384 Probable thioredoxin related protein RB12541 Probable thioredoxin RB9386 FixW Probable FixW protein 2.2 Stress/detoxification response RB6751 ClpC Negative regulator of genetic competence ClpC/MecB RB10826 ClpP_1 ATP-dependent clp protease proteolytic subunit (EC 3.4.21.92) RB10829 ClpP_2 ATP-dependent clp protease proteolytic subunit (EC 3.4.21.92) RB11179 UspA_1 Hypothetical protein uspA RB11183 UspA_2 Hypothetical protein uspA RB4433 Probable DNA protection during starvation protein RB10727 Manganese-containing catalase RB8238 PrdX2 Peroxiredoxin 2 RB4586 Tpx Probable thiol peroxidase (EC 1.11.1.-) RB6688 SodA Superoxide dismutase, Mn family (EC 1.15.1.1) RB4438 Probable general stress protein 26 0.54 1.00 1.00 0.30 1 1 0.51 0.52 0.21 1 0.19 1 0.95 1.00 1.00 1 1 0.28 0.29 0.29 0.16 0.22 0.27 2.3 Transport/binding proteins and lipoproteins 2.3.1 ABC transporter systems RB8584 FruA PTS system, fructose-specific enzyme II, BC component (EC 2.7.1.69) RB1248 ABC-type multidrug transport system, ATPase component RB6236 Putative ABC transporter ATP-binding protein RB8375 ABC transporter RB9998 Probable ABC-type transporter ATP-binding protein 4.46 17.4 1 0.50 6.06 4.79 5.06 6.32 29.0 37.0 62.7 30.6 1 1 1 0.44 0.60 0.17 0.39 2.3.2 Others RB8565 IolI 4.36 33.6 1 0.70 2.4 Cell wall and outer membrane binding proteins RB6428 Hypothetical fasciclin protein 6.97 17.3 1 1.17 3 Genetic information processing 3.1 DNA replication RB10108 DnaN DNA polymerase III, beta chain (EC 2.7.7.7) RB4654 Hypothetical nuclease protein 4.80 4.49 48.6 37.7 1 0.17 0.77 3.2 RNA biosynthesis 3.2.1 Sigma factors RB8580 Probable sigma-54 modulation protein 6.15 12.7 1 0.80 Similar to IolI protein © 2008 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim 1.00 www.proteomics-journal.com Proteomics 2008, 8, 1608–1623 Microbiology 1615 SP score PHX % Vol. 1 0.27 0.52 Table 1. Continued Accession Protein number name Function pI Mr 3.2.2 Regulation and signal transduction 3.2.2.1 Sigma 54-dependent enhancer binding proteins RB4487 NtrC_1 Nitrogen assimilation regulatory protein RB2743 Nitrate/nitrite regulatory protein narP 4.58 6.13 56.2 23.0 3.2.2.2 MoxR-related transcriptional regulatory proteins RB1798 MoxR_2 Methanol dehydrogenase regulator (MoxR) homolog 4.57 40.6 0.21 3.2.3 RNA polymerases RB1964 RpoA_1 Probable DNA-directed RNA polymerase alpha chain (EC 2.7.7.6) RB12626 RpoA_3 DNA-directed RNA polymerase alpha chain (EC 2.7.7.6) RB5414 RpoB DNA-directed RNA polymerase beta chain (EC 2.7.7.6) 4.67 55.6 1 0.33 4.91 5.02 36.8 140.3 1 1 0.79 0.29 3.2.4 Transcription, elongation and termination RB10458 Grp RNA-binding protein RB5507 NusA Transcription terminator protein A RB7898 NusG Transcription antiterminator 10.32 4.00 4.08 21.1 54.0 25.1 1 1 1 0.82 0.17 0.33 3.2.5 RNA modification RB5804 Pnp_2 Polyribonucleotide nucleotidyltransferase (EC 2.7.7.8) 5.38 88.2 1 0.88 3.3 Protein synthesis 3.3.1 Translation elongation factors RB12577 Efp_2 Translation elongation factor EF-P RB5434 FusA_1 Elongation factor G (EF-G) RB10640 Tsf Elongation factor Ts (EF-Ts) RB7894 TufA Translational elongation factor-Tu RB1270 Putative translation initiation inhibitor RB6395 Probable PKR inhibitor (translation regulation) 5.75 4.69 4.60 5.34 4.96 4.57 28.4 77.8 35.8 43.2 16.5 85.7 1 1 1 1 0.18 0.56 0.59 1.80 0.21 0.18 3.3.2 Translation termination RB3886 Frr Ribosome recycling factor (ribosome releasing factor) (RRF) 4.65 22.2 3.3.3 Ribosomal proteins 3.3.3.1 Large subunit RB9916 RplI Ribosomal protein L9 RB12840 RplJ Probable 50S ribosomal protein L10 RB12842 RplL Probable 50S ribosomal protein L7/L12 RB9923 RplY Probable 50S ribosomal protein L25 RB7846 RpmC Probable 50S ribosomal protein L29 6.87 4.92 4.29 4.44 10.48 19.4 18.8 14.5 22.8 8.3 1 1 1 1 1 0.51 0.63 1.91 0.68 0.52 3.3.3.2 Small subunit RB2543 RpsA_2 30S ribosomal protein S1 RB9920 RpsF Probable ribosomal protein S6 RB12824 RpsP 30S ribosomal protein S16 4.41 4.27 5.35 76.5 15.5 15.9 1 1.08 0.49 0.45 3.3.4 Protein folding and targeting RB9103 ClpB Chaperone clpB RB5681 Tig Trigger factor (TF) RB9105 DnaK_4 Chaperone protein dnaK (heat shock 70 kDa protein) (HSP70) RB8966 GroEL_1 60 kDa chaperonin RB8970 GroEL_2 60 kDa chaperonin RB10629 GroEL_3 60 kDa chaperonin RB8969 GroES_2 10 kDa chaperonin RB10627 GroES_3 10 kDa chaperonin 4.83 4.44 4.56 5.29 4.80 4.87 4.87 3.99 99.5 66.6 76.1 69.8 57.5 57.5 10.6 10.0 3.3.5 Aminoacyl-tRNA sythetases RB8253 AspS Aspartyl-tRNA synthetase (EC 6.1.1.12) RB7600 GltX Glutamyl-tRNA synthetase (EC 6.1.1.17) RB10547 GlyS Glycyl-tRNA synthetase (EC 6.1.1.14) 4.85 5.09 5.87 66.2 60.2 63.8 © 2008 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim 0.69 1 1 1 1 1 1 1 0.35 0.64 1.12 1.43 1.19 1.70 1.08 0.79 0.19 0.21 0.30 www.proteomics-journal.com 1616 C. X. Hieu et al. Proteomics 2008, 8, 1608–1623 Table 1. Continued Accession Protein number name Function pI Mr SP score PHX % Vol. 4 Conserved hypothetical proteins 4.1 Oxidoreductases RB1555 Probable NADH-dependent dehydrogenase (EC 1.-.-.-) RB1939 Probable oxidoreductase (EC 1.-.-.-) RB5140 Probable NADH-dependent dehydrogenase RB8799 Probable NADH-dependent dyhydrogenase RB2656 Probable oxidoreductase RB12038 Hypothetical protein (EC 1.-.-.-) 6.13 6.35 5.72 6.41 4.34 5.55 52.2 55.1 67.6 51.8 37.8 42.3 1.00 1 1 1 0.79 0.78 0.43 0.35 0.27 0.26 4.2 Hydrolases RB3405 Putative hydrolase (EC 3.2.1.23) 4.96 86.1 1.00 4.3 Others RB4405 RB4474 RB10934 RB12301 RB4347 RB12297 RB569 RB12844 RB6430 RB9101 RB2647 RB8246 RB11176 RB9261 RB4129 RB2901 RB2640 RB5657 RB11728 RB9218 RB10061 RB12035 RB10078 RB85 RB10028 RB6610 RB11010 RB3070 RB6386 RB9132 RB520 RB9849 RB5608 Probable DNA-binding protein Hypothetical UPF0337 protein RB4474 Hypothetical UPF0337 protein RB10934 Conserved hypothetical protein Conserved hypothetical protein Conserved hypothetical protein Conserved hypothetical protein Conserved hypothetical protein Conserved hypothetical protein Conserved hypothetical protein Conserved hypothetical protein Probable transmembrane protein Conserved hypothetical protein Conserved hypothetical protein Conserved hypothetical protein Conserved hypothetical protein Hypothetical protein signal peptide Conserved hypothetical protein Hypothetical protein (EC 5.3.1.-) Conserved hypothetical protein Conserved hypothetical protein Conserved hypothetical protein Conserved hypothetical protein Conserved hypothetical protein Conserved hypothetical protein Conserved hypothetical protein Conserved hypothetical protein Conserved hypothetical protein Conserved hypothetical protein Conserved hypothetical protein Conserved hypothetical protein Conserved hypothetical protein Putative large multi-functional protein 4.28 8.22 4.32 4.36 4.73 4.41 6.63 6.08 5.30 6.96 4.92 6.43 4.76 5.03 4.62 4.77 4.62 4.96 4.55 4.73 4.34 4.55 4.73 5.06 4.66 5.47 4.88 5.90 5.95 5.78 4.84 8.69 4.78 13.7 18.5 7.9 20.5 17.5 17.9 29.8 37.8 32.3 20.3 27.9 29.6 17.2 45.9 46.5 42.8 38.1 14.6 32.4 25.7 40.2 38.7 76.9 45.7 39.7 137.8 30.9 55.9 42.0 46.5 41.3 31.5 50.1 1.00 6.94 4.29 4.72 5.19 4.35 4.79 6.53 4.77 4.27 25.1 61.3 27.1 49.4 36.5 13.0 52.5 25.7 47.7 5 Hypothetical proteins RB11107 Hypothetical protein RB12461 Hypothetical protein RB3689 Hypothetical protein RB1535 Hypothetical protein RB1260 Hypothetical protein RB10956 Hypothetical protein RB9527 Hypothetical protein RB2125 Hypothetical protein RB512 Hypothetical protein © 2008 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim 0.99 1 0.72 1 1.00 1 1 0.87 1 1.00 1.00 1.00 0.99 1 1 1 1 1.00 0.99 1.00 1 1 1.00 1.00 1.00 0.99 1 1 1 1 1.00 1.00 1.00 1.00 1 1 1 1 1 1.00 1.00 1.00 1.00 1 0.52 0.64 0.62 2.19 1.91 1.85 1.30 0.90 0.90 0.87 0.80 0.63 0.61 0.61 0.51 0.36 0.33 0.33 0.30 0.28 0.25 0.25 0.23 0.21 0.20 0.19 0.18 0.18 0.18 0.18 0.17 0.17 0.16 1.29 0.76 0.50 0.41 0.36 0.33 0.31 0.31 0.29 www.proteomics-journal.com Proteomics 2008, 8, 1608–1623 Microbiology 1617 SP score % Vol. Table 1. Continued Accession Protein number name Function pI RB7203 RB7235 RB12968 RB9030 RB1002 RB6127 RB4480 Hypothetical protein Hypothetical protein Hypothetical protein Hypothetical protein Hypothetical protein Hypothetical protein Hypothetical protein 4.63 4.72 4.65 3.95 5.25 4.37 6.51 Mr 16.8 29.4 74.6 91.5 77.5 156.6 9.4 PHX 1 0.99 1.00 1 1 0.86 1.00 0.24 0.24 0.22 0.21 0.20 0.20 0.16 SP score, signal peptide scores derived from the SignalP 3.0 server. PHX, the predicted highly expression level based on codon usage optimization described by Karlin et al. [20]. % Vol., the relative portion (given as % volume) of individual protein spots of the total protein present on the gel as quantified by the Delta 2D software. Figure 2. Sections of 2-D gels of intracellular R. baltica proteins demonstrating the efficiency of the separation between master gel pH 4–7 and narrow range pH 4.5– 5.5. Newly identified proteins are underlined. Both gels were stained with colloidal CBB. 3.4 Cell wall proteome Figure 3. Numbers of intracellular proteins identified by the three distinct proteomics approaches. Proteins/peptides were separated (1) by 1-D SDS-PAGE combined with nHPLC and followed by ESI MS/MS, (2) by 2-DE prior to MALDI-TOF/TOF, (3) by nHPLC coupled with ESI MS/MS. from 2-D gels or in a gel-free analysis of this protein fraction (Table S1 in Supporting Information). We therefore suggest that R. baltica cells do not secret degradative enzymes in considerable amounts into the surrounding environment but rather attach such enzymes onto their cell surface or secret them into the peripheral ribosome-free paryphoplasm [23]. © 2008 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim Cell wall studies on budding bacteria of the Planctomycetes group began two decades ago with the finding that these bacteria lack an ordinary Gram-negative type of murein, and instead, carry a stable protein envelope [15]. The amino acid composition of the CWPs revealed a higher relative portion of proline and cysteine in these proteins than in the overall theoretical proteome. However, protein profiles of such a cell wall with a presumably high degree of crosslinking disulfide bonds are still an open question. In cell wall preparations that were treated with boiling 10% SDS, we identified in total 148 proteins (including 15 signal peptide containing proteins). A buffer containing 10 mM DTT, even with a lower SDS content (2%), could extract nearly as many proteins as the method using boiling 10% SDS solution. Ten of the identified proteins (GroEL_1, IlvC, TufA, RpsF, RB850, RB7455, RB1753, RB2830, RB4347, and RB8246) were found in both methods. In all cell wall preparations a considerable number of contaminating cytoplasmic proteins were found. Interestingly, some proteins (RB850, RB7455, and RB2247) that were specifically identified in cell wall fractions www.proteomics-journal.com 1618 C. X. Hieu et al. are rich in cysteine and proline, fitting to the results of former analyses of the amino acid composition of the proteinaceous cell wall [15]. They belong to a novel YTV protein family that consists of five members and is uniquely found in the R. baltica genome [5]. YTV proteins contain several protein domains with a typical signature of repeated tyrosine, threonine, and valine residues. The three YTV proteins found in our cell wall preparations have more than three repeated YTV domains and a larger molecular mass than the other YTV family members (Fig. 4). Disulfide bonds may be formed between the cysteine residues of these YTV proteins in the R. baltica cell wall. We suggest that these proteins are important structural components of the R. baltica cell wall. This assumption is supported by the fact that the three identified YTV proteins are PHX proteins (Table S1 in Supporting Information). A more rigid cell wall fractionation method using the nonionic detergent Triton X-100 resulted in an improvement in protein extraction and identification of a total of 30 proteins from the R. baltica CWP fraction (Table S1 in Supporting Information). In this cell wall preparation, a protein, RB10948, was identified that is predicted to be a membraneassociated protein with low similarity (12%) to cytoskeletal keratin type II belonging to the eukaryotic intermediate filament family proteins. Altogether, we could identify in different approaches 93 cell wall-associated proteins (Table S1 in Supporting Information), 39 of these proteins contain signal peptides and/or transmembrane helices (Table 2). Further studies of the localization of these proteins could bring new insights into the structure of the cell wall of Planctomycetes. 3.5 Overall proteome dataset In summary, we have used three distinct, preanalytical separation techniques (1-D and 2-DE and HPLC separation) prior to MS analysis of R. baltica SH1T proteins and have identified a total of 1115 nonredundant proteins (Fig. 3, Proteomics 2008, 8, 1608–1623 Table S1 in Supporting Information). Seven hundred nine of these proteins are newly identified in comparison to previous publications [10–12, 24]. So far, with the contribution of our study, 1267 of the 7325 putative protein-coding ORFs (accounting for 17.3%) were shown to be expressed at the protein level under the growth conditions used. In this extensive study, 190 proteins with so far unknown functions were identified that are probably unique for Planctomycetes because no orthologs of the corresponding genes were found in other bacteria. Two hundred sixty-one of the identified proteins (20.6%) contain a predicted signal peptide and are supposedly cell wall or membrane-associated or secreted into the paryphoplasm. The proteins identified in growing R. baltica cells fulfill a variety of cellular functions ranging from replication, transcription and translation, glycolysis and pentose phosphate pathway, tricarboxylic acid cycle to biosynthetic pathways of nucleotides, amino acids, and fatty acids. This allows a more detailed depiction of fundamental physiological pathways of growing R. baltica cells. The data indicate that this representative of the Planctomycetes uses mainly the same metabolic network as other aerobic heterotrophic bacteria (Fig. 5). Since the most abundant protein spots on 2-D gels are enzymes of the glycolysis and the pentose phosphate cycle, these metabolic pathways are likely to be the central routes for carbon metabolism of R. baltica cells living in mineral medium with glucose as a sole carbon source. This result is in accordance with bioinformatic predictions and previous proteome studies of R. baltica cells growing with different carbon sources [12]. Surprisingly, although there was only glucose present as a carbon source in the medium, enzymes for the degradation of other carbohydrates, such as xylose or Nacetylglucosamine, were found to be expressed in R. baltica. Those carbohydrates seem to be preferred carbon and energy sources of this bacterium in its natural environment and therefore favor a constitutive expression of these enzymes. Moreover, we could also elucidate some specific metabolic features of R. baltica. We identified two abundant phosphofructokinases (EC 2.7.1.90, PfkA RB7572, Pfk RB10591) which utilize pyrophosphate rather than ATP to convert fructose-6-phosphate to fructose-1,6-bisphosphate (Figs. 1, 5B). This result confirms previous studies revealing a phosphofructokinase activity with pyrophosphate as phosphoryl donor in growing R. baltica cells [12]. In terms of their sequence similarity, the two proteins are not paralogous (only 36% similarity). The protein PfkA is closely related to eukaryotic phosphofructokinases, Pfk on the other hand shows higher similarities to bacterial phosphofructokinases. 3.5.1 Chaperone proteins Figure 4. The novel and unique protein family with YTV domains (Pfam accession number PF07639). YTV domains contain repeated tyrosine, threonine, and valine residues. Regions in the proteins with less relative similarity to the YTV domain signature are called YTV context. The YTV proteins are predicted to be important components of the proteinaceous R. baltica cell wall. © 2008 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim Chaperones, a protein family which promotes the folding of proteins and proper assembly of unfolded polypeptides generated under stress conditions, typically are among the most abundant cytoplasmic proteins. In the genome sequence of R. baltica, the chaperone genes are duplicated and organized www.proteomics-journal.com Microbiology Proteomics 2008, 8, 1608–1623 1619 Table 2. List of the proteins containing either a signal peptide or transmembrane helices which were identified in cell wall protein fractions Accession number RB85 RB569 RB812 RB850 RB1002 RB1555 RB1566 RB1656 RB2016 RB2247 RB2586 RB2647 RB2830 RB3462 RB3639 RB4255 RB4256 RB4613 RB6127 RB6668 RB7455 RB7590 RB7627 RB7762 RB8018 RB8238 RB8246 RB8728 RB9034 RB9498 RB9566 RB10458 RB10581 RB10948 RB11154 RB11762 RB12148 RB12451 RB12707 Protein name ComQ EutM PrdX2 ArsA_9 Grp StrI Prc Function SP score TM Conserved hypothetical protein Conserved hypothetical protein Hypothetical protein Hypothetical protein containing YTV domains Hypothetical protein Probable NADH-dependent dehydrogenase Similar to general secretory pathway protein D Conserved hypothetical protein Hypothetical protein Hypothetical protein containing YTV domains Probable ethanolamine utilization protein, EutM Conserved hpothetical protein Hypothetical protein Hypothetical protein Conserved hypothetical protein Hypothetical protein Conserved hypothetical protein Conserved hypothetical protein Hypothetical protein Conserved hypothetical protein Hypothetical protein containing YTV domains Probable proteinase (EC 3.4.-.-) Conserved hypothetical protein Probable xanthan lyase Conserved hypothetical protein Peroxiredoxin 2 Probable transmembrane protein Putative oxidoreductase Conserved hypothetical protein Arylsulfatase (EC 3.1.6.1) Hypothetical protein RNA-binding protein Conserved hypothetical protein Conserved hypothetical protein Probable RND efflux membrane fusion protein Probable streptomycin biosynthesis protein, strI Periplasmic tail-specific proteinase (EC 3.4.21.-) Conserved hypothetical protein Conserved hypothetical protein 1.00 0.87 1 2 1 1 1 1 2 2 1 1 1 1 1 1 0.86 1.00 0.80 1.00 1.00 1.00 1.00 1.00 1.00 1 1 1 1.00 0.95 0.99 0.91 1.00 0.86 0.96 0.94 1.00 1.00 1 2 1 1 1 1 1 1 2 1 1 1 1 1 1 1 1 1 SP score, signal peptide scores derived from the SignalP 3.0 server. TM, number of the predicted transmembrane helices. in three operons (groS (RB281), groEL1 (RB8966) – groES1 (RB8969) – groEL2 (RB8970), and groES2 (RB10627) – groEL3 (RB10629)). Such a duplication of chaperone encoding genes was observed only in few bacteria species such as some aproteobacteria (Bradyrhizobium japonicum, Rhodopseudomonas palustris, Rhizobium meliloti, and Rhizobium loti) and several Vibrio species (Vibrio cholerae, V. parahaemolyticus, and V. vulnificus) [25]. Except for GroS (RB281), which shows only a low sequence similarity with the other GroES proteins, all chaperones were identified in the master gel (Fig. 1). © 2008 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim Interestingly, one of the GroESL operons in the R. baltica genome consists of one 10 kDa chaperone gene (groES1) and two flanking 60 kDa chaperone genes (groEL1, groEL2). Such an operon structure has not been observed in other bacteria. 3.5.2 Transporter proteins Belonging to a phylogenetic distinct bacteria branch, R. baltica and other members of the Planctomycetes contain an organized intracellular compartment, termed pirellulosome www.proteomics-journal.com 1620 C. X. Hieu et al. Proteomics 2008, 8, 1608–1623 A [23]. Although the membrane and intracellular transport systems of the Planctomycetes had been expected to be highly developed, Glockner et al. [4] found that the predicted number of ABC transport-related genes was only one-third of that of the free-living bacterium Streptomyces coelicolor A3. We identified altogether 39 putative transporter/binding proteins belonging to 29 transporter groups (amounting to 17.8% of the predicted transporter proteins in TransportDB [26], http://www.membranetransport.org/). Among them, there are 17 ABC transporters for oligopeptides, carbohydrates, or inorganic nutrients according to the Megx database [27] (http://www.megx.net). Furthermore, we could identify a secondary sodium-glucose cotransporter (RB6538) and an ammonium transporter (RB297). These proteins may play an important role in the transport of nutrients into the cell when R. baltica cells grow in the mineral medium containing glucose and ammonium as carbon and nitrogen sources. 3.5.3 Sigma factors and transcriptional regulators In order to adapt to changing environmental conditions, R. baltica cells possess a complex regulatory system with 49 putative sigma factors and 66 two-component system proteins [8]. In growing R. baltica cells, we identified a sole repre© 2008 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim sentative of the s54 sigma factor (RB6491) and five of the overall 16 s54-dependent transcriptional activators (RB2731, RB4487, RB8745, RB10323, and RB12108) known as enhancer-binding proteins (EBPs). An in silico study of Studholme and Dixon [9] illustrated that all above mentioned EBPs contain a helix-turn-helix DNA binding domain in their C-terminal region. Two-component system response regulator domains were found in the N-terminal regions of the RB2731, RB4487, RB10323, and RB12108 proteins. However, although the EBP genes RB2731 and RB10323 possibly form operons with the histidine kinase genes RB2728 and RB10322, we could not identify these two kinase proteins in our proteome studies. Interestingly, one EBP (RB8745) identified on our master gel contained both a GAF domain, an ubiquitous domain mediating protein dimerization and a “forkhead-associated” domain (FHA) in its N-terminal amino acid sequence. Because FHA domains recognize phosphothreonine residues, this finding suggests a regulatory link between the s54 regulon and the abundant Ser/Thr protein kinases [9]. A protein with the same domain pattern in the d-proteobacterium Myxococcus xanthus is known to mediate phosphorylationdependent protein–protein interactions in cell shape, type III secretion, and sporulation processes [28, 29]. www.proteomics-journal.com Proteomics 2008, 8, 1608–1623 Microbiology 1621 B Figure 5. Depiction of metabolic pathways of R. baltica. The pathways were depicted from the overall proteome dataset (combining our data and data from ref. [11]). Gray bars show enzymes not yet annotated. Dotted lines are enzymes not yet identified in the proteome studies. The 200 most abundant enzymes in the master gels (given in Fig. 1 and Table 1) are underlined. (A) Amino acid, nucleotide, and cofactor metabolism pathways. (B) Carbohydrate and fatty acid metabolism pathways. On our master gels four (about 23%) of the predicted s70 factors containing both typical regions, region 2 (core RNA polymerase binding motif) and region 4 (DNA-binding motif), were identified [9]. These results suggest that R. baltica cells may recruit alternative s70 factors as a means of switching on specific regulons. 3.5.4 Sulfatases In the R. baltica SH1T published genome annotation, there are surprisingly 110 theoretical ORFs showing significant homology to either prokaryotic or eukaryotic enzymes that hydrolyze various sulfate esters (Pfam Ac: PF00884) [4]. A highly enantioselective sulfatase activity of whole resting R. baltica cells was revealed with strict retention of configuration [30]. Such sulfatases might become biotechnologically important for industrial production when they are used in combination with a stereocomplementary inverting enzyme in a deracemization strategy [31]. © 2008 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim Sixteen sulfatases were identified by our proteome approach including four N-acetyl-galactosamine 6-sulfatases (GALNS, EC. 3.1.6.4: RB3309, RB3403, RB4851, RB7560), Nacetylglucosamine-6-sulfatase (EC. 3.1.6.14: RB973), two aryl sulfatases (ArsA, EC. 3.1.6.1: RB3877, RB9498), two probable alkyl sulfatases (RB7598, RB11502), two iduronate-2-sulfatases (EC. 3.1.6.13: RB2254, RB3755), heparan N-sulfatase (RB2732) and four putative aryl sulfatases (EC. 3.1.6.8: RB2367, RB5378, RB6873, RB7995). The identification of five sulfatases that are involved in the cleavage of sulfated high molecular weight glycoproteins (N-acetyl-galactosamine and N-acetylglucosamine) supports the hypothesis that R. baltica can use mucin as a source of nitrogen and organic carbon [12]. Especially marine invertebrates produce a large variety of mucus secretions which are rich in such glycoproteins [32]. The N-acetylglucosamine-6-sulfatase (RB973) gene is located next to a gene that encodes an N-acetylglucosamine6-phosphate deacetylase (nagA, RB977), both genes probably form an operon. The quite high expression of RB9498 (aryl www.proteomics-journal.com 1622 C. X. Hieu et al. sulfatase) and RB11502 (alkyl sulfatase) under exponential growth conditions indicates a crucial role of these sulfatases in the mobilization of sulfated carbon and energy sources and so in the metabolism of R. baltica cells in its natural marine environment [30]. 4 Concluding remarks With 709 novel proteins from different proteomics approaches in this study, we could enlarge the current R. baltica protein catalog to 1267 identified entries. The identified proteins comprise 17.3% of the theoretical proteome of R. baltica. From 668 functionally assigned proteins, house-keeping metabolic routes of carbohydrate, amino acid, nucleic acid, fatty acid metabolism pathways could be depicted (Fig. 5). The comprehensive analysis of the intracellular proteome will promote further studies of the R. baltica physiology in its marine environment. Beside this, the proteome analyses of this study led to the identification of 190 hypothetical proteins, which are predicted to be unique for Planctomycetes. These results do not only support the information from the genome annotation but also suggest that these proteins may be involved in cellular processes or metabolic pathways that are characteristic for planctomycetes. Cellular location and function of these proteins could be interpreted both by bioinformatics analysis and experimental data by analyzing different subproteomes (as shown in Table S1 in Supporting Information). Furthermore, this is the first proteome study of the special, very rigid proteinaceous cell wall of R. baltica. The identification of a special protein family containing YTV domains as important constituents of the protein layer of this marine bacterium requires further investigations. Finally, a public database (Marine2D, http://www.marine2D.de/) that comprises the proteome data and genomic information of this marine bacterium was established. The authors would like to thank H. Teeling (MPI of Marine Microbiology in Bremen) for help with genome annotation, and H. Mehlan (Decodon GmbH) for the construction of the Marine2D database. Hieu Cao Xuan was supported by the Vietnamese Ministry of Education and Training (MOET) and the Deutscher Akademischer Austausch Dienst (DAAD). This work was supported by the Bundesministerium für Bildung und Forschung (BMBF, grant number 03F0364B), the TBI of Mecklenburg-Vorpommern (AZ 230-630.8-TIFA-356), and the Max Planck Society. The authors have declared no conflict of interest. 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