Download Detailed proteome analysis of growing cells of the planctomycete

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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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].
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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
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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.
Proteomics 2008, 8, 1608–1623
5
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