Characterization of the Insoluble Proteome of Lactococcus lactis by SDS-PAGE LC-MS/MS Leads to the Identification of New Markers of Adaptation of the Bacteria to the Mouse Digestive Tract Jasna Beganovic´,†,# Alain Guillot,† Maarten van de Guchte,‡ Anne Jouan,§ Christophe Gitton,| Valentin Loux,⊥ Karine Roy,| Sylvie Huet,§ Herve´ Monod,§ and Ve´ronique Monnet*,†,| INRA, PAPPSO (Plate-Forme d’Analyse Prote´omique de Paris Sud-Ouest), UR895 Ge´ne´tique Microbienne, UR341 Mathe´matique et Informatique Applique´es, UR477 Biochimie Bacte´rienne, UR1077 Mathe´matique, Informatique, Ge´nome, Domaine de Vilvert, F-78352 Jouy en Josas, France Received February 3, 2009
We characterized the insoluble proteome of Lactococcus lactis using 1D electrophoresis-LC-MS/MS and identified 313 proteins with at least two different peptides. The identified proteins include 89 proteins having a predicted signal peptide and 25 predicted to be membrane-located. In addition, 67 proteins had alkaline isoelectric point values. Using spectra and peptide counts, we compared protein abundances in two different conditions: growth in rich medium, and after transit in the mouse digestive tract. We identified the large mechanosensitive channel and a putative cation transporter as membrane markers of bacterial adaptation to the digestive tract. Keywords: Lactococcus lactis • cell envelope • proteome • protein abundance • comparative proteomics • LC-MS/MS • digestive tract • spectral counting • peptide counting • membrane protein
1. Introduction Proteins located in the cell envelope are key molecules that interface bacteria with their environment. They provide various functionalities that allow bacteria to adapt to various environments, such as internalization of nutrients, export of byproducts, adhesion to biological and abiotic surfaces, and colonization of host cells. In pathogens, cell envelope proteins are involved in bacterial virulence and in survival during the infection process. These proteins are indeed major actors in the interaction with human cells and more particularly with the gastrointestinal epithelium.1 In the case of nonpathogenic bacteria such as lactic acid bacteria, several genes coding for surface proteins were also found to be induced in the gastrointestinal tract of mice and are probably important for survival in this environment as well as for pathogen exclusion in the case of probiotic strains.2 Despite the importance of cell-envelope associated proteins, their characterization by proteomics approaches is still poorly documented. Their analysis is difficult due to several factors, including their relative low abundance in crude cell extracts * To whom correspondence should be addressed. Dr Ve´ronique Monnet, INRA, Unite´ de Biochimie Bacte´rienne, Domaine de Vilvert, F-78352 Jouy en Josas cedex. Tel: 33.1.34.65.21.49. Fax: 33 0.1.34.65.21.63. E-mail:
[email protected]. † PAPPSO (Plate-Forme d’Analyse Prote´omique de Paris Sud-Ouest). # Present address: Faculty of Food Technology and Biotechnology, University of Zagreb, Croatia. ‡ UR895 Ge´ne´tique Microbienne. § UR341 Mathe´matique et Informatique Applique´es. | UR477 Biochimie Bacte´rienne. ⊥ UR1077 Mathe´matique, Informatique, Ge´nome. 10.1021/pr9000866
2010 American Chemical Society
as a consequence of the hydrophobicity of many membrane proteins, that can contain up to 15 membrane-spanning regions.3,4 Most analyses of bacterial proteomes reported up to now are based on 2D electrophoresis of proteins, either from whole bacterial cells5 or from cytoplasmic6-9 or secreted10,11 fractions, and lead to the establishment of specific reference maps. Cellenvelope proteins are under-represented, and membrane and basic proteins are generally absent from these proteomes. In the past few years, several attempts to characterize the interesting group of surface proteins have been published.9 Some of these studies proposed new selective extraction, labeling or solubilization methods for envelope proteins.12,13 Targeted strategies were also developed to gain access to specific groups of proteins such as LPXTG proteins14 or proteins that protrude from the cell wall and from which fragments can be released by trypsin treatment of whole cells.14-16 In this context, gelfree approaches are emerging in proteomic studies as efficient means to separate and identify membrane and cell-wall proteins.3,17-19 In parallel, much progress has been made in the field of LC-MS/MS-based quantitative proteomics20-22 using the observed correlation between the counts of protein specific spectra or peptides obtained by mass spectrometry and the relative abundance of the proteins. In the present work, we successfully combined the use of SDS-PAGE, LC-MS/MS and spectral or peptide counting methods, to identify, in one experiment, 313 proteins that belong to the insoluble proteome of Lactococcus lactis grown in rich medium (RM). This insoluble proteome complements the already known cytoplasmic proteome which contains 275 proteins visible on a pH 4-7 2D gel.6,23 The relative abundances Journal of Proteome Research 2010, 9, 677–688 677 Published on Web 12/15/2009
research articles of the proteins in two environments, that is, during growth in RM or recovered from mice feces after transit of the digestive tract (DT), were compared and statistical analysis of the results allowed the identification of new markers of adaptation of the bacteria to the digestive tract. The increasing interest in the use of L. lactis, which is “generally recognized as safe”, to be used in novel applications such as live vaccines24 emphasizes the importance of studying the behavior of these bacteria in the digestive tract.
2. Materials and Methods 2.1. L. lactis IL1403 Data and Protein Localization Prediction. L. lactis protein sequences were extracted from an automatic reannotation of the original sequence (accession number AE005176) using AGMIAL, a microbial genome annotation platform (http://genome.jouy.inra.fr/public-agmial).25 Small differences in gene prediction were observed compared with the original annotation. When the new gene prediction matched the original annotation, the original protein ID was conserved. Proteins without protein ID correspond to newly predicted genes. Protein localization was predicted using the bacterial localization prediction tool Psortb (version.2.0),26 and tools dedicated to the prediction of signal peptide cleavage sites (SignalP, version 2.0),27 transmembrane helices (TMH) (TMHMM, version 2.0)28 and lipoprotein signal peptides (LipoP, version 1.0).29 The Psortb results served as a guide for final localization predictions. The proteins for which Psortb provided no prediction were classified on the basis of the following criteria, which were also used to correct some of the Psortb predictions: Proteins with two or more predicted TMH were classified as membrane proteins. Predicted lipoproteins were classified as cell envelope associated. Among the remaining proteins, those for which both SignalP-NN and SignalP-HMM predicted a cleavable signal peptide were classified as extracellular. Proteins with one predicted TMH and no consensus signal peptide prediction were classified as cell envelope associated. Remaining proteins were classified as cytoplasmic. For a number of proteins, these classifications were overruled on the basis of the annotated function. The codon adaptation index (CAI) was calculated for all ORFs of L. lactis IL1403 to obtain an estimation of the expected relative protein expression levels using codonmixture 1.0. (http://genome.jouy.inra.fr/~pnicolas/codonmixture). Each protein was assigned to a functional category following the Clusters of Orthologous Groups of proteins (COGs) present in the L. lactis database on NCBI with slight modifications. Proteins were classified in 8 categories: translation, other cell function, transporters, cell wall or membrane biogenesis, replication, recombination and repair, post-translational modification, protein turnover and chaperons, nucleotide metabolism and unknown proteins. 2.2. Bacterial Strains and Growth Conditions. The L. lactis IL2661 strain used in this study is identical to strain IL1403 of which the genome is completely sequenced,30 but contains the pIL9 plasmid carrying the lac operon.31 An overnight culture of L. lactis grown in M17 containing 0.5% lactose (M17Lac) was used to inoculate 400 mL of M17Lac broth, to obtain an optical density at 600 nm (OD600nm) of 0.05 (UVIKON 931 spectrophotometer, KONTRON Instruments). Bacteria were then cultivated at 30 °C until an OD600nm of 0.7 (exponential phase). The same strain was used to colonize C3H/HeN germ-free and 8-week-old mice kept in sterile incubators and isolated 678
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Beganovic´ et al. 23
from the mice feces as described earlier. Mice received a lactose (4.5% w/v) solution in water as a drinking solution. A Nycodenz gradient centrifugation procedure, initially described for isolation of bacteria from soils,32,33 was applied to recover L. lactis from feces from eight mice to give one pooled sample. Afterward, bacteria grown on M17lac or recovered from feces were harvested by centrifugation at 8000g for 10 min at 4 °C. The cell pellet was washed twice with ice-cold 200 mM Naphosphate buffer pH 6.4 and resuspended in disruption buffer: 20 mM Na-phosphate buffer pH 6.4 containing 10 mM tributylphosphine, a cocktail of protease inhibitors (P8465; Sigma Aldrich, St Louis, MO, 20-fold diluted) and catalase 40 U/mL (C3155; Sigma Aldrich, St Louis, MO) to limit isoform formation. An L. lactis strain, carrying the mscL gene under the control of the nisin inducible nisA promoter,34 was used to validate the relative protein quantification method used in this work. For this purpose, the 391-bp MscL encoding open reading frame was amplified by PCR using IL1403 genomic DNA as the template, the primers U-Mscl-NcoI (CATGCCATGGCCTTAAAGGAATTTAAAAACTTTA [NcoI site underlined]) and L-Mscl-XbaI (CTAGTCTAGATCATTATTGTTTTTTCAATAAATC [XbaI site underlined]), and the high fidelity DNA polymerase Phusion (Finnzymes). The PCR product was cloned downstream of the nisA promoter into the pMSP3545 plasmid (obtained from NIZO, The Netherlands Institute for Dairy Research, Ede, The Netherlands)35 using Escherichia coli TG1 as a host. The resulting plasmid, pMSPmscL, was subsequently transferred to L. lactis IL2661 by electroporation. The resulting strain was grown in M17lac medium in presence of 0, 0.1, 1, or 5 ng of nisin/mL. 2.3. Preparation of Protein Extracts. Protein extracts were obtained after mechanical disruption of the bacteria with two different devices, adapted to the volume of the bacterial suspension obtained after growth in M17 medium or recovered from feces. The protein extract of M17 grown cells was prepared essentially as previously described.36 After a washing step, the cells were disrupted using a BAZIC Z cell disrupter (Celld, Warwickshire, U.K.). The suspensions were then centrifuged at 5000g for 20 min at 4 °C to remove unbroken bacteria and large cellular debris. The supernatants were collected and subjected to ultracentrifugation at 220 000g for 30 min at 4 °C to separate the “cell envelope pellets” from cytosolic proteins. Finally, the pellets were resuspended in disruption buffer (see section 2.2) and sonicated for 15 min at 4 °C in an ultrasonic bath. Bacteria extracted from feces were mechanically disrupted with an FP120 FastPrepcell disruptor (Bio 101Systems, Qbiogen, Inc., Irvine, CA) using two 30 s cycles of homogenization at maximum speed with a 1 min interval between cycles. The suspension was then centrifuged at 10 000g for 15 min at 4 °C to remove unbroken cells and large cellular debris. The supernatant was centrifuged at 220 000g for 30 min at 4 °C to separate the cell envelope pellets from soluble cytosolic proteins. Finally, the pellets were resuspended in disruption buffer and sonicated for 15 min at 4 °C in an ultrasonic bath. Protein extracts were prepared for each condition (M17 medium and mice feces) in two independent experiments. Protein concentration was determined by the Bradford protein assay37 using bovine serum albumin as a standard. 2.4. Protein Separation: 1D Electrophoresis and Western Blot. Ten micrograms of resuspended cell-envelope proteins was totally solubilized in 20 µL of 6% glycerol, 25 mM DTT, 2% SDS, 50 mM Tris, pH 6.8, 0.1% bromophenol blue (final
Comparison of Protein Abundance in L. lactis Insoluble Proteomes concentrations) by sonication for 15 min at 4 °C in an ultrasonic bath. The proteins were then separated by denaturing SDSPAGE on 4-12% polyacrylamide minigels in MES buffer (200 V, 110 mA for 45 min). The gel was stained with Blue Safe Stain (Invitrogen, Carlsbad, CA) while shaking on an orbital shaker for 60 min after which the gel was washed twice with 100 mL of Milli-Q water. Cell-envelope proteins from L. lactis IL2661 (pMSPmscL) grown with increasing concentrations of nisin were also separated by SDS-PAGE and transferred onto a nitrocellulose membrane (Biorad 162-0113) in Tris 25 mM, glycine 192 mM, methanol 4%. After two washes (10 and 5 min) in 100 mL of PBS buffer containing 0.5% Tween20, the membrane was blocked with BSA (1% in PBS-Tween20) during 1 h. It was then incubated for 2 h with 15 mL of a 1/1000 diluted antiserum against MscL provided by B. Poolman (Dept. Biochemistry, Univ. Groningen, The Netherlands), washed four times during 5 min with 100 mL of PBSTween20, incubated during 1 h with 15 mL of 1/7500 diluted horseradish peroxidase (HRP) goat anti-rabbit IgG (UP511380, Interchim) and finally washed three times during 5 min with 100 mL of TBS-Tween20. The blot was then revealed using the opti4CN substrate kit (Biorad) and scanned (8 bits, 256 levels of gray). The measure of spot volumes was performed with Progenesis Samespot software (Nonlinear Dynamics) and calculated as follows: spot area × spot pixel intensity - background intensity, using arbitrary units. 2.5. In-Gel Digestion and Sample Preparation. From the stained SDS-PAGE gels, each gel lane corresponding to one extract was cut in 2-3 mm slices, to collect up to 35 gel sections per sample. Each gel slice was washed twice with 50 mM NH4HCO3 and 50% CH3CN, dried at room temperature and digested overnight at 37 °C with 100 ng of sequencing grade modified trypsin (Promega, Madison, WI) in 17 µL of 50 mM NH4HCO3. The resulting peptides were extracted in several steps: the supernatant of trypsin hydrolysis was transferred to a new tube and the gel slices were extracted (a) with 25 µL of 50 mM NH4HCO3 and (b) two times with 25 µL of HCOOH 0.1%, CH3CN 50%. For each extraction, the gel slices were incubated for 15 min at room temperature while shaking. The supernatants of each extraction were pooled with the original trypsin digest supernatant and dried for 1 h in a Speed-Vacuum concentrator. The peptides were then resuspended in 25 µL of precolumn loading buffer (0.08% trifluoroacetic acid (TFA) and 2% acetonitrile (ACN) in H2O), prior to LC-MS/MS analysis. 2.6. LC-MS/MS Analysis. LC-MS/MS analysis was performed on an Ultimate 3000 LC system (Dionex, Voisins le Bretonneux, France) connected to a linear ion trap mass spectrometer (LTQ, Thermo Fisher) by a nanoelectrospray interface to realize the separation, ionization and fragmentation of peptides, respectively. Four microliters of tryptic digest peptide mixture were loaded at a flow rate of 20 µL/min onto a precolumn (Pepmap C18; 0.3 × 5 mm, 100 Å, 5 µm; Dionex). After 4 min, the precolumn was connected to the separating nanocolumn Pepmap C18 (0.075 × 15 cm, 100 Å, 3 µm) and the gradient was started at 300 nL/min. All peptides were separated on the nanocolumn using a linear gradient from 2 to 36% of buffer B, over 18 min. The eluting buffers were the following: A, 0.1% HCOOH, 2% CH3CN; eluting buffer B, 0.1% HCOOH, 80% CH3CN. Including the regeneration step, the run-length was 50 min. Ionization was performed on the liquid junction with a spray voltage of 1.3 kV applied to a noncoated capillary probe (PicoTip EMITER 10 µm i.d.; New Objective). Peptide ions were analyzed as follows: (i) full Ms scan (m/z 300-2000), (ii) ZoomScan (scan
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of the 3 major ions), (iii) MS/MS on these 3 ions with classical peptide fragmentation parameters: Qz ) 0.25, activation time ) 30 ms, collision energy ) 40%. The time during which the same ion cannot be reanalyzed was set to 30 s. 2.7. Protein Identification. Protein identification was performed using Bioworks 3.2 software (Thermo scientific). The raw data were converted and filtered in peak lists with default data generation parameters for LTQ mass spectrometry. All peak lists of precursor and fragment ions were matched automatically against a protein database. The strain IL1403 database containing 2501 open reading frames was created and modified by the integration of additional information on subcellular localization, CAI, gene name and accession number. The Bioworks search parameters included: trypsin specificity allowing one missed cleavage, variable oxidation of methionine. The mass tolerance was fixed to 1.4 Da for precursor ions and 0.5 Da for fragment ions. The search result was filtered using Bioworks 3.2. A multiple threshold filter applied at the peptide level consisted of the following criteria: Xcorrelation score (Xcorr) > 1.7, 2.5, and 3.0 for mono-, di-, and tricharged peptides, respectively; peptide probabilities lower than 0.01; ∆Cn defined by [(Xcorr1 - Xcorr2)/Xcorr1] bigger than 0.1 and only the first match result for each identified peptide. Proteins that did not have two unique peptides identified from a single gel slice or adjacent gel slices were filtered. The percentage of false positive identifications was evaluated by querying the peak list of precursors and fragment ions with the same cutoff parameters in the reverse database (all protein sequences of the IL1403 database were inversed). We used the formula given by Dreisbach et al.38 (2nreverse/[nreverse + nforward]) to estimate the false positive rate. 2.8. Relative Protein Quantitation. Two measurements: spectra counts (numbers of spectra identified for a protein during MS/MS analysis in all gel slices of each extract) and peptide counts (number of different peptides identified for a protein in all gel slices of each extract) were used for relative protein quantification. All data corresponding to the analyses of all gel slices for a given extract were merged and converted to XML format by a script written by Benoıˆt Valot (PAPPSO platform). These numbers were used to realize a comparative statistical analysis of the Lactococcus strain in the two different conditions (M17 and digestive tract). Significance of the observed differences between the two conditions was evaluated with statistical modeling and testing methods. 2.9. Statistical Data Analysis. All statistical analyses were performed using the R statistical software.39,40 2.9.1. Modeling and Parameter Estimation. There were four biological samples in the experiment, corresponding to two independent extracts for each of the two conditions to be compared: M17 rich medium (RM) and digestive tract (DT). The two extracts of each condition played the role of replicates in the statistical analysis. We denote by Yptr the observation of either the spectra or peptide counts for protein p in the sample associated with extract r (r ) 1 or 2) of condition t (t ) 1 for RM and t ) 2 for DT). The two response variables were analyzed separately but with the same approach based on the general model: Yptr ) µ + (P)p + (T)t + (PT)pt + (T/R)tr + Eptr
(1)
where, according to the usual analysis-of-variance terminology, µ is the general mean, (P)p is called the main effect of protein Journal of Proteome Research • Vol. 9, No. 2, 2010 679
research articles p, (T)t is the main effect of condition t, (PT)pt is the protein × condition interaction between protein p and condition t, (T/ R)tr is the main effect of extract r within condition t, and Eptr is a random error term that is assumed to be independent between observations. The entities of interest in this model are the interaction parameters (PT)pt, because they represent the abundance differences between conditions which are specific to each protein p. More precisely, (PT)p1 - (PT)p2 quantifies the difference in the abundance of protein p between conditions RM and DT, minus the average difference over all proteins. Note that in Model (1), (PT)pt parameters are not uniquely defined but their linear combinations (PT)p1 - (PT)p2 are.39 A major problem when analyzing proteomics data is the lack of variance homogeneity in the error term Eptr. To cope with this problem, we proceeded in three steps which involved fitting successive versions of Model (1) to data together with ˆ ptr, defined as the differences between studying their residuals E the observations and the fitted values. First, we fitted Model (1) assuming a common variance parameter for all errors Eptr and searched for transformations on the response variables that stabilized the variability of residuals with respect to fitted values. The square root transformation was chosen for spectra counts, whereas it appeared unnecessary to transform peptide counts. In what follows, Yptr in Model (1) will now denote either the square root of the spectra counts or the untransformed peptide counts. Second, we looked for an appropriate linear model, called Model (2), to relate the logarithm of the variance of Eptr (denoted by σ2pt) to the condition and to two protein characteristics: molecular mass and hydrophobicity (as characterized by presence/absence of transmembrane helices). The factorial effects to include in this model were determined by examining 2 scatterplot smoothing curves40 of log(Eˆptr ) versus protein biomass. For spectra counts (respectively, for peptide counts), the chosen Model (2) for log(σ2pt) consisted of biomass polynomial effects up to degree 3 with linear, quadratic and cubic coefficients depending on the environment (respectively, depending on the environment and on protein hydrophobicity). Third, Model (1′), that is, Model (1) combined with Model (2) for the variance of Eptr, was fitted to counting data. We used an iterative procedure to estimate the parameters by (i) fitting ˆ ptr; (ii) fitting Model Model (1) by least-squares to get residuals E 2 ˆ (2) on log(Eptr) by least-squares to get estimated variances σˆ pt; (iii) fitting Model (1) again by weighted least-squares with weights on Yptr proportional to σˆ pt (see ref 41), to get the final parameter estimates before evaluating and testing the abundance differences of proteins. 2.9.2. Identification of Proteins with Significant Abundance Differences. Abundance difference of protein p between condiˆ p of Dp ) tions RM and DT was quantified by the estimate D [(PT)p1 - (PT)p2]/2. To restrict attention to proteins with a significant abundance difference, the hypotheses H0,p:Dp ) 0 ˆ p/σˆ D,p, where were tested by Wald tests based on the t-values D ˆ p. The tests were performed σˆ D,p denotes the standard error on D assuming a normal distribution on Eptr in Model (1′) and using the Bonferroni and the Benjamini and Hochberg corrections for multiple testing adjustment.42,43 A few significant proteins did not satisfactorily fit the statistical model, because the counts obtained in the replications were more divergent than reasonably expected according to their estimated variance. These proteins were identified in order to exert more caution in their interpretation. 680
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3. Results 3.1. Insoluble Proteome Analysis of L. lactis Grown in Rich Medium. Our objective was to characterize the insoluble proteome of L. lactis, which is expected to be complementary to the soluble proteome and to contain proteins located in the bacterial cell-envelope, at the interface with the environment. After L. lactis growth in RM, 313 distinct proteins, representing a wide range of CAI and isoelectric points, were identified with at least two different peptides by 1D electrophoresis-LC-MS/ MS (Supplementary Table 1). Because of the limited size of the L. lactis genome, no peptide was identified when the peak list of precursors and fragment ions was queried in the reverse L. lactis database, indicating that the false positive rate is below 0.6% (the value that would have been obtained if one protein had been identified). Of the 313 distinct proteins identified, in silico analysis indicated that 89 contain amino acid sequences predicting that they are exported, located in the cell membrane, or associated with the cell wall. The remaining 224 proteins did not appear to contain identifiable motifs, and hence were considered as cytoplasmic proteins. The proportion of proteins identified varied (from 8% to 24% of the number of open reading frames) according to the predicted localization of the proteins and is maximal (24%) for the group of proteins predicted to be cell-wall associated (Supplementary Table 1). A majority of the proteins detected are involved in translation, transport of different molecules, cell wall or membrane biogenesis, or protein folding. We detected 25 out of 442 predicted membrane proteins containing at least 2 transmembrane helices (TMH), and belonging to a group of proteins not visible with a classical 2D-PAGE approach44 (Table 1). Most of them were identified with low spectra counts due to their intrinsic high hydrophobicity. Among them, we identified highly hydrophobic proteins such as the potassium uptake protein (KupB) or the fructosespecific PTS system enzyme IIBC component (FruA) that are predicted to contain as many as 12 or 10 TMH, respectively. Lipoproteins were very efficiently detected (12 out of 33 predicted) with high number of peptides and spectra and consequently high protein coverage (average coverage range 45%). Most of these are substrate binding components of ABC transporters (Table 2). We also identified proteins with LysM cell wall binding domains such as the surface exposed protein N-acetylglucosaminidase AcmA, a cell wall hydrolase that contains three LysM domains.45 Other proteins that were detected include proteins involved in cell division (Fts family proteins) or in peptidoglycan metabolism (N-acetyl-muramidase MurA; undecaprenyl-PP-MurNAc-pentapeptide-UDPGlcNAc GlcNAc transferase MurG; UDP-N-acetylglucosamine pyrophosphorylase GlmA; UDP-MurNac-alanine ligase MurC) and all in silico predicted representatives of the penicillin binding protein family possessing a transmembrane anchor (PonA, Pbp2A, Pbp1B, Pbp2B)9 and probably highly active during exponential growth.46 Not one of the proteins predicted to be covalently bound to the cell wall via a LPXTG (8 proteins) or a WxL47(Serror and Brinster, personal communication) (7 proteins) motif was identified in our experiment. Although our extract is clearly enriched in envelope associated proteins, most of the proteins (71%) identified by our approach are predicted to be cytoplasmic. Further analysis of this group revealed that most of them belong to large protein complexes or machineries involved in translation, cell division, nucleotide
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Comparison of Protein Abundance in L. lactis Insoluble Proteomes
Table 1. List of Identified Membrane Proteins in the Insoluble Proteome of L. lactis Grown in Rich Mediuma protein description
Xcorrelation score
spectra counts
100.22
35
4
100.24
34
10
170.21
27
2
130.23
20
2
110.23
14
7
30.18
14
3
110.18
11
8
100.22
10
4
Sugar, amino acid and other transporters
60.27
7
6
0.596
Sugar, amino acid and other transporters
40.17
7
4
Eep
0.499
60.20
6
4
Unknown protein DHH subfamily 1 protein
YbjB YhfB
0.521 0.462
50.19 50.19
6 5
4 2
ABC transporter permease and substrate binding protein Putative SpoIIIJ family protein Potassium uptake protein
YsaB
0.433
Posttranslational modification, protein turnover, chaperons Unknown protein Proteins involved in other cell functions Sugar, amino acid and other transporters
50.20
5
10
YfgG
0.562
Unknown protein
40.15
5
5
KupB
0.451
10.17
5
12
Sensor protein kinase
KinB
0.419
40.19
4
10
Sensor protein kinase
KinD
0.399
30.17
3
2
Orf2 Unknown protein Cell division protein
pi359 Yxwj FtsX
0.493 0.490 0.512
30.15 30.15 20.15
3 3 3
12 3 3
Preprotein translocase SecY subunit
SecY
0.499
20.16
3
4
Cardiolipin synthase
ClsB
0.379
20.13
2
2
Putative glycosyl transferase ykoT Unknown protein
YkoT
0.456
Sugar, amino acid and other transporters Proteins involved in other cell functions Proteins involved in other cell functions Unknown protein Unknown protein Cell wall/membrane biogenesis Posttranslational modification, protein turnover, chaperons Cell wall/membrane biogenesis Unknown protein
20.23
2
2
YbgB
0.404
Unknown protein
20.14
2
8
Mannose-specific PTS system component IID Fructose-specific PTS system enzyme IIBC component NADH dehydrogenase Cell division protein FtsH homologue Glutamine ABC transporter permease and substrate binding protein Mannose-specific PTS system component IIC Cation-transporting ATPase ABC transporter ATP-binding and permease protein ABC transporter ATP binding and permease protein ABC transporter ATP binding and permease protein Probable protease eep
name
CAI
PtnD
0.726
FruA
0.546
NoxB
0.622
FtsH
0.629
GlnP
0.570
PtnC
0.713
YoaB
0.494
YhcA
0.495
YdaG
0.566
YdbA
functional categories
Sugar, amino acid and other transporters Sugar, amino acid and other transporters Proteins involved in other cell functions Cell wall/membrane biogenesis Sugar, amino acid and other transporters
Sugar, amino acid and other transporters Sugar, amino acid and other transporters Sugar, amino acid and other transporters
TMH
a
The proteins are given with their CAI values, the number of predicted transmembrane helixes (TMH) according to TMHMM (see Material and Methods), the number of spectra obtained during identification and the Xcorrelation score.
metabolism, secretion that are associated with the cell membrane. Proteins involved in translation and ribosomal structure were substantially over-represented in this set. Eighteen of the 20 aminoacyl-tRNA synthetases were present in our extract as well as almost all 30S and 50S ribosomal structural proteins (Table 3). 3.2. Comparison of Insoluble Proteomes of L. lactis in RM and DT. We analyzed and compared the insoluble proteomes of L. lactis in two extracts for each of the two different
conditions tested: the rich medium (RM) M17 and feces of mice after transit through the digestive tract (DT). Previous work done on the cytoplasmic fraction of L. lactis present in feces demonstrated that the proteome of this fraction is very similar to that of the same bacteria taken from mice cecum.23 Consequently, our feces sample will be called DT sample throughout the text, although some differences probably exist between bacteria in the digestive tract itself and in feces. The Journal of Proteome Research • Vol. 9, No. 2, 2010 681
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Beganovic´ et al. a
Table 2. List of Identified Lipoproteins name
functional category
CAI
Xcorrelation score
spectra counts
MalE BmpA FrdC YvdF PmpA OptA YtcC YfcG PstE YjgC ZitS YpcG
Sugar, amino acid and other transporters Proteins involved in other cell functions Proteins involved in other cell functions Sugar, amino acid and other transporters Post-translational modification, protein turnover, chaperons Sugar, amino acid and other transporters Unknown protein Sugar, amino acid and other transporters Sugar, amino acid and other transporters Sugar, amino acid and other transporters Sugar, amino acid and other transporters Sugar, amino acid and other transporters
0.638 0.665 0.534 0.565 0.605 0.629 0.714 0.478 0.493 0.509 0.452 0.590
130.25 120.19 70.17 50.19 40.20 40.17 40.17 30.18 30.17 30.17 20.17 20.15
33 21 7 5 5 4 4 3 4 5 2 2
a The proteins are given with their CAI values, the number of spectra obtained during identification and the Xcorrelation score. The proteins are classified by decreasing spectra counts.
relative abundances of proteins under these two conditions were estimated through label-free proteomics approaches.48,49 For the comparison, we considered the 792 proteins detected with at least one peptide or one spectrum in at least one extract (Supplementary Table 2). Spectra and peptide counts were submitted to statistical analysis necessitating the development of an appropriate model to account for variance heterogeneity. We found that square root transformation stabilized the variance of spectra count data more appropriately than logarithmic transformation, whereas no transformation was needed for peptide count data. After square root transformation for spectra counts, the variance of count data still appeared to depend on the condition and on protein abundance and, for peptide counts only, on protein hydrophobicity. The variance was larger in the DT environment and its logarithm varied nonlinearly with abundance (results not shown). These relationships and the need to model variance with respect to covariates were stronger for peptide counts than for spectra counts (results not shown). Once the models were determined, the statistical tests on spectra and peptide counts allowed to classify proteins according to their absolute Student t-values and restricted to significant differences (Table 4). When applying the Bonferroni method, the abundance of 15 proteins significantly differed between RM and DT according to either spectra or peptide counts, of which 9 differed significantly for both counting variables. Among the proteins identified as more abundant after growth in RM were proteins associated with translation (t-RNA synthetases LysS, ProS, ThrS, GltX, GlyT; ribosomal proteins RpsD, RplO, RpsG, RpsR; elongation factor Tsf), transcription (RNA polymerase subunits RpoB, RpoC), DNA replication (helicases RheA and RheB; gyrase GyrA) transport of nutrients (ABC transporter ATP binding protein, YahG; maltose binding protein, MalE), cell wall biosynthesis (LPS synthesis, YcbJ; D-Ala-D-Ala ligase, DltA) as well as various metabolic enzymes (peptidases PepV and PepX, enolase, 6-phosphofructokinase, etc.). The most obvious difference between L. lactis grown in RM and after transit in DT was a switch between two sugar binding proteins of ABC transporters. MalE was abundant in RM medium containing lactose as main carbon source while YpcG was more abundant after transit in DT indicating an adaptation to different but nonidentified sugar source(s). The abundance of the soluble subunit IIAB of the main PTS permease, PtnAB, in DT indicated the probable activation of sugar transport in this environment. Furthermore, the 6-phosphogluconate de682
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hydrogenase (GntZ) was more abundant in DT indicating gluconate catabolism in DT and activation of the pentose phosphate pathway. In addition, we identified two enzymes probably involved in sugar catabolism more abundant after DT transit: GalK, the galactokinase and YpcC probably carrying an endo-beta-N-acetylglucosaminidase activity. Finally, two other enzymes, the dihydroxyacetone kinase, DhaM, and the NADdependent alcohol and aldehyde dehyrogenase, AdhE, were more abundant in DT samples. Interestingly, we found the large mechanosensitive channel (MscL) containing two transmembrane helices more abundant in L. lactis after transit in DT. We also observed a higher production of a putative cation transport ATPase, PacL containing eight transmembrane helices in DT. Markers of stress response were more abundant after DT transit as exemplified by ClpE and Ytgh, a protein homologous to Gls24; the housekeeping surface protease HtrA, a key factor under specific stress conditions,50 and RecN involved in DNA repair were also more abundant in the DT sample. Two pathways involved in energy production or pH homeostasis were clearly activated in DT samples: the arginine deiminase pathway involving the arginine deiminase (ArcA and the ornithine carbamoyltransferase, ArcB) and the F0F1-type ATP synthase (higher abundance of two of its subunits). Finally, several other proteins were also found more abundant in DT samples. They are involved in purine biosynthesis (GuaB), cell division (FtsZ and FtsY), riboflavin synthesis (RibA), teichoı¨c acid synthesis (YmjF) and S-adenosyl methionine synthesis (MetK). 3.3. Verification of the Validity of the Spectral Counting Approach Applied to Membrane Proteins. We verified the accuracy of the link between spectra counts and membrane protein abundance using a L. lactis strain harboring a nisininducible mscL gene, encoding the integral membrane protein MscL. After induction of mscL expression, we followed the presence of the protein in the bacterial cell envelope fraction using either Western blotting with MscL targeted antibodies or the spectral counting method. We visualized MscL, as already observed,51 as a fuzzy band on the blot (Figure 1C). The quantification of band volumes correlates well with the spectra counts measured in the same gel lanes (Figure 1B,D).
4. Discussion 4.1. Insoluble Proteome View. Our approach uses a new and easily applicable methodology to detect a significant
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Comparison of Protein Abundance in L. lactis Insoluble Proteomes
Table 3. List of Identified Cytoplasmic Proteins Involved in Translation or Belonging to Ribosomal Structuresa protein description
name
CAI
Xcorrelation score
spectra counts
50S ribosomal protein L2 30S ribosomal protein S2 30S ribosomal protein S4 Elongation factor Tu (EF-Tu) 30S ribosomal protein S3 30S ribosomal protein S13 50S ribosomal protein L15 30S ribosomal protein S5 30S ribosomal protein S1 50S ribosomal protein L13 50S ribosomal protein L19 Arginyl-tRNA synthetase (ArgRS) 50S ribosomal protein L4 30S ribosomal protein S7 50S ribosomal protein L5 Prolyl-tRNA synthetase 50S ribosomal protein L16 50S ribosomal protein L9 50S ribosomal protein L21 GTP-binding protein TypA/BipA Elongation factor Ts (EF-Ts) ATP-dependent RNA helicase 50S ribosomal protein L1 Lysyl-tRNA synthetase Methionyl-tRNA synthetase (MetRS) Asparagine synthetase 30S ribosomal protein S10 50S ribosomal protein L6 50S ribosomal protein L14 50S ribosomal protein L23 Putative glycyl-tRNA synthetase beta subunit Glutamine synthetase 30S ribosomal protein S20 50S ribosomal protein L22 30S ribosomal protein S12 30S ribosomal protein S18 50S ribosomal protein L3 Alanyl-tRNA synthetase (AlaRS) Aspartyl-tRNA synthetase (AspRS) Glutamyl-tRNA synthetase Translation initiation factor IF-2 ATP-dependent RNA helicase RNA polymerase sigma factor rpoD 30S ribosomal protein S15 Asparaginyl-tRNA synthetase (AsnRS) Isoleucyl-tRNA synthetase (IleRS) Polyribonucleotide nucleotidyltransferase 30S ribosomal protein S8 30S ribosomal protein S19 50S ribosomal protein L7/L12 Histidyl-tRNA synthetase Tyrosyl-tRNA synthetase 1 tRNA-guanosine methyltransferase RNA methyltransferase, TrmA family GTP-binding protein Serine/threonine protein kinase 30S ribosomal protein S9 Phenylalanyl-tRNA synthetase beta chain Threonyl-tRNA synthetase (ThrRS) S-adenosylmethionine:tRNA ribosyltransferase-isomerase Putative methyltransferase S-adenosyl-methyltransferase mraW RRNA methylase 50S ribosomal protein L10 50S ribosomal protein L30 Aspartyl/glutamyl-tRNA(Asn/Gln) amidotransferase subunit B
RplB RpsB RpsD Tuf RpsC RpsM RplO RpsE RpsA RplM RplS ArgS RplD RpsG RplE ProS RlpL RplI RplU TypA Tsf RheB RplA LysS MetRS AsnH RpsJ RplF RplN RplW GlyS GlnA RpsT RplV RpsL RpsR RplC AlaS AspS GltX InfB RheA RpoD RpsO AsnS IleS PnpA RpsH RpsS RplL HisS TyrS TrmH YljE YyaL PknB RpsI PheT ThrS QueA YriA MraW SunL RplJ RpmD GatB
0.754 0.839 0.854 0.814 0.768 0.746 0.791 0.713 0.756 0.760 0.810 0.603 0.727 0.794 0.800 0.592 0.698 0.629 0.781 0.744 0.741 0.564 0.797 0.689 0.562 0.629 0.730 0.766 0.694 0.732 0.592 0.653 0.788 0.697 0.731 0.743 0.695 0.568 0.576 0.657 0.661 0.679 0.611 0.859 0.666 0.640 0.555 0.815 0.649 0.859 0.512 0.564 0.434 0.479 0.579 0.481 0.692 0.536 0.649 0.548 0.362 0.440 0.441 0.760 0.734 0.503
70.24 130.21 130.27 140.21 80.22 80.21 70.27 80.23 100.21 80.18 60.22 100.17 40.20 50.18 40.21 100.20 30.20 60.18 40.21 100.20 70.18 80.22 60.22 80.24 90.16 90.19 50.21 40.18 30.22 60.20 70.17 60.21 20.19 20.19 30.15 40.18 30.22 60.17 60.21 60.17 50.19 60.18 60.22 40.20 50.19 50.19 50.19 30.18 30.17 20.15 30.17 40.24 40.14 40.15 40.17 30.16 30.15 30.21 20.17 30.13 30.17 30.16 30.18 20.24 20.17 20.19
34 33 32 23 22 19 19 18 17 17 17 15 13 11 11 11 10 10 10 10 10 10 9 9 9 9 8 8 8 8 8 8 7 7 6 6 6 6 6 6 6 6 6 5 5 5 5 4 4 4 4 4 4 4 4 3 3 3 3 3 3 3 3 2 2 2
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Table 3. Continued protein description
name
CAI
Xcorrelation score
spectra counts
Ribonuclease R 2 (RNase R 2) (VacB protein homologue 2) Pseudouridine synthase Peptide chain release factor 1 (RF-1) Methionine aminopeptidase Peptidyl-prolyl cis-trans isomerase
Rnr2 RluB PrfA PepM PpiA
0.450 0.486 0.486 0.563 0.521
20.14 20.14 20.18 20.19 20.20
2 2 2 2 2
a Proteins are given with their CAI values, their Xcorrelation score, the number of spectra obtained during identification. Proteins are classified by decreasing spectra counts.
Table 4. Classification of Proteins with Different Abundances between Environments RM and DT According to the Square Root of Spectra Counts or to Peptide Countsa
a Proteins are classified according to their t-values; in the upper left grey part, the proteins that are more abundant in the DT (negative estimate); in the lower white right part, the proteins that are more abundant in RM (positive estimate); marked with an asterisk, the proteins with divergence between replicates larger than expected from the variance estimates.
number of proteins associated with the bacterial cell envelope that are usually poorly or not revealed by classical 2D gel analysis. In one experiment, we detected 313 different proteins, including proteins with low CAI or with transmembrane helices as well as basic proteins. Our method is especially efficient to detect lipoproteins, in majority basic proteins, but also transmembrane proteins which play essential roles in the interaction between the bacteria and their environment. Previous experiments made on the same bacterium showed that neither the use of alkaline 2D gels52 nor that of pH 4-7 2D gels applied to a cell envelope-associated protein extract36 allowed the efficient detection of a high number of lipoproteins. Our work also confirmed the predicted presence of many proteins with unknown functions at the surface of L. lactis. We did not detect any proteins linked to the cell wall via LPXTG or WxL motifs, which may be due to expression levels too low to allow detection under the conditions tested. This 684
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point will become clearer when experimental data from the use of our protocol will start accumulating. The numerous proteins predicted as cytoplasm-located in our cell-envelope extract belong to three main categories: proteins from big cellular machineries (translation, transcription, cell division, secretion) which copurified with the bacterial envelopes, cytoplasmic subunits of transporters and proteins having affinity for the inner part of the membrane. In the group of proteins with affinity for the envelope, we identified ribosomal proteins which were found associated to the bacterial membrane even after extensive washing in pathogenic bacteria.53 As another example, we identified the Xaa-Pro dipeptidylpeptidase PepX, which was predicted to be cytoplasmic, but whose location was demonstrated to be membrane associated by previous immunomicroscopy detection.54 Other bacterial proteins, known as “moonlighting” proteins, that is, proteins that can have several roles in bacteria55 also
Comparison of Protein Abundance in L. lactis Insoluble Proteomes
Figure 1. (A) 1D SDS-PAGE of cell-envelope enriched protein extract prepared from L. lactis overexpressing the mscL gene after growth in presence of 0, 0.1, 1, or 5 ng/mL of nisin. The gel slices which were cut and submitted to mass spectrometry analysis are visualized and named a, b, c, d. (B) Sums of the spectral count obtained for the four slices of each gel lane during the identification process of MscL by mass spectrometry. (C) Western blot obtained with an anti-MscL serum. The localization of the slices submitted to mass spectrometry analysis was reported on the figure. (D) Spot volumes measured on the Western blot as described in the Material and Methods (arbitrary units).
emerged from our study with very high protein coverage and spectra numbers. These putatively nonclassically secreted proteins would have different roles when located in the cytoplasm and at the surface where they have been described to participate in biological processes such as virulence or interaction with the host.55-57 Among these proteins, we found the elongation factor Tuf, recently identified as an actor in adhesion to epithelial cells at the surface of Lactobacillus johnsonii56 (Table 5). 4.2. Protein Abundance Comparison Method and Validation. Our analysis of protein abundance is based on spectra and peptide counts. Spectral counting at the protein level was evaluated as a promising approach to measure protein abundance changes, especially in bacteria where genome sizes and consequently number of proteins are limited.58 In these cases, the risk of matching of one peptide with several proteins is negligible. This method was recently applied to identify proteome variation of Porphyromonas gingivalis in two different environments (intracellular vs extracellular).59 In our control experiment, where we expressed mscL to varying levels, we observed that the relative quantification of MscL by spectral counting correlated to the relative quantification by Western blotting. This experiment confirmed the applicability of the spectral counting method to membrane proteins. Using spectral or peptide counting at the protein level combined with a newly developed statistical analysis model, we found less than 50 proteins whose abundance varied when bacteria were placed in two different environments: rich
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medium versus mice feces after transit of the digestive tract. This number of proteins may seem very low compared to the drastic environmental change the bacteria encountered and the resulting expected changes in metabolism, but represents the proteins for which the differences between the two environments are statistically significant in the insoluble proteome. This relatively low number is probably due to the use of the spectral counting method which is not very efficient to quantify proteins of low abundance, detected by low numbers of peptides in some samples while absent from others.60 In these conditions, we probably saw only the most obvious differences. Among them, the proteins we found more abundant in RM are involved in translation, transcription, DNA replication, nutrient transport and cell wall biosynthesis. This observation is in agreement with the much higher metabolic activity expected in this environment, and already observed by cytoplasmic proteome analysis when bacteria are grown in RM compared to DT.23 The present study extends this view to parts of transporters (YagG, MalE) whose specificity needs to be specified and to basic proteins such as ribosomal proteins. 4.3. Markers of Adaptation to the Digestive Tract. At the current state of knowledge, bacterial adaptation to the digestive tract environment is poorly documented, especially for lactic acid bacteria used in food technology. One can hypothesize that, after oral ingestion, bacteria have to face various stresses including high concentrations of gastric acid, bile salts and mucins, low pH, osmolarity changes, limited oxygen availability, nutrient scavenging and hosts defense mechanisms, and that these factors will have an effect on bacterial gene expression.61 4.3.1. Diversification of Carbon Sources. Although carbon starvation was attenuated by the addition of lactose to the diet of the mice,62 our results clearly show that L. lactis had to diversify its use of carbon sources in the DT. This adaptation is a common trait of bacterial adaptation to the DT,2,63 and a general response to carbon starvation.64 The most obvious shift occurred between two sugar binding proteins of ABC transporters. MalE was abundant in RM and disappeared in DT in which YpcG, whose specificity is unknown, appeared. In addition, L. lactis activated the upper part of the pentose phosphate pathway in the DT via the 6-phosphogluconate dehydrogenase GntZ. This result reinforces previous work identifying the phosphogluconolactonase YwcC, acting upstream of GntZ, as more abundant in the digestive tract.23 In addition, we identified two enzymes probably involved in sugar catabolism more abundant after DT transit: GalK, the galactokinase and YpcC probably carrying an endo-beta-N-acetylglucosaminidase activity. Finally, two other enzymes, the dihydroxyacetone kinase, DhaM, and the NAD-dependent alcohol and aldehyde dehyrogenase, AdhE, probably involved in glycerolipid metabolism are more abundant after DT transit as already observed for L. lactis23 or Lactobacillus plantarum.2 4.3.2. Response to Osmotic Downshift. MscL was found more abundant in DT only on the basis of spectra counts. The absence of difference on peptide counts can be explained by the small size of the protein (13 kDa) and the presence of two transmembrane helices limiting the number of possible peptides visible using MS/MS. The mechanosensitive channel MscL was shown, in vitro, to play the main role in the protection of L. lactis against osmotic downshifts, that is, to conditions in which the external osmolyte concentration decreases. In these conditions, MscL releases compatible solutes such as glycine and betaine as well as water avoiding excessive bacterial Journal of Proteome Research • Vol. 9, No. 2, 2010 685
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Table 5. List of Identified Proteins Homologous to Proteins Already Described As Moonlighting Proteins in Other Bacteriaa protein name Enolase 1 DNA-directed RNA polymerase beta chain Elongation factor Tu Cell division protein FtsH homologue Heat shock protein DnaK Elongation factor G 50S ribosomal protein L19 Dihydrolipoamide acetyltransferase componet of PDH complex 60 kDa chaperonin DNA-directed RNA polymerase beta chain Lipoamide dehydrogenase componet of PDH complex Glyceraldehyde 3-phosphate dehydrogenase Glutamine synthetase PDH E1 component beta subunit 30S ribosomal protein S9
name predicted localization Xcorrelation score spectra counts CAI reference EnoA RpoC Tuf FtsH DnaK TuF RplS PdhC GroEL RpoB PdhD GapB GlnA PdhB RpsI
cytosol cytosol cytosol membrane cytosol cytosol cytosol cytosol cytosol cytosol cytosol cytosol cytosol cytosol cytosol
120.23 230.2 140.21 130.23 160.22 110.21 60.22 110.21 120.21 130.25 90.29 50.20 60.21 40.24 30.15
42 29 23 20 17 17 17 16 14 13 11 11 8 6 3
0.853 0.715 0.814 0.629 0.768 0.807 0.810 0.632 0.651 0.665 0.615 0.894 0.653 0.607 0.692
55 55 55,56 57 55 55 57 55 55 55 55 55 55 55 55
a Proteins are given with their predicted localization, Xcorrelation score, CAI, spectra counts obtained during identification. They are ordered by decreasing spectra counts.
swelling.51 A recent work demonstrated that, in Bacillus subtilis, the disruption of the mscL gene induced a severe survival defect upon an osmotic down-shock.65 Osmotic downshift is probably one of the stresses L. lactis has to face in the DT and could explain why we observed higher amounts of MscL in DT samples. MscL from E. coli excretes, in addition to low molecular weight molecules, some cytoplasmic proteins such as the elongation factor Tuf.66 The same elongation factor was found more abundant after transit of L. lactis through the digestive tract suggesting a possible similar MscL excretion system. 4.3.3. Response to Stress and Production of Energy. Markers of stress response were more abundant after DT transit, underlining that survival in this environment was challenging for L. lactis. The Clp family of proteins is classically associated with stress response and, as expected, we found different Clp profiles in the two environments. ClpC, which is generally described as a molecular chaperone, was more abundant in RM, while ClpE and ClpB that are associated with heat shock and pyromicine resistance67 appeared in the DT. In addition, Ytgh appeared to be more abundant in DT samples. This protein is homologous to Gls24, which is a general stress protein, synthesized in response to carbohydrate starvation or bile salt stress in Enterococcus faecalis.68 The housekeeping surface protease, HtrA, a key factor under specific stress conditions50 was also more abundant in the DT sample. We also observed a higher production of a putative cation transport ATPase, PacL. Calcium transport is poorly documented in bacteria but was associated to stress response in sporulating bacteria.69 GuaB, YmjF and FtsZ may also be associated to stress responses since they have been associated to responses to salt stress70 and in vivo survival in Listeria71 and to heat shock in L. lactis,72 respectively. L. lactis activated two other ways to produce energy or to maintain pH homeostasis: the arginine demiminase pathway involving the arginine deiminase ArcA and the ornithine carbamoyltransferase, ArcB. In addition to energy production, this pathway also produces NH3 and participates in pH homeostasis. The activation of this pathway was also observed using a 2D approach.23 The F0F1-type ATP synthase was also activated as revealed by the higher abundance of two of its subunits (AtpD, AtpG). In conclusion, the method we used to compare bacterial proteomes allows the identification of proteins whose abundance 686
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varies between two different conditions. Some of the proteins we identified were already known to vary between rich medium and after transit of the mouse digestive tract while others, belonging to the cell-envelope fraction such as YpcG, MalE, MscL, PacL or HtrA, were new in this context. We demonstrated with MscL that the spectral counting method can be successfully applied to membrane proteins even to those which contain very few peptides detectable by mass spectrometry.
Acknowledgment. This study has been carried out with the financial support of the Commission of the European Communities Marie Curie Project LABHEALTH (MEST-CT-2004-514428) and of the Ile de France regional council. Jasna Beganovic´ was recipient of a Marie Curie fellowship for Early Stage Research Training (EST). We warmly thank Mickael Meyrand for the construction of the MscL overproducing strain and Bert Poolman and Joost Folgering for providing antibodies directed against MscL. We also thank Michel-Yves Mistou for his critical reading of the manuscript; the NIZO for providing the plasmid pMSP3545 and the Germ-Free Animal Facilities of UR910 Ecologie et Physiologie du Syste`me Digestif (INRA, Jouy-en-Josas, France) for providing the animals and feces samples. Supporting Information Available: List of the 313 proteins identified in the insoluble proteome of L. lactis grown in rich medium. List of proteins identified in the insoluble proteomes of L. lactis in RM or DT. This material is available free of charge via the Internet at http://pubs.acs.org. References (1) Bierne, H.; Cossart, P. Listeria monocytogenes surface proteins: from genome predictions to function. Microbiol. Mol. Biol. Rev. 2007, 71 (2), 377–97. (2) Bron, P. A.; Grangette, C.; Mercenier, A.; de Vos, W. M.; Kleerebezem, M. Identification of Lactobacillus plantarum genes that are induced in the gastrointestinal tract of mice. J. Bacteriol. 2004, 186 (17), 5721–9. (3) Cordwell, S. J. Technologies for bacterial surface proteomics. Curr. Opin. Microbiol. 2006, 9 (3), 320–9. (4) Santoni, V.; Molloy, M.; Rabilloud, T. Membrane proteins and proteomics: un amour impossible. Electrophoresis 2000, 21 (6), 1054–70. (5) Wang, R.; Prince, J. T.; Marcotte, E. M. Mass spectrometry of the M. smegmatis proteome: protein expression levels correlate with function, operons, and codon bias. Genome Res. 2005, 15 (8), 1118– 26.
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