Stable Isotope Labeling by Amino Acids in Cell Culture (SILAC

May 28, 2010 - Stable Isotope Labeling by Amino Acids in Cell Culture (SILAC). Applied ... Sequence Analysis, Technical University of Denmark, 2800 Ly...
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Stable Isotope Labeling by Amino Acids in Cell Culture (SILAC) Applied to Quantitative Proteomics of Bacillus subtilis Boumediene Soufi,†,‡ Chanchal Kumar,†,# Florian Gnad,†,⊥ Matthias Mann,† Ivan Mijakovic,*,§ and Boris Macek*,†,| Max Planck Institute for Biochemistry, Am Klopferspitz 18, 82152 Martinsried, Germany, Center for Biological Sequence Analysis, Technical University of Denmark, 2800 Lyngby, Denmark, Micalis, AgroParisTech-INRA, Domaine de Vilvert, 78352 Jouy-en-Josas, France, and Proteome Center Tuebingen, Auf der Morgenstelle 15, 72076 Tu ¨ bingen, Germany Received February 17, 2010

We applied stable isotope labeling by amino acids in cell culture (SILAC) to large-scale quantitative proteomics analyses of the model bacterium Bacillus subtilis in two physiological conditions: growth on succinate and growth under phosphate starvation. Using a B. subtilis strain auxotrophic for lysine and high accuracy mass spectrometry for downstream analysis, we identified and quantified changes in the levels of more than 1500 proteins in each of the tested conditions with high biological and technical reproducibility. With a total of 1928 identified proteins, this study presents one of the most comprehensive quantitative proteomics studies in bacteria, covering more than 75% of the B. subtilis genes expressed in the log phase of growth. Furthermore, we detect and quantify dynamics of 35 Ser/Thr/ Tyr phosphorylation sites under growth on succinate, and 10 phosphorylation sites under phosphate starvation, demonstrating the full compatibility of the method with site-specific detection and quantitation of phosphorylation events in bacteria. Keywords: SILAC • Orbitrap • proteomics • bacteria • starvation • gluconeogenic growth

Introduction Systems biology relies on global analytical methodologies such as genomics, transcriptomics, and proteomics to provide a quantitative description of gene expression. Although the most accurate measurements of the protein-coding gene expression can be performed at the protein level, the increase of system complexity in direction genome-transcriptomeproteome1 makes global measurements of protein levels an extremely challenging task. Consequently, most of the comprehensive quantitative gene expression analyses are still done at the transcriptome level. Recent developments in the field of quantitative gel-free proteomics, based on protein labeling with stable isotopes and high accuracy mass spectrometry, bring the promise of more powerful protein detection coupled with accurate methods of quantitation. * Corresponding authors: Prof. Dr. Boris Macek, Proteome Center Interdepartmental Institute for Cell Biology, University of Tuebingen, Auf der Morgenstelle 15, 72076 Tuebingen, Germany. E-mail: [email protected]. Phone: +49/(0)7071/29-70556. Fax: +49/(0)7071/29-5779. Prof. Ivan Mijakovic, Ph.D., Micalis, AgroParisTech-INRA, Route de Thiverval, F-78850 Thiverval-Grignon, France. E-mail: [email protected]. Phone: +33(0)1 30 81 45 40. Fax: +33(0)1 30 81 54 57. † Max Planck Institute for Biochemistry. ‡ Technical University of Denmark. § AgroParisTech-INRA. | Proteome Center Tuebingen. # Present address: Lilly Singapore Centre for Drug Discovery, 8A Biomedical Grove #02-05, Immunos, Biopolis, 138648, Singapore. ⊥ Present address: Department of Systems Biology, Harvard Medical School, 200 Longwood Avenue, Boston, MA 02115, USA.

3638 Journal of Proteome Research 2010, 9, 3638–3646 Published on Web 05/28/2010

Among several techniques of metabolic labeling,2 stable isotope labeling by amino acids in cell culture (SILAC) stands out as a simple and efficient way of introducing a defined stable isotope label into all cellular proteins through an amino acid, mostly lysine and arginine.3 Since its introduction in 2002,4 SILAC has been employed in numerous quantitative proteomics analyses of eukaryotic cell lines and protozoa, demonstrating its potential to accurately quantify proteins to an unprecedented depth. In a recent comparison of haploid vs diploid yeast, all expressed gene products have been identified and quantified, making yeast the first organism in which mass spectrometry-based proteomics reached such a depth of analysis.5 Because of the relatively small genome sizes and fast growth, bacteria present a class of organisms that is especially amenable for SILAC labeling. Yet, global analyses of protein expression using SILAC in bacteria have been very limited,6 and other methods for protein labeling, such as 15N labeling,6,7 or classical quantitative proteomics based on 2D gels, are still widespread in proteomics studies of bacteria. In the model Gram-positive bacterium Bacillus subtilis, proteomic studies conducted by 2Dgel electrophoresis of protein extracts coupled to protein identification by mass spectrometry commenced early.8 This technique allowed researchers to chart the “vegetative” proteome of exponentially growing B. subtilis in optimal conditions,9 as well as the restructured proteome in response to high salinity,10 the presence of antibiotics,11 and glucose starvation.12 Gel-based proteomics has often been supplemented by tran10.1021/pr100150w

 2010 American Chemical Society

SILAC of Bacillus subtilis scriptional analysis, for example, in phosphate starvation response,13 glucose repression,14 and a number of other conditions.15-17 There is a good reason for combining these two approaches, since the overlap between transcriptome and proteome recorded under the same conditions was so far rather limited, due to posttranscriptional regulation and technical limitations of the employed methods. For example, the detected transcriptome of B. subtilis grown in chemically defined minimal medium comprised about 70% of the total genome,18 while the proteomics approach allowed the detection of only one-half of the expressed genes at the protein level.19 Therefore, the detection limit of the proteomic approaches has so far been one of the principal technical challenges in global gene expression studies, next to the challenge of accurate and reliable quantitation of proteins. Here we employ SILAC labeling and quantitation in combination with high accuracy mass spectrometry to analyze the proteome dynamics of B. subtilis in response to two nutritional challenges: the gluconeogenic growth on succinate and phosphate starvation. Response to phosphate starvation is one of the most thoroughly studied adaptation responses of B. subtilis.13,20,21 When the phosphate in the medium is exhausted, B. subtilis switches on the so-called Pho regulon20 to decrease metabolic activity under the transcriptional control of the twocomponent system PhoP/R. The Pho regulon comprises functions related to phosphatase scavenging and reutilization, among which the most elaborate one is the switching of the exopolysaccharide type.22 In addition to the Pho regulon, general stress response, controlled by σB is also induced by phosphate starvation. B. subtilis also exhibits elaborate mechanisms of adaptation to various types of carbon sources.23 Rapidly metabolizable sugars such as glucose, mannose, or fructose are imported by a ubiquitous transport system known as the phosphoenolpyruvate-dependent transport system (PTS). When glucose is abundant, there is a strong glycolytic flux and the energy created by oxidative phosphorylation is used for biosynthesis, sustaining rapid growth. Concomitantly, genes encoding functions related to influx and utilization of alternative carbon sources are repressed; this phenomenon is known as catabolic repression.24 When B. subtilis is grown on alternative carbon sources, such as organic acids (malate, succinate), this trend is reversed.25 Metabolic fluxes are redirected toward gluconeogenesis, and the catabolite repression is alleviated. These two global nutritional challenges are among the most extensively studied cases of cellular regulation in Firmicutes, and we thus decided to use them as a showcase of improvements that are achieveable using quantitative high accuracy mass spectrometry. We report highly reproducible identification and quantitation of more than 1500 proteins in each of the analyzed conditions, with a combined coverage of >75% of B. subtilis genes expressed in the log phase of growth. We also perform a Ser/Thr/Tyr-phosphoproteomic analysis to demonstrate that this approach is applicable to site-specific identification and quantitation of posttranslational modifications.

Materials and Methods Cell Culture and SILAC Labeling. B. subtilis mutant [∆Arg (GH)3, ∆Lys-21, ∆metB5, ∆pheA12, ∆purA16, ∆rplV1] was obtained from the Bacillus Genetic Stock Center (http:// www.bgsc.org/). Cells were grown in the minimal medium (8 mM MgSO4, 27 mM KCl, 7 mM sodium citrate, 50 mM Tris pH 7.5, 2 mM CaCl2, 0.6 mM KH2PO4, 15 mM (NH4)2SO4, 10 µM MnSO4, 1 µM FeSO4, 0.5% glucose, 780 µM tryptophan, 1 mM

research articles each of glutamic acid, phenylalanine, and methionine and 250 µM each of uracil, adenine, guanine, cytosine, and thymine). All experiments were performed in biological duplicates. Control cells were grown in 500 mL minimal medium containing 0.025% of “heavy” lysine 13C615N2 (Cambridge Isotope Laboratories). Cells were grown at 37 °C to an OD600 ) 0.4. Special care was taken to inoculate and harvest all cultures at the same OD. For the first test condition (growth on succinate, a non-PTS carbon source), cells were grown in 200 mL minimal media containing 0.025% “light” lysine 12C614N2 (Sigma) and 0.5% succinate (instead of 0.5% glucose). For the second test condition (growth under phosphate starvation), cells were grown in 200 mL of minimal medium containing 0.025% “light” lysine, 0.5% glucose and 75 µM KH2PO4. In both test conditions, cells were grown at 37 °C with vigorous shaking to an OD600 ) 0.4. In order to improve the biological reproducibility, cells were inoculated from synchronized exponential overnight cultures to avoid gene expression differences commonly associated with stationary phase cells. All cultures were precipitated by centrifugation (5 min at 6000g) and resuspended in lysis buffer containing 50 mM TrisCl (pH 7.5), 5 mg/mL lysozyme, and 5 mM of each of the following phosphatase inhibitors: sodium fluoride, 2-glycerol phosphate, sodium vanadate, and sodium pyrophosphate (Sigma). Cell wall lysis was performed for 15 min at 37 °C, and cell membranes were disrupted by sonication at amplitude 40% for 2 min. Benzonase (100 µg/mL) (Merck) was added to the lysate and incubated for an additional 10 min at 37 °C. N-Octylglucoside detergent (Sigma) was added to a final concentration of 1% for more efficient extraction and solubilization of membrane proteins. Cellular debris was removed by centrifugation at 25000g for 30 min. The crude protein extract was precipitated using chloroform/methanol extraction and the precipitate was redissolved in 6 M urea/2 M thiourea. “Heavy” and “light” extracts from each condition were then mixed 1:1, based on the protein content measured by the Bradford assay (Bio-Rad). Sample Preparation and Mass Spectrometry. SILAC Incorporation Analysis. About 20 µg of protein extract of the “heavy” culture was digested in-solution as described previously.26 Briefly, proteins from the crude protein extract were reduced with 1 mM dithiothreitol (DTT), alkylated with 5 mM iodoacetamide, predigested with the endoproteinase Lys-C (Wako) for 3 h and digested further with Lys-C overnight. The resulting peptide mixture was cleaned using Stage-tips27 and subjected to nano-LC-MS measurement an LTQ-Orbitrap mass spectrometer (see below) without prior peptide separation. Proteome Analysis. About 5 mg of the lyophilized crude protein extract from each condition was used for the subsequent proteomics and phosphoproteomics analyses. For proteomics analysis, two orthogonal analytical strategies were performed: the GeLC-MS and the off-gel isoelectric focusing (IEF) of peptides with subsequent LC-MS measurement. For the GeLC-MS analysis, 150 µg of protein extract was separated on a 10 cm, 4-12% gradient SDS-PAGE gel (Invitrogen). The resulting lanes were cut into 10 slices which were subjected to in-gel digestion with Lys-C, essentially as described previously.28 Upon extraction from the gel, peptides were cleaned using Stage-tips and analyzed using nano-LC-MS on an LTQOrbitrap mass spectrometer (see below). For the off-gel IEF of peptides, 150 µg of the protein extract was subjected to insolution digestion with Lys-C, as described previously.29,30 The resulting peptide mixture was applied onto a 13-cm, pH 3-10, Journal of Proteome Research • Vol. 9, No. 7, 2010 3639

research articles IEF strip (GE Healthcare) and focused on a 3100 OffGel Separator (Agilent), using the procedure and the default method 12PE01 (20 kVh at 50 µA) provided by the manufacturer. After the IEF, peptides from each of the 12 OffGel wells were cleaned using Stage-tips and analyzed using nano-LCMS. Samples from each analyzed biological condition were run in biological replicates. In addition, the offgel measurements were performed in duplicate to assess the technical reproducibility of the LC-MS setup (Supplementary Figure 1, Supporting Information). Phosphoproteome Analysis. For the phosphoproteome analysis, a total of 4.5 mg of the protein extract was subjected to in-solution digestion with Lys-C and two stages of phosphopeptide enrichment using strong cation exchange (SCX) and TiO2 chromatographies, essentially as described previously29-32 (Supplementary Figure 1, Supporting Information). After washing twice with 80% acetonitrile/0.2% trifluoroacetic acid, peptides were eluted from the TiO2 beads with 0.5% ammonium hydroxide solution in 40% acetonitrile (pH 10.5) and concentrated in a vacuum centrifuge. For analysis by LC-MS, samples were mixed with 1% trifluoroacetic acid/2% acetonitrile to a final volume of 10 µL. LC-MS/MS Analysis. All LC-MS analyses were performed on an 1100 nano-HPLC (Agilent) coupled to the LTQ-Orbitrap mass spectrometer (Thermo Fisher) as previously described.31 The LTQ-Orbitrap was operated in the positive ion mode, with the following acquisition cycle: a full scan recorded in the orbitrap analyzer at resolution R ) 60 000 was followed by MS/ MS of the five most intense peptide ions in the LTQ analyzer. In the case of phosphopeptides-enriched samples, “multistage activation” at 97.97, 48.99, and 32.66 Thompson (Th) relative to the precursor ion was enabled in all MS/MS events. To improve the mass accuracy of precursor ions, the “lock mass”33 option was utilized in all full scans. Data Processing and Bioinformatics Analysis. Raw MS spectra were processed using the MaxQuant software suite (version 1.0.11.5),34,35 and peak lists were searched using the MASCOT 2.2 search engine (Matrix Science) against a concatenated forward and reversed protein database of B. subtilis and Saccharomyces cerevisae. The forward database, downloaded from the Comprehensive Microbial Resource (CMR) (http:// cmr.jcvi.org/tigr-scripts/CMR), contained 10 793 protein sequences and 26 commonly observed contaminants. The following database search criteria were applied: SILAC pairs were searched with carbamidomethylation and either “light” (Lys0) or “heavy” (Lys8) lysine as fixed modifications and oxidation (M) and phosphorylation (STY) as variable modifications. Ion “singlets” (without SILAC pairs) were searched with carbamidomethylation as fixed and oxidation (M), phosphorylation (STY), and “heavy” (Lys8) lysine as variable modifications. LysC was defined as protease and two missed cleavages were allowed. Mass tolerance was 7 ppm at the precursor, and 0.5 Da at the fragment ion level. Mascot search results were parsed by the MaxQuant at the FDR 1% at both the peptide and protein level. For SILAC quantitation, only proteins with two or more quantitation events (ratio counts) were considered. The final reported protein ratio presents a ratio of light vs heavy SILAC signal calculated as a median of ratios obtained in all biological and technical repeats where the corresponding protein was identified. KEGG Enrichment Analysis-Based Hierarchical Clustering. To elucidate the systems level behavior in our quantitative B. subtilis proteome, the method of “proteomic phenotyping”36 3640

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Soufi et al. was employed at the KEGG pathway level. Briefly, for each of the quantified data sets (growth on succinate and phosphate starvation) the quantified proteome was divided into three quantiles corresponding to percentage cutoffs of 0, 25%, 75%, and 100%. The enrichment analysis for KEGG pathways was done for these quantiles with respect to the complete B. subtilis KEGG annotation by the hyper geometric test using BINGO.37 For hierarchical clustering, we first collated all the categories obtained after enrichment along with their p values and then filtered for those categories which were at least enriched in one of the quantiles with p value < 0.05. Categories, which did not have a p value after collation in any quantile, were provided a very conservative p value of 1. This filtered p value matrix was transformed by the function x ) -log10 (p value). Finally, these x values were transformed to z-score for each KEGG pathway by using the transformation [x-mean(x)]/sd(x). These z-scores were then clustered by one-way hierarchical clustering using “Euclidean distance” as the distance function and “average linkage clustering” method available in Genesis.38 Gene Annotation Retrieval. To provide the most recent gene annotation for all proteins detected in this study, we used BLASTP39 and mapped CMR accession numbers (e.g., Bsu2384) to their corresponding locus names (e.g., BSU23850) according to their sequence similarity. As BLAST creates local sequence alignments only, we used Needle40 software to derive global sequence alignments for the best matching pairs. In each supplementary table, we included the corresponding locus names, gene names, and descriptions as annotated by the SubtiWiki41 database, as well as the relative identity between the CMR entry and the locus entry.

Results B. subtilis strain auxotrophic for lysine was grown in a minimal medium containing either the “light” (12C614N2) or “heavy” (13C615N2) form of lysine. “Light” cell cultures were grown in two physiological conditions: in the first, glucose was replaced by succinate, a non-PTS carbon source; in the second, the concentration of phosphate was decreased to induce starvation and metabolic stress (Supplementary Figure 1, Supporting Information). Measured growth curves of bacterial cultures from both experiments are presented in the Supplementary Figure 2, Supporting Information. Protein extraction, peptide and phosphopeptide analysis, and quantification were performed as described in Materials and Methods. SILAC Label Incorporation. To assess the SILAC label incorporation, “heavy” cell culture (grown on 13C615N2 lysine) was sampled at an OD600 ) 0.4, corresponding to culture density after seven generations. After in-solution digestion and subsequent LC-MS analysis, 913 peptides were quantified with a median H/L ratio of 56.5, corresponding to 98.3% incorporation of the SILAC label (Supplementary Table 1, Supporting Information). This correlates with the full theoretical incorporation of 97-99% enriched 13C615N2 lysine (manufacturer’s specification) and confirms complete labeling of the B. subtilis culture (Figure 1). Initial SILAC experiments with B. subtilis were done using both “heavy” lysine and “heavy” arginine; however, the use of arginine led to significant conversion to proline and hence interfered with quantitation (data not shown). Such conversion of arginine was previously reported in several cell lines,42 but similar behavior of lysine was not reported. Indeed, the SubtiPathways41 and the KEGG database

SILAC of Bacillus subtilis

research articles peptide sequences (Supplementary Table 1, Supporting Information), with an average absolute precursor ion mass accuracy of 0.71 ppm and standard deviation of 0.99 ppm. Identified peptides mapped to 1928 protein groups at 1% FDR, comprising more than 75% of gene products reported to be expressed in the log phase of growth.9

Figure 1. SILAC incorporation. Histogram of heavy/light (H/L) ratios measured in the protein extract after harvesting at OD600 ) 0.4. The ratios are binned into log2 units. The median log2(H/L) equaled 5.82, pointing to the incorporation efficiency of 98.25%.

do not list any active pathway that would enable conversion of lysine to any other amino acid in B. subtilis.43 Biological and Technical Reproducibility of SILAC Quantitation. Our experimental setup resulted in 224 LC-MS measurements and 2 254 239 acquired MS/MS spectra. The database search led to identification of 13 604 nonredundant

To assess biological reproducibility, two bacterial cultures were grown under each of the tested conditions (succinate or low phosphate). To assess technical reproducibility, all LC-MS measurements were performed in duplicate. In both tested conditions, high correlation coefficients were achieved for biological and technical replicates (Figure 2). Despite the high reproducibility of our results, we note that the proteins of low abundance are prone to fluctuations in measured ratios and therefore need to be analyzed with extra scrutiny. For example, we observed instances where one gene of the operon is regulated and the other(s) are not. One such example is that of ribosomal proteins, discussed below. Other examples include the case of acetoin dehydrogenase A and B, where the expression of the gene acuB is unaffected under growth on succinate, whereas its operonic neighbor acuA is 4-fold repressed. Under phosphate starvation, the large 12gene operon for purine biosynthesis is repressed; however, individual genes display a large variability in expression levels, with a 20-fold difference between the most strongly

Figure 2. Biological and technical reproducibility of quantitation. (a) “Succinate” experiment - technical replicates (left); “Succinate” experiment - biological replicates (right). (b) “Low phosphate” experiment - technical replicates (left); “Low phosphate” experiment biological replicates (right). Journal of Proteome Research • Vol. 9, No. 7, 2010 3641

research articles repressed purF and least affected purH. Although the reason for these discrepancies is most likely of a technical nature, another explanation could lie in presently uncharacterized mechanisms of transcriptional and post-transriptional control in bacteria, which might influence protein levels by differential mRNA degradation, alternative terminators, or similar mechanisms. Additional global quantitative proteomic studies in bacteria are underway and they are expected to shed more light on this issue. Quantitative Proteome and Phosphoproteome of B. subtilis. Of 1928 protein groups identified in both experiments, 1730 protein groups fulfilled the requirement of having at least two quantitation events (ratio counts) in each experiment and were therefore considered as quantified. Of these, 1614 protein groups were quantified in the cells grown on succinate, and 1566 protein groups were quantified in the cells grown on low phosphate (all identified and quantified protein groups are presented in the Supplementary Table 2, Supporting Information). As expected, in both tested conditions the majority of protein ratios was distributed around log2 ) 0 (demonstrating little or no change), with a median log2 ratio at -0.21 in succinate, and 0.011 in the low phosphate experiment. The overall ratio distribution in the succinate experiment was shifted toward negative values, pointing to protein downregulation as a predominant process in this tested condition (Figure 3). For quantitative phenotyping of the tested data sets, each ratio distribution was divided into three quantiles, and KEGG pathway enrichment analysis was performed separately for protein populations belonging to each quantile. The results of the KEGG pathway enrichment analysis are presented in Figure 4 and discussed below. In the phosphoproteome study, 35 phosphorylation sites were detected in the succinate and 10 were detected in the low phosphate experiment (all identified phosphorylation sites are presented in the Supplementary Table 3, Supporting Information). In total, 27 phosphoproteins were detected, seven in both experiments (AbrB, YbbT, RsbV, ThiA, IspU, PtsH, FbaA). Data on detected phosphopeptides were uploaded to the PHOSIDA database (www.phosida.com). Compared to our previous qualitative phosphoproteome study in B. subtilis grown in LB medium,30 less phosphorylation sites in the minimal synthetic medium were identified, especially under phosphate starvation; however, of the detected phosphorylation sites 21 were previously not reported (Table 1), which could reflect qualitative phosphoproteome changes related to nutrient availability. It should also be noted that the biomass used for sampling was greatly reduced in the present study, since the phosphoproteome was not the primary target, and this likely affected the overall depth of the phosphoproteome analysis. Nevertheless, the results presented here constitute the first quantitative study of protein Ser/Thr/Tyr phosphorylation in bacteria at the phosphorylation site level.

Discussion Phosphate Starvation. Previous transcriptomic and proteomic studies of B. subtilis under phosphate starvation identified overexpression of different members of the Pho and σB regulons, but with only limited overlap.13,21 Interestingly, they identified mostly the overexpressed genes, and very few repressed genes, with the exception of the enzymes for teichoic acid biosynthesis. Our approach allowed the identification of the majority of differentially regulated 3642

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Soufi et al.

Figure 3. Fold-change distributions of the proteome. (a) Quantitative proteome of B. subtilis with succinate as the carbon source. (b) Quantitative proteome of B. subtilis under phosphate starvation. The distributions are divided into three quantiles (0-25%, 25-75%, 75-100%) which were further used for proteomic phenotyping. The color bar on top represents the quantiles.

proteins reported in these studies. Moreover, our data provide new insights in the global slowdown and reshaping of the cellular metabolism. Intracellular levels of a number of transcription factors were attenuated in the phosphatestarved culture. They comprised the global repressor of stationary phase functions AbrB, the global nutrient-sensing transcription regulator CodY, purine biosynthesis regulator PurR, and about a dozen other transcription regulators of known and unknown functions. In addition, AbrB was found to become phosphorylated at Ser86, a residue in the Cterminal domain important for its multimerization.44 An ever larger number of transcription factors was overproduced under phosphate starvation, which include the alternative

research articles

SILAC of Bacillus subtilis

Figure 4. Functional phenotyping of the B. subtilis proteome. The quantiles resulting from quantitative proteome comparison in Figure 3 were separately analyzed for enriched KEGG pathways and clustered for the z-transformed p values. The color bar on top represents the quantiles. Representative pathways enriched in the protein population of each quantile are annotated for the “Succinate” and “Low phosphate” cases. Table 1. New Ser/Thr/Tyr Phosphorylation Sites Detected in This Study (“ph” ) phosphorylation; “ox” ) oxidation; “ac” ) acetylation)a gene name

phosphopeptide sequence

phosphorylation site

succinate

75 µM KH2PO4

gyrB abrB metS ydcC groES yfjC thiA thiA thiA yjbK ylbO (gerR) recA ilvC yukF yurJ ywrJ ywqD (ptkA) ywqD (ptkA) tyrZ rocF

S(ph)ALEISNLPGK EGAEQIIS(ph)EIQNQLQNLK VVS(ph)IDQSLPK M(ox)LPT(ph)QEIT(ph)FNK TAS(ph)GIVLPDSAK MES(ph)SEGLLAK KNDLS(ph)EAEAINK EFVDTGS(ph)NLYQ EFVDTGSNLY(ph)Q S(ph)QEIEIEFK MVVS(ph)KEDGRM(ox)K S(ph)DRQAALDM(ox)ALK EAVVS(ph)VAQN RIS(ph)QIAEIDLK (ac)MAS(ph)LTFEHVK GVSNNIIELINAS(ph)GEPVIWK HSEYGYY(ph)GTK HSEYGY(ph)YGTK M(ox)LLART(ph)IVRMYHGEK NLNS(ph)VLAGNEK

Ser400 Ser86 Ser652 Thr197;Thr201 Ser36 Ser126 Ser565 Ser586 Tyr589 Ser2 Ser180 Ser2 Ser338 Ser395 Ser3 Ser208 Tyr228 Tyr227 Thr311 Ser68

+ + + + + + + + + + + + + + + + + + + +

+ + -

a A complete list of detected phosphorylation sites together with newly annotated gene names is presented in the Supplementary Table 3, Supporting Information.

sigma factors, sporulation related regulators (Spo0A, Spo0J, Spo0F), stress response regulator HrcA, and the regulator of degradative enzymes DegR. Interestingly, we were also able to document signal transduction leading up to SigB regulon activation at the level of protein phosphorylation. Under the conditions of starvation, RsbV, an anti-SigB antagonist, is expected to be less phosphorylated by RsbW, and thus released.45 This is exactly what we observed, with RsbV phosphorylation exhibiting a 10-fold drop in phosphatestarved culture. These changes lead to a global reshaping of

the proteome, where the most biosynthetic and growthrelated functions are repressed, and biodegradative and other specific functions are activated. Among the repressed functions the most notable are biosynthesis of fatty acids, peptidoglycan, purines, amino acids and cofactors, parts of the pentose phosphate pathway, DNA replication (polymerases I and III, gyrase, helicase loader), nitrate assimilation, parts of glycolysis and the TCA cycle, certain types of motility systems (CheA, CheW, FliD, FliS), and the ATP synthesis. Two glycolytic enzymes, phosphoglucomutase and Journal of Proteome Research • Vol. 9, No. 7, 2010 3643

research articles aldolase, were also increasingly phosphorylated, which in concert to their decreased levels, might indicate that these phosphorylations have an inhibitory effect on their enzyme activities, and therefore contribute to decreasing the glycolytic flux under these conditions. Interestingly, B. subtilis seems to favor an alternative, thermodynamically more potent phosphate donor creatine-phosphate, since the creatine kinase is overproduced in phosphate-starved cells. Among the overproduced enzymes we identified RNA polymerase, general stress proteins (ClpC, chaperones), enzymes for breakdown of amino acids and nucleotides, and RNases and proteases. Another interesting observation is the pronounced reshaping of the ABC transporter complement. There is a large number of changes among these systems, reflecting the change in the overall choice of transported compounds. Interestingly, we also observed a variation in a significant number of ribosomal proteins, some of which were depleted and others overproduced. The overall stoichiometry of ribosomal assemblies is known to be fixed, except for minor exceptions,46 and the overall number of ribosomes changes in concert with growth rate. Therefore, a concerted decrease in the level of ribosomal proteins would be expected during phosphate starvation, related to the strong reduction of growth rate. It is possible that increased RNase and proteolytic activity leads to degradation of ribosomal components at different rates. Growth on Succinate. During growth on succinate, carbon catabolite repression in B. subtilis is alleviated and metabolic fluxes are redirected toward gluconeogenesis. Our results reflect this overall trend, with several notable exceptions, such as the depletion of some PTS transporters and some enzymes of the oxidative phosphorylation (expected to be repressed when glycolysis generates enough ATP). Some complications in interpreting the metabolic setup in this case could arise from the fact that our strain was an auxotroph for several amino acids and nucleotides, whose presence in the medium provides additional nutrient sources. Among the most depleted proteins we identified in succinate-grown cells was the CcpA corepressor and general PTS component HPr and several gluconeogenic reactions were activated. Sucinate-CoA synthetase, which catalyzes the conversion of succinyl-CoA to succinate, was overproduced and dephosphorylated during growth on succinate. This presumably allows the repartition of the carbon source that must for one part be converted to oxaloacetate and be fed into the gluconeogenic pathway, and for the other part participate in amino acid biosynthesis. Surprisingly, proteins involved in DNA recombination and damage-repair were also downregulated. To our knowledge, no direct relationship between carbon source and DNA recombination and repair has been previously recorded. Interestingly, most ribosomal proteins were present in increased amounts in the succinate-grown cells, despite a slightly decreased growth rate compared to the glucose-grown culture. Polyketide, glycan, lipoic acid, and sphingolipid synthesis was among the strongly activated pathways. Amino acid metabolism underwent rearrangements, with activation of lysine and branched-chain amino acids degradation and repression of their synthesis, and activation of aromatic amino acid biosynthesis. Protein phosphorylation seems to play several prominent regulatory roles in gluconeogenic conditions. Dephosphorylation of HPr Ser46 in the absence of glucose leads to alleviation of carbon catabolite repression by CcpA. Interestingly, RecA, the 3644

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Soufi et al. general DNA recombination and DNA lesion-repair protein, whose synthesis was down-regulated under growth on succinate, was also increasingly phosphorylated at its residue serine 2. Phosphorylation in this region that is involved in monomer-monomer interaction47 could have a disruptive effect on the formation of the RecA filament, and therefore contribute to the same physiological effect as the depletion of the RecA protein. The BY-kinase PtkA,48 involved in controlling the DNA metabolism of B. subtilis,49 was found to be increasingly phosphorylated in its C-terminal polytyrosine cluster. So far, physiological triggers for its activation have not been identified, and this is the first indication that glucose starvation might constitute an activating signal for PtkA.

Conclusions This study presents the first systematic application of SILAC to investigation of metabolic adaptation of B. subtilis at the proteome level. We demonstrate that a lysine auxotrophic strain of B. subtilis can be fully labeled with heavy lysine and that its proteome can be accurately and reproducibly quantified at unprecedented depth. Furthermore, we show that this approach is fully compatible with the measurement of global phosphorylation dynamics at the phosphorylation site level, which so far has not been achieved in bacteria. Because of their small genome sizes and relatively simple proteomes due to lack of alternative splicing, bacteria are especially amenable to peptide-based (“shot-gun”) proteomics. We predict that SILAC and similar methods of metabolic labeling will soon play a dominant role in bacterial proteomics. Abbreviations: SILAC, stable isotope labeling by amino acids in cell culture; OD, optical density; PTS, phosphoenolpyruvate: sugar phosphotransferase system; FDR, false discovery rate; ppm, parts-per-million.

Acknowledgment. This work was supported by grants from the Max-Planck Society to M.M., from the Institut National de la Recherche Agronomique (INRA) and Lundbeckfonden to I.M., and from the Landesstiftung Baden-Wu ¨ rttemberg to B.M. We are grateful to Vincent Fromion for discussing the compliance of our data to the established operonic structure. We would also like to thank Mogens Kilstrup for fruitful discussions and insights regarding bacterial growth and physiological experimental conditions. Supporting Information Available: SILAC workflow applied in this study (Figure S1); B. subtilis growth curves from the “succinate” and “low phosphate” experiments (Figure S2); assigned spectra of identified phosphopeptides (Figure S3); identified peptides (Table S1); identified and quantified proteins (Table S2); identified and quantified phosphorylation sites (Table S3). This material is available free of charge via the Internet at http://pubs.acs.org. References (1) de Hoog, C. L.; Mann, M. Proteomics. Annu. Rev. Genomics Hum. Genet. 2004, 5, 267–293. (2) Ong, S. E.; Mann, M. Mass spectrometry-based proteomics turns quantitative. Nat. Chem. Biol. 2005, 1 (5), 252–262. (3) Ong, S. E.; Mann, M. A practical recipe for stable isotope labeling by amino acids in cell culture (SILAC). Nat. Protoc. 2006, 1 (6), 2650–2660. (4) Ong, S. E.; Blagoev, B.; Kratchmarova, I.; Kristensen, D. B.; Steen, H.; Pandey, A.; Mann, M. Stable isotope labeling by amino acids

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