Inverse Regulation in the Metabolic Genes pckA and metE Revealed

ARTICLE pubs.acs.org/jpr

Inverse Regulation in the Metabolic Genes pckA and metE Revealed by Proteomic Analysis of the Salmonella RcsCDB Regulon Alberto Paradela,†,‡ Javier F. Mariscotti,‡,§ Rosana Navajas,† Antonio Ramos-Fernandez,† Juan Pablo Albar,† and Francisco García-del Portillo*,§ †

Laboratorio de Proteomica, Centro Nacional de Biotecnología, Consejo Superior de Investigaciones Científicas (CSIC), Madrid, Spain Departamento de Biotecnología Microbiana, Centro Nacional de Biotecnología, Consejo Superior de Investigaciones Científicas (CSIC), Madrid, Spain

§

bS Supporting Information ABSTRACT:

The RcsC, RcsD, and RcsB proteins compose a system used by enteric bacteria to sense envelope stress. Signal transmission occurs from the sensor RcsC to the transcriptional regulator RcsB. Accessory proteins, such as IgaA, are known to adjust the response level. In a previous transcriptomic study, we uncovered 85 genes differentially expressed in Salmonella enterica serovar Typhimurium igaA mutants. Here, we extended these observations to proteomics by performing differential isotope-coded protein labeling (ICPL) followed by liquid chromatographyelectrospray ionization tandem mass spectrometry. Five-hundred five proteins were identified and quantified, with 75 of them displaying significant changes in response to alterations in the RcsCDB system. Divergent expression at the RNA and protein level was observed for the metabolic genes pckA and metE, involved in gluconeogenesis and methionine synthesis, respectively. When analyzed in diverse environmental conditions, including the intracellular niche of eukaryotic cells, inverse regulation was more evident for metE and in bacteria growing in defined minimal medium or to stationary phase. The RcsCDB system was also shown to repress the synthesis of the small RNA FnrS, previously reported to modulate metE expression. Collectively, these findings provide new insights into post-transcriptional regulatory mechanisms involving the RcsCDB system and its control over metabolic functions. KEYWORDS: Salmonella, RcsCDB, regulon, proteomics, transcriptomics, ICPL (isotope-coded protein labeling), MetE, FnrS

’ INTRODUCTION The proteome of any living organism is considered as a highly dynamic collection of proteins with alterations paralleled by those previously occurring at the transcriptome level. Recently, evidence has been found for multiple post-transcriptional regulatory mechanisms occurring in both eukaryotic and prokaryotic systems.1,2 In bacteria, protein translation is affected by varied mechanisms such as the RNase-mediated decay of the mRNA (mRNA) or the binding efficiency of ribosomes to a concrete Shine-Dalgarno site.3 Other factors affecting translation rates include changes in the folding of the untranslated region (UTR) located at the 50 -end of some mRNAs. These changes occur upon binding of low-molecular weight effectors (amino acids, vitamins, coenzymes) in “riboswitches” structures or the intervention of small noncoding r 2011 American Chemical Society

RNAs with capacity to perform base-pairing in these regions. Regulatory and degradative proteases with specificity for endogenous substrates also affect the amount of the ultimate protein product.46 Large-scale proteomic and transcriptomic analyses allow to gain a global view of alterations resulting from a given stress or environmental change. When combined, these two experimental approaches provide information not only about the dynamics of the transcriptional and translational machineries but also on posttranscriptional and post-translational regulatory mechanisms. Despite being technically rather simple in microbial systems, combined Received: December 29, 2010 Published: June 10, 2011 3386

dx.doi.org/10.1021/pr101294v | J. Proteome Res. 2011, 10, 3386–3398

Journal of Proteome Research

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Table 1. S. Typhimurium Strains Used in This Studya strain

Figure 1. (A) Scheme showing the RcsCfRcsDfRcsB phosphorelay of S. Typhimurium and the accessory membrane protein IgaA that acts as a repressor of the system.20,23 (B) Typical expression pattern of a gene negatively regulated by the RcsCDB system in wild-type, igaA1, igaA1rcsB, and rcsB strains. As an example, the behavior in defined ISM medium of the RcsCDB-regulated gene pbgP, involved in lipopolysaccharide biosynthesis and coregulated by the two-component regulatory system PhoP-PhoQ,49 is shown.

proteomic and transcriptomic studies are relatively uncommon. The few exceptions include recent studies examining bacterial responses at different growth rates, in diverse redox metabolic states, during symbiosis with plants and when coping stresses linked to lowoxygen exposure, high salt, alkali pH or nutrient limitation.715 Most of these studies describe cases with divergent mRNA-protein expression profiles. The number of genes-proteins exhibiting divergent patterns ranged from 30% of the total of analyzed genes9 to a few specific cases.10 Limited correlation between transcription and protein expression profiles has for example been found in bacteria lacking the RNA-binding proteins Hfq and SmpB.16 In a previous study, we reported genome expression profiling data of the intracellular bacterial pathogen Salmonella enterica serovar Typhimurium (hereafter referred to as S. Typhimurium).17 A total of 85 genes were identified showing expression changes upon activation or repression of the RcsCDB regulatory system.17 This signaltransduction system is conserved in most enteric bacteria and responds to high osmolarity and envelope stress.18,19 Mechanistically, the system operates by a phosphorelay initiated upon signal perception at the level of the membrane sensor kinase RcsC, which further transmits the phosphate group to RcsD and from this to the response regulator RcsB (Figure 1A). Phosphorylated RcsB (RcsB∼P) regulates expression of many genes, including those involved in colanic capsule synthesis and flagella.20 Nonphosphorylated RcsB has also been recently shown to regulate gene expression.17 The RcsCDB phosphorelay is modulated by a series of accessory proteins, which either stimulate or repress the phosphate flow.21,22 For example, the outer membrane protein RcsF acts as an activator of the RcsCDB phosphorelay by a mechanism involving the sensor kinase RcsC. Another accessory protein is the inner membrane protein IgaA, which has been identified in S. Typhimurium as an attenuator of the system.20,23 The repression exerted by IgaA on the RcsCDB system is essential for ensuring viability since igaA null mutants are lethal.20 Virulence is impaired in strains carrying the partial loss-of-function allele igaA1, consisting in a R188H nonconservative amino acid change that affects protein stability.23 This allele was identified as a mutation leading to increased growth rate of S. Typhimurium inside fibroblasts.24 The IgaA1 variant is unable to repress the RcsCDB phosphorelay, and as a result, the regulon becomes activated.20,23 Indicators of this activation are the up-regulation in the production of colanic acid capsule and the repression of flagella synthesis, processes

a

relevant

source or

genotype

reference

SL1344

hisG64, rpsL, mouse virulent isolate

34

SV4406

rcsB70::Tn10dCm

20

SV4450

igaA1

20

MD0862

igaA1 rcsB70::Tn10dCm

50

MD0242

gmd::3xflag-Kn

23

MD0237

igaA1 rcsC::3xflag gmd::3xflag-Kn

23

MD0244 MD0245

igaA1 rcsB70::Tn10dCm gmd::3xflag-Kn rcsB70::Tn10dCm gmd::3xflag-Kn

23 23

MD1682

glpK::3xflag-Kn

This work

MD1683

igaA1 glpK::3xflag-Kn

This work

MD1684

igaA1 rcsB70::Tn10dCm glpK::3xflag-Kn

This work

MD2922

rcsB70::Tn10dCm glpK::3xflag-Kn

This work

MD1685

pckA::3xflag-Kn

This work

MD1686

igaA1 pckA::3xflag-Kn

This work

MD1687 MD2923

igaA1 rcsB70::Tn10dCm pckA::3xflag-Kn rcsB70::Tn10dCm pckA::3xflag-Kn

This work This work

MD1688

metE::3xflag-Kn

This work

MD1689

igaA1 metE::3xflag-Kn

This work

MD1690

igaA1 rcsB70::Tn10dCm metE::3xflag-Kn

This work

MD2924

rcsB70::Tn10dCm metE::3xflag-Kn

This work

MD2918

ΔfnrS metE::3xflag

This work

MD2919

igaA1 ΔfnrS metE::3xflag

This work

MD2920 MD2921

igaA1rcsB70::Tn10dCm ΔfnrS metE::3xflag rcsB70::Tn10dCm ΔfnrS metE::3xflag

This work This work

MD1690

igaA1 rcsB70::Tn10dCm metE::3xflag-Kn

This work

All strains are isogenic derivates of SL1344.

controlled positively and negatively by RcsCDB, respectively.20 Mutations causing activation of the RcsCDB system due to gain-of-function mutations in RcsC also attenuate virulence.25,26 Recently, post-transcriptional regulatory mechanisms involving the RcsCDB system were inferred in Escherichia coli. Thus, RcsCDB represses production of the small regulatory RNA (sRNA) RprA.27,28 In S. Typhimurium, RprA was shown to affect the expression of genes involved in colanic acid capsule synthesis, positively regulated by the RcsCDB system.29 To identify post-transcriptional regulatory events involving the RcsCDB system, we analyzed the proteome of S. Typhimurium mutant strains having defects in this system and grown as in our previous transcriptomic study.17,30 Samples were labeled at the peptide level with the isotope-coded protein labeling (ICPL) reagent and subsequently analyzed by liquid chromatography/ electrospray ionization tandem mass spectrometry.31 For the ICPLworkflow, peptide labeling instead of reaction at the protein level increases notoriously protein identification rates and the reliability of the quantification values.32 A total of 505 protein species were identified and quantified across all strains analyzed. Among these, we focused in two proteins, PckA (phosphopyruvate carboxykinase) and MetE (5-methyl-tetrahydropteroyltriglutamate-homocystein-Smethyltransferase), exhibiting opposite changes at the protein and RNA level. This regulation was analyzed in differential environmental conditions, including the intracellular niche of eukaryotic cells. The contribution to this inverse regulation of the small RNA FnrS, recently described as negative regulator of metE expression in E. coli,33 was also investigated. 3387

dx.doi.org/10.1021/pr101294v |J. Proteome Res. 2011, 10, 3386–3398


’ MATERIALS AND METHODS Bacterial Strains and Growth Conditions

The S. Typhimurium strains used in this study are listed in Table 1. Derivate strains were constructed from the virulent strain SL1344 (hisG rpsL),34 as described.23 Addition of 3xflag epitope tags at the 30 ends of the chromosomal copies of glpK, gmd, pckA, and metE genes was carried out as described.35 The sequences of the primers used are detailed in supplementary Table S1 (see Supporting Information). To construct the ΔfnrS deletion mutant, the one-step inactivation procedure described by Datsenko and Wanner was used.36 The oligonucleotides used for this procedure were: pKD3-fnrS-up (50 -GCG AAG TCA ATA AAC CAT CTA CCT ATT CGG GGC AAT ATC TCT CGT GTA GGC TGG AGC TGC TTC-30 ) and pKD3-fnrSdown (50 -GTC ATT CAG ACT CTT AAA GGG TAG ACG CAG ATA GTC TAC AGG CAT ATG AAT ATC CTC CTT AGT-30 ). The ΔfnrS deletion was verified by PCR on chromosomal DNA using primers in regions flanking the fnrS gene: fnrS-up (50 -TAG TTG TCC TGA TCG GTG GTG TTA C-30 ) and fnrSdown (50 - GAT GCG ACA TTT TAA TCG GTT CTT C-30 ). Bacterial cultures were prepared at 37 °C in shaking conditions (180 rpm) using LuriaBertani (LB) medium or defined minimal intracellular-salt (ISM) medium pH 7.4. This ISM medium contained 170 mM K2PO4/KH2PO4, 0.5 mM MgSO4, 1 μM CaCl2, 6 mM K2SO4, 5 mM NH4Cl, 5 mM NaCl, 50 μg mL1 histidine and 2 μg mL1 nicotinic acid.30 38 mM glycerol was used as carbon source. For exponential-phase extracts, the overnight culture was diluted 1/100 in fresh medium and collected at an optical density (absorbance at 600 nm) of 0.20.4. In the case of LB medium, the remaining culture was maintained for an additional 18 h to prepare stationary-phase extracts. When appropriate, the LB or ISM-glycerol media were supplemented with kanamycin (30 μg mL1) or chloramphenicol (10 μg mL1). Infection of Eukaryotic Cells and Isolation of Intracellular Salmonella

Human telomerase reverse transcriptase-immortalized BJ-5ta fibroblasts (ATCC CRL-4001) were used. BJ-5ta fibroblasts were propagated in a 4:1 ratio of Dulbecco’s modified Eagle’s medium (DMEM):medium 199 containing 10% FBS, 1 mM sodium pyruvate and 4 mM L-glutamine. Prior to infection, bacteria were grown overnight at 37 °C in 1 mL of LB medium in standing (notshaking) conditions. These bacteria were spun down (4300 g, 4 °C, 2 min) and washed using Hank’s buffered saline solution (HBSS) (Invitrogen). For each time point, 3 107 human BJ-5ta fibroblasts were seeded in BioDish-XL 500 cm2 plates (BD Biosciences, ref 351040) and infected at a multiplicity of infection of 50:1 (bacteria:fibroblast) when reaching 4060% confluency. After 30 min, the infected fibroblasts were washed five times with prewarmed HBSS. These cells were then incubated for one hour in fresh culture medium containing 50 μg mL1 of gentamicin to kill any remaining extracellular bacteria. The culture medium was then replaced with new fresh tissue culture medium containing 10 μg mL1 gentamicin until 24 h postinfection. Cell cultures were processed as previously described.37 Briefly, the infected cells were washed five times with HBSS, lysed in a solution containing 0.1% SDS/1% acidic phenol/19% ethanol in water and the intracellular bacteria collected by centrifugation (27 500 g, 4 °C, 30 min). Intracellular bacteria were washed twice with cold phosphate buffered saline (PBS) pH 7.4, recovered by centrifugation (15 000 g, 4 °C, 10 min), and finally processed for total protein extracts or RNA extraction (see below).

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Protein Digestion and ICPL-Labeling

Total protein extracts were prepared from bacteria grown at 37 °C in ISM-glycerol medium at exponential phase (OD6000.2). Bacteria contained in 10 mL of culture were collected by centrifugation (8000 g, 10 min, 4 °C), washed in PBS buffer pH 7.4, resuspended in buffer A (1% SDS, 100 mM TrisHCl, pH 7.0) to a density of 5 107 bacteria/μL and boiled for 510 min. These samples were kept at 20 °C. The ICPL-reagent protocol was optimized for labeling of 100 μg of each individual sample per experiment. Thus, 100 μg of total protein extracts from wild-type, igaA1 or igaA1rcsB strains were individually dissolved in 8 M urea 25 mM ammonium bicarbonate, reduced and alkylated with iodoacetamide. Urea concentration was reduced to 2 M with 25 mM ammonium bicarbonate and the sample digested with trypsin (Roche Diagnostics GmbH, Mannheim, Germany) with a ratio of 20:1 overnight at 37 °C as previously described.38 Digested samples were dried and suspended in 50 μL of 0.2% TFA in water and salts and urea removed using high-capacity OMIX C18 tips (Varian, Palo Alto, CA). Labeling with any of the four versions of the ICPL reagent (Serva Electrophoresis, Heidelberg, Germany) was performed at the peptide level according to the manufacturer’s instructions and following the scheme depicted in Supplementary Figure S1 (Supporting Information). Briefly, 100 μg of each sample was individually dissolved in 20 μL of lysis buffer (containing 6 M of guanidinium chloride). Three μL of ICPL reagent was added to each individual sample, shaken, sonicated for 1 min and overlaid with N2 to exclude oxidation (schema depicted in Supplementary Table S2, Supporting Information). Reaction was incubated at 25 °C for 2 h. Labeling was stopped by quenching excess of reagent with hydroxylamine. At this point, samples were combined and 4 μL of NaOH 2N added to destroy esterification products. Finally, the same amount of HCl 2N was added to neutralize the sample. Combined samples were dried in speed-vac and stored dry at 20 °C until needed. 2D-nano LCESIMSMS Analysis

ICPL-labeled combined samples (200 μg per experiment) were dissolved in 100 μL of 10 mM NH4OH in water, pH 9.5 and fractionated in a wide-pH range 5 μm particle size, 100 2.1 mm reversed phase column (Fortis technologies, UK) using a Knauer Smartline HPLC system. HPLC-grade methanol, water and ammonium hydroxide used in the first-dimension chromatography were obtained from Sigma-Aldrich (St. Louis, MO). Gradient elution was performed according to the following scheme: isocratic conditions of 10 mM NH4OH in water, pH 9.5 for 5 min, a linear increase to 25% B (10 mM NH4OH in 80% methanol, 20% water, pH 9.5) in 10 min followed by a linear increase to 75% B in 40 min, a linear increase to 100% B in five minutes, isocratic conditions of 100% B for five minutes, and return to initial conditions in two minutes. Flow-rate was 150 μL/min. Injection volume was 100 μL and the wavelength was monitored at 214 nm. About 2530 individual HPLC fractions (2 min each), were collected in each experiment, dried in a speed-vac and stored at 20 °C until needed. Second dimension of the 2D-nano LCESIMSMS analysis was performed using an Ultimate 3000 nanoHPLC (Dionex, Sunnyvale, California) coupled to an HCT Ultra ion-trap mass spectrometer (Bruker Daltonics, Bremen, Germany). The analytical column used was a silica-based reversed phase column C18 PepMap 75 μm 15 cm, 3 μm particle size and 100 Å pore size (Dionex, Sunnyvale, California). The trap column was C18 PepMap (Dionex, Sunnyvale, California) with 5 μm particle diameter, 100 Å pore size and switched online with the analytical column. The loading pump 3388


Journal of Proteome Research delivered a solution of 0.1% trifluoroacetic acid in 98% water/2% acetonitrile (LabScan, Gliwice, Poland) at 30 μL/min. The nanopump provided a flow-rate of 300 nL/min and was operated under gradient elution conditions, using 0.1% formic acid (Fluka, Buchs, Switzerland) in water as mobile phase A, and 0.1% formic acid in 80% acetonitrile/20% water as mobile phase B. Gradient elution was performed according the following scheme: isocratic conditions of 96% A:4% B for five minutes, a linear increase to 40% B in 60 min, a linear increase to 95% B in one minute, isocratic conditions of 95% B for seven minutes and return to initial conditions in 10 min. Injection volume was 5 μL and the wavelength was monitored at 214 and 280 nm. The LC system was coupled via a nanospray source (Bruker Daltonics, Bremen, Germany) to a 3D ion trap mass spectrometer operating in positive ion mode with the capillary voltage set at 1400 V. Automatic data-dependent acquisition allowed to obtain sequentially both full scan (m/z 3501500) MS spectra followed by tandem MS CID spectra of the four most abundant ions. Dynamic exclusion was applied to prevent the same m/z from being isolated for 1 min after its fragmentation. Protein Identification and Quantitative Analyses

MS and MS/MS data obtained for individual HPLC fractions were merged using the Analysis Combiner tool and subsequently processed as a single experiment using DataAnalysis 3.4 (Bruker Daltonics, Bremen, Germany). For protein identification, MSMS spectra (in the form of mascot generic files) were searched against the Salmonella enterica serovar Typhimurium LT2 SGSC1412 database, downloaded from the Comprehensive Microbial Resource (http://cmr.jcvi.org) and containing 4553 protein-coding genes and their corresponding reversed sequences. Sequence reversal was done using the program pSCAN (http://pfind.ict. ac.cn/pscan.htm). Database searches were done using a licensed version of Mascot v.2.2.04 (www.matrixscience.com; Matrix Science, London, U.K.). Search parameters were set as follows: carbamidomethyl cysteine as fixed modification, oxidized methionine and ICPL-labeling of lysine residues and/or peptide amino termini as variable ones. Peptide mass tolerance was set at 0.6 Da both in MS and MS/MS mode, and 1 missed cleavage was allowed. In most cases, an accuracy of (0.10.2 Da was found both for MS and MS/MS spectra. FDR e 5% for peptide identification were manually assessed as follows: after database searching, a set of peptide matches was ranked according to their corresponding Mascot scores. This list contains peptide sequences matching either forward or reversed database sequences. Then, a subset containing 5% of peptides matching the reversed sequences was extracted, and all the proteins resulted for that FDR were used for further quantitative analysis. Qualitative and quantitative analyses were performed by WARP-LC 1.1 using the parameters described above. First, all the peptides are identified and then, based on a single ICPL-labeled identified peptide, the software calculates the extracted ion chromatogram for the putative ICPL-labeled pair according to: (a) the mass shift defined by the labeling reagent, (b) a mass tolerance of 0.5 Da, and (c) a retention time tolerance of 40 s. Relative ratios between light and heavy ICPL-labeled peptides were calculated based on the intensity signals of their corresponding monoisotopic peaks, and according to these individual peptide ratios the software calculates the protein ratio. Only proteins quantified with at least two peptides were considered. In those cases where the ratio of the pair intensity signals was r g 50 and, taking into account that the dynamic range of the instrument was exceeded, the software assumed there was a “singlet”. For these so-called “singlets” (pairs in which only one partner can be

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quantified reliably), an additional manual assessment and verification of ratio value was performed, assigning them an arbitrary value of regulation of 50. Ratios were log2-transformed, normalized by subtracting the median value, and a zero-centered normal probability density function was least-squares fitted to each data set. The least-squares estimator of the standard deviation s was used to compute the p-values: Z ∞ Nð0, sÞdx pi ¼ xi

where xi is a given log2Ratio value. Recall that each set of ratios contains a substantial proportion of events under the alternative hypothesis, i.e. the absolute number of truly null features used in the FDR calculation is overestimated. This makes the estimation of differential expression thresholds generally conservative. p-Values where then sorted in ascending order and FDR values were computed as follows: FDRi ¼

N pi i

Since differential expression events were mainly concentrated in the right tails, we computed FDR thresholds separately for the left and right tails. Differential expression ratios below a False Discovery Rate (FDR) threshold of 5% were called significant. Finally, the correlation between the proteomic and transcriptomic data was calculated as the Pearson’s correlation coefficient: n

i yÞ ∑ ðxi xÞðy ̅ ̅ i¼1

r ¼ sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi n

n

2 2 ðxi xÞ ∑ ̅ ∑ ðyi yÞ ̅ i¼1 i¼1

where x and y represent the set of proteomic and transcriptomic data, respectively, and x and y are their respective average values. Western Blot Analysis

Protein extracts were prepared from bacteria grown at 37 °C to exponential phase in ISM-glycerol or LB media (OD600 = 0.20.4) or in LB medium up to stationary phase (OD600 ≈ 3.0). Bacteria were collected by centrifugation (8000 g, 10 min, 4 °C), washed in PBS buffer, pH 7.4, and suspended in the appropriate volume of Laemmli sample buffer (1.3% SDS, 10%, v/v, glycerol, 50 mM Tris/ HCl, 1.8% β-mercaptoethanol, 0.02% bromophenol blue, pH 6.8). Samples were adjusted to equal number of bacteria for loading in the gels (4.0 106 to 1 107 bacteria depending on the protein). Proteins were resolved by Tris-Glycine-PAGE39 using 10% gels and transferred onto polyvinyliden-difluoride (PVDF) membranes using a semidry electrophoresis transfer apparatus (Bio-Rad). Flag-tagged proteins were detected with the mouse monoclonal antiflag antibody (clone M2, 1:10 000; Sigma) and a goat antimouse horseradish peroxidase (HRP)-conjugated secondary antibody (Bio-Rad Life Science). For recognition of the chaperonin GroEL, an HPR-conjugated anti-GroEL rabbit antibody was used (dilution 1:10 000, Sigma). Proteins were visualized by chemoluminescence using the luciferin-luminol reagents. RNA Techniques and RT-PCR Assays

For RNA extraction, an amount of ca. 2 109 bacteria corresponding to log-phase cultures, standing overnight cultures, or those collected from a cell culture BioDish-XL 500 cm2 plate (BD Biosciences, ref 351040) containing infected fibroblasts, were used. To instantly stop bacterial metabolism before RNA

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Table 2. S. Typhimurium Proteins Identified by ICPL-Protein Labeling Followed by LCESIMS/MS Analysis proteins identified proteins identified (FDR e 5%)

and quantified (FDR e 5%)

WT (12C6) vs igaA1 (13C6)

509

482

370

WT (13C6) vs igaA1 (12C6)

509

460

349

igaA1 (2D4) vs igaA1rcsB (2D413C6)

428

413

313

igaA1 (2D413C6) vs igaA1rcsB (2D4)

340

318

190

comparisona

a

proteins identified and quantified (# pep >2)

sum per comparison (with no redundancy) 446

total proteins identified 505

353

See Supplementary Table S2 (Supporting Information) for details of the isotope-swapping in the diverse comparisons.

extraction, chilled 5% phenol in ethanol at a 1:5 ratio (vol/vol) was added to the cultures of extracellular bacteria. The cultures were then incubated for 30 min on ice and bacteria harvested by centrifugation (10 000 g, 4 °C, 5 min). RNA was isolated and purified with the SV total RNA isolation system kit (Promega, ref Z3100). RNA was treated with DNase I for 1 h at 37 °C (Turbo DNA-free kit, Ambion/Applied Biosystems, Austin, TX). RNA integrity and concentration were assessed by agarose-TAE electrophoresis and absorbance at 260 nm, respectively. RT-PCR was performed using a one-step RT-PCR kit (Qiagen) as previously described.40 Briefly, RNA samples were diluted to 20 ng μL1 and further normalized against the RT-PCR-amplified 16S rRNA product, used as an internal control. The RT-PCR was carried out in a final volume of 25 μL consisting of 5 μL buffer (5), 1 μL of deoxynucleoside triphosphates (10 mM), 3 μL each of the forward and reverse primers (5 mM), 1 μL of RT-PCR enzyme mix, and ∼20 ng of RNA and RNase-free water supplemented to 25 μL. RTPCR cycling conditions were as follows: 50 °C for 35 min and 95 °C for 15 min, followed by 1826 cycles of 94 °C for 30 s, 55 °C for 30 s, and 72 °C for 40 s and then an extra step of elongation at 72 °C for 10 min. Oligonucleotides used in these RT-PCR assays are listed in Supplementary Table S1 (Supporting Information). Real-Time Quantitative PCR and Expression Data Analysis

For cDNA library construction, 0.22 μg of total RNA were used as template with the high-capacity cDNA archive kit (Applied Biosystems), which includes a mix of random hexamers for onestep run of 10 min at 25 °C, 2 h at 37 °C and 5 min at 85 °C. For real-time quantitative PCR (qRT-PCR), we used 2 ng of the cDNA library as template that was added to the Power Sybr Green PCR master mix (Applied Biosystems) in a 10 μL final volume. The reactions were carried out in an ABI Prism 7300 instrument (Applied Biosystems) using standard reaction conditions: 10 min at 95 °C; 45 cycles of 15 s at 95 °C and 1 min at 60 °C; dissociation curve of 15 s at 95 °C, 1 min at 60 °C and a progressive temperature increase until 95 °C. Each cDNA sample was run in triplicate, and expression data from each condition were obtained from a minimum of two independent experiments. To assess the lack of DNA contamination, equivalent amounts of cDNA libraries produced with and without reverse transcriptase (RT) were compared by qRT-PCR analysis. When feasible, primers were designed using Primer Express 3.0 (Applied Biosystems) and the quality of manually designed primers was examined with the same software. Primers are listed in Supplementary Table S1 (Supporting Information). To determine the amplification efficiency in each qRT-PCR assay, standard curves for each primer pair starting at 12 ng of a cDNA library mix and four points of 1/5 serial dilutions were performed. Ct data from each experimental condition was subtracted from the minimal Ct value in each run (ΔCt) and converted into fold-expression by raising the

efficiency of each qPCR assay to the ΔCt value. 16S rRNA was used as internal control gene. Expression data were analyzed with the GraphPad Prism 5.0 software (GraphPad Inc., San Diego, CA) using two-way analysis of variance (ANOVA) and Bonferroni post-test. p-Values lower than 0.05 were considered significant.

’ RESULTS Proteome Analysis of S. Typhimurium Strains Displaying Different Activity of the RcsCDB Regulatory System

The RcsCDB regulon was recently analyzed by genome expression profiling of wild-type virulent strain S. Typhimurium SL1344 and isogenic igaA1 and igaA1rcsB derivates.17 These derivates differ from wild-type bacteria in the activity level of the RcsCDB system, increased in igaA1 bacteria23 but abrogated in the igaA1rcsB double mutant due to the lack of the response regulator RcsB. The behavior of these strains for a representative gene regulated negatively by the RcsCDB system, pbgP,17 is shown in Figure 1B. The effect of these mutations at the proteome level was investigated by differential isotope-coded protein labeling (ICPL) approach combined with LCESIMS/MS. ICPL-labeling at the protein level is known to yield 3040% of unlabeled peptides.32 ICPL-labeling at the peptide level was used in order to increase the number of peptides amenable for quantification purposes, as it was confirmed in recent reports.32,41 Total protein extracts were prepared from wild-type, igaA1 and igaA1rcsB strains grown in defined ISM medium at exponential phase (OD600 = 0.2). This experimental condition was selected due to the marked activation of RcsCDB system observed in bacteria carrying defects in IgaA and growing in this medium.23 Two independent wild-type/igaA1 proteomic comparisons were performed applying 12C13C isotope-swapping in two biological replicates (Supplementary Table S2, Supporting Information). Five hundred nine unique proteins were identified in both replicates with a false discovery rate (FDR) e 5% (Table 2, Supplementary Table S3, Supporting Information). To improve the quality of the quantitative data, only those proteins with at least two unique peptides identified and quantified with a FDR e 5% were considered, rendering 370 and 349 unique proteins in each replicate (Table 2, Supplementary Table S4, Supporting Information). The sum of both lists generated a nonredundant list of 446 unique protein species, identified and quantified with at least two different peptides (Table 2, Supplementary Table S4, Supporting Information). This protein identification rate represented ∼10% of the S. Typhimurium predicted proteome.42 Regarding the other comparison, igaA1 versus igaA1rcsB, two independent quantitative proteomic analyses were also performed following a similar 12 C13C isotope-swapping strategy. Both analyses yielded a total of 428 and 340 unique protein species identified with a FDR e 5%, respectively (Table 2, Supplementary Table S3, 3390



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Figure 2. Correlation between transcriptomic and proteomic data obtained in S. Typhimurium mutants exhibiting distinct activities in the RcsCDB system. (A) Venn diagram showing the overlap between RcsCDB-regulated transcripts and proteins identified in bacteria growing in defined ISM medium. Only those functions displaying expression changes higher than 4-fold in transcriptomics or 2-fold in proteomics were considered; (B) relative quantification values expressed as log2(ratio) obtained in transcriptomic and proteomic analyses of the comparison igaA1/wild-type. Pearson’s correlation coefficient value R is also shown; (C) same as panel B but for the igaA1/igaA1rcsB comparison. Only genes/proteins appearing in all the four proteomic and transcriptomic experiments were considered for the analyses shown in panels B and C.

Supporting Information). As in the previous case, only those proteins identified with at least two unique peptides were considered for the quantitative analysis, reducing these numbers to 313 and 190 different proteins, respectively. The sum of both lists generated a nonredundant list of 353 unique proteins, identified and quantified with at least two different peptides and with a FDR e 5% (Table 2, supplementary Table S4). This identification rate represented roughly ∼8% of the S. Typhimurium predicted proteome. The proteome analysis performed on both comparisons (igaA1/wt and igaA1/igaA1rcsB) led to the identification of 505 unique protein species. Summary of Quantitative Proteomic Results

WARP LC-calculated protein ratios were log2-transformed, normalized by subtracting the median value, and a zero-centered normal probability density function was least-squares fitted to each data set. Statistically significant up- and down-regulated proteins were determined considering a FDR threshold lower than 5%. Protein expression profiles in both comparisons are depicted in Supplementary Figure S2 (Supporting Information). Four-hundred forty-six unique proteins were identified and quantified with at least two different peptides in the case of the igaA1 vs wild-type comparison (Table 2, Supplementary Table S4, Supporting Information). Of these, 46 showed a statistically significant up-regulation in the igaA1 mutant when compared

with the wild-type strain, meanwhile seven proteins showed a statistically significant down-regulation (Supplementary Table S5, Supporting Information). Of the 353 protein species identified and quantified in the igaA1 vs igaA1rcsB comparison, 36 were significantly up-regulated in the igaA1 strain while 3 proteins were more abundant in the igaA1rcsB double mutant (Supplementary Table S5, Supporting Information). Of note, among the proteins with altered expression characterized in the igaA1/wild-type and igaA1/ igaA1rcsB comparisons, only 17 proteins (∼23%) were common to both lists. This result is consistent with previous data indicating that the RcsCDB regulatory system displays basal activity in wild-type bacteria.17 Such basal activity makes distinguishable the expression profile of wild-type bacteria versus those of either rcsB or igaA1rcsB strains. Examples are the STM1862-STM1863-STM1864 and yciGFE operons, which are less expressed in rcsB mutants compared to wildtype bacteria.17 This proteomic study therefore allowed the identification of 75 proteins differentially expressed upon stimulation and/or inactivation of the RcsCDB system (Supplementary Table S5, Supporting Information). These data suggest that the RcsCDB regulatory system could control ∼2% of the S. Typhimurium proteome (∼4500 protein species).42 This value contrasts with the percentage of the proteome affected by the absence of RNA-binding proteins as Hfq and SmpB, which was estimated in the order of 17% (781 proteins) and 4.2% (189 proteins), respectively.16 3391


3392

pckA

metE

STM3500

STM3965

protein A

glycerol kinase

S-methyltransferase

triglutamate homocysteine

5-methyltetrahydropteroyl-

carboxykinase

synthetase phosphoenolpyruvate

S-adenosylmethionine

FeS cluster formation

scaffold protein involved in

dehydratase

GDP-D-mannose

p

0.22 0.73

1.16 0.07

1.78 0.0048

0.45 0.48

0.23 0.71

1.88 0.003

0.03 0.96 0.84 0.18

exp1

p

0.50 0.22

0.73 0.07

0.41 0.307

0.24 0.56

0.31 0.45

2.92 5.7 1013

0.00 1.00 0.46 0.25

exp 2

p

0.91 0.058

1.08 0.052

1.05 0.033

1.26 0.027

0.93 0.050

8.21 9.8 107

0.02 0.97 0.99 0.040

exp1

p

1.37 0.025

1.49 0.0098

0.46 0.447

0.21 0.65

0.93 0.072

9.09 1.0 108

0.06 0.88 1.07 0.032

exp 2

RNA

p

0.58 0.39

p

p

1.00 0.036

0.82 0.0252

0.43 0.289

1.11 0.0052

0.87 0.028

p

1.53 0.026

1.27 0.0045

1.77 0.0055

0.69 0.090

0.81 0.0880

10.17 4.00 108

0.75 0.040 1.28 0.0035

exp 2

RNA

10.13 6.00 108

0.98 0.012 1.30 0.0045

exp1

2.25 2.7 108 1.66 0.018

0.01 0.97

0.12 0.77

4.37 0

0.22 0.58 0.26 0.52

exp 2

1.26 0.061 0.59 0.14

0.80 0.23

0.71 0.29

0.40 0.55

2.01 0.003

0.85 0.20 0.04 0.95

exp1

protein

IgaA1/IgaA1rcsB (log2 ratio) d

Shown are fold changes, expressed as log2 ratios, between samples of the two independent comparisons (igaA1/WT and igaA1/igaA1rcsB). Only those proteins identified in all the four proteomic assays and displaying discrepancies in the fold-changes at the protein and RNA levels are listed. b Accesion number corresponding to the ortholog gene of the S. enterica serovar Typhimurium strain LT2, with genome annotation avalilable in databases: http://www.ncbi.nlm.nih.gov/sites/entrez?Db=genome&Cmd=Retrieve&dopt=Protein+Table&list_uids=202. c Indicated in bold are genes analyzed individually for expression at the RNA and protein level. The gmd (GDP-D-mannose dehydratase) and glpK (glycerol kinase) genes were used as controls of concordant changes identified by proteomics and transcriptomics (see text for details). d p-Values were computed in the proteomic analyses by the least-squares estimation of the standard deviation (see Material and Methods). In the case of the transcriptomic (microarray) analyses, p-values were obtained using the limma package of the Bioconductor project (http://bioconductor.org) as described.17 Fold changes highlighted in bold correspond to those discrepancies between protein and RNA analyses with statistically significant fold-changes.

a

metK

STM3090

glpK

nifU

STM2542

STM4086

gmd

STM2109

acnB aconitate hydratase 2 ompA outer membrane

STM0158 STM1070

protein name

genec

systematic

gen nameb

protein

IgaA1/WT (log2 ratio) d

Table 3. Discrepancies Found between Proteomic and Transcriptomic Data Collected in the igaA1/WT and igaA1/igaA1rcsB Comparisonsa

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Journal of Proteome Research Comparison of Proteomic and Transcriptomic Data Relative to the RcsCDB System

The proteomic data obtained in this study were consistent with genome expression profiles described by us and other groups for S. Typhimurium strains displaying distinct activities of the RcsCDB system. A significant percentage of proteins up-regulated in the igaA1 mutant, which has a constitutively active RcsCDB system, were enzymes involved in colanic acid capsule synthesis. These proteins include the products encoded by the wza-wzb, wcaABCDEF-gmd-wcaGHI-manC, cpsG-wcaJ, and wzxC-wcaKLM operons (Supplementary Table S5, Supporting Information). The pronounced changes observed in the expression of these proteins agree with data previously reported at the transcriptomic level.17,40,43 Analysis of other proteins encoded by genes positively regulated by the RcsCDB system, as the yjbEFGH and galETKM operons, showed similar trends. Proteins down-regulated in igaA1 bacteria and encoded by previously characterized RcsCDB-repressed genes were also found, as it was the case of the flagellin FljB (Supplementary Table S5, Supporting Information).17 Comparative analysis of proteomes from igaA1 and igaA1rcsB strains led to a better definition of the RcsCDB regulon. The lack of the response regulator RcsB affected negatively the expression of a significant number of proteins (n = 36), including those encoded by genes involved in production of the colanic acid capsule (8 out of 36) (Supplementary Table S5, Supporting Information). Likewise, three proteins exhibited a pattern consistent with a negative regulation exerted by RcsB, that is, up-regulated in the igaA1rcsB mutant. All of them followed a similar regulatory trend as their respective genes in the transcriptomic analysis17 such as those involved in flagellar synthesis (fljB) (Supplementary Tables S5, Supporting Information). Nonetheless, differences in the absolute fold-change detected by the two global techniques were common. For example, when considering significance threshold considered for all biological replicates made in the two techniques (2e log2M g2 and 1e log2M g1 for transcripts and proteins, respectively), only 15 overlaps were found among the 85 transcripts and 75 proteins differentially expressed in igaA mutants (Figure 2A, Supplementary Table S5, Supporting Information).17 This low overlap rate (1720%) found for the RcsCDB regulon at the RNA and protein level agrees however with the values reported in S. Typhimurium by other authors for the number of Hfq- or SmpBregulated proteins and transcripts.16 Post-transcriptional Regulation in the RcsCDB-Regulated Genes pckA and metE

Proteins for which quantitative data were consistently obtained in the four proteomic and transcriptomic experiments were compared to get further insights into the correlation between proteomic and transcriptomic data. This analysis was made irrespective of whether the proteins were differentially regulated or not. A total of 131 proteins were included in this category (Supplementary Table S6, Supporting Information). The comparison of the expression level of these 131 elements at the RNA and protein level resulted in Pearson’s correlation coefficient (R) values close to 0.75 (Figure 2B, C). Such correspondence sustained the reliability of our experimental approach. Several discrepancies at the protein and RNA levels were found (Table 3). Thus, the metabolic genes pckA, encoding the gluconeogenic enzyme phosphoenolpyruvate-carboxykinase, and metE, which encodes the enzyme 5-methyl-tetrahydropteroyl-triglutamate-homocysteine S-methyltransferase involved in methionine biosynthesis, exhibited opposite transcriptional and translational patterns (Figure 2B, C). For pckA, the transcriptomic

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data inferred this gene as an RcsCDB-repressed gene, with log2 values of differential expression ranging in 1.05/0.46 and 1.66/1.77 for the igaA1/wild-type and igaA1/igaA1rcsB comparisons, respectively (Table 3). These differences in RNA level were in marked contrast with the data obtained at the protein level, with positive log2 values (ranging from 0.4 to 2.25) in the four experiments (Table 3). Conversely, transcriptomics data classified metE as an RcsCDB-induced gene, with log2 values of 1.08/ 1.49 and 0.87/1.27 for the igaA1/wild-type and igaA1/igaA1rcsB comparisons, respectively. However, the proteomic analysis resulted in negative log2 values for this gene (Table 3). On the basis of the existing precedents of post-transcriptional regulatory mechanisms involving the S. Typhimurium RcsCDB system,27 we sought to confirm by alternative approaches the probable inverse regulation operating in pckA and metE. To that aim, we generated tagged bacterial strains carrying a 3xflag sequence at the 30 -end of the chromosomal copies of pckA and metE. This procedure, known as ‘one-step chromosomal tagging’, is widely used in Salmonella genetics35 and ensures that the regulation acting on these genes is not altered. The pckA::3Xflag and metE::3Xflag alleles were introduced in four genetic backgrounds: wild-type, igaA1, igaA1rcsB, and rcsB (see Table 1). Bacterial extracts were prepared from these strains in identical growth conditions as for the global analyses (defined ISM medium, exponential phase) to monitor gene expression and protein content by RT-PCR and Western assays. As controls, analogous series of isogenic tagged-strains carrying gmd::3Xflag and glpK::3xflag alleles were constructed (Table 1). gmd encodes GDP-mannose dehydratase, a enzyme involved in synthesis of colanic acid capsule that is positively regulated by the RcsCDB system at the RNA and protein level17,23 whereas glpK encodes glycerol kinase and displays lower expression levels upon RcsCDB activation, both at the RNA and protein level (Table 3).17 A correspondence was observed between the data obtained in the RT-PCR and Western assays and those collected in the proteomic and transcriptomic data (Figure 3A, B). Changes in expression among the different strains were however more evident for metE than for pckA at both RNA and protein level (Figure 3B). However, quantitative RT-PCR data revealed that changes in metE and pckA expression were significant when comparing wild-type and igaA1 strains (Figure 3C). Data obtained with metE were of interest since a higher relative amount of RNA detected in conditions of RcsCDB activation correlated to lower protein levels (Figure 3B). Altogether, these data unraveled post-transcriptional regulatory processes implicating the RcsCDB system and the metabolic genes pckA and metE. Expression of pckA and metE in Different Growth Conditions

The inverse regulation observed for pckA and metE in defined ISM medium was examined in other growth conditions as nutrient-rich LB medium (exponential and stationary growth phases) and in a model involving infection and persistence within cultured human fibroblasts. RNA and protein extracts were prepared from wild-type, igaA1, igaA1rcsB and rcsB strains to monitor pckA and metE expression in extracellular and intracellular bacteria. RT-PCR and Western assays revealed that among all the conditions tested, inverse regulation was observed only for metE and in bacteria collected from LB stationary phase (Figure 4). Compared to defined ISM medium, metE was poorly expressed in all these new conditions tested, which agrees with the presence of methionine in the LB medium and probably also in the intracellular phagosomal niche inhabited by the bacteria inside eukaryotic cells. Interestingly, MetE protein was observed in intracellular bacteria at lower levels in igaA1 with respect to wild-type bacteria 3393



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Figure 3. Inverse regulation of the pckA and metE metabolic genes of S. Typhimurium demonstrated by RT-PCR and Western analyses. Appropriate derivate strains carrying pckA::3xflag- and metE::3xflag-tagged versions were used (see Table 1). gmd (positively regulated by RcsCDB) and glpK (negatively regulated by RcsCDB) were included as control genes. (A) Semiquantitative RT-PCR and Western assays revealing concordance in the changes detected at the mRNA and protein levels for gmd and glpK; (B) Same assays as in panel A but for the metabolic genes pckA and metE. Compared to the wild-type strain, the igaA1 mutant contained lower mRNA and higher protein level for the case of pckA and the reverse for metE. Expression of 16S rRNA and the chaperonin GroEL were determined as loading controls for the RT-PCR and Western assays, respectively; (C) real-time quantitative PCR -RT-qPCR- assays showing relative expression data for the four genes analyzed: gmd, glpK, metE, pckA. Samples in all cases were prepared from bacteria grown in defined ISM medium. Data represent the mean and the standard deviation of three independent experiments. *, P = 0.010.05; ***, P < 0.001 (Two-way ANOVA, Bonferroni post-test).

after 24 h postinfection of human fibroblasts. This change paralleled those found in ISM medium (Figure 3B) and in LB stationary phase (Figure 4). However, no inverse regulation for metE was observed at the RNA level in bacteria collected from eukaryotic cells (Figure 4). These findings suggested that the regulation exerted by the RcsCDB system on metE at the transcriptional and post-transcriptional levels could vary under certain growth conditions. S. Typhimurium RcsCDB System Regulates metE via the Small Regulatory RNA FnrS

Small regulatory RNA (sRNA) molecules play essential roles in modulating expression of many bacterial genes.44,45 Recent analysis on the regulation of anaerobic metabolism in E. coli by the sRNA FnrS uncovered metE as a candidate target gene that was repressed following FnrS overexpression.33 To determine whether FnrS was required for the regulation exerted by the S. Typhimurium RcsCDB system on metE, we generated a new set of isogenic wild-type, igaA1, igaA1rcsB and rcsB strains defective for this sRNA. Lack of FnrS altered metE expression pattern at the transcriptional level. Thus, the abundance of metE transcript in igaA1 strains was evident only in an fnrS+ background (Figure 5A, 5B). In contrast, the lack of FnrS had no effect on MetE protein levels. Compared to the other isogenic strains tested, the amount of MetE protein remained significantly low in both igaA1 and igaA1ΔfnrS strains (Figure 5C). Interestingly, quantitative RT-PCR demonstrated that production of the sRNA FnrS was itself negatively regulated by the RcsCDB system

(Figure 5D). Altogether, these data indicated that RcsCDB may affect metE transcription by modulating expression of the sRNA FnrS and that another RcsCDB-regulated function could control translation (or stability) of the MetE protein independently of FnrS.

’ DISCUSSION This study was designed to analyze proteome changes following activation of the S. Typhimurium RcsCDB regulatory system, repressed in wild-type bacteria at a large extent by the accessory membrane protein IgaA.23 A total of 505 proteins were identified and quantified by ICPL and ESI-MS/M across all extracts prepared from strains displaying distinct activities of the RcsCDB regulon.17,23 This identification rate, which accounts for a ∼11% of the theoretical S. Typhimurium proteome, contrasts with the 1621 proteins reported by Ansong et al. in 2009 using the same bacterium but with an LCMS approach based in a LTQ-Orbitrap mass spectrometer.16 Besides the intrinsic differences derived from the bacterial strains and/or the method used to prepare the protein extracts, the high performance known for the LTQ Orbitrap mass spectrometer may explain such varied identification rates. On the other hand, the 505 proteins identified in our study are in the range of proteomes reported in other bacteria such as Streptomyces coelicolor using similar stable isotope-based proteomic approaches. In this case, 680 proteins (14% of the theoretical proteome of this organism) were identified with more than two 3394



Figure 4. Regulation of pckA and metE in S. Typhimurium grown in different environmental conditions. RNA and protein extracts were prepared from bacteria grown in LB to exponential (OD600 = 0.4) or stationary phase (OD600 = 3.0). Samples were also prepared from intracellular bacteria collected at 24 h postinfection of cultured human fibroblasts BJ5ta (see Material and Methods). Expression of 16S rRNA and the chaperonin GroEL was determined as loading controls for the RT-PCR and Western assays, respectively. The data shown are representative of a minimum of three independent experiments.

unique peptide hits and 99% of confidence score.9 Taking into account these data, the proteome described in our study is considered representative of the growth conditions selected for the analysis. Our interest then focused in comparing such quantitative proteomic data with previous genome expression analyses performed in identical experimental conditions17 to get new insights into the composition of the RcsCDB regulon and the regulatory mechanisms involved. To our knowledge, the proteomic study presented here provides the first insights at a global scale on the number and type of proteins regulated by the RcsCDB system. Remarkably, a low overlap (1720%) was observed between the 85 transcripts and 75 proteins assigned to the RcsCDB regulon (Figure 2A). This

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finding was however in good correlation with the overlap described for transcripts and proteins affected upon loss of global regulators, such as the RNA-binding proteins Hfq and Smp.16 As a representative example, Ansong et al.16 reported 781 proteins and 492 transcripts with altered expression in hfq mutants. Of these, only 113 overlap. Similarly, a 30% overlap was found between transcripts shown by deep-sequencing to be altered in expression in hfq mutants and the analysis of mRNAs coimmunoprecipitated with Hfq.46 Analyses performed in Streptomyces coelicolor in different growth phases also uncovered a significant percentage of divergence, 30%, between gene and protein expression data.9 Most of the recent studies addressing parallel analysis at RNA and protein levels in other bacteria also reported the existence of divergent RNA-protein dynamics,715,47 denoting the prevalence of post-transcriptional regulatory mechanisms in bacteria. Additional potential sources of variability include measurement errors and limitations of the techniques employed. For example, log2(ratio) values >5 are extremely difficult to measure in most mass spectrometers while higher log2(ratio) values are routinely found using transcriptomic approaches. Despite the fact that our comparison of the transcriptomic and proteomic data resulted in Pearson’s correlation coefficient (R) values of 0.75 (Figure 2), we found a set of functions exhibiting opposite regulation, that is, down-regulated at the protein level and up-regulated at the mRNA level or viceversa (Table 3). Opposite regulatory trends have previously been reported in recent global studies performed in Streptomyces coelicolor and Helicobacter pylori.9,47 In this latter study, the authors found six H. pylori proteins with divergent RNA and protein profiles and highlighted two genes, tsaA and ftsA, encoding a metabolic enzyme and a cell division protein, respectively. An opposite behavior concerning the RNA and protein levels was found for these two genes, an observation that was supported with paralleled RT-PCR and Western assays.47 Similarly, we found the metabolic genes pckA and metE exhibiting opposite regulation in bacteria grown in defined ISM medium. PckA and MetE participate in unrelated pathways as the gluconeogenesis and the synthesis of methionine, raising the question of why these two specific functions display this particular type of regulation. MetE is of special interest since there is another enzyme, MetH, which catalyzes the same conversion of L-homocysteine to L-methionine but depending of cobalamin (vitamin B12) as cofactor. The extensive analysis of metE and pckA expression performed in diverse environmental conditions revealed that metE, but not pckA, is subjected to inverse regulation in other conditions besides the defined ISM medium. Concretely, metE expression exhibits such regulation in bacteria grown to stationary phase in LB medium. Interestingly, the IgaA1 mutant protein is degraded when bacteria reach the stationary phase in LB medium.23 These findings suggest that inverse regulation of metE may be achieved only upon a high level of response of the RcsCDB system, as it occurs in the igaA1 mutant in defined ISM medium or in stationary phase in LB. This postulate could also explain our inability to detect opposite regulation of metE or pckA in bacteria located inside eukaryotic cells. Thus, the data collected in diverse infection models indicate that S. Typhimurium virulence relies on a tight repression of the RcsCDB system upon host colonization.23,25,26 Of interest, the “artificial” induction the RcsCDB system in intracellular bacteria due to the igaA1 mutation resulted in lower levels of both the metE transcript and the MetE protein (Figure 4). This observation contrasts with the opposite regulatory trend that the bacteria exhibit 3395



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Figure 5. Small RNA FnrS, regulated negatively by S. Typhimurium RcsCDB system, represses metE transcription without altering MetE protein levels. (A) metE expression in the presence/absence of FnrS as assessed by semiquantitative RT-PCR; (B) relative amount of the metE transcript determined by real-time quantitative PCR -RT-qPCR- in the same set of strains as in panel A; (C) MetE protein amount contained in isogenic strains expressing or lacking the small RNA FnrS; (D) relative amount of fnrS transcript quantified in strains displaying different activities of the RcsCDB system: wild-type (low); igaA1 (high); igaA1rcsB (null) and rcsB (null). Samples in all cases were prepared from bacteria grown in defined ISM medium to exponential phase (OD600 = 0.2). Data represent the mean and the standard deviation of three independent experiments. *, P = 0.01 to 0.05; ***, P < 0.001 (Two-way ANOVA, Bonferroni post-test).

when growing in defined ISM medium or in LB at stationary phase. These differences could be tentatively interpreted as distinct mechanisms acting on metE at the transcriptional and post-transcriptional level that could be directly or indirectly controlled by the RcsCDB system. The analysis of the role of the small RNA FnrS in such regulation sustains this hypothesis since the lack of this regulatory element uncoupled the changes observed at the transcriptional and translation level. The data obtained in our study demonstrate that FnrS is repressed by the RcsCDB system and are also consistent at the RNA level with a negative regulation of FnrS on either metE transcription or the stability of the metE transcript. At the protein level, further work is required to decipher whether other putative RcsCDB-regulated functions could repress MetE translation independently of FnrS. In this scenario, transcriptional regulation exerted by RcsB directly on the metE promoter is also possible. In silico analysis of the promoter indeed reveals a putative “RcsAB box”48 located in positions 161/148 from the translation start site (TaAaacT: cgTCatA versus consensus TaAGaat:atTCctA). Future mutagenesis studies directed to this potential regulatory site could shed new light into the mechanisms used by the RcsCDB system to modulate metE expression at RNA and protein level.

In summary, comparison of the proteomic and transcriptomic data in S. Typhimurium mutants with defects in regulatory and accessory proteins of the RcsCDB system has allowed us to define in more detail the composition of this regulon. Other novel regulatory processes, such as the inverse regulation exhibited by the metE and pckA genes and the control in the expression of the small RNA FnrS by the RcsCDB system, have confirmed the importance of post-transcriptional regulation for the normal functioning of this sensory system.

’ ASSOCIATED CONTENT

bS

Supporting Information Supplementary Tables S1S6 and Figures S1S2. This material is available free of charge via the Internet at http:// pubs.acs.org.

’ AUTHOR INFORMATION Corresponding Author

*Tel. (+34) 91 5854923; Fax (+34) 91 5854506; E-mail: [email protected]. 3396


Journal of Proteome Research Author Contributions ‡

These authors contributed equally to this work.

’ ACKNOWLEDGMENT We thank M. Laura Navarro and Diana Barroso for their technical assistance and the anonymous reviewers for their suggestions of new experiments. This work was supported by grants BIO2010-18885 and CSD2008-00013-INTERMODS from the Spanish Ministry of Science and Innovation. CNBCSIC Proteomics Facility is a member of ProteoRed-Instituto Nacional de Proteomica, funded by Instituto de Salud Carlos III. ’ REFERENCES (1) Galperin, M. Y. Diversity of structure and function of response regulator output domains. Curr. Opin. Microbiol. 2010, 13 (2), 150–9. (2) Krol, J.; Loedige, I.; Filipowicz, W. The widespread regulation of microRNA biogenesis, function and decay. Nat. Rev. Genet. 2010, 11 (9), 597–610. (3) Kaberdin, V. R.; Blasi, U. Translation initiation and the fate of bacterial mRNAs. FEMS Microbiol. Rev. 2006, 30 (6), 967–79. (4) Ehrmann, M.; Clausen, T. Proteolysis as a regulatory mechanism. Annu. Rev. Genet. 2004, 38, 709–24. (5) Schmidt, R.; Bukau, B.; Mogk, A. Principles of general and regulatory proteolysis by AAA+ proteases in Escherichia coli. Res. Microbiol. 2009, 160 (9), 629–36. (6) Van Melderen, L.; Aertsen, A. Regulation and quality control by Lon-dependent proteolysis. Res. Microbiol. 2009, 160 (9), 645–51. (7) Mukhopadhyay, A.; Redding, A. M.; Joachimiak, M. P.; Arkin, A. P.; Borglin, S. E.; Dehal, P. S.; Chakraborty, R.; Geller, J. T.; Hazen, T. C.; He, Q.; Joyner, D. C.; Martin, V. J.; Wall, J. D.; Yang, Z. K.; Zhou, J.; Keasling, J. D. Cell-wide responses to low-oxygen exposure in Desulfovibrio vulgaris Hildenborough. J. Bacteriol. 2007, 189 (16), 5996–6010. (8) Klusener, S.; Hacker, S.; Tsai, Y. L.; Bandow, J. E.; Gust, R.; Lai, E. M.; Narberhaus, F. Proteomic and transcriptomic characterization of a virulence-deficient phosphatidylcholine-negative Agrobacterium tumefaciens mutant. Mol. Genet. Genomics 2010, 283 (6), 575–89. (9) Jayapal, K. P.; Philp, R. J.; Kok, Y. J.; Yap, M. G.; Sherman, D. H.; Griffin, T. J.; Hu, W. S. Uncovering genes with divergent mRNA-protein dynamics in Streptomyces coelicolor. PLoS One 2008, 3 (5), e2097. (10) Pan, C.; Oda, Y.; Lankford, P. K.; Zhang, B.; Samatova, N. F.; Pelletier, D. A.; Harwood, C. S.; Hettich, R. L. Characterization of anaerobic catabolism of p-coumarate in Rhodopseudomonas palustris by integrating transcriptomics and quantitative proteomics. Mol. Cell. Proteomics 2008, 7 (5), 938–48. (11) Mostertz, J.; Hochgrafe, F.; Jurgen, B.; Schweder, T.; Hecker, M. The role of thioredoxin TrxA in Bacillus subtilis: a proteomics and transcriptomics approach. Proteomics 2008, 8 (13), 2676–90. (12) Jennings, L. K.; Chartrand, M. M.; Lacrampe-Couloume, G.; Lollar, B. S.; Spain, J. C.; Gossett, J. M. Proteomic and transcriptomic analyses reveal genes upregulated by cis-dichloroethene in Polaromonas sp. strain JS666. Appl. Environ. Microbiol. 2009, 75 (11), 3733–44. (13) Giotis, E. S.; Muthaiyan, A.; Blair, I. S.; Wilkinson, B. J.; McDowell, D. A. Genomic and proteomic analysis of the AlkaliTolerance Response (AlTR) in Listeria monocytogenes 10403S. BMC Microbiol. 2008, 8, 102. (14) Hahne, H.; Mader, U.; Otto, A.; Bonn, F.; Steil, L.; Bremer, E.; Hecker, M.; Becher, D. A comprehensive proteomics and transcriptomics analysis of Bacillus subtilis salt stress adaptation. J. Bacteriol. 2010, 192 (3), 870–82. (15) Delmotte, N.; Ahrens, C. H.; Knief, C.; Qeli, E.; Koch, M.; Fischer, H. M.; Vorholt, J. A.; Hennecke, H.; Pessi, G. An integrated proteomics and transcriptomics reference data set provides new insights into the Bradyrhizobium japonicum bacteroid metabolism in soybean root nodules. Proteomics 2010, 10 (7), 1391–400.

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