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J Bacteriol. Mar 2012; 194(6): 1317–1330.
PMCID: PMC3294845

Analysis of the Pseudomonas aeruginosa Regulon Controlled by the Sensor Kinase KinB and Sigma Factor RpoN

Abstract

Alginate overproduction by Pseudomonas aeruginosa, also known as mucoidy, is associated with chronic endobronchial infections in cystic fibrosis. Alginate biosynthesis is initiated by the extracytoplasmic function sigma factor (σ22; AlgU/AlgT). In the wild-type (wt) nonmucoid strains, such as PAO1, AlgU is sequestered to the cytoplasmic membrane by the anti-sigma factor MucA that inhibits alginate production. One mechanism underlying the conversion to mucoidy is mutation of mucA. However, the mucoid conversion can occur in wt mucA strains via the degradation of MucA by activated intramembrane proteases AlgW and/or MucP. Previously, we reported that the deletion of the sensor kinase KinB in PAO1 induces an AlgW-dependent proteolysis of MucA, resulting in alginate overproduction. This type of mucoid induction requires the alternate sigma factor RpoN (σ54). To determine the RpoN-dependent KinB regulon, microarray and proteomic analyses were performed on a mucoid kinB mutant and an isogenic nonmucoid kinB rpoN double mutant. In the kinB mutant of PAO1, RpoN controlled the expression of approximately 20% of the genome. In addition to alginate biosynthetic and regulatory genes, KinB and RpoN also control a large number of genes including those involved in carbohydrate metabolism, quorum sensing, iron regulation, rhamnolipid production, and motility. In an acute pneumonia murine infection model, BALB/c mice exhibited increased survival when challenged with the kinB mutant relative to survival with PAO1 challenge. Together, these data strongly suggest that KinB regulates virulence factors important for the development of acute pneumonia and conversion to mucoidy.

INTRODUCTION

The genetic disease cystic fibrosis (CF) manifests in multiple systems of the body; the most life-threatening complication is the predisposition to bacterial respiratory infections (25). In decades past, Staphylococcus aureus and Haemophilus influenzae were recognized as the dominant pathogens that infected the CF patient airway. Antibiotic therapies have decreased the incidence of these pathogens. However, infection of the CF patient's airway with the Gram-negative opportunistic pathogen Pseudomonas aeruginosa remains of great concern. Once P. aeruginosa colonizes the lung, it multiplies to high cell densities and forms biofilms, where neither the host immune response nor antibiotic therapies are effective in complete eradication of the organism.

P. aeruginosa has numerous genes and phenotypes that contribute to the persistence in the CF lung. The phenotype known as mucoidy is caused by the overproduction of the exopolysaccharide alginate. Typically, nonmucoid environmental strains initially colonize the lungs of CF patients. However, isolation of stable mucoid strains from sputum samples signals the onset of chronic infection of the CF lung (25). One of the master regulators of alginate production is the sigma factor σ22 also known as AlgU (AlgT). AlgU activates alginate overproduction by regulating the expression of transcription factors (2, 16, 23) leading to transcriptional activation of the algD biosynthetic operon (17, 26, 36). The genes controlled by AlgU and their role in the alginate machinery have been extensively reviewed elsewhere (46). MucA is an anti-sigma factor and the primary negative regulator of alginate overproduction. MucA directly sequesters AlgU to the inner membrane (48). Constitutively mucoid CF isolates typically harbor mutations in mucA (36), but mutations in the other negative regulators of AlgU, mucB and mucD, have been identified in mucoid CF isolates (8). If the mucA gene is wild type (wt), MucA must be proteolytically degraded to activate AlgU (45). MucA can be degraded by proteases AlgW and MucP, which are the homologues of Escherichia coli DegS and RseP (45). AlgW protease can be activated by accumulation of envelope proteins (45). Once an envelope protein binds to and activates AlgW, cleavage of MucA occurs at several specific residues in the periplasmic C terminus of MucA (6). After AlgW cleavage of MucA, it is hypothesized that MucP will then further digest MucA (45), which could activate AlgU. An exception has been observed where it appears that MucP can cleave MucA independent of AlgW (13). MucB and MucD also participate in modulating AlgU activity through regulated proteolytic degradation of MucA. MucB (24, 36) protects the C terminus of MucA from proteolytic degradation (6), and MucD is a chaperone protease (58, 63) that regulates protein quality in the periplasm, which may indirectly control MucA degradation (13).

The mucoid phenotype can also be regulated via environmental sensing. Environmental nonmucoid strains have the capacity to become mucoid regardless of mucA mutation (10). One method used by bacteria to sense the environment is through two-component regulatory systems which are comprised of a cytoplasmic membrane-bound histidine kinase, a sensor, and a response regulator, usually a transcription factor. Phosphorylation and dephosphorylation are mechanisms of signal transduction across the inner membrane and can result in changes in gene expression mediated by the response regulator. P. aeruginosa strain PAO1 has 63 sensors and 64 response regulators (47). We recently reported that the inactivation of a gene encoding a histidine kinase, KinB, resulted in alginate overproduction (12). In the PAO1 kinB strain, the activated protease AlgW degrades MucA (Fig. 1). Furthermore, regulated proteolysis of MucA is dependent on the cognate response regulator of kinB, which is known as AlgB, as well as the rpoN gene encoding σ54 (12). A CF isolate with the kinB mutation has been identified (12). AlgB has been suggested to be an atypical response regulator because AlgB-dependent transcription in alginate regulation does not require phosphorylation (35), and it can be inferred that AlgB would not be phosphorylated in the absence of KinB.

Fig 1
P. aeruginosa sensor kinase KinB regulation of alginate overproduction. KinB is a sensor kinase that localizes to the inner membrane. KinB is also a negative regulator of alginate overproduction (12). We have previously shown that in the absence of kinB ...

In this study, we analyzed the whole transcriptomes and proteomes of PAO1 kinB and PAO1 kinB ΔrpoN to identify the genes and proteins that are uniquely controlled by KinB and RpoN. Our data indicated that KinB, in concert with RpoN, controls alginate and rhamnolipid expression as well as structural pilus genes and other potentially novel factors. In the kinB mutant, RpoN alone represses pyochelin, type IVb pili, antibiotic efflux, and quorum-sensing (QS) genes. In light of these data, we hypothesized that KinB may control virulence in P. aeruginosa which was corroborated in an acute murine infection model. Collectively, our data show that KinB is a pleiotropic regulator that through RpoN controls a regulon consisting of approximately 20% of the PAO1 genome and includes the alginate biosynthetic pathway as well as key determinants of virulence.

MATERIALS AND METHODS

Bacterial strains and culture medium conditions.

Bacterial strains and plasmids used in this study are indicated in Table S1 in the supplemental material. P. aeruginosa strains were grown at 37°C on Pseudomonas isolation agar (PIA) plates (Difco). Lysogeny broth (LB)-Miller agar plates were prepared with 10 g of tryptone, 5 g of NaCl, 10 g of yeast extract, and 15 g of agar per liter.

RNA isolation and preparation for Affymetrix GeneChip analysis.

P. aeruginosa strains were streaked on PIA plates and harvested in a 2:1 solution of phosphate-buffered saline (PBS) and RNA Protect (Qiagen) as per the manufacturer's instructions. After a 10-min incubation at 20°C, cells were centrifuged at 4,000 × g, and the supernatant was removed. The cell pellet was then resuspended in 1 ml of TRIzol reagent (Invitrogen), and total RNA was purified following the manufacturer's instructions. Total RNA was DNase treated with Ambion's Turbo DNA Free reagent. DNase-treated RNA was used as a template in a 35-cycle PCR to assess the presence of contaminating DNA. RNA quality and the presence of residual DNA were checked on an Agilent Bioanalyzer 2100 electrophoretic system pre- and post-DNase treatment. Ten micrograms of total RNA was used for cDNA synthesis, fragmentation, and labeling according to the Affymetrix GeneChip P. aeruginosa genome array expression analysis protocol. Briefly, random hexamers (Invitrogen) were added (final concentration, 25 ng μl−1) to the 10 μg of total RNA along with in vitro transcribed Bacillus subtilis control spikes (as described in the Affymetrix GeneChip P. aeruginosa genome array expression analysis protocol).

cDNA was synthesized using Superscript II (Invitrogen) according to the manufacturer's instructions under the following conditions: 25°C for 10 min, 37°C for 60 min, 42°C for 60 min, and 70°C for 10 min. RNA was removed by alkaline treatment and subsequent neutralization. The cDNA was purified with use of a QIAquick PCR purification kit (Qiagen) and eluted in 40 μl of buffer EB (10 mM Tris-HCl, pH 8.5). The cDNA was fragmented by DNase I (0.6 U μg−1 of cDNA; Amersham) at 37°C for 10 min and then end labeled with biotin-ddUTP with use of an Enzo BioArray Terminal Labeling kit (Affymetrix) at 37°C for 60 min. Proper cDNA fragmentation and biotin labeling were determined by gel mobility shift assay with NeutrAvadin (Pierce), followed by electrophoresis through a 5% polyacrylamide gel and subsequent DNA staining with SYBR green I (Roche).

Microarray data analysis.

Microarray data were generated using Affymetrix protocols as previously described (22, 34, 40, 42). Absolute expression transcript levels were normalized for each chip by globally scaling all probe sets to a target signal intensity of 500. Three statistical algorithms (detection, change call, and signal log ratio) were then used to identify differential gene expression in experimental and control samples. The detection metric (presence, absence, or marginal) for a particular gene was determined using default parameters in MAS software (version 5.0; Affymetrix). Batch analysis was performed in MAS to make pairwise comparisons between individual experimental and control GeneChips in order to generate change calls and a signal log ratio for each transcript. These data were imported into Data Mining Tools (version 3.0; Affymetrix). Transcripts that were absent under both control and experimental conditions were eliminated from further consideration. Statistical significance of signals between the control and experimental conditions (P < 0.05) for individual transcripts was determined using the t test. We defined a positive change call as one in which greater than 50% of the transcripts had a call of increased (I) or marginally increased (MI) for upregulated genes and decreased (D) or marginally decreased (MD) for downregulated genes. Finally, the mean value of the signal log ratios from each comparison file was calculated. Only those genes that met the above criteria and had a mean signal log ratio of greater than or equal to 1 for upregulated transcripts and less than or equal to 1 for downregulated transcripts were kept in the final list of genes. Signal log ratio values were converted from log2 and expressed as fold changes.

RNA isolation and reverse transcriptase-quantitative PCR (RT-qPCR).

P. aeruginosa strains were streaked on PIA plates and harvested in a 2:1 solution of PBS and RNA Protect (Qiagen) as per the manufacturer's instructions. After a 10-min incubation at 20°C, cells were centrifuged at 4,000 × g, and the supernatant was removed. The cell pellet was then resuspended in 1 ml of TRIzol reagent (Invitrogen), and total RNA was purified following the manufacturer's instructions. Total RNA was DNase treated with Ambion's Turbo DNA Free reagent. DNase-treated RNA was used as a template in a 35-cycle PCR to assess the presence of contaminating DNA. cDNA samples were generated from 1 μg of total RNA using a TaqMan reverse transcription kit (Applied Biosystems) according to the manufacturer's instructions in a total volume of 100 μl. Twenty-microliter qPCR mixtures were set up using 1 μl of cDNA, 10 μl of 2× FastStart Universal SYBR green qPCR master mix (Roche), and 4 pmol of each primer (see Table S1 in the supplemental material). qPCRs were performed in 96-well plates in an ABI Prism 7900HT Fast Real Time thermocycler. The program used consisted of an initial 10-min incubation at 95°C, followed by 40 cycles of 15 s at 95°C and 1 min at 60°C. Primer specificity was verified by use of a denaturation step following the last amplification cycle. Threshold cycle (CT) values were collected with a manual threshold of 0.2. Each target was tested in quadruplicate, and the average of the four CT values was used for analysis. The average CT was converted to a relative transcript number by the equation n = 2(40−CT) as previously described (41). These values were then standardized to the determined value for omlA, and the number of target transcripts per omlA transcript was calculated. These normalized values were used to determine fold changes.

Motility assays.

Swimming and swarming assays were performed as described previously (33). Briefly, strains were grown overnight on tryptic soy agar (TSA; Remel) and used to inoculate freshly prepared TSA plates with either 0.4% agar (swimming) or 0.5% agar (swarming). Samples were spotted in triplicate, and the radius of the colony was measured at 24 h postinoculation. Reported values are the average with standard deviation of three replicates. Two methods were employed for twitching motility, subsurface and surface twitching motility assays. Subsurface twitching motility assays were performed as previously described (1). Zones of motility after 48 h of incubation at 37°C were measured after the agar was removed and cells attached to the plastic surface of the petri dish were stained with 0.5%, wt/vol, crystal violet. Surface twitching motility assays were performed as previously described (30, 39). Briefly, buffered twitching motility plates were prepared (10 mM Tris, pH 7.6, 8 mM MgSO4, 1 mM NaPO4, pH 7.6, and 1.5% agar) and dried for 48 h. Overnight cultures were subcultured, grown to an optical density at 590 nm (OD590) of 1.2, and collected by centrifugation to 9 × 109 cells/ml in morpholinepropanesulfonic acid (MOPS) buffer (10 mM MOPS, pH 7.6, 8 mM MgSO4). A 2.5-μl volume of this suspension was placed onto the surface of the agar, and the zone of twitching after 48 h was captured on a Nikon TE2000-U microscope with a 20× objective and a DS-Fi1 digital camera (Nikon).

Rhamnolipid production assay.

Semiquantitative measurements of rhamnolipid production were performed using minimal medium containing cetyl trimethylammonium bromide (CTAB; Acros) and methylene blue (MB; Sigma-Aldrich). Agar plates were made as previously described (43) using 1.6% (vol/vol) glycerol as the sole carbon source. Wells were prepared by pressing a heated 4-mm-diameter glass rod into the agar. Overnight cultures were adjusted by dilution to an OD600 of 0.2, and 10 μl of cultures was inoculated in the wells. Plates were incubated at 37°C for 48 h and subsequently stored at 4°C for 24 h to increase the contrast of the blue halo indicative of surfactant diffusion. Diameters were measured and compared with a standard of known rhamnolipid concentration.

iTRAQ MALDI-TOF/TOF proteome analysis of PAO1, PAO1 kinB, PAO1 kinB ΔalgU, and PAO1 kinB ΔrpoN.

Strains PAO1, PAO1 kinB, PAO1 kinB ΔalgU, and PAO1 kinB ΔrpoN were cultured on PIA medium for 24 h at 37°C. Total protein samples were prepared using a ProteaPrep Cell Lysis kit, per the manufacturer's instructions. Total protein samples (500 μg) were acetone precipitated with a 1:6 dilution and incubated overnight at −20°C. The resulting pellets were reconstituted in 160 μl of dissolution buffer for use with isobaric tags for relative and absolute quantification (iTRAQ), to which 8 μl of iTRAQ denaturant was added. A total of 16 μl of reducing agent was added, and the samples were incubated for 60 min at 60°C. Then, 8 μl of cysteine blocking reagent was added, and the samples were incubated for another 10 min in the dark. Trypsin (2 μg) was added to the samples for proteolytic degradation, and the reaction was performed overnight at 37°C. Each sample, PAO1, PAO1 kinB, PAO1 kinB ΔalgU, and PAO1 kinB ΔrpoN, was labeled with iTRAQ reagents 114, 115, 116, and 117, respectively. The labeling of each sample was performed as per the instructions with the kit. The labeling reaction mixture was incubated at room temperature for 60 min. To stop the reactions, 100 μl of distilled H2O (dH2O) was added, and the samples were incubated for 30 min at room temperature. The samples were frozen and then lyophilized. The lyophilized samples were then reconstituted in 50 μl of SCX buffer (Protea Biosciences) for fractionation. The samples were transferred to equilibrated SCX ProteaTip Spin Tips and centrifuged at low speed (4,000 rpm) for 2 min. The column was then washed to elute salts and other contaminants with a 50-μl rinse solution (5 mM ammonium formate in 10% acetonitrile). The Spin Tip was then added to a clean centrifuge tube, and fractions were collected by 150 μl of eight different elution solutions (20, 40, 60, 80, 100, 150, 250, and 500 mM) with 5 mM ammonium formate in 10% acetonitrile. The collected fractions were then cleaned by repeated lyophilization and reconstitution in 0.1 M acetic acid. After final lyophilization, the labeled and digested peptides were reconstituted in 12 μl of liquid chromatography buffer (0.1% trifluoroacetic acid [TFA] in water) for liquid chromatography–matrix-assisted laser desorption ionization (LC-MALDI) spotting. An ABI Tempo LC-MALDI instrument was used, running version 2.00.09 software. A Merck Chromolith CapRod monolith column was injected with 10 μl of sample. A 30-min separation gradient with two components was used for LC spotting. Buffer A consisted of 0.1% acetic acid and 2% acetonitrile, and buffer B consisted of 0.1% acetic acid and 90% acetonitrile. The ratio of percent A to percent B over the 30-min gradient is available upon request from Protea Biosciences. Spotted samples were then analyzed by an ABI 4800 MALDI-tandem time of flight (TOF/TOF) analyzer with 400 Series Explorer software running acquisition in reflector mode, positive ion mode. A mass range (m/z) of 850 to 4,000 was set with 400 laser shots per spectrum. A minimum signal-to-noise ratio of 10 was required for acquisition. Data collected by MALDI-TOF/TOF were then analyzed by ABI ProteinPilot software, version 3.0, using the search engine Paragon with iTRAQ sampling. The ProteinPilot software used the P. aeruginosa PAO1 proteome to identify peptides detected in the iTRAQ analysis of the total protein samples. The software reported the number of unique peptides that were found for each protein as well as the percent coverage of the protein. A ratio was derived that compares a peptide from one labeled sample to another labeled sample. If more than one unique peptide was detected and analyzed for a protein, then a P value was reported to suggest if the protein is significantly up- or downregulated between the two samples analyzed.

Murine acute pneumonia model.

P. aeruginosa strains were grown on Pseudomonas isolation agar (PIA; Becton Dickinson) plates for 24 h at 37°C and suspended in 1× PBS to an OD650 of 1.8. Inocula were diluted 1:1 with 1× PBS to obtain the desired challenge dose in 20 μl. Six- to 8-week-old female BALB/c mice (Harlan Laboratories) were anesthetized by intraperitoneal injection of 0.2 ml of ketamine (6.7 mg/ml) and xylazine (1.3 mg/ml) in 0.9% saline. Anesthetized animals were placed on their backs, and 10-μl inocula were pipetted directly into each nostril (20 μl total). Bacterial doses were verified immediately after infection by serial dilution in 1× PBS–1% bovine serum albumin (BSA) and plating on PIA. All animals were carefully observed for the duration of the trials. The University of Virginia Animal Care and Use Committee approved all procedures used in this work.

Microarray data accession number.

The microarray data are available on the GEO (Gene Expression Omnibus) website at http://www.ncbi.nlm.nih.gov/projects/geo under accession number GSE35248.

RESULTS

Transcriptome analysis of PAO1 kinB and PAO1 kinB ΔrpoN strains.

Typically, wt strains of P. aeruginosa such as PAO1 express a nonmucoid phenotype when grown on a common laboratory medium such as PIA. Previous studies established the role of MucA, MucB, and MucD as negative alginate regulators (4, 24, 36). We recently showed that the inactivation or deletion of kinB caused AlgW-mediated proteolysis of MucA, which also results in alginate overproduction (12). Interestingly, in the kinB mutant, AlgU activity and MucA degradation required the transcriptional regulator AlgB and alternate sigma factor RpoN (12). From these data a proteolysis-mediated model of alginate regulation was proposed where AlgB and RpoN control the expression of factors that can activate AlgW to degrade MucA. Therefore, the loss of rpoN would cause a decrease in the expression of alginate synthesis genes due to the lowered AlgU activity (Fig. 1). The effect of AlgB on the P. aeruginosa transcriptome, as has been previously shown (32), is through binding to PalgD to activate transcription of the alginate biosynthetic operon. In order to understand the effect of the kinB and rpoN mutations on the transcriptome, microarray analysis was performed on the mucoid kinB mutant, and data were compared to results of the nonmucoid kinB rpoN double mutant. To our surprise, a vast number of genes (926 genes) were dysregulated due to deletion of rpoN in the kinB mutant (Fig. 2A and Tables 1 and and2;2; see also Table S2 in the supplemental material). Previous microarray analysis indicated that the loss of rpoN caused the dysregulation of 62 genes in P. aeruginosa strain PAK (14). Here, we observed that the KinB-RpoN regulon is made up of 926 genes, which represent approximately 20% of the PAO1 genome (see Table S2). Of these 926 genes, 499 were repressed, and 427 were activated (Fig. 2A). Seventeen of the 62 previously reported dysregulated genes due to loss of rpoN were confirmed (see Table S2). Since the kinB strain is mucoid and has high AlgU activity (12), we expected to observe genes in the KinB regulon that were dependent on AlgU. Multiple transcriptome studies have been performed with mucA mutants (1821) and compounds that activate regulated proteolysis of MucA (57, 59). We chose to define genes as AlgU dependent based on an analysis by Tart et al. (50), which utilized an isogenic algU pair to define the AlgU regulon. We observed that 139 AlgU-dependent genes (50) were also downregulated in the PAO1 kinB ΔrpoN strain, confirming our previous hypothesis that RpoN regulates AlgU activity. Interestingly, we observed 360 genes that were dependent on RpoN and that had not been found to be AlgU regulated (Fig. 2A; see also Table S2 in the supplemental material).

Fig 2
Microarray analysis of KinB regulon of P. aeruginosa. (A) The numbers of genes dysregulated are indicated and subdivided by genes activated (red) and genes repressed (green) due to the deletion of rpoN in the kinB mutant. Genes which are repressed are ...
Table 1
KinB regulon genes with decreased expression due to rpoN deletion
Table 2
KinB regulon genes with increased expression due to rpoN deletion

KinB-RpoN regulon genes were grouped by PseudoCAP class (56) and plotted with respect to activation or repression in Fig. 2B. A large number of hypothetical genes were dysregulated but are not indicated in Fig. 2B. A total of 182 hypothetical genes were activated, and 155 hypothetical genes were repressed when the kinB rpoN double mutant was compared to the kinB strain. In the kinB mutant, deletion of rpoN caused activation of 18 genes in the adaptation and protection class but also repressed 7 genes (Fig. 2B). Of particular interest, 30 genes involved in amino acid biosynthesis and metabolism were repressed due to the deletion of rpoN in the kinB mutant. Eighteen genes encoding secreted factors were activated in the kinB rpoN double mutant (Fig. 2B). We also observed that genes encoding 34 membrane proteins were activated, and 50 were repressed due to the rpoN deletion in the kinB background. These data support the hypothesis that a connection between RpoN and outer membrane protein expression exists. Similarly, a large number of genes which encode proteins that function in small-molecule transport were repressed in the kinB rpoN double mutant (Fig. 2B).

AlgW expression in the kinB and kinB ΔrpoN mutants.

In our previous study, MucA degradation in the kinB mutant was abrogated by the deletion of rpoN (12). We hypothesized that this may be due to the loss of AlgW protease activity or the absence of an unknown factor (12). Transcriptome analysis showed that the expression of algW decreased by 11-fold in the PAO1 kinB ΔrpoN strain compared to PAO1 kinB (Table 1). To validate this, reverse transcriptase quantitative PCR (RT-qPCR) was performed and showed that algW expression was 2.9-fold lower in the PAO1 kinB ΔrpoN mutant than in the PAO1 kinB strain (Table 3). PAO1 also showed less AlgW expression than PAO1 kinB under the same conditions (Table 3). These data suggest that AlgW expression is affected by RpoN and KinB, which supports our previously proposed hypothesis that RpoN may affect the AlgW protease activity and/or expression.

Table 3
RT-qPCR analysis of KinB-RpoN regulon genes

KinB and RpoN control expression of alginate biosynthetic genes and genes associated with carbohydrate metabolism.

Comparison of the PAO1 kinB and PAO1 kinB ΔrpoN transcriptomes indicated that alginate biosynthetic gene expression was significantly lower in PAO1 kinB ΔrpoN, as would be expected in any comparison between a mucoid and a nonmucoid strain (Table 1). Since AlgU activity in PAO1 kinB depends on RpoN (12), it is not surprising to see that many AlgU-dependent genes are also dependent on RpoN in PAO1 kinB (Table 1). Interestingly, algW was the only alginate gene detected that is only RpoN dependent and not dependent on both RpoN and AlgU. These data suggest a hierarchy of alginate gene expression where RpoN controls regulatory elements such as AlgW that can activate the AlgU pathway leading to alginate synthesis. In the transcriptome analysis (Table 1), no significant changes in algU and mucA expression were observed between PAO1 kinB and PAO1 kinB ΔrpoN strains. However, RT-qPCR showed that algU and mucA expression levels decreased 10.5- and 8.8-fold, respectively, due to loss of rpoN (Table 3), as was previously shown (12).

Examination of the RpoN-dependent genes in a kinB background revealed a chromosomal region from PA2134 to PA2192 comprised of 54 genes whose expression was dependent on RpoN in PAO1 kinB (Table 1). Other investigators have noticed that this same chromosomal region was differentially expressed in planktonic and biofilm stationary-phase growth transcriptomes (53). In our study, all of these genes were dependent on RpoN. Here, this region of genes will be referred to as the PA21XX region. Interestingly, in mucoid strains, a subset of these genes in the middle of this chromosomal region were previously shown to be regulated by AlgU (PA2146, PA2147, PA2161, PA2165, PA2170, PA2171, PA2172, PA2173, and PA2176) (50) (Table 1). Three of these genes (PA2161, PA2167, and PA2172) have been shown to be AlgU dependent (61).

iTRAQ analysis of the kinB mutant proteome.

While transcriptome analysis provided new insights into the effect of the loss of kinB, we sought to further characterize the effect by examining the proteome of P. aeruginosa strains PAO1, PAO1 kinB, PAO1 kinB ΔalgU, and PAO1 kinB ΔrpoN by using isobaric tags for relative and absolute quantification (iTRAQ). iTRAQ uses the analysis of MALDI-TOF (matrix-assisted laser desorption ionization–time of flight) mass spectrometry (MS) with tagged peptides that allow multiple protein samples to be examined and the relative quantity of proteins to be measured. Our data for iTRAQ analysis of the strains are shown in Table 4 and Table S3 in the supplemental material. A total of 1,448 peptides that had 95% confidence values were observed in the four-sample multiplex, and of these, 740 were distinct and corresponded to 121 proteins to which relative intensity and P values could be assigned. Similar to the transcriptome analyses, PA2169, PA2171, PA2184, and PA2190 were upregulated in the kinB mutant (Table 4) and repressed in the PAO1 kinB ΔrpoN strain. However, we could determine that only PA2184 and PA2190 were upregulated to a statistically significant level (see Table S3 in the supplemental material), but PA2169 and PA2171 were not significantly upregulated. The most plausible explanation is that since a distinct tryptic peptide must be found in each sample, some proteins will lack enough trypsin recognition sites. However, our analysis of the kinB mutant as well as PAO1 kinB ΔalgU and PAO1 kinB ΔrpoN mutants supported the transcriptome analysis and suggests that the PA21XX chromosomal region is controlled by RpoN and KinB and may play a role in metabolic pathways.

Table 4
iTRAQ quantitative proteomic analysis of strains PAO1, PAO1 kinB, PAO1 kinB ΔalgU, and PAO1 kinB ΔrpoN

RpoN negatively regulates pyochelin synthesis.

Transcriptome analysis of nonmucoid strain PAO1 kinB ΔrpoN strain revealed that the entire pyochelin biosynthetic operon was upregulated compared to the level in the mucoid PAO1 kinB (Table 2). Pyochelin is one of the iron siderophores P. aeruginosa uses to scavenge iron from the environment. Fold changes of 7.1, 21.3, and 12.2 were observed for pyochelin genes fptA, pchG, and pchA, respectively (Table 2). RT-qPCR was utilized to validate these gene expression changes because proteomic analysis failed to detect the biosynthetic enzymes. As shown in Table 3, RT-qPCR indicated fold changes of −2.2, −3.2, and −2.2 for fptA, pchG, and pchA, respectively. Our data suggest that rpoN may be a negative regulator of pyochelin biosynthesis gene expression in the mucoid PAO1 kinB mutant.

KinB and RpoN regulation of motility.

RpoN positively regulates both flagella (52) and type IVa pili (29) in P. aeruginosa. Furthermore, AlgU has been shown to repress both flagella (50) and pili (3). However, in the kinB mutant, increased type IVb pilus gene expression was observed when rpoN was absent (Table 2). Multiple distinct genetic systems contribute to overall cellular motility of P. aeruginosa. To ascertain the effects of the rpoN deletion in the kinB mutant, motility assays measuring swimming, swarming, and twitching were performed. Using 0.3%, 0.5%, and 1% agar, respectively, swimming, swarming, and twitching were measured, and results are indicated in Fig. 3A. Both surface and subsurface twitching motility assays were performed to assess type IV pilus functionality (1, 39). The kinB mutation decreased all three forms of motility. Not surprisingly, rpoN deletion completely abrogated swimming and twitching motility; however, a small amount of swarming was observed in the kinB ΔrpoN strain (Fig. 3A and B). Interestingly, deletion of algU restored the kinB mutant back to wild-type levels of swimming and swarming but not twitching motility (Fig. 3A).

Fig 3
Motility of P. aeruginosa kinB mutant strains. (A) Swimming, swarming, and twitching motility are shown for the strain indicated, with standard deviations. (B) Representative surface twitching motility. Cultures adjusted to an A590 of 1.2 were centrifuged ...

Quorum-sensing gene systems are negatively controlled by RpoN in the kinB mutant.

P. aeruginosa has an elaborate quorum-sensing (QS) system utilizing three autoinducer molecules. N-3-Oxododecanoyl-homoserine lactone (3OC12-HSL) is produced by LasI (55), and at the threshold concentration, enough 3OC12-HSL binds to LasR to allow for population-wide transcriptional activation (55). Upon dimerization of LasR, expression of QS genes will occur (15). However, no significant change in either lasI or lasR expression was observed in the transcriptome analysis (Table 2). Previously, we noted a high number of QS-regulated proteins identified in the kinB mutant (11). The transcriptome analysis revealed a large number of QS-regulated genes with increased expression levels in the PAO1 kinB ΔrpoN strain (Table 2). The expression levels of rhlI were increased in the PAO1 kinB ΔrpoN strain (Tables 2 and and3).3). This suggests that the increased expression of QS genes may be due to the second class of P. aeruginosa QS molecules, N-butyryl-homoserine lactone (C4-HSL). A number of genes that are controlled by both 3OC12-HSL and C4-HSL (49) also showed increased expression levels in the PAO1 kinB ΔrpoN strain (Table 2). Therefore, it is difficult to determine which QS signal is responsible for increased QS-dependent gene expression. QS-controlled lasB, which encodes the LasB protease, was increased 3-fold in the transcriptome analysis and 2-fold by RT-qPCR. Furthermore, the rhamnolipid genes rhlA and rhlB were 10-fold upregulated in PAO1 kinB ΔrpoN in the transcriptome analysis (Table 2) and 7.7 and 27.8, respectively, by RT-qPCR (Table 3). PAO1 and PAO1 kinB ΔrpoN produced three times more rhamnolipids than PAO1 kinB, indicating that RpoN repressed this product of the Rhl quorum-sensing system (Fig. 4A and B). The increased levels of rhamnolipid production may explain the residual swarming observed in the PAO1 kinB ΔrpoN mutant (Fig. 3A). The discrepancy between the measured amounts of rhamnolipids and the transcription of rhlAB are likely due to posttranscriptional regulation by other factors such as the small noncoding RNA binding protein RsmA (28). Other QS-regulated genes were noted in our transcriptome analysis. Alkaline protease operon apr-aprF was upregulated, yet aprA encoding alkaline protease was not significantly upregulated in the transcriptome (Table 2). However, RT-qPCR revealed that aprA was 12-fold upregulated in PAO1 kinB ΔrpoN over PAO1 kinB (Table 3). The third class of QS inducers, Pseudomonas quinolone signal (PQS), were not dysregulated by the kinB mutation or the combination of the kinB and rpoN mutations. Collectively, our microarray analysis and RT-qPCR data suggest that rpoN may repress the Rhl system in the kinB mutant.

Fig 4
Rhamnolipid production of P. aeruginosa PAO1, kinB, and kinB ΔrpoN strains. (A) Rhamnolipid content is shown for the strains indicated, with PAO1 ΔrhlA as a negative control. ND, statistical analysis was not determined due to the values ...

KinB, RpoN, and AlgU control virulence of P. aeruginosa in a murine model.

Our transcriptomic, proteomic, and phenotypic analyses of the mucoid PAO1 kinB strain and nonmucoid PAO1 kinB ΔrpoN double mutant showed that KinB controls not only alginate but also a wide array of genes and virulence factors. Since sensor kinases monitor the environment and activate responses, KinB may be required for virulence. Furthermore, other investigators have shown that kinB is required for virulence in a zebra fish model (7). To test this hypothesis, an acute pneumonia model was utilized with BALB/c mice infected by the intranasal route. Since the kinB mutant is mucoid, PDO300, a mucoid mucA22 mutant (37) with a wild-type kinB, was used for comparison. With a dose of 1 × 107 CFU of bacteria, 0% of BALB/c mice survived infection by PAO1 after 40 to 50 h (Fig. 5). Similar mortality was observed with mucoid strain PDO300 (Fig. 5). However, PAO1 kinB did not cause the death of any mice (Fig. 5). PAO1 kinB ΔrpoN caused slightly greater mortality than PAO1 kinB; however, 75% of the mice survived the infection (Fig. 5). Interestingly, nonmucoid PAO1 kinB ΔalgU caused a similar rate of mortality as PAO1 (Fig. 5). Collectively, these data suggest that KinB/RpoN regulates virulence factors and may be required for virulence in acute infection by P. aeruginosa.

Fig 5
Survival of BALB/c challenged with P. aeruginosa strains. The percent survival of female BALB/c mice is plotted over time (n = 8 mice per group) with each mouse receiving a comparable dose of 1 × 107 CFU via intranasal delivery. Statistical analyses ...

DISCUSSION

Our data suggest that KinB negatively regulates alginate overproduction due to algW expression. Furthermore, KinB and RpoN may regulate the carbon flux from carbohydrate metabolism. We anticipated and found that the expression of the alginate biosynthetic operon was decreased in the kinB ΔrpoN double mutant; however, we did not expect the downregulation of a 54-gene chromosomal region, PA21XX (PA2134 to PA2192) (Table 1). It seems probable that the chromosomal region PA21XX is responsible for carbohydrate metabolism. RpoN has been inactivated from mucA mucoid mutants before by our laboratory (unpublished results) and by others (52), and no change in the mucoid phenotype was observed. However, the kinB mutant (12) and the unidentified muc-23 mutant PAO579 require rpoN for alginate production (5). One observation that may help to explain the differences between mucA strains and wt strains is that the PA21XX chromosomal region is mixed with both RpoN-AlgU (9 genes)-dependent genes and RpoN-only (45 genes)-dependent genes. In terms of organization, the AlgU-RpoN-dependent genes are located near the middle of the operon. Within this core set of genes, glgA encodes glycogen synthase. It is possible that AlgU controls storage of carbohydrate supply, whereas RpoN controls catabolism of glycogen via the PA21XX chromosomal region. It has been demonstrated previously that alginate production by muc-23 mutant strain PAO579 is nitrogen dependent (5). Interestingly, since most of the PA21XX chromosomal region is RpoN dependent, it seems probable that strains other than the kinB strain also utilize this chromosomal region. Our data intercalated with previous studies by other investigators (50, 54, 61) suggest that there may be another tier of regulation above alginate synthesis and MucA proteolysis at the level of management of carbohydrate supply.

Given the number of P. aeruginosa two-component systems (47) and sigma factors (44) in the PAO1 genome, it is not hard to imagine many possible gene expression patterns. To the best of our knowledge, this work is the first example of transcriptome and proteome analyses of a strain lacking a sensor kinase and an alternative sigma factor. The unmistakable mucoid phenotype prompted our approach to characterizing the regulation by sensor kinase KinB and sigma factor RpoN. In this study we focused on the regulon controlled by RpoN in the kinB mutant. However, there are likely many genes that are differentially regulated due to the loss of kinB that are independent of RpoN. In order to determine which genes are directly controlled by KinB, a Venn diagram comparing the transcriptomes of PAO1 versus PAO1 kinB to PAO1 kinB ΔrpoN versus PAO1 kinB is shown in Fig. 6. There are 486 genes that appear to be directly controlled by KinB despite the fact that the RNA used for the PAO1 kinB microarray was isolated from cells cultured on LB agar and that the RNA used for the PAO1 microarray was from cells grown to stationary phase in LB broth (34). Of note, 80% of genes repressed in PAO1 kinB ΔrpoN compared to PAO1 kinB were also repressed in the PAO1 strain compared to PAO1 kinB. This diagram also illustrates that there may be far more genes affected by the loss of kinB but are independent of rpoN.

Fig 6
Venn diagrams comparing the transcriptomes of the strains detailed in this study with additional comparison to wt strain PAO1. The strains in this study (PAO1 kinB and PAO1 kinB ΔrpoN) were cultured on PIA; but the PAO1 strain for comparison was ...

The kinB mutant strain displays a white phenotype dissimilar from mucA22 strains such as PDO300 (data not shown). Siderophore pyocyanin production does not seem evident on visual inspection of the kinB mutant due to the lack of pigment production of the strain on agar medium. Loss of rpoN caused a robust upregulation of pyochelin, which is mostly thought of as the minor siderophore. It seems that the QS system is upregulated to keep the need to acquire iron via pyochelin from the environment. Other investigators have shown that low iron can upregulate QS (31). Furthermore, studies have shown that rhlAB (38) and rhlI (51) are dependent on RpoN. It has been shown that RpoN negatively regulates QS (27), but, here, in the kinB mutant background, iron regulation and QS can be tied together with RpoN.

Recently, a hypothesis has been derived which directly ties nitrogen regulation to quorum sensing (62). In that work, the authors suggest that nitrogen availability alerts the organism to express various factors when metabolically prudent (62). Assimilation of this rule into the data described in this work suggests that loss of rpoN may arrest bacteria in a growth phase. Strains PAO1 and PAO1 ΔrpoN do not show visible differences in growth on standard rich culture medium (data not shown), but loss of rpoN renders the organism nonmotile. Conversely, deletion of rpoN from the kinB mutant causes a small-colony variant phenotype (data not shown). These data suggest that rpoN may be involved in additional physiological behaviors besides just motility, nitrogen metabolism, and virulence. Our data, as well as those of others, implicate rpoN in both direct and indirect mechanisms causing differential gene expression.

The pleiotropic effects of the inactivation of kinB, as observed in the transcriptome and proteome, indicate that kinB could impact the virulence of the organism. In Table 5 the phenotypic changes of each of the strains are indicated. Previously, we had shown that algU and rpoN are required for the mucoid phenotype of the kinB mutant (12). Our transcriptome analysis indicated that a large region of genes was repressed with the mucoid phenotype (Table 1). This observation was confirmed by both RT-qPCR and iTRAQ proteomic analysis (Tables 3 and and4).4). Another novel phenotype observed in the kinB regulon was the dramatic upregulation of pyochelin biosynthesis genes due to the deletion of rpoN (Table 2). Previously, mucoid isolates have been shown to produce less pyochelin (60). Here, we have observed their upregulation in a nonmucoid kinB rpoN mutant (Table 2). Pyochelin production has been shown to increase virulence of a subset of strains (9). These data beg the question as to why pyochelin is differentially regulated in mucoid strains. Since the kinB strain is mucoid and has high AlgU activity, we would expect that the kinB mutant would be less motile, as other investigators have described (50). We observed low motility for the kinB rpoN double mutant as expected and wild-type amounts of swimming and swarming motility for the kinB algU mutant (Fig. 3A). However, our analyses did not reveal the RpoN-dependent changes in pilin (pilA) (14). It is possible that the expression difference in these mutants was too low to be detected. However, another possibility is that the other phenotypes as noted here, such as rhamnolipid production, may account for this discrepancy. Another interesting change observed in the proteome was the dysregulation of rhamnolipid production (Table 1). Loss of rpoN in the mucoid mutant increased rhamnolipid production in the kinB mutant (Fig. 4A and B). Due to the variety of phenotypes observed in our study, it seemed pertinent to observe the effect of kinB on virulence in an acute pneumonia model (Fig. 5). To our surprise, the kinB strain was attenuated in virulence and caused no mortality even though the mucoid strain PDO300 (PAO1 mucA22) was just as virulent as PAO1 (Fig. 5). A previous study has shown that inactivation of algU causes increased virulence in nonmucoid strain PAO1 (64). This could explain why the kinB algU double mutant causes increased mortality compared to the kinB mutant. At first glance, Table 5 would seem to indicate that motility and virulence directly correlate. However, both the kinB mutant and PDO300 have similar motility levels (data not shown). These data suggest that effect of kinB on virulence is not due to the mucoid phenotype or motility but may be attributed to one or more of the other genes of the regulon. Our virulence data with BALB/c mice corroborate the recent observation that kinB controls virulence in PA14 against zebrafish embryos (7). In addition to the phenotypes described here, others have shown that KinB regulates pyocyanin, elastase, and biofilm formation (7). Here, we also observed increased expression of elastase (lasB) (Tables 2 and and3).3). Interestingly, the authors of the aforementioned study showed that the kinase activity of KinB was dispensable for virulence (7). Collectively, these data indicate control of the KinB regulon is pleiotropic and complex.

Table 5
Summary of P. aeruginosa gene classes and phenotypes regulated by KinB and RpoNa

P. aeruginosa has a large genome encoding many two-component systems and a vast number of virulence factors (47). P. aeruginosa is a danger to the health and well-being of persons living with CF. We have employed “-omic” techniques and phenotypic analyses to describe a regulon of genes controlled by the sensor kinase KinB through direct and indirect pathways. We found that KinB and RpoN control far more genes and phenotypes than we would have imagined. What does this mean in terms of the larger picture of overall pathogenesis of P. aeruginosa? At this juncture we could only speculate as to what environmental cues activate KinB to relay signals through AlgB and RpoN to coordinate expression of virulence. The inactivation of KinB resulted in the activation of alginate overproduction (12), and here we observed attenuation of virulence. These data suggest that stable mucoid strains that are not virulent may be useful as attenuated vaccine strains. With its tool box of virulence genes, preventing P. aeruginosa infections may be the best chance for prolonging the lives of CF patients. As proteomic analysis catches up to total transcriptome analysis, the regulation of virulence may become clearer and also increase hope for treatment and control of P. aeruginosa opportunistic infection.

Supplementary Material

Supplemental material:

ACKNOWLEDGMENTS

F.H.D. was supported by training grants from the NASA Graduate Student Researchers Program (NNX06AH20H), NASA West Virginia Space Grant Consortium, and a postdoctoral fellowship from the Cystic Fibrosis Foundation (DAMRON10F0). J.P.O. was partially supported by NIH through University of Virginia Infectious Disease training grant AI07406. J.J.V. was supported by the NIH through the University of Virginia Biodefense Research and Career training grant 5T32AI055432. J.B.G. was supported by grants from the NIH (R01 AI068112) and the Cystic Fibrosis Foundation (GOLDBERG10G0). M.J.S was supported by NIH R01-AI050812-01. H.D.Y. was supported by the NASA West Virginia Space Grant Consortium, NIH P20 RR016477 to the West Virginia IDeA Network for Biomedical Research Excellence, and the Cystic Fibrosis Foundation (YU11G0). This work was also partially funded by Progenesis Technologies, LLC.

We thank Matt Powell of Protea Biosciences for assistance with the iTRAQ proteomic analysis. We thank Mike Vasil of University of Colorado for the generous gift of the PAO1 ΔrhlA strain and Dennis Ohman of Virginia Commonwealth University for the generous gift strain PDO300. Finally, we also thank the anonymous reviewers for the helpful comments.

Footnotes

Published ahead of print 30 December 2011

Supplemental material for this article may be found at http://jb.asm.org/.

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