Computational prediction of the Crc regulon identifies genus-wide and species-specific targets of catabolite repression control in Pseudomonas bacteria

  • Patrick Browne1,

    Affiliated with

    • Matthieu Barret1,

      Affiliated with

      • Fergal O'Gara1Email author and

        Affiliated with

        • John P Morrissey2Email author

          Affiliated with

          BMC Microbiology201010:300

          DOI: 10.1186/1471-2180-10-300

          Received: 15 July 2010

          Accepted: 25 November 2010

          Published: 25 November 2010

          Abstract

          Background

          Catabolite repression control (CRC) is an important global control system in Pseudomonas that fine tunes metabolism in order optimise growth and metabolism in a range of different environments. The mechanism of CRC in Pseudomonas spp. centres on the binding of a protein, Crc, to an A-rich motif on the 5' end of an mRNA resulting in translational down-regulation of target genes. Despite the identification of several Crc targets in Pseudomonas spp. the Crc regulon has remained largely unexplored.

          Results

          In order to predict direct targets of Crc, we used a bioinformatics approach based on detection of A-rich motifs near the initiation of translation of all protein-encoding genes in twelve fully sequenced Pseudomonas genomes. As expected, our data predict that genes related to the utilisation of less preferred nutrients, such as some carbohydrates, nitrogen sources and aromatic carbon compounds are targets of Crc. A general trend in this analysis is that the regulation of transporters is conserved across species whereas regulation of specific enzymatic steps or transcriptional activators are often conserved only within a species. Interestingly, some nucleoid associated proteins (NAPs) such as HU and IHF are predicted to be regulated by Crc. This finding indicates a possible role of Crc in indirect control over a subset of genes that depend on the DNA bending properties of NAPs for expression or repression. Finally, some virulence traits such as alginate and rhamnolipid production also appear to be regulated by Crc, which links nutritional status cues with the regulation of virulence traits.

          Conclusions

          Catabolite repression control regulates a broad spectrum of genes in Pseudomonas. Some targets are genus-wide and are typically related to central metabolism, whereas other targets are species-specific, or even unique to particular strains. Further study of these novel targets will enhance our understanding of how Pseudomonas bacteria integrate nutritional status cues with the regulation of traits that are of ecological, industrial and clinical importance.

          Background

          The genus Pseudomonas is an important group of microorganisms that occupy a wide variety of habitats including soil [1], the rhizosphere [2], food [3] and mammalian hosts [4]. Some species are important plant or human pathogens, whereas others are involved in processes such as bioremediation [5], biocontrol [68], nutrient cycling [9] or biotechnological processes [10]. A key aspect of the lifestyle of Pseudomonads is their ability to adapt, grow and compete in a wide variety of habitats. Thus, Pseudomonads require great flexibility in controlling their diverse array of metabolic pathways and, like most microorganisms, have global regulatory systems that ensure that the best nutrient source is utilised and almost depleted before less favoured nutrient sources are exploited [1113].

          Pseudomonads favour the utilisation of organic acids, particularly tricarboxylic acid (TCA) cycle intermediates, and amino acids over various other carbon sources such as carbohydrates or hydrocarbons [14]. This is in contrast to the majority of well-studied Enterobacteriaceae and Firmicutes, which favour glucose and use a system known as carbon catabolite repression (CCR) or catabolite repression control (CRC) to regulate carbon utilisation. The mechanism of CCR in Enterobacteriaceae and Firmicutes centres on a protein phosphorylation cascade and also involves transcriptional regulation mediated through cyclic AMP (cAMP) binding to the cAMP receptor protein (Crp) (for review see [11, 12]). Although Pseudomonads possess a Crp homolog, Vfr, this protein is not involved in carbon source regulation, at least in P. aeruginosa PAO1 [15]. In fact, the CRC mechanism used by Pseudomonads to regulate carbon source utilisation is fundamentally different to CCR of Enterobacteriaceae and Firmicutes.

          A central mediator of CRC is the Crc protein, which acts as a post-transcriptional regulator of target genes [16]. The post-transcriptional action of Crc relies on the binding of Crc to an unpaired A-rich motif in the 5'-end of a target mRNA causing inhibition of the initiation of translation [17, 18]. It is still not fully understood how Crc activity is regulated in different Pseudomonas species, nor whether a common unified regulatory system is employed. In P. aeruginosa, activity is regulated by small RNA, CrcZ, which has five A-rich motifs, that binds to the Crc protein and sequesters it [17]. Levels of the CrcZ sRNA, in turn, are regulated by a two-component system (CbrA/CbrB) and by RpoN. Interestingly, CbrAB and NtrBC form a network to control the C/N balance in both P. aeruginosa and P. fluorescens [1921]. Furthermore, the presence of a readily available nitrogen source enhances the magnitude of CRC [22], two observations that are suggestive of a link between regulatory systems controlling C and N utilisation. Although the crcZ gene is present in other Pseudomonads, its role in regulating CRC outside of P. aeruginosa has not yet been demonstrated. Indeed, in P. putida, crc mRNA and Crc protein levels are higher under conditions where CRC is active, a phenomenon not observed in P. aeruginosa, suggesting that an alternative system of regulating CRC may be used in this species [23, 24].

          Much of what is known about CRC comes from work on mutants lacking the Crc protein in P. aeruginosa and P. putida. Initially, the key work in identifying the CRC system came from the isolation and characterisation of a P. aeruginosa crc mutant [25]. In this mutant, the succinate-mediated catabolite repression control (CRC) of glucose and mannitol transport and Entner-Doudoroff pathway enzymes was alleviated, thereby establishing the importance of Crc. More recently, the role of Crc has been examined on a global scale in P. putida [26] and P. aeruginosa [27] by carrying out transcriptome and proteome analyses of crc mutants. No less than 134 targets in P. putida and 65 targets in P. aeruginosa were differentially altered in expression in rich media as a result of a crc mutation. This indicates that crc is an important global regulator that superimposes an additional layer of regulation over many metabolic pathways that are otherwise regulated locally by specific regulatory elements that control only one or a few genes. The global analyses of the P. putida and P. aeruginosa crc mutants indicates that CRC is responsible for the hierarchical assimilation of amino acids from rich media, with pathways required for assimilation of valine, isoleucine, leucine, tyrosine, phenylalanine, threonine, glycine and serine inhibited by Crc [26, 27]. Additionally, the P. aeruginosa crc mutation was shown to alter the expression of targets with roles in anaerobic respiration, antibiotic resistance and virulence [27]. Recent work on a crc mutant of P. putida DOT-T1E established that Crc is not involved in the induction of pathways for nutrient utilisation since the mutant grows on the same range of carbon and nitrogen sources as the wild type strain [28]. This is in contrast to the E. coli CCR system where the cAMP-CRP complex is responsible for the induction of genes for utilisation of less favoured carbon sources such as lactose [29].

          The role of CRC in regulating linear and aromatic hydrocarbon utilisation pathways in P. putida has received a lot of attention because of the potential implications of CRC on bioremediation processes. The utilisation of alkanes and a wide range of aromatic compounds including benzene and toluene are subject to CRC in P. putida [16, 3034]. Indeed Crc mediated post-transcriptional control of the pheA and pheB toluene degradation genes [31], the benR activator of benzene degradation [33], the alkS activator of alkane degradation [16], the xylR activator of the TOL genes and xylB (benzyl alcohol dehydrogenase) [34] and the bkdR activator of branched-chain keto acid dehydrogenase [35] has been demonstrated. The Crc protein is known to bind in vitro to mRNAs of the PU, PM, PS1, PR1, promoters and mRNAs of the xylMABN (and weakly to xylL and xylQ) genes of the pWW0 toluene degradation plasmid in P. putida indicating that these targets may also be regulated by directly by Crc [34]. Besides the role that CRC plays in the bioremediation activities of P. putida, little else is known about the control that CRC imposes on the ecological functions of Pseudomonads other than for virulence-associated functions in Pseudomonas aeruginosa. A crc mutant of P. aeruginosa PA14 was defective in biofilm formation and type IV pilus-mediated twitching motility [36]. and a crc mutant of P. aeruginosa PAO1 displayed increased susceptibility to some antibiotics as well as defects in type III secretion, motility and expression of quorum sensing-regulated virulence factors [27]. Given the range of ecological functions that Pseudomonas may perform there is great scope for Crc to be a significant regulator beyond the realm of primary metabolism. For instance, glucose metabolism is subject to CRC and gluconate is a product of glucose metabolism. Gluconate itself is linked to phosphate solubilisation [9] and biocontrol [37] and there is a link between the ability to produce gluconate and the levels of antimicrobial compounds produced such as 2,4-diacetylphloroglucinol and pyoluteorin [38]. Additionally, recent evidence indicates that there is a link between primary metabolism and secondary metabolism controlled by the GacS/Rsm system [39]. This suggests that there is great potential for CRC to interact with other regulatory networks, at least indirectly, and it is therefore a high priority to better understand the Crc regulon.

          Based on the size of the Crc product and the proposed mechanism of action, it is thought that Crc binding must occur within -70 to +16 bp relative to the origin of translation [18]. There remain, however, very few known direct targets of Crc: only benR [33], alkS [18], xylR and xylB [34] mRNAs from P. putida and amiE mRNA [17] (product of the amidase gene amiE), in P. aeruginosa have been demonstrated to bind Crc. To extend the number of direct targets known, we carried out a bioinformatic analysis using genome information from sequenced Pseudomonas strains. By identifying the specific targets in pathways that are known to be regulated by CRC, it will be possible to determine precisely how different Pseudomonads control nutrient uptake and utilisation. Furthermore, the analysis is expected to identify new pathways and processes, not previously known to be CRC-regulated. A better understanding of how Pseudomonas species use CRC will enhance knowledge of the ecology of these bacteria and will facilitate efforts to exploit the metabolic capacity of these bacteria in industrial and environmental microbiology.

          Results and discussion

          Crc binding site detection: estimation of the numbers of Crc regulated genes in Pseudomonas

          Following the recent discovery that the Crc protein binds to an A-rich motif in the -70 to +16 region of target mRNAs [17, 18] a search was performed to catalogue the occurrences of this motif in the forward orientation in the upstream regions of all protein-encoding genes in Pseudomonas strains present in the RSAT database [40] at the time of analysis. These were P. aeruginosa (PAO1, PA14, PA7 and LESB58), P. fluorescens (Pf0-1, Pf-5 and SBW25), P. putida (KT2440, F1 and W619) and P. syringae (B728a and DC3000). It was reasoned that if a gene is under direct Crc control, the binding site should be present in that gene in all representatives of a particular species. Accordingly, only genes with the A-rich motif (AAnAAnAA) in the upstream region of intraspecies orthologs for all strains of a given species were considered as candidates (Additional file 1). In total, 421 candidate genes were identified, with an estimated false discovery rate of 27% (see materials and methods). P. aeruginosa has the highest number (215) of Crc candidates, P. syringae and P. putida had 143 and 133, respectively while P. fluorescens has the lowest number (84) (Figure 1). This difference in the number of possible CRC-regulated genes is likely to be a consequence of the taxonomic organisation within the genus, in particular the diversity of P. fluorescens species. A consequence of this diversity is that the core genome of P. fluorescens is significantly smaller than that of P. aeruginosa and so the pool of orthologous genes that are potentially regulated by Crc is lower [4145]. Twelve Crc candidates are common to all four Pseudomonas species while a further 28 Crc candidates are present in three out of the four species examined (Figure 1). Taken together, these 40 Crc candidates represent the predicted core Crc regulon of Pseudomonas (Table 1). Many of these Crc candidates are annotated as having roles in nutrient transport and metabolism, fitting with the idea of CRC as a means of controlling hierarchical assimilation of nutrients from the environment. Most putative Crc targets are not part of the core regulon and are confined to a single or two species. These include the three Crc target genes (alkS, benR of P. putida and amiE of P. aeruginosa) that have been experimentally shown to bind Crc in the 5' region of the mRNA [17, 18, 33]. No orthologues of benR or amiE were detected outside of P. putida or P. aeruginosa species, respectively, and so these are species-specific targets. The absence of alkS in our dataset is due to its location on a mobile element (the P. putida OCT plasmid) that is only present in some strains of P. putida. In summation, the Pseudomonas regulatory network controlled by Crc ranges from genes that are regulated at a genus-wide level, down to genes that may only be regulated in certain strains within a particular species.
          http://static-content.springer.com/image/art%3A10.1186%2F1471-2180-10-300/MediaObjects/12866_2010_Article_1256_Fig1_HTML.jpg
          Figure 1

          Interspecific variations of the Crc regulon. Venn diagram showing a four way comparison of Crc candidates in P. aeruginosa, P. fluorescens, P. putida and P. syringae. Numbers in parentheses correspond to total number of Crc candidates in each species.

          Table 1

          Predicted core Crc regulon of Pseudomonas.

          Gene name

          Function

          PAO1

          PA14

          PA7

          LESB58

          Pf0-1

          Pf-5

          SBW25

          KT2440

          F1

          W619

          B728a

          DC3000

           
           

          Amino acid transport and metabolism

                       

          putP

          Sodium/proline symporter

          0783

          54150

          4736

          45601

          0453

          0496

          0452

          4946

          4818

          0522

          NM

          NM

           
           

          Probable amino acid permease

          0789

          54040

          4729

          45531

          4561

          4906

          1103

          1059

          1100

          1089

          NO

          NO

           

          phhA

          Phenylalanine-4-hydroxylase

          0872

          52990

          4644

          44441

          1499

          1611

          4458

          4490

          1424

          3779

          NM

          NM

          Y

          gltI

          Probable binding protein component of ABC transporter

          1342

          46910

          4043

          38391

          4535

          4871

          1139

          1071

          1112

          1100

          NM

          NM

           
           

          Probable sodium/alanine symporter

          2252

          35460

          2989

          30521

          NM

          NM

          NM

          0496

          0530

          0551

          5052

          5500

           
           

          Probable glutamine synthetase

          2040

          38140

          3247

          32821

          5408

          5930

          5849

          5184

          5091

          0279

          4868

          5310

           
           

          Carbohydrate transport and metabolism

                       

          mltE

          Probable binding protein component of ABC maltose/mannitol transporter

          2338

          34420

          2925

          29651

          2640

          3070

          2745

          NO

          NO

          2041

          2440

          2707

           

          oprB

          Glucose/carbohydrate outer membrane porin precursor

          3186

          23030

          1943

          18821

          4366

          4613

          4842

          1019

          1057

          4205

          1117

          1296

          Y

           

          Probable binding protein component of ABC sugar transporter

          3190

          22980

          1939

          18781

          4370

          4617

          4846

          1015

          1053

          4209

          1113

          1292

          Y

          lldP

          L-lactate permease

          4770

          63080

          5490

          51551

          0753

          0817

          5278

          4735

          4601

          0696

          NO

          NO

           

          fruA

          PTS fructose IIC component

          NM

          NM

          NM

          NM

          0795

          0861

          0806

          0795

          0818

          4398

          0823

          0956

          Y

          glpF

          Glycerol uptake facilitator protein

          NM

          NM

          NM

          NM

          4531

          4867

          1143

          1076

          1117

          1105

          3904

          4167

           
           

          Energy production and conservation

                       

          coxB

          Cytochrome c oxidase, subunit II

          0105

          01290

          0180

          01061

          0079

          0061

          0058

          0103

          0119

          0122

          NO

          NO

           

          pntB

          Pyridine nucleotide transhydrogenase, beta subunit

          0196

          02470

          0277

          01971

          0112

          0113

          0111

          0155

          0173

          5072

          NM

          NO

           

          acsA

          Acetyl-coenzyme A synthetase

          0887

          52800

          4627

          44291

          4293

          4522

          4766

          4487

          1428

          3776

          3572

          1825

           
           

          Putative glycerate 2-kinase

          1499

          45030

          3833

          39131

          1595

          1698

          1800

          4300

          1569

          3625

          NO

          NO

           
           

          Probable D-beta-hydroxybutyrate permease

          2004

          38580

          3285

          33191

          3078

          3575

          2629

          3074

          2650

          2717

          NO

          NO

           
           

          Putative acetate transporter

          3235

          22340

          1890

          18321

          1607

          1711

          1813

          1742

          3977

          1292

          3757

          1623

           

          cstA

          Probable carbon starvation protein

          4606

          60950

          5246

          49911

          4883

          5352

          5333

          4641

          4503

          0798

          4273

          4638

           

          phaC1

          Poly(3-hydroxyalkanoic acid) synthase 1

          5056

          66820

          5794

          54461

          0394

          0434

          0396

          5003

          4877

          0461

          NO

          NO

           

          phaF

          polyhydroxyalkanoate synthesis protein PhaF

          5060

          66875

          5799

          54501

          NM

          NM

          NM

          5007

          4881

          0457

          0391

          5147

           
           

          Alginate metabolism

                       

          algP

          Alginate regulatory protein

          5253

          69370

          5998

          56471

          NM

          NM

          NM

          0194

          0215

          0263

          0054

          0136

           
           

          Lipid metabolism

                       

          fadD2

          Long-chain-fatty-acid--CoA ligase

          3300

          21340

          1824

          17661

          4354

          4599

          4830

          4550

          1339

          3845

          3836

          4098

           

          estA

          Esterase

          5112

          67510

          5845

          55021

          NM

          NM

          NM

          0418

          0452

          4784

          4606

          0569

           
           

          Polyamine metabolism

                       

          aphA

          Acetylpolyamine aminohydrolase

          1409

          46230

          3930

          40041

          5631

          6145

          6061

          5340

          5249

          0133

          NO

          NO

           
           

          Xenobiotic degradation and transport

                       

          pcaK

          Benzoate transport

          NM

          NM

          NM

          NM

          1266

          1316

          1362

          1376

          4347

          1016

          2124

          2340

           
           

          DNA replication, recombination and repair

                       

          recA

          RecA protein

          3617

          17530

          1523

          14181

          1175

          1231

          1189

          1629

          4088

          4030

          1378

          4033

           

          hupA

          HU family DNA-binding protein

          5348

          70600

          6125

          57431

          5600

          6102

          6032

          5313

          5222

          0160

          NM

          NM

          Y

           

          nucleotide transport and metabolism

                       
           

          Probable transporter

          1519

          44800

          3815

          38911

          1711

          4364

          4355

          4284

          1584

          3610

          NO

          NO

           

          xdhA

          Xanthine dehydrogenase

          1524

          44710

          3809

          38041

          1797

          1889

          4592

          4278

          1590

          3604

          NM

          NM

           
           

          Translation

                       

          rplR

          50S ribosomal protein L18

          4247

          09010

          0853

          06811

          5063

          5566

          5511

          0470

          0503

          4733

          4532

          0642

           

          tufB

          Elongation factor Tu

          4277

          08830

          0835

          06631

          5081

          5584

          5529

          0452

          0485

          4751

          4550

          0624

           
           

          Unknown function

                       
           

          Hypothetical protein

          0754

          54540

          4766

          45891

          NM

          NM

          NM

          1418

          4303

          1058

          3966

          4232

          Y

           

          Probable transporter

          1507

          44950

          3824

          39041

          1701

          4371

          4364

          4290

          1578

          3616

          NO

          NO

           
           

          Probable major facilitator superfamily (MFS) transporter

          3709

          16410

          1427

          12731

          3359

          2486

          2159

          0057

          0073

          0076

          NO

          NO

           
           

          Hypothetical protein

          3923

          13130

          1184

          10541

          4708

          5116

          0923

          0765

          0792

          4424

          0991

          1149

           
           

          Probable ATP-binding component of ABC transporter

          4461

          57930

          5034

          48401

          0858

          0916

          0883

          0953

          0992

          4262

          4146

          4452

           
           

          Hypothetical protein

          4570

          60480

          5210

          49531

          4863

          5332

          5174

          0685

          0716

          4500

          NO

          NO

           
           

          Hypothetical protein

          5052

          66760

          5789

          54421

          0398

          0438

          0400

          5090

          4963

          0375

          NM

          NM

           
           

          Phosphotransferase domain-containing protein

          NM

          NM

          NM

          NM

          1487

          1597

          4880

          4500

          1412

          3789

          3586

          1810

           

          Loci with an A-rich motif in the upstream region in all strains tested for at least three species are shown. The numbers under strain names indicate the locus id, according to Genbank annotation, of the locus with the A-rich motif in the upstream region. NO (no ortholog) indicates that no orthologous locus was found. NM (no motif) indicates that the orthologous locus did not have the A-rich motif in the upstream region. Y indicates that the locus is has increased transcript and/or protein levels in a crc mutant of P. putida KT2442 (a spontaneous rifampicin resistant mutant of KT2440) [26].

          Comparison of predicted Crc-regulated candidates to experimental datasets

          Other studies have made use of crc mutant strains derived from P. putida KT2440 and P. aeruginosa PAO1 to experimentally identify possible targets of the CRC system. To assess the level of concordance between the bioinformatic and experimental approaches, the datasets were compared (Table 2 and Table 3). Recently, transcriptome and proteome comparisons of transcript and protein abundances of P. putida KT2442, a spontaneous rifampicin-resistant mutant of strain KT2440, and an isogenic crc mutant have been performed in rich media [26]. The transcriptome and proteome data sets identified 134 genes that were differentially altered in expression either at transcriptional or translational level in the crc mutant. We compared this list of 134 genes to the lists of genes identified in our bioinformatic analysis, with the results presented in table 2. The initial comparison was to the 133 candidate genes that were bioinformatically predicted to be the core Crc regulon of P. putida and then to ensure that possible positive matches were not overlooked, we extended the comparison to the longer list of 294 candidates identified in P. putida strain KT2440 (only targets present in all three P. putida strains were shown in additional file 1). 18 common targets between the predicted P. putida Crc regulon and the transcriptome/proteome data were identified, and another 5 possible targets are seen when the comparison is with the full KT2440 list of candidates.
          Table 2

          Comparison of predicted Crc regulon of P. putida with transcriptome and proteome data.

          Gene name

          putida a

          KT2440b

          Function

          mRNA

          Protein

           

          NO

          PP_0267

          outer membrane ferric siderophore receptor

          nd

          1.6

          fruR

          NM

          PP_0792

          FruR transcriptional regulator

          nd

          2.3

          fruA

          PP_0795

          PP_0795

          PTS fructose IIC component

          2.1

          nd

          gap-1

          PP_1009

          PP_1009

          glyceraldehyde-3-phosphate dehydrogenase, type I

          2.7

          3.3

           

          PP_1015

          PP_1015

          probable binding protein component of ABC sugar transporter

          2.3

          4.9

          oprB-1

          PP_1019

          PP_1019

          Glucose/carbohydrate outer membrane porin OprB precursor

          3.5

          2.9

           

          PP_1059

          PP_1059

          probable amino acid permease

          6.4

          nd

          aatJ

          PP_1071

          PP_1071

          probable binding protein component of ABC transporter

          3.3

          7.7

           

          NM

          PP_1400

          dicarboxylate MFS transporter

          2.5

          nd

          tctC

          PP_1418

          PP_1418

          hypothetical protein

          1.6

          3.4

          cspA-1

          PP_1522

          PP_1522

          cold shock protein CspA

          1.9

          3.5

          ansA

          PP_2453

          PP_2453

          L-asparaginase, type II

          2.4

          3.1

           

          PP_3123

          PP_3123

          3-oxoacid CoA-transferase subunit B

          9.1

          4.5

           

          NO

          PP_3434

          hypothetical protein

          6.7

          nd

           

          NM

          PP_3530

          conserved hypothetical protein

          2.0

          nd

           

          PP_3593

          PP_3593

          amino acid ABC transporter, periplasmic amino acid-binding protein

          nd

          6.3

          bkdA-1

          PP_4401

          PP_4401

          3-methyl-2-oxobutanoate dehydrogenase

          3.2

          1.6

          phhA

          PP_4490

          PP_4490

          phenylalanine-4-hydroxylase

          2.8

          1.9

           

          PP_4495

          PP_4495

          aromatic amino acid transport protein AroP2

          2.6

          nd

          hmgA

          PP_4621

          PP_4621

          homogentisate 1,2-dioxygenase

          5.0

          7.8

           

          PP_4636

          PP_4636

          acetyl-CoA acetyltransferase

          3.6

          2.3

          hupA

          PP_5313

          PP_5313

          probable DNA-binding protein

          3.8

          nd

          accC-2

          PP_5347

          PP_5347

          acetyl-CoA carboxylase subunit A

          2.4

          nd

          Genes differentially regulated, based on transcriptome and proteome data, in rich media in a crc mutant of P. putida KT2442 [26] are cross referenced with (a) predicted Crc targets from three P. putida strains (KT2440, F1 and W619) and (b) with predicted Crc targets from P. putida KT2440 alone. Values of mRNA and protein indicate the relative levels of transcripts and protein in transcriptome and proteome analyses respectively [26]. NO (no ortholog) indicates that no orthologous loci were detected in either or both of P. putida F1 and W619. NM (no motif) indicates that no A-rich motif was detected in the upstream region of the orthologous loci in P. putida F1 and W619.

          Table 3

          Comparison of predicted Crc regulon of P. aeruginosa with proteome data.

          Gene name

          PAO1

          Function

          protein

           

          PA0534

          conserved hypothetical protein

          2.03

          hpd

          PA0865

          4-hydroxyphenylpyruvate dioxygenase

          4.71

          oprD

          PA0958

          Basic amino acid, basic peptide and imipenem outer membrane porin OprD precursor

          1.75

           

          PA1069

          hypothetical protein

          4.28

           

          PA2553a

          probable acyl-CoA thiolase

          1.59

           

          PA2555

          probable AMP-binding enzyme

          1.54

           

          PA2776

          conserved hypothetical protein

          1.71

           

          PA3187b

          probable ATP-binding component of ABC transporter

          10.28

          edd

          PA3194

          phosphogluconate dehydratase

          2.17

           

          PA4500

          probable binding protein component of ABC transporter

          3.48

           

          PA4502c

          probable binding protein component of ABC transporter

          3.35

           

          PA4506c

          probable ATP-binding component of ABC dipeptide transporter

          8.43

          dadA

          PA5304

          D-amino acid dehydrogenase, small subunit

          2.36

          Genes differentially regulated, based on proteome data, in rich media in a crc mutant of P. aeruginosa PAO1 [27] are cross referenced with predicted targets from all P. aeruginosa strains considered in this study. Values of protein indicate relative levels of protein in the crc mutant relative to levels in the wild type strain. Some genes are proximal to, and possibly in operons with, bioinformatically predicted Crc targets: (a) PA2553 is proximal to PA2555, (b) PA3187 is proximal to PA3186 and (c) PA4502 and PA4506 are proximal to PA4501.

          A proteomic analysis comparing the wild type strain P. aeruginosa PAO1 to an isogenic crc mutant in LB broth was also recently performed [27]. Under these conditions, 46 proteins were present at higher levels in the crc mutant compared to the wild type strain, suggesting that these targets are negatively regulated by the CRC system. Comparing those 46 experimentally-identified targets with the 215 predicted Crc targets identified in our bioinformatic study, it is seen that 13 of the 46 targets overlap (Table 3). Of these, 9 common targets have a predicted Crc binding site in the gene itself and a further 4 targets are in operons downstream of predicted Crc targets (Table 3). When the comparison is expanded to include all 279 candidates identified in PAO1 no new matches were found. The authors of that study identified putative Crc-binding sites in the 5' region of 23 of the 46 genes, and suggested that these may be subject to direct Crc mediated regulation [27]. The criteria applied for identifying putative Crc-binding sites was less strict than our study (with respect to consensus and distance from AUG codon), which explains the difference between the 13 binding sites we propose and the 23 postulated by these authors.

          The fact that 18/23 overlaps are in the core P. putida regulon (and a further 2 are only excluded because orthologues are absent) and that no new overlaps with experimental data are introduced when the predicted Crc-regulon of P. aeruginosa is considered reinforces the validity of using the presence of the motif in orthologous genes within a species as a selection criterion in the global bioinformatic screen. Although the overlap between the experimental and bioinformatic datasets appears low for P. putida - 18(23)/267 genes - this should not be entirely unexpected. Genes predicted by the bioinformatics but not identified experimentally could simply be because they were below experimental detection limits, or more likely because the growth conditions used favoured some classes of genes. Of course, some hits may represent false positives, and our analysis predicted that there are rates of 18% and 26% false positive hits for P. aeruginosa and P. putida respectively. These are also possible explanations for differences between our data set and the PAO1 proteome data despite the higher level of overlap between our data with PAO1 (13/46) than between our data with KT2440. It is interesting that all three studies identify amino acid metabolism as an important component of the Crc-regulon. This reflects Crc metabolic adaptations in a nutrient rich environment (which was the experimental condition) where various amino acids are the major carbon sources. Performing the transcriptome/proteome experiments under different growth conditions, would be likely to yield a different set of genes. Conversely, there were also targets identified in the experimental studies that did not feature in the bioinformatic analysis. The most likely explanation for this is that these are indirect rather than direct targets of Crc as they lack the predicted Crc binding site. It is also possible, however, that the strict criteria used in the bioinformatic analysis excluded some genuine targets, or that Crc has alternative or additional binding sites, perhaps used only under certain conditions. From comparing all the data, we can already see that this was probably the case with the bkdA1 gene, which was identified as a target experimentally in both P. putida and P. aeruginosa, but bioinformatically only in P. putida (Table 2). The proposed Crc binding site in P. aeruginosa is AACAAGAGAAACAA [27], which differs in some positions to the consensus AAnAAnAA used in the bioinformatic analysis. Ultimately, protein-mRNA binding studies will be needed to resolve all these Crc-binding possibilities.

          Crc regulates carbohydrate and amino acid utilisation

          In order to find a common pattern of Crc regulation in Pseudomonas spp., we examined the function associated with the Crc candidates. In Pseudomonads, intermediates of the TCA cycle such as succinate or citrate cause catabolic repression of pathways involved in metabolism of carbohydrates, amino acids and other carbon sources [14, 46]. Therefore, it is not surprising to find predicted Crc targets involved in such pathways. Indeed, our analysis highlights six interspecies Crc candidates involved in carbohydrate metabolism (Table 1). Since these candidates are all related to transport, it is tempting to speculate that Crc is responsible for direct down-regulation of transporters of carbohydrate utilisation, which would result in the indirect down-regulation of the relevant catabolic enzymes due to the lack of inducing molecules in the cytoplasm. Closer inspection of the intra-species Crc candidates, however, shows that some genes linked to carbohydrate metabolism could also be directly regulated by Crc (Additional file 1). For example, in P. aeruginosa and P. fluorescens species, the gene, zwf, encoding glucose-6-phosphate dehydrogenase has a Crc motif, whereas in P. putida and P. syringae species, the gene, gap-1, encoding glyceraldehyde-3-phosphate dehydrogenase has a Crc motif. When viewed in an integrated way, it is seen that there are two distinct patterns to the regulation of genes in this class (Figure 2). When present, sugar transporters are generally subject to CRC control, whereas the regulation of downstream sugar metabolism is species-specific with respect to genes encoding catabolic enzymes. Interestingly, the same trend is observed for amino acid metabolism where most of the interspecies Crc candidates are involved in transport (Table 1), whereas intraspecies candidates are involved in metabolism (Additional file 1).
          http://static-content.springer.com/image/art%3A10.1186%2F1471-2180-10-300/MediaObjects/12866_2010_Article_1256_Fig2_HTML.jpg
          Figure 2

          Predicted Crc regulon of carbohydrate metabolism in Pseudomonas. Selected genes involved in carbohydrate transport and metabolism are shown along with their status vis a vis (predicted) Crc regulation. Genes from P. aeruginosa (squares), P. fluorescens (circles), P. putida (triangles) and P. syringae (diamonds) are shown, with filled/unfilled symbols indicating that the target in that species is/is not predicted to be regulated by Crc. An asterisk (*) after a symbol indicates where an orthologous locus is absent in the relevant species. OM - outer membrane; PP - periplasm; IM - inner membrane; ED - Entner-Doudoroff pathway; EMP - Embden-Meyerhoff pathway; 2-K-3-DG-6-P - 2-keto-3-deoxygluconate-6-phosphate. OprB - carbohydrate porin B; GlpF - glycerol transporter; FruAB - fructose phosphotransferase system; Mtr - mannitol transporter subunit; GtsA - glucose transporter subunit; GntP - gluconate transporter; KguT - 2-ketogluconate transporter; Mdh - mannitol dehydrogenase; AlgA - mannose-6-P isomerase; Zwf - glucose-6-P dehydrogenase; Edd - gluconate-6-P dehydratase; KguE - xylose isomerase; GapA - glyceraldehyde-3-P dehydrogenase; Eno - phosphopyruvate hydratase. Some steps of the Embden-Meyerhoff pathway are abbreviated with a dashed line for clarity.

          It is notable that another gene, cstA, with a predicted role in carbon starvation stress alleviation was also implicated as a Crc candidate. The CstA protein is involved in peptide transport that would assist the cell in escaping carbon starvation [47]. In Escherichia coli, induction of the cstA gene depends on cAMP and Crp [48] indicating that this locus is subject to CCR in E. coli. It is intriguing that a locus is implicated as being subject to catabolite repression in both Escherichia and Pseudomonas, despite differences in preferred nutrient sources and mechanisms of catabolite repression.

          Crc regulates transcriptional activators that are induced during stationary phase

          Crc also seems to regulate proteins involved in transcriptional regulation, as previously described [33]. Indeed the gene, hupA, encoding a bacterial histone like protein (HU-like protein), possesses a Crc motif in the P. aeruginosa, P. putida and P. fluorescens species. HU proteins are ubiquitous DNA binding factors that are involved in the structural maintenance of the bacterial chromosome and other events that require DNA binding [49]. In contrast to the structurally related integration host factor (IHF), HU proteins bind DNA in a sequence-independent manner. Generally, Pseudomonas possesses five HU/IHF copies per genome [50]. Two of these ORFs encode the two subunits of the IHF (integration host factor) protein (ihfA and ihfB), whereas hupA (or hupP), hupB and hupN encode HU-like proteins. Although the precise role of hupA is not known, HU-like proteins are required for transcription from the σ54-dependent Ps promoter of the toluene degradation pathway in P. putida [51], which is known to be subject to control by the CRC system. Identification of the Crc motif would be consistent with the idea that Crc impacts indirectly on the transcription level of a subset of genes through translational regulation of the regulatory genes hupA or ihfB. This may also explain some of the indirect targets of Crc identified in the transcriptome/proteome analysis discussed earlier [26]. The expression of hupA, hupB and hupN has been monitored during P. putida KT2440 growth [52]. Interestingly, whereas hupB and hupN transcript abundances are maximal in exponential phase, hupA expression seems to be activated during stationary phase. Remarkably, another Crc candidate of P. aeruginosa and P. syringae, ihfB, has increased expression during transition of cells from exponential growth to stationary phase [53]. This observation is not an isolated phenomenon as other predicted Crc targets, for example cstA [47, 48] and polyhydroxyalkanoate biosynthesis (phaC1 or phaZ) [54], are also induced at the onset of stationary phase. CRC is depressed during stationary phase [24] so these observations on expression are consistent with a role for Crc in repressing expression of target genes during active growth.

          Crc regulates virulence-related traits

          It was shown previously that a crc mutant of P. aeruginosa PA14 was defective for biofilm formation and type IV pilus-mediated twitching motility [36] and a crc mutant of P. aeruginosa PAO1 is compromised in type III secretion, motility, expression of quorum sensing-regulated virulence factors and was less virulent in a Dictyostelium discoideum model [27]. Therefore, we searched for bioinformatic evidence that Crc integrates nutritional status cues with the regulation of virulence-related traits. We postulate that Crc might regulate some steps in alginate biosynthesis in Pseudomonas. Alginate production is linked to the conversion of microcolonies from a non-mucoid to a mucoid phenotype. In P. aeruginosa this phenotype marks the transition to a more persistent state during pulmonary infection, characterised by antibiotic resistance and accelerated pulmonary decline [55]. The regulation of alginate production in Pseudomonas is highly complex and involves the interaction of many regulatory systems [56]. In this study, the transcriptional activator AlgP, involved in the transcription of a key alginate biosynthetic gene, algD [57] encoding GDP-mannose 6-dehydrogenase, is predicted, to be directly regulated by Crc in P. aeruginosa, P. putida and P. syringae species. In this case, the interspecific Crc regulation blocks the synthesis of a transcriptional regulator which leads to indirect regulation of the biosynthetic pathway, reminiscent of the cases of alkS and benR in P. putida [18]. Nevertheless, at the species level, Crc is also predicted to regulate some enzymes directly. In P. aeruginosa, Crc also is predicted to bind to alg8 and algF transcripts which encode a subunit of alginate polymerase [58, 59] and an alginate acetylation protein [60] respectively. The synthesis of the alginate precursor, mannose-6-phosphate, encoded by algA, is predicted to be under the control of Crc in P. fluorescens only (Figure 2). The additional levels of regulation of alginate in P. aeruginosa, could reflect the importance of this exopolysaccharide for persistence in specialised ecological niches, including inside the host.

          Another interesting Crc target is estA encoding an autotransporter protein with esterase activity [61] that is indispensable for rhamnolipid production [62]. Rhamnolipids are surface-active molecules that play a role in biofilm fluidity [63] and are toxic against a variety of microorganisms [64]. Preliminary experiments confirm that rhamnolipid production is a Crc-regulated trait in P. aeruginosa (data not shown). Moreover, inactivation of the estA gene in P. aeruginosa also influenced other virulence-related functions like swimming, twitching and swarming in a rhamnolipid-independent fashion [62]. Rhamnolipids have numerous features in common with polyhydroxyalkanoic acid (PHAs), a metabolic storage material involved in bacterial stress-resistance and biofilm formation [65]. Firstly they are both synthesised in response to the presence of excess carbon where other nutrients, such as nitrogen or phosphorus, are growth limiting [54, 64, 66]. Secondly, both molecules are composed of 3-hydroxydecanoic acids connected by ester bonds. Interestingly, phaC1 [67] and phaF [68] encoding a PHA polymerase and PHA transcriptional regulator respectively are also predicted to be Crc regulated in P. aeruginosa, P. putida and P. syringae species. Notwithstanding the role of PHA in attachment of P. aeruginosa to surfaces [65], the implication of Crc in PHA production may also be interesting from an industrial point of view since it is hoped that PHA accumulating bacteria may be exploited in bioplastic production [69]. Under high carbon:nitrogen ratios, PHA and rhamnolipids are produced and represent carbon sinks to accommodate an inability to metabolise an excess of carbon over nitrogen. One possible function of the CRC system is to integrate C/N metabolism by regulating the production of carbon sink compounds such as PHA and rhamnolipid. This could be mediated by the CbrAB/NtrBC links outlined earlier.

          Conclusions

          CRC is an important global control network employed by Pseudomonas to optimise growth with available nutrients in a variety of environments. This analysis aimed to predict the set of targets that are directly regulated by the Crc protein in four species of Pseudomonas. As expected, genes involved in the metabolism of less favoured nutrients were identified. An interesting feature, however, was that the regulation of transporters is a conserved feature of Crc regulation in Pseudomonas spp. while the regulation of particular enzymatic steps and transcriptional activators is generally present in a more species-dependent manner. This suggests that different Pseudomonas species have fine-tuned CRC to reflect the ecology of that particular species. In addition to anticipated effects on sugar metabolism, there are indications from the data that Crc may play a role in maintaining the carbon/nitrogen balance in Pseudomonas and this is worthy of further study. It was postulated that identifying Crc targets might enhance knowledge of some applied aspects of Pseudomonas and one example of this was the prediction that Crc regulates steps in polyhydroxyalkanoate (PHA) synthesis in P. putida, as this is of interest for the production of biodegradable bioplastics. In the case of P. aeruginosa, the analysis revealed that alginate production and other traits linked to virulence may be under CRC control. It was especially intriguing to discover that Crc may play a role in regulation of globally important DNA binding proteins such as HU and IHF and thus regulate, indirectly, many pathways that depend on the DNA bending properties of these proteins for transcription or repression. These novel aspects of Crc regulation therefore deserve further investigation given the potential that it may enhance our understanding of the integration of nutritional status cues with the regulation of important activities of the Pseudomonas.

          Methods

          Positions -70 to +16 relative to the origin of translation of all protein encoding genes of available Pseudomonas spp. were downloaded from the regulatory sequence analysis tool (RSAT) [40] using the retrieve sequence function. Genes containing an A-rich (AAnAAnAA) motif in the -70 to +16 region were identified using a script in Perl. Translated protein encoding sequences were downloaded from the Pseudomonas genome database [70] and used to create local blast databases with formatdb [71]. Orthologous genes were identified as best hits using blastp analysis (blastall v2.2.22) [71, 72] against local databases. Cut-offs of 50% identity over at least 80% of the sequence length and an expected value (e-value) of 1e-10 were applied. Orthology was confirmed by reciprocating the blastp analysis. Since the A-rich motif is short and degenerate it is expected that occurrences of the A-rich motif that are unrelated to Crc binding will be detected in this analysis, giving rise to false positive hits. In order to estimate the rate of false positive hits in our analysis we searched for the A-rich motif in the reverse orientation of the upstream regions of orthologous loci [73]. Since the A-rich motif in the reverse orientation is unrelated to Crc binding it is reasoned that this estimates the rate of occurrence of the A-rich motif in the sequence fragments tested. Predictably it was found that the use of more strains per species resulted in lower estimated rates of false positives (P. aeruginosa - 4 strains, 18% estimated false positives; P. fluorescens - 3 strains, 32% estimated false positives; P. putida - 3 strains, 26% estimated false positives; P. syringae - 2 strains, 41% estimated false positives). Thus, it is estimated, based on the weighted mean false discovery rate, that approximately 73% of the Crc candidates in additional file 1 are genuine targets for Crc binding. Functional information about the translated protein sequences was obtained from the sequence headers and by performing Blast2GO analysis [74].

          Declarations

          Acknowledgements

          This research was supported in part by grants awarded by the Science Foundation of Ireland (grants 04/BR/B0597, 07/IN.1/B948, 08/RFP/GEN1295, 08/RFP/GEN1319 and 09/RFP/BMT2350), the Department of Agriculture, Fisheries and Food (RSF grants 06-321 and 06-377; FIRM grants 06RDC459, 06RDC506 and 08RDC629), the European Commission (grant FP6#O36314 and Marie Currie TOK:TRAMWAYS), Irish Research Council for Science Engineering and Technology (grant 05/EDIV/FP107/INTERPAM), the Marine Institute (Beaufort award C&CRA2007/082), the Health Research Board (grants RP/2006/271 and RP/2007/290). P.B. is supported by a STRIVE Doctoral Scholarship from the Environmental Protection Agency, Ireland and the Department of Environment, Heritage and Local Government provided by the Irish Government under the National Development Plan 2007-2013 (EPA-2006-S-21). We thank Pat Higgins for ongoing techncial support and members of our groups for useful discussions.

          Authors’ Affiliations

          (1)
          BIOMERIT Research Centre, Microbiology Department University College Cork
          (2)
          Microbiology Department University College Cork

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          This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://​creativecommons.​org/​licenses/​by/​2.​0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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