Open Access

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

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.
https://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).
https://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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