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Analysis of regulatory networks in Pseudomonas aeruginosa by genomewide transcriptional profiling
Analysis of regulatory networks in Pseudomonas aeruginosa by genomewide transcriptional profiling Andrew L Goodman and Stephen Lory Transcriptional profiling using DNA microarrays has proved to be a valuable tool for dissecting bacterial adaptation to various environments, including human hosts. Analysis of genomes and transcriptomes of Pseudomonas aeruginosa shows that this bacterium possesses and expresses a core set of genes, including virulence factors, which allow it to thrive in a range of environments. Transcriptional regulators previously thought to control single virulence traits are now shown to regulate complex global signaling networks. Microarray-based research has led to the discovery of upstream regulators and downstream components of these pathways, as well as probed the response to antibiotics, environmental stresses and other bacteria. Independent studies have highlighted the role of media composition, the makeup of the physical environment and experimental methods in the outcome of microarray analyses. A compilation of all the published data clearly shows transcriptional regulation of genes in all functional classes. Under conditions examined to date, slightly more than a quarter of the genome is regulated, suggesting that P. aeruginosa may use much of its genome for conditions unexplored in the laboratory. Addresses Department of Microbiology and Molecular Genetics, Harvard Medical School, 200 Longwood Avenue, Warren Alpert Building, Room 363, Boston, MA 02115, USA Corresponding author: Stephen Lory e-mail: [email protected]
Current Opinion in Microbiology 2004, 7:39–44 This review comes from a themed issue on Host–microbe interactions: bacteria Edited by Craig Roy and Philippe Sansonetti 1369-5274/$ – see front matter ß 2003 Elsevier Ltd. All rights reserved. DOI 10.1016/j.mib.2003.12.009
Abbreviations AHL acyl-homoserine lactones CF cystic fibrosis QS quorum sensing TTSS type III secretion system
Introduction Pseudomonas aeruginosa is a ubiquitous Gram-negative bacterium that can transition from its environmental niche in the soil and water to become an important and dangerous human pathogen. The bacterium can cause nosocomial pneumonia, catheter and urinary tract infections, sepsis in burn wound and immunocompromised patients, and chronic pulmonary inflammation in www.sciencedirect.com
hosts rendered susceptible by cystic fibrosis (CF) [1–5]. It appears that a diverse set of adaptive responses allow P. aeruginosa to establish this range of infections and in the case of CF cause chronic and inevitably fatal disease. The completion of the P. aeruginosa PAO1 genome sequencing project [6] and the subsequent development of high-density DNA microarrays to profile the genome and transcriptome at a global level have opened a window to the strategies used by this pathogen to cause disease. In the past few years, microarray studies have collected a wealth of data on P. aeruginosa strain diversity, transcriptional regulation, environmental adaptation and pathogenesis. These investigations illustrate the complexity of P. aeruginosa signaling networks and reveal a highly regulated genome well adapted for a wide range of environmental niches.
Environmental and clinical strains share core virulence factors High-density microarrays based largely on the genome of the fully annotated strain PAO1 provide a reference to examine strain diversity across environmental and clinical isolates. By hybridizing chromosomal DNA to a PAO1based microarray, it is possible to survey genomes for genes present in the reference strain but missing in the interrogated strains. Wolfgang et al. [7] hybridized DNA from 18 clinical and environmental isolates and found a remarkable degree of gene conservation, suggesting that strains are not restricted to specific niches but instead possess a core set of genes that allow the bacteria to thrive in a range of environments. Additionally, PAOl genes ‘missing’ in the isolates studied were often replaced by clusters of novel, strain-specific genes. Although such studies are limited by the gene diversity present in the reference strain, chromosomal hybridization provides a map for potential integration sites for the DNA segments acquired by horizontal gene transfer known as ‘genomic islands’. Several such islands have been previously described at polymorphic regions of the P. aeruginosa chromosome, including the characterized flagellin glycosylation island [8] and the island encoding the enzymes for the biosynthesis of O-side chains of lipopolysaccharide [9] as well as genomic islands of unknown function [10,11].
Signaling networks coordinate transcriptional programs The conclusion that environmental and clinical strains share the vast majority of their genes emphasizes the importance of coordinate transcriptional control in niche adaptation. To this end, the P. aeruginosa genome Current Opinion in Microbiology 2004, 7:39–44
40 Host–microbe interactions: bacteria
encodes a large number (550) of transcriptional regulators, dedicating nearly ten percent of its coding capacity to this particular class of proteins [6]. Microarray studies have provided insight into the complexity and interconnectedness of transcriptional regulatory networks and the role that transcriptional regulatory elements play in assimilation of signals originating from a multitude of different environments. The bacterial flagellum, the primary organ of swimming motility, has been shown to play a role in biofilm formation and other pathogenic adaptations [12]. It has been directly implicated in virulence on the basis of work in animal models of infection and serves as a pattern recognition molecule involved in the mobilization of the innate host response [13,14]. Dasgupta et al. [15] examined the transcriptional profiles of mutants in several regulators of flagellar biogenesis to explore the intricate control mechanisms that govern the synthesis of this complex organelle. Flagellar assembly is regulated in a four-step hierarchy dictated by sequential transcription of regulators of specific flagellar components. New genes controlled by this regulatory hierarchy proved to be involved in swimming motility, illustrating that transcriptional profiling can suggest function for hypothetical ORFs in the genome. Over 80% of CF patients become chronically colonized by P. aeruginosa that has undergone a phenotypic switch to mucoidy [16]. This overproduction of the exopolysaccharide alginate increases inflammation in the lung and is associated with declining lung function [17]. Conversion to mucoidy occurs when mutation in anti-sigma factor MucA leads to the release of the sequestered alternate sigma factor AlgU, resulting in the transcription of a set of genes encoding enzymes and accessory proteins responsible for alginate production. AlgU also controls genes other than those involved in alginate production, suggesting that the conversion to mucoidy is part of a global adaptation to the host environment [18]. To explore the genes under AlgU control, Firoved et al. [19] compared the transcriptional profiles of a mucA mutant with wildtype P. aeruginosa and an algU/mucA double mutant. Microarray studies confirmed the upregulation of alginate genes in strains with free AlgU. Surprisingly, the authors found an equal or greater upregulation of a range of toxin/ secretion genes, transport genes and enzymes, which indicates that alginate overproduction is part of a larger global adaptation response controlled by AlgU. The AlgU regulon also included genes encoding lipoproteins that stimulate production of pro-inflammatory chemokines by macrophages, highlighting the ability of the host to sense and respond to products of coordinately regulated virulence genes. Microarray profiling studies of genetically manipulated strains have been complemented by the use of media Current Opinion in Microbiology 2004, 7:39–44
conditions that trigger transcriptional pathways known to be important for pathogenesis. Biosynthesis of the P. aeruginosa type III secretion system (TTSS), a major virulence factor, is induced by host-cell contact [20,21] or by growth in low-calcium media. Wolfgang et al. [22] took advantage of this observation to look for upstream regulators of the TTSS. Microarray examination of gene expression in P. aeruginosa grown in high- and low-calcium media led to the identification of eleven calciumdependent transcriptional regulators. To determine if any of these proteins were also involved in contact-mediated TTSS activation, mutants in each of these regulators were then assayed for TTSS-dependent, contact-induced tissue culture cell cytotoxicity. Adenylate cyclase gene cyaB was found to connect the environmental stimulus (calcium limitation or host-cell contact) with activation of TTSS. Thus transcriptional profiling in specific media conditions allowed the identification of upstream regulators required for host cell-mediated TTSS activation.
Microarrays illuminate bacterial response to its environment Although technical obstacles have impeded direct transcriptional profiling of P. aeruginosa in the infected tissues, several groups have attempted to recreate components of physiologically relevant niches in the laboratory. Transcriptional profiling in these artificial environments has been used to gain a better understanding of the nature and function of signaling pathways activated during specific stages of infection. It has long been recognized that host proteins sequester free iron, creating an iron-limiting environment for pathogenic bacteria [23,24]. P. aeruginosa is known to coordinate multiple iron acquisition systems to thrive in lowiron niches. Two studies have investigated the global response to iron by significantly different experimental methods. Ochsner et al. [25] studied steady-state iron starvation response by growing bacteria to stationary phase in iron-replete or iron-deplete media, while Palma et al. [26] examined the iron response of exponentially growing bacteria by harvesting RNA shortly after adding iron to iron-deplete cultures. Both groups confirmed irondependent responses in known iron acquisition genes, such as pyoverdine, pyochelin and haem uptake, and found many novel iron-dependent genes and regulatory pathways, including complete operons of genes with no known function. Using their microarray data and genomic context to provide clues for annotation, Ochner et al. found that some of these were novel genes involved in pyoverdine synthesis. Significantly, many transcriptional regulators were found to be iron-dependent, suggesting that a complex transcriptional hierarchy governs the response of this bacterium to iron limitation. It also should be noted that only about half of the genes found to be iron-responsive under the conditions used by Ochner et al. were also detected in the study by Palma et al., www.sciencedirect.com
Analysis of regulatory networks in Pseudomonas aeruginosa by genomewide transcriptional profiling Goodman and Lory 41
emphasizing the importance of growth phase and environmental conditions in transcriptional response. In addition to the stress of iron limitation, it is also believed that P. aeruginosa undergoes oxidative stress during infection via superoxide generated by polymorphonuclear leukocytes. Salunkhe et al. [27] mimicked this condition in vitro by treating various P. aeruginosa strains with paraquat, which continuously releases superoxide. Although many responses were strain-specific, one complete operon of unknown function was consistently upregulated in the presence of paraquat, associating it with superoxide stress response.
Bacteria sense and respond to each other at a global scale Resistance to stresses and antibiotics might be mediated by growth as a biofilm, a sessile community of cells characteristic of chronic CF infection [28]. Although genetic studies suggest that there are discrete, separable steps in biofilm formation [12,29], microarrays have been used thus far only to characterize mature biofilms. Greenberg and co-workers [30] used a continuous-culture technique to compare planktonic bacteria to those attached to granite pebbles. Seventy-three biofilm-regulated genes were detected (1% of the genome), a surprisingly low number considering the multitude of characteristics ascribed to biofilm growth. In addition to repression of motility genes, a widespread induction of temperate bacteriophage genes was observed by microarray and validated by the detection of 100–1000 times more Pf1 phage in the biofilm system than the planktonic system. Drug efflux systems were not differently transcribed in this system, although tolA — the product of which can alter lipopolysaccharide structure to reduce affinity of aminoglycosides — was activated. The expression of rpoS, which encodes the alternative stationary phase sigma factor, was repressed in biofilm growth. This repression was shown to have physiological significance; bacteria with mutations in the rpoS gene formed biofilms that colonized more of the surface, formed larger structures, and were more drug resistant. To further characterize the antibiotic resistance of bacteria in the biofilm, the transcriptional profile of a biofilm exposed to tobromycin was analyzed. Fourteen genes including two drug efflux systems were activated upon exposure to the antibiotic; it is unclear, however, if this induction is biofilmdependent as these genes might be also induced in response to tobromycin in planktonic cultures. A primary signaling molecule in a biofilm may come from the bacteria themselves; strains deficient in quorum sensing (QS) are unable to form the fully developed, drugresistant biofilms characteristic of CF infection [31,32]. The observation that bacteria grow to high density in the lung and produce significant levels of acyl-homoserine lactones (AHL) suggests that QS might play an important www.sciencedirect.com
role in chronic bacterial colonization [28]. Three independent laboratories have used microarrays to examine the global QS response, analyzing the response to the effect of purified AHL on autoinducer synthase mutants [33–35]. Two of the resulting papers have been reviewed previously [36] and a third further characterized the system by examining the effects of a QS inhibitor at a genomewide level. A wealth of information is now available on the QS regulon; in addition to the experiments described above these laboratories have looked at AHL receptor mutants [33] and the effects of growth kinetics [33–35] and environment [33,34] on QS. Integrating these three datasets yields a core set of 77 QS-induced genes, approximately half of the total list generated by each group [35]. Even less agreement was reached regarding QS-repressed genes. Variations in experimental conditions, including amount of AHL added and media used, might explain these differences. Hentzer et al. [35] targeted QS as a system amenable to inhibition without promoting selection for resistant mutants seen with bacteriocidal antibiotics. The group isolated a furanone derivative that seemed to block QS and characterized its action by microarray. Eighty percent of the genes repressed by the furanone were also downregulated in an AHL synthase mutant, including genes involved in AHL synthesis and the cognate receptor genes. This transcriptional ‘fingerprint’ of an antibacterial compound opens the door to detailed, gene-by-gene understanding of drug action. This study demonstrates the power of microarrays to illuminate Pseudomonas pathogenesis at many levels, including cell–cell communication, biofilm growth and drug response.
Conclusions What can we learn from assimilating the wealth of data gathered in these studies? A basic compendium of all genes reported as regulated in published microarray studies shows that to date over a quarter of the genome is regulated under the conditions studied in the laboratory (Figure 1). It is clear that this result is biased by the choice of environmental conditions studied by individual investigators, with an emphasis towards those that simulate conditions presumed to exist in an infected patient. It is also important to note that post-transcriptional regulation can also control protein levels in P. aeruginosa. That said, it is remarkable that the few experiments outlined above have uncovered regulation in a full quarter of the genome; future experiments will continue to reveal novel networks of transcriptional regulation in this organism. Looking closer at the assembled list of changing genes, it is evident that in nearly every functional category a significant portion of genes are transcriptionally regulated (Figure 2). This further suggests that all of these protein classes have the potential to be regulated at the transcriptional level and that such regulation in this organism Current Opinion in Microbiology 2004, 7:39–44
42 Host–microbe interactions: bacteria
Figure 1
Changing in 1 experiment Changing in 2 experiments 3 4 5 Changing in 6-9 experiments Not changing in any experiment Current Opinion in Microbiology
Combined analysis of published Pseudomonas aeruginosa microarray experiments shows that over one-quarter of the genome is transcriptionally regulated. Partitions represent percentage of the genome; numbers indicate how many experiments have shown this genome percentage to be regulated.
extends far further than we have currently observed. The finding that transcriptional regulators represent a highly conserved functional group across clinical and environmental strains of P. aeruginosa indicates that transcriptional regulation is absolutely essential for the success of the species. In this regard, a community-wide project of inactivation of every regulatory gene would be useful in defining complex regulatory networks, analogous to the effort undertaken in Saccharomyces cerevisiae [37]. It is also noteworthy that the same percentage of genes is regulated in the hypothetical/unknown functional class as in the other annotated classes. This suggests that these hypothetical ORFs are not a discrete functional unit independent of the biology pursued in the laboratory; instead, the compilation of array results in Figure 2 predicts that the hypothetical/unknown genes are integrated into the pathogenic processes already under study. Therefore, it is expected that global transcriptional analysis in P. aeruginosa will continue to contribute in a significant way towards assigning function to unknown
Figure 2
Quinolone signal response Secreted Factors (toxins, enzymes, alginate) Chemotaxis Motility and Attachment Protein secretion/export apparatus Adaptation, protection Related to phage, transposon, or plasmid Central intermediary metabolism Energy metabolism Two-component regulatory systems Transport of small molecules Carbon compound catabolism Membrane proteins Chaperones & heat shock proteins Amino acid biosynthesis and metabolism Hypothetical, unclassified, unknown Transcription, RNA processing and degradation Antibiotic resistance and susceptibility Nucleotide biosynthesis and metabolism Putative enzymes Biosynthesis of cofactors, prosthetic groups and carriers Transcriptional regulators Cell wall / LPS / capsule Fatty acid and phospholipid metabolism Cell division Translation, post-translational modification, degradation DNA replication, recombination, modification and repair 0
20
40
60
80
100
Percent regulated Current Opinion in Microbiology
Annotation of genes found changing in published Pseudomonas aeruginosa microarray experiments shows transcriptional regulation in all functional classes. Hypothetical genes (arrow) have been regulated to the same extent as genes with known function. Annotations were derived from http://www.pseudomonas.com [38]. Current Opinion in Microbiology 2004, 7:39–44
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Analysis of regulatory networks in Pseudomonas aeruginosa by genomewide transcriptional profiling Goodman and Lory 43
proteins, provided a reliable system for integrating results generated by individual investigators is developed.
Acknowledgements The authors would like to thank Roger Smith and Matthew Wolfgang for helpful discussion. ALG is a Howard Hughes Medical Institute Predoctoral Fellow.
References and recommended reading Papers of particular interest, published within the annual period of review, have been highlighted as: of special interest of outstanding interest 1.
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10. Liang X, Pham XQ, Olson MV, Lory S: Identification of a genomic island present in the majority of pathogenic isolates of Pseudomonas aeruginosa. J Bacteriol 2001, 183:843-853. 11. Kiewitz C, Larbig K, Klockgether J, Weinel C, Tummler B: Monitoring genome evolution ex vivo: reversible chromosomal integration of a 106 kb plasmid at two tRNA(Lys) gene loci in sequential Pseudomonas aeruginosa airway isolates. Microbiology 2000, 146:2365-2373. 12. O’Toole GA, Kolter R: Flagellar and twitching motility are necessary for Pseudomonas aeruginosa biofilm development. Mol Microbiol 1998, 30:295-304. 13. Feldman M, Bryan R, Rajan S, Scheffler L, Brunnert S, Tang H, Prince A: Role of flagella in pathogenesis of Pseudomonas aeruginosa pulmonary infection. Infect Immun 1998, 66:43-51. 14. Ciacci-Woolwine F, McDermott PF, Mizel SB: Induction of cytokine synthesis by flagella from Gram-negative bacteria may be dependent on the activation or differentiation state of human monocytes. Infect Immun 1999, 67:5176-5185. www.sciencedirect.com
15. Dasgupta N, Wolfgang MC, Goodman AL, Arora SK, Jyot J, Lory S, Ramphal R: A four-tiered transcriptional regulatory circuit controls flagellar biogenesis in Pseudomonas aeruginosa. Mol Microbiol 2003, 50:809-824. The authors use microarrays to dissect the transcriptional hierarchy controlling flagellar biosynthesis and show that mutants in relevant regulators block transcription of downstream structural and regulatory components of this organelle. 16. FitzSimmons SC: The changing epidemiology of cystic fibrosis. J Pediatr 1993, 122:1-9. 17. Govan JR, Deretic V: Microbial pathogenesis in cystic fibrosis: mucoid Pseudomonas aeruginosa and Burkholderia cepacia. Microbiol Rev 1996, 60:539-574. 18. Firoved AM, Boucher JC, Deretic V: Global genomic analysis of AlgU (sigma(E))-dependent promoters (sigmulon) in Pseudomonas aeruginosa and implications for inflammatory processes in cystic fibrosis. J Bacteriol 2002, 184:1057-1064. 19. Firoved AM, Deretic V: Microarray analysis of global gene expression in mucoid Pseudomonas aeruginosa. J Bacteriol 2003, 185:1071-1081. Global transcriptional profiling comparisons of mucoid and isogenic non-mucoid Pseudomonas aeruginosa reveal that AlgU, the master regulator of alginate biosynthesis, also directs a global adaptation upon mucoid conversion. 20. Dacheux D, Attree I, Toussaint B: Expression of ExsA in trans confers type III secretion system-dependent cytotoxicity on noncytotoxic Pseudomonas aeruginosa cystic fibrosis isolates. Infect Immun 2001, 69:538-542. 21. Vallis AJ, Yahr TL, Barbieri JT, Frank DW: Regulation of ExoS production and secretion by Pseudomonas aeruginosa in response to tissue culture conditions. Infect Immun 1999, 67:914-920. 22. Wolfgang MC, Lee VT, Gilmore ME, Lory S: Coordinate regulation of bacterial virulence genes by a novel adenylate cyclasedependent signaling pathway. Dev Cell 2003, 4:253-263. Transcriptional profiles of bacteria grown in the presence and absence of calcium provided a list of calcium-dependent transcriptional regulators. Among these, adenylate cyclase was found to be a calcium-dependent, contact-induced activator of type III secretion. 23. Haas B, Kraut J, Marks J, Zanker SC, Castignetti D: Siderophore presence in sputa of cystic fibrosis patients. Infect Immun 1991, 59:3997-4000. 24. Handfield M, Lehoux DE, Sanschagrin F, Mahan MJ, Woods DE, Levesque RC: In vivo-induced genes in Pseudomonas aeruginosa. Infect Immun 2000, 68:2359-2362. 25. Ochsner UA, Wilderman PJ, Vasil AI, Vasil ML: GeneChip expression analysis of the iron starvation response in Pseudomonas aeruginosa: identification of novel pyoverdine biosynthesis genes. Mol Microbiol 2002, 45:1277-1287. This paper reports the stationary-phase response to prolonged iron starvation at a global level and illustrates that co-regulation of hypothetical genes with known genes provides clues for their function. 26. Palma M, Worgall S, Quadri LE: Transcriptome analysis of the Pseudomonas aeruginosa response to iron. Arch Microbiol 2003, 180:374-379. The immediate response of exponentially growing cells to iron was examined by microarray profiling of iron-starved cultures shortly after iron addition. Many new genes not previously associated with iron response, including virulence factors, were found to have iron-dependent regulation. 27. Salunkhe P, von Gotz F, Wiehlmann L, Lauber J, Buer J, Tu¨ mmler B: GeneChip expression analysis of the response of Pseudomonas aeruginosa to paraquat-induced superoxide stress. Genome Lett 2002, 1:165-174. 28. Singh PK, Schaefer AL, Parsek MR, Moninger TO, Welsh MJ, Greenberg EP: Quorum-sensing signals indicate that cystic fibrosis lungs are infected with bacterial biofilms. Nature 2000, 407:762-764. 29. Sauer K, Camper AK, Ehrlich GD, Costerton JW, Davies DG: Pseudomonas aeruginosa displays multiple phenotypes during development as a biofilm. J Bacteriol 2002, 184:1140-1154. Current Opinion in Microbiology 2004, 7:39–44
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30. Whiteley M, Bangera MG, Bumgarner RE, Parsek MR, Teitzel GM, Lory S, Greenberg EP: Gene expression in Pseudomonas aeruginosa biofilms. Nature 2001, 413:860-864.
Autoinducer synthase mutants were examined by microarray profiling in the presence and absence of autoinducer. Kinetic and media effects were also examined to create a list of quorum sensing-regulated genes.
31. Shih PC, Huang CT: Effects of quorum-sensing deficiency on Pseudomonas aeruginosa biofilm formation and antibiotic resistance. J Antimicrob Chemother 2002, 49:309-314.
35. Hentzer M, Wu H, Andersen JB, Riedel K, Rasmussen TB, Bagge N, Kumar N, Schembri MA, Song Z, Kristoffersen P et al.: Attenuation of Pseudomonas aeruginosa virulence by quorum sensing inhibitors. EMBO J 2003, 22:3803-3815. This paper characterizes the mode of action of a furanone analog that inhibits bacterial quorum sensing. Transcriptional profiles of autoinducer synthase mutants in the presence or absence of exogenous autoinducer determined the set of quorum sensing-regulated genes. This set of genes was specifically affected by addition of the furanone analog, establishing a gene-by-gene ‘fingerprint’ of drug action.
32. De Kievit TR, Gillis R, Marx S, Brown C, Iglewski BH: Quorumsensing genes in Pseudomonas aeruginosa biofilms: their role and expression patterns. Appl Environ Microbiol 2001, 67:1865-1873. 33. Schuster M, Lostroh CP, Ogi T, Greenberg EP: Identification, timing, and signal specificity of Pseudomonas aeruginosa quorum-controlled genes: a transcriptome analysis. J Bacteriol 2003, 185:2066-2079. The quorum sensing regulon was investigated by transcriptional profiling of autoinducer synthase mutants in the presence or absence of autoinducer. Autoinducer receptor mutants and the kinetics of regulation were also investigated, establishing approximately 6% of the genome as quorum regulated. 34. Wagner VE, Bushnell D, Passador L, Brooks AI, Iglewski BH: Microarray analysis of Pseudomonas aeruginosa quorumsensing regulons: effects of growth phase and environment. J Bacteriol 2003, 185:2080-2095.
Current Opinion in Microbiology 2004, 7:39–44
36. Vasil ML: DNA microarrays in analysis of quorum sensing: strengths and limitations. J Bacteriol 2003, 185:2061-2065. 37. Que QQ, Winzeler EA: Large-scale mutagenesis and functional genomics in yeast. Funct Integr Genomics 2002, 2:193-198. The systematic deletion of each open reading frame in Saccharomyces cerevisiae is described. This community-built resource provides a valuable tool for functional genomics and the dissection of regulatory networks. 38. Pseudomonas aeruginosa Community Annotation Project on World Wide Web URL: http://www.pseudomonas.com.
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Current Opinion in Microbiology 2004, 7:39–44 This review comes from a themed issue on Host–microbe interactions: bacteria Edited by Craig Roy and Philippe Sansonetti 1369-5274/$ – see front matter ß 2003 Elsevier Ltd. All rights reserved. DOI 10.1016/j.mib.2003.12.009
Abbreviations AHL acyl-homoserine lactones CF cystic fibrosis QS quorum sensing TTSS type III secretion system
Introduction Pseudomonas aeruginosa is a ubiquitous Gram-negative bacterium that can transition from its environmental niche in the soil and water to become an important and dangerous human pathogen. The bacterium can cause nosocomial pneumonia, catheter and urinary tract infections, sepsis in burn wound and immunocompromised patients, and chronic pulmonary inflammation in www.sciencedirect.com
hosts rendered susceptible by cystic fibrosis (CF) [1–5]. It appears that a diverse set of adaptive responses allow P. aeruginosa to establish this range of infections and in the case of CF cause chronic and inevitably fatal disease. The completion of the P. aeruginosa PAO1 genome sequencing project [6] and the subsequent development of high-density DNA microarrays to profile the genome and transcriptome at a global level have opened a window to the strategies used by this pathogen to cause disease. In the past few years, microarray studies have collected a wealth of data on P. aeruginosa strain diversity, transcriptional regulation, environmental adaptation and pathogenesis. These investigations illustrate the complexity of P. aeruginosa signaling networks and reveal a highly regulated genome well adapted for a wide range of environmental niches.
Environmental and clinical strains share core virulence factors High-density microarrays based largely on the genome of the fully annotated strain PAO1 provide a reference to examine strain diversity across environmental and clinical isolates. By hybridizing chromosomal DNA to a PAO1based microarray, it is possible to survey genomes for genes present in the reference strain but missing in the interrogated strains. Wolfgang et al. [7] hybridized DNA from 18 clinical and environmental isolates and found a remarkable degree of gene conservation, suggesting that strains are not restricted to specific niches but instead possess a core set of genes that allow the bacteria to thrive in a range of environments. Additionally, PAOl genes ‘missing’ in the isolates studied were often replaced by clusters of novel, strain-specific genes. Although such studies are limited by the gene diversity present in the reference strain, chromosomal hybridization provides a map for potential integration sites for the DNA segments acquired by horizontal gene transfer known as ‘genomic islands’. Several such islands have been previously described at polymorphic regions of the P. aeruginosa chromosome, including the characterized flagellin glycosylation island [8] and the island encoding the enzymes for the biosynthesis of O-side chains of lipopolysaccharide [9] as well as genomic islands of unknown function [10,11].
Signaling networks coordinate transcriptional programs The conclusion that environmental and clinical strains share the vast majority of their genes emphasizes the importance of coordinate transcriptional control in niche adaptation. To this end, the P. aeruginosa genome Current Opinion in Microbiology 2004, 7:39–44
40 Host–microbe interactions: bacteria
encodes a large number (550) of transcriptional regulators, dedicating nearly ten percent of its coding capacity to this particular class of proteins [6]. Microarray studies have provided insight into the complexity and interconnectedness of transcriptional regulatory networks and the role that transcriptional regulatory elements play in assimilation of signals originating from a multitude of different environments. The bacterial flagellum, the primary organ of swimming motility, has been shown to play a role in biofilm formation and other pathogenic adaptations [12]. It has been directly implicated in virulence on the basis of work in animal models of infection and serves as a pattern recognition molecule involved in the mobilization of the innate host response [13,14]. Dasgupta et al. [15] examined the transcriptional profiles of mutants in several regulators of flagellar biogenesis to explore the intricate control mechanisms that govern the synthesis of this complex organelle. Flagellar assembly is regulated in a four-step hierarchy dictated by sequential transcription of regulators of specific flagellar components. New genes controlled by this regulatory hierarchy proved to be involved in swimming motility, illustrating that transcriptional profiling can suggest function for hypothetical ORFs in the genome. Over 80% of CF patients become chronically colonized by P. aeruginosa that has undergone a phenotypic switch to mucoidy [16]. This overproduction of the exopolysaccharide alginate increases inflammation in the lung and is associated with declining lung function [17]. Conversion to mucoidy occurs when mutation in anti-sigma factor MucA leads to the release of the sequestered alternate sigma factor AlgU, resulting in the transcription of a set of genes encoding enzymes and accessory proteins responsible for alginate production. AlgU also controls genes other than those involved in alginate production, suggesting that the conversion to mucoidy is part of a global adaptation to the host environment [18]. To explore the genes under AlgU control, Firoved et al. [19] compared the transcriptional profiles of a mucA mutant with wildtype P. aeruginosa and an algU/mucA double mutant. Microarray studies confirmed the upregulation of alginate genes in strains with free AlgU. Surprisingly, the authors found an equal or greater upregulation of a range of toxin/ secretion genes, transport genes and enzymes, which indicates that alginate overproduction is part of a larger global adaptation response controlled by AlgU. The AlgU regulon also included genes encoding lipoproteins that stimulate production of pro-inflammatory chemokines by macrophages, highlighting the ability of the host to sense and respond to products of coordinately regulated virulence genes. Microarray profiling studies of genetically manipulated strains have been complemented by the use of media Current Opinion in Microbiology 2004, 7:39–44
conditions that trigger transcriptional pathways known to be important for pathogenesis. Biosynthesis of the P. aeruginosa type III secretion system (TTSS), a major virulence factor, is induced by host-cell contact [20,21] or by growth in low-calcium media. Wolfgang et al. [22] took advantage of this observation to look for upstream regulators of the TTSS. Microarray examination of gene expression in P. aeruginosa grown in high- and low-calcium media led to the identification of eleven calciumdependent transcriptional regulators. To determine if any of these proteins were also involved in contact-mediated TTSS activation, mutants in each of these regulators were then assayed for TTSS-dependent, contact-induced tissue culture cell cytotoxicity. Adenylate cyclase gene cyaB was found to connect the environmental stimulus (calcium limitation or host-cell contact) with activation of TTSS. Thus transcriptional profiling in specific media conditions allowed the identification of upstream regulators required for host cell-mediated TTSS activation.
Microarrays illuminate bacterial response to its environment Although technical obstacles have impeded direct transcriptional profiling of P. aeruginosa in the infected tissues, several groups have attempted to recreate components of physiologically relevant niches in the laboratory. Transcriptional profiling in these artificial environments has been used to gain a better understanding of the nature and function of signaling pathways activated during specific stages of infection. It has long been recognized that host proteins sequester free iron, creating an iron-limiting environment for pathogenic bacteria [23,24]. P. aeruginosa is known to coordinate multiple iron acquisition systems to thrive in lowiron niches. Two studies have investigated the global response to iron by significantly different experimental methods. Ochsner et al. [25] studied steady-state iron starvation response by growing bacteria to stationary phase in iron-replete or iron-deplete media, while Palma et al. [26] examined the iron response of exponentially growing bacteria by harvesting RNA shortly after adding iron to iron-deplete cultures. Both groups confirmed irondependent responses in known iron acquisition genes, such as pyoverdine, pyochelin and haem uptake, and found many novel iron-dependent genes and regulatory pathways, including complete operons of genes with no known function. Using their microarray data and genomic context to provide clues for annotation, Ochner et al. found that some of these were novel genes involved in pyoverdine synthesis. Significantly, many transcriptional regulators were found to be iron-dependent, suggesting that a complex transcriptional hierarchy governs the response of this bacterium to iron limitation. It also should be noted that only about half of the genes found to be iron-responsive under the conditions used by Ochner et al. were also detected in the study by Palma et al., www.sciencedirect.com
Analysis of regulatory networks in Pseudomonas aeruginosa by genomewide transcriptional profiling Goodman and Lory 41
emphasizing the importance of growth phase and environmental conditions in transcriptional response. In addition to the stress of iron limitation, it is also believed that P. aeruginosa undergoes oxidative stress during infection via superoxide generated by polymorphonuclear leukocytes. Salunkhe et al. [27] mimicked this condition in vitro by treating various P. aeruginosa strains with paraquat, which continuously releases superoxide. Although many responses were strain-specific, one complete operon of unknown function was consistently upregulated in the presence of paraquat, associating it with superoxide stress response.
Bacteria sense and respond to each other at a global scale Resistance to stresses and antibiotics might be mediated by growth as a biofilm, a sessile community of cells characteristic of chronic CF infection [28]. Although genetic studies suggest that there are discrete, separable steps in biofilm formation [12,29], microarrays have been used thus far only to characterize mature biofilms. Greenberg and co-workers [30] used a continuous-culture technique to compare planktonic bacteria to those attached to granite pebbles. Seventy-three biofilm-regulated genes were detected (1% of the genome), a surprisingly low number considering the multitude of characteristics ascribed to biofilm growth. In addition to repression of motility genes, a widespread induction of temperate bacteriophage genes was observed by microarray and validated by the detection of 100–1000 times more Pf1 phage in the biofilm system than the planktonic system. Drug efflux systems were not differently transcribed in this system, although tolA — the product of which can alter lipopolysaccharide structure to reduce affinity of aminoglycosides — was activated. The expression of rpoS, which encodes the alternative stationary phase sigma factor, was repressed in biofilm growth. This repression was shown to have physiological significance; bacteria with mutations in the rpoS gene formed biofilms that colonized more of the surface, formed larger structures, and were more drug resistant. To further characterize the antibiotic resistance of bacteria in the biofilm, the transcriptional profile of a biofilm exposed to tobromycin was analyzed. Fourteen genes including two drug efflux systems were activated upon exposure to the antibiotic; it is unclear, however, if this induction is biofilmdependent as these genes might be also induced in response to tobromycin in planktonic cultures. A primary signaling molecule in a biofilm may come from the bacteria themselves; strains deficient in quorum sensing (QS) are unable to form the fully developed, drugresistant biofilms characteristic of CF infection [31,32]. The observation that bacteria grow to high density in the lung and produce significant levels of acyl-homoserine lactones (AHL) suggests that QS might play an important www.sciencedirect.com
role in chronic bacterial colonization [28]. Three independent laboratories have used microarrays to examine the global QS response, analyzing the response to the effect of purified AHL on autoinducer synthase mutants [33–35]. Two of the resulting papers have been reviewed previously [36] and a third further characterized the system by examining the effects of a QS inhibitor at a genomewide level. A wealth of information is now available on the QS regulon; in addition to the experiments described above these laboratories have looked at AHL receptor mutants [33] and the effects of growth kinetics [33–35] and environment [33,34] on QS. Integrating these three datasets yields a core set of 77 QS-induced genes, approximately half of the total list generated by each group [35]. Even less agreement was reached regarding QS-repressed genes. Variations in experimental conditions, including amount of AHL added and media used, might explain these differences. Hentzer et al. [35] targeted QS as a system amenable to inhibition without promoting selection for resistant mutants seen with bacteriocidal antibiotics. The group isolated a furanone derivative that seemed to block QS and characterized its action by microarray. Eighty percent of the genes repressed by the furanone were also downregulated in an AHL synthase mutant, including genes involved in AHL synthesis and the cognate receptor genes. This transcriptional ‘fingerprint’ of an antibacterial compound opens the door to detailed, gene-by-gene understanding of drug action. This study demonstrates the power of microarrays to illuminate Pseudomonas pathogenesis at many levels, including cell–cell communication, biofilm growth and drug response.
Conclusions What can we learn from assimilating the wealth of data gathered in these studies? A basic compendium of all genes reported as regulated in published microarray studies shows that to date over a quarter of the genome is regulated under the conditions studied in the laboratory (Figure 1). It is clear that this result is biased by the choice of environmental conditions studied by individual investigators, with an emphasis towards those that simulate conditions presumed to exist in an infected patient. It is also important to note that post-transcriptional regulation can also control protein levels in P. aeruginosa. That said, it is remarkable that the few experiments outlined above have uncovered regulation in a full quarter of the genome; future experiments will continue to reveal novel networks of transcriptional regulation in this organism. Looking closer at the assembled list of changing genes, it is evident that in nearly every functional category a significant portion of genes are transcriptionally regulated (Figure 2). This further suggests that all of these protein classes have the potential to be regulated at the transcriptional level and that such regulation in this organism Current Opinion in Microbiology 2004, 7:39–44
42 Host–microbe interactions: bacteria
Figure 1
Changing in 1 experiment Changing in 2 experiments 3 4 5 Changing in 6-9 experiments Not changing in any experiment Current Opinion in Microbiology
Combined analysis of published Pseudomonas aeruginosa microarray experiments shows that over one-quarter of the genome is transcriptionally regulated. Partitions represent percentage of the genome; numbers indicate how many experiments have shown this genome percentage to be regulated.
extends far further than we have currently observed. The finding that transcriptional regulators represent a highly conserved functional group across clinical and environmental strains of P. aeruginosa indicates that transcriptional regulation is absolutely essential for the success of the species. In this regard, a community-wide project of inactivation of every regulatory gene would be useful in defining complex regulatory networks, analogous to the effort undertaken in Saccharomyces cerevisiae [37]. It is also noteworthy that the same percentage of genes is regulated in the hypothetical/unknown functional class as in the other annotated classes. This suggests that these hypothetical ORFs are not a discrete functional unit independent of the biology pursued in the laboratory; instead, the compilation of array results in Figure 2 predicts that the hypothetical/unknown genes are integrated into the pathogenic processes already under study. Therefore, it is expected that global transcriptional analysis in P. aeruginosa will continue to contribute in a significant way towards assigning function to unknown
Figure 2
Quinolone signal response Secreted Factors (toxins, enzymes, alginate) Chemotaxis Motility and Attachment Protein secretion/export apparatus Adaptation, protection Related to phage, transposon, or plasmid Central intermediary metabolism Energy metabolism Two-component regulatory systems Transport of small molecules Carbon compound catabolism Membrane proteins Chaperones & heat shock proteins Amino acid biosynthesis and metabolism Hypothetical, unclassified, unknown Transcription, RNA processing and degradation Antibiotic resistance and susceptibility Nucleotide biosynthesis and metabolism Putative enzymes Biosynthesis of cofactors, prosthetic groups and carriers Transcriptional regulators Cell wall / LPS / capsule Fatty acid and phospholipid metabolism Cell division Translation, post-translational modification, degradation DNA replication, recombination, modification and repair 0
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Percent regulated Current Opinion in Microbiology
Annotation of genes found changing in published Pseudomonas aeruginosa microarray experiments shows transcriptional regulation in all functional classes. Hypothetical genes (arrow) have been regulated to the same extent as genes with known function. Annotations were derived from http://www.pseudomonas.com [38]. Current Opinion in Microbiology 2004, 7:39–44
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Analysis of regulatory networks in Pseudomonas aeruginosa by genomewide transcriptional profiling Goodman and Lory 43
proteins, provided a reliable system for integrating results generated by individual investigators is developed.
Acknowledgements The authors would like to thank Roger Smith and Matthew Wolfgang for helpful discussion. ALG is a Howard Hughes Medical Institute Predoctoral Fellow.
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