Response of Pseudomonas aeruginosa PAO1 to low shear modelled microgravity involves AlgU regulation


  • Aurélie Crabbé,

    1. Laboratory of Microbial Interactions, Department of Molecular and Cellular Interactions, Flanders Institute for Biotechnology (VIB), Vrije Universiteit Brussel, Pleinlaan 2, B-1050 Brussels, Belgium.
    2. Expertise Group Molecular and Cellular Biology, Institute for Environment, Health and Safety, Belgian Nuclear Research Center (SCK·CEN), Boeretang 200, B-2400 Mol, Belgium.
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    • Present addresses: Center for Infectious Diseases and Vaccinology, The Biodesign Institute, Arizona State University, 1001 S. McAllister Avenue, Tempe, AZ 85287, USA;

  • Benny Pycke,

    1. Expertise Group Molecular and Cellular Biology, Institute for Environment, Health and Safety, Belgian Nuclear Research Center (SCK·CEN), Boeretang 200, B-2400 Mol, Belgium.
    2. Laboratory for Microbial Ecology and Technology (LabMET), Ghent University, Coupure Links 653, B-9000 Gent, Belgium.
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    • Center for Environmental Biotechnology, The Biodesign Institute, Arizona State University, 1001 S. McAllister Avenue, Tempe, AZ 85287, USA.

  • Rob Van Houdt,

    1. Expertise Group Molecular and Cellular Biology, Institute for Environment, Health and Safety, Belgian Nuclear Research Center (SCK·CEN), Boeretang 200, B-2400 Mol, Belgium.
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  • Pieter Monsieurs,

    1. Expertise Group Molecular and Cellular Biology, Institute for Environment, Health and Safety, Belgian Nuclear Research Center (SCK·CEN), Boeretang 200, B-2400 Mol, Belgium.
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  • Cheryl Nickerson,

    1. School of Life Sciences, The Biodesign Institute, Center for Infectious Diseases and Vaccinology, Arizona State University, 1001 South McAllister Avenue, Tempe, AZ 85287, USA.
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  • Natalie Leys,

    Corresponding author
    1. Expertise Group Molecular and Cellular Biology, Institute for Environment, Health and Safety, Belgian Nuclear Research Center (SCK·CEN), Boeretang 200, B-2400 Mol, Belgium.
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  • Pierre Cornelis

    1. Laboratory of Microbial Interactions, Department of Molecular and Cellular Interactions, Flanders Institute for Biotechnology (VIB), Vrije Universiteit Brussel, Pleinlaan 2, B-1050 Brussels, Belgium.
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E-mail; Tel. (+32) 14 33 27 26; Fax (+32) 14 31 47 93.


As a ubiquitous environmental organism that is occasionally part of the human flora, Pseudomonas aeruginosa could pose a health hazard for the immunocompromised astronauts during long-term missions. Therefore, insights into the behaviour of P. aeruginosa under spaceflight conditions were gained using two spaceflight-analogue culture systems: the rotating wall vessel (RWV) and the random position machine (RPM). Microarray analysis of P. aeruginosa PAO1 grown in the low shear modelled microgravity (LSMMG) environment of the RWV, compared with the normal gravity control (NG), revealed an apparent regulatory role for the alternative sigma factor AlgU (RpoE-like). Accordingly, P. aeruginosa cultured in LSMMG exhibited increased alginate production and upregulation of AlgU-controlled transcripts, including those encoding stress-related proteins. The LSMMG increased heat and oxidative stress resistance and caused a decrease in the oxygen transfer rate of the culture. This study also showed the involvement of the RNA-binding protein Hfq in the LSMMG response, consistent with its previously identified role in the Salmonella LSMMG and spaceflight response. The global transcriptional response of P. aeruginosa grown in the RPM was highly similar to that in NG. Fluid mixing was assessed in both systems and is believed to be a pivotal factor contributing to transcriptional differences between RWV- and RPM-grown P. aeruginosa. This study represents the first step towards the identification of virulence mechanisms of P. aeruginosa activated in response to spaceflight-analogue conditions, and could direct future research regarding the risk assessment and prevention of Pseudomonas infections during spaceflight and in immunocompromised patients.


At the advent of an era where science and technology is making long-term spaceflight missions to the Moon and Mars achievable, increased health risks are arising that are associated with the sustained stay of astronauts in isolated, confined environments. One of the critical factors to ensure safety, health and performance of the crewmembers is anticipating the risk for infectious disease during space exploration and habitation.

The ubiquitous Gram-negative bacterium Pseudomonas aeruginosa has previously caused respiratory and urinary tract infections in crewmembers of Apollo missions during and/or immediately after spaceflight (Taylor, 1974; Hawkins and Ziegelschmid, 1975). Pseudomonas aeruginosa is typically found in water and soil, and is occasionally part of the healthy human flora. As a result, Pseudomonas sp. have been isolated from surfaces and the potable water system of the International Space Station (ISS), despite stringent disinfection regimes (Bruce et al., 2005; Novikova et al., 2006). Besides being an environmental organism, P. aeruginosa is an important opportunistic pathogen in patients with both primary and acquired immunodeficiencies, accounting for 11–14% of nosocomial infections (Driscoll et al., 2007). Importantly, spaceflight conditions are known to compromise the immune system in multiple fashions (reviewed by Sonnenfeld, 2005). Earth-based models, such as hind limb unloading of rodents and bed-rest studies of human, simulate aspects of the altered immune response in space (reviewed by Sonnenfeld, 2005). Mice exposed tohind limb unloading showed a compromised resistance to infection by the Gram-negative pathogens Klebsiella pneumoniae and P. aeruginosa (Belay et al., 2002; Aviles et al., 2003). Furthermore, previous studies have demonstrated an increased virulence and stress resistance of bacterial pathogens in response to both spaceflight conditions and low shear modelled microgravity (LSMMG), compared with the ground controls (Nickerson et al., 2000; Wilson et al., 2002; 2007; 2008; Lynch et al., 2004).

As opportunities for bacterial experiments aboard the ISS are often limited due to constraints in crew time and safety reasons, specialized lab scale bioreactors have been developed to study certain aspects of microgravity on cellular reactions on Earth. The rotating wall vessel (RWV) bioreactor and the random positioning machine (RPM) are the most commonly used devices for this purpose. In the RWV, cells continuously fall at terminal velocity in a low fluid-shear suspension environment, which is inherent to microgravity (reviewed by Nickerson et al., 2004). In the RPM, cells are subjected to random orientations and accelerations, which is thought to result in the random alteration and thus theoretical reduction of the gravity vector (van Loon, 2007). Although the RPM has rarely been used for bacterial experiments (de Vet and Rutgers, 2007; Mastroleo et al., 2009), many reports exist on its use to study microgravity-analogue-induced changes in liquid cultures of suspended eukaryotic cells, mostly on leucocytes (Schwarzenberg et al., 2000; Boonyaratanakornkit et al., 2005).

The present study aimed at assessing the behaviour of P. aeruginosa PAO1 in response to microgravity-analogue conditions, emphasizing on pathogenicity mechanisms and stress resistance. Therefore, global transcriptional analysis and complementary phenotypic and physiologic assays were performed on P. aeruginosa grown in the RWV and RPM. To the best of our knowledge, this study presents the first global response of P. aeruginosa to spaceflight-analogue conditions, and the first direct comparison for bacteria between the most commonly used ground-based microgravity simulators.


Microarray analysis of P. aeruginosa PAO1 in response to cultivation in the RWV and the RPM

General observations.  Bacterial counts of P. aeruginosa PAO1 cultivated for 24 h in LSMMG, randomized gravity (RG) and normal gravity (NG) revealed no significant differences in total colony-forming units (cfu) (data not shown). The NG-grown bacteria were partially clustered on the membrane of the RWV bioreactors, presumably due to sedimentation. Analysis of differential gene expression revealed 134 and 9 genes induced in LSMMG and RG, respectively, compared with the NG control, applying a twofold threshold (P ≤ 0.05). Adopting a 1.5-fold threshold (P ≤ 0.05), 330 and 67 genes were differentially regulated in LSMMG and RG, respectively, as compared with NG. No genes in LSMMG and RG showed a significant downregulation compared with the NG control (≥ 1.50-fold, P ≤ 0.05). The induced genes were distributed throughout the P. aeruginosa PAO1 genome and were often adjacent, indicating organization in transcriptional units (operons). Interestingly, 75% (6 out of 9) of the RG-induced genes were also found differentially expressed in LSMMG, as compared with NG. Overall, these results indicate that RG and NG were sensed in a similar manner by P. aeruginosa PAO1 while LSMMG led to a more divergent transcriptomic profile.

Based on the set criteria of significance (twofold threshold, P ≤ 0.05), pathways and classes of genes involved in the response of P. aeruginosa PAO1 to microgravity-analogue conditions (LSMMG and RG) were identified. By including significant differentially regulated genes with fold-induction values between 1.5 and 2.0, the number of genes belonging to these pathways could be enlarged, thus creating a more complete picture of the response elicited by microgravity-analogue conditions in P. aeruginosa PAO1 (Table 1). A proposed hierarchical classification of the main regulatory pathways induced by LSMMG is provided in Fig. 1 and is discussed in more details in the following sections.

Table 1.  Selected genes differentially expressed in low shear modelled microgravity (LSMMG) and randomized gravity (RG) as compared with the normal gravity control (NG), organized in functional groups (fold threshold ≥ 1.5).
Gene numberGene nameFold change LSMMG/NGFold change RG/NGFunctionAlgUaHfqbQScMotdStresse02fATPg
PA0038 1.75Hypothetical proteinx      
PA0141 2.36Hypothetical protein x     
PA0179 1.56Putative two-component response regulator     x 
PA0200 2.381.79Hypothetical protein x   x 
PA0376rpoH1.97RNA polymerase factor sigma-32x   x  
PA0408pilG2.751.95Twitching motility protein PilG   x   
PA0409pilH2.00Twitching motility protein PilH   x   
PA0456 1.89Putative cold-shock protein x  x  
PA0460 1.89Hypothetical proteinxx     
PA0527dnr1.75Transcriptional regulator Dnr x   x 
PA0537 1.95Hypothetical proteinx      
PA0542 1.55Hypothetical protein x     
PA0546metK1.71S-adenosylmethionine synthetase     x 
PA0576rpoD2.52RNA polymerase sigma factor RpoD   x   
PA0588 1.75Hypothetical proteinx    x 
PA0762algU3.032.03RNA polymerase sigma factor AlgUx      
PA0763mucA3.03Anti-sigma factor MucAx      
PA0764mucB2.45Negative regulator for alginate biosynthesis MucBx      
PA0765mucC1.51Positive regulator for alginate biosynthesis MucCx      
PA0805 2.39Hypothetical proteinx      
PA0833 2.34Hypothetical proteinx      
PA0835pta2.25Phosphate acetyltransferase     x 
PA0836 2.091.65Acetate kinase     x 
PA0854fumC21.62Fumarate hydratasexx    x
PA0856 2.19Hypothetical proteinx      
PA0857bolA1.58Morphogene protein BolAx      
PA0905rsmA3.121.94Carbon storage regulatorxΔ x    
PA0943 1.51Hypothetical protein x     
PA0962 2.051.64DNA-binding stress protein    xx 
PA0973oprL2.37Peptidoglycan associated lipoprotein OprL precursor     x 
PA0996pqsA1.56Coenzyme A ligase  x    
PA0997pqsB1.93Beta-keto-acyl-acyl-carrier protein synthase-like protein  x  x 
PA1049pdxH1.94Pyridoxamine 5′-phosphate oxidase x     
PA1076 3.161.85Hypothetical protein     x 
PA1092fliC1.73Flagellin type B   x x 
PA1093fleL1.65Filament length control   x   
PA1094fliD2.30Flagellar capping protein FliD   x   
PA1095fliS1.94Flagellar protein FliS   x   
PA1096fleP1.87Type IV pilli length control   x   
PA1102fliG1.53Flagellar motor switch protein G   x   
PA1296 1.53Putative 2-hydroxyacid dehydrogenasex      
PA1429 2.02Cation-transporting P-type ATPase     x 
PA1430lasR1.52Transcriptional regulator LasR  x    
PA1431rsaL1.922.84Regulatory protein RsaL  x  x 
PA1454fleN1.60Flagellar synthesis regulator FleN   x   
PA1455fliA2.621.57Flagellar biosynthesis sigma factor   x   
PA1456cheY1.78Two-component response regulator CheY   x   
PA1457cheZ1.70Chemotaxis protein CheZ   x   
PA1464cheW1.69Putative purine-binding chemotaxis protein   x   
PA1471 2.63Hypothetical proteinx      
PA1544anr1.84Transcriptional regulator Anr x x x 
PA1546hemN2.601.59Coproporphyrinogen III oxidase x   x 
PA1552ccoP11.53Cytochrome c     xx
PA1553ccoO11.71Cytochrome c oxidase subunit     xx
PA1556ccoO21.801.56Cytochrome c oxidase subunit x   xx
PA1557ccoN21.54Cytochrome oxidase subunit (cbb3-type) x   xx
PA1561aer2.301.70Aerotaxis receptor Aer   x x 
PA1562acnA1.71Aconitate hydratasex    xx
PA1580gltA1.56Type II citrate synthasexΔ     x
PA1582sdhD1.51Succinate dehydrogenase (D subunit)      x
PA1583sdhA2.35Succinate dehydrogenase flavoprotein subunit     xx
PA1584sdhB2.19Succinate dehydrogenase iron-sulfur subunit      x
PA1589sucD1.57Succinyl-CoA synthetase subunit alpha      x
PA1596htpG3.04Heat shock protein 90    xx 
PA1609fabB1.563-Oxoacyl-(acyl carrier protein) synthase I x     
PA1610fabA1.803-Hydroxydecanoyl-(acyl carrier protein) dehydratase x     
PA1673 2.741.95Hypothetical protein x   x 
PA1746 2.001.72Hypothetical protein x   x 
PA1776sigX1.51RNA polymerase sigma factor SigX x     
PA1777oprF1.67Major porin and structural outer membrane porin OprF precursorx    x 
PA1787acnB1.63Bifunctional aconitate hydratase 2/2-methylisocitrate dehydratase     xx
PA1789uspL2.451.94Hypothetical protein x  xx 
PA1800tig1.60Trigger factor     x 
PA1801clpP1.63ATP-dependent Clp protease proteolytic subunitx      
PA1802clpX2.49ATP-dependent protease ATP-binding subunit ClpXx   x  
PA1803lon2.54Lon protease  x x  
PA1852 1.59Hypothetical protein x     
PA2016gnyR1.98Regulator of liu genes     x 
PA2023galU1.72UTP-glucose-1-phosphate uridylyltransferasex      
PA2119 2.11Alcohol dehydrogenase (Zn-dependent) x   x 
PA2249bkdB1.65Branched-chain alpha-keto acid dehydrogenase subunit E2x      
PA2486 2.72Hypothetical proteinx      
PA2562 2.74Hypothetical proteinx      
PA2620clpA2.161.61ATP-binding protease component ClpAxΔ      
PA2621clpS2.081.85ATP-dependent Clp protease adaptor protein ClpSxΔ      
PA2622cspD1.721.99Cold-shock protein CspD    x  
PA2623icd2.30Isocitrate dehydrogenase     xx
PA2634 2.39Isocitrate lyase x     
PA2638nuoB1.88NADH dehydrogenase subunit B     xx
PA2639nuoD1.52Bifunctional NADH : ubiquinone oxidoreductase subunit C/D     xx
PA2640nuoE1.56NADH dehydrogenase subunit E     xx
PA2753 2.821.81Hypothetical protein x   x 
PA2754 1.991.67Hypothetical proteinxx   x 
PA2805 2.311.69Hypothetical protein x     
PA2830htpX1.78Heat shock protein HtpX    x  
PA2867 2.07Putative chemotaxis transducerx  x   
PA2883 2.15Hypothetical proteinx      
PA2951etfA2.28Electron transfer flavoprotein alpha-subunit      x
PA2952etfB2.13Electron transfer flavoprotein beta-subunit      x
PA2953 1.97Electron transfer flavoprotein-ubiquinone oxidoreductase      x
PA2966acpP1.90Acyl carrier protein x     
PA2967fabG1.773-Ketoacyl-(acyl-carrier-protein) reductase x     
PA2987 1.81ABC transporter ATP-binding proteinx      
PA3014foaA2.22Multifunctional fatty acid oxidation complex subunit alphax      
PA3017 1.51Hypothetical protein    x  
PA3115fimV1.77Motility protein FimV   x   
PA3126ibpA3.09Heat-shock protein IbpA    x  
PA3262 1.71Putative peptidyl-prolyl cis-trans isomerase, FkbP-typex      
PA3278 2.031.60Hypothetical protein x     
PA3309uspK1.87Hypothetical protein x  xx 
PA3337rfaD2.781.70ADP-l-glycero-d-mannoheptose 6-epimerase x   x 
PA3349 2.39Probable chemotaxis protein   x   
PA3351flgM1.611.52FlgM   x   
PA3352 2.34Hypothetical protein   x   
PA3418ldh1.55Leucine dehydrogenase     x 
PA3465 1.84Hypothetical protein     x 
PA3477rhlR1.52Transcriptional regulator RhlR xx    
PA3525argG1.88Argininosuccinate synthase      x
PA3531bfrB3.311.76BacterioferritinxΔ    x 
PA3572 2.462.15Hypothetical protein x     
PA3613 1.64Hypothetical protein x     
PA3614 1.92Hypothetical protein x     
PA3621fdxA1.95Ferredoxin I x     
PA3635eno1.67Phosphopyruvate hydratase     x 
PA3655tsf1.57Elongation factor Ts     x 
PA3788 1.64Hypothetical proteinx      
PA3819 1.99Hypothetical proteinx      
PA3879narL2.141.84Transcriptional regulator NarL     x 
PA3880 1.62Hypothetical protein x     
PA3940 2.481.56Putative DNA binding proteinxx     
PA4067oprG2.831.57Outer membrane protein OprG precursor x     
PA4133 1.71Cytochrome c oxidase subunit (cbb3-type)x    xx
PA4236katA1.70Catalase    xx 
PA4238rpoA2.32DNA-directed RNA polymerase alpha subunit     x 
PA4244rplO3.2050S ribosomal protein L15x      
PA4245rpmD2.2150S ribosomal protein L30xΔ      
PA4246rpsE2.0030S ribosomal protein S5     x 
PA4248rplF2.2950S ribosomal protein L6xΔ      
PA4249rpsH1.8530S ribosomal protein S8x      
PA4253rplN2.3150S ribosomal protein L14xΔ      
PA4261rplW3.2050S ribosomal protein L23x      
PA4263rplC2.7450S ribosomal protein L3     x 
PA4265tufA1.57Elongation factor Tu     x 
PA4266fusA12.36Elongation factor Gx    x 
PA4273rplA2.4250S ribosomal protein L1     x 
PA4311 1.77Hypothetical proteinx      
PA4328uspM1.60Hypothetical protein x  xx 
PA4348 2.85Hypothetical protein x     
PA4352uspN1.54Hypothetical protein x  xx 
PA4385groEL1.81Chaperonin GroEL    xx 
PA4386groES4.34Co-chaperonin GroES    x  
PA4403secA1.59Translocase     x 
PA4429 1.74Putative cytochrome c1 precursor      x
PA4430 1.57Putative cytochrome b      x
PA4433rplM2.3350S ribosomal protein L13 x     
PA4457 1.72Arabinose-5-phosphate isomerase KdsD proteinx      
PA4542clpB2.24ClpB proteinxΔ      
PA4577 2.371.52Hypothetical protein x   x 
PA4587ccpR1.91Cytochrome c551 peroxidase precursor     xx
PA4611 2.592s.43Hypothetical protein x     
PA4661pagL2.231.93Lipid A 3-O-deacylasex      
PA4671 2.1750S ribosomal protein L25/general stress protein Ctc    x  
PA4736 1.55Hypothetical proteinx      
PA4761dnaK2.17Molecular chaperone DnaK    xx 
PA4762grpE2.03Heat shock protein GrpE    x  
PA4880 1.59Putative bacterioferritinxx     
PA4943 1.77GTP-binding protein x     
PA4944hfq2.61RNA-binding protein HfqxΔx     
PA5027 1.70Hypothetical protein x   xx
PA5053hslV3.67ATP-dependent protease peptidase subunit    x  
PA5054hslU1.82ATP-dependent protease ATP-binding subunit HslU    x  
PA5060phaF2.641.62Polyhydroxyalkanoate synthesis protein PhaFx      
PA5107blc1.57Outer membrane lipoprotein Blcx      
PA5108 1.55Hypothetical proteinx      
PA5170arcD1.79Arginine/ornithine antiporter     x 
PA5171arcA2.462.25Arginine deiminase     x 
PA5172arcB2.422.38Ornithine carbamoyltransferase     x 
PA5173arcC1.852.21Carbamate kinase     x 
PA5178 1.86LysM domain/BON superfamily proteinx      
PA5182 2.11Hypothetical proteinx      
PA5208 2.09Hypothetical protein     x 
PA5212 1.841.73Hypothetical proteinx      
PA5227 1.79hypothetical proteinx      
PA5253algP1.63Alginate regulatory protein AlgPx      
PA5261algR2.25Alginate biosynthesis regulatory protein AlgRx      
PA5271 1.58Hypothetical proteinx      
PA5300cycB2.04Cytochrome c5      x
PA5306 1.69Hypothetical proteinx      
PA5316rpmB2.5350S ribosomal protein L28xΔ      
PA5424 1.58Hypothetical proteinx      
PA5427adhA2.86Alcohol dehydrogenase x   x 
PA5461 1.63Hypothetical protein x     
PA5475 1.79Hypothetical protein x   x 
PA5489dsbA1.76Thiol : disulfide interchange protein DsbAx      
PA5490cc41.88Cytochrome c4 precursor      x
PA5526 2.65Hypothetical proteinx      
PA5554atpD1.641.54F0F1 ATP synthase subunit beta     xx
PA5556atpA1.64F0F1 ATP synthase subunit alpha     xx
PA5557atpH1.97F0F1 ATP synthase subunit delta      x
PA5558atpF2.05F0F1 ATP synthase subunit B      x
PA5559atpE2.51F0F1 ATP synthase subunit C      x
PA5570rpmH2.1750S ribosomal protein L34xΔ      
Figure 1.

Proposed hierarchical ranking of the main regulatory pathways induced in LSMMG, based on literature. For clarity purposes, not all LSMMG-genes were included and genes under control of RpoD were not connected. References: 1 (Qiu et al., 2008); 2 (Wozniak and Ohman, 1994); 3 (Ramsey and Wozniak, 2005); 4 (Firoved et al., 2002); 5 (Malhotra et al., 2000); 6 (Shi et al., 2007); 7 (Sonnleitner et al., 2006); 8 (Wood and Ohman, 2009); 9 (Nakahigashi et al., 1998); 10 (Alvarez-Ortega and Harwood, 2007); 11 (Comolli and Donohue, 2004); 12 (Filiatrault et al., 2005); 13 (Platt et al., 2008); 14 (Rompf et al., 1998); 15 (Schreiber et al., 2006); 16 (Rampioni et al., 2007); 17 (Rampioni et al., 2006); 18 (Kato et al., 2008); 19 (Gamper et al., 1991); 20 (Pessi et al., 2001); 21 (Boes et al., 2008); 22 (Lizewski et al., 2004); 23 (Takaya et al., 2008); 24 (Schurr and Deretic, 1997); 25 (Bang et al., 2005).

Alternative sigma factor AlgU.  The transcription of algU (3.03-fold) and genes under control of AlgU were induced in LSMMG as compared with the NG control. While algU was also upregulated in RG conditions, albeit to a smaller extent than in LSMMG, the majority of genes under control of AlgU were not significantly affected under this condition.

Activation of AlgU relies on the degradation or inactivation of its anti-sigma factor MucA and subsequent release of the sequestered AlgU (reviewed by Ramsey and Wozniak, 2005). The proteases ClpX and ClpP (induced in LSMMG by 2.5- and 1.6-fold respectively) could partly account for the liberation of AlgU, which can result in the transcription of the alginate biosynthesis operon (Qiu et al., 2008). Also the genes encoding AlgR and AlgP, which positively regulate alginate biosynthesis, were upregulated in LSMMG. Genes dependent of AlgR (AlgU regulated) were also induced in LSMMG, i.e. lon[encoding the Lon protease, inhibition of quorum sensing (QS)], ibpA and hslV (heat shock response) (Lizewski et al., 2004). However, several genes known to be under negative control of AlgR (Lizewski et al., 2004) were also induced in LSMMG (e.g. hemN, arcA, PA1414, ccoO2, PA4348).

Furthermore, other genes that were reported to be under direct control or positively regulated by AlgU were significantly upregulated in LSMMG (Table 1). Those include the sigma factor rpoH and heat-shock response genes (dnaK, grpE, groEL, groES), dsbA (involved in folding of proteins with disulfide bonds), oprF (encoding the major outer membrane porin), mucABC (involved in post-transcriptional control of AlgU), phaF[encoding a negative regulator of polyhydroxyalkanoate (PHA) synthesis] and rplOW (encoding ribosomal subunits) (Arsene et al., 2000; Malhotra et al., 2000; Firoved et al., 2002; Firoved and Deretic, 2003). In addition, based on homology with the plant pathogen Xylella fastidiosa, the upregulation in LSMMG of hfq- and Hfq-regulon genes (further discussed below), rsmA (encoding a negative post-transcriptional QS regulator), bfrAB (encoding bacterioferritin), clpABS (encoding proteases), gltA (encoding citrate synthase), rplNF and rpmBDH (encoding ribosomal subunit proteins) may also be under control of AlgU (Shi et al., 2007).

Hfq and quorum sensing.  The gene coding for the RNA binding protein Hfq and a part of the genes belonging to the Hfq regulon were upregulated when the transcriptional profiles of P. aeruginosa PAO1 grown in LSMMG and NG were compared (Table 1). The hfq gene was not differentially expressed in RG as compared with NG and only two genes of the Hfq regulon were differentially expressed above the twofold threshold, i.e. PA3572 and PA4611.

Comparative analysis of the LSMMG upregulated genes (1.5-fold ratio) and the gene set under positive control of Hfq (Sonnleitner et al., 2006), identified a significant overlap of 50 genes (P ≤ 0.05) (Table 1). The overlap between LSMMG-induced genes and genes under negative control of Hfq was not statistically significant. Interestingly, hfq transcription is under control of AlgU in the plant pathogen X. fastidiosa (Shi et al., 2007) and AlgU was upregulated in LSMMG-grown P. aeruginosa in the present study (see above). Hfq regulates about 5% of the transcriptome in P. aeruginosa, in part through its positive effect on the sigma factor RpoS and the QS system (Sonnleitner et al., 2006). However, neither RpoS nor major QS genes showed a significant differential expression above the twofold threshold in the test conditions. Correspondingly, the negative QS regulators rsmA and the lon-protease (Pessi et al., 2001; Takaya et al., 2008) showed a significant upregulation in LSMMG (3.12-fold and 1.94-fold respectively). In addition, we observed that a negative regulator of the las QS system, i.e. rsaL, was induced in LSMMG compared with NG (Rampioni et al., 2006; 2007).

Environmental stress genes.  Among the genes with the highest fold-induction ratios in LSMMG were those involved in the response and resistance of P. aeruginosa to stress, including groEL, htpX, grpE, katA, PA3017 (usp encoding for a universal stress protein), and uspKLMNO (Table 1). Interestingly, AlgU (upregulated in LSMMG) controls the expression of (i) genes involved in the heat shock response of P. aeruginosa PAO1 through its positive regulation of rpoH (1.97-fold induced in LSMMG) (Schurr and Deretic, 1997; Malhotra et al., 2000; Firoved et al., 2002) and (ii) universal stress proteins through anr (Alvarez-Ortega and Harwood, 2007; Boes et al., 2008) (see below). Moreover, AlgU is involved in the resistance to oxidative stress (Martin et al., 1994).

Microaerophilic/anaerobic gene expression.  A notable number of genes upregulated in LSMMG as compared with NG have previously been identified to be involved in the metabolism of P. aeruginosa in microaerophilic and/or anaerobic conditions (Filiatrault et al., 2005; Alvarez-Ortega and Harwood, 2007; Platt et al., 2008) (Table 1). Indeed, the overlap between the LSMMG gene set and the microaerophilic/anaerobic transcriptome of P. aeruginosa (Alvarez-Ortega and Harwood, 2007) was confirmed not to be a chance event, as determined by hypergeometric distribution. A part of the gene transcripts involved in microaerophilic and anaerobic metabolism, which were shown upregulated in LSMMG, were also upregulated when RG was compared with NG, albeit in a majority of the cases to a smaller extent and below the twofold threshold.

Growth of P. aeruginosa in an environment with low oxygen levels requires the expression of terminal oxidases with a high affinity for oxygen (Comolli and Donohue, 2004; Alvarez-Ortega and Harwood, 2007). Two genes of the cbb3-2 type cytochrome oxidase operon (ccoO2, ccoN2), which play a significant role for growth of P. aeruginosa in oxygen-limiting conditions, were found to be slightly upregulated in LSMMG but not in RG, when compared with the NG control. Furthermore, other genes which are specific for growth in microaerophilic conditions (Alvarez-Ortega and Harwood, 2007) were upregulated in LSMMG (i.e. rsaL, uspK, katA, ccpR) and many genes previously identified as upregulated under both anaerobic and microaerophilic conditions (Filiatrault et al., 2005; Alvarez-Ortega and Harwood, 2007; Platt et al., 2008) were also more transcribed in LSMMG compared with NG. The oxygen-responsive transcription regulator Anr presumably played an important role in the LSMMG response because the anr gene and several of the microaerophilic/anaerobic genes induced in LSMMG are under control of Anr (aer, ccoO2, ccoN2, hemN, arcDABC, uspKLMO) (Rompf et al., 1998; Comolli and Donohue, 2004; Schreiber et al., 2006; Boes et al., 2008). Interestingly, Aer (under control of Anr) controls aerotaxis in P. aeruginosa (reviewed by Kato et al., 2008) which might have played a role in the LSMMG response because genes involved in chemotaxis and motility were upregulated in LSMMG compared with NG (see below).

Under anaerobic conditions, an alternative electron acceptor such as nitrate or nitrite is necessary for the production of ATP through the respiratory chain in P. aeruginosa. Other than the activator of nitrate reduction, NarL, none of the gene transcripts involved in denitrification were more abundant in LSMMG than NG. Pseudomonas aeruginosa is able to ferment arginine and pyruvate as carbon and energy source in anoxic conditions (Vander Wauven et al., 1984; Schreiber et al., 2006). The arginine deiminase pathway, under control of Anr, is comprised of the genes arcABC (encoding enzymes), and arcD, encoding an arginine-ornithine antiporter (Luthi et al., 1986; 1990; Haas et al., 1987). All four members of the arginine deiminase pathway showed a higher expression in LSMMG as compared with NG. The genes of the arginine deiminase operon (arcABC) were the only genes showing a more than twofold upregulation in RG compared with NG.

In addition, all of the genes identified to play the most important roles in pyruvate fermentation, i.e. arcABC, oprL, uspK and uspN, were upregulated in LSMMG compared with NG (Schreiber et al., 2006). In contrast to denitrification and arginine fermentation, pyruvate fermentation does not sustain anaerobic cell proliferation but is important for long-term anaerobic survival of P. aeruginosa (Eschbach et al., 2004; Schreiber et al., 2006).

Motility and chemotaxis.  The synthesis and assembly of the multi-component single polar flagellum of P. aeruginosa is intricately regulated. A four-tiered hierarchy (Class I to IV) of transcriptional regulation has been proposed by Dasgupta and colleagues, where each class is at least dependent on the expression of genes from its preceding classes (Dasgupta et al., 2003). Representative members of Class I (fliA, rpoD), Class II (fliD, fliS, fliG, fleN) and Class IV (fliC, fleP, cheY, cheZ, flgM) were found to be transcribed to a greater extent in LSMMG and some to a lesser extent in RG (all below the twofold threshold) compared with the NG control (Table 1). While no genes of Class III were significantly upregulated in LSMMG, the transcription of the listed Class IV genes is indicative for gene transcription dependent of Class I, II and III. Interestingly, several genes involved in chemotaxis (cheY, cheZ, cheW) and more specifically in aerotaxis (i.e. aer, anr) were upregulated in LSMMG compared with NG. In addition, the response regulators encoded by pilG and pilH, as well as fimV, involved in twitching motility or flagella-independent surface translocation of P. aeruginosa (Darzins and Russell, 1997; Semmler et al., 2000) showed a higher transcription level in the LSMMG condition, but not in RG.

Other functional classes and hypothetical proteins.  Pathways involved in energy production of P. aeruginosa were induced in LSMMG as reflected by an increased transcription of genes encoding citric acid cycle proteins (sdhABCD, gltA, acnAB, icd, fumC2), electron transport chains proteins (etfAB, PA2953), NADH dehydrogenases (nuoBDE), cytochromes (see above) and ATP synthase subunits (atpADEFH). An important number of genes encoding ribosomal proteins were also found induced in LSMMG compared with NG (i.e. frr, PA4671, rplABCDEFJKLMNOPQRSTUVWX, rpmBDFHIJ, rpsABCDEFGHIJKLMNPQRSTU) (data not shown). Many of these housekeeping genes are under control of the sigma factor RpoD (reviewed by Potvin et al., 2008), which was upregulated in LSMMG (2.52-fold).

In the present study, 39 hypothetical proteins (based on gene annotation extracted from the NCBI database, accession number NC_002516) were at least twofold upregulated in LSMMG-cultured cells compared with bacteria grown in NG. A majority of these hypothetical proteins were categorized in Table 1 because published literature linked their expression to a specific environmental condition (microaerophilic/anaerobic metabolism), regulon (AlgU, Hfq) or function (citric acid cycle, motility, stress response) found to be of importance in the LSMMG response of P. aeruginosa. A main part of the remaining hypothetical genes have been reported to be differentially expressed under given cellular challenges but appear not to be related to identified LSMMG-induced pathways in this study. Interestingly, one hypothetical gene (PA2737) has, to our knowledge, not been reported as differentially regulated in previously studied conditions and its induction could potentially be characteristic of the cellular response to the specific environment of LSMMG.

Quantitative real-time PCR analysis

In order to confirm the gene expression data of algU and hfq, which were identified as two main regulators in the LSMMG response using microarray analysis, quantitative real-time PCR (qRT-PCR) was performed. The algU and hfq genes were both found significantly induced in LSMMG (P < 0.05) when compared with NG (7.0-fold for AlgU, 4.9-fold for Hfq) and RG (3.2-fold for AlgU, 1.96-fold for Hfq) conditions, confirming the Affymetrix transcriptomic data.

Alginate production in response to microgravity-analogue conditions

Microarray analysis of P. aeruginosa PAO1 grown in LSMMG as compared with NG identified several genes involved in the production of the exopolysaccharide alginate (Table 1). Consequently, the borate-carbazole assay was performed to assess the amount of alginate produced in the test conditions. A significantly higher production of alginate in LSMMG compared with NG (2.3-fold) and RG (2.1-fold) was observed (Fig. 2). These data are in agreement with the transcriptional profile and confirm again the similarity of RG- and NG-induced responses in P. aeruginosa PAO1.

Figure 2.

Alginate concentration in LSMMG, NG and RG as determined with the borate-carbazole assay.

Environmental stress response in LSMMG compared with NG

In order to investigate if a higher transcription of stress-associated genes in LSMMG leads to an increased resistance to environmental stresses, P. aeruginosa PAO1 was exposed to oxidative, heat and acid stress conditions after cultivation in the RWV (Fig. 3). Resistance to oxidative stress was increased for LSMMG cultures when exposed for 15 (4.6-fold) and 60 min (6.6-fold). Pseudomonas aeruginosa PAO1 grown in LSMMG showed a 2.8-fold higher resistance (P < 0.05) to a 15 min heat shock in comparison with cultivation in the NG control position. Sixty minutes' exposure to 50°C did not lead to significant differences in the survival percentage of LSMMG and NG cultures. The tolerance to acid stress was not significantly different for both test conditions (P > 0.05).

Figure 3.

Susceptibility of P. aeruginosa PAO1 grown in LSMMG and NG conditions to different environmental stresses: oxidative stress (0.88 M H2O2, 15 and 60 min) (A), heat shock (50°C, 15 and 60 min) (B), and acid stress (pH 3.3, 15 and 60 min) (C). Results are presented as the percentage (in log10 units) survival following stress exposure (as a function of the original cell number).

Oxygen availability in LSMMG and NG

The microarray profile of P. aeruginosa PAO1 in LSMMG indicated an induction of different mechanisms to grow and survive in microaerophilic or anaerobic conditions, as compared with NG. To determine if LSMMG and NG cultures show differences in their oxygen levels after 24 h of cultivation, the dissolved oxygen (DO) concentration was measured and compared with the DO of an overnight shaking culture. In the three tested growth conditions, the DO concentration was in the range of the detection limit of the probe, i.e. 0.1 mg l−1. As no apparent differences in bulk oxygen content of LSMMG and NG cultures could be demonstrated, the oxygen transfer coefficient (kLa) value for LSMMG and NG grown cultures was determined. Figure 4 presents the oxygen transfer rate of LSMMG and NG cultures (non-viable cells and medium). Interestingly, the transfer of oxygen into the medium can be reduced by alginate (Hassett, 1996), which was determined as more abundant in LSMMG (Fig. 2). To assess the influence of alginate on the oxygen transfer rate, sterile LB supplemented with 0, 1, 5 and 10 mg ml−1 sodium alginate (in LB) were included in the experiment. As shown in Fig. 4 and confirmed by the kLa-value, the oxygen transfer rate of LSMMG cultures (kLa = 0.004 s−1) was 1.5-fold lower (significant with P < 0.05) than that of NG (kLa = 0.006 s−1). The kLa-value as a function of the alginate concentration is presented in Fig. 5, clearly showing that the oxygen transfer is inversely proportional to the alginate concentration. An alginate concentration of 1 mg ml−1 rendered a kLa value comparable to that of LB medium (P > 0.05), indicating that the quantified difference in alginate concentration for LSMMG (33.65 ± 8.01 µg ml−1) and NG (14.8 ± 4.90 µg ml−1) could not have caused the observed decrease in oxygen transfer rate. Determination of kLa values in cell-free supernatants of LSMMG and NG cultures showed no differences (data not shown), indicating that a cell-associated component might be at the origin of a lower oxygen transfer rate in LSMMG-grown cultures.

Figure 4.

Follow-up of the oxygen transfer into LSMMG and NG cultures as a function of time. Data for blanco LB medium and 10 mg ml−1 alginate were included as a point of comparison. Initial oxygen deprivation was done by gassing with argon. These sigmoid curves were subsequently used to calculate the volumetric mass transfer coefficient kLa, according to the dynamic method.

Figure 5.

KLa-value (indicative for the oxygen transfer rate) as a function of the alginate concentration.

Assessment of fluid mixing in the RWV and RPM

In order to clarify the observed differences in gene expression profiles between LSMMG and RG as compared with the NG control, the level of fluid mixing was assessed in the three test conditions based on the dispersal of a drop of crystal violet (see Supporting information). Assessment of the dynamics of fluid mixing of the culture media between the RWV and RPM demonstrated important differences, with much lower levels of mixing observed in the RWV. Specifically, the fluid-shear level in LSMMG was shown to be limited because the drop of crystal violet had the same momentum as the surrounding liquid and remained stationary respective to the inlet port (supplementary movie files). This observation indicated that in a fully filled vessel, the medium appeared immobile (i.e. the so-called solid body mass rotation). Nevertheless, a gradual dispersal of the dye occurred parallel to the circumference of the bioreactor in line with the original injected drop (Fig. 6). After 12 h of rotation, the dye covered the majority of the circumference with a diameter similar to the original drop (Fig. 6). In the NG position, the crystal violet drop settled to the bottom of the vessel, dispersing along the surface of the membrane and was completely homogenized after 12 h of rotation (Fig. 6). Consequently, the dispersal of crystal violet was shown to be different and faster in NG compared with LSMMG, indicating different mixing rates in both test conditions. Crystal violet injection in a RWV bioreactor mounted on the RPM (RG condition) led to immediate mixing upon entry of the dye in the vessel, indicating an important difference in mixing compared with LSMMG. This observation is due to the continuous acceleration changes underwent by the vessel, and consequently by the medium and cells, during random movement of the RPM.

Figure 6.

Dispersal of crystal violet 20 min (A) and 12 h (B) after injection through the sample port of the RWV bioreactors in LSMMG and NG conditions.


Exploring the response of opportunistic pathogens to spaceflight conditions is not solely important in the frame of preventing infectious disease in astronauts but also allows for the study of bacteria in physiologically relevant environments which cannot be achieved with conventional technologies. Low fluid-shear conditions, which are inherently linked to microgravity, are encountered by bacteria during their natural course of infection and culturing under such conditions can be a potential tool to unravel novel pathways involved in the infection process (Nickerson et al., 2004; Wilson et al., 2007; 2008; Crabbéet al., 2008). During this study, the transcriptional and physiological response of P. aeruginosa PAO1 to microgravity-analogue conditions (LSMMG and RG) was determined, employing two commonly applied techniques, the RWV and the RPM. While 134 genes were significantly more transcribed in the LSMMG culture conditions of the RWV, only nine genes showed a differential expression in the RPM compared with the control condition. These distinct results prompted us to scrutinize the role of mixing, because a low fluid shear and mixing environment is required to study the indirect effects of microgravity on Earth. The indirect effects of microgravity are induced by microgravity-mediated changes in the extracellular environment (e.g. mechanical forces of the surrounding liquid, nutrients and wastes surrounding the cell) while direct effects refer to the sensing of and responding to changes in g-force as a direct effect of weight (on the cell and cell components) (Albrecht-Buehler, 1991; Klaus et al., 2004). Theoretical assessment of single cell gravitational sensing predicted that forces exerted by cell weight may be too small to be sensed and that indirect effects prevail (Albrecht-Buehler, 1991). Others, however, claim that gravity can be sensed directly at the single cell level (Goldermann and Hanke, 2001). In the RWV, cells are grown in a low fluid-shear environment, which is an indirect effect of microgravity. Low fluid-shear levels in LSMMG have been demonstrated through mathematical calculation (Nauman et al., 2007) and were confirmed with the crystal violet dispersal assay in the present study. The mixing level in NG and RG conditions was found to be higher in comparison with the LSMMG condition in the present study. Correspondingly, eukaryotic monolayer cultures exposed to RG conditions experienced fluid-shear levels up to 44 times the shear in LSMMG (Pardo et al., 2005). Consequently, both low fluid mixing and shear, which are characteristic during spaceflight, may not be accurately represented in the RPM. Based on the conclusions made above, the RWV is believed to be more appropriate to study the LSMMG-induced response of cells in a suspension environment, rather than the RPM. It is, however, important to mention that, besides the low fluid-shear environment encountered in low-Earth orbit, other environmental factors (such as irradiation and vibration) may contribute to the bacterial response to spaceflight conditions. Indeed, the RWV technology allows to reproduce only certain aspects of the microgravity environment, which could at least partly explain differences between LSMMG- and spaceflight-induced bacterial responses. On the other hand, similarities between spaceflight and LSMMG responses can presumably be ascribed to the analogous low fluid-shear conditions in-flight and in the RWV.

This study indicated that the alternative sigma factor AlgU (also known as AlgT or sigma 22 in P. aeruginosa, analogue of RpoE or sigma 24 in other bacteria) plays a regulatory role in the response of P. aeruginosa to LSMMG. The activity of AlgU is regulated by proteins encoded by the algUmucABCD operon (reviewed by Ramsey and Wozniak, 2005). In non-mucoid strains of P. aeruginosa or in the absence of specific environmental stimuli, the cytoplasmic domain of the anti-sigma factor MucA binds AlgU, resulting in a low expression of promoters targeted by AlgU. The periplasmic domain of MucA is associated with MucB, which is a negative regulator of alginate biosynthesis (Wood and Ohman, 2009). The role of MucC remains to be determined. The higher transcription of mucA and mucB in LSMMG can be explained by autoregulation of the algUmucABCD operon by AlgU as a negative feedback mechanism (DeVries and Ohman, 1994; Wood and Ohman, 2009). Alginate production, transcription of heat shock genes through the sigma factor rpoH and other genes under control of AlgU (e.g. dsbA, oprF, phaF, foaA, algR) were increased in LSMMG. Among the LSMMG-induced AlgU-controlled genes, oprF, algR, rpoH and PA0856 have known AlgU-dependent promoters (Firoved et al., 2002). Bacterial alginates are linear exopolysaccharides which consist of O-acetylated β-1,4-linked β-d-mannuronic acid and its C5 epimer α-l-guluronic acid (Evans and Linker, 1973). The only bacterial genera known to produce alginates are Pseudomonas (Evans and Linker, 1973) and Azotobacter (Pindar and Bucke, 1975). Alginate restricts the diffusion of anti-microbial agents and confers resistance to human immune defence mechanisms, by avoiding phagocytic uptake, scavenging reactive oxygen intermediates and suppressing leucocyte function (Baltimore and Mitchell, 1980; Learn et al., 1987; Eftekhar and Speert, 1988; Simpson et al., 1989). AlgU is required for the production of alginate by acting directly on the transcription of the alginate biosynthesis operon, or indirectly by activating the transcriptional regulator AlgR (Martin et al., 1994; Wozniak and Ohman, 1994; Ramsey and Wozniak, 2005). Alginate regulatory gene transcripts (algU, algR, algP) and, correspondingly, alginate itself were shown more abundant in LSMMG. Furthermore, expression of the negative regulator of PHA synthesis PhaF (Hoffmann and Rehm, 2004) was induced. Because alginate and PHA biosynthetic enzymes compete for the common precursor acetyl-CoA (Pham et al., 2004), this finding indicates that acetyl-CoA might be directed towards the production of alginate in LSMMG. Besides the regulatory role of AlgR in alginate biosynthesis, this transcriptional regulator activates and represses the transcription of many other genes (Lizewski et al., 2004). The transcription of AlgR-dependent genes ipbA, lon and hslV was induced in LSMMG. In addition, genes under negative control of AlgR also showed an upregulation in LSMMG (Lizewski et al., 2004). A majority of these genes, however, also belong to the Hfq and/or microaerophilic/anaerobic regulon, which is activated in LSMMG (see below). These findings indicate a very complex, regulatory network in which regulation might occur at transcriptional, post-transcriptional and post-translational level.

Specific environmental stimuli, e.g. heat shock, cell wall inhibitory antibiotics, nutrient limitation, osmotic and oxidative stress, and copper sulfate, are known to induce expression of AlgU in Pseudomonas sp. (Terry et al., 1992; Schurr et al., 1995; Keith and Bender, 1999; Wood and Ohman, 2009). However, the receptor(s) and signal transduction pathway(s) that activate AlgU and its regulon remain to be identified for the LSMMG-associated stimuli. It is interesting to note that several genes induced by the cell wall-disrupting antibiotic d-cycloserine (Wood and Ohman, 2009) were also upregulated by the LSMMG condition. Adopting a 1.5-fold threshold in the LSMMG gene set, 50 genes overlapped between LSMMG and d-cycloserine upregulated genes (P < 0.05, combining the 15 and 60 min exposure transcriptomes), e.g. phaF, foaA, PA2562, PA2486, mucABC. A majority of the latter gene set is dependent or under control of AlgU (44 out of 50) (Wood and Ohman, 2009). Consequently, AlgU might be an important regulator following the sensing of mechanical or chemical cell wall signals.

Another interesting fact is that alginate overproduction (AlgU dependent) of P. aeruginosa has been associated with phosphate limitation (Terry et al., 1992). Interestingly, phosphate was proposed to play a key role in the LSMMG-induced stress resistance of Salmonella enterica (Wilson et al., 2008), as supplementation of LB medium with phosphate nullified the differences in stress resistance between LSMMG and NG conditions. Hypothetically, decreased phosphate availability in LSMMG, due to low homogenization of culture medium, could have activated AlgU and AlgU-controlled pathways. However, other than katA, which is induced upon phosphate limitation (Yuan et al., 2005), genes of the Pho regulon were not differentially regulated at the transcriptional level in the test conditions.

Interestingly, the AlgU regulon for the plant pathogen X. fastidiosa revealed that the conserved RNA binding protein Hfq was positively regulated by AlgU (Shi et al., 2007). The AlgU and Hfq protein sequences of P. aeruginosa PAO1 and X. fastidiosa share 56% and 75% identity respectively. The major physiological functions of Hfq encompass stability control of small regulatory RNAs (sRNAs) and mRNAs as well as positive and negative translational regulation of target mRNAs by sRNAs (reviewed by Valentin-hansen et al., 2004; Majdalani et al., 2005). Also in S. enterica, RpoE has been suggested to positively regulate Hfq transcription, indirectly via the transcriptional activation of RpoH (Bang et al., 2005). On the other hand, Hfq was shown to exert a negative post-transcriptional effect on RpoE in S. enterica, Vibrio cholerae and Escherichia coli (Ding et al., 2004; Figueroa-bossi et al., 2006; Guisbert et al., 2007). Indeed, rpoE itself and RpoE-controlled gene transcription, among which genes encoding heat shock proteins, was induced in hfq mutants of V. cholerae and E. coli. As AlgU and the characteristic AlgU-controlled pathways (heat shock, alginate biosynthesis) were reported not to be affected in an hfq mutant of P. aeruginosa (Schreiber et al., 2006), the stability of algU-mRNA is presumably not under control of Hfq in this bacterium. Hfq has been shown to be involved in the LSMMG and spaceflight microgravity response of S. enterica (Nickerson et al., 2000; Wilson et al., 2007; 2008) and was upregulated when P. aeruginosa PAO1 was exposed to LSMMG in the present study. While for S. enterica, downregulation of Hfq in both LSMMG and spaceflight microgravity led to differential expression of genes under both positive and negative control of Hfq, the upregulation of Hfq for P. aeruginosa in LSMMG suggested mainly a positive regulatory role. Hfq has been shown to be important for virulence and stress resistance of several (opportunistic) pathogens, including P. aeruginosa PAO1, partly through its action on the sigma factor rpoS and the QS system (Sonnleitner et al., 2003; Ding et al., 2004; Sittka et al., 2007). The QS system of P. aeruginosa has been extensively reviewed (Diggle et al., 2006; Venturi, 2006). Hfq exerts a positive effect on the Rhl-QS system of P. aeruginosa PAO1, through stabilization of the regulatory RNA rsmY, which in turn inactivates the negative post-transcriptional Rhl-QS regulator RsmA (Sonnleitner et al., 2006). As none of the major QS-regulated genes were differentially expressed above the twofold threshold in the test conditions, the positive effect of Hfq on the Rhl-QS system could have been counterbalanced by the upregulation of the negative QS regulators rsmA and the lon-protease in LSMMG (Pessi et al., 2001; Takaya et al., 2008). In addition, we observed that a negative regulator of the las QS system, i.e. rsaL, was induced in LSMMG compared with NG (Rampioni et al., 2006; 2007). Interestingly, Hfq is a positive regulator of genes involved in the microaerophilic/anaerobic metabolism of P. aeruginosa (anr, hemN, ccoO2, ccoN2), also upregulated in LSMMG. In addition, 16 other genes belonging to the LSMMG-induced Hfq regulon are described as being upregulated in microaerophilic/anaerobic conditions (Table 1). Consequently, it is worthwhile to further explore the role of Hfq in the response of P. aeruginosa PAO1 to oxygen-limiting conditions. Collectively, these findings indicate that hfq is an important regulator of the LSMMG response across the bacterial species border.

A significant number of genes involved in microaerophilic/anaerobic metabolism and in the fermentation of arginine and pyruvate were upregulated in LSMMG conditions. Among others, the anaerobic regulator Anr and genes under control of Anr were induced in LSMMG. Anr induces the transcription of the cell surface chemoreceptor Aer (induced in LSMMG), which detects and responds to the chemotactic ligand oxygen, by redirecting the flagellar movement towards or away from oxygen through communication via chemotactic proteins (reviewed by Kato et al., 2008). Correspondingly, several genes involved in the P. aeruginosa chemotaxis and motility were upregulated in LSMMG compared with NG. Collectively, the above data suggest that different metabolic pathways were activated in LSMMG to ensure growth and survival in this environment with apparent lower oxygen availability. Previous studies have demonstrated that the oxygen level in a shake culture of P. aeruginosa rapidly decreases, leading to an apparent microaerophilic/anaerobic growth environment after 3–4 h of cultivation (Sabra et al., 2002). The DO levels in both LSMMG and NG cultures were also below the detection limit of the oxygen probe after 24 h of cultivation. Interestingly, the oxygen transfer rate, as reflected by the volumetric mass transfer coefficient kLa, was higher in NG than LSMMG cultures. While high amounts of alginate reduced the oxygen transfer from air into LB [in agreement with the study of (Hassett, 1996), the low amounts of alginate quantified in LSMMG and NG could not account for the significant difference in kLa-values. Herewith, the presence of a yet unidentified factor responsible for decreasing the oxygen transfer rate is proposed. This factor is presumably present in the cell fraction and not in the spent culture supernatant. Kim and colleagues (2003) linked a decrease in the oxygen transfer rate in P. aeruginosa cultures to a depletion of iron into the culture medium (Kim et al., 2003). In the present study, no evidence arose that would indicate iron depletion in LSMMG because none of the genes involved in the transcriptional response of P. aeruginosa to iron-limiting conditions (Ochsner et al., 2002) were differentially expressed in LSMMG and NG. Importantly, hemN and anr mutants demonstrated a decreased virulence in the lettuce model of infection (Filiatrault et al., 2006), indicating that growth of P. aeruginosa PAO1 in a microaerophilic or anaerobic environment could be of importance in pathogenicity.

Besides alginate, which can confer a certain degree of stress resistance to P. aeruginosa, several gene transcripts involved in environmental stress tolerance, i.e. heat shock proteins, catalase and universal stress proteins, were significantly more abundant in LSMMG. Importantly, AlgU is involved in the resistance of P. aeruginosa to heat shock (through rpoH regulation) and oxidative stress (Martin et al., 1994; Schurr and Deretic, 1997; Malhotra et al., 2000; Firoved et al., 2002). In accordance with the gene expression data, P. aeruginosa grown in LSMMG showed an increased resistance to heat shock and oxidative stress, while no significant differences in the acid stress tolerance were observed. As alginate is thought to scavenge oxygen radicals (Learn et al., 1987), the higher alginate concentration in LSMMG might have acted as a long-lasting barrier protecting the cells from H2O2. In addition, the induced transcription of katA (encoding a catalase) could have accounted for a higher turnover of H2O2 to H2O and O2 in LSMMG. The increased oxidative stress resistance observed over time might reflect enzymatic degradation of H2O2. Conversely, the increased heat shock tolerance in LSMMG could only be observed after a 15 min exposure time. Indeed, the cellular damage after 60 min exposure to high temperature might have been too important to compensate with the increased chaperone concentration in LSMMG. Correspondingly with the results of this study, Wilson and colleagues (2002) have shown an increased heat shock resistance for S. enterica grown in LSMMG compared with NG. In addition, increased osmotic and acid stress tolerance was demonstrated for S. enterica (Wilson et al., 2002). In contrast, E. coli MG1655 did not show any differences in its stress resistance profile when cultured in LSMMG and NG (Tucker et al., 2007), indicating that the responses between bacterial species might differ significantly.

Overall, the LSMMG-induced response of P. aeruginosa PAO1 shows similarities with the molecular profile of other bacteria studied in spaceflight and LSMMG conditions. More specifically, genes encoding chaperones, citric acid cycle enzymes, ATP synthases, cytochromes and ribosomal subunits have been reported to be upregulated in LSMMG and in space (Wilson et al., 2007; 2008; Leys et al., 2009; Mastroleo et al., 2009). Other LSMMG-induced genes and pathways were unique for each studied bacterium. As differences in low fluid-shear levels are multifactorial and are presumably associated with alterations in mechanical forces, availability of nutrients, oxygen concentrations, etc., the response to each of these shear-related factors may differ or be similar for different bacterial species. Besides genomic differences between bacteria, their preferred niche and how an environmental signal relates to this niche may play a role in these observations.

In conclusion, this study showed for the first time that cultivation of P. aeruginosa PAO1 in the LSMMG environment of the RWV induces a transcriptomic and physiologic response with possible implications for virulence. AlgU was identified as one of the key regulators in the LSMMG-induced response and this alternative sigma factor presumably upregulated the transcription of genes downstream involved in environmental stress resistance, alginate production and microaerophilic/anaerobic metabolism. Moreover, AlgU might control hfq transcription in P. aeruginosa PAO1, which has previously been linked with bacterial response to space and LSMMG conditions. The identification of the sensor which activates the AlgU cascade in LSMMG remains to be determined and is an important element to further unravel the virulence mechanisms of P. aeruginosa on Earth and, potentially, in space. The observations during the present study in combination with a decreased immune responsiveness of astronauts could become problematic during long-term spaceflight missions and should direct future research regarding the risk assessment and prevention of infections caused by this opportunistic pathogen.

Experimental procedures

Microgravity-analogue conditions

The RWV and RPM were used in the present study to grow P. aeruginosa PAO1 in microgravity-analogue conditions. The RWV is a widely used methodology to study bacterial behaviour in conditions of LSMMG. These conditions are achieved through continuous rotation around a horizontal axis of cylindrical bioreactors that are completely filled with growth medium (Nickerson et al., 2004). Counteracting sedimentation of bacterial cells at adequate rotation speed in the RWV leads to maintained cell suspension within a restricted orbit. The fluid-shear force in a RWV bioreactor is calculated to be less than 0.001 Pa (Gao et al., 1997; Nauman et al., 2007). A RWV bioreactor rotating along the vertical axis is conventionally used as a NG control. The RPM mimics aspects of microgravity in 3-D through rotation around two orthogonal axes. The system is comprised of an inner frame (containing the attached sample) and an outer frame, which is independently rotating but linked to the inner frame. The rotation of the inner and outer frame occurs within a restricted 3-D volume at a random orientation and time interval using software developed by the Dutch Space Agency. A theoretical reduction of the g-vector is aimed to be obtained over time, resulting in an average g-vector of 10−2–10−3 (reviewed by van Loon, 2007). The RPM does, however, not remove the constant unidirectional force that gravity exerts on the cells.

Bacterial strain, culture medium and conditions

The wild-type P. aeruginosa PAO1 strain (ATCC 15692) was used in this study and all cultures were grown in Lennox L Broth Base (LB) (Life Technologies) at 28°C. An overnight shaking culture (125 r.p.m.) of P. aeruginosa in LB was washed and diluted in 0.85% NaCl solution to an OD600 of 1. This bacterial suspension was used to inoculate fresh LB medium at a final concentration of 10−4 cfu ml−1. Synthecon RWV bioreactors (50 or 10 ml) were filled with inoculated medium so that no headspace (i.e. no bubbles) was present. Other than for stress-resistance assays, for which 10 ml capacity bioreactors were used, RWV bioreactors with a capacity of 50 ml were adopted for all experiments. Identical bioreactors were mounted in triplicate on (i) a RWV device in vertical position (LSMMG) (Cellon); (ii) a RWV device in horizontal position (NG) and (iii) the centre of the inner RPM frame (RG) (Fokker Space), and placed in a large humidified (70–80% relative humidity) culture chamber, to avoid evaporation of culture medium through the gas-permeable membrane at the back of each vessel. A 25 r.p.m. rotation speed was adopted for the RWV cultures, while RPM cultures were randomly rotated at 10 r.p.m. (60° s−1), according to the instructions of the manufacturer. Bacteria were grown in the three described test conditions for 24 h. After 24 h of cultivation, the content of every bioreactor was gently mixed by pipetting and divided into several aliquots. Ten millilitres of culture from each growth condition was immediately fixed with RNA Protect Reagent (Qiagen), following the manufacturer's instructions, and fixed cell pellets were frozen at –20°C until RNA extraction. Samples were immediately exposed to different stresses or stored at –20°C for alginate quantification and oxygen transfer rate determination (see below). The bacterial density was assessed by viable count on LB agar (Invitrogen).

RNA extraction, labelling and Affymetrix GeneChip analysis

The extraction of total RNA was performed with the Total RNA Isolation System (Promega). RNA quality and quantity were assessed using the Agilent Bioanalyzer 2100 electrophoresis system and the Nanodrop ND-1000 spectrophotometer (NanoDrop technologies) respectively. cDNA synthesis and further processing for the transcriptomic analysis were performed according to the protocol of the GeneChip® manufacturer (Affymetrix). Ten micrograms of total RNA per sample was used for cDNA synthesis and a Poly-A RNA control was added to the RNA sample using the GeneChip eukaryotic poly-A control kit (Affymetrix). The synthesis of cDNA was performed using denatured total RNA (70°C for 10 min), 13 ng µl−1 random hexamer primers (Roche), 0.5 U µl−1 SUPERase In (Ambion), 25 U µl−1 SuperScript II reverse transcriptase (Invitrogen), 0.5 nmol µl−1 deoxynucleoside triphosphates in 1× first strand buffer with 10 nmol µl−1 dithiothreitol. The latter mix was placed in a Thermocycler operated at 25°C for 10 min, 37°C for 60 min, 42°C for 60 min and 70°C for 10 min. The remaining RNA was degraded by treatment with 1 N NaOH, incubation for 30 min at 65°C and subsequent neutralization with 1 N HCl. cDNA was purified using a MiniElute PCR purification kit (Qiagen), where after the concentration of cDNA was assessed using the Nanodrop ND-1000 spectrophotometer (NanoDrop technologies). Eight micrograms of cDNA was fragmented to a size range of 50–200 nucleotides with DNase I (Promega) for 10 min at 37°C where after the enzyme was inactivated at 99°C for 10 min. The fragmented cDNA was verified by loading a sample on a 2% agarose gel containing 0.1‰ (v/v) GelRed (Biotium). Fragmented cDNA was end-labelled with biotin-dUTP using the GeneChip® DNA labelling reagent (Affymetrix) and terminal deoxynucleotidyl transferase (Promega) in 1× reaction buffer (Promega) at 37°C for 60 min. The reaction was stopped using 0.5 M EDTA. Biotinylated cDNA was hybridized to GeneChip®P. aeruginosa Genome arrays (Affymetrix) by incubation in a GeneChip® hybridization oven 640 at 50°C with orbital shaking at 60 r.p.m. for 16 h in the following MES-buffer [2-(N-morpholino) ethanesulfonic acid]: 0.1 µg µl−1 herring sperm DNA (Promega), 0.5 µg µl−1 bovine serum albumine (Invitrogen), 7.8% dimethyl sulfoxide (Sigma-Aldrich), 50 mM B2 control oligo (Affymetrix) in 1× hybridization buffer (made according to instructions of Affymetrix GeneChip®). Microarray slides were washed according to the manufacturer's protocol and stained using a fluidics station 450 (Affymetrix) and applying the Midi_euk2v3-450 protocol. Briefly, the staining procedure was comprised of the following steps: (i) binding of streptavidin (Invitrogen) to the biotin-labelled hybridized cDNA; (ii) binding of a biotin-conjugated streptavidin antibody (Labconsult) and (iii) binding of R-phycoerythrin-conjugated streptavidin (Invitrogen). Microarrays were scanned at the emission wavelength of 570 nm where after the data analysis took place. Raw Affymetrix data were pre-processed using the Affy package in BioConductor as follows: (i) background correction based on the robust multichip average (RMA) convolution model (Irizarry et al., 2003); (ii) quantile normalization to make expression values from different arrays more comparable (Bolstad et al., 2003) and (iii) summarization of multiple probe intensities for each probe set to one expression value per gene using RMA (Irizarry et al., 2003). To test for differential expression between the different microgravity-analogue conditions (LSMMG and RG) and the reference condition (NG), the Bayesian adjusted t-statistics was used as implemented in the LIMMA package (version 2.5.15) (Smyth, 2004). P-values were corrected for multiple testing using the Benjamini and Hochberg's method to control the false discovery rate (Benjamini and Hochberg, 1995). Only fold change ratios with adjusted P-values below 0.05 were included as statistically significant. Microarray analysis was performed on all three biological replicates. Based on quality control analysis of the different samples, we identified one of the biological replicates of LSMMG as an outlier, which was discarded for further analysis. The full description of the array analysis platform and the complete array data have been deposited at the Gene Expression Omnibus website ( under accession number GSE16970.

Quantitative real-time PCR analysis

RNA extraction, reverse transcription, primer design, RNA and cDNA quality control, and qRT-PCR analysis were performed as described previously (Crabbéet al., 2008). The list of qRT-PCR primers used in this study is presented in Table S1. Data were normalized using the rrnB gene as an internal control (Crabbéet al., 2008; Wilson et al., 2008), according to the method of Livak and colleagues (Livak and Schmittgen, 2001). The experiment was performed at least in biological triplicate and technical duplicate.

Quantification of uronic acids

The removal of cell material and subsequent isolation of uronic acids from 5 ml supernatants was performed as previously described (Crabbéet al., 2008). Following uronic acid extraction, the quantification was done by applying the modified carbazole assay of Knutson and Jeanes (1968), adapted by May and Chakrabarty (1994). A standard curve of sodium alginate (Fluka), ranging from 10 to 1000 µg ml−1, was included in the experiment.

Stress-resistance assays

For all stress assays, bacteria were immediately subjected to the respective stress upon removal from the RWV bioreactors. Specifically, 100 µl of bacterial culture was diluted 10-fold in 0.85% NaCl solution containing 0.05% (v/v) HCl (pH = 3.3) for acid stress or 0.88 M H2O2 for oxidative stress respectively. For thermal stress-resistance assays, bacteria were diluted 10-fold in 0.85% NaCl and exposed to 50°C using a heating block. After an exposure period of 15 or 60 min, cultures were resuspended in 0.85% NaCl and viable counts were determined. The survival percentage was calculated by relating the colony forming units (cfu ml−1) at time zero (i.e. before addition of the stress) to the cfu ml−1 after exposure to the respective stress.

Dissolved oxygen determination in RWV bioreactors

The measurement of the DO concentration in the culture media in RWV bioreactors was performed using a Knick KNI913 oxygen metre. After cultivation of bacteria for 24 h in the RWV bioreactors, the DO probe was inserted into the culture medium through the main port of the RWV bioreactor and dissolved O2 readings were recorded.

Oxygen transfer rate assessment in LSMMG and NG cultures

Aliquots (10 ml) of LSMMG- and NG-grown cultures of P. aeruginosa PAO1 were frozen at –20°C for at least 3 weeks to sterilize the culture in a non-invasive manner. After thawing, plating on solid LB medium revealed no viable cells. The use of complete cultures (i.e. non-viable cells and culture medium) was adopted to study the influence of both cell-bound and soluble factors on the oxygen transfer rate. Ten millilitres of thawed cultures were transferred into 50 ml glass measure cups. A continuous stirring was established using a small magnet, rotating at a speed of 200 r.p.m. The transfer of oxygen from the air into the culture was assessed by applying the ‘Dynamic method’ (reviewed by Garcia-Ochoa and Gomez 2009), taking into account an Oxygen Uptake Rate of zero (due to the absence of biologic oxygen consuming elements). In the initial phase, stirred cultures were omitted of oxygen by bubbling argon gas into the liquid until an oxygen concentration lower than the detection limit of the probe (0.1 mg l−1) was measured. In a second phase, the liquid was put in contact again with ambient air, at a continuous stirring rate, and the increase in oxygen concentration as a function of time was measured until saturation was reached. The oxygen mass transfer rate is described as proportional to the latter concentration gradient, reflected in the volumetric mass transfer coefficient kLa. The kLa is determined based on the following equation:


where CL is the DO concentration in the bulk liquid, C* the oxygen saturation concentration in the bulk liquid and t the time. The resulting curves are sigmoidal and are modelled by the 3-parameter Gompertz model, providing a regression coefficient of at least 0.999. The saturation concentration in the culture liquid of each test condition is extracted from the Gompertz equation, as described by Zwietering and colleagues (1990). The latter value is used as C* in Equation 1 in order to plot ln (1 – CL/C*) as compared with time. The obtained curve was fitted again with the 3-parameter Gompertz model, which allowed the calculation of the lag phase (i.e. the time to initiate exponential oxygen increase in the bulk liquid) and the slope (i.e. kLa). The oxygen transfer rate experiment was performed in technical duplicate for each biological replicate. In addition, the oxygen transfer rate was determined for sterile LB medium supplemented with 0, 1, 5 and 10 mg ml−1 sodium alginate (Fluka) in LB medium (all performed in triplicate).

Assessment of fluid-mixing in LSMMG, NG and RG

RWV bioreactors were completely filled with distilled water and air bubbles were removed to avoid undesired fluid-shear. A 1 ml syringe containing 100 µl 0.03% crystal violet (Merck) was mounted on one sample port of the bioreactor. The RWV bioreactors were placed on the RWV and RPM devices to obtain the LSMMG, NG and RG test positions. After 10 min of rotation, allowing equilibration of the fluid relative to the bioreactor, approximately 50 µl of crystal violet was gently injected into the bioreactor while it was rotating. The dispersal of the crystal violet solution into the water was monitored with online video recording (see supplementary movie files).

Statistical analysis and bioinformatics

All experiments were performed in triplicate, unless stated otherwise. The statistical determination of significance (α = 0.05) was calculated with Microsoft Office Excel 2003 using a two-sample Student's t-test on the biological replicates of each experimental condition. For statistical analysis of three samples, the stats-package in R 2.9.0 was used by performing analysis of variance (aov), followed by a Tukey multiple comparison test (TukeyHSD). Data were checked for normality (Shapiro-Wilk test) and homoscedasticity (Bartlett test). Calculation of the statistical significance of the overlap between the present microarray gene sets and previously published microarray data was performed by applying the hypergeometric distribution method (Fury et al., 2006).


This research was funded by a space research grant from the European Space Agency (ESA) and the Belgian Science Policy (Belspo). Benny Pycke was supported by the Institute for the Promotion of Innovation through Science and Technology in Flanders (IWT-Vlaanderen) through the PhD scholarship IWT-SB/53360. Cheryl Nickerson was funded by NASA grants NNX09AH40G, NCC2-1362 and NNJ04HF75F. The authors would like to express their gratitude to Ann Janssen and Ilse Coninx (SCK·CEN) for technical assistance. We are grateful to Dr Proto Pippia and Dr Mariantonia Meloni for providing us access to their ESA RPM facility and Gavino Campus for technical support (University of Sassari, Italy). We thank Dr Mark Ott (Johnson Space Center, Houston, USA) and Ir. Hugo Moors (SCK·CEN) for fruitful discussions.