Open Access

Dictyostelium transcriptional responses to Pseudomonas aeruginosa: common and specific effects from PAO1 and PA14 strains

  • Sergio Carilla-Latorre1,
  • Javier Calvo-Garrido1,
  • Gareth Bloomfield2,
  • Jason Skelton3,
  • Robert R Kay2,
  • Alasdair Ivens3,
  • José L Martinez4 and
  • Ricardo Escalante1Email author
Contributed equally
BMC Microbiology20088:109

https://doi.org/10.1186/1471-2180-8-109

Received: 06 March 2008

Accepted: 30 June 2008

Published: 30 June 2008

Abstract

Background

Pseudomonas aeruginosa is one of the most relevant human opportunistic bacterial pathogens. Two strains (PAO1 and PA14) have been mainly used as models for studying virulence of P. aeruginosa. The strain PA14 is more virulent than PAO1 in a wide range of hosts including insects, nematodes and plants. Whereas some of the differences might be attributable to concerted action of determinants encoded in pathogenicity islands present in the genome of PA14, a global analysis of the differential host responses to these P. aeruginosa strains has not been addressed. Little is known about the host response to infection with P. aeruginosa and whether or not the global host transcription is being affected as a defense mechanism or altered in the benefit of the pathogen. Since the social amoeba Dictyostelium discoideum is a suitable host to study virulence of P. aeruginosa and other pathogens, we used available genomic tools in this model system to study the transcriptional host response to P. aeruginosa infection.

Results

We have compared the virulence of the P. aeruginosa PAO1 and PA14 using D. discoideum and studied the transcriptional response of the amoeba upon infection. Our results showed that PA14 is more virulent in Dictyostelium than PA01using different plating assays. For studying the differential response of the host to infection by these model strains, D. discoideum cells were exposed to either P. aeruginosa PAO1 or P. aeruginosa PA14 (mixed with an excess of the non-pathogenic bacterium Klebsiella aerogenes as food supply) and after 4 hours, cellular RNA extracted. A three-way comparison was made using whole-genome D. discoideum microarrays between RNA samples from cells treated with the two different strains and control cells exposed only to K. aerogenes. The transcriptomic analyses have shown the existence of common and specific responses to infection. The expression of 364 genes changed in a similar way upon infection with one or another strain, whereas 169 genes were differentially regulated depending on whether the infecting strain was either P. aeruginosa PAO1 or PA14. Effects on metabolism, signalling, stress response and cell cycle can be inferred from the genes affected.

Conclusion

Our results show that pathogenic Pseudomonas strains invoke both a common transcriptional response from Dictyostelium and a strain specific one, indicating that the infective process of bacterial pathogens can be strain-specific and is more complex than previously thought.

Background

Nosocomial infections caused by opportunistic pathogens are one of the most important health problems in developed countries. Depending on the geographic location, P. aeruginosa is the first or second causative agent of nosocomial infections [1, 2]. P. aeruginosa infects patients suffering from AIDS, people at intensive care units, and burned people among others, and is the major cause of morbidity and mortality in patients with cystic fibrosis, the most prevalent hereditary disease in Caucasian populations [3]. A successful infection by this type of pathogens depends on the interplay of multiple factors including the susceptibility of the host, the virulence of the strain and its resistance to antibiotics [4]. Previous work has shown that the physiological fitness and the virulence of P. aeruginosa and other opportunists are affected by the expression of antibiotic resistance mechanisms such as MDR-pumping systems [58].

The pathogenicity of Pseudomonas aeruginosa involves various components operating at different levels. The flagella and pili facilitate contact with the bacterium's cell target and play a role in its adhesion, which is a critical step in the infection [9, 10]. After contact, the type III secretion system is able to inject into the cytoplasm of the target cell a series of cytotoxic molecules that act at various levels. The mechanism of action involves, in many cases, the presence of host cofactors still unidentified [11]. Other virulence factors involve products secreted into the extracellular medium by systems I and II such as elastase, alkaline phosphatase and exotoxin A among others. The expression of many of these virulence factors is regulated by a mechanism of bacteria-to-bacteria cell signalling known as quorum-sensing [12]. Despite the functional and genomic similarity among different P. aeruginosa strains [13, 14], some differences in their pathogenicity have been observed [15]. For example, the clinical isolate PA14 is more virulent than PAO1 in a wide range of hosts [1517]. It has been shown that the genome of PA14 contains two pathogenicity islands that are not present in PAO1 and it has been proposed that the virulence in this organism (and the difference between PA14 and PAO1) is the result of a pool of pathogenicity genes interacting in various combinations in different genetic backgrounds [15]. In spite of these suggestions, the cause of the different virulence behavior of PAO1 and PA14 is not yet fully understood.

Although most of the work on pathogenesis has been focused on understanding the bacterial factors that render a virulence phenotype, increasing attention is being paid to the host and those aspects connected to the susceptibility or resistance to infection. Understanding the host-pathogen relationship, at both the cellular and molecular level, is essential to identify new targets and develop new strategies to fight infection. Molecular analysis of host-pathogen interactions would benefit from the use of model systems allowing a systematic study of the factors involved. In this regard the social amoeba D. discoideum has proven particularly useful for its ease of handling, genetic tractability [1822] and fully sequenced genome [23].

D. discoideum is a soil microorganism that feeds on bacteria by phagocytosis. The interaction between bacteria and their natural predators (Dictyostelium, other protists and worms) is believed to have shaped both predators [24] and bacterial evolution. As a consequence, some of the mechanisms developed by bacteria to avoid the activity of their natural predators in the environment might have been adapted later in evolution to allow the infection of higher organisms such as humans [25]. Specifically, it was found that the quorum-sensing mechanisms and type III secretion, which are essential factors in the infectivity to humans are also responsible for the infectivity of P. aeruginosa in D. discoideum [18, 20, 21].

Our previous studies have shown the utility of this model system of infection to analyze the virulence of other opportunistic pathogens like Stenotrophomonas maltophilia [7]. It has been also demonstrated the validity of D. discoideum as a model of infection by intracellular pathogens such as Legionella, Cryptococcus and Mycobacterium [19, 22]. Consequently, the conservation of the mechanisms of infection needed to infect mammals and D. discoideum in a wide variety of pathogens reinforces the use of this system as a valid model to study host-pathogen relations. We have used whole-genome D. discoideum microarrays to study global host transcription upon infection with Pseudomonas aeruginosa PAO1 and PA14 to determine whether or not transcription is being affected as a defense mechanism or altered in the benefit of the pathogen.

Results

Pseudomonas aeruginosa strains PAO1 and PA14 show a different virulence behavior in D. discoideum

PAO1 and PA14 are two clinical isolates of P. aeruginosa frequently used as model strains to analyze the virulence of this bacterial pathogen. Since they behave differently in some aspects dealing with the expression of virulence determinants, we wanted to compare the differential response of the host to these strains. For this purpose, we made use of D. discoideum as a model for virulence. As a first step a plating assay of virulence was set up. Figure 1 shows a representative experiment of three independent assays in which D. discoideum cells were grown in association with bacteria on nutrient SM plates. Klebsiella aerogenes, a non -pathogenic bacteria, was used as an appropriate food supply and P. aeruginosa mixed at the indicated proportions. An effect in the size of the clearing plaques could already be seen when only 3.5% of P. aeruginosa cells were mixed with 96.5% of K. aerogenes cells and this effect was even clearer using 17% of P. aeruginosa cells. When the behavior of the strains was analyzed in more detail, it was found that PAO1 is reproducibly more permissive than PA14 as observed by the higher growth of D. discoideum on PAO1. The differences in the area of the cleared bacterial lawn between PAO1 and PA14 were measured for the condition corresponding to the 3.5 % mixture. The average area and the standard deviation were 1.65 ± 1.2 mm2 for PAO1 and 0.11 ± 0.07 mm2 for PA14 (the number of clear plaques measured in each condition was 50). The significance of differences between groups as determined by Student's t-test was p < 10-8. To further confirm these results a different plating assay was performed on non-nutrient agar. PAO1, PA14 and K. aerogenes were previously grown in LB overnight, washed out of the media by centrifugation and deposited with D. discoideum cells in agar plates at the indicated proportions. Under these conditions the difference in the virulence between PAO1 and PA14 was even more evident as shown in a representative experiment in Figure 2. Interestingly PAO1 is permissive to D. discoideum growth under these non-nutrient conditions. However, PA14 still shows a strong virulence against D. discoideum. All together these results suggest that PA14 is more virulent than PAO1 in the D. discoideum model of virulence.
Figure 1

PA14 is more virulent than PAO1 in SM-plating assay. Approximately 100 D. discoideum cells were cultivated in SM-plates with the indicated proportion of Klebsiella and Pseudomonas strains (PAO1 or PA14) previously grown and adjusted to the same optical density. Plates were maintained at 22°C for 5 days. Growth of D. discoideum is severely affected by the presence of Pseudomonas but the inhibition is stronger when PA14 is used.

Figure 2

PA14 is more virulent than PAO1 in PDF-agar plating assay. D. discoideum cells were cultivated in non-nutrient agar on a lawn of Klebsiella and Pseudomonas (PAO1 and PA14) at the indicated proportion. Under these conditions PA14 maintain a high virulence as seen by the strong inhibition of D. discoideum growth. Heat inactivated PA14 is used as a control.

Pseudomonas aeruginosa induces a specific gene expression response in Dictyostelium

Little is known about the interplay between the host and the pathogen in terms of gene expression responses. We wanted to determine if there is a specific gene expression response of D. discoideum to their interaction with P. aeruginosa. D. discoideum cells were exposed to P. aeruginosa strains PAO1 and PA14 mixed with an excess of K. aerogenes in HL5 for 4 hours. K. aerogenes alone was used as a control to which the gene expression levels were compared. RNA was extracted from D. discoideum and used to study the global pattern of gene expression using whole-genome D. discoideum microarrays (see Additional file 1 for the complete data). Using a P < 0.05 cutoff, there were 752 genes whose expression was significantly different between the PAO1-treated cells and the controls and 624 genes between PA14-treated cells and controls (Table 1 summarizes the results at different P values and log-ratios). The heat map shown in Figure 3 indicates that the responses were broadly comparable between the two strains with very few genes oppositely altered in the two conditions. The differences in the gene expression are approximately in the range between four-fold repression and three-fold induction (log-ratios between of -2 to +1.5 as shown in the histogram of Figure 3). These results were validated by real time PCR of the same samples used for the transcriptomic assays, measuring the expression of 7 representative genes that were up-regulated or down-regulated in the different conditions. Figure 4 shows a good correlation between the data obtained from the microarray transcriptomic experiment as compared with that obtained by quantitative RT-PCR. Although the log-ratio changes in the gene expression showed some differences the overall trend were consistent, supporting the reliability of our data.
Table 1

Differential genes at p < 0.05 and different Log 2 ratios

 

PAO1 vs Control

PA14 vs Control

PAO1+PA14 vs Control

PAO1 vs PA14

Log 2 ratio

752

623

364 (Table 2)

169 (Table 3)

(>+0.5 or <-0.5)

461 down 291 up

396 down 227 up

258 down 106 up

60 down 109 up

Log 2 ratio

150

125

70

35

(>+1 or <-1)

126 down 24 up

105 down 20 up

66 down 4 up

14 down 21 up

Figure 3

Pseudomonas aeruginosa induces gene expression changes in Dictyostelium. Heat map comparing the genes significantly altered (p < 0.05) between PAO1-treated cells versus control (975) and PA14-treated cells versus control (838). Each row of the plot is a gene and was colored according to the log2ratio of expression with red meaning up-regulation in relation to the controls and blue downregulation. The histogram shows the range of changes in a log2 scale. The data presented are for the three independent experiments combined. The heatmap was generated using the heatmap.2 function of the gplots package in R [47]. The dendrogram was generated using Euclidean distance and the "complete" agglomeration method.

Figure 4

Correlation of microaray and real-time PCR. Real-time PCR measurements of the mRNA levels for seven representative genes whose expression were affected in the array. Upper panel shows a direct comparison of the changes in a log2 scale for PAO1 versus control and the lower panel shows the same genes for PA14 versus control. Blue bars corresponded to quantitative real time PCR and the purple bars to the array data. The array data and the real time PCR displayed are the combination of three independent biological experiments. The correlation coefficients were: R2 = 0.87 for PAO1 and R2 = 0.91 for PA14.

Common and specific responses of D. discoideumto the infection with PAO1 and PA14 strains

As shown in Table 1 there were 364 genes that showed similar differential regulation with both bacterial strains compared with the controls (labeled as PAO1+PA14 vs control). We have considered in the analysis those genes showing differences in log-ratios that are higher than +0.5 or lower than -0.5. Interestingly the expression of another group of 169 genes (labeled as PAO1 vs PA14) was different depending on whether the infecting strain was PAO1 or PA14. We have studied in detail both groups by manual annotation and categorizing using the extended categorization for D. discoideum previously described [26]. Genes of unknown function and those showing weak homologies were not included in the list. Table 2 contains the genes that were similarly regulated upon infection with any of both strains, and in Figure 5 the genes are categorized by function (see Additional file 2 for the complete data). The first interesting conclusion from this experiment is the existence of a common transcriptional response that affects many different genes that are involved in a wide range of functions. The proportion of the genes that were downregulated by the treatment with both strains of P. aeruginosa is higher (258 genes) compared to those upregulated (106 genes). This difference is more evident in categories such as stress response and transport (Figure 5).
Table 2

Genes differentially expressed upon infection with PAO1 and PA14 versus Klebsiella

Cell proliferation

   

Gene ID

Gene function and name

Log 2 ratio: PAO1-C

PA14-C

DDB0216882

Cyclin-dependent kinase regulatory

-0,89

-0,97

DDB0188449

cdc40, conserved splicing factor

0,68

0,78

DDB0205486

CDK family protein kinase

0,81

0,89

DDB0168249

cdk1, "cyclin-dependent_kinase, p34-cdc2_protein"

-0,78

-0,83

DDB0216532

cdk10;"putative_CDK_family_protein_kinase

0,91

1,08

DDB0185341

PP-loop family

-0,60

-0,70

DDB0205486

putative protein serine/threonine kinase, CDK family protein kinase

0,68

0,64

DDB0218360

PhoPQ-activated pathogenicity-related protein

-0,59

-0,57

Cellular biogenesis and organization

  

DDB0189693

copA, coatomer protein complex alpha subunit

0,52

0,68

DDB0216892

lvsB;"BEACH_domain-containing_protein"

1,05

0,91

DDB0202609

Transport protein particle (TRAPP)

-0,77

-0,61

DDB0187558

putative mitochondrial import inner membrane translocase

-0,65

-0,65

DDB0186481

atg9, APG9, "autophagy_protein_9"

-0,88

-0,77

DDB0217942

Putative Mpv17/PMP22 family

-0,66

-0,93

Energy

  

DDB0217090

Isocitrate lyase family

-0,80

-0,84

DDB0192001

ppkA, "poly_P_kinase, polyphosphate_kinase"

-0,65

-0,51

DDB0190821

sdhB, complex II, iron-sulfur protein (IP) subunit

0,74

0,53

DDB0190821

sdhB;"complex_II,(ubiquinone), succinic_dehydrogenase"

0,59

0,61

DDB0167662

similar to Coenzyme Q9

-0,71

-0,72

DDB0204006

AMPK beta-2 chain

0,52

0,73

Metabolism

  

DDB0187528

cysteine dioxygenase

0,69

0,66

DDB0185702

hgd;"homogentisate_1,2-dioxygenase"

1,05

1,10

DDB0184361

Thiamine pyrophosphate enzyme

-0,57

-0,88

DDB0186120

gshA, "gamma_glutamylcysteine_synthetase, glutamate-cysteine_ligase"

-1,72

-0,94

DDB0167249

Aldehyde dehydrogenase

-0,77

-0,94

DDB0192169

alrA;"aldehyde_reductase, aldo-keto_reductase"

-0,54

-0,88

DDB0218652

alrB;"aldo-keto_reductase"

-0,60

-0,69

DDB0189745

alrC;"aldo-keto_reductase"

-0,84

-0,72

DDB0186332

alrE;"aldo-keto_reductase"

-1,23

-0,99

DDB0204015

D-Lactate dehydrogenase

0,92

0,65

DDB0187572

Endoglucanase_E_like

0,64

0,78

DDB0203268

glgB;"1,4-alpha-glucan_branching_enzyme, branching_enzyme"

1,02

0,85

DDB0187562

glk;glucokinase

0,69

0,56

DDB0217973

Gluconolactonase

0,85

0,82

DDB0202855

Glycoside hydrolase

-1,63

-1,45

DDB0202233

Glycosyl hydrolase family 7

-1,26

-1,38

DDB0167594

Glycosyl hydrolases family

0,60

0,56

DDB0204016

gpt10;"putative_glycophosphotransferase"

-0,69

-1,11

DDB0204037

Legume lectins beta-chain signature

-0,86

-1,16

DDB0206405

Mannosyl oligosaccharide glucosidase

-0,80

-0,68

DDB0205896

NAD-dependent epimerase/dehydratase family protein

-2,12

-1,73

DDB0204752

Phosphoglycerate mutase family

0,64

0,61

DDB0190464

Predicted kinase related to galactokinase and mevalonate kinase

0,52

1,02

DDB0186919

zinc-containing alcohol dehydrogenase

-2,09

-1,99

DDB0168737

zinc-containing alcohol dehydrogenase (ADH)

-1,35

-1,03

DDB0169356

carboxylic ester hydrolase

-1,03

-0,72

DDB0190523

CAS1;"cycloartenol_synthase"

0,85

0,88

DDB0185601

cutA;"fatty_acid_elongase_3-ketoacyl-CoA_synthase, long_chain_fatty_acid_elongase"

-0,93

-0,62

DDB0188166

delta-24-sterol methyltransferase

-1,42

-1,74

DDB0186908

eapA;"alkyl-dihydroxyacetonephosphate_synthase"

0,72

0,81

DDB0187604

enoyl-CoA hydratase/isomerase domain-containing protei

-0,65

-0,63

DDB0205302

Enoyl-CoA hydratase/isomerase family

-0,64

-0,68

DDB0205157

fadB, des5-2, "delta_5_fatty_acid_desaturase"

0,89

1,11

DDB0190288

fcsB, fatty acyl-CoA synthetase, long-chain-fatty-acid-CoA ligase

-1,45

-1,57

DDB0191679

GNS1/SUR4 family

-1,03

-0,59

DDB0205505

mfeB;"hypothetical_peroxisomal_multifunctional_enzyme_2"

-1,06

-0,65

DDB0191653

patatin family protein

-2,07

-1,77

DDB0218187

Perilipin family

-1,35

-1,04

DDB0184443

Saposin (B) Domains

0,59

0,59

DDB0190553

Similar to sterol-C4-methyl oxidase-like

-0,71

-0,91

DDB0217332

stearoyl-CoA desaturase

0,90

1,02

DDB0206478

allC;"allantoate_amidinohydrolase, allantoicase"

-1,12

-1,25

DDB0205700

cysteine desulfurase

-1,31

-1,26

DDB0169540

MOSC domain

-1,00

-0,67

DDB0187599

5 prime nucleotidase family

-0,90

-0,70

DDB0187063

3-methyl-2-oxobutanoate hydroxymethyltransferase

-1,83

-1,41

DDB0190860

adenine phosphoribosyltransferase

-0,98

-0,75

DDB0203073

adenosine deaminase-related growth factor

-1,35

-0,74

DDB0215237

ATP:D-ribose_5-phosphotransferase, ribokinase

-0,56

-0,51

DDB0206047

CTP synthase

-1,01

-1,20

DDB0187738

Cytidine and deoxycytidylate deaminase zinc-binding region

-0,76

-0,73

DDB0191911

putative RNA methylase

-1,01

-1,21

DDB0185785

Putative RNA methylase family

-0,59

-0,63

DDB0219236

pyrK, cytidylate kinase

-0,98

-0,71

DDB0217423

rnrB_2, ribonucleotide reductase small subunit

-0,79

-0,84

DDB0215284

tRNA/rRNA methyltransferase SpoU family protein

-0,77

-0,93

DDB0186269

Thioredoxin family

-1,41

-1,91

DDB0206431

FAD binding domain

-1,63

-1,13

DDB0187958

gchA, "GTP_cyclohydrolase_I"

-0,91

-0,89

DDB0185963

Oxysterol-binding protein

-0,55

-0,75

DDB0205608

pks18, putative polyketide synthase

0,54

0,60

DDB0168380

pks5, putative polyketide synthase

0,57

0,99

DDB0219613

stlB, putative polyketide synthase

0,68

1,05

DDB0186173

Histidine acid phosphatase

-0,79

-0,80

DDB0184156

Acetyltransferase (GNAT) family

0,81

0,78

DDB0205937

Aldehyde dehydrogenase family

1,02

0,81

DDB0183800

dihydrolipoamide_dehydrogenase

-0,60

-0,57

DDB0188526

FAD binding domain

-1,14

-0,93

DDB0169374

haloacid dehalogenase-like hydrolase

-1,26

-1,45

DDB0204714

hemA, ALAS, "5-aminolevulinate_synthase, ALA_synthase"

1,00

0,99

DDB0187575

monooxygenase, FAD-binding

-1,30

-1,14

DDB0203608

NADH:flavin oxidoreductase/NADH oxidase domain-containing protein

-2,99

-2,49

DDB0186877

Predicted hydrolases or acyltransferases

-0,70

-0,83

DDB0203708

Putative dehydrogenase domain

0,60

0,66

DDB0186921

Putative quinone oxidoreductase

-0,68

-0,80

DDB0218378

selD, selenophosphate synthase

0,83

0,85

DDB0219578

short chain dehydrogenase

-0,56

-0,76

DDB0168766

short chain dehydrogenase

0,75

0,90

DDB0201995

Short-chain alcohol dehydrogenase

-0,64

-0,69

DDB0191047

Sucrolytic enzyme/ferredoxin homolog protei

-0,76

-0,55

DDB0205223

Ubiquinone biosynthesis protein

1,02

0,99

Movement

  

DDB0190345

Actin

-0,62

-0,64

DDB0216677

tubB;"beta_tubulin"

0,78

0,66

Protein targeting

  

DDB0189280

CLN3 protein;Major Facilitator Superfamily

-1,38

-0,81

DDB0186130

pigF, phosphatidylinositol glycan, class Fphosphoethanolamine N- methyltransferase family

-1,13

-1,33

DDB0187271

Protein prenyltransferase, alpha subunit

-0,69

-0,66

DDB0187195

Ubiquitin family protein

-0,68

-0,56

DDB0217546

vps13B, vacuolar protein sorting-associated protein

0,80

0,57

DDB0205767

Importin-beta N-terminal domain

-1,39

-0,91

DDB0219696

CSN3, COP9 signalosome complex subunit 3

0,86

0,56

DDB0188097

mppB;"mitochondrial_processing_peptidase_beta_subunit"

0,85

0,93

DDB0188792

npl4, nuclear protein localization 4

-1,46

-1,26

DDB0189322

Peptidase family M41

0,66

0,82

DDB0204548

putative E3 ubiquitin ligase

0,56

0,88

DDB0203213

Putative serine protease

0,57

0,64

DDB0190305

RING-finger-containing ubiquitin ligase

-1,71

-2,23

DDB0191910

sigB, GP63, orfGP63", "leishmanolysin_family_protein, peptidase

-1,44

-1,19

DDB0219558

usp12, putative ubiquitin carboxyl-terminal hydrolase (UCH)

0,81

0,50

DDB0188490

usp40, putative ubiquitin carboxyl-terminal hydrolase (UCH)

0,83

0,60

Signal transduction

  

DDB0167328

ArfGAP, Arf GTPase activating protein

0,58

0,67

DDB0187828

gacX, RacGAP

0,62

0,52

DDB0217797

gpaG, "G-protein_subunit_alpha_7"

-0,93

-0,77

DDB0205484

GTPase-activator protein for Ras-like

0,58

0,69

DDB0202545

rabX;"Rab_GTPase"

-0,59

-0,76

DDB0186244

abkD, AdckB2, "putative_ABC1_family_protein_kinase"

0,80

0,88

DDB0217600

nek3, putative protein serine/threonine kinase

0,55

0,56

DDB0203684

tyrosine kinase-like

0,53

0,56

DDB0189806

vwkA;"protein_serine/threonine_kinase"

-0,77

-0,67

DDB0205355

Calcineurin-like phosphoesterase

-1,27

-0,86

DDB0218779

pdsA, "PDE, pde1, pdeA", "cAMP_phosphodiesterase"

0,79

1,40

DDB0204820

rabS;"Rab_GTPase"

-0,80

-0,87

DDB0190872

NLI interacting factor-like phosphatase

-0,55

-0,60

DDB0185382

Protein phosphatase 5, catalytic subunit

-0,68

-0,62

DDB0186390

protein tyrosine phosphatase

-0,85

-0,74

DDB0218065

ptpB, DdPTPa, "phosphotyrosine_phosphatase_ptp2

-1,20

-1,01

DDB0189698

Tyrosine specific protein phosphatases family

-0,76

-0,89

DDB0203756

G-protein-coupled receptor (GPCR) family protein

0,69

0,99

DDB0189216

gacD, RacGAP

-0,63

-0,58

DDB0217433

Regulator of G protein signaling

0,90

0,72

DDB0169375

cGMP-specific phosphodiesterase

-0,83

-0,79

DDB0167494

plbF, PLB, "phospholipase_B-like"

-1,17

-1,01

DDB0168860

sgkA, "SK, SPHK", "sphingosine_kinase"

-0,97

-0,79

DDB0167227

Cytochrome b5-like Heme/Steroid binding domain

-0,71

-1,57

DDB0216720

Tetraspanin family

-0,73

-0,68

Stress response

  

DDB0202483

AhpC/TSA family

-0,51

-0,68

DDB0203727

AhpC/TSA family protein. Thioredoxin-like

-1,25

-1,89

DDB0203727

AhpC/TSA family protein. Thioredoxin-like

-1,11

-2,00

DDB0205904

AhpC/TSA family protein. Thioredoxin-like

-1,10

-1,63

DDB0168230

Cytochrome P450

0,61

0,67

DDB0217979

cytochrome P450 family protein

-1,11

-1,24

DDB0187276

cytochrome P450 family protein

-0,73

-0,88

DDB0186118

cytochrome P450 family protein

-0,54

-0,51

DDB0167587

Glutathione S-transferase

-2,24

-2,02

DDB0218804

Glutathione S-transferase

-1,07

-0,86

DDB0185602

putative FMN-dependent NAD(P)H:quinone reductase

-0,66

-0,93

DDB0168563

putative glutathione S-transferase

-0,64

-1,23

DDB0201962

Ku70-binding family protein

-0,60

-0,51

DDB0191833

TFIIH4, "TFIIH_subunit, general_transcription_factor_IIH,_polypeptide_4"

-0,81

-0,90

DDB0204089

NUDIX hydrolase family

-0,89

-1,14

DDB0185428

Strictosidine synthase

-1,74

-1,01

DDB0169033

trap1, Dd-trap1, "TNF_receptor-associated_protein", member of the HSP90 fam

-1,93

-1,56

DDB0188234

Chaperone clpB

-1,00

-1,25

DDB0192088

heat shock cognate protein

-0,82

-0,99

DDB0192086

heat shock protein, 70 kDa heat shock protein

-0,94

-1,18

DDB0169051

Hsp20/alpha crystallin family

-1,13

-1,30

DDB0169044

Hsp20/alpha crystallin family

-1,07

-1,12

DDB0169207

hspG12, heat shock protein Hsp20 domain-containing protein

-0,72

-0,86

Transcription

  

DDB0204405

CRTF;"transcription_factor"

0,97

1,15

DDB0167879

IWS1 C-terminus

0,70

0,69

DDB0205969

snd1, tudor domain-containing protein

0,66

0,79

DDB0188840

TFIIAL, "transcription_factor_IIA"

-0,79

-0,55

DDB0167865

ddx52, DEAD/DEAH box helicase

-0,55

-0,55

DDB0184074

ddx6, DEAD/DEAH box helicase

0,62

0,66

DDB0184228

DEAD/DEAH box helicase

-0,63

-0,60

DDB0206136

myb domain-containing protein

0,59

0,58

DDB0189583

rpmA;"DNA-dependent_RNA_polymerase"

-1,33

-1,17

DDB0186101

pwp2, ortholog of H. sapiens and S. cerevisiae PWP2

-0,80

-0,88

DDB0192008

rpa2, RNA polymerase I, second largest subunit

-0,98

-1,13

DDB0218008

rpc4;"putative_RNA_polymerase_III_subunit"

-0,58

-0,55

DDB0216877

tRNA pseudouridine synthase

-0,56

-0,65

DDB0204724

DNA helicase TIP49, TBP-interacting protei

0,63

0,64

DDB0219410

pirin-like protein

-0,99

-0,81

Translation

  

DDB0167043

Ribosomal protein L10

-0,86

-1,37

DDB0190639

Fibrillarin

-0,65

-0,84

DDB0184302

Mitochondrial small ribosomal subunit Rsm22

-0,70

-0,72

DDB0205674

MPP10, U3 small nucleolar ribonucleoprotein

-0,77

-0,66

DDB0201601

mrpl11, S60 ribosomal protein L11, mitochondrial

-0,91

-1,26

DDB0204554

Ribosomal protein L28

-0,50

-0,69

DDB0188692

Ribosomal protein S8e

-0,94

-0,79

DDB0183814

Ribosomal RNA processing protein 4

0,58

0,56

DDB0188661

rps9, "rp1024, v12", "40S_ribosomal_protein_S9

0,60

0,70

DDB0219852

u3 small nucleolar RNA interacting protein 2, putative

-0,75

-0,63

DDB0191852

eukaryotic translation initiation factor 3 subunit 5

0,59

0,54

DDB0203843

Eukaryotic translation initiation factor 6 (EIF-6)-like protein

-0,96

-0,64

DDB0189529

gfm2, mitochondrial translation elongation factor G

-0,59

-0,50

DDB0202851

NMD3 family

-0,95

-0,97

DDB0168814

aspartyl-tRNA_synthetase

0,70

0,98

Transport

  

DDB0217304

ABC transporter AbcG17

-1,58

-1,44

DDB0167281

ABC transporter mdrA2

-2,65

-1,44

DDB0167281

ABC transporter mdrA2

-2,26

-1,45

DDB0191940

abcB2;"ABC_transporter_B_family_protein"

0,87

0,93

DDB0188931

abcE1;"RNaseL_inhibitor-like_protein, non-transporter_ABC_protein"

0,61

0,74

DDB0189332

amino acid permease family protein

-0,72

-0,87

DDB0168564

Amino acid/polyamine transporter

-1,28

-1,06

DDB0190286

mcfF, Mitochondrial carrier protein

-0,99

-0,86

DDB0216936

mftA;"carrier_protein_RIM"

-0,91

-1,05

DDB0188529

nucleoporin family protein

-0,94

-0,72

DDB0189222

ccsA, copper chaperone for superoxide dismutase

-0,69

-0,62

DDB0205129

Co/Zn/Cd efflux system component

-0,56

-0,71

DDB0202441

nhe1, DdNHE1, "Na-H_exchanger, sodium/hydrogen_exchanger"

-0,77

-0,59

DDB0168533

porA;porin

0,68

0,59

DDB0218156

P-type cation-transporting ATPase

-0,69

-0,57

DDB0189480

mcfT, mitochondrial substrate carrier family protein

-0,74

-0,86

DDB0202337

Nodulin, Major Facilitator Superfamily

-0,60

-0,68

DDB0185520

Nucleoside transporter

-0,53

-0,56

DDB0203447

Sugar (and other) transporter

1,01

0,71

DDB0168979

Sugar transport proteins signature 1

-0,78

-1,28

DDB0189650

sodium/potassium-transporting ATPase alpha chain 2

-1,48

-0,81

DDB0190036

Major Facilitator Superfamily

-1,13

-1,05

DDB0205693

Major Facilitator Superfamily

-1,33

-1,08

Figure 5

Functional categories of the genes affected similarly by the exposure with PAO1 and PA14. The genes whose expression was altered by PAO1 and PA14 were manually annotated (see Table 2) and grouped in functional categories. The size of the blue bars indicates the number of genes upregulated in each category related to the control and purple bars the number of genes downregulated.

Table 3 displays the D. discoideum genes whose expression changed differentially between PAO1 and PA14 infection and Figure 6 shows the number of genes in each category (see Additional file 3 for complete data). In general a higher proportion of the genes showed a higher level of expression by PAO1 infection (109 genes) when compared with the levels observed by PA14. On the other hand, 60 genes behaved oppositely showing lower levels of expression upon exposure to PAO1 compared to those levels obtained after PA14 infection. Interestingly, all the genes represented in the categories stress-response and protein targeting had a higher level of expression in the cells exposed to PAO1 compared to PA14. The behavior of these genes in comparison with the control is also displayed in Table 3.
Table 3

Genes differentially expressed upon infection with PAO1 versus PA14

Cellular Biogenesis and organization

   

Gene ID

Gene function and name

Log2 ratio: PAO1-14

PAO1-C

PA14-C

DDB0187116

vps13A, vacuolar protein sorting-associated protein

0,81

0,67

-0,13

DDB0189855

vps46, Vacuolar Protein Sorting

0,83

0,59

-0,24

Energy

   

DDB0204335

cxgE, "cox7E, coxVIIe", "cytochrome_c_oxidase_subunit_VII_E"

1, 20

0,80

-0,40

Metabolism

   

DDB0190752

diaminopimelate epimerase

1,08

0,02

-1,06

DDB0204319

Hydroxymethyltransferase

0,63

0,43

-0,20

DDB0168738

putative arginine deiminase

-0,62

-0,78

-0,16

DDB0205389

acly, ATP citrate lyase

0,89

0,75

-0,15

DDB0169357

methylenetetrahydrofolate dehydrogenase

0,78

0,21

-0,57

DDB0187393

NAD-dependent epimerase/dehydratase family protein

-0,91

-1,18

-0,28

DDB0205386

putative ATP citrate synthase

0,96

0,79

-0,17

DDB0205339

rpe;"ribulose_phosphate_3-epimerase"

0,78

0,43

-0,35

DDB0187942

Short-chain alcohol dehydrogenase of unknown specificit

0,70

0,43

-0,27

DDB0187544

smlA

1,43

1,00

-0,43

DDB0217455

zinc-containing alcohol dehydrogenase (ADH)

-1,21

-1,16

0,06

DDB0217374

zinc-containing alcohol dehydrogenase (ADH)

-1,02

-1,37

-0,35

DDB0190948

acid ceramidase-like protein

-0,87

-0,31

0,56

DDB0188248

Acyltransferase

0,64

0,16

-0,48

DDB0219652

cinB, "esterase/lipase/thioesterase_domain-containing_protein

0,92

1,28

0,36

DDB0189754

esterase/lipase/thioesterase domain-containing protein

1,07

1,07

0,00

DDB0185740

esterase/lipase/thioesterase domain-containing protein

1,08

1,27

0,20

DDB0184141

Phosphate acyltransferases

0,55

-0,26

-0,82

DDB0167446

pks16, putative fatty acid synthase

1,19

1,81

0,63

DDB0189182

Putative esterase/lipase/thioesterase

0,99

1,43

0,44

DDB0191907

dUTP diphosphatase

0,61

0,14

-0,47

DDB0217901

purH, AICAR transformylase/IMP cyclohydrolase

0,89

0,34

-0,55

DDB0167009

pyr4;"dihydroorotate_dehydrogenase, dihydroorotate_oxidase"

0,68

0,18

-0,50

DDB0217842

rpiA;"ribose-5-phosphate_isomerase"

0,63

0,42

-0,21

DDB0189571

Sulfite reductase, alpha subunit (flavoprotein)

-0,89

-0,09

0,79

DDB0202318

Cyclopropyl sterol isomerase

0,73

0,31

-0,42

DDB0167227

Cytochrome b5-like Heme/Steroid binding domain

0,86

-0,71

-1,57

DDB0168923

dihydropteridine reductase

0,60

0,14

-0,47

DDB0185998

ERG24, Ergosterol biosynthesis

0,59

-0,02

-0,61

DDB0219255

Fe(II) oxygenase superfamily

1,14

0,91

-0,23

DDB0217308

Predicted iron-dependent peroxidase

-0,55

-0,12

0,43

DDB0192180

putative O-methyltransferase

0,56

0,71

0,15

DDB0202301

putative SAM dependent methyltransferase

0,88

0,94

0,05

DDB0167345

short-chain dehydrogenase/reductase (SDR) family protein

0,73

0,56

-0,17

Movement

   

DDB0188280

myoB, "DMIB, abmB", "myosin_IB"

-0,85

-0,30

0,55

DDB0167337

myoD, DMID, "myosin_ID_heavy_chain"

-0,81

-0,76

0,05

Multicellular organization

   

DDB0216906

comC;"FIBROSURFIN_PRECURSOR"

-0,65

-0,39

0,26

Protein destination

   

DDB0189735

homolog to co-chaperone p23

0,56

0,45

-0,11

DDB0187409

Acetyltransferase (GNAT) family

0,57

0,33

-0,24

DDB0189799

SET domain-containing protein

0,52

0,33

-0,19

DDB0202482

26S proteasome non-ATPase regulatory subunit 9

0,78

1,25

0,47

DDB0219654

Cysteine proteinase 1 precursor

0,70

0,61

-0,09

DDB0167298

Dipeptidyl aminopeptidase

0,51

-0,07

-0,59

DDB0190542

Probable proteasome subunit beta type 3

0,57

-0,06

-0,63

DDB0216902

prtA, M3L, "proteosomal_alpha-subunit_M3"

0,93

0,94

0,01

DDB0216901

prtB, M3R, "proteosomal_alpha-subunit_7-1"

1,37

1,30

-0,06

DDB0186869

small ubiquitin-like protein

0,61

0,33

-0,28

DDB0217344

ubiquitin-like domain containing CTD phosphatase

0,52

0,29

-0,23

Signal transduction

   

DDB0169410

gpaB, "Ga2, Galpha2, gpa2", "G-protein_subunit_alpha_2"

-0,82

-0,55

0,27

DDB0190318

rab1C;"Rab_GTPase"

0,87

0,76

-0,10

DDB0202066

pakC, STE20 family protein kinase

-0,61

-0,18

0,43

DDB0169250

putative protein serine/threonine kinase

-0,87

-0,58

0,29

DDB0205782

roco6;"putative_protein_serine/threonine_kinase

-0,61

-0,48

0,14

DDB0167076

sepA, putative protein serine/threonine kinase

-0,57

-0,31

0,26

DDB0217465

fslH, G-protein-coupled receptor (GPCR) family protein

0,71

0,24

-0,46

DDB0205174

grlF, GABA-B receptor-like protein

-1,41

-1,54

-0,12

DDB0168770

grlJ, GABA-B receptor-like protein

-0,63

-0,96

-0,32

DDB0204083

grlL, GABA-B receptor-like protein

-1,84

-1,25

0,60

DDB0229801

grlQ, G-protein-coupled receptor (GPCR) family protein

0,77

0,98

0,21

DDB0167432

gacFF, RacGAP

-0,57

0,08

0,66

DDB0167384

putative guanine nucleotide exchange factor (GEF)

-0,70

-0,01

0,70

DDB0167541

dpoA;"prolyl_oligopeptidase"

-0,66

-0,11

0,55

Stress response and cell rescue

   

DDB0218719

AhpC/TSA family protein

0,82

0,76

-0,05

DDB0205882

AhpC/TSA family protein. Thioredoxin-like

0,75

0,57

-0,18

DDB0217383

Cytochrome P450

1,16

0,42

-0,74

DDB0168563

putative glutathione S-transferase

0,59

-0,64

-1,23

DDB0167491

alyA;lysozyme

0,63

0,73

0,10

DDB0167489

alyB;lysozyme

0,69

0,75

0,07

DDB0167490

alyC;lysozyme

0,71

0,87

0,16

Transcription

   

DDB0206051

member of NOD protein family

1,05

0,90

-0,15

DDB0202276

srfC;"putative_MADS-box_transcription_factor"

-0,90

-0,30

0,60

DDB0217613

wrky1;"putative_WRKY_transcription_factor"

1,04

1,11

0,07

DDB0167422

putative histone acetyltransferase

-0,64

-0,55

0,09

Translation

   

DDB0201621

mrps2, ribosomal protein S2, mitochondrial

0,75

0,49

-0,27

DDB0218535

Eukaryotic elongation factor 1 (EF1) alpha subfamily

-0,83

-0,18

0,65

DDB0167339

tRNA-ribosyltransferase

-0,52

-0,56

-0,04

Transport facilitation

   

DDB0187089

abcC5;"ABC_transporter_C_family_protein"

-1,12

-0,68

0,44

DDB0218568

Copper-transporting P-type ATPase

-1,14

-0,60

0,54

DDB0204460

patA, PAT1, "Ca2+-ATPase, P-type_ATPase"

-0,93

-0,15

0,78

DDB0205031

potassium channel tetramerization domain-containing protein

0,92

0,84

-0,08

DDB0217251

P-type cation-transporting ATPase

-2,09

-1,46

0,63

DDB0186223

P-type cation-transporting ATPase

-0,69

-0,40

0,29

DDB0167361

vatE;"vacuolar_H+-ATPase_E_subunit"

0,54

0,47

-0,08

DDB0217699

phospholipid-translocating P-type ATPase family protein

-0,67

-0,63

0,04

DDB0187945

Sugar (and other) transporter

-0,90

-0,78

0,11

DDB0206579

mcfQ, mitochondrial substrate carrier family protein

0,65

0,78

0,13

DDB0192172

mcfZ, mitochondrial substrate carrier family protein

0,73

0,92

0,18

DDB0183815

Mitochondrial carrier protein

0,55

0,81

0,26

Figure 6

Functional categories of the genes differentially affected by the exposure to PAO1 and PA14. The genes whose expression was differentially altered in PAO1 versus PA14 were manually annotated (see Table 3) and grouped in functional categories. The size of the blue bars indicates the number of genes upregulated in PAO1 versus PA14 in each category and purple bars the number of genes downregulated in PAO1 versus PA14.

Discussion

P. aeruginosa is able to infect D. discoideum cells using several virulence traits that are similar to those used to infect mammalian cells and other hosts [18]. The clinical P. aeruginosa isolates PA14 and PAO1 have been used independently to study the infection of Dictyostelium by Pseudomonas in two different laboratories [20, 21]. However, no direct comparison had been reported so far between these strains in this pathogenicity model. We now report that PA14 is indeed more virulent in D. discoideum using different plating assays. Since P. aeruginosa is phagocytosed at much lower rate than the non-pathogenic K. aerogenes (commonly used to grow Dictyostelium) [20], the assays were designed to provide sufficient food to D. discoideum to avoid cell starvation. Thus, K. aerogenes was always used in excess together with the pathogenic strains. In the first assay (Figure 1) a nutrient plate was used to allow the growth of bacteria and D. discoideum simultaneously. Under these conditions the presence of PA14 inhibits D. discoideum growth to a greater extent than PAO1. To avoid differences in the growth rates between bacteria that might alter their final proportions, a non-nutrient assay was performed (Figure 2). In these experiments, D. discoideum feed on bacteria that have been previously grown and deposited at different proportions in non-nutrient agar. Interestingly, PAO1 is not virulent in this condition suggesting that bacterial growth is necessary for the expression of the virulence in this strain. However, even in these conditions PA14 is capable of inhibiting D. discoideum growth. Some studies have suggested that PA14 pathogenicity is multifactorial and required the action of multiple virulence mechanisms [15, 27]. These differences between strains prompted us to study the transcriptional profile of D. discoideum upon infection with PAO1 and PA14 to gain insights not only into the possible common transcriptional response but also into any specific response that could explain the observed differences in their virulence. Pilot experiments showed that 4 hours of exposure of D. discoideum cells to either Pseudomonas aeruginosa strains did not result in any apparent cell death or change in cell morphology (data not shown). Since we wanted to study the early transcriptional response we chose this short time of exposure to avoid changes due to cell death. The existence of a rapid gene expression response between 1–6 hours upon exposure of D. discoideum cells to Legionella, an intracellular pathogen, has also been described [28].

Our results show the existence of a common transcriptional response to the infection with P. aeruginoa PAO1 or PA14 that affects 364 genes grouped in many different cellular functions. The complexity of the observed transcriptional changes could be the result of the induction of D. discoideum defensive responses or triggered by P. aeruginosa to make a less hostile cell environment that would support a better survival of the pathogen. In this scenario downregulation of genes involved in stress response might be beneficial for a successful infection. Interestingly, we have observed a clear down-regulation of genes dedicated to stress in the common response to PAO1 and PA14 but also in the specific response to the more virulent strain PA14. For example the gene coding for Strictosidine synthase (DDB0185428) which is involved in the synthesis of alkaloids related to defense mechanisms in plants [29], Trap1 (DDB0169033) that plays a central role in cell cycle regulation and differentiation [30] or the genes coding for lysozymes involved in bacterial degradation (DDB0167491) [31], to mention just a few.

Besides stress response other categories are affected by an overall downregulation such as metabolism, translation and transport facilitation. A subcategorization of the genes included in metabolism (see supplementary table 2) showed that all the genes coding for proteins involved in nucleotide metabolism were downregulated in the common response suggesting an effect on cell proliferation. Moreover, a number of other downregulated genes are directly involved in cell growth as demonstrated functionally in previous studies. This is the case for example of DDB0192001 (ppkA, polyphosphate kinase), whose disruption leads to reduced growth on bacteria [32, 33], DDB0186120 (gcsA, glutamylcysteine synthetase), which is essential for cell growth as mutants in the gene are not viable in the absence of glutathione [34]. DDB0168860 (sgkA, sphingosine kinase) that is involved in cell proliferation [35], among others that have been annotated in Supplementary Table 2, 3.

Two different expression microarray analysis in mammals upon infection with Pseudomonas aeruginosa have been reported. In the first report epithelial cells were exposed to the pathogen for 3 hours, a short exposure similar to our experimental design. Unfortunately the number of genes represented in the array was very limited (1500 cDNAs) [36]. Only 22 genes were differentially regulated and we have not found any homologous gene in common. The other work reported the analysis of Pseudomonas aeruginosa corneal infection using an oligonucleotide microarray [37]. This experiment is not directly comparable to ours since a long exposure to the pathogen (1 day) was performed to assure an infection process. As a consequence most of the regulated genes were associated with the immune response and apoptosis, aspects that are not present in Dictyostelium.

D. discoideum is also susceptible to the infection by Legionella pneumophila, a facultative intracellular parasite, which uses different infective mechanisms from P. aeruginosa. It is important to note here that the transcriptional response of D. discoideum upon infection with Legionella [28] was essentially different to the one we report for P. aeruginosa. Only 8 genes were found to be altered in both experiments (DDB0186332, DDB0219578, DDB0167879, DDB0205386, DDB0185740, DDB0167345, DDB0201617, DDB0202615). This indicates that the host response is rather specific of the type of infection and the bacterial pathogen involved. Nevertheless, some responses can be also common. For instance, DDB0202615 (nramp1, natural resistance-associated macrophage protein) whose expression is downregulated in PAO1 and PA14, plays an important role in Legionella infection since the null mutant has increased sensitivity to the infection [38]. Nramp1 transports metal cations out of the phagolysosome in an ATP-dependent process. This activity is believed to be necessary to avoid the growth of intracellular pathogens and might also contribute to the efficient killing of other bacterial pathogens.

The variety of genes whose expression is altered by P. aeruginosa infection suggests a complex scenario in which a combined downregulation of the expression of some of the mentioned genes might affect D. discoideum fitness thus favoring the infection. The precise role of these genes in the pathogenesis and the mechanisms that regulates their expression will promote further investigation.

Conclusion

Our results showed that P. aeruginosa PA14 is more virulent than PAO1 in the D. discoideum model using different plating assays. The transcriptional responses of D. discoideum infected by either P. aeruginosa PAO1 or PA14 were analyzed by whole-genome microarrays and the expression of 364 genes changed similarly upon infection with any of both strains as compared with the control. Effects on metabolism, signaling, stress response and cell cycle can be inferred from the genes affected. Interestingly there were 169 genes differentially regulated between PAO1 and PA14, and this differential response might contribute to the different virulence behavior displayed by these two model strains. This is a starting point to begin to understand the complex relationships between environmental opportunistic pathogens and their natural hosts. Besides, our data support the idea that the host responses to different isolates of the same bacterial pathogen are largely different, thus indicating that the crosstalk between the pathogen and its host is more specific and more complex than previously thought.

Methods

D. discoideumgrowth and plating assays

Dictyostelium AX4 cells were grown axenically in HL5 medium (14 g/l tryptone, 7 g/l yeast extract, 0.35 g/l Na2HPO4, 1.2 g/l KH2PO4, 14 g/l glucose, pH 6.5) or in association with Klebsiella aerogenes on SM plates (10 g/l glucose, 1 g/l yeast extract, 10 g/l peptone, 1 g/l MgSO4·6H20, 1.9 g/l KH2PO4, 0.6 g/l K2HPO4, 20 g/l agar, pH 6.5) [39].

For the nutrient SM-plating assay Pseudomonas aeruginosa (PAO1 and PA14) and Klebsiella aerogenes (KA) were grown overnight in LB. After washing, bacteria were resuspended with PDF (20 mM KCl; 9 mM K2HPO4, 13 mM KH2PO4, 1 mM CaCl2; 1 mM MgSO4; pH: 6.4) and the optical density (OD) determined at 600 nm. After adjusting to 0.5 OD units, 300 μl of Klebsiella and Pseudomonas at the indicated proportions were plated in SM-agar plates with approximately 100 D. discoideum cells.

For non-nutrient plating assay bacteria were grown as before, washed and resuspended in PDF (at an OD of 15 units at 600 nm. 100 μl of bacteria at the indicated proportions were mixed with D. discoideum cells and deposited in a drop over a PDF-based non-nutrient agar plates.

Microarrays

Dictyostelium cells (5 × 107 cells) were deposited in 10 ml of HL5 (without antibiotics) in shaking culture and exposed during 4 hours to 1.0 OD (approximate multiplicity of infection: 1000) of Klebsiella aerogenes as a control or to a mixture of Pseudomonas aeruginosa (PAO1 and PA14) and Klebsiella (used in excess to provide similar conditions of food supply). The proportion of Klebsiella to Pseudomonas (either PAO1 or PA14) was 7:3. Previous experiments showed that these proportions are adequate for a clear inhibition of Dictyostelium growth in plating assays (see the results section). Dictyostelium cells were separated from the bacteria by gentle centrifugation (twice at 1000 rpm, 5 minutes) and RNA isolated by Trizol (Life Technologies) according to manufacturer's instructions. Three independent biological experiments were performed making a total of three independent treatments for each condition (Klebsiella control, PAO1, and PA14). RNA from the three treatments were paired in all three combinations (Control/PAO1; Control/PA14 and PAO1/PA14) and hybridized to three different arrays. The same was performed for the other two biological replicates making a total of 9 microarrays hybridized. One of the three biological replicates was hybridized in the opposite dye orientation to the other two. The arrays, and protocols for labelling, hybridisation and scanning were as previously described [40]. Background fluorescence was subtracted [41], linear models were fitted and the significance of apparent changes in expression was assessed using limma [42, 43]. The data were normalised within each array with the printtip loess algorithm to counteract scanning and spatial biases, and further between each array to normalise mean absolute deviations using the 'scale' algorithm [44].

Preliminary analysis using ANOVA methods indicated that many genes had significant differences in expression between treatments, so we proceeded to examine each pairwise contrast in turn. We filtered out less reliable data by selecting genes with p-values adjusted for multiple testing [45] less than 0.05, making use of the moderated t statistics calculated by the eBayes function of limma. Since many genes passing this cutoff showed small changes in expression, we filtered further by absolute log2ratio. The commonly-used cutoff of greater than 2-fold change would have excluded a large number of genes that appeared to change in expression quite consistently, so we used the less stringent criterion of absolute log2ratio >0.5. A lower log2ratio would have included genes with differences in expression too small to be corroborated by other methods. The array design is available from ArrayExpress [46] under the accession A-SGRP-3. The array experiment was deposited in the ArrayExpress database under the accession E-TABM-464.

Quantitative PCR

The same RNA samples subjected to microarray study were used as templates for retrotranscription with High Capacity cDNA Reverse transcription kit (Applied Biosystems) using 250 ng of each RNA in a final volume of 20 μl. For each sample, a triplicated blank was used. This cDNAs served as template in the PCR reaction carried out in 7900 HT Fast Real- Time PCR System using Power Sybrgreen PCR Master Mix 2× with 300 nM oligonucleotides concentration in a final volume of 10 μl. The results were acquired with SDS 2.3 software by Applied Biosystems and handled with EXCEL software by Microsoft. A total of seven genes were studied for each sample and their amount were related to one control gene, DDB0217951, whose expression is not affected by the treatments with the different strains.

Notes

Declarations

Acknowledgements

This work was supported by grants BMC2006-00394 from the Spanish Ministerio de Educación y Ciencia (R.E.), CCG07-CSIC/SAL-1959 from Comunidad de Madrid/CSIC (R.E.) and BIO2005-04278, LSHM-CT-2005-518152 and LSHM-CT-2005-018705 to J.M. We would like to thank Theresa Feltwell for technical assistance with the microarray experiment. GB, JS and AI were supported by the Wellcome Trust. JC is recipient of a pre-doctoral fellowship from Spanish Ministerio de Educación y Ciencia.

Authors’ Affiliations

(1)
Instituto de Investigaciones Biomédicas Alberto Sols, Universidad Autónoma de Madrid-Consejo Superior de Investigaciones Científicas
(2)
MRC Laboratory of Molecular Biology
(3)
Wellcome Trust Sanger Institute
(4)
Centro Nacional de Biotecnología, CSIC, Madrid and CIBERESP

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© Carilla-Latorre et al; licensee BioMed Central Ltd. 2008

This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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