A two-component response regulator, gltR, is required for glucose transport activity in Pseudomonas aeruginosa PAO1

Sage, Andrew; Proctor, W D; Phibbs, Paul V.

doi:10.1128/jb.178.20.6064-6066.1996

Cited by 27 publications

(18 citation statements)

References 20 publications

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“…In Pseudomonas aeruginosa, Sage et al (32) reported that the gltR gene encoded a product homologous to the response element of two-component systems, whose disruption caused the loss of glucose transport activity. Inactivation of the gltR-2 gene in P. putida had an effect on growth in the presence of glucose that resulted in a prolonged lag when cells were transferred, for example, from M9 minimal medium with citrate to glucose-containing medium as the sole C source.…”

Section: Discussionmentioning

confidence: 99%

A Set of Activators and Repressors Control Peripheral Glucose Pathways in Pseudomonas putida To Yield a Common Central Intermediate

2008

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Pseudomonas putida KT2440 channels glucose to the central Entner-Doudoroff intermediate 6-phosphogluconate through three convergent pathways. The genes for these convergent pathways are clustered in three independent regions on the host chromosome. A number of monocistronic units and operons coexist within each of these clusters, favoring coexpression of catabolic enzymes and transport systems. Expression of the three pathways is mediated by three transcriptional repressors, HexR, GnuR, and PtxS, and by a positive transcriptional regulator, GltR-2. In this study, we generated mutants in each of the regulators and carried out transcriptional assays using microarrays and transcriptional fusions. These studies revealed that HexR controls the genes that encode glucokinase/glucose 6-phosphate dehydrogenase that yield 6-phosphogluconate; the genes for the Entner-Doudoroff enzymes that yield glyceraldehyde-3-phosphate and pyruvate; and gap-1, which encodes glyceraldehyde-3-phosphate dehydrogenase. GltR-2 is the transcriptional regulator that controls specific porins for the entry of glucose into the periplasmic space, as well as the gtsABCD operon for glucose transport through the inner membrane. GnuR is the repressor of gluconate transport and gluconokinase responsible for the conversion of gluconate into 6-phosphogluconate. PtxS, however, controls the enzymes for oxidation of gluconate to 2-ketogluconate, its transport and metabolism, and a set of genes unrelated to glucose metabolism.

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Section: Discussionmentioning

confidence: 99%

A Set of Activators and Repressors Control Peripheral Glucose Pathways in Pseudomonas putida To Yield a Common Central Intermediate

2008

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“…It resulted in the identification of all except two of the genes involved in the degradation of the different carbohydrates studied ( Fig. 2; Lessie & Phibbs, 1984;Temple et al, 1998): the fructose utilization gene cluster (PP0792-PP0795), the glucose utilization gene cluster (PP1010-PP1012; Sage et al, 1996), the zwf-pgleda gene cluster (PP1021-PP1024; Petruschka et al, 2002;Hager et al, 2000), the 2-ketogluconate utilization gene cluster (PP3376-PP3380; Swanson et al, 2000) and the gluconate utilization gene cluster (PP3415-PP3417). Only the genes encoding glucose dehydrogenase and gluconate dehydrogenase were not positively identified.…”

Section: Hcamentioning

confidence: 99%

Multivariate analysis of microarray data by principal component discriminant analysis: prioritizing relevant transcripts linked to the degradation of different carbohydrates in Pseudomonas putida S12

et al. 2006

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The value of the multivariate data analysis tools principal component analysis (PCA) and principal component discriminant analysis (PCDA) for prioritizing leads generated by microarrays was evaluated. To this end, Pseudomonas putida S12 was grown in independent triplicate fermentations on four different carbon sources, i.e. fructose, glucose, gluconate and succinate. RNA isolated from these samples was analysed in duplicate on an anonymous clone-based array to avoid bias during data analysis. The relevant transcripts were identified by analysing the loadings of the principal components (PC) and discriminants (D) in PCA and PCDA, respectively. Even more specifically, the relevant transcripts for a specific phenotype could also be ranked from the loadings under an angle (biplot) obtained after PCDA analysis. The leads identified in this way were compared with those identified using the commonly applied fold-difference and hierarchical clustering approaches. The different data analysis methods gave different results. The methods used were complementary and together resulted in a comprehensive picture of the processes important for the different carbon sources studied. For the more subtle, regulatory processes in a cell, the PCDA approach seemed to be the most effective. Except for glucose and gluconate dehydrogenase, all genes involved in the degradation of glucose, gluconate and fructose were identified. Moreover, the transcriptomics approach resulted in potential new insights into the physiology of the degradation of these carbon sources. Indications of iron limitation were observed with cells grown on glucose, gluconate or succinate but not with fructose-grown cells. Moreover, several cytochrome- or quinone-associated genes seemed to be specifically up- or downregulated, indicating that the composition of the electron-transport chain in P. putida S12 might change significantly in fructose-grown cells compared to glucose-, gluconate- or succinate-grown cells.

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“…The growth substrate(s) within the CF lung is not known, but high concentrations of proteins and amino acids have been found in CF sputum (2,33,48). Since glucose-grown bacteria served as the control comparison in the GeneChip experiments and glucose metabolism is well understood in P. aeruginosa (7,19,22,36,41), we hypothesized that our array data would provide information regarding carbon metabolism in CF sputum. Eleven genes involved in branch chain and aromatic amino acid catabolism were highly up-regulated during growth in CF sputum (Table 1), and genes involved in biosynthesis of these amino acids were repressed.…”

Section: Medium Increased P Aeruginosa Growth Yields (Data Not Shown)mentioning

confidence: 99%

Cystic Fibrosis Sputum Supports Growth and Cues Key Aspects of Pseudomonas aeruginosa Physiology

et al. 2005

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The opportunistic human pathogen Pseudomonas aeruginosa causes persistent airway infections in patients with cystic fibrosis (CF). To establish these chronic infections, P. aeruginosa must grow and proliferate within the highly viscous sputum in the lungs of CF patients. In this study, we used Affymetrix GeneChip microarrays to investigate the physiology of P. aeruginosa grown using CF sputum as the sole source of carbon and energy. Our results indicate that CF sputum readily supports high-density P. aeruginosa growth. Furthermore, multiple signals, which reduce swimming motility and prematurely activate the Pseudomonas quinolone signal cell-to-cell signaling cascade in P. aeruginosa, are present in CF sputum. P. aeruginosa factors critical for lysis of the common CF lung inhabitant Staphylococcus aureus were also induced in CF sputum and increased the competitiveness of P. aeruginosa during polymicrobial growth in CF sputum.

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A two-component response regulator, gltR, is required for glucose transport activity in Pseudomonas aeruginosa PAO1

Cited by 27 publications

References 20 publications

A Set of Activators and Repressors Control Peripheral Glucose Pathways in Pseudomonas putida To Yield a Common Central Intermediate

A Set of Activators and Repressors Control Peripheral Glucose Pathways in Pseudomonas putida To Yield a Common Central Intermediate

Multivariate analysis of microarray data by principal component discriminant analysis: prioritizing relevant transcripts linked to the degradation of different carbohydrates in Pseudomonas putida S12

Cystic Fibrosis Sputum Supports Growth and Cues Key Aspects of Pseudomonas aeruginosa Physiology

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