Project description:A hallmark of the biofilm architecture is the presence of microcolonies. However, little is known about the underlying mechanisms governing microcolony formation. In the human pathogen Pseudomonas aeruginosa, microcolony formation is dependent on the two-component regulator MifR, with mifR mutant biofilms exhibiting an overall thin structure lacking microcolonies, and overexpression of mifR resulting in hyper-microcolony formation. Here, we made use of the distinct MifR-dependent phenotypes to elucidate mechanisms associated with microcolony formation. Using global transcriptomic and proteomic approaches, we demonstrate that cells located within microcolonies experience stressful, oxygen limited, and energy starving conditions, as indicated by the activation of stress response mechanisms and anaerobic and fermentative processes, in particular pyruvate fermentation. Inactivation of genes involved in pyruvate utilization including uspK, acnA and ldhA abrogated microcolony formation in a manner similar to mifR inactivation. Moreover, depletion of pyruvate from the growth medium impaired biofilm and microcolony formation, while addition of pyruvate significantly increased microcolony formation. Addition of pyruvate partly restored microcolony formation in M-bM-^HM-^FmifR biofilms. Moreover, addition of pyruvate to or expression of mifR in lactate dehydrogenase (ldhA) mutant biofilms did not restore microcolony formation. Consistent with the finding of denitrification genes not demonstrating distinct expression patterns in biofilms forming or lacking microcolonies, addition of nitrate did not alter microcolony formation. Our findings indicate the fermentative utilization of pyruvate to be a microcolony-specific adaptation to the oxygen limitation and energy starvation of the P. aeruginosa biofilm environment. For biofilm growth experiments, three independent replicates of P. aeruginosa strains PAO1 and M-NM-^TmifR were grown as biofilms in a flow-through system using a once-through continuous flow tube reactor system for biofilm sample collection and in flow cells (BioSurface Technologies) for the analysis of biofilm architecture as previously described (Sauer et al., 2002, Sauer et al., 2004, Petrova & Sauer, 2009). Cells were treated with RNAprotect (Qiagen) and total RNA was extracted using an RNeasy mini purification kit (Qiagen) per the manufacturerM-bM-^@M-^Ys instructions. RNA quality and the presence of residual DNA were checked on an Agilent Bioanalyzer 2100 electrophoretic system pre- and post-DNase treatment. Ten micrograms of total RNA was used for cDNA synthesis, fragmentation, and labeling according to the Affymetrix GeneChip P. aeruginosa genome array expression analysis protocol. Sauer, K., A. K. Camper, G. D. Ehrlich, J. W. Costerton & D. G. Davies, (2002) Pseudomonas aeruginosa displays multiple phenotypes during development as a biofilm. J. Bacteriol. 184: 1140-1154. Sauer, K., M. C. Cullen, A. H. Rickard, L. A. H. Zeef, D. G. Davies & P. Gilbert, (2004) Characterization of nutrient-induced dispersion in Pseudomonas aeruginosa PAO1 biofilm. J. Bacteriol. 186: 7312-7326. Petrova, O. E. & K. Sauer, (2009) A novel signaling network essential for regulating Pseudomonas aeruginosa biofilm development. PLoS Pathogens 5: e1000668.

Project description:Oberhardt2008 - Genome-scale metabolic network of Pseudomonas aeruginosa (iMO1056) This model is described in the article: Genome-scale metabolic network analysis of the opportunistic pathogen Pseudomonas aeruginosa PAO1. Oberhardt MA, Puchałka J, Fryer KE, Martins dos Santos VA, Papin JA. J. Bacteriol. 2008 Apr; 190(8): 2790-2803 Abstract: Pseudomonas aeruginosa is a major life-threatening opportunistic pathogen that commonly infects immunocompromised patients. This bacterium owes its success as a pathogen largely to its metabolic versatility and flexibility. A thorough understanding of P. aeruginosa's metabolism is thus pivotal for the design of effective intervention strategies. Here we aim to provide, through systems analysis, a basis for the characterization of the genome-scale properties of this pathogen's versatile metabolic network. To this end, we reconstructed a genome-scale metabolic network of Pseudomonas aeruginosa PAO1. This reconstruction accounts for 1,056 genes (19% of the genome), 1,030 proteins, and 883 reactions. Flux balance analysis was used to identify key features of P. aeruginosa metabolism, such as growth yield, under defined conditions and with defined knowledge gaps within the network. BIOLOG substrate oxidation data were used in model expansion, and a genome-scale transposon knockout set was compared against in silico knockout predictions to validate the model. Ultimately, this genome-scale model provides a basic modeling framework with which to explore the metabolism of P. aeruginosa in the context of its environmental and genetic constraints, thereby contributing to a more thorough understanding of the genotype-phenotype relationships in this resourceful and dangerous pathogen. This model is hosted on BioModels Database and identified by: MODEL1507180020. To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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Pseudomonas aeruginosa

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