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Quorum quenching analysis in Pseudomonas aeruginosa and Escherichia coli: network topology and inhibition mechanism effect on the optimized inhibitor dose

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Abstract

The discovery of quorum sensing as a mechanism for regulating specific phenotypes in bacteria based on population density has conveyed attention to find molecules capable of interfering quorum sensing networks (QSN) in a process coined quorum quenching. Here, we examined the dynamics of Escherichia coli AI-2 and Pseudomonas aeruginosa QSN exposed to signal degraders or competitors for binding transcriptional regulators. Stability analysis was performed for E. coli and P. aeruginosa finding no multistability in E. coli. However, our model allowed to discern that quenchers influence P. aeruginosa QSN multistability by reducing the interval of the amount of molecules of the extracellular signal that originate several steady states. We proposed a simulated annealing algorithm to optimize the quencher dose based on stochastic kinetics. E. coli QSN requires around 640 while P. aeruginosa QSN needs 253 quencher molecules per microorganism. This dose was found to be negatively influenced by the quencher-signal affinity.

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Acknowledgments

A.F.G.B was supported by a Fulbright scholarship

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Authors and Affiliations

  1. Grupo de diseño de productos y procesos (GDPP), Department of Chemical Engineering, Universidad de los Andes, Carrera 1ª No. 19ª40, Bogotá, Colombia

    Andrés Fernando González Barrios, Virginia Covo & Luis Miguel Medina

  2. Centro de Investigación en Microbiología (CIMIC), Universidad de Los Andes, Cra 1E No 18A-10, Bogotá, Colombia

    Martha Vives-Florez

  3. Department of Chemical Engineering, VirginiaTech, 142D Randolph Hall, Blacksburg, VA, 24061, USA

    Luke Achenie

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  1. Andrés Fernando González Barrios
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  2. Virginia Covo
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Correspondence to Andrés Fernando González Barrios.

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Barrios, A.F.G., Covo, V., Medina, L.M. et al. Quorum quenching analysis in Pseudomonas aeruginosa and Escherichia coli: network topology and inhibition mechanism effect on the optimized inhibitor dose. Bioprocess Biosyst Eng 32, 545–556 (2009). https://doi.org/10.1007/s00449-008-0276-7

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  • DOI: https://doi.org/10.1007/s00449-008-0276-7

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