Dynamic regulation of<i>N</i>-acyl-homoserine lactone production and degradation in<i>Pseudomonas putida</i>IsoF

Fekete, Ágnes; Kuttler, Christina; Rothballer, Michael; Hense, Burkhard A.; Fischer, Doreen; Buddrus-Schiemann, Katharina; Lucio, Marianna; Müller, Johannes; Schmitt‐Kopplin, Philippe; Hartmann, Anton

doi:10.1111/j.1574-6941.2009.00828.x

Cited by 88 publications

(94 citation statements)

References 60 publications

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“…AI-2 is an unavoidable waste product of an essential metabolic pathway in many bacteria. Interestingly, a similar AI dynamic is observed during the growth of P. putida IsoF, although its AI (mainly 3-oxo-decanoyl-homoserine lactone) is not known to be a waste product (105). Due to different stabilities of AIs and some AIreceptor complexes, actual AI concentrations may either reflect more the actual population (for unstable AIs) or integrate information over several generations (for stable AIs).…”

Section: Variability Of the Ai Production Rate: The Hybrid Push-pull mentioning

confidence: 74%

“…AI induces the transcription of RsaL and QteE (76) and activates QscR as a ligand (104). Furthermore, AI levels may be controlled by self-produced AI-degrading enzymes (105). In Vibrio fischeri, the expression of the AI receptor LuxR is upregulated at intermediary but downregulated at high AI concentrations, which was assumed to maintain light production within a certain strength range (106).…”

Section: Role Of Ai Systems and Accessory Mechanisms In Homeostasis Omentioning

confidence: 99%

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Core Principles of Bacterial Autoinducer Systems

Hense

Schuster

2015

Microbiol Mol Biol Rev

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163

156

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SUMMARY Autoinduction (AI), the response to self-produced chemical signals, is widespread in the bacterial world. This process controls vastly different target functions, such as luminescence, nutrient acquisition, and biofilm formation, in different ways and integrates additional environmental and physiological cues. This diversity raises questions about unifying principles that underlie all AI systems. Here, we suggest that such core principles exist. We argue that the general purpose of AI systems is the homeostatic control of costly cooperative behaviors, including, but not limited to, secreted public goods. First, costly behaviors require preassessment of their efficiency by cheaper AI signals, which we encapsulate in a hybrid “push-pull” model. The “push” factors cell density, diffusion, and spatial clustering determine when a behavior becomes effective. The relative importance of each factor depends on each species' individual ecological context and life history. In turn, “pull” factors, often stress cues that reduce the activation threshold, determine the cellular demand for the target behavior. Second, control is homeostatic because AI systems, either themselves or through accessory mechanisms, not only initiate but also maintain the efficiency of target behaviors. Third, AI-controlled behaviors, even seemingly noncooperative ones, are generally cooperative in nature, when interpreted in the appropriate ecological context. The escape of individual cells from biofilms, for example, may be viewed as an altruistic behavior that increases the fitness of the resident population by reducing starvation stress. The framework proposed here helps appropriately categorize AI-controlled behaviors and allows for a deeper understanding of their ecological and evolutionary functions.

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Section: Variability Of the Ai Production Rate: The Hybrid Push-pull mentioning

confidence: 74%

Section: Role Of Ai Systems and Accessory Mechanisms In Homeostasis Omentioning

confidence: 99%

Core Principles of Bacterial Autoinducer Systems

Hense

Schuster

2015

Microbiol Mol Biol Rev

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163

156

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“…Aybey and E. Demirkan (Fekete et al, 2010). It has been reported that signal molecules are produced maximally when cultures reach the late stationary phase of growth (Gallagher et al, 2002; Lépine et al, 2003).…”

Section: Discussionmentioning

confidence: 99%

“…Bars show the mean¡SEM (n53 independent experiments) with statistical differences at P,0.05 and P,0.01 indicated by * and **, respectively. Journal of Medical Microbiology 65 (Fekete et al, 2010). It has been reported that signal molecules are produced maximally when cultures reach the late stationary phase of growth (Gallagher et al, 2002;Lépine et al, 2003).…”

Section: Discussionmentioning

confidence: 99%

Inhibition of quorum sensing-controlled virulence factors in Pseudomonas aeruginosa by human serum paraoxonase

Aybey

Demirkan

2016

Journal of Medical Microbiology

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The role of quorum sensing (QS) in the regulation of virulence factor production in Pseudomonas aeruginosa is well established. Increased antibiotic resistance in this bacterium has led to the search for new treatment options, and inhibition of the QS system has been explored for potential therapeutic benefits. If the use of QS inhibitory agents were to lead to a reduction in bacterial virulence, new approaches in the treatment of P. aeruginosa infections could be further developed. Accordingly, we examined whether human serum paraoxonase 1 (hPON1), which uses lactonase activity to hydrolyse N-acyl homoserine lactones, could cleave P. aeruginosa-derived signalling molecules. hPON1 was purified using ammonium sulfate precipitation and hydrophobic interaction chromatography (Sepharose 4B-L-tyrosine-1-naphthylamine). Different concentrations of hPON1 were found to reduce various virulence factors including pyocyanin, rhamnolipid, elastase, staphylolytic LasA protease and alkaline protease. Although treatment with 0.1-10 mg hPON1 ml 21 did not show a highly inhibitory effect on elastase and staphylolytic LasA protease production, it resulted in good inhibitory effects on alkaline protease production at concentrations as low as 0.1 mg ml 21. hPON1 also reduced the production of pyocyanin and rhamnolipid at a concentration of 1.25 mg ml 21(within a range of 0.312-5 mg ml 21). In addition, rhamnolipid, an effective biosurfactant reported to stimulate the biodegradation of hydrocarbons, was able to degrade oil only in the absence of hPON1. INTRODUCTIONQuorum sensing (QS) is a process of bacterial communication used to collectively control group behaviours (Ng & Bassler, 2009;Rutherford & Bassler, 2012). Pseudomonas aeruginosa perceives its local population density using a QS system: as the concentration of the signalling molecules passes a given threshold, the bacteria regulate production of certain virulence factors. To orchestrate the synchronous production of virulence factors, P. aeruginosa relies on two major LuxI/R QS systems: the Las and Rhl systems (Pesci et al., 1997;Schuster et al., 2003). The LuxI homologues LasI and RhlI are responsible for synthesis of the Las and Rhl signals, N-(3-oxododecanoyl)-L-homoserine lactone (PAI1) and N-butanoyl-L-homoserine lactone (PAI2), respectively. PAI1 activates the LuxR-type transcription factor LasR, and, in turn, LasR-PAI1 induces the production of LasB elastase, LasA protease, alkaline protease and LasRI (Gambello et al., , 1993Seed et al., 1995). PAI2 cooperates with the cognate regulator RhlR to enhance the production of rhamnolipid, pyocyanin and LasB elastase (Latifi et al., 1995;Pearson et al., 1995). Expression of many of the virulence factors in P. aeruginosa is controlled by the QS system (Venturi, 2006). Different strains of P. aeruginosa secrete several extracellular proteolytic enzymes that have been implicated as virulence factors. These enzymes include protease IV, alkaline protease, and LasA and LasB elastases (Caballero et al., 2001). These proteases pr...

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“…Several model approaches have also been proposed for Pseudomonas putida, in closed systems (batch) as well as in continuous cultures (chemostat) [16][17][18]. The goal of this manuscript is to review such models, investigating mathematical properties and principles underlying the equations.…”

Section: Introductionmentioning

confidence: 99%

Mathematical Modeling of Bacteria Communication in Continuous Cultures

Barbarossa

Kuttler²

2016

Applied Sciences

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Quorum sensing is a bacterial cell-to-cell communication mechanism and is based on gene regulatory networks, which control and regulate the production of signaling molecules in the environment. In the past years, mathematical modeling of quorum sensing has provided an understanding of key components of such networks, including several feedback loops involved. This paper presents a simple system of delay differential equations (DDEs) for quorum sensing of Pseudomonas putida with one positive feedback plus one (delayed) negative feedback mechanism. Results are shown concerning fundamental properties of solutions, such as existence, uniqueness, and non-negativity; the last feature is crucial for mathematical models in biology and is often violated when working with DDEs. The qualitative behavior of solutions is investigated, especially the stationary states and their stability. It is shown that for a certain choice of parameter values, the system presents stability switches with respect to the delay. On the other hand, when the delay is set to zero, a Hopf bifurcation might occur with respect to one of the negative feedback parameters. Model parameters are fitted to experimental data, indicating that the delay system is sufficient to explain and predict the biological observations.

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Dynamic regulation ofN-acyl-homoserine lactone production and degradation inPseudomonas putidaIsoF

Cited by 88 publications

References 60 publications

Core Principles of Bacterial Autoinducer Systems

Core Principles of Bacterial Autoinducer Systems

Inhibition of quorum sensing-controlled virulence factors in Pseudomonas aeruginosa by human serum paraoxonase

Mathematical Modeling of Bacteria Communication in Continuous Cultures

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