Quorum Sensing and Self-Quorum Quenching in the Intracellular Pathogen Brucellamelitensis

Terwagne, Matthieu; Mirabella, Aurélie; Lemaire, Juliette; Deschamps, Chantal; Bolle, Xavier De; Letesson, Jean‐Jacques

doi:10.1371/journal.pone.0082514

Cited by 36 publications

(17 citation statements)

References 76 publications

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“…The dependence of biotic signal degradation on bacterial cell density and the possibility of its feedback-dependent control (Quorum Quenching) in realistic QS systems2526 intriguingly suggest that the trade-off studied in this paper may be amenable to a subtle regulation. A key role is indeed played by the interplay between signal diffusivity and degradation that sets the characteristic distance over which signal molecules can diffuse, on average, before they are degraded.…”

Section: Discussionmentioning

confidence: 92%

Modeling quorum sensing trade-offs between bacterial cell density and system extension from open boundaries

Marenda

Zanardo

Trovato

et al. 2016

Sci Rep

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Bacterial communities undergo collective behavioural switches upon producing and sensing diffusible signal molecules; a mechanism referred to as Quorum Sensing (QS). Exemplarily, biofilm organic matrices are built concertedly by bacteria in several environments. QS scope in bacterial ecology has been debated for over 20 years. Different perspectives counterpose the role of density reporter for populations to that of local environment diffusivity probe for individual cells. Here we devise a model system where tubes of different heights contain matrix-embedded producers and sensors. These tubes allow non-limiting signal diffusion from one open end, thereby showing that population spatial extension away from an open boundary can be a main critical factor in QS. Experimental data, successfully recapitulated by a comprehensive mathematical model, demonstrate how tube height can overtake the role of producer density in triggering sensor activation. The biotic degradation of the signal is found to play a major role and to be species-specific and entirely feedback-independent.

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Section: Discussionmentioning

confidence: 92%

Modeling quorum sensing trade-offs between bacterial cell density and system extension from open boundaries

Marenda

Zanardo

Trovato

et al. 2016

Sci Rep

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“…Because B. abortus is not synchronizable as of yet, we monitored the activity of CtrA target promoters at the single cell level, and we then sorted bacteria according to their size to reconstruct their cell cycle. A reporter system was designed to monitor the activity of ccrM (p ccrM ), repAB (p repAB ), ctrA (p ctrA ), and pleC (p pleC ) promoters by fusing each of them to a gene coding for an unstable GFP (GFP-ASV) (Andersen et al, 1998) on a medium-copy replicative vector (Terwagne et al, 2013). The ccrM and ctrA transcription follows a tightly regulated profile throughout C. crescentus cell cycle while PleC protein amount remains stable (Zweiger et al, 1994;Quon et al, 1996;Wheeler and Shapiro, 1999).…”

Section: The Activity Of Ctra Target Promoters Varies In Function Of mentioning

confidence: 99%

CtrA controls cell division and outer membrane composition of the pathogen Brucella abortus

Francis

Poncin

Fioravanti

et al. 2017

Molecular Microbiology

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Brucella abortus is a pathogen infecting cattle, able to survive, traffic, and proliferate inside host cells. It belongs to the Alphaproteobacteria, a phylogenetic group comprising bacteria with free living, symbiotic, and pathogenic lifestyles. An essential regulator of cell cycle progression named CtrA was described in the model bacterium Caulobacter crescentus. This regulator is conserved in many alphaproteobacteria, but the evolution of its regulon remains elusive. Here we identified promoters that are CtrA targets using ChIP-seq and we found that CtrA binds to promoters of genes involved in cell cycle progression, in addition to numerous genes encoding outer membrane components involved in export of membrane proteins and synthesis of lipopolysaccharide. Analysis of a conditional B. abortus ctrA loss of function mutant confirmed that CtrA controls cell division. Impairment of cell division generates elongated and branched morphologies, that are also detectable inside HeLa cells. Surprisingly, abnormal bacteria are able to traffic to the endoplasmic reticulum, the usual replication niche of B. abortus in host cells. We also found that CtrA depletion affected outer membrane composition, in particular the abundance and spatial distribution of Omp25. Control of the B. abortus envelope composition by CtrA indicates the plasticity of the CtrA regulon along evolution.

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“…This raises interesting questions about the role of extracellular AI-degrading (quorum-quenching [QQ]) enzymes in AI-producing species, which are able to degrade their own AI (148,149). QQ enzymes have been interpreted mainly in terms of disturbing AI systems of other species, limiting or terminating cooperation activities in certain growth states, or using AIs just as nutrients (149,150). However, the efficiency of extracellular QQ enzymes for these purposes has yet to be proven in situ.…”

Section: Figmentioning

confidence: 99%

Core Principles of Bacterial Autoinducer Systems

Hense

Schuster

2015

Microbiol Mol Biol Rev

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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Quorum Sensing and Self-Quorum Quenching in the Intracellular Pathogen Brucellamelitensis

Cited by 36 publications

References 76 publications

Modeling quorum sensing trade-offs between bacterial cell density and system extension from open boundaries

Modeling quorum sensing trade-offs between bacterial cell density and system extension from open boundaries

CtrA controls cell division and outer membrane composition of the pathogen Brucella abortus

Core Principles of Bacterial Autoinducer Systems

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