Escherichia coli autoinducer-2 uptake network does not display hysteretic behavior but AI-2 synthesis rate controls transient bifurcation

A Holistic Approach from Systems Biology Reveals the Direct Influence of the Quorum-Sensing Phenomenon on Pseudomonas aeruginosa Metabolism to Pyoverdine Biosynthesis

Computational modeling and simulation of biological systems have become valuable tools for understanding and predicting cellular performance and phenotype generation. This work aimed to construct, model, and dynamically simulate the virulence factor pyoverdine (PVD) biosynthesis in Pseudomonas aeruginosa through a systemic approach, considering that the metabolic pathway of PVD synthesis is regulated by the quorum-sensing (QS) phenomenon. The methodology comprised three main stages: (i) Construction, modeling, and validation of the QS gene regulatory network that controls PVD synthesis in P. aeruginosa strain PAO1; (ii) construction, curating, and modeling of the metabolic network of P. aeruginosa using the flux balance analysis (FBA) approach; (iii) integration and modeling of these two networks into an integrative model using the dynamic flux balance analysis (DFBA) approximation, followed, finally, by an in vitro validation of the integrated model for PVD synthesis in P. aeruginosa as a function of QS signaling. The QS gene network, constructed using the standard System Biology Markup Language, comprised 114 chemical species and 103 reactions and was modeled as a deterministic system following the kinetic based on mass action law. This model showed that the higher the bacterial growth, the higher the extracellular concentration of QS signal molecules, thus emulating the natural behavior of P. aeruginosa PAO1. The P. aeruginosa metabolic network model was constructed based on the iMO1056 model, the P. aeruginosa PAO1 strain genomic annotation, and the metabolic pathway of PVD synthesis. The metabolic network model included the PVD synthesis, transport, exchange reactions, and the QS signal molecules. This metabolic network model was curated and then modeled under the FBA approximation, using biomass maximization as the objective function (optimization problem, a term borrowed from the engineering field). Next, chemical reactions shared by both network models were chosen to combine them into an integrative model. To this end, the fluxes of these reactions, obtained from the QS network model, were fixed in the metabolic network model as constraints of the optimization problem using the DFBA approximation. Finally, simulations of the integrative model (CCBM1146, comprising 1123 reactions and 880 metabolites) were run using the DFBA approximation to get (i) the flux profile for each reaction, (ii) the bacterial growth profile, (iii) the biomass profile, and (iv) the concentration profiles of metabolites of interest such as glucose, PVD, and QS signal molecules. The CCBM1146 model showed that the QS phenomenon directly influences the P. aeruginosa metabolism to PVD biosynthesis as a function of the change in QS signal intensity. The CCBM1146 model made it possible to characterize and explain the complex and emergent behavior generated by the interactions between the two networks, which would have been impossible to do by studying each system's individual components or scales separately. This work is the first in silico report of an integrative model comprising the QS gene regulatory network and the metabolic network of P. aeruginosa.

Quorum sensing mediates gut bacterial communication and host-microbiota interaction

Gut bacteria employ quorum sensing (QS) to coordinate their activities and communicate with one another, this process relies on the production, detection, and response to autoinducers, which are extracellular signaling molecules. In addition to synchronizing behavioral activities within the species, QS plays a crucial role in the gut host-microbiota interaction. In this review, an overview of classical QS systems is presented as well as the interspecies communication mediated by QS, and recent advances in the host-microbiota interaction mediated by QS. A greater knowledge of the communication network of gut microbiota is not only an opportunity and a challenge for developing nutritional and therapeutic strategies against bacterial illnesses, but also a means for improving gut health.

An anthranilic acid-responsive transcriptional regulator controls the physiology and pathogenicity of Ralstonia solanacearum

Quorum sensing (QS) is widely employed by bacterial cells to control gene expression in a cell density-dependent manner. A previous study revealed that anthranilic acid from Ralstonia solanacearum plays a vital role in regulating the physiology and pathogenicity of R. solanacearum. We reported here that anthranilic acid controls the important biological functions and virulence of R. solanacearum through the receptor protein RaaR, which contains helix-turn-helix (HTH) and LysR substrate binding (LysR_substrate) domains. RaaR regulates the same processes as anthranilic acid, and both are present in various bacterial species. In addition, anthranilic acid-deficient mutant phenotypes were rescued by in trans expression of RaaR. Intriguingly, we found that anthranilic acid binds to the LysR_substrate domain of RaaR with high affinity, induces allosteric conformational changes, and then enhances the binding of RaaR to the promoter DNA regions of target genes. These findings indicate that the components of the anthranilic acid signaling system are distinguished from those of the typical QS systems. Together, our work presents a unique and widely conserved signaling system that might be an important new type of cell-to-cell communication system in bacteria.

Bacterial wilt caused by Ralstonia solanacearum is one of the most widespread, harmful and destructive plant diseases in the world. Our previous study showed that the pathogenic bacterium R. solanacearum uses anthranilic acid to regulate the important biological functions, virulence and the production of quorum sensing signals. Here, we show that RaaR, a transcriptional regulator from R. solanacearum, was first identified to regulate the same phenotypes as anthranilic acid. Anthranilic acid binds to the LysR_substrate domain of RaaR and enhances the regulatory activity of RaaR to control the target gene expression, including the QS signal synthase encoding genes, phcB and solI. Both the anthranilic acid synthase TrpEG and the response regulator RaaR are present in diverse bacteria, suggesting that the anthranilic acid-type signaling system is widespread. Together, our work describes a system where a pathogen uses a single protein to control the bacterial physiology and pathogenesis by responding to anthranilic acid.

Research progress of bacterial quorum sensing receptors: Classification, structure, function and characteristics

The microbial community is an important part of the natural ecosystem, and the quorum sensing system is a momentous communication tool for the microbial community to connect to the surrounding environment. Quorum sensing is a process of cell-cell communication that relies on the production, release, and detection of extracellular signaling molecules, which are called autoinducers. Quorum sensing systems in bacteria consist of two main components: a receptor protein and an autoinducer. The binding of autoinducer to its receptor activates the target gene, which then performs the corresponding function in bacteria. In a natural environment, different bacterial species possess quorum sensing receptors that are structurally and functionally different. So far, many bacterial quorum sensing receptors have been identified and the structure and function of some receptors have been characterized. There are many reviews about quorum sensing and quorum sensing receptors, but there are few reviews that describe various types of quorum sensing in different environments with receptors as the core. Therefore, we summarize the well-defined quorum sensing receptors involved in intra-species and inter-species cell-cell communication, and describe the structure, function, and characteristics of typical receptors for different types of quorum sensing. A systematic understanding of quorum sensing receptors will help researchers to further explore the signaling mechanism and regulation mechanism of quorum sensing system, provide help to clarify the role and function of quorum sensing in natural ecosystems, then provide theoretical basis for the discovery or synthesis of new targeted drugs that block quorum sensing.

Anthranilic acid from Ralstonia solanacearum plays dual roles in intraspecies signalling and inter-kingdom communication

Quorum sensing (QS) signals are widely utilized by bacteria to regulate biological functions in response to cell population density. Previous studies have demonstrated that Ralstonia solanacearum employs two different types of QS systems. We report here that anthranilic acid controls important biological functions and the production of QS signals in R. solanacearum. It was demonstrated that the biosynthesis of anthranilic acid is mainly performed by TrpEG. The accumulation of anthranilic acid and the transcription of trpEG occur in a cell density-dependent manner in R. solanacearum. Both the anthranilic acid and TrpEG homologues are conserved in various bacterial species. Moreover, we show that Sporisorium scitamineum sexual mating and hypha formation are strongly inhibited by the addition of exogenous anthranilic acid. Our results suggest that anthranilic acid is important for the physiology of bacteria in addition to its role in inter-kingdom communication.

Modeling Quorum Sensing Dynamics and Interference on Escherichia coli

Bacteria control the expression of specific genes by Quorum Sensing (QS). This works using small signaling molecules called Autoinducers (AIs), for example, the Autoinducer-2 (AI-2). In this work, we present a mathematical model that represents the AI-2 dynamics on Escherichia coli, which is linked to the cell growth and the lsr operon expression. The model is adjusted using experimental data. Our results suggest that the extracellular AI-2 activity level depends on the cell growth rate, and this activity depends on the cell exponential growth phase. The model was adapted to simulate the interference of QS mechanisms in a co-culture of two E. coli strains: a wild type strain and a knock out strain that detects AI-2 but does not produce it. Co-culture simulations unveiled two conditions to avoid the QS on the wild strain: when the knock out takes control of the growth medium and overcomes the wild strain, or when is pre-cultured to its mid-exponential phase and then added to the wild strain culture. Model simulations unveiled new insights about the interference of bacterial communication and offer new tools for QS control.

Information transmission in microbial and fungal communication: from classical to quantum

Microbes have their own communication systems. Secretion and reception of chemical signaling molecules and ion-channels mediated electrical signaling mechanism are yet observed two special ways of information transmission in microbial community. In this article, we address the aspects of various crucial machineries which set the backbone of microbial cell-to-cell communication process such as quorum sensing mechanism (bacterial and fungal), quorum sensing regulated biofilm formation, gene expression, virulence, swarming, quorum quenching, role of noise in quorum sensing, mathematical models (therapy model, evolutionary model, molecular mechanism model and many more), synthetic bacterial communication, bacterial ion-channels, bacterial nanowires and electrical communication. In particular, we highlight bacterial collective behavior with classical and quantum mechanical approaches (including quantum information). Moreover, we shed a new light to introduce the concept of quantum synthetic biology and possible cellular quantum Turing test.

Mathematical model of LsrR-binding and derepression in Escherichia coli K12

Quorum sensing (QS) enables bacterial communication and collective behavior in response to self-secreted signaling molecules. Unlocking its genetic regulation will provide insight towards understanding its influence on pathogenesis, formation of biofilms, and many other phenotypes. There are few datasets available that link QS-mediated gene expression to its regulatory components and even fewer mathematical models that incorporate known mechanistic detail. By integrating these data with annotated sequence information, mathematical inferences can be pieced together that shed light on regulatory structure. A first principles model, developed here for the E. coli QS system, builds on known mechanistic detail and is used to develop a working model of LuxS-regulated (Lsr) activity. That is, our model is meant to discriminate among hypothetical mechanisms governing lsr transcriptional regulation. Our simulations are in qualitative agreement with experimentally observed data. Importantly, our results point to the importance of transcriptional regulator, LsrR, cycling on genetic control. We also found several experimental observations in E. coli and homologous systems that were not explained by current mechanistic understanding. For example, by comparing simulations with reports of the integrating host factor in Aggrigatibacter actinomycetemcomitans, we conclude that additional transcriptional components are likely involved. An iterative process of simulation and experiment, therefore, is needed to inform new experiments and incorporate new model detail, the benefit of which will more rapidly validate mechanistic understanding.

Mathematical Modelling of Bacterial Quorum Sensing: A Review

Bacterial quorum sensing (QS) refers to the process of cell-to-cell bacterial communication enabled through the production and sensing of the local concentration of small molecules called autoinducers to regulate the production of gene products (e.g. enzymes or virulence factors). Through autoinducers, bacteria interact with individuals of the same species, other bacterial species, and with their host. Among QS-regulated processes mediated through autoinducers are aggregation, biofilm formation, bioluminescence, and sporulation. Autoinducers are therefore "master" regulators of bacterial lifestyles. For over 10 years, mathematical modelling of QS has sought, in parallel to experimental discoveries, to elucidate the mechanisms regulating this process. In this review, we present the progress in mathematical modelling of QS, highlighting the various theoretical approaches that have been used and discussing some of the insights that have emerged. Modelling of QS has benefited almost from the onset of the involvement of experimentalists, with many of the papers which we review, published in non-mathematical journals. This review therefore attempts to give a broad overview of the topic to the mathematical biology community, as well as the current modelling efforts and future challenges.

Fatty acid modulation of autoinducer (AI-2) influenced growth and macrophage invasion by Salmonella Typhimurium

Autoinducer-2 (AI-2) is a small molecule that is involved in bacterial cell-to-cell signaling whose precursor formation is mediated by luxS. A luxS mutant of Salmonella Typhimurium PJ002 (ΔluxS) was grown in glucose-containing M-9 minimal medium supplemented with varying concentrations (1×, 10×, and 100×) of long-chain fatty acids (linoleic acid, oleic acid, palmitic acid, and stearic acid) to study the influence of fatty acids on growth rate and macrophage invasion. Additionally, in vitro synthesized AI-2 was added to this medium to identify the influence of AI-2 on S. Typhimurium PJ002 (ΔluxS) growth rate and macrophage invasion. The growth rate constant (k) for each experimental treatment was determined based on OD₆₀₀ values recorded during 12 h of incubation. There was a significant (p=0.01) increase in the growth rate of S. Typhimurium PJ002 (ΔluxS) in the presence of AI-2 when compared to the phosphate-buffered saline (PBS) control. However, fatty acids either singly or in a mixture were unable to influence AI-2's effect on growth rate. The presence of AI-2 significantly (p=0.02) decreased the invasiveness of S. Typhimurium PJ002 (ΔluxS) towards the murine macrophage cell line, RAW 264.7. However, the fatty acid mixture was able to reverse this reduction and restore invasiveness to background levels. These results suggest that, while AI-2 may enhance the growth rate and reduce macrophage invasion by the luxS mutant S. Typhimurium PJ002 (ΔluxS), fatty acids may influence the virulence in S. Typhimurium (PJ002) by modulating AI-2 activity.