Deterministic and stochastic modelling of endosome escape by Staphylococcus aureus: ?quorum? sensing by a single bacterium

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 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.

Apparent role for Borrelia burgdorferi LuxS during mammalian infection

The Lyme disease spirochete, Borrelia burgdorferi, controls protein expression patterns during its tick-mammal infection cycle. Earlier studies demonstrated that B. burgdorferi synthesizes 4,5-dihydroxy-2,3-pentanedione (autoinducer-2 [AI-2]) and responds to AI-2 by measurably changing production of several infection-associated proteins. luxS mutants, which are unable to produce AI-2, exhibit altered production of several proteins. B. burgdorferi cannot utilize the other product of LuxS, homocysteine, indicating that phenotypes of luxS mutants are not due to the absence of that molecule. Although a previous study found that a luxS mutant was capable of infecting mice, a critical caveat to those results is that bacterial loads were not quantified. To more precisely determine whether LuxS serves a role in mammalian infection, mice were simultaneously inoculated with congenic wild-type and luxS strains, and bacterial numbers were assessed using quantitative PCR. The wild-type bacteria substantially outcompeted the mutants, suggesting that LuxS performs a significant function during mammalian infection. These data also provide further evidence that nonquantitative infection studies do not necessarily provide conclusive results and that regulatory factors may not make all-or-none, black-or-white contributions to infectivity.

Selective heterogeneity in exoprotease production by Bacillus subtilis

Bacteria have elaborate signalling mechanisms to ensure a behavioural response that is most likely to enhance survival in a changing environment. It is becoming increasingly apparent that as part of this response, bacteria are capable of cell differentiation and can generate multiple, mutually exclusive co-existing cell states. These cell states are often associated with multicellular processes that bring benefit to the community as a whole but which may be, paradoxically, disadvantageous to an individual subpopulation. How this process of cell differentiation is controlled is intriguing and remains a largely open question. In this paper, we consider an important aspect of cell differentiation that is known to occur in the gram-positive bacterium Bacillus subtilis: we investigate the role of two master regulators DegU and Spo0A in the control of extra-cellular protease production. Recent work in this area focussed the on role of DegU in this process and suggested that transient effects in protein production were the drivers of cell-response heterogeneity. Here, using a combination of mathematical modelling, analysis and stochastic simulations, we provide a complementary analysis of this regulatory system that investigates the roles of both DegU and Spo0A in extra-cellular protease production. In doing so, we present a mechanism for bimodality, or system heterogeneity, without the need for a bistable switch in the underlying regulatory network. Moreover, our analysis leads us to conclude that this heterogeneity is in fact a persistent, stable feature. Our results suggest that system response is divided into three zones: low and high signal levels induce a unimodal or undifferentiated response from the cell population with all cells OFF and ON, respectively for exoprotease production. However, for intermediate levels of signal, a heterogeneous response is predicted with a spread of activity levels, representing typical "bet-hedging" behaviour.

Cross-strain quorum sensing inhibition by Staphylococcus aureus. Part 2: A spatially inhomogeneous model

Staphylococcus aureus uses quorum sensing (QS) to enhance its pathogenicity. An intriguing aspect of this is that different strains are capable of inactivating the QS systems of opposing strains. In Part 1 of this study, we presented a model of this phenomenon in a well-mixed environment; here, we incorporate spatial structure. Two competitive strains occupying adjacent habitats with freely diffusing QS signal molecules (QSSMs) are considered. We investigate the effect of the QSSM diffusion coefficient and the relative size of the two populations on the ability of one population to dominate the other. Regarding population size, a larger population is generally at an advantage (initial conditions permitting), while the implications of different diffusivities are more complex and depend upon the sizes of the populations.

Design principles of the bacterial quorum sensing gene networks

Bacterial quorum sensing (QS) has attracted much interest as the manifestation of collective behavior in prokaryotic organisms once considered strictly solitary. Significant amount of genetic, biochemical, and structural data which, has been accumulated in studies on QS in many species allows us to map properties of specific molecules and their interactions on the observed population-wide bacterial behavior. The present review attempts to give a systems biology perspective on the structure of genetic regulatory networks that control QS and considers functional implications of a variety of design principles that recur in the organization of these networks across species.

Mathematical modelling of the agr operon in Staphylococcus aureus

Staphylococcus aureus is a pathogenic bacterium that utilises quorum sensing (QS), a cell-to-cell signalling mechanism, to enhance its ability to cause disease. QS allows the bacteria to monitor their surroundings and the size of their population, and S. aureus makes use of this to regulate the production of virulence factors. Here we describe a mathematical model of this QS system and perform a detailed time-dependent asymptotic analysis in order to clarify the roles of the distinct interactions that make up the QS process, demonstrating which reactions dominate the behaviour of the system at various timepoints. We couple this analysis with numerical simulations and are thus able to gain insight into how a large population of S. aureus shifts from a relatively harmless state to a highly virulent one, focussing on the need for the three distinct phases which form the feedback loop of this particular QS system.

Cell–cell communication by quorum sensing and dimension-reduction

Several bacterial taxa change their behavior if the population density exceeds a certain threshold. This phenomenon is the consequence of a communication system between the bacteria and is called quorum sensing (QS). Up to now, this phenomenon is mostly modeled at population level. However, new experimental techniques allow for single cell analysis. We introduce a modeling approach for the description of this QS system, including a discussion of the regulatory network and its bistable behavior. Based on this single-cell model we develop and analyze a spatially structured model for a cell population. Special attention is given to the scaling behavior w.r.t. the cell size (leading to an approximation theorem for stationary solutions) and its consequences for the interpretation of cell communication (QS versus diffusion sensing). Concluding, we apply the modeling approach to spatially structured experimental data.

Functionality of Borrelia burgdorferi LuxS: the Lyme disease spirochete produces and responds to the pheromone autoinducer-2 and lacks a complete activated-methyl cycle

Borrelia burgdorferi produces Pfs and LuxS enzymes for breakdown of the toxic byproducts of methylation reactions, producing 4,5-dihydroxy-2,3-pentanedione (DPD), adenine, and homocysteine. DPD and its spontaneously rearranged derivatives constitute a class of bacterial pheromones named autoinducer-2 (AI-2). We describe that B. burgdorferi produces DPD during laboratory cultivation. Furthermore, addition of in vitro synthesized DPD to cultured B. burgdorferi resulted in altered expression levels of a specific set of bacterial proteins, among which is the outer surface lipoprotein VlsE. While a large number of bacteria utilize homocysteine, the other LuxS product, for synthesis of methionine as part of the activated-methyl cycle, B. burgdorferi was found to lack that ability. We propose that the main function of B. burgdorferi LuxS is to synthesize DPD and that the Lyme disease spirochete utilizes a form of DPD as a pheromone to control gene expression.

Synthesis of autoinducer 2 by the lyme disease spirochete, Borrelia burgdorferi

Defining the metabolic capabilities and regulatory mechanisms controlling gene expression is a valuable step in understanding the pathogenic properties of infectious agents such as Borrelia burgdorferi. The present studies demonstrated that B. burgdorferi encodes functional Pfs and LuxS enzymes for the breakdown of toxic products of methylation reactions. Consistent with those observations, B. burgdorferi was shown to synthesize the end product 4,5-dihydroxy-2,3-pentanedione (DPD) during laboratory cultivation. DPD undergoes spontaneous rearrangements to produce a class of pheromones collectively named autoinducer 2 (AI-2). Addition of in vitro-synthesized DPD to cultured B. burgdorferi resulted in differential expression of a distinct subset of proteins, including the outer surface lipoprotein VlsE. Although many bacteria can utilize the other LuxS product, homocysteine, for regeneration of methionine, B. burgdorferi was found to lack such ability. It is hypothesized that B. burgdorferi produces LuxS for the express purpose of synthesizing DPD and utilizes a form of that molecule as an AI-2 pheromone to control gene expression.