Cross-Strain Quorum Sensing Inhibition by Staphylococcus aureus. Part 1: A Spatially Homogeneous Model

Mathematical model predicts anti-adhesion-antibiotic-debridement combination therapies can clear an antibiotic resistant infection

As antimicrobial resistance increases, it is crucial to develop new treatment strategies to counter the emerging threat. In this paper, we consider combination therapies involving conventional antibiotics and debridement, coupled with a novel anti-adhesion therapy, and their use in the treatment of antimicrobial resistant burn wound infections. Our models predict that anti-adhesion-antibiotic-debridement combination therapies can eliminate a bacterial infection in cases where each treatment in isolation would fail. Antibiotics are assumed to have a bactericidal mode of action, killing bacteria, while debridement involves physically cleaning a wound (e.g. with a cloth); removing free bacteria. Anti-adhesion therapy can take a number of forms. Here we consider adhesion inhibitors consisting of polystyrene microbeads chemically coupled to a protein known as multivalent adhesion molecule 7, an adhesin which mediates the initial stages of attachment of many bacterial species to host cells. Adhesion inhibitors competitively inhibit bacteria from binding to host cells, thus rendering them susceptible to removal through debridement. An ordinary differential equation model is developed and the antibiotic-related parameters are fitted against new in vitro data gathered for the present study. The model is used to predict treatment outcomes and to suggest optimal treatment strategies. Our model predicts that anti-adhesion and antibiotic therapies will combine synergistically, producing a combined effect which is often greater than the sum of their individual effects, and that anti-adhesion-antibiotic-debridement combination therapy will be more effective than any of the treatment strategies used in isolation. Further, the use of inhibitors significantly reduces the minimum dose of antibiotics required to eliminate an infection, reducing the chances that bacteria will develop increased resistance. Lastly, we use our model to suggest treatment regimens capable of eliminating bacterial infections within clinically relevant timescales.

From Staphylococcus aureus gene regulation to its pattern formation

The focus of this paper is to develop a new partial differential equation model for the pattern formation of the human pathogen Staphylococcus aureus, starting from a newly developed model of selected gene regulation mechanisms. In our model, we do not only account for the bacteria densities and nutrient concentrations, but also for the quorum sensing and biofilm components, since they enable bacteria to coordinate their behavior and provide the environment in which the colony grows. To this end, we model the relevant gene regulation systems using ordinary differential equations and therefrom derive our evolution equations for quorum sensing and biofilm environment by time-scale arguments. Furthermore, we compare and validate our model and the corresponding simulation results with biological real data observations of Staphylococcus aureus mutant colony growth in the laboratory. We show that we are able to adequately display the qualitative biological features of pattern formation in selected mutants, using the parameter changes indicated by the gene regulation mechanisms.

Predictive modelling of a novel anti-adhesion therapy to combat bacterial colonisation of burn wounds

As the development of new classes of antibiotics slows, bacterial resistance to existing antibiotics is becoming an increasing problem. A potential solution is to develop treatment strategies with an alternative mode of action. We consider one such strategy: anti-adhesion therapy. Whereas antibiotics act directly upon bacteria, either killing them or inhibiting their growth, anti-adhesion therapy impedes the binding of bacteria to host cells. This prevents bacteria from deploying their arsenal of virulence mechanisms, while simultaneously rendering them more susceptible to natural and artificial clearance. In this paper, we consider a particular form of anti-adhesion therapy, involving biomimetic multivalent adhesion molecule 7 coupled polystyrene microbeads, which competitively inhibit the binding of bacteria to host cells. We develop a mathematical model, formulated as a system of ordinary differential equations, to describe inhibitor treatment of a Pseudomonas aeruginosa burn wound infection in the rat. Benchmarking our model against in vivo data from an ongoing experimental programme, we use the model to explain bacteria population dynamics and to predict the efficacy of a range of treatment strategies, with the aim of improving treatment outcome. The model consists of two physical compartments: the host cells and the exudate. It is found that, when effective in reducing the bacterial burden, inhibitor treatment operates both by preventing bacteria from binding to the host cells and by reducing the flux of daughter cells from the host cells into the exudate. Our model predicts that inhibitor treatment cannot eliminate the bacterial burden when used in isolation; however, when combined with regular or continuous debridement of the exudate, elimination is theoretically possible. Lastly, we present ways to improve therapeutic efficacy, as predicted by our mathematical model.

Three-Dimensional Numerical Simulations of Biofilm Dynamics with Quorum Sensing in a Flow Cell

We develop a multiphasic hydrodynamic theory for biofilms taking into account interactions among various bacterial phenotypes, extracellular polymeric substance (EPS), quorum sensing (QS) molecules, solvent, and antibiotics. In the model, bacteria are classified into down-regulated QS, up-regulated QS, and non-QS cells based on their QS ability. The model is first benchmarked against an experiment yielding an excellent fit to experimental measurements on the concentration of QS molecules and the cell density during biofilm development. It is then applied to study development of heterogeneous structures in biofilms due to interactions of QS regulation, hydrodynamics, and antimicrobial treatment. Our 3D numerical simulations have confirmed that (i). QS is beneficial for biofilm development in a long run by building a robust EPS population to protect the biofilm; (ii). biofilms located upstream can induce QS downstream when the colonies are close enough spatially; (iii). QS induction may not be fully operational and can even be compromised in strong laminar flows; (v). the hydrodynamic stress alters the biofilm morphology. Through further numerical investigations, our model suggests that (i). QS-regulated EPS production contributes to the structural formation of heterogeneous biofilms; (ii) QS down-regulated cells tend to grow at the surface of the biofilm while QS up-regulated ones tend to grow in the bulk; (iii) when nutrient supply is sufficient, QS induction might be more effective upstream than downstream; (iv) QS may be of little benefit in a short timescale in term of fighting against invading strain/species; (v) the material properties of biomass (bacteria and EPS) have strong impact on the dilution of QS molecules under strong shear flow. In addition, with this modeling framework, hydrodynamic details and rheological quantities associated with biofilm formation under QS regulation can be resolved.

Influence of the AgrC-AgrA complex on the response time of Staphylococcus aureus quorum sensing

The Staphylococcus aureus agr quorum-sensing system plays a major role in the transition from the persistent to the virulent phenotype. S. aureus agr type I to IV strains are characterized by mutations in the sensor domain of the histidine kinase AgrC and differences in the sequences of the secreted autoinducing peptides (AIP). Here we demonstrate that interactions between the cytosolic domain of AgrC (AgrCCyto) and the response regulator domain of AgrA (AgrARR) dictate the spontaneity of the cellular response to AIP stimuli. The crystal structure of AgrCCyto provided a basis for a mechanistic model of AgrC-AgrA interactions. This model enabled an analysis of the biochemical and biophysical parameters of AgrC-AgrA interactions in the context of the conformational features of the AgrC-AgrA complex. This analysis revealed distinct sequence and conformational features that determine the affinity, specificity, and kinetics of the phosphotransfer reaction. This step, which governs the response time for transcriptional reengineering triggered by an AIP stimulus, is independent of the agr type and similar for agonist and antagonist stimuli. These experimental data could serve as a basis on which to validate simulations of the quorum-sensing response and for strategies that employ the agr quorum-sensing system to combat biofilm formation in S. aureus infections.