Bistable Bacterial Growth Dynamics in the Presence of Antimicrobial Agents

Dynamical model of antibiotic responses linking expression of resistance genes to metabolism explains emergence of heterogeneity during drug exposures

Antibiotic responses in bacteria are highly dynamic and heterogeneous, with sudden exposure of bacterial colonies to high drug doses resulting in the coexistence of recovered and arrested cells. The dynamics of the response is determined by regulatory circuits controlling the expression of resistance genes, which are in turn modulated by the drug's action on cell growth and metabolism. Despite advances in understanding gene regulation at the molecular level, we still lack a framework to describe how feedback mechanisms resulting from the interdependence between expression of resistance and cell metabolism can amplify naturally occurring noise and create heterogeneity at the population level. To understand how this interplay affects cell survival upon exposure, we constructed a mathematical model of the dynamics of antibiotic responses that links metabolism and regulation of gene expression, based on the tetracycline resistancetetoperon inE. coli. We use this model to interpret measurements of growth and expression of resistance in microfluidic experiments, both in single cells and in biofilms. We also implemented a stochastic model of the drug response, to show that exposure to high drug levels results in large variations of recovery times and heterogeneity at the population level. We show that stochasticity is important to determine how nutrient quality affects cell survival during exposure to high drug concentrations. A quantitative description of how microbes respond to antibiotics in dynamical environments is crucial to understand population-level behaviors such as biofilms and pathogenesis.

β-lactam-induced OMV release promotes polymyxin tolerance in Salmonella enterica sv. Typhi

The rise of multidrug-resistant bacteria is a global concern, leading to a renewed reliance on older antibiotics like polymyxins as a last resort. Polymyxins, cationic cyclic peptides synthesized nonribosomally, feature a hydrophobic acyl tail and positively charged residues. Their antimicrobial mechanism involves initial interaction with Gram-negative bacterial outer-membrane components through polar and hydrophobic interactions. Outer membrane vesicles (OMVs), nano-sized proteoliposomes secreted from the outer membrane of Gram-negative bacteria, play a crucial role in tolerating harmful molecules, including cationic peptides such as polymyxins. Existing literature has documented environmental changes' impact on modulating OMV properties in Salmonella Typhimurium. However, less information exists regarding OMV production and characteristics in Salmonella Typhi. A previous study in our laboratory showed that S. Typhi ΔmrcB, a mutant associated with penicillin-binding protein (PBP, a β-lactam antibiotic target), exhibited hypervesiculation. Consequently, this study investigated the potential impact of β-lactam antibiotics on promoting polymyxin tolerance via OMVs in S. Typhi. Our results demonstrated that sub-lethal doses of β-lactams increased bacterial survival against polymyxin B in S. Typhi. This phenomenon stems from β-lactam antibiotics inducing hypervesiculation of OMVs with higher affinity for polymyxin B, capturing and diminishing its biologically effective concentration. These findings suggest that β-lactam antibiotic use may inadvertently contribute to decreased polymyxin effectivity against S. Typhi or other Gram-negative bacteria, complicating the effective treatment of infections caused by these pathogens. This study emphasizes the importance of evaluating the influence of β-lactam antibiotics on the interaction between OMVs and other antimicrobial agents.

Dynamical model of antibiotic responses linking expression of resistance to metabolism explains emergence of heterogeneity during drug exposures

Antibiotic responses in bacteria are highly dynamic and heterogeneous, with sudden exposure of bacterial colonies to high drug doses resulting in the coexistence of recovered and arrested cells. The dynamics of the response is determined by regulatory circuits controlling the expression of resistance genes, which are in turn modulated by the drug's action on cell growth and metabolism. Despite advances in understanding gene regulation at the molecular level, we still lack a framework to describe how feedback mechanisms resulting from the interdependence between expression of resistance and cell metabolism can amplify naturally occurring noise and create heterogeneity at the population level. To understand how this interplay affects cell survival upon exposure, we constructed a mathematical model of the dynamics of antibiotic responses that links metabolism and regulation of gene expression, based on the tetracycline resistance tet operon in E. coli. We use this model to interpret measurements of growth and expression of resistance in microfluidic experiments, both in single cells and in biofilms. We also implemented a stochastic model of the drug response, to show that exposure to high drug levels results in large variations of recovery times and heterogeneity at the population level. We show that stochasticity is important to determine how nutrient quality affects cell survival during exposure to high drug concentrations. A quantitative description of how microbes respond to antibiotics in dynamical environments is crucial to understand population-level behaviors such as biofilms and pathogenesis.

Probing the Gelation Synergies and Anti-Escherichia coli Activity of Fmoc-Phenylalanine/Graphene Oxide Hybrid Hydrogel

The N-fluorenyl-9-methyloxycarbonyl (Fmoc)-protected amino acids have shown high antimicrobial application potential, among which the phenylalanine derivative (Fmoc-F) is the most well-known representative. However, the activity spectrum of Fmoc-F is restricted to Gram-positive bacteria only. The demand for efficient antimicrobial materials expanded research into graphene and its derivatives, although the reported results are somewhat controversial. Herein, we combined graphene oxide (GO) flakes with Fmoc-F amino acid to form Fmoc-F/GO hybrid hydrogel for the first time. We studied the synergistic effect of each component on gelation and assessed the material's bactericidal activity on Gram-negative Escherichia coli (E. coli). GO flakes do not affect Fmoc-F self-assembly per se but modulate the elasticity of the gel and speed up its formation. The hybrid hydrogel affects E. coli survival, initially causing abrupt bacterial death followed by the recovery of the surviving ones due to the inoculum effect (IE). The combination of graphene with amino acids is a step forward in developing antimicrobial gels due to their easy preparation, chemical modification, graphene functionalization, cost-effectiveness, and physicochemical/biological synergy of each component.

Interplay between strain fitness and transmission frequency determines prevalence of antimicrobial resistance

The steep rise of infections caused by bacteria that are resistant to antimicrobial agents threatens global health. However, the association between antimicrobial use and the prevalence of resistance is not straightforward. Therefore, it is necessary to quantify the importance of additional factors that affect this relationship. We theoretically explore how the prevalence of resistance is affected by the combination of three factors: antimicrobial use, bacterial transmission, and fitness cost of resistance. We present a model that combines within-host, between-hosts and between-populations dynamics, built upon the competitive Lotka-Volterra equations. We developed the model in a manner that allows future experimental validation of the findings with single isolates in the laboratory. Each host may carry two strains (susceptible and resistant) that represent the host’s commensal microbiome and are not the target of the antimicrobial treatment. The model simulates a population of hosts who are treated periodically with antibiotics and transmit bacteria to each other. We show that bacterial transmission results in strain co-existence. Transmission disseminates resistant bacteria in the population, increasing the levels of resistance. Counterintuitively, when the cost of resistance is low, high transmission frequencies reduce resistance prevalence. Transmission between host populations leads to more similar resistance levels, increasing the susceptibility of the population with higher antimicrobial use. Overall, our results indicate that the interplay between bacterial transmission and strain fitness affects the prevalence of resistance in a non-linear way. We then place our results within the context of ecological theory, particularly on temporal niche partitioning and metapopulation rescue, and we formulate testable experimental predictions for future research.

Eugenol-Functionalized Magnetite Nanoparticles Modulate Virulence and Persistence in Pseudomonas aeruginosa Clinical Strains

Efficient antibiotics to cure Pseudomonas aeruginosa persistent infections are currently insufficient and alternative options are needed. A promising lead is to design therapeutics able to modulate key phenotypes in microbial virulence and thus control the progression of the infectious process without selecting resistant mutants. In this study, we developed a nanostructured system based on Fe₃O₄ nanoparticles (NPs) and eugenol, a natural plant-compound which has been previously shown to interfere with microbial virulence when utilized in subinhibitory concentrations. The obtained functional NPs are crystalline, with a spherical shape and 10-15 nm in size. The subinhibitory concentrations (MIC 1/2) of the eugenol embedded magnetite NPs (Fe₃O₄@EUG) modulate key virulence phenotypes, such as attachment, biofilm formation, persister selection by ciprofloxacin, and the production of soluble enzymes. To our knowledge, this is the first report on the ability of functional magnetite NPs to modulate P. aeruginosa virulence and phenotypic resistance; our data highlights the potential of these bioactive nanostructures to be used as anti-pathogenic agents.