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Teuber Carvalho, J. P.

Publications and source records attributed to Teuber Carvalho, J. P..

2 recordsLinked to original sources

Slow trajectories generate divergent cell fates following antibiotic stress

Single-cell microfluidic experiments have shown that upon abrupt exposures to antibiotics, genetically homogeneous microbial populations undergo divergent cell fates. The mechanism underlying this divergence is not clear and in particular, the emergence of a range of distinct slow-growing phenotypes cannot be explained by models relying on bistability alone. Here, we propose a model for gene expression and growth dynamics during antibiotic exposures, which is informed by well-known scaling relations connecting proteome allocation and cell growth. In our model, resources available for transcription and translation of resistance genes act like generalized momenta and their initial variation is predictive of cell fate. Our model reproduces key experimental observations, including the prediction of specific phenotypes and a critical threshold in initial resource allocation that predicts cell survival. These results offer an alternative mechanism for the emergence of phenotypic diversity where slow cell growth effectively stabilizes cellular states along the trajectory that are far from the final stable states predicted by fixed points.

microbiology↗

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 drugs 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.

microbiology↗