Quantifying Dynamic Phenotypic Heterogeneity in Resistant Escherichia coli under Translation‐Inhibiting Antibiotics

Abstract

Abstract Understanding the phenotypic heterogeneity of antibiotic‐resistant bacteria following treatment and the transitions between different phenotypes is crucial for developing effective infection control strategies. The study expands upon previous work by explicating chloramphenicol‐induced phenotypic heterogeneities in growth rate, gene expression, and morphology of resistant Escherichia coli using time‐lapse microscopy. Correlating the bacterial growth rate and cspC expression, four interchangeable phenotypic subpopulations across varying antibiotic concentrations are identified, surpassing the previously described growth rate bistability. Notably, bacterial cells exhibiting either fast or slow growth rates can concurrently harbor subpopulations characterized by high and low gene expression levels, respectively. To elucidate the mechanisms behind this enhanced heterogeneity, a concise gene expression network model is proposed and the biological significance of the four phenotypes is further explored. Additionally, by employing Hidden Markov Model fitting and integrating the non‐equilibrium landscape and flux theory, the real‐time data encompassing diverse bacterial traits are analyzed. This approach reveals dynamic changes and switching kinetics in different cell fates, facilitating the quantification of observable behaviors and the non‐equilibrium dynamics and thermodynamics at play. The results highlight the multi‐dimensional heterogeneous behaviors of antibiotic‐resistant bacteria under antibiotic stress, providing new insights into the compromised antibiotic efficacy, microbial response, and associated evolution processes.

Keywords

• bacterial antibiotic resistance
• gene expression network
• growth rate
• landscape and flux theory
• phenotypic heterogeneity
• quantitative dynamics