ABSTRACT
Bacterial antibiotic resistance is typically quantified by the minimum inhibitory concentration (MIC), which is defined as the minimal concentration of antibiotic that inhibits bacterial growth starting from a standard cell density. However, when antibiotic resistance is mediated by degradation, the collective inactivation of antibiotic by the bacterial population can cause the measured MIC to depend strongly on the initial cell density. In cases where this inoculum effect is strong, the relationship between MIC and bacterial fitness in the antibiotic is not well defined. Here, we demonstrate that the resistance of a single, isolated cell-which we call the single-cell MIC (scMIC)-provides a superior metric for quantifying antibiotic resistance. Unlike the MIC, we find that the scMIC predicts the direction of selection and also specifies the antibiotic concentration at which selection begins to favor new mutants. Understanding the cooperative nature of bacterial growth in antibiotics is therefore essential in predicting the evolution of antibiotic resistance.
Subject(s)
Anti-Bacterial Agents/pharmacology , Cefotaxime/pharmacology , Drug Resistance, Bacterial , Escherichia coli/growth & development , Escherichia coli/drug effects , Evolution, Molecular , Genetic Fitness , Microbial Sensitivity Tests , Models, Biological , Single-Cell Analysis/methodsABSTRACT
Inactivation of ß-lactam antibiotics by resistant bacteria is a 'cooperative' behavior that may allow sensitive bacteria to survive antibiotic treatment. However, the factors that determine the fraction of resistant cells in the bacterial population remain unclear, indicating a fundamental gap in our understanding of how antibiotic resistance evolves. Here, we experimentally track the spread of a plasmid that encodes a ß-lactamase enzyme through the bacterial population. We find that independent of the initial fraction of resistant cells, the population settles to an equilibrium fraction proportional to the antibiotic concentration divided by the cell density. A simple model explains this behavior, successfully predicting a data collapse over two orders of magnitude in antibiotic concentration. This model also successfully predicts that adding a commonly used ß-lactamase inhibitor will lead to the spread of resistance, highlighting the need to incorporate social dynamics into the study of antibiotic resistance.
Subject(s)
Escherichia coli/drug effects , Gene Transfer, Horizontal/drug effects , Plasmids/metabolism , Quorum Sensing/genetics , beta-Lactam Resistance/drug effects , Ampicillin/pharmacology , Anti-Bacterial Agents/pharmacology , Bacterial Load/drug effects , Escherichia coli/enzymology , Escherichia coli/genetics , Models, Genetic , Plasmids/agonists , beta-Lactam Resistance/genetics , beta-Lactamase Inhibitors , beta-Lactamases/genetics , beta-Lactamases/metabolismABSTRACT
Formation of enzyme-oligoamine complexes was suggested as an approach to obtain biocatalysts with enhanced resistance towards inactivation in water-organic media. Complex formation results in broadening (by 20-40% v/v ethanol) of the range of cosolvent concentrations where the enzyme retains its catalytic activity (stabilization effect). At moderate cosolvent concentrations (20-40% v/v) complex formation activates the enzyme (by 3-6 times). The magnitude of activation and stabilization effects increases with the number of possible electrostatic contacts between the protein surface and the molecules of oligoamines (OA). Circular dichroism spectra in the far-UV region show that complex formation stabilizes protein conformation and prevents aggregation in water-organic solvent mixtures. Two populations of the complexes with different thermodynamic stabilities were found in alpha-chymotrypsin (CT)-OA systems depending on the CT/OA ratio. The average dissociation constants and stoichiometries of both low- and high-affinity populations of the complexes were estimated. It appears that it is the low-affinity sites on the CT surface that are responsible for the activation effect.
