The emergence of antibiotic resistance in bacteria is a persistent challenge to public health. beta-lactam antibiotics, which kill bacteria by inhibiting cell wall synthesis, are both the oldest and most widely used class of antibiotics. Bacteria can gain resistance to these antibiotics by expressing the enzyme lactamase, which inactivates the antibiotic. This inactivation of the antibiotic may be a cooperative behavior because the entire cell population benefits from the removal of the antibiotic. In this proposal we quantify the cooperative nature of bacterial growth in beta-lactam antibiotics and explore the consequences of this cooperation for the evolution of antibiotic resistance. We hypothesize that integrating modeling with quantitative measurements of the cooperative bacterial growth in antibiotics will yield novel insights into the evolution of antibiotic resistance. Preliminary experiments have characterized the manner in which bacterial populations collectively break down antibiotics in the environment. Modeling of these cooperative growth dynamics makes surprising yet testable predictions regarding which mutations in beta-lactamase will spread throughout the population. We focus on mutations in beta-lactamase that allow the enzyme to break down a wide range of clinically important drugs- such versions of the enzyme are called Extended Spectrum beta-Lactamases (ESBL) and are a serious public health concern. The approach described here is a natural extension of the PI's previous experiments studying evolutionary and population dynamics in microbes.
Three specific aims will guide our study of the evolutionary dynamics of antibiotic resistance: 1) Determine how the cooperative nature of bacterial growth in beta-lactam antibiotics influences the direction of selection and the conditions that lead to the sprea of more resistant mutants. 2) Determine whether sensitive bacteria, which lack the plasmid encoding beta-lactamase, can act as """"""""cheaters"""""""" and take advantage of the resistant bacteria that are inactivating the antibiotic. 3) Explore the conditions in which two bacterial strains, eah resistant to a single antibiotic, can cooperatively grow in a multi-drug environment. These studies present a novel set of quantitative approaches to understand the evolutionary dynamics of bacteria in the presence of antibiotics. We expect that these studies will provide new insight into the evolutionary origin of antibiotic resistance and will also help to clarify the conditions n which evolution can favor the emergence of cooperative behaviors.
Antibiotics represent one of our most powerful tools in the war against disease, although the emergence of antibiotic resistance in bacteria challenges this progress. This project proposes to use quantitative analysis techniques combined with ideas from evolutionary game theory in order to characterize the ability of bacteria to evolve resistance against clinically important antibiotics.
| Gokhale, Shreyas; Conwill, Arolyn; Ranjan, Tanvi et al. (2018) Migration alters oscillatory dynamics and promotes survival in connected bacterial populations. Nat Commun 9:5273 |
| Ratzke, Christoph; Denk, Jonas; Gore, Jeff (2018) Ecological suicide in microbes. Nat Ecol Evol 2:867-872 |
| Hoek, Tim A; Axelrod, Kevin; Biancalani, Tommaso et al. (2016) Resource Availability Modulates the Cooperative and Competitive Nature of a Microbial Cross-Feeding Mutualism. PLoS Biol 14:e1002540 |
| Yurtsev, Eugene Anatoly; Conwill, Arolyn; Gore, Jeff (2016) Oscillatory dynamics in a bacterial cross-protection mutualism. Proc Natl Acad Sci U S A 113:6236-41 |
| Artemova, Tatiana; Gerardin, Ylaine; Dudley, Carmel et al. (2015) Isolated cell behavior drives the evolution of antibiotic resistance. Mol Syst Biol 11:822 |
| Vega, Nicole M; Gore, Jeff (2014) Collective antibiotic resistance: mechanisms and implications. Curr Opin Microbiol 21:28-34 |
| Yurtsev, Eugene A; Chao, Hui Xiao; Datta, Manoshi S et al. (2013) Bacterial cheating drives the population dynamics of cooperative antibiotic resistance plasmids. Mol Syst Biol 9:683 |
