Final Report Summary - ARISE (The Ecology of Antibiotic Resistance)
Antibiotic production and resistance are both particularly common in the dense competitive communities of the soil environment. Indeed, most clinically used antibiotics are products of soil bacteria or derivatives of these products, and many clinically common resistance mechanisms originated in soil communities. Often considered in terms of an evolutionary arms race, the warfare among bacteria and between humans and bacteria is one of an escalating nature. Interestingly, in natural environments, and in contrast to theoretical models, diverse microbial communities are continuously and stably inhabited by both producers and resistants.
In this research projects we aimed to (1) identify the rules that govern the ecology of antibiotic resistance and how resistance allows and possibly dictates the diversity of microbial communities; (2) establish novel research tools to screen and identify compounds that select against resistance; (3) observe and study the evolution of antibiotic resistance in a laboratory setting.
Studying the ecology and community structure of microbial communities we have established an experimental system for high-throughput species identification on single grains of soil. Applying this method to hundreds of soil grains we found that species are distributed lognormally across these microhabitats. The observation of prevalent diversity of coexisting species is not well explained by ecological models which consider pairwise interactions. Extending these models to include high-order interactions in which species can affect the pairwise interaction of other species we identified how antibiotic production and degradation can set both an upper and lower limit on the diversity of microbial communities, often dictating greater diversity than can be explained by pairwise interactions. Considering a the effect of the environment, we also showed that fast oscillations in environmental conditions can facilitate diversity.
As bacteria in the soil are continuously exposed to diverse stresses and antibiotics, we sought to find how bacteria are affected by simultaneous antibiotic exposures. Two competing null models are often used to predict the combined effect of drugs: response additivity (Bliss) and dosage additivity (Loewe). To distinguish between those two models we contrasted their predictions with high-throughput measurements of growth. Our measurements rejected Loewe’s model and provided basis of a new model of “dosage-orthogonality” framework for drug additivity.
As antibiotic exposure drives resistance to higher levels, we considered ecologically-inspired strategies for constraining and reversing evolution of resistance. We identified how specific drugs and temporally alternative drug treatments can constrain evolution of resistance. We have further established a method for high-throughput screening and identification of compounds that select against resistance.
Studying the impact of spatial environment on evolution of antibiotic resistance, we developed an apparatus, the Microbial Evolution and Growth Arena (MEGA) plate, which allows direct observation of fundamental evolutionary phenomena in real time. Facilitating the evolution of antibiotic resistance in a laboratory spatially structured setting which allows for multiple diverse mutants to evolve and coexist, it also facilitates the study of antibiotic resistance evolution. Interestingly, we observed such co-existing and co-evolving lineages also in studies of human infections which allowed us to unravel within-host migration and evolution of pathogens.
In this research projects we aimed to (1) identify the rules that govern the ecology of antibiotic resistance and how resistance allows and possibly dictates the diversity of microbial communities; (2) establish novel research tools to screen and identify compounds that select against resistance; (3) observe and study the evolution of antibiotic resistance in a laboratory setting.
Studying the ecology and community structure of microbial communities we have established an experimental system for high-throughput species identification on single grains of soil. Applying this method to hundreds of soil grains we found that species are distributed lognormally across these microhabitats. The observation of prevalent diversity of coexisting species is not well explained by ecological models which consider pairwise interactions. Extending these models to include high-order interactions in which species can affect the pairwise interaction of other species we identified how antibiotic production and degradation can set both an upper and lower limit on the diversity of microbial communities, often dictating greater diversity than can be explained by pairwise interactions. Considering a the effect of the environment, we also showed that fast oscillations in environmental conditions can facilitate diversity.
As bacteria in the soil are continuously exposed to diverse stresses and antibiotics, we sought to find how bacteria are affected by simultaneous antibiotic exposures. Two competing null models are often used to predict the combined effect of drugs: response additivity (Bliss) and dosage additivity (Loewe). To distinguish between those two models we contrasted their predictions with high-throughput measurements of growth. Our measurements rejected Loewe’s model and provided basis of a new model of “dosage-orthogonality” framework for drug additivity.
As antibiotic exposure drives resistance to higher levels, we considered ecologically-inspired strategies for constraining and reversing evolution of resistance. We identified how specific drugs and temporally alternative drug treatments can constrain evolution of resistance. We have further established a method for high-throughput screening and identification of compounds that select against resistance.
Studying the impact of spatial environment on evolution of antibiotic resistance, we developed an apparatus, the Microbial Evolution and Growth Arena (MEGA) plate, which allows direct observation of fundamental evolutionary phenomena in real time. Facilitating the evolution of antibiotic resistance in a laboratory spatially structured setting which allows for multiple diverse mutants to evolve and coexist, it also facilitates the study of antibiotic resistance evolution. Interestingly, we observed such co-existing and co-evolving lineages also in studies of human infections which allowed us to unravel within-host migration and evolution of pathogens.