Abstracts (first author)

Invited Speaker 

The evolutionary causes and consequences of divergence in antibiotic resistance between bacterial species: insights from comparative and experimental studies

Author(s): MacLean C


This talk will address two unresolved problems: i) Why do species of bacteria differ in their intrinsic resistance to low levels of antibiotics that are found in natural environments? And, ii) How does variation between species of bacteria impact their ability to evolve resistance to high levels of antibiotics that are found in clinical environments? To address these questions, we study antibiotic resistance in bacteria from the genus Pseudomonas: this genus of bacteria shows exceptional levels of phenotypic and genetic diversity, and antibiotic resistance in P.aeruginosa has emerged an important clinical problem. First, I will show how integrating phenotypic and functional genetic measures of resistance into a comparative framework can be used to infer historical patterns of selection on antibiotic resistance and identify the genetic mechanisms that underlie variation in resistance levels between species of bacteria. This comparative work shows that selection drives divergence in resistance among species, and that the underlying genetic mechanism driving this divergence is changes in the number of antibiotic efflux pumps. Second, I will present the results of experiments that explore the ability of different species of Pseudomonas to adapt to a common selective pressure , in the form of a lethal dose of the antibiotic rifampicin. These experiments show that different species vary substantially in their ability to evolve rifampicin resistance, and that this divergence is driven by variation in the mutation spectrum and epistasis for fitness across species. Importantly, we find no evidence of a connection between intrinsic resistance and the ability to evolve high levels of antibiotic resistance. In a broader evolutionary framework, this talk will provide novel insights into how and why species show convergent and divergent adaptations to common selective pressures at a molecular and phenotypic level.

Abstracts (coauthor)


The anthropogenic use of antibiotics has driven the rapid evolution of high levels of resistance, but the extent to which antibiotics imposed selection for resistance in the pre-human era remains largely unknown. Using a novel comparative approach to study resistance, we show that selection for low levels of antibiotic resistance (“intrinsic resistance”) is ancient in bacteria from the genus Pseudomonas. At a phenotypic level, we show that divergence in intrinsic resistance between species does not correlate to evolutionary distance, suggesting that the evolution of resistance is driven by selection, and not neutral drift. Consistent with this argument, we find evidence of a genome-wide trade-off between intrinsic resistance and competitive ability, implying that selection is required to maintain resistance. Recent work has shown that hundreds of genes with diverse functional roles contribute to intrinsic resistance, but we show that approximately 80% of the phenotypic variation in resistance between species is determined by variation in a small number of genes with known roles in protection against antibiotics, most of which are antibiotic efflux pumps. Importantly, these are the same genes that provide the dominant mechanism for the evolution of resistance to clinical doses of antibiotics in Pseudomonas. In summary, our study shows that antibiotics represent an ancient evolutionary pressure, suggesting that historical patterns of evolution of resistance are likely to play a key role in determining the potential for evolving resistance to clinical doses of antibiotics.


Chairman: Octávio S. Paulo
Tel: 00 351 217500614 direct
Tel: 00 351 217500000 ext22359
Fax: 00 351 217500028
email: mail@eseb2013.com


XIV Congress of the European Society for Evolutionary Biology

Organization Team
Department of Animal Biology (DBA)
Faculty of Sciences of the University of Lisbon
P-1749-016 Lisbon


Computational Biology & Population Genomics Group