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The twelve E. coli populations of the long-term evolution experiment (LTEE). Photo credit: Brian Baer and Neerja Hajela.
The evolution of antibiotic resistance in a long-term experiment with E. coli

Environmental changes are common and a source of selection that maintains a trait in one environment, may not be present in another. Relaxed selection can therefore lead to trait decay and reduced fitness in an organism's prior environment. However, whether and how such trait decay affects the subsequent evolvability of populations upon the restoration of selection is not well-understood. In my research, I use E. coli from a long-term evolution experiment (LTEE), and antibiotic resistance as a model trait, to address this question. Specifically, I examine i.) whether intrinsic resistance is maintained in LTEE lines given that they have evolved in the absence of drugs for decades; and ii.) their potential to evolve increased resistance when drugs are introduced. I focus on the role that genetic background plays in resistance evolvability.

PicturePhoto credit: Aleksandra Krolik / EMBL
Predicting pathogen evolutionary potential

Antibiotics have become an essential part of modern medicine since their discovery and introduction in the mid-20th century. However, their benefits are diminishing because pathogens are quickly evolving resistance to these drugs. Given this evolution and subsequent global spread of resistance, bacterial infections are becoming progressively more difficult to treat with current drug therapies. Developing an improved understanding of the forces underlying and shaping antibiotic resistance is therefore a critical public health goal.


The benefit of current antimicrobial surveillance and diagnostic approaches lies in their ability to determine whether a pathogen is resistant to antibiotics, and the genetic basis underlying this phenotype. This information is used to inform patient treatment, and to assess the emergence and spread of resistance geographically and across time. However, ​these approaches have an important limitation: they are not suitable for predicting whether a pathogen has the potential to evolve increased antibiotic resistance in the future. This objective should be an integral component of surveillance and patient treatment in light of the global threat of antibiotic resistance. Three large-scale objectives of my research program are therefore to i.) combine theoretical models with empirical studies on the various genetic and demographic factors that may influence a population's resistance potential; ii.) develop technologies that can accurately and rapidly assess a pathogen's capacity to evolve resistance by incorporating this knowledge; and iii.) apply these technologies in a clinical setting to both improve patient treatment outcomes and hamper the spread of resistant microbes.

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