Speaker
Description
Random mutations and demographic events make evolution inherently stochastic, despite the deterministic force of natural selection. In order to better understand the genetic and ecological factors that drive evolutionary predictability, we use the evolution of resistance to beta-lactam antibiotics in Escherichia coli as experimental model. I will present recent work on the joint effect of mutation and selection bias in bacterial populations of different size, which shows that different-sized populations use similar numbers, but different types of mutations with distinct consequences for the level of resistance. For example, large populations more often use gain-of-function point mutations, including mutations activating a beta-lactamase, leading to high-level resistance, whereas small populations fix more often large deletions and duplications, including the deletion of the same inactive beta-lactamase, leading to 10 times lower resistance levels. We find that these distinct mutation choices can be largely predicted based solely on the rates and fitness effects of different mutation classes. However, some mutation choices require more subtle information about dose-dependent fitness effects of different mutations. If time allows, I may also present recent work on how social interactions within and between bacterial genotypes affect the emergence and selection of resistant mutants.
