@article{9410,
  abstract     = {Antibiotic concentrations vary dramatically in the body and the environment. Hence, understanding the dynamics of resistance evolution along antibiotic concentration gradients is critical for predicting and slowing the emergence and spread of resistance. While it has been shown that increasing the concentration of an antibiotic slows resistance evolution, how adaptation to one antibiotic concentration correlates with fitness at other points along the gradient has not received much attention. Here, we selected populations of Escherichia coli at several points along a concentration gradient for three different antibiotics, asking how rapidly resistance evolved and whether populations became specialized to the antibiotic concentration they were selected on. Populations selected at higher concentrations evolved resistance more slowly but exhibited equal or higher fitness across the whole gradient. Populations selected at lower concentrations evolved resistance rapidly, but overall fitness in the presence of antibiotics was lower. However, these populations readily adapted to higher concentrations upon subsequent selection. Our results indicate that resistance management strategies must account not only for the rates of resistance evolution but also for the fitness of evolved strains.},
  author       = {Lagator, Mato and Uecker, Hildegard and Neve, Paul},
  issn         = {1744957X},
  journal      = {Biology letters},
  number       = {5},
  publisher    = {Royal Society of London},
  title        = {{Adaptation at different points along antibiotic concentration gradients}},
  doi          = {10.1098/rsbl.2020.0913},
  volume       = {17},
  year         = {2021},
}

@article{6467,
  abstract     = {Fitness interactions between mutations can influence a population’s evolution in many different ways. While epistatic effects are difficult to measure precisely, important information is captured by the mean and variance of log fitnesses for individuals carrying different numbers of mutations. We derive predictions for these quantities from a class of simple fitness landscapes, based on models of optimizing selection on quantitative traits. We also explore extensions to the models, including modular pleiotropy, variable effect sizes, mutational bias and maladaptation of the wild type. We illustrate our approach by reanalysing a large dataset of mutant effects in a yeast snoRNA (small nucleolar RNA). Though characterized by some large epistatic effects, these data give a good overall fit to the non-epistatic null model, suggesting that epistasis might have limited influence on the evolutionary dynamics in this system. We also show how the amount of epistasis depends on both the underlying fitness landscape and the distribution of mutations, and so is expected to vary in consistent ways between new mutations, standing variation and fixed mutations.},
  author       = {Fraisse, Christelle and Welch, John J.},
  issn         = {1744957X},
  journal      = {Biology Letters},
  number       = {4},
  publisher    = {Royal Society of London},
  title        = {{The distribution of epistasis on simple fitness landscapes}},
  doi          = {10.1098/rsbl.2018.0881},
  volume       = {15},
  year         = {2019},
}