To investigate the cost of antibiotic resistance versus the potential for resistant clones to adapt in maintaining polymorphism for resistance. Materials & methods: Experimental evolution of Escherichia coli carrying different resistance alleles was performed under an environment devoid of antibiotics and evolutionary parameters estimated from their frequencies along time. Results & conclusion: Costly resistance mutations were found to coexist with lower cost resistances for hundreds of generations, contrary to the hypothesis that the cost of a resistance dictates its extinction. Estimated evolutionary parameters for the different resistance backgrounds suggest a higher adaptive potential of clones with costly antibiotic resistance mutations, overriding their initial cost of resistance and allowing their maintenance in the absence of drugs.

Mutations conferring resistance to antibiotics are typically costly in the absence of the drug, but bacteria can reduce this cost by acquiring compensatory mutations. Thus, the rate of acquisition of compensatory mutations and their effects are key for the maintenance and dissemination of antibiotic resistances. While compensation for single resistances has been extensively studied, compensatory evolution of multiresistant bacteria remains unexplored. Importantly, since resistance mutations often interact epistatically, compensation of multiresistant bacteria may significantly differ from that of single-resistant strains. We used experimental evolution, next-generation sequencing, in silico simulations, and genome editing to compare the compensatory process of a streptomycin and rifampicin double-resistant Escherichia coli with those of single-resistant clones. We demonstrate that low-fitness double-resistant bacteria compensate faster than single-resistant strains due to the acquisition of compensatory mutations with larger effects. Strikingly, we identified mutations that only compensate for double resistance, being neutral or deleterious in sensitive or single-resistant backgrounds. Moreover, we show that their beneficial effects strongly decrease or disappear in conditions where the epistatic interaction between resistance alleles is absent, demonstrating that these mutations compensate for the epistasis. In summary, our data indicate that epistatic interactions between antibiotic resistances, leading to large fitness costs, possibly open alternative paths for rapid compensatory evolution, thereby potentially stabilizing costly multiple resistances in bacterial populations.

One of the simplest models of adaptation to a new environment is Fisher's Geometric Model (FGM), in which populations move on a multidimensional landscape defined by the traits under selection. The predictions of this model have been found to be consistent with current observations of patterns of fitness increase in experimentally evolved populations. Recent studies investigated the dynamics of allele frequency change along adaptation of microbes to simple laboratory conditions and unveiled a dramatic pattern of competition between cohorts of mutations, i.e., multiple mutations simultaneously segregating and ultimately reaching fixation. Here, using simulations, we study the dynamics of phenotypic and genetic change as asexual populations under clonal interference climb a Fisherian landscape, and ask about the conditions under which FGM can display the simultaneous increase and fixation of multiple mutations-mutation cohorts-along the adaptive walk. We find that FGM under clonal interference, and with varying levels of pleiotropy, can reproduce the experimentally observed competition between different cohorts of mutations, some of which have a high probability of fixation along the adaptive walk. Overall, our results show that the surprising dynamics of mutation cohorts recently observed during experimental adaptation of microbial populations can be expected under one of the oldest and simplest theoretical models of adaptation-FGM.

The study of adaptation in microorganisms has led to a significant expansion in knowledge at many biological levels, ranging from biochemistry and genetics, to ecology and demography. Experimental evolution, in particular, has been invaluable at elucidating how complex the adaptive dynamics in microbial populations can be. One of the most fundamental characteristics of these dynamics is the distribution of beneficial mutations driving the adaptive process. How often do microorganisms acquire these mutations? And what are their expected effects? These questions have been at the heart of evolutionary biology from the very beginning, and the studies that have tackled these difficult issues have been tremendously enlightening about adaptive processes. However, the increasing awareness of the complexity of the environment where microorganisms live requires constant development of new approaches to answer these fundamental questions about their evolution. Large population sizes lead to increased levels of clonal interference, and thus to a deviation from the expected outcome in classical regimes of periodic selection. Genetic variation within an evolving population, which is now easily detected by sequencing technologies, can create complex interactions between phenotypes. Environments with antagonistic biotic interactions, pose very different selective pressures from the ones experienced when a species grows alone. All these factors influence adaptation in microorganisms and, importantly, drive the pathogenicity traits that create severe clinical and epidemiological problems.