Abstract:Antibiotic combination therapies are an approach used to counter the evolution of resistance; their purported benefit is they can stop the successive emergence of independent resistance mutations in the same genome. Here, we show that bacterial populations with ‘mutators’, organisms with defects in DNA repair, readily evolve resistance to combination antibiotic treatment when there is a delay in reaching inhibitory concentrations of antibiotic—under conditions where purely wild-type populations cannot. In popu… Show more
“…This concentration of rifampicin was selected as we determined it to be the minimum inhibitory concentration (MIC) of the WT strain measured in MH. We used AUC as a measure of fitness as it is a well-used growth metric that integrates various features of the bacterial growth curve, such as growth rate and lag phase [ 29 – 32 ]. Figure 1 Investigating genotype-by-environment interactions of rpoB mutations.…”
The fitness effects of antibiotic resistance mutations are a major driver of resistance evolution. While the nutrient environment affects bacterial fitness, experimental studies of resistance typically measure fitness of mutants in a single environment only. We explored how the nutrient environment affected the fitness effects of rifampicin-resistant
rpoB
mutations in
Escherichia coli
under several conditions critical for the emergence and spread of resistance—the presence of primary or secondary antibiotic, or the absence of any antibiotic. Pervasive genotype-by-environment (GxE) interactions determined fitness in all experimental conditions, with rank order of fitness in the presence and absence of antibiotics being strongly dependent on the nutrient environment. GxE interactions also affected the magnitude and direction of collateral effects of secondary antibiotics, in some cases so drastically that a mutant that was highly sensitive in one nutrient environment exhibited cross-resistance to the same antibiotic in another. It is likely that the mutant-specific impact of
rpoB
mutations on the global transcriptome underpins the observed GxE interactions. The pervasive, mutant-specific GxE interactions highlight the importance of doing what is rarely done when studying the evolution and spread of resistance in experimental and clinical work: assessing fitness of antibiotic-resistant mutants across a range of relevant environments.
“…This concentration of rifampicin was selected as we determined it to be the minimum inhibitory concentration (MIC) of the WT strain measured in MH. We used AUC as a measure of fitness as it is a well-used growth metric that integrates various features of the bacterial growth curve, such as growth rate and lag phase [ 29 – 32 ]. Figure 1 Investigating genotype-by-environment interactions of rpoB mutations.…”
The fitness effects of antibiotic resistance mutations are a major driver of resistance evolution. While the nutrient environment affects bacterial fitness, experimental studies of resistance typically measure fitness of mutants in a single environment only. We explored how the nutrient environment affected the fitness effects of rifampicin-resistant
rpoB
mutations in
Escherichia coli
under several conditions critical for the emergence and spread of resistance—the presence of primary or secondary antibiotic, or the absence of any antibiotic. Pervasive genotype-by-environment (GxE) interactions determined fitness in all experimental conditions, with rank order of fitness in the presence and absence of antibiotics being strongly dependent on the nutrient environment. GxE interactions also affected the magnitude and direction of collateral effects of secondary antibiotics, in some cases so drastically that a mutant that was highly sensitive in one nutrient environment exhibited cross-resistance to the same antibiotic in another. It is likely that the mutant-specific impact of
rpoB
mutations on the global transcriptome underpins the observed GxE interactions. The pervasive, mutant-specific GxE interactions highlight the importance of doing what is rarely done when studying the evolution and spread of resistance in experimental and clinical work: assessing fitness of antibiotic-resistant mutants across a range of relevant environments.
“…Thus, some mutators could gain an additional advantage – over and above the effect of high mutation rate – if their mutation bias opposes that of the ancestor. Mutators are observed frequently in natural microbial populations as well as in clinical settings, where they are often associated with drug resistance [46,47]. Testing the impacts of mutation bias shifts on evolutionary dynamics is thus a promising direction for future research.…”
Mutation bias is an important factor determining the diversity of genetic variants available for selection, and can therefore constrain the genetic paths to adaptation. An additional constraint emerges over the course of evolution. As adaptation proceeds and some beneficial mutations are fixed, new beneficial mutations become rare, limiting further adaptation. Recent theoretical work predicts that these constraints may be alleviated by a change in the direction of mutation bias (i.e., a bias reversal). As populations sample previously underexplored types of mutations, the distribution of fitness effects (DFE) of mutations should shift towards more beneficial mutations. Here, we test this prediction using Escherichia coli, which has a transition mutation bias, with ~55% of single-nucleotide mutations being transitions compared to the unbiased expectation of ~33% transitions. We generated mutant strains with a wide range of mutation biases from 96% transitions to 98% transversions, either reinforcing or reversing the wild type transition bias. Quantifying DFEs of hundreds of single mutations obtained from mutation accumulation experiments for each strain, we find strong support for the theoretical prediction. Strains that oppose the ancestral bias (i.e., with a strong transversion bias) have DFEs with the highest proportion of beneficial mutations, whereas strains that exacerbate the ancestral transition bias have up to 10-fold fewer beneficial mutations. Such dramatic differences in the DFE should drive large variation in the rate and outcome of adaptation. Our results thus strongly suggest an important and generalized evolutionary role for mutation bias shifts.
“…Mutation supply is a key evolutionary hurdle often limiting the adaptation of populations [ 1 , 65 – 67 ]. As mutation supply depends on population size, one might expect the supply of mutations, for instance to AMR, to be severely limited in small populations, such as the small number of cells forming an infectious propagule of E .…”
Section: Discussionmentioning
confidence: 99%
“…Uncovering the mechanisms behind environmentally responsive mutagenesis informs our understanding of evolution, notably antimicrobial resistance, where mutation supply can be critical [ 1 , 2 ]. Microbial mutation rates are responsive to a wide variety of environmental factors including population density [ 3 ], temperature [ 4 ], growth rate [ 5 , 6 ], stress [ 7 , 8 ], growth phase [ 9 ], and nutritional state [ 10 ].…”
Mutagenesis is responsive to many environmental factors. Evolution therefore depends on the environment not only for selection but also in determining the variation available in a population. One such environmental dependency is the inverse relationship between mutation rates and population density in many microbial species. Here, we determine the mechanism responsible for this mutation rate plasticity. Using dynamical computational modelling and in culture mutation rate estimation, we show that the negative relationship between mutation rate and population density arises from the collective ability of microbial populations to control concentrations of hydrogen peroxide. We demonstrate a loss of this density-associated mutation rate plasticity (DAMP) when Escherichia coli populations are deficient in the degradation of hydrogen peroxide. We further show that the reduction in mutation rate in denser populations is restored in peroxide degradation-deficient cells by the presence of wild-type cells in a mixed population. Together, these model-guided experiments provide a mechanistic explanation for DAMP, applicable across all domains of life, and frames mutation rate as a dynamic trait shaped by microbial community composition.
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