Evolutionary trajectories are constrained by tradeoffs when mutations that benefit one life history trait incur fitness costs in other traits. As resistance to tetracycline antibiotics by increased efflux can be associated with a 10%, or more, increase in length of the Escherichia coli chromosome, we sought costs of resistance associated with doxycycline. However, it was difficult to identify any because E.coli's growth rate (r), carrying capacity (K) and drug efflux rate increased during evolutionary experiments where E.coli was exposed to doxycycline. Moreover, these improvements remained following drug withdrawal. We sought mechanisms for this seemingly unconstrained adaptation particularly as these traits ought to tradeoff according to rK selection theory. Using prokaryote and eukaryote microbes, including clinical pathogens, we therefore show r and K can tradeoff, but need not, because of 'rK trade-ups'. r and K only tradeoff in sufficiently carbon-rich environments where growth is inefficient.We then used E. coli ribosomal RNA (rrn) knockouts to determine specific mutations, namely changes in rrn operon copy number, than can simultaneously maximise r and K. The optimal genome has fewer operons, and therefore fewer functional ribosomes, than the ancestral strain. It is, therefore, unsurprising for r-adaptation in the presence of a ribosome-inhibiting antibiotic, doxycycline, to also increase population size. Although E. coli can evolve to grow faster and to larger population sizes in the presence of antibiotics when compared to their absence, we found two costs to this improvement: an elongated lag phase and the loss of stress protection genes. 1 IntroductionTradeoffs lie at the heart of a cross-kingdom research effort that seeks to explain how biodiversity is generated and maintained. [1][2][3][4][5] Two traits engage in an evolutionary tradeoff when beneficial mutations for one trait are deleterious for the other, and vice versa, and many theories agree 2,[6][7][8][9][10][11] that genetic polymorphisms are maintained when tradeoffs have an appropriate geometry. Less clear, however, are the physical, chemical and physiological forces that create tradeoffs in the first place 12 and tradeoffs needed for the theories to work can be difficult to isolate in practise. [13][14][15][16][17][18] It is essential for medicine that we understand tradeoffs. The term 'superbug' refers to a pathogenic microorganism that resists treatment by antibiotics with no apparent cost, or tradeoff, in terms of its pathogenicity. An evolutionary route to superbug status is thought to occur when a pathogen first adopts costly drug resistance mutations, a process that sees resistance traded against proliferation rate in antibiotic-free environments. Thereafter, other mutations compensate for those costs, yielding strains that are both drug resistant and capable of rapid proliferation. 19, 20 Tradeoffs are, however, sometimes observed in pathogens. A genomic study of a clinical pathogen using several antibiotic classes 21 showed res...
Background: Conjugation plays a major role in the transmission of plasmids encoding antibiotic resistance genes in both clinical and general settings. The conjugation efficiency is influenced by many biotic and abiotic factors, one of which is the taxonomic relatedness between donor and recipient bacteria. A comprehensive overview of the influence of donor-recipient relatedness on conjugation is still lacking, but such an overview is important to quantitatively assess the risk of plasmid transfer and the effect of interventions which limit the spread of antibiotic resistance, and to obtain parameter values for conjugation in mathematical models. Therefore, we performed a meta-analysis on reported conjugation frequencies from Escherichia coli donors to various recipient species. Results: Thirty-two studies reporting 313 conjugation frequencies for liquid broth matings and 270 conjugation frequencies for filter matings were included in our meta-analysis. The reported conjugation frequencies varied over 11 orders of magnitude. Decreasing taxonomic relatedness between donor and recipient bacteria, when adjusted for confounding factors, was associated with a lower conjugation frequency in liquid matings. The mean conjugation frequency for bacteria of the same order, the same class, and other classes was 10, 20, and 789 times lower than the mean conjugation frequency within the same species, respectively. This association between relatedness and conjugation frequency was not found for filter matings. The conjugation frequency was furthermore found to be influenced by temperature in both types of mating experiments, and in addition by plasmid incompatibility group in liquid matings, and by recipient origin and mating time in filter matings. Conclusions: In our meta-analysis, taxonomic relatedness is limiting conjugation in liquid matings, but not in filter matings, suggesting that taxonomic relatedness is not a limiting factor for conjugation in environments where bacteria are fixed in space.
Plasmids are important vectors for the spread of genes among diverse populations of bacteria.However, there is no standard method to determine the rate at which they spread horizontally via conjugation. Here, we compare commonly used methods on simulated data, and show that the conjugation rate estimates often depend strongly on the time of measurement, the initial population densities, or the initial ratio of donor to recipient populations. We derive a new 'end-point' measure to estimate conjugation rates, which extends the Simonsen method to include the effects of differences in population growth and conjugation rates from donors and transconjugants.We further derive analytical expressions for the parameter range in which these approximations remain valid. All tools to estimate conjugation rates are available in an R package and Shiny app.The result is a set of guidelines for easy, accurate, and comparable measurement of conjugation rates and tools to verify these rates.Plasmids are extra-chromosomal, self-replicating genetic elements that can spread between bac-1 teria via conjugation. They spread genes within and between bacterial species and are a primary 2 source of genetic innovation in the prokaryotic realm [1,2]. Genes disseminated by plasmids include 3 virulence factors, heavy metal and antibiotic resistance, metabolic genes, as well as genes involved 4 in cooperation and spite [2,3,4,5]. To understand how these traits shape the ecology and evolution 5 of bacteria [6], it is of fundamental importance to understand how plasmids spread. 6
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