Adaptive Laboratory Evolution of Antibiotic Resistance Using Different Selection Regimes Lead to Similar Phenotypes and Genotypes

Jahn, Leonie Johanna; Munck, Christian; Ellabaan, Mostafa Mostafa Hashim; Sommer, Morten Otto Alexander

doi:10.3389/fmicb.2017.00816

Cited by 71 publications

(80 citation statements)

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“…Mutations in fusA1 were found in several different species including E. coli , S. typhimurium , and S. aureus (Ibacache-Quiroga et al, 2018; Jahn et al, 2017; Johanson and Hughes, 1994; Kim et al, 2014; Mogre et al, 2014; Norström et al, 2007), and all laboratory studies reported these mutations either in response to aminoglycoside selection or as a direct cause of aminoglycoside resistance (Figure 4). Mutations in cyoA and cyoB were also found in E. coli and S. typhimurium in these experiments (Ibacache-Quiroga et al, 2018; Jahn et al, 2017; Johanson and Hughes, 1994). Multiple sequence alignment of these genes across species revealed that fusA1 mutations were localized to two primary regions across all species and were primarily nonsynonymous SNPs, and eight positions exhibit amino acid-level parallelism across species (Figure 4, Table S1, Figure S5) (Wattam et al, 2017).…”

Section: Resultsmentioning

confidence: 99%

Parallel evolution of tobramycin resistance across species and environments

Scribner

Santos-López

Marshall

et al. 2019

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words)13 An important problem in evolution is identifying the genetic basis of how different species adapt 14 to similar environments. Understanding how various bacterial pathogens evolve in response to 15 antimicrobial treatment is a pressing example of this problem, where discovery of molecular 16 parallelism could lead to clinically useful predictions. Evolution experiments with pathogens in 17 environments containing antibiotics combined with periodic whole population genome 18 sequencing can be used to characterize the evolutionary dynamics of the pathways to 19 antimicrobial resistance. We separately propagated two clinically relevant Gram-negative 20 pathogens, Pseudomonas aeruginosa and Acinetobacter baumannii, in increasing 21 concentrations of tobramycin in two different environments each: planktonic and biofilm. 22Independent of the pathogen, populations adapted to tobramycin selection by parallel evolution 23 of mutations in fusA1, encoding elongation factor G, and ptsP, encoding phosphoenolpyruvate 24 phosphotransferase. As neither gene is a direct target of this aminoglycoside, both are relatively 25 novel and underreported causes of resistance. Additionally, both species acquired antibiotic-26 associated mutations that were more prevalent in the biofilm lifestyle than planktonic, in electron 27 transport chain components in A. baumannii and LPS biosynthesis enzymes in P. aeruginosa 28 populations. Using existing databases, we discovered both fusA1 and ptsP mutations to be 29 prevalent in antibiotic resistant clinical isolates. Additionally, we report site-specific parallelism of 30 fusA1 mutations that extend across several bacterial phyla. This study suggests that strong 31 selective pressures such as antibiotic treatment may result in high levels of predictability in 32 molecular targets of evolution despite differences between organisms' genetic background and 33 environment.The notion that evolution can be forecasted at the level of phenotype, gene, or even 36 amino acid is no longer a fantasy in the post-genomic era (Lässig et al., 2017). If we 37 acknowledge that most forecasting efforts rely on history to anticipate the future, the explosive 38 growth of whole-genome sequencing (WGS) sets the stage to resolve evolutionary phenomena 39 in action and suggest the next selected path. Among the best examples, bacterial populations 40 exposed to strong selection like antibiotics and analyzed by WGS are likely to identify gene 41 regions that produce resistance (Ahmed et al., 2018a; Cooper, 2018; Feng et al., 2016; Palmer 42 and Kishony, 2013). Repeated instances of the same antibiotic selection may enrich the same 43 types of mutations and ultimately enable some measure of predictability (Ibacache-Quiroga et 44 al., 2018; Wong et al., 2012). For instance, we can be confident that exposure of many bacteria 45 to high doses of fluoroquinolones like ciprofloxacin may select for substitutions in residues 83 or 46 87 of the drug target, DNA gyrase A (Fàbrega et al., 2009; Wong and Kassen, 2011).47 Furt...

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Section: Resultsmentioning

confidence: 99%

Parallel evolution of tobramycin resistance across species and environments

Scribner

Santos-López

Marshall

et al. 2019

Preprint

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“…The Amk3 mutant had point mutations in nuoH, cpxR, crr, fusA, and rffG, as well as a small deletion in lrhA. While the effect of crr, rffG, and lrhA mutations are not clear, the fusA, cpxR, and nuoH mutations are often associated with resistance towards aminoglycosides 18,19,23 . The nuo genes encode subunits of NADH:quinone oxidoreductase I, which maintains the PMF to fuel respiration 24 .…”

Section: Resultsmentioning

confidence: 99%

“…In addition to promoting antibiotic resistance development in circulating strains, epistatic interactions between resistance determinants may also lead to increased resistance or collateral sensitivity during sequential treatment regimes in individual patients [15][16][17][18] . For example, mutations associated with aminoglycoside resistance confer sensitivity towards multiple antibiotic drug classes, including beta-lactams, quinolones, and tetracyclines [17][18][19] , and this sensitivity is thought to arise from a reduction in the proton motive force (PMF) that reduces the uptake of aminoglycoside, while simultaneously decreasing multidrug efflux 18 .…”

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confidence: 99%

Dominant resistance and negative epistasis can limit the co-selection of de novo resistance mutations and antibiotic resistance genes

et al. 2020

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To tackle the global antibiotic resistance crisis, antibiotic resistance acquired either vertically by chromosomal mutations or horizontally through antibiotic resistance genes (ARGs) have been studied. Yet, little is known about the interactions between the two, which may impact the evolution of antibiotic resistance. Here, we develop a multiplexed barcoded approach to assess the fitness of 144 mutant-ARG combinations in Escherichia coli subjected to eight different antibiotics at 11 different concentrations. While most interactions are neutral, we identify significant interactions for 12% of the mutant-ARG combinations. The ability of most ARGs to confer high-level resistance at a low fitness cost shields the selective dynamics of mutants at low drug concentrations. Therefore, high-fitness mutants are often selected regardless of their resistance level. Finally, we identify strong negative epistasis between two unrelated resistance mechanisms: the tetA tetracycline resistance gene and loss-of-function nuo mutations involved in aminoglycoside tolerance. Our study highlights important constraints that may allow better prediction and control of antibiotic resistance evolution.

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“…Experimental evolution of antibiotic-resistant mutants from sensitive parent strains, followed by whole genome sequencing to identify resistance-conferring mutations, is an approach that has been applied to a number of species (16)(17)(18)(19)(20)(21), including P. aeruginosa (22)(23)(24)(25)(26)(27)(28). Studies have confirmed the involvement of genes previously proposed to be associated with resistance in P. aeruginosa.…”

Section: Introductionmentioning

confidence: 88%

A large-scale comparison shows that genetic changes causing antibiotic resistance in experimentally evolvedPseudomonas aeruginosapredict those in naturally evolved bacteria

Wardell¹,

Rehman²,

Martin³

et al. 2019

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Pseudomonas aeruginosa is an opportunistic pathogen that causes a wide range of acute and chronic infections. An increasing number of isolates have acquired mutations that make them antibiotic resistant, making treatment more difficult. To identify resistance-associated mutations we experimentally evolved the antibiotic sensitive strain P. aeruginosa PAO1 to become resistant to three widely used anti-pseudomonal antibiotics, ciprofloxacin, meropenem and tobramycin. Mutants were able to tolerate up to 2048-fold higher concentrations of antibiotic than strain PAO1. Genome sequences were determined for thirteen mutants for each antibiotic. Each mutant had between 2 and 8 mutations. There were at least 8 genes mutated in more than one mutant per antibiotic, demonstrating the complexity of the genetic basis of resistance. Additionally, large deletions of up to 479kb arose in multiple meropenem resistant mutants. For all three antibiotics mutations arose in genes known to be associated with resistance, but also in genes not previously associated with resistance. To determine the clinical relevance of mutations uncovered in experimentallyevolved mutants we analysed the corresponding genes in 457 isolates of P. aeruginosa from patients with cystic fibrosis or bronchiectasis as well as 172 isolates from the general environment. Many of the genes identified through experimental evolution had changes predicted to be function-altering in clinical isolates but not in isolates from the general environment, showing that mutated genes in experimentally evolved bacteria can predict those that undergo mutation during infection. These findings expand understanding of the genetic basis of antibiotic resistance in P. aeruginosa as well as demonstrating the validity of experimental evolution in identifying clinically-relevant resistance-associated mutations.' ImportanceThe rise in antibiotic resistant bacteria represents an impending global health crisis. As such, understanding the genetic mechanisms underpinning this resistance can be a crucial piece of the puzzle to combatting it. The importance of this research is that by experimentally evolving P. aeruginosa to three clinically relevant antibiotics, we have generated a catalogue of genes that can contribute to resistance in vitro. We show that many (but not all) of these genes are clinically relevant, by identifying variants in clinical isolates of P. aeruginosa. This research furthers our understanding of the genetics leading to resistance in P. aeruginosa and provides tangible evidence that these genes can play a role clinically, potentially leading to new druggable targets or inform therapies.

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Adaptive Laboratory Evolution of Antibiotic Resistance Using Different Selection Regimes Lead to Similar Phenotypes and Genotypes

Cited by 71 publications

References 81 publications

Parallel evolution of tobramycin resistance across species and environments

Parallel evolution of tobramycin resistance across species and environments

Dominant resistance and negative epistasis can limit the co-selection of de novo resistance mutations and antibiotic resistance genes

A large-scale comparison shows that genetic changes causing antibiotic resistance in experimentally evolvedPseudomonas aeruginosapredict those in naturally evolved bacteria

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