Evolutionary Mechanisms Shaping the Maintenance of Antibiotic Resistance

Durão, Paulo; Balbontín, Roberto; Gordo, Isabel

doi:10.1016/j.tim.2018.01.005

Cited by 199 publications

(137 citation statements)

References 147 publications

(134 reference statements)

Supporting

Mentioning

131

Contrasting

Unclassified

Order By: Relevance

“…For example, genes that can confer antibiotic resistance are likely to only be beneficial in environments where the antibiotic is present, and either confer no benefit or have a cost in its absence 29 . In other words, as the antibiotic concentration rises, presence of a resistance gene goes from normally deleterious (or neutral) to highly advantageous, to the point of potentially becoming essential under constant antibiotic pressure 30 .…”

Section: Resultsmentioning

confidence: 99%

Selection-based model of prokaryote pangenomes

Domingo-Sananes

McInerney

2019

Preprint

View full text Add to dashboard Cite

10The genomes of different individuals of the same prokaryote species can vary widely in gene 11 content, displaying different proportions of core genes, which are present in all genomes, and 12 accessory genes, whose presence varies between genomes. Together, these core and 13 accessory genes make up a species' pangenome. The reasons behind this extensive diversity 14 in gene content remain elusive, and there is an ongoing debate about the contribution of 15 accessory genes to fitness, that is, whether their presence is on average advantageous, 16 neutral, or deleterious. In order to explore this issue, we developed a mathematical model to 17 simulate the gene content of prokaryote genomes and pangenomes. Our model focuses on 18 testing how the fitness effects of genes and their rates of gene gain and loss would affect the 19 properties of pangenomes. We first show that pangenomes with large numbers of low-20 frequency genes can arise due to the gain and loss of neutral and nearly neutral genes in a 21 population. However, pangenomes with large numbers of highly beneficial, low-frequency 22 genes can arise as a consequence of genotype-by-environment interactions when multiple 23 niches are available to a species. Finally, pangenomes can arise, irrespective of the fitness 24 effect of the gained and lost genes, as long as gene gain and loss rates are high. We argue 25 that in order to understand the contribution of different mechanisms to pangenome diversity, 26it is crucial to have empirical information on population structure, gene-by-environment 27 interactions, the distributions of fitness effects and rates of gene gain and loss in different 28 prokaryote groups. 30 42 43Pangenomes arise as a consequence of gene acquisition via horizontal gene transfer (HGT), 44 and gene loss 2 . These processes ultimately result in general patterns observed across 45 prokaryotes, which include an increase in the number of known accessory genes as more 46 genomes from the same species are sequenced -along with a slower decrease in the number 47 of core genes-and a U-shaped gene frequency distribution or spectrum 2,10,11 . The U shape 48 2 of this distribution indicates that there is a large proportion of genes present in a single or very 49 few genomes, few genes present at intermediate frequencies, and substantial proportion of 50 core genes. Although across prokaryotic life there is a certain amount of commonality in the 51 shape of the gene content frequency distributions, different prokaryote species manifest 52 significant differences in the proportions of core and accessory genes [12][13][14] . 54Although we know that HGT and gene loss result in pangenomes, what is less clear are the 55 forces that lead to high diversity in gene content and U-shaped gene frequency distributions. 56According to mathematical models, gain and loss of entirely neutral genes along a simulated 57 phylogeny or population can in principle predict the U-shaped gene frequency distributions of 58 pangenomes 11,15 . However, the fit to real data i...

show abstract

Section: Resultsmentioning

confidence: 99%

Selection-based model of prokaryote pangenomes

Domingo-Sananes

McInerney

2019

Preprint

View full text Add to dashboard Cite

show abstract

“…Its dissemination depends on the rate at which resistances are acquired and on their effects on bacterial fitness (cost). The latter is influenced by the environment, by interactions between the resistances and their genetic background (epistasis) and by the subsequent acquisition of mutations compensating for fitness defects (compensatory evolution) (2). Despite its importance, the causes of this cost are not completely understood (3), and the identification of its causes has become the Holy Grail in the AR field.…”

Section: Main Textmentioning

confidence: 99%

“…These are typically involved in essential functions, such as transcription, translation, DNA replication or cell wall biosynthesis. Resistance mutations cause alterations in the structure of the target protein, rendering it insensitive to the drug, but often also affecting its function (4,2). Rifampicin and streptomycin resistance mutations (Rif R and Str R ) paradigmatically represent resistances to antibiotics targeting transcription and translation, respectively.…”

Section: Main Textmentioning

confidence: 99%

DNA breaks-mediated cost reveals RNase HI as a new target for selectively eliminating antibiotic resistance

Balbontín

Frazão

Gordo

2019

Preprint

Self Cite

View full text Add to dashboard Cite

Antibiotic resistance often causes a fitness cost to bacteria in the absence of the drug. The cost is the main determinant of the prevalence of resistances upon reducing antibiotics use.Understanding its causes is considered the Holy Grail in the antibiotic resistance field. We show that most of the variation in the cost of resistances common in pathogens can be explained by DNA breaks, a previously unsuspected cause. We demonstrate that the cost can be manipulated by targeting the RNase responsible for degrading R-loops, which cause DNA breaks. Indeed, lack of RNase HI drives resistant clones to extinction in populations with high initial frequency of resistance. Thus, RNase HI provides a promising target for antimicrobials specific against resistant bacteria, which we validate using a repurposed drug. These results show previously unknown effects of resistance on bacterial physiology and provide a framework for the development of new strategies against antibiotic resistance.Rifampicin and streptomycin resistance mutations (Rif R and Str R ), common in pathogenic bacteria, map to the genes rpoB and rpsL, encoding the β' subunit of the RNA polymerase and the 30S ribosomal subunit protein S12, respectively. Rif R and Str R mutations are representative examples of resistances to antibiotics targeting transcription and translation, respectively. Rif R mutations show different costs ( Jin & Gross, 1989, Reynolds, 2000 , and these are commonly attributed to alterations in the rates of transcription initiation, elongation, slippage or termination (

show abstract

“…Chromosomal encoded resistance mutations often map onto genes coding for essential cellular functions, such as transcription, translation, or cell-wall biogenesis (see e.g. 7,8 ). Resistance tends to be highly epistatic and pleiotropic 9–11 and typically entails fitness costs in the absence of antibiotics 12–15 .…”

Section: Introductionmentioning

confidence: 99%

“…The existence of AR costs predicts that a susceptible strain should out-compete a resistant strain, and a decrease of resistance levels to a given antibiotic should occur if the use of that drug is halted in clinical settings. This strategy should be effective when the cost of resistance is high 16–19 , allowing for the elimination of the AR strain before evolutionary compensation for the cost of resistance occurs 8 . Thus, the efficacy of controlling the spread of AR by suspending the usage of an antibiotic is critically dependent on the relative fitness of resistant and sensitive genotypes in the absence of antibiotic.…”

Section: Introductionmentioning

confidence: 99%

Dysbiosis personalizes fitness effect of antibiotic resistance in the mammalian gut

Cardoso

Durão

Amicone

et al. 2019

Preprint

Self Cite

View full text Add to dashboard Cite

10The fitness cost of antibiotic resistance in the absence of antibiotics is crucial to the 11 success of suspending antibiotics as a strategy to lower resistance. Here we show that 12 after antibiotic treatment the cost of resistance within the complex ecosystem of the 13 mammalian gut is personalized. Using mice as an in vivo model, we find that the 14 fitness effect of the same resistant mutation can be deleterious in a host, but neutral or 15 even beneficial in other hosts. Such antagonistic pleiotropy is shaped by the 16 microbiota, as in germ-free mice resistance is consistently costly across all hosts. An 17 eco-evolutionary model of competition for resources identifies a general mechanism 18 underlying between host variation and predicts that the dynamics of compensatory 19 evolution of resistant bacteria should be host specific, a prediction that was supported 20 by experimental evolution in vivo. The microbiome of each human is close to unique 21 and our results suggest that the short-term costs of resistance and its long-term within-22 host evolution will also be highly personalized, a finding that may contribute to the 23 observed variable outcome of control therapies. 24 25 few studies where pathogens 25-31 were tested during in vivo colonization and infection 51suggest that fitness costs of AR are not always high in the context of bacterial 52 colonization or virulence. Yet to the best of our knowledge, no study so far has 53 analyzed the temporal dynamics of resistant strains colonizing the key ecosystem of 54 the gut microbiota. In particular, it is currently unclear how the results from in vitro 55 studies or in the context of invasive pathogens are informative about AR in gut 56 commensal strains, which are by far the main colonizers of a natural complex 57 ecosystem. Here, we performed in vivo competitive fitness assays, mathematical 58 modeling and in vivo experimental evolution to unravel the fitness effects of AR in 59 commensal E. coli colonizing its natural environment. 60 61 RESULTS 62Competitive fitness of AR in the mouse gut 63We focused on common resistance mutations to streptomycin -Str R (rpsL K43T ) and 64 rifampicin-Rif R (rpoB H526Y ), and also studied double resistant clones -Str R Rif R 65 (rpsL K43T rpoB H526Y ). These have been identified in many important pathogens, such 66as Mycobacterium tuberculosis and Salmonella, and also in pathogenic and 67 commensal E. coli [32][33][34] . 68To query how inter-species interactions, present in the natural ecosystem comprising 69 the mammalian gut, influence the costs of AR, we performed competitive fitness 70 assays in mice that have a complex microbiota (SPF mice). To mimic conditions 71where the rise of AR can occur, mice were given an antibiotic treatment -72 streptomycin -for a week (see Fig. 1a and Methods). Such treatment is known to 73 cause perturbations in the microbiota species composition and also to break 74 colonization resistant to E. coli 35 , thus increasing the probability that colonization by 75

show abstract

Evolutionary Mechanisms Shaping the Maintenance of Antibiotic Resistance

Cited by 199 publications

References 147 publications

Selection-based model of prokaryote pangenomes

Selection-based model of prokaryote pangenomes

DNA breaks-mediated cost reveals RNase HI as a new target for selectively eliminating antibiotic resistance

Dysbiosis personalizes fitness effect of antibiotic resistance in the mammalian gut

Contact Info

Product

Resources

About