Fitness costs associated with the acquisition of antibiotic resistance

Hernando-Amado, Sara; Sanz-García, Fernando; Blanco, Paula; Martínez, José Luis

doi:10.1042/ebc20160057

Cited by 72 publications

(55 citation statements)

References 137 publications

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“…This uniformity of fitness effects across environments is unlikely to be true for many genes 23 . 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

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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...

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Section: Resultsmentioning

confidence: 99%

Selection-based model of prokaryote pangenomes

Domingo-Sananes

McInerney

2019

Preprint

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“…For the long-term maintenance of antibiotic resistance genes in bacterial communities, two parallel evolutionary forces are at play: selection promoting resistance phenotypes, and selection leading to reduction of the fitness costs associated with carrying resistance genes (Andersson and Hughes 2010 ; Baquero, Coque and de la Cruz 2011 ; Hernando-Amado et al . 2017 ). As discussed earlier, gain and establishment of resistance genes in a bacterial population are largely dependent on a direct antibiotic selection pressure (Martinez 2011 ).…”

Section: Evolutionary Processes Influencing Environmental Antibiotic mentioning

confidence: 99%

“…In both cases, compensatory mechanisms, including additional mutations, can reduce fitness costs over time (Andersson 2003 ; Hernando-Amado et al . 2017 ). Under antibiotic selection, evolution of a bacterial population towards mutation-mediated resistance depends on both the population size and the mutation rate (Perron et al .…”

Section: Evolutionary Processes Influencing Environmental Antibiotic mentioning

confidence: 99%

Environmental factors influencing the development and spread of antibiotic resistance

Bengtsson‐Palme

Kristiansson

Larsson

2017

FEMS Microbiology Reviews

787

481

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Antibiotic resistance and its wider implications present us with a growing healthcare crisis. Recent research points to the environment as an important component for the transmission of resistant bacteria and in the emergence of resistant pathogens. However, a deeper understanding of the evolutionary and ecological processes that lead to clinical appearance of resistance genes is still lacking, as is knowledge of environmental dispersal barriers. This calls for better models of how resistance genes evolve, are mobilized, transferred and disseminated in the environment. Here, we attempt to define the ecological and evolutionary environmental factors that contribute to resistance development and transmission. Although mobilization of resistance genes likely occurs continuously, the great majority of such genetic events do not lead to the establishment of novel resistance factors in bacterial populations, unless there is a selection pressure for maintaining them or their fitness costs are negligible. To enable preventative measures it is therefore critical to investigate under what conditions and to what extent environmental selection for resistance takes place. In addition, understanding dispersal barriers is not only key to evaluate risks, but also to prevent resistant pathogens, as well as novel resistance genes, from reaching humans.

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“…Resistance can be acquired via horizontal transfer of antibiotic resistance genes (HGT), or through mutation ( Hernando-Amado et al, 2017 ). While exhaustive information is available on the mechanisms of antibiotic resistance at the basic science and epidemiological levels, the evolutionary trajectories leading to high level antimicrobial resistance, as well as the reproducibility of these trajectories among populations evolving concurrently, have been studied in less detail.…”

Section: Introductionmentioning

confidence: 99%

Mutational Evolution of Pseudomonas aeruginosa Resistance to Ribosome-Targeting Antibiotics

2018

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The present work examines the evolutionary trajectories of replicate Pseudomonas aeruginosa cultures in presence of the ribosome-targeting antibiotics tobramycin and tigecycline. It is known that large number of mutations across different genes – and therefore a large number of potential pathways – may be involved in resistance to any single antibiotic. Thus, evolution toward resistance might, to a large degree, rely on stochasticity, which might preclude the use of predictive strategies for fighting antibiotic resistance. However, the present results show that P. aeruginosa populations evolving in parallel in the presence of antibiotics (either tobramycin or tigecycline) follow a set of trajectories that present common elements. In addition, the pattern of resistance mutations involved include common elements for these two ribosome-targeting antimicrobials. This indicates that mutational evolution toward resistance (and perhaps other properties) is to a certain degree deterministic and, consequently, predictable. These findings are of interest, not just for P. aeruginosa, but in understanding the general rules involved in the evolution of antibiotic resistance also. In addition, the results indicate that bacteria can evolve toward higher levels of resistance to antibiotics against which they are considered to be intrinsically resistant, as tigecycline in the case of P. aeruginosa and that this may confer cross-resistance to other antibiotics of therapeutic value. Our results are particularly relevant in the case of patients under empiric treatment with tigecycline, which frequently suffer P. aeruginosa superinfections.

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Fitness costs associated with the acquisition of antibiotic resistance

Cited by 72 publications

References 137 publications

Selection-based model of prokaryote pangenomes

Selection-based model of prokaryote pangenomes

Environmental factors influencing the development and spread of antibiotic resistance

Mutational Evolution of Pseudomonas aeruginosa Resistance to Ribosome-Targeting Antibiotics

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