Understanding, predicting and manipulating the genotypic evolution of antibiotic resistance

Palmer, Adam C.; Kishony, Roy

doi:10.1038/nrg3351

Cited by 256 publications

(226 citation statements)

References 49 publications

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“…Taking into consideration all information on the response at the molecular level of E. coli to exposure to antibiotics in this and other studies (5,6,10,25,(31)(32)(33)(34), a picture emerges of the cell exploring all possible escape routes both at the transcriptional and at the mutational level. The overall result is an intricate set of interactions between mutations and quantitative adaptations at the enzymatic level that result in enhanced resistance of the cell to the antibiotic it encounters.…”

Section: Discussionmentioning

confidence: 96%

Interaction between Mutations and Regulation of Gene Expression during Development of De Novo Antibiotic Resistance

Händel

Schuurmans

Feng

et al. 2014

Antimicrob Agents Chemother

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bBacteria can become resistant not only by horizontal gene transfer or other forms of exchange of genetic information but also by de novo by adaptation at the gene expression level and through DNA mutations. The interrelationship between changes in gene expression and DNA mutations during acquisition of resistance is not well documented. In addition, it is not known whether the DNA mutations leading to resistance always occur in the same order and whether the final result is always identical. The expression of >4,000 genes in Escherichia coli was compared upon adaptation to amoxicillin, tetracycline, and enrofloxacin. During adaptation, known resistance genes were sequenced for mutations that cause resistance. The order of mutations varied within two sets of strains adapted in parallel to amoxicillin and enrofloxacin, respectively, whereas the buildup of resistance was very similar. No specific mutations were related to the rather modest increase in tetracycline resistance. Ribosome-sensed induction and efflux pump activation initially protected the cell through induction of expression and allowed it to survive low levels of antibiotics. Subsequently, mutations were promoted by the stress-induced SOS response that stimulated modulation of genetic instability, and these mutations resulted in resistance to even higher antibiotic concentrations. The initial adaptation at the expression level enabled a subsequent trial and error search for the optimal mutations. The quantitative adjustment of cellular processes at different levels accelerated the acquisition of antibiotic resistance. The de novo acquisition of resistance against antibiotics is known to be accompanied by certain mutations and differential expression of specific genes (1-5). The "radical-based" theory (6, 7) proposes that bactericidal antibiotics cause cell death by a single mechanism, driven by the accumulation of oxygen radicals in the cells. In that case, the cellular response to sublethal concentrations of antibiotics should be similar even for compounds belonging to different classes of bactericidal drugs, such as beta-lactams or fluoroquinolones. The outcome might differ for bacteriostatic drugs, for example, tetracycline. The radical-based theory, however, is the subject of debate (8). The revelation of a common denominator for the adaptation processes to different antibiotics might illuminate the question of a single mechanism from a different angle.Resistance can easily be induced in Escherichia coli by exposure to stepwise increasing sublethal antibiotic concentrations (9). The effects of the acquisition of resistance to amoxicillin on the overall physiology is a complex set of adaptations at the gene expression level, preventing metabolic costs at the expense of the ecological range (10). After the initial stage, the prolonged exposure to antibiotics modulates the SOS response, leading in turn to mutations that cause resistance (11). The mutations generate more permanent resistance, which remains long after the antibiotic pressure has been ...

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Section: Discussionmentioning

confidence: 96%

Interaction between Mutations and Regulation of Gene Expression during Development of De Novo Antibiotic Resistance

Händel

Schuurmans

Feng

et al. 2014

Antimicrob Agents Chemother

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“…However, the long-term use of antibiotics increased the risk of new antibiotic-resistant strain generation through the process of evolutionary pressure. [8][9][10]34 An alternative way to combat implant infection efficiently with a reduced antibiotics usage and shorter administration time is highly desired. Combination therapy of locally released Ag and systemic antibiotics treatment has been proved to be a considerable strategy due to their synergistic effect.…”

Section: Discussionmentioning

confidence: 99%

“…The rising trend in drug resistance can be partially attributed to the long-term use of antibiotics, promoting the formation of new resistant bacteria through the process of evolutionary pressure. [8][9][10] In addition, biofilm, a multiple bacteria structure embedded in a three-dimensional extracellular polymeric substance, is a physical barrier to antibiotic molecules and protects the bacteria present inside from the attack of antibiotics. 11,12 Compared to planktonic bacteria, an incredible 1,000-fold higher dosage of antibiotic is needed to kill bacteria inside the biofilm.…”

Section: Introductionmentioning

confidence: 99%

Silver-loaded nanotubular structures enhanced bactericidal efficiency of antibiotics with synergistic effect in vitro and in vivo

Cheng

et al. 2017

IJN

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Antibiotic-resistant bacteria have become a major issue due to the long-term use and abuse of antibiotics in treatments in clinics. The combination therapy of antibiotics and silver (Ag) nanoparticles is an effective way of both enhancing the antibacterial effect and decreasing the usage of antibiotics. Although the method has been proved to be effective in vitro, no in vivo tests have been carried out at present. Herein, we described a combination therapy of local delivery of Ag and systemic antibiotics treatment in vitro in an infection model of rat. Ag nanoparticle-loaded TiO 2 nanotube (NT) arrays (Ag-NTs) were fabricated on titanium implants for a customized release of Ag ion. The antibacterial properties of silver combined with antibiotics vancomycin, rifampin, gentamicin, and levofloxacin, respectively, were tested in vitro by minimum inhibitory concentration (MIC) assay, disk diffusion assay, and antibiofilm formation test. Enhanced antibacterial activity of combination therapy was observed for all the chosen bacterial strains, including gram-negative Escherichia coli (ATCC 25922), gram-positive Staphylococcus aureus (ATCC 25923), and methicillin-resistant Staphylococcus aureus (MRSA; ATCC 33591 and ATCC 43300). Moreover, after a relative short (3 weeks) combinational treatment, animal experiments in vivo further proved the synergistic antibacterial effect by X-ray and histological and immunohistochemical analyses. These results demonstrated that the combination of Ag nanoparticles and antibiotics significantly enhanced the antibacterial effect both in vitro and in vivo through the synergistic effect. The strategy is promising for clinical application to reduce the usage of antibiotics and shorten the administration time of implant-associated infection.

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“…To achieve this, there are expanding efforts worldwide to transform current diagnostics from the common culture-based methods to genomics-based tools. The application of next-generation sequencing (NGS) technologies has the potential to significantly accelerate bacterial species identification and resistance profiling (61) and could provide information, not only on the current drug susceptibility of a pathogen, but also on its potential to evolve resistance (62). However, precise prediction of antibiotic resistance profiles solely based on genotypes is still challenging due to the complexity of some resistance mechanisms (63).…”

Section: Discussionmentioning

confidence: 99%

Transcriptome Profiling of Antimicrobial Resistance in Pseudomonas aeruginosa

Khaledi

Schniederjans

Pohl

et al. 2016

Antimicrob Agents Chemother

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e Emerging resistance to antimicrobials and the lack of new antibiotic drug candidates underscore the need for optimization of current diagnostics and therapies to diminish the evolution and spread of multidrug resistance. As the antibiotic resistance status of a bacterial pathogen is defined by its genome, resistance profiling by applying next-generation sequencing (NGS) technologies may in the future accomplish pathogen identification, prompt initiation of targeted individualized treatment, and the implementation of optimized infection control measures. In this study, qualitative RNA sequencing was used to identify key genetic determinants of antibiotic resistance in 135 clinical Pseudomonas aeruginosa isolates from diverse geographic and infection site origins. By applying transcriptome-wide association studies, adaptive variations associated with resistance to the antibiotic classes fluoroquinolones, aminoglycosides, and ␤-lactams were identified. Besides potential novel biomarkers with a direct correlation to resistance, global patterns of phenotype-associated gene expression and sequence variations were identified by predictive machine learning approaches. Our research serves to establish genotype-based molecular diagnostic tools for the identification of the current resistance profiles of bacterial pathogens and paves the way for faster diagnostics for more efficient, targeted treatment strategies to also mitigate the future potential for resistance evolution.

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Understanding, predicting and manipulating the genotypic evolution of antibiotic resistance

Cited by 256 publications

References 49 publications

Interaction between Mutations and Regulation of Gene Expression during Development of De Novo Antibiotic Resistance

Interaction between Mutations and Regulation of Gene Expression during Development of De Novo Antibiotic Resistance

Silver-loaded nanotubular structures enhanced bactericidal efficiency of antibiotics with synergistic effect in vitro and in vivo

Transcriptome Profiling of Antimicrobial Resistance in Pseudomonas aeruginosa

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