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Compensation of the metabolic costs of antibiotic resistance by physiological adaptation in Escherichia coli.

ABSTRACT: Antibiotic resistance is often associated with metabolic costs. To investigate the metabolic consequences of antibiotic resistance, the genomic and transcriptomic profiles of an amoxicillin-resistant Escherichia coli strain and the wild type it was derived from were compared. A total of 125 amino acid substitutions and 7 mutations that were located <1,000 bp upstream of differentially expressed genes were found in resistant cells. However, broad induction and suppression of genes were observed when comparing the expression profiles of resistant and wild-type cells. Expression of genes involved in cell wall maintenance, DNA metabolic processes, cellular stress response, and respiration was most affected in resistant cells regardless of the absence or presence of amoxicillin. The SOS response was downregulated in resistant cells. The physiological effect of the acquisition of amoxicillin resistance in cells grown in chemostat cultures consisted of an initial increase in glucose consumption that was followed by an adaptation process. Furthermore, no difference in maintenance energy was observed between resistant and sensitive cells. In accordance with the transcriptomic profile, exposure of resistant cells to amoxicillin resulted in reduced salt and pH tolerance. Taken together, the results demonstrate that the acquisition of antibiotic resistance in E. coli is accompanied by specifically reorganized metabolic networks in order to circumvent metabolic costs. The overall effect of the acquisition of resistance consists not so much of an extra energy requirement, but more a reduced ecological range.

SUBMITTER: Handel N

PROVIDER: S-EPMC3719774 | biostudies-literature | 2013 Aug

REPOSITORIES: biostudies-literature

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Compensation of the metabolic costs of antibiotic resistance by physiological adaptation in Escherichia coli.

Händel Nadine N Schuurmans J Merijn JM Brul Stanley S ter Kuile Benno H BH

Antimicrobial agents and chemotherapy 20130528 8

Antibiotic resistance is often associated with metabolic costs. To investigate the metabolic consequences of antibiotic resistance, the genomic and transcriptomic profiles of an amoxicillin-resistant Escherichia coli strain and the wild type it was derived from were compared. A total of 125 amino acid substitutions and 7 mutations that were located <1,000 bp upstream of differentially expressed genes were found in resistant cells. However, broad induction and suppression of genes were observed w ...[more]

PMID: 23716056

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Project description:Antimicrobial resistance (AMR) is becoming one of the largest threats to public health worldwide, with the opportunistic pathogen Escherichia coli playing a major role in the AMR global health crisis. Unravelling the complex interplay between drug resistance and metabolic rewiring is key to understand the ability of bacteria to adapt to new treatments and to the development of new effective solutions to combat resistant infections. We developed a computational pipeline that combines machine learning with genome-scale metabolic models (GSMs) to elucidate the systemic relationships between genetic determinants of resistance and metabolism beyond annotated drug resistance genes. Our approach was used to identify genetic determinants of 12 AMR profiles for the opportunistic pathogenic bacterium E. coli. Then, to interpret the large number of identified genetic determinants, we applied a constraint-based approach using the GSM to predict the effects of genetic changes on growth, metabolite yields, and reaction fluxes. Our computational platform leads to multiple results. First, our approach corroborates 225 known AMR-conferring genes, 35 of which are known for the specific antibiotic. Second, integration with the GSM predicted 20 top-ranked genetic determinants (including accA, metK, fabD, fabG, murG, lptG, mraY, folP, and glmM) essential for growth, while a further 17 top-ranked genetic determinants linked AMR to auxotrophic behavior. Third, clusters of AMR-conferring genes affecting similar metabolic processes are revealed, which strongly suggested that metabolic adaptations in cell wall, energy, iron and nucleotide metabolism are associated with AMR. The computational solution can be used to study other human and animal pathogens. IMPORTANCE Escherichia coli is a major public health concern given its increasing level of antibiotic resistance worldwide and extraordinary capacity to acquire and spread resistance via horizontal gene transfer with surrounding species and via mutations in its existing genome. E. coli also exhibits a large amount of metabolic pathway redundancy, which promotes resistance via metabolic adaptability. In this study, we developed a computational approach that integrates machine learning with metabolic modeling to understand the correlation between AMR and metabolic adaptation mechanisms in this model bacterium. Using our approach, we identified AMR genetic determinants associated with cell wall modifications for increased permeability, virulence factor manipulation of host immunity, reduction of oxidative stress toxicity, and changes to energy metabolism. Unravelling the complex interplay between antibiotic resistance and metabolic rewiring may open new opportunities to understand the ability of E. coli, and potentially of other human and animal pathogens, to adapt to new treatments.

Project description:Biofilms are considered to be highly resistant to antimicrobial agents. Several mechanisms have been proposed to explain this high resistance of biofilms, including restricted penetration of antimicrobial agents into biofilms, slow growth owing to nutrient limitation, expression of genes involved in the general stress response, and emergence of a biofilm-specific phenotype. However, since combinations of these factors are involved in most biofilm studies, it is still difficult to fully understand the mechanisms of biofilm resistance to antibiotics. In this study, the antibiotic susceptibility of Escherichia coli cells in biofilms was investigated with exclusion of the effects of the restricted penetration of antimicrobial agents into biofilms and the slow growth owing to nutrient limitation. Three different antibiotics, ampicillin (100 microg/ml), kanamycin (25 microg/ml), and ofloxacin (10 microg/ml), were applied directly to cells in the deeper layers of mature biofilms that developed in flow cells after removal of the surface layers of the biofilms. The results of the antibiotic treatment analyses revealed that ofloxacin and kanamycin were effective against biofilm cells, whereas ampicillin did not kill the cells, resulting in regrowth of the biofilm after the ampicillin treatment was discontinued. LIVE/DEAD staining revealed that a small fraction of resistant cells emerged in the deeper layers of the mature biofilms and that these cells were still alive even after 24 h of ampicillin treatment. Furthermore, to determine which genes in the biofilm cells are induced, allowing increased resistance to ampicillin, global gene expression was analyzed at different stages of biofilm formation, the attachment, colony formation, and maturation stages. The results showed that significant changes in gene expression occurred during biofilm formation, which were partly induced by rpoS expression. Based on the experimental data, it is likely that the observed resistance of biofilms can be attributed to formation of ampicillin-resistant subpopulations in the deeper layers of mature biofilms but not in young colony biofilms and that the production and resistance of the subpopulations were aided by biofilm-specific phenotypes, like slow growth and induction of rpoS-mediated stress responses.

Dataset Information

Compensation of the metabolic costs of antibiotic resistance by physiological adaptation in Escherichia coli.

Publications

Compensation of the metabolic costs of antibiotic resistance by physiological adaptation in Escherichia coli.

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