Literature DB >> 11512526

Low-level antibacterial resistance: a gateway to clinical resistance.

F Baquero1.   

Abstract

The huge amount of antibiotic substances released in the human environment has probably resulted in an acceleration in the rate of bacterial evolution. It is to note that most interactions between chemotherapeutic agents and microbial populations occur at very low antibiotic concentrations. Thus, natural selection is expected to act on very small increases in the bacterial ability to resist to antibiotic inhibitory effects. On the other hand, there is a wealth of mechanisms to resist to these low antibiotic concentrations. The progressive enrichment in low-level resistant populations favours secondary selections for more specific and effective mechanisms of resistance, particularly in treated patients. These adaptations may have a biological cost in the absence of antibiotics, but frequently compensatory mutations occur, minimizing such genetic burden. In this way, a phenomenon of directional selection takes place, with low possibilities of return to susceptibility. Moreover, low antibiotic concentrations are not only able to select low-level antibiotic resistant variants, but may produce a substantial stress in bacterial populations, that eventually influences the rate of genetic variation and the diversity of adaptive responses. More attention should be devoted to the mechanisms of low-level resistance in microorganisms, as they can serve as stepping stones to develop high level, clinically relevant resistance. These mechanisms should be identified early in the development of drugs in order to adapt the therapeutic strategies (for instance dosage) to minimize the selection of low-level resistant variants, as frequently they emerge by means of concentration-specific selection. At the same time, conventional susceptibility testing should probably be able to detect low-level resistance, and not only clinically-relevant resistance. We should be vigilant of the evolutionary trends of microorganisms; for that a purpose, knowledge of the biology and epidemiology of low-level resistance is becoming a real need.

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Year:  2001        PMID: 11512526     DOI: 10.1054/drup.2001.0196

Source DB:  PubMed          Journal:  Drug Resist Updat        ISSN: 1368-7646            Impact factor:   18.500


  51 in total

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2.  Escherichia coli mutators present an enhanced risk for emergence of antibiotic resistance during urinary tract infections.

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3.  Presence of multidrug-resistant enteric bacteria in dairy farm topsoil.

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4.  Detection of Salmonella enterica subpopulations by phenotype microarray antibiotic resistance patterns.

Authors:  Jean Guard-Bouldin; Cesar A Morales; Jonathan G Frye; Richard K Gast; Michael Musgrove
Journal:  Appl Environ Microbiol       Date:  2007-10-26       Impact factor: 4.792

5.  Microbes have the last word. A drastic re-evaluation of antimicrobial treatment is needed to overcome the threat of antibiotic-resistant bacteria.

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Journal:  EMBO Rep       Date:  2007-07       Impact factor: 8.807

Review 6.  What is a resistance gene? Ranking risk in resistomes.

Authors:  José L Martínez; Teresa M Coque; Fernando Baquero
Journal:  Nat Rev Microbiol       Date:  2014-12-15       Impact factor: 60.633

7.  Genetic determinants involved in the susceptibility of Pseudomonas aeruginosa to beta-lactam antibiotics.

Authors:  Carolina Alvarez-Ortega; Irith Wiegand; Jorge Olivares; Robert E W Hancock; José Luis Martínez
Journal:  Antimicrob Agents Chemother       Date:  2010-08-02       Impact factor: 5.191

8.  Molecular detection of rifampin and isoniazid resistance to guide chronic TB patient management in Burkina Faso.

Authors:  Paolo Miotto; Nuccia Saleri; Mathurin Dembelé; Martial Ouedraogo; Gisèle Badoum; Gabriele Pinsi; Giovanni B Migliori; Alberto Matteelli; Daniela M Cirillo
Journal:  BMC Infect Dis       Date:  2009-08-28       Impact factor: 3.090

9.  Characterization of the polymyxin B resistome of Pseudomonas aeruginosa.

Authors:  Lucía Fernández; Carolina Alvarez-Ortega; Irith Wiegand; Jorge Olivares; Dana Kocíncová; Joseph S Lam; José Luis Martínez; Robert E W Hancock
Journal:  Antimicrob Agents Chemother       Date:  2012-10-15       Impact factor: 5.191

10.  Genome-scale identification method applied to find cryptic aminoglycoside resistance genes in Pseudomonas aeruginosa.

Authors:  Julie M Struble; Ryan T Gill
Journal:  PLoS One       Date:  2009-11-11       Impact factor: 3.240

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