Literature DB >> 17616621

Evaluating the effects of chlortetracycline on the proliferation of antibiotic-resistant bacteria in a simulated river water ecosystem.

Jeannette Muñoz-Aguayo1, Kevin S Lang, Timothy M LaPara, Gerardo González, Randall S Singer.   

Abstract

Antibiotics and antibiotic metabolites have been found in the environment, but the biological activities of these compounds are uncertain, especially given the low levels that are typically detected in the environment. The objective of this study was to estimate the selection potential of chlortetracycline (CTC) on the antibiotic resistance of aerobic bacterial populations in a simulated river water ecosystem. Six replicates of a 10-day experiment using river water in continuous flow chemostat systems were conducted. Each replicate used three chemostats, one serving as a control to which no antibiotic was added and the other two receiving low and high doses of CTC (8 microg/liter and 800 microg/liter, respectively). The addition of CTC to the chemostats did not impact the overall level of cultivable aerobic bacteria (P = 0.51). The high-CTC chemostat had significantly higher tetracycline-resistant bacterial colony counts than both the low-CTC and the control chemostats (P < 0.035). The differences in resistance between the low-CTC and control chemostats were highly nonsignificant (P = 0.779). In general a greater diversity of tet resistance genes was detected in the high-CTC chemostat and with a greater frequency than in the low-CTC and control chemostats. Low levels of CTC in this in vitro experiment did not select for increased levels of tetracycline resistance among cultivable aerobic bacteria. This finding should not be equated with the absence of environmental risk, however. Low concentrations of antibiotics in the environment may select for resistant bacterial populations once they are concentrated in sediments or other locations.

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Year:  2007        PMID: 17616621      PMCID: PMC2042072          DOI: 10.1128/AEM.00708-07

Source DB:  PubMed          Journal:  Appl Environ Microbiol        ISSN: 0099-2240            Impact factor:   4.792


  26 in total

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4.  Promoting resistance by the emission of antibiotics from hospitals and households into effluent.

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5.  Urban contribution of pharmaceuticals and other organic wastewater contaminants to streams during differing flow conditions.

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Review 6.  Environmental antimicrobial contamination from terraccumulation and diffuse pollution pathways.

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Review 7.  Can landscape ecology untangle the complexity of antibiotic resistance?

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8.  Antibiotic resistance in Acinetobacter spp. isolated from sewers receiving waste effluent from a hospital and a pharmaceutical plant.

Authors:  L Guardabassi; A Petersen; J E Olsen; A Dalsgaard
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9.  Diverse tetracycline resistance genotypes of Megasphaera elsdenii strains selectively cultured from swine feces.

Authors:  Thaddeus B Stanton; Jennifer S McDowall; Mark A Rasmussen
Journal:  Appl Environ Microbiol       Date:  2004-06       Impact factor: 4.792

10.  Enzyme-linked immunosorbent assay for ultratrace determination of antibiotics in aqueous samples.

Authors:  Kuldip Kumar; Anita Thompson; Ashok K Singh; Yogesh Chander; Satish C Gupta
Journal:  J Environ Qual       Date:  2004 Jan-Feb       Impact factor: 2.751

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Review 3.  Antibiotic resistance genes in China: occurrence, risk, and correlation among different parameters.

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4.  Chronic exposure of river sediments to environmentally relevant levels of tetracycline affects bacterial communities but not denitrification rates.

Authors:  Céline Roose-Amsaleg; Chen Yan; Anne-Marie Hoang; Anniet M Laverman
Journal:  Ecotoxicology       Date:  2013-10-09       Impact factor: 2.823

5.  A simple model of tetracycline antibiotic resistance in the aquatic environment (with application to the Poudre River).

Authors:  Ferdi L Hellweger; Xiaodan Ruan; Sarah Sanchez
Journal:  Int J Environ Res Public Health       Date:  2011-02-15       Impact factor: 3.390

6.  Modeling transport of antibiotic resistant bacteria in aquatic environment using stochastic differential equations.

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Journal:  Sci Rep       Date:  2020-09-15       Impact factor: 4.379

  6 in total

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