The DOI matches its Crossref record; 20 of the 20 references it cites were found in a registry or an index.
Concentration-dependent activity in natural environments
Open accessAbstract
Bacterial responses to antibiotics are concentration-dependent. At high concentrations, antibiotics exhibit antimicrobial activities on susceptible cells, while subinhibitory concentrations induce diverse biological responses in bacteria. At non-lethal concentrations, bacteria may sense antibiotics as extracellular chemicals to trigger different cellular responses, which may include an altered antibiotic resistance/tolerance profile. In natural settings, microbes are typically in polymicrobial communities and antibiotic-mediated interactions between species may play a significant role in bacterial community structure and function. However, these aspects have not yet fully been explored at the community level. Here we discuss the different types of interactions mediated by antibiotics and non-antibiotic metabolites as a function of their concentrations and speculate on how these may amplify the overall antibiotic resistance/tolerance and the spread of antibiotic resistance determinants in a context of polymicrobial community.
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Cites
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Cited by
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The Evolution and Ecology of Bacterial Warfare
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Antibiotic resistance in urban runoff
Almakki, A., Jumas-Bilak, E., Marchandin, H., & Licznar-Fajardo, P. (2019) · Science of The Total Environment · doi:10.1016/j.scitotenv.2019.02.183 · Cited by 194
Metadata
- Authors
- Bernier, S. P., & Surette, M. G.
- Venue
- Frontiers in Microbiology
- Publisher
- Frontiers Media SA
- Published
- 2013
- Volume
- 4
- Pages
- 20-20
- ISSN
- 1664-302X (ISSN-L)
- OpenAlex
- W1573761949
- Cited by
- 260
- References
- 20
- FWCI
- 10.52
- Type
- journal article
- Language
- en
- Open access
- gold
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Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
