The DOI matches its Crossref record; 44 of the 44 references it cites were found in a registry or an index.
Induction and Inhibition of Pseudomonas aeruginosa Quorum Sensing by Synthetic Autoinducer Analogs
Open accessAbstract
We synthesized a library of Pseudomonas aeruginosa autoinducer analogs with variation targeted to the homoserine lactone (HSL) moiety and discovered a new agonist, 3-oxo-C(12)-(2-aminocyclohexanol), capable of activating LasR as a transcription factor. We reconstructed two sets of focused libraries against the quorum-sensing transcription factors LasR and RhlR, respectively. Opposing the prediction that both proteins should have the same binding site for HSL, it was surprising to find that these two related proteins respond to different structural motifs. This suggests that the HSL binding site differs in these proteins. We also found that subtle structural modifications to the agonists yielded compounds with antagonist activity. We performed a series of assays to show that inhibition of quorum sensing by these antagonists significantly reduced the production of virulence factors and biofilm formation.
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Self-citationBiofilm formation as microbial development
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Cited by
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Small molecule control of bacterial biofilms
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Metadata
- Authors
- Smith, K. M., Bu, Y., & Suga, H.
- Venue
- Chemistry & Biology
- Publisher
- Elsevier BV
- Published
- 2003-01
- Volume
- 10
- Issue
- 1
- Pages
- 81-89
- ISSN
- 1074-5521 (ISSN-L)
- OpenAlex
- W2075736573
- Cited by
- 320
- References
- 44
- FWCI
- 5.90
- Type
- journal article
- Language
- en
- Open access
- bronze
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- Smith, K. M., Bu, Y., & Suga, H. (2003). Induction and Inhibition of Pseudomonas aeruginosa Quorum Sensing by Synthetic Autoinducer Analogs. Chemistry & Biology. https://doi.org/10.1016/s1074-5521(03)00002-4
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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.
