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Record W1990506242 · doi:10.1007/s12275-008-0054-x

Modulation of secreted virulence factor genes by subinhibitory concentrations of antibiotics in Pseudomonas aeruginosa

2008· article· en· W1990506242 on OpenAlexaff
Lixin Shen, Ying Shi, Dan Zhang, Jinhua Wei, Michael G. Surette, Kangmin Duan

Bibliographic record

VenueThe Journal of Microbiology · 2008
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBacterial biofilms and quorum sensing
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsPyocyaninMicrobiologyPseudomonas aeruginosaVirulenceTetracyclineBiologyVirulence factorQuorum sensingAmpicillinAntibioticsSwarming motilityRhamnolipidGeneVancomycinAzithromycinPathogenBacteriaGeneticsStaphylococcus aureus

Abstract

fetched live from OpenAlex

Recent studies have shown that subinhibitory antibiotics play important roles in regulating bacterial genes including virulence factor genes. In this study, the expression of 13 secreted virulence related gene clusters of Pseudomonas aeruginosa, an important opportunistic pathogen, was examined in the presence of subinhibitory concentrations of 4 antibiotics: vancomycin, tetracycline, ampicillin and azithromycin. Activation of gene expression was observed with phzAl, rhlAB, phzA2, lasB, exoY, and exoS. Subinhibitory concentrations of vancomycin resulted in more than 10-fold increase of rhlAB and phzA2 transcription. Both rhamnolipid production and pyocyanin production were significantly elevated, correlating phenotypes with the increased transcription. P. aeruginosa swarming and swimming motility also increased. Similar results were observed with subinhibitory tetracycline, azithromycin and ampicillin. These results indicate that the antibiotics at low concentrations can up-regulate virulence factors and therefore influence bacterial pathogenesis.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.246

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.011
GPT teacher head0.218
Teacher spread0.207 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations63
Published2008
Admission routes1
Has abstractyes

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