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Record W2122432936 · doi:10.1093/jac/dkm397

MarA-mediated overexpression of the AcrAB efflux pump results in decreased susceptibility to tigecycline in Escherichia coli

2007· article· en· W2122432936 on OpenAlexaff
David Keeney, Alexey Ruzin, Fionnuala McAleese, Ellen Murphy, Patricia A. Bradford

Bibliographic record

VenueJournal of Antimicrobial Chemotherapy · 2007
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAntibiotic Resistance in Bacteria
Canadian institutionsWomen's Health Research Institute
Fundersnot available
KeywordsBiologyTransposon mutagenesisTransposable elementEscherichia coliTigecyclineEffluxMicrobiologyInsertion sequenceMutantPlasmidMolecular biologyGeneGeneticsAntibiotics

Abstract

fetched live from OpenAlex

OBJECTIVES: The purpose of this study was to characterize decreased susceptibility to tigecycline in clinical isolates of Escherichia coli obtained during Phase 3 clinical trials. METHODS: Gene expression was analysed by transcriptional profile analysis and RT-PCR. Transposon mutagenesis with IS903kan was used for selection of transposon mutants. Transposon insertions were mapped by DNA sequencing and PCR analyses. The MICs were determined by broth microdilution. RESULTS: Both transcriptional profile analysis and Taqman RT-PCR demonstrated increased expression levels of MarA, a transcriptional activator, and AcrAB, an RND-type efflux pump, in the strains with elevated tigecycline MICs. Transposon mutagenesis generated nine mutants, the majority of which had either marA or acrB inactivated. Sequence analysis revealed a single nucleotide insertion in the open reading frame of the marR gene in less-susceptible strains of E. coli. CONCLUSIONS: This study suggested that a loss of MarR functionality due to a frameshift mutation resulted in constitutive overproduction of MarA and AcrAB and, consequently, in decreased susceptibility to tigecycline in clinical isolates of E. coli.

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)0.001
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 score0.281
Threshold uncertainty score0.520

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.007
GPT teacher head0.261
Teacher spread0.253 · 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

Citations154
Published2007
Admission routes1
Has abstractyes

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