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Record W4413610935 · doi:10.1039/d5cb00092k

Contributions of β-lactamase substrate specificity and outer membrane permeability to the antibiotic sheltering of β-lactam-susceptible bacteria

2025· article· en· W4413610935 on OpenAlexafffund
Montserrat Mora-Ochomogo, Mitchell A Jeffs, Josephine L Liu, Christopher T. Lohans

Bibliographic record

VenueRSC Chemical Biology · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAntibiotic Resistance in Bacteria
Canadian institutionsQueen's University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsBacteriaAntibioticsLactamMicrobiologyBacterial outer membraneBiologyChemistryBiochemistryEscherichia coliStereochemistryGenetics

Abstract

fetched live from OpenAlex

The use of β-lactam antibiotics is threatened by antibiotic resistant bacteria that produce β-lactamases. These enzymes not only protect the bacteria that produce them but also shelter other bacteria in the same environment that would otherwise be susceptible. While this phenomenon is of clinical significance, many of the factors that contribute to β-lactamase-mediated antibiotic sheltering have not been well-studied. We report the development of a luminescence assay to directly monitor the survival of β-lactam-susceptible bacteria in the presence of β-lactamase-producing bacteria and β-lactam antibiotics. This method provides a rapid and scalable means of quantifying antibiotic sheltering in mixed microbial populations. We applied this assay to investigate the contributions of several factors to sheltering, including the class of β-lactam, the substrate specificity of the β-lactamase, and the cell wall permeability of the β-lactamase-producing bacterium. Our results show that the extent of sheltering that occurs not only depends on the particular combination of β-lactam and β-lactamase, but is also greatly impacted by the ability of a β-lactamase to access its β-lactam substrates.

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 score0.007
Threshold uncertainty score0.545

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 studies0.0000.001
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 head0.009
GPT teacher head0.266
Teacher spread0.257 · 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

Citations2
Published2025
Admission routes2
Has abstractyes

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