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Record W2007709133 · doi:10.1111/1574-6968.12185

Role of uropathogenic<i>Escherichia coli</i>OmpT in the resistance against human cathelicidin LL-37

2013· article· en· W2007709133 on OpenAlexafffund
John R. Brannon, Jenny‐Lee Thomassin, Isabelle Desloges, Samantha Gruenheid, Hervé Le Moual

Bibliographic record

VenueFEMS Microbiology Letters · 2013
Typearticle
Languageen
FieldImmunology and Microbiology
TopicAntimicrobial Peptides and Activities
Canadian institutionsMcGill University
FundersCanadian Institutes of Health Research
KeywordsEscherichia coliMicrobiologyCathelicidinBacterial outer membraneBiologyMutantAntimicrobial peptidesChemistryAntimicrobialBiochemistryGene

Abstract

fetched live from OpenAlex

Uropathogenic Escherichia coli (UPEC) strains are among the most prevalent causative agents of urinary tract infections. To establish infection, UPEC must overcome the bactericidal action of host antimicrobial peptides. Previously, the enterohaemorrhagic E. coli outer membrane protease, OmpT, was shown to degrade and inactivate the human antimicrobial peptide LL-37. This study aims to investigate the involvement of UPEC OmpT in LL-37 degradation. An ompT deletion mutant was generated in the prototypical UPEC strain CFT073. Western blot analysis showed that the OmpT protein level is moderate in CFT073. In agreement, OmpT was shown to partially cleave LL-37. However, no difference in the minimum inhibitory concentration of LL-37 was observed between CFT073 and the ompT mutant. Plasmid complementation of ompT, which led to increased OmpT levels, resulted in complete cleavage of LL-37 and a fourfold increase in the minimum inhibitory concentration. The analysis of other UPEC isolates showed similar OmpT activity levels as CFT073. Although UPEC OmpT can cleave LL-37, we conclude that the low level of OmpT limits its contribution to LL-37 resistance. Collectively, these data suggest that UPEC OmpT is likely accompanied by other LL-37 resistance mechanisms.

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 score0.075
Threshold uncertainty score0.985

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 science0.0010.000
Research integrity0.0000.001
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 head0.196
Teacher spread0.189 · 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

Citations36
Published2013
Admission routes2
Has abstractyes

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