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Subinhibitory Concentrations of the Deformylase Inhibitor Actinonin Increase Bacterial Release of Neutrophil-Activating Peptides: a New Approach to Antimicrobial Chemotherapy

2003· article· en· W2103634797 on OpenAlexaff
Huamei Fu, Cláes Dahlgren, Johan Bylund

Bibliographic record

VenueAntimicrobial Agents and Chemotherapy · 2003
Typearticle
Languageen
FieldMedicine
TopicPeptidase Inhibition and Analysis
Canadian institutionsChildren’s Health Research Institute
FundersMedical Research CouncilStiftelsen för Strategisk Forskning
KeywordsInnate immune systemAntimicrobial peptidesAntimicrobialBiochemistryBiologyPeptideSecretionMicrobiologyEscherichia coliReceptorChemistry

Abstract

fetched live from OpenAlex

Bacterial protein synthesis starts with a formylated methionine residue, and this residue is sequentially cleaved away by a unique peptide deformylase (PDF) and a methionine aminopeptidase to generate mature proteins. The formylation-deformylation of proteins is a unique hallmark of bacterial metabolism and has recently become an attractive target for the development of antimicrobial agents. The innate immune system uses the formylation of bacterial proteins as a target, and professional phagocytes, e.g., neutrophils, express specific receptors for bacterium-derived formylated peptides. Activation of formyl peptide receptors (FPR) mediates neutrophil migration and the release of oxygen radicals and other antimicrobial substances from these cells. We hypothesize that the use of a PDF inhibitor would increase the production of proinflammatory peptides from the bacteria and thus trigger a more pronounced innate immune response. We tested this hypothesis by exposing Escherichia coli to subinhibitory doses of the PDF inhibitor actinonin and show that actinonin indeed increases the production and secretion of neutrophil-activating peptides that activate human neutrophils through FPR. These findings could be potentially used as a new approach to antibacterial chemotherapy.

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 score0.017
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.015
GPT teacher head0.246
Teacher spread0.231 · 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

Citations30
Published2003
Admission routes1
Has abstractyes

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