Subinhibitory Concentrations of the Deformylase Inhibitor Actinonin Increase Bacterial Release of Neutrophil-Activating Peptides: a New Approach to Antimicrobial Chemotherapy
Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".