Bacterial peptide deformylase inhibitors induce prophages in competitors
Bibliographic record
Abstract
Abstract While antibiotics mediate chemical warfare among microbes, their roles in the wild extend beyond direct growth inhibition( 1 ). Some antibiotics have the potential to mediate interference competition by triggering a bacterial stress response that subsequently activates endogenous viruses integrated in bacterial genomes (prophages). Canonically, this activation is regulated by the SOS response upon DNA damage. Here we show that a metabolite produced by natural isolates of Vibrio ordalii circumvents the SOS response by directly triggering prophage induction in other Vibrio species, co-occurring in the same environment. While the metabolite was previously classified as a broad-spectrum antibiotic, we observe how it acts as a peptide deformylase inhibitor that specifically induces certain prophages, even when target bacterial cells carry multiple other prophages. Its biosynthetic gene cluster, or ord cluster, also encodes its own peptide deformylase (OrdE) which provides self-immunity to producer strains. Likewise, among natural Vibrio isolates that carry similar prophages, resistance against the ord metabolite was found in those that had acquired a divergent second peptide deformylase. Finally, we show that prophage induction by the ord cluster prevents slower-growing producer strains from being outcompeted by their otherwise fast-growing competitors if they carry an inducible prophage. Thus, we demonstrate how natural products play additional impactful roles in communities beyond antibiotic activity and that prophage induction serves as an interference competition strategy, sustaining community diversity.
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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".