Discovery, characterization and <i>in vivo</i> activity of pyocin SD2, a protein antibiotic from <i>Pseudomonas aeruginosa</i>
Bibliographic record
Abstract
Increasing rates of antibiotic resistance among Gram-negative pathogens such as Pseudomonas aeruginosa means alternative approaches to antibiotic development are urgently required. Pyocins, produced by P. aeruginosa for intraspecies competition, are highly potent protein antibiotics known to actively translocate across the outer membrane of P. aeruginosa. Understanding and exploiting the mechanisms by which pyocins target, penetrate and kill P. aeruginosa is a promising approach to antibiotic development. In this work we show the therapeutic potential of a newly identified tRNase pyocin, pyocin SD2, by demonstrating its activity in vivo in a murine model of P. aeruginosa lung infection. In addition, we propose a mechanism of cell targeting and translocation for pyocin SD2 across the P. aeruginosa outer membrane. Pyocin SD2 is concentrated at the cell surface, via binding to the common polysaccharide antigen (CPA) of P. aeruginosa lipopolysaccharide (LPS), from where it can efficiently locate its outer membrane receptor FpvAI. This strategy of utilizing both the CPA and a protein receptor for cell targeting is common among pyocins as we show that pyocins S2, S5 and SD3 also bind to the CPA. Additional data indicate a key role for an unstructured N-terminal region of pyocin SD2 in the subsequent translocation of the pyocin into the cell. These results greatly improve our understanding of how pyocins target and translocate across the outer membrane of P. aeruginosa. This knowledge could be useful for the development of novel anti-pseudomonal therapeutics and will also support the development of pyocin SD2 as a therapeutic in its own right.
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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) | 0.000 | 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".