Characterizing a Novel Phage Tail-Like Bacteriocin in Salmonella and Escherichia
Bibliographic record
Abstract
Bacteriophage tail-like bacteriocins, or tailocins, are phage tail-like particles that facilitate interbacterial competition. They are evolutionarily and structurally related to phage tails, and have evolved numerous times from different phages throughout several bacterial classes. In this work, I present a comprehensive analysis of a novel tailocin cluster that we identified in Salmonella and Escherichia. I demonstrate that this tailocin cluster is conserved throughout several S. enterica subspecies, and that it is conserved at the same locus in the closely related genus Escherichia. I define the essential components of this tailocin, describe several features that are novel in the context of tailocin biology, and demonstrate that this tailocin can be classified into groups on the basis of its receptor binding proteins. I then focus on tailocin regulation in Salmonella, showing that this tailocin is DNA damage-inducible and characterizing expression from several tailocin promoters. I identify the tailocin transcriptional regulator, and I illustrate that overexpression of this regulator can be used to specifically induce tailocin production for downstream characterization. This work expands upon a previously unappreciated aspect of Salmonella and Escherichia biology, and lays the groundwork for further tailocin characterization in these genera.
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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".