Bacteriocin Occurrence and Activity in Escherichia coli Isolated from Bovines and Wastewater
Bibliographic record
Abstract
The increasing prevalence of antimicrobial resistant (AMR) E. coli and related Enterobacteriaceae is a serious problem necessitating new mitigation strategies and antimicrobial agents. Bacteriocins, functionally diverse toxins produced by most microbes, have long been studied for their antimicrobial potential. Bacteriocins have once again received attention for their role as probiotic traits that could mitigate pathogen burden and AMR bacteria in livestock. Here, bacteriocins were identified by activity screening and whole-genome sequencing of bacteriocin-producers capable of inhibiting bovine and wastewater E. coli isolates enriched for resistance to cephalosporins. Producers were tested for activity against shiga toxin-producing E. coli (STEC), AMR E. coli, and related enteric pathogens. Multiple bacteriocins were found in 14 out of 90 E. coli isolates tested. Based on alignment within BACTIBASE, colicins M, B, R, Ia, Ib, S4, E1, E2, and microcins V, J25, and H47, encoded by identical, variant, or truncated genes were identified. Although some bacteriocin-producers exhibited activity against AMR and STEC E. coli in agar-based assays, most did not. Despite this idiosyncrasy, liquid co-cultures of all bacteriocinogenic isolates with luciferase-expressing generic (K12) or STEC E. coli (EDL933) resulted in inhibited growth or reduced viability. These abundant toxins may have real potential as next-generation control strategies in livestock production systems but separating the bacteriocin from its immunity gene may be necessary for such a strategy to be effective.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".