Characterization of Non-O157 STEC Infecting Bacteriophages Isolated from Cattle Faeces in North-West South Africa
Bibliographic record
Abstract
Non-O157 Shiga toxin-producing Escherichia coli (STEC) E. coli are emerging pathotypes that are frequently associated with diseases in humans around the world. The consequences of these serogroups for public health is a concern given the lack of effective prevention and treatment measures. In this study, ten bacteriophages (phages; SA20RB, SA79RD, SA126VB, SA30RD, SA32RD, SA35RD, SA21RB, SA80RD, SA12KD and SA91KD) isolated from cattle faeces collected in the North-West of South Africa were characterized. Activity of these phages against non-O157 STEC isolates served as hosts for these phages. All of the phages except SA80RD displayed lytic against non-O157 E. coli isolates. Of 22 non-O157 E. coli isolates, 14 were sensitive to 9 of the 10 phages tested. Phage SA35RD was able to lyse 13 isolates representing a diverse group of non-O157 E. coli serotypes including a novel O-antigen Shiga toxigenic (wzx-Onovel5:H19) strain. However, non-O157 E. coli serotypes O76:H34, O99:H9, O129:H23 and O136:H30 were insensitive to all phages. Based on transmission electron microscopy, the non-O157 STEC phages were placed into Myoviridae (n = 5) and Siphoviridae (n = 5). Genome of the phage ranged from 44 to 184.3 kb. All but three phages (SA91KD, SA80RD and SA126VB) were insensitive to EcoRI-HF and HindIII nucleases. This is the first study illustrating that cattle from North-West South Africa harbour phages with lytic potentials that could potentially be exploited for biocontrol against a diverse group of non-O157 STEC isolated from the same region.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.008 | 0.002 |
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; both teacher heads agree on what is shown here.
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".