Genomic Profiling of Non-O157 Shiga Toxigenic <i>Escherichia coli</i> -Infecting Bacteriophages from South Africa
Bibliographic record
Abstract
Background: Non-O157 Shiga toxigenic Escherichia coli (STEC) are one of the most important food and waterborne pathogens worldwide. Although bacteriophages (phages) have been used for the biocontrol of these pathogens, a comprehensive understanding of the genetic characteristics and lifestyle of potentially effective candidate phages is lacking. Materials and Methods: In this study, 10 non-O157-infecting phages previously isolated from feedlot cattle and dairy farms in the North-West province of South Africa were sequenced, and their genomes were analyzed. Results: Comparative genomics and proteomics revealed that the phages were closely related to other E. coli-infecting Tunaviruses, Seuratviruses, Carltongylesviruses, Tequatroviruses, and Mosigviruses from the National Center for Biotechnology Information GenBank database. Phages lacked integrases associated with a lysogenic cycle and genes associated with antibiotic resistance and Shiga toxins. Conclusions: Comparative genomic analysis identified a diversity of unique non-O157-infecting phages, which could be used to mitigate the abundance of various non-O157 STEC serogroups without safety concerns.
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 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.012 | 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 teacher head, 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".