Comparative genomics and proteomics analysis of phages infecting multi-drug resistant Escherichia coli O177 isolated from cattle faeces
Bibliographic record
Abstract
The increasing prevalence of antimicrobial-resistant (AMR) pathogens has become a major global health concern. To address this challenge, innovative strategies such as bacteriophage therapy must be optimised. Genomic characterisation is a crucial step in identifying suitable phage candidates for combating AMR pathogens. The aim of this study was to characterise seven phages that infect the Escherichia coli O177 strain using a whole genome sequencing. The analysis of genome sequences revealed that these phages had linear dsDNA, with genome sizes spanning from 136, 483 to 166,791 bp and GC content varying from 35.39 to 43.63%. Taxonomically, the phages were classified under three different subfamilies (Stephanstirmvirinae, Tevenvirinae, and Vequintavirinae) and three genera (Phapecoctavirus, Tequatrovirus, and Vequintavirus) within the class Caudoviricetes. In silico PhageAI analysis predicted that all the phages were virulent, with confidence levels between 96.07 and 97.26%. The phage genomes contained between 66 and 82 ORFs, which encode hypothetical and putative functional proteins. In addition, the phage genomes contained core genes associated with molecular processes such as DNA replication, transcription modulation, nucleotide metabolism, phage structure (capsid and tail), and lysis. None of the genomes carried genes associated with undesirable traits such as integrase, antimicrobial resistance, virulence, and toxins. The study revealed high genome and proteome homology among E. coli O177 phages and other known Escherichia phages. The results suggest that the seven phages are new members of the genera Phapecoctavirus, Tequatrovirus, and Vequintavirus under the subfamilies Stephanstirmvirinae, Tevenvirinae, and Vequintavirinae, respectively.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".