Bacteriophage-Induced Lipopolysaccharide Mutations in Escherichia coli Lead to Hypersensitivity to Food Grade Surfactant Sodium Dodecyl Sulfate
Bibliographic record
Abstract
Bacteriophages (phages) are considered as one of the most promising antibiotic alternatives in combatting bacterial infectious diseases. However, one concern of employing phage application is the emergence of bacteriophage-insensitive mutants (BIMs). Here, we isolated six BIMs from E. coli B in the presence of phage T4 and characterized them using genomic and phenotypic methods. Of all six BIMs, a six-amino acid deletion in glucosyltransferase WaaG likely conferred phage resistance by deactivating the addition of T4 receptor glucose to the lipopolysaccharide (LPS). This finding was further supported by the impaired phage adsorption to BIMs and glycosyl composition analysis which quantitatively confirmed the absence of glucose in the LPS of BIMs. Since LPSs actively maintain outer membrane (OM) permeability, phage-induced truncations of LPSs destabilized the OM and sensitized BIMs to various substrates, especially to the food-grade surfactant sodium dodecyl sulfate (SDS). This hypersensitivity to SDS was exploited to design a T4–SDS combination which successfully prevented the generation of BIMs and eliminated the inoculated bacteria. Collectively, phage-driven modifications of LPSs immunized BIMs from T4 predation but increased their susceptibilities as a fitness cost. The findings of this study suggest a novel strategy to enhance the effectiveness of phage-based food safety interventions.
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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) | 0.001 | 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".