Transcriptional Response of Salmonella enterica to Bacteriophage Treatments with Differential Multiplicities of Infection
Bibliographic record
Abstract
Salmonella enterica (S. enterica) is a causative agent of numerous foodborne outbreaks, as current industrial measures may be <90% effective. Therefore, bacteriophages have been suggested as an antimicrobial treatment against S. enterica, but it is currently unclear if there is an optimal bacteriophage multiplicity of infection (MOI) against S. enterica. Two bacteriophage cocktails at MOIs 1, 10, 100, 1000 and 10,000 were co-inoculated against four S. enterica strains (S. Enteritidis, S. Newport, S. Muenchen and S. Typhimurium), and populations were estimated on days 0–3. The transcriptional profiles of 20 genes previously indicated to be differentially expressed after bacteriophage treatment were studied by extracting RNA from all four S. enterica strains after bacteriophage SE14, SF5 and SF6 treatment on days 0, 1 and 3, and RT-qPCR was conducted to determine the expression of the 20 selected genes. The results showed that an MOI of 1000 was the most optimal in reducing S. Enteritidis populations to undetectable levels from day 0 to 3. The cas1 (SOS response) and mod (DNA modification and recombination) genes were highly upregulated between 2.5- and 5-fold on day 0 for S. Enteritidis S5-483 and S. Typhimurium S5-536 at MOIs of 1000 and 10,000. On day 3, hsdS (DNA modification and recombination) was upregulated by ~1-fold for S. enteritidis S5-483 after an MOI of 1000. Understanding an optimal bacteriophage MOI can be beneficial to implementing effective and optimal bacteriophage treatments in the industry. Knowledge of S. enterica’s transcriptional response after bacteriophage treatment provides further insight into how S. enterica can survive bacteriophage infection.
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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) | 0.001 | 0.001 |
| 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 | 0.001 |
| 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".