Resistance development and transcriptional responses of Salmonella enterica strains to bacteriophage SF1 treatment on Arabidopsis thaliana
Bibliographic record
Abstract
This study explores the prevalence and mechanisms of in vitro and in planta phage resistance in Salmonella enterica, a critical concern for food safety. We examined three Salmonella Typhimurium strains, ST001, ST536, and ST580, that developed phage resistance on Arabidopsis plants. Strains ST001 and ST536 exhibited strong phage resistance and cross-resistance to heat, while ST580 was more susceptible, showing greater population declines and requiring a longer adaptation period. Interestingly, despite gaining phage resistance, all three strains became more sensitive to oxidative and acidic stressors, suggesting potential applications for food industry controls. RNA-seq analysis indicated diverse transcriptional responses to phage exposure: ST580 displayed significant gene expression changes, while ST001 and ST536 increased resistance through enhanced membrane protein synthesis and upregulated ribosome and membrane protein localization genes. ST001 also reduced O-antigen synthesis, blocking the SF1 phage receptor. These transcriptional trends were corroborated by phage attachment assays and membrane protein measurements, highlighting distinct adaptive mechanisms. Unlike ST001 and ST536, ST580's resistance was inadequate to prevent phage adsorption, leading to more reactive transcriptional responses. This research underscores the complexity of phage-host interactions and reveals potential trade-offs, such as increased sensitivity to oxidative and acidic stresses. It offers insights into the dynamics of phage resistance, which could improve phage applications against foodborne pathogens, enhancing the efficacy and sustainability of phage-based 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) | 0.001 | 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) | 0.002 | 0.001 |
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".