Changes in Gene Transcription Induced by Hydrogen Peroxide Treatment of Verotoxin-Producing Escherichia coli O157:H7 and Non-O157 Serotypes on Romaine Lettuce
Bibliographic record
Abstract
Disease outbreaks of verotoxin-producing Escherichia coli (VTEC) O157:H7 and non-O157 serotypes associated with leafy green vegetables are becoming a growing concern. A better understanding of the behaviour of VTEC, particularly non-O157 serotypes, on lettuce under stress conditions is necessary for designing more effective control strategies. Hydrogen peroxide can be used as a sanitizer to reduce the microbial load in leafy green vegetables, particularly in fresh produce destined for organic market. In this study, we tested the hypothesis that hydrogen peroxide treatment of contaminated lettuce affects in the same manner transcription of stress-associated and virulence genes in VTEC strains representing O157 and non-O157 serotypes. Six VTEC isolates representing serotypes O26:H11, O103:H2, O104:H4, O111:NM, and O145:NM and O157:H7 were included in this study. The results indicate that 50 mM H2O2 caused a population reduction of 2.4 - 2.8 log10 (compared to non-treated control samples) in all six VTEC strains present on Romaine lettuce. Following the treatment, the transcription of genes related to oxidative stress (oxyR and sodA), general stress (uspA and rpoS), starvation (phoA), acid stress (gadA, gadB and gadW), and virulence (stx1A, stx2A and fliC) were dramatically downregulated in all six VTEC serotypes (P≤0.05) compared to not treated control samples. Therefore, VTEC O157:H7 and non-O157 serotypes on lettuce showed similar survival rates and gene transcription profiles in response to 50 mM H2O2 treatment. Thus, the results derived from this study provide a basic understanding of the influence of H2O2 treatment on the survival and virulence of VTEC O157:H7 and non-O157 serotypes on lettuce.
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 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".