Transcriptome Analysis of <i>Escherichia coli</i> O157:H7 Exposed to Lysates of Lettuce Leaves
Bibliographic record
Abstract
Harvesting and processing of leafy greens inherently cause plant tissue damage, creating niches on leaves that human pathogens can exploit. We previously demonstrated that Escherichia coli O157:H7 (EcO157) multiplies more rapidly on shredded leaves than on intact leaves (M. T. Brandl, Appl. Environ. Microbiol. 74:5285-5289, 2008). To investigate how EcO157 cells adapt to physicochemical conditions in injured lettuce tissue, we used microarray-based whole-genome transcriptional profiling to characterize gene expression patterns in EcO157 after 15- and 30-min exposures to romaine lettuce lysates. Multiple carbohydrate transport systems that have a role in the utilization of substrates known to be prevalent in plant cells were activated in EcO157. This indicates the availability to the human pathogen of a variety of carbohydrates released from injured plant cells that may promote its extensive growth in leaf lysates and, thus, in wounded leaf tissue. In addition, microarray analysis revealed the upregulation of numerous genes associated with EcO157 attachment and virulence, with oxidative stress and antimicrobial resistance (including the OxyR and Mar regulons), with detoxification of noxious compounds, and with DNA repair. Upregulation of oxidative stress and antimicrobial resistance genes in EcO157 was confirmed on shredded lettuce by quantitative reverse transcription-PCR. We further demonstrate that this adaptation to stress conditions imparts the pathogen with increased resistance to hydrogen peroxide and calcium hypochlorite. This enhanced resistance to chlorinated sanitizers combined with increased expression of virulence determinants and multiplication at sites of injury on the leaves may help explain the association of processed leafy greens with outbreaks of EcO157.
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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 | 0.001 | 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".