Examination of Stress and Virulence Gene Expression in <i>Escherichia coli</i> O157:H7 Using Targeted Microarray Analysis
Bibliographic record
Abstract
Escherichia coli O157:H7 poses a threat to humans through food- and water-borne transmission. To investigate how environmental stresses affect the Escherichia coli O157:H7 transcriptome, we designed a targeted microarray consisting of stress response and virulence genes (n = 125) to analyze the impact of acidified (pH 3.5), cold (7.5 degrees C), and fresh tryptic soy broth (TSB) (37 degrees C) on E. coli O157:H7 stress response and virulence gene expression. Nutrient replenishment with fresh TSB resulted in 72 differentially expressed genes (> or = 1.5-fold change; p < 0.05), with 65 induced. All queried global and specific stress regulators were affected, as were 12 virulence genes. Cold-shocked cells displayed 17 differentially expressed genes, with 10 being induced. Induction of rpoS, members of the sigma(H) regulon (clpB, dnaK, ftsH), and acid resistance (AR) genes (gadA, gadX) was observed. Porin transcript (ompC, ompF) and gapA and tufA ancillary genes were repressed. Acid shock resulted in 24 differentially expressed genes, with 21 induced. No induction of any stationary phase AR system was observed, though acid-coping mechanisms were recruited, including mar and phoB induction, and repression of ompC and ompF. Stress regulators were induced, including relA, soxS, rpoE, and rpoH. The microarray data were validated by quantitative real-time polymerase chain reaction. Exposure to sublethal stress events led to the induction of diverse stress response networks. In the food chain, sublethal events may render cells increasingly resistant to future stresses, potentially leading to increased survival.
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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 | 0.000 |
| 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".