Acid-stress-induced changes in enterohaemorrhagic Escherichia coli O157 : H7 virulence
Bibliographic record
Abstract
Enterohaemorrhagic Escherichia coli (EHEC) O157 : H7 is naturally exposed to a wide variety of stresses including gastric acid shock, and yet little is known about how this stress influences virulence. This study investigated the impact of acid stress on several critical virulence properties including survival, host adhesion, Shiga toxin production, motility and induction of host-cell apoptosis. Several acid-stress protocols with relevance for gastric passage as well as external environmental exposure were included. Acute acid stress at pH 3 preceded by acid adaptation at pH 5 significantly enhanced the adhesion of surviving organisms to epithelial cells and bacterial induction of host-cell apoptosis. Motility was also significantly increased after acute acid stress. Interestingly, neither secreted nor periplasmic levels of Shiga toxin were affected by acid shock. Pretreatment of bacteria with erythromycin eliminated the acid-induced adhesion enhancement, suggesting that de novo protein synthesis was required for the enhanced adhesion of acid-shocked organisms. DNA microarray was used to analyse the transcriptome of an EHEC O157 : H7 strain exposed to three different acid-stress treatments. Expression profiles of acid-stressed EHEC revealed significant changes in virulence factors associated with adhesion, motility and type III secretion. These results document profound changes in the virulence properties of EHEC O157 : H7 after acid stress, provide a comprehensive genetic analysis to substantiate these changes and suggest strategies that this pathogen may use during gastric passage and colonization in the human gastrointestinal tract.
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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) | 0.000 | 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".