The metabolic enzyme <scp>AdhE</scp> controls the virulence of <scp><i>E</i></scp><i>scherichia coli</i> <scp>O</scp>157:<scp>H</scp>7
Bibliographic record
Abstract
Classical studies have focused on the role that individual regulators play in controlling virulence gene expression. An emerging theme, however, is that bacterial metabolism also plays a key role in this process. Our previous work identified a series of proteins that were implicated in the regulation of virulence. One of these proteins was AdhE, a bi-functional acetaldehyde-CoA dehydrogenase and alcohol dehydrogenase. Deletion of its gene (adhE) resulted in elevated levels of extracellular acetate and a stark pleiotropic phenotype: strong suppression of the Type Three Secretion System (T3SS) and overexpression of non-functional flagella. Correspondingly, the adhE mutant bound poorly to host cells and was unable to swim. Furthermore, the mutant was significantly less virulent than its parent when tested in vivo, which supports the hypothesis that attachment and motility are central to the colonization process. The molecular basis by which AdhE affects virulence gene regulation was found to be multifactorial, involving acetate-stimulated transcription of flagella expression and post-transcriptional regulation of the T3SS through Hfq. Our study reveals fascinating insights into the links between bacterial physiology, the expression of virulence genes, and the underlying molecular mechanism mechanisms by which these processes are regulated.
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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) | 0.000 | 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".