A Positive Regulatory Loop Controls Expression of the Locus of Enterocyte Effacement-Encoded Regulators Ler and GrlA
Bibliographic record
Abstract
The formation of attaching and effacing (A/E) lesions on intestinal epithelial cells is an essential step in the pathogenesis of human enteropathogenic and enterohemorrhagic Escherichia coli and of the mouse pathogen Citrobacter rodentium. The genes required for the development of the A/E phenotype are located within a pathogenicity island known as the locus of enterocyte effacement (LEE). The LEE-encoded transcriptional regulators Ler, an H-NS-like protein, and GrlA, a member of a novel family of transcriptional activators, positively control the expression of the genes located in the LEE and their corresponding virulence. In this study, we used C. rodentium as a model to study the mechanisms controlling the expression of Ler and GrlA. By deletion analysis of the ler and grlRA regulatory regions and complementation experiments, negative and positive cis-acting regulatory motifs were identified that are essential for the regulation of both genes. This analysis confirmed that GrlA is required for the activation of ler, but it also showed that Ler is required for the expression of grlRA, revealing a novel regulatory loop controlling the optimal expression of virulence genes in A/E pathogens. Furthermore, our results indicate that Ler and GrlA induce the expression of each other by, at least in part, counteracting the repression mediated by H-NS. However, whereas GrlA is still required for the optimal expression of ler even in the absence of H-NS, Ler is not needed for the expression of grlRA in the absence of H-NS. This type of transcriptional positive regulatory loop represents a novel mechanism in pathogenic bacteria that is likely required to maintain an appropriate spatiotemporal transcriptional response during infection.
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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) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".