Autoactivation of the AggR regulator of enteroaggregative<i>Escherichia coli in vitro</i>and<i>in vivo</i>
Bibliographic record
Abstract
Enteroaggregative Escherichia coli (EAEC) causes diarrhea in diverse populations worldwide. The AraC-like regulator AggR is a key virulence regulator in EAEC. AggR-regulated genes include those encoding the Aggregative Adherence Fimbria, the dispersin protein, and a type VI secretion system. This study characterizes the regulation of the aggR promoter (P(aggR)). Using primer extension analysis, the transcriptional start site of the aggR promoter was located 40 nucleotides upstream of the translational start. P(aggR) was found to be autoregulated and DNA footprinting revealed the presence of two AggR-binding sites: one upstream of the transcriptional start site and one downstream. Additionally, P(aggR) was found to be positively regulated by the DNA-binding protein FIS and negatively regulated by the global regulator H-NS. To further understand this complex regulation scheme, a bacterial luciferase reporter system was used with a mouse model of EAEC colonization. This allowed for the in vivo measurement of P(aggR), P(fis), and P(hns) activity. EAEC present in the mouse intestine possessed relatively high levels of P(fis) and P(aggR) activity and a low level of P(hns) when compared with in vitro experiments. The data provide significant insights into the regulation cascade leading to aggR expression in the mammalian intestine during EAEC 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) | 0.000 | 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 | 0.001 |
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".