RegA, an AraC-Like Protein, Is a Global Transcriptional Regulator That Controls Virulence Gene Expression in <i>Citrobacter rodentium</i>
Bibliographic record
Abstract
Citrobacter rodentium is an attaching and effacing pathogen which causes transmissible colonic hyperplasia in mice. Infection with C. rodentium serves as a model for infection of humans with enteropathogenic and enterohemorrhagic Escherichia coli. To identify novel colonization factors of C. rodentium, we screened a signature-tagged mutant library of C. rodentium in mice. One noncolonizing mutant had a single transposon insertion in an open reading frame (ORF) which we designated regA because of its homology to genes encoding members of the AraC family of transcriptional regulators. Deletion of regA in C. rodentium resulted in markedly reduced colonization of the mouse intestine. Examination of lacZ transcriptional fusions using promoter regions of known and putative virulence-associated genes of C. rodentium revealed that RegA strongly stimulated transcription of two newly identified genes located close to regA, which we designated adcA and kfcC. The cloned adcA gene conferred autoaggregation and adherence to mammalian cells to E. coli strain DH5alpha, and a kfc mutation led to a reduction in the duration of intestinal colonization, but the kfc mutant was far less attenuated than the regA mutant. These results indicated that other genes of C. rodentium whose expression required activation by RegA were required for colonization. Microarray analysis revealed a number of RegA-regulated ORFs encoding proteins homologous to known colonization factors. Transcription of these putative virulence determinants was activated by RegA only in the presence of sodium bicarbonate. Taken together, these results show that RegA is a global regulator of virulence in C. rodentium which activates factors that are required for intestinal colonization.
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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 | 0.000 |
| 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".