Bacterial Regulon Evolution: Distinct Responses and Roles for the Identical OmpR Proteins of Salmonella Typhimurium and Escherichia coli in the Acid Stress Response
Bibliographic record
Abstract
The evolution of new gene networks is a primary source of genetic innovation that allows bacteria to explore and exploit new niches, including pathogenic interactions with host organisms. For example, the archetypal DNA binding protein, OmpR, is identical between Salmonella Typhimurium serovar Typhimurium and Escherichia coli, but regulatory specialization has resulted in different environmental triggers of OmpR expression and largely divergent OmpR regulons. Specifically, ompR mRNA and OmpR protein levels are elevated by acid pH in S. Typhimurium but not in E. coli. This differential expression pattern is due to differences in the promoter regions of the ompR genes and the E. coli ompR orthologue can be made acid-inducible by introduction of the appropriate sequences from S. Typhimurium. The OmpR regulon in S. Typhimurium overlaps that of E. coli at only 15 genes and includes many horizontally acquired genes (including virulence genes) that E. coli does not have. We found that OmpR binds to its genomic targets in higher abundance when the DNA is relaxed, something that occurs in S. Typhimurium as a result of acid stress and which is a requirement for optimal expression of its virulence genes. The genomic targets of OmpR do not share a strong nucleotide sequence consensus: we propose that the ability of OmpR to recruit additional genes to its regulon arises from its modest requirements for specificity in its DNA targets with its preference for relaxed DNA allowing it to cooperate with DNA-topology-based allostery to modulate transcription in response to acid stress.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".