Antagonistic regulation of motility and transcriptome expression by RpoN and RpoS in <i>Escherichia coli</i>
Bibliographic record
Abstract
Bacteria generally possess multiple σ factors that, based on structural and functional similarity, divide into two families: σ(70) and σ(N) . Many studies have revealed σ factor competition within the σ(70) family, while the competition between σ(N) and σ(70) families has yet to be fully explored. Here we report a global antagonistic effect on gene expression between two alternative σ factors, σ(N) (RpoN) and a σ(70) family protein σ(S) (RpoS). Mutations in rpoS and rpoN were found to inversely affect a number of cellular traits, such as the expression of flagellar genes, σ(N) -controlled growth on poor nitrogen sources, and σ(S) -directed expression of acid phosphatase AppA. Transcriptome analysis reveals that about 60% of genes in the RpoN regulon are under reciprocal RpoS control. Furthermore, loss of RpoN led to increased levels of RpoS, while RpoN levels were unaffected by the rpoS mutation. Expression of the flagellar σ(F) factor (FliA), another σ(70) family protein, is controlled positively by RpoN but negatively by RpoS. This positive control by RpoN is likely mediated through the flagellar regulator FlhDC, whose expression is RpoN-dependent. These findings unveil a complex regulatory interaction among σ(N) , σ(S) and σ(F) , which modulates motility, nitrogen utilization, stress response and many other cellular functions.
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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 | 0.001 | 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".