Broad-scale redistribution of mRNA abundance and transcriptional machinery in response to growth rate in Salmonella enterica serovar Typhimurium
Bibliographic record
Abstract
We have investigated the connection between the four-dimensional architecture of the bacterial nucleoid and the organism's global gene expression programme. By localizing the transcription machinery and the transcriptional outputs across the genome of the model bacterium Salmonella enterica serovar Typhimurium at different stages of the growth cycle, a surprising disconnection between gene dosage and transcriptional output was revealed. During exponential growth, gene output occurred chiefly in the Ori (origin), Ter (terminus) and NSL (non-structured left) domains, whereas the Left macrodomain remained transcriptionally quiescent at all stages of growth. The apparently high transcriptional output in Ter was correlated with an enhanced stability of the RNA expressed there during exponential growth, suggesting that longer mRNA half-lives compensate for low gene dosage. During exponential growth, RNA polymerase (RNAP) was detected everywhere, whereas in stationary phase cells, RNAP was concentrated in the Ter macrodomain. The alternative sigma factors RpoE, RpoH and RpoN were not required to drive transcription in these growth conditions, consistent with their observed binding to regions away from RNAP and regions of active transcription. Specifically, these alternative sigma factors were found in the Ter macrodomain during exponential growth, whereas they were localized at the Ori macrodomain in stationary phase.
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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) | 0.000 | 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".