Identification of a comprehensive set of transcriptional regulators involved in the long-term survivability of Escherichia coli in soil
Bibliographic record
Abstract
Bacteria that typically do not thrive in soil can survive therein for long periods. While much research has been conducted on the external environmental factors affecting the long-term survival of bacteria in soil, their inherent factors are poorly understood. To adapt to environmental changes, bacteria alter their gene expression patterns using transcriptional regulators such as sigma factors. Using Escherichia coli as a model bacterium, we examined the effects of each transcriptional regulator on the long-term survivability of E. coli in soil. The survivability of 294 E. coli strains deficient in transcriptional regulators in soil was measured over 6 weeks. The results showed that ten strains deficient in transcription factors significantly reduced survivability, whereas four deficient strains increased it. The functions common to several of these transcriptional regulators included carbon and nitrogen metabolism, stationary phase adaptation, and osmotic stress adaptation. These transcription factors are often global regulators and conserved among other pathogenic bacterial species. Taken together, we successfully identified a comprehensive set of transcription factors involved in the long-term survival of E. coli in soil. These findings will be useful for understanding the mechanisms underlying the adaptation of microorganisms to soil environments.
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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 | 0.001 | 0.001 |
| 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".