Gre factors protect against phenotypic diversification and cheating in <i>Escherichia coli</i> populations under toxic metabolite stress
Bibliographic record
Abstract
Abstract Nitric oxide (·NO) is one of the toxic metabolites that bacteria can be exposed to within phagosomes. Gre factors, which are also known as transcript cleavage factors or transcription elongation factors, relieve back-tracked transcription elongation complexes by cleaving nascent RNAs, which allows transcription to resume after stalling. Here we discovered that loss of both Gre factors in E. coli , GreA and GreB, significantly compromised ·NO detoxification through a phenotypic diversification of the population. Under normal culturing conditions, both wild-type and Δ greA Δ greB synthesized protein uniformly. However, treatment with ·NO led to bimodal protein expression in Δ greA Δ greB , whereas wild-type remained unimodal. Interestingly, exposure to another toxic metabolite of phagosomes, hydrogen peroxide (H 2 O 2 ), produced similar results. We found that the diversification in Δ greA Δ greB cultures required E. coli RNAP, occurred at the level of transcription, and could produce cheating where transcriptionally-deficient cells benefit from the detoxification activities of the transcriptionally-proficient subpopulation. Collectively, these results indicate that Gre factors bolster bacterial defenses by preventing phenotypic diversification and cheating in environments with fast-diffusing toxic metabolites. Importance Toxic metabolite stress occurs in a broad range of contexts that are important to human health, microbial ecology, and biotechnology; whereas Gre factors are highly conserved throughout the bacterial kingdom. Here we discovered that the Gre factors of E. coli prevent phenotypic diversification under toxic metabolite stress. Such conformist regulation improves populationwide removal of those stressors and protects against cheating, where one subpopulation commits resources to counter a threat, and the other subpopulation does not, yet both subpopulations benefit.
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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 | 0.001 |
| 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".