Loss of Gre factors leads to phenotypic heterogeneity and cheating in <i>Escherichia coli</i> populations under nitric oxide 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 Escherichia coli , GreA and GreB, significantly compromised ·NO detoxification due to ·NO-induced phenotypic heterogeneity in Δ greA Δ greB populations, which did not occur in wild-type cultures. Under normal culturing conditions, both wild-type and Δ greA Δ greB synthesized transcripts uniformly, whereas treatment with ·NO led to bimodal transcript levels in Δ greA Δ greB that were unimodal in wild-type. Interestingly, exposure to another toxic metabolite of phagosomes, hydrogen peroxide (H 2 O 2 ), produced analogous results. Furthermore, we showed that loss of Gre factors led to cheating under ·NO stress where transcriptionally deficient cells benefited from the detoxification activities of the transcriptionally proficient subpopulation. Collectively, these results show that loss of Gre factor activities produces phenotypic heterogeneity under ·NO and H 2 O 2 stress that can yield cheating between subpopulations. 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 loss of Gre factors in E. coli leads to phenotypic heterogeneity under ·NO and H 2 O 2 stress, which we further show with ·NO results in cheating between subpopulations. Collectively, these data suggest that Gre factors play a role in coping with toxic metabolite stress, and that loss of Gre factors can produce cheating between neighbors.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".