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Record W4313526025 · doi:10.1101/2023.01.02.522506

Gre factors protect against phenotypic diversification and cheating in <i>Escherichia coli</i> populations under toxic metabolite stress

2023· preprint· en· W4313526025 on OpenAlexfundno aff
Darshan M. Sivaloganathan, Xuanqing Wan, Mark P. Brynildsen

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2023
Typepreprint
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBacterial Genetics and Biotechnology
Canadian institutionsnot available
FundersDivision of Chemical, Bioengineering, Environmental, and Transport SystemsNatural Sciences and Engineering Research Council of CanadaKeio UniversityNational Science Foundation
KeywordsMetaboliteBiologyPopulationEscherichia coliPhenotypeMicrobiologyBiochemistryGene

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.025
GPT teacher head0.231
Teacher spread0.205 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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