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Record W2119534200 · doi:10.1371/journal.pone.0038426

Ribonuclease E Modulation of the Bacterial SOS Response

2012· article· en· W2119534200 on OpenAlexfundno aff
Robert Manasherob, Christine Miller, Kwang-sun Kim, Stanley N. Cohen

Bibliographic record

VenuePLoS ONE · 2012
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBacterial Genetics and Biotechnology
Canadian institutionsnot available
FundersNational Institute of Allergy and Infectious DiseasesInstitute of GeneticsNational Institute of General Medical SciencesNational Institutes of Health
KeywordsSOS responseRepressor lexARNase PEndoribonucleaseBiologyRNase MRPCell biologyRibonucleaseDNA repairDNA damageRNase HGeneticsMolecular biologyDNARNAGeneTranscription factorRepressor

Abstract

fetched live from OpenAlex

Plants, animals, bacteria, and Archaea all have evolved mechanisms to cope with environmental or cellular stress. Bacterial cells respond to the stress of DNA damage by activation of the SOS response, the canonical RecA/LexA-dependent signal transduction pathway that transcriptionally derepresses a multiplicity of genes-leading to transient arrest of cell division and initiation of DNA repair. Here we report the previously unsuspected role of E. coli endoribonuclease RNase E in regulation of the SOS response. We show that RNase E deletion or inactivation of temperature-sensitive RNase E protein precludes normal initiation of SOS. The ability of RNase E to regulate SOS is dynamic, as down regulation of RNase E following DNA damage by mitomycin C resulted in SOS termination and restoration of RNase E function leads to resumption of a previously aborted response. Overexpression of the RraA protein, which binds to the C-terminal region of RNase E and modulates the actions of degradosomes, recapitulated the effects of RNase E deficiency. Possible mechanisms for RNase E effects on SOS are discussed.

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 integrity0.0000.000
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 head0.021
GPT teacher head0.209
Teacher spread0.188 · 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

Citations24
Published2012
Admission routes1
Has abstractyes

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Same venuePLoS ONESame topicBacterial Genetics and BiotechnologyFrench-language works237,207