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Differential modulation of <i>E. coli</i> mRNA abundance by inhibitory proteins that alter the composition of the degradosome

2006· article· en· W2109425758 on OpenAlexfundno aff
Junjun Gao, Kangseok Lee, Meng Zhao, Ji Qiu, Xiaoming Zhan, Ankur Saxena, Christopher J. Moore, Stanley N. Cohen, George Georgiou

Bibliographic record

VenueMolecular Microbiology · 2006
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBacterial Genetics and Biotechnology
Canadian institutionsnot available
FundersNational Institute of General Medical SciencesNational Institutes of HealthPetroleum Technology Research Centre
KeywordsBiologyDegradosomeAbundance (ecology)Composition (language)Modulation (music)Messenger RNAInhibitory postsynaptic potentialCell biologyBiochemistryEcologyGenePhysicsNeuroscienceRNA editing

Abstract

fetched live from OpenAlex

In Escherichia coli the initial step in the processing or decay of many messenger and structural RNAs is mediated by the endonuclease RNase E, which forms the core of a large RNA-catalysis machine termed the degradosome. Previous experiments have identified a protein that globally modulates RNA abundance by binding to RNase E and regulating its endonucleolytic activity. Here we report the discovery of RraB, which interacts with a different site on RNase E and interferes with cleavage of a different set of transcripts. We show that expression of RraA or RraB in vivo is accompanied by dramatic, distinct, and inhibitor-specific changes in degradosome composition--and that these are in turn associated with alterations in RNA decay and global transcript abundance profiles that are dissimilar to the profile observed during simple RNase E deficiency. Our results reveal the existence of endonuclease binding proteins that modulate the remodelling of degradosome composition in bacteria and argue that such degradosome remodelling is a mechanism for the differential regulation of RNA cleavages in E. coli.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.008
Threshold uncertainty score0.407

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.003
GPT teacher head0.177
Teacher spread0.174 · 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 teacher head, 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

Citations122
Published2006
Admission routes1
Has abstractyes

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