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Record W4213432794 · doi:10.1371/journal.pbio.3001528

The noncoding RNA CcnA modulates the master cell cycle regulators CtrA and GcrA in Caulobacter crescentus

2022· article· en· W4213432794 on OpenAlexaff
Wanassa Beroual, Karine Prévost, David Lalaouna, Nadia Ben Zaina, Odile Valette, Yann Denis, Meriem Djendli, Gaël Brasseur, Matteo Brilli, Marta Garrido, José I. Jiménez‐Zurdo, Éric Massé, Emanuele G. Biondi

Bibliographic record

VenuePLoS Biology · 2022
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBacterial Genetics and Biotechnology
Canadian institutionsUniversité de Sherbrooke
FundersAgence Nationale de la Recherche
KeywordsCaulobacter crescentusBiologyCell cycleCell biologyRegulatorCell divisionGeneticsComputational biologyCellGene

Abstract

fetched live from OpenAlex

Bacteria are powerful models for understanding how cells divide and accomplish global regulatory programs. In Caulobacter crescentus, a cascade of essential master regulators supervises the correct and sequential activation of DNA replication, cell division, and development of different cell types. Among them, the response regulator CtrA plays a crucial role coordinating all those functions. Here, for the first time, we describe the role of a novel factor named CcnA (cell cycle noncoding RNA A), a cell cycle-regulated noncoding RNA (ncRNA) located at the origin of replication, presumably activated by CtrA, and responsible for the accumulation of CtrA itself. In addition, CcnA may be also involved in the inhibition of translation of the S-phase regulator, GcrA, by interacting with its 5' untranslated region (5' UTR). Performing in vitro experiments and mutagenesis, we propose a mechanism of action of CcnA based on liberation (ctrA) or sequestration (gcrA) of their ribosome-binding site (RBS). Finally, its role may be conserved in other alphaproteobacterial species, such as Sinorhizobium meliloti, representing indeed a potentially conserved process modulating cell cycle in Caulobacterales and Rhizobiales.

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 score0.161
Threshold uncertainty score0.404

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 science0.0000.001
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 head0.008
GPT teacher head0.198
Teacher spread0.190 · 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

Citations13
Published2022
Admission routes1
Has abstractyes

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