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Record W2127550934 · doi:10.1261/rna.901908

Two molecular features contribute to the Argonaute specificity for the microRNA and RNAi pathways in <i>C. elegans</i>

2008· article· en· W2127550934 on OpenAlexafffund
Guillaume Jannot, Marie-Eve L. Boisvert, Isabelle Banville, Martin J. Simard

Bibliographic record

VenueRNA · 2008
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA Interference and Gene Delivery
Canadian institutionsUniversité LavalHôtel-Dieu de Québec
FundersNational Center for Research ResourcesNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health ResearchNational Institutes of Health
KeywordsArgonauteDicerBiologyRNA interferenceRNA silencingTrans-acting siRNAGene silencingmicroRNARNA-induced silencing complexRasiRNACaenorhabditis elegansCell biologySmall interfering RNAGeneticsPiwi-interacting RNARNAComputational biologyGene

Abstract

fetched live from OpenAlex

In Caenorhabditis elegans, specific Argonaute proteins are dedicated to the RNAi and microRNA pathways. To uncover how the precise Argonaute selection occurs, we designed dsRNA triggers containing both miRNA and siRNA sequences. While dsRNA carrying nucleotides mismatches can only enter the miRNA pathway, a fully complementary dsRNA successfully rescues let-7 miRNA function and initiates silencing by RNAi. We demonstrated that RDE-1 is essential for RNAi induced by the perfectly paired trigger, yet is not required for silencing by the let-7 miRNA. In contrast, ALG-1/ALG-2 are required for the miRNA function, but not for the siRNA-directed gene silencing. Finally, a dsRNA containing a bulged miRNA and a perfectly paired siRNA can enter both pathways suggesting that the sorting of small RNAs occurs after that the dsRNA trigger has been processed by Dicer. Thus, our data suggest that the selection of Argonaute proteins is affected by two molecular features: (1) the structure of the small RNA duplex; and (2) the Argonautes specific characteristics.

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 score0.091
Threshold uncertainty score0.324

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 head0.013
GPT teacher head0.236
Teacher spread0.223 · 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

Citations45
Published2008
Admission routes2
Has abstractyes

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