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Argonaute Pull-Down and RISC Analysis Using 2’-O-Methylated Oligonucleotides Affinity Matrices

2011· article· en· W130693167 on OpenAlexafffund
Guillaume Jannot, Alejandro Vasquez-Rifo, Martin J. Simard

Bibliographic record

VenueMethods in molecular biology · 2011
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA Research and Splicing
Canadian institutionsUniversité LavalHôtel-Dieu de Québec
FundersCanadian Institutes of Health Research
KeywordsArgonauteOligonucleotideBiologyRNARNA-induced silencing complexCaenorhabditis elegansComputational biologyRNA interferenceCell biologyGeneticsDNAGene

Abstract

fetched live from OpenAlex

During the last decade, several novel small non-coding RNA pathways have been unveiled, which reach out to many biological processes. Common to all these pathways is the binding of a small RNA molecule to a protein member of the Argonaute family, which forms a minimal core complex called the RNA-induced silencing complex or RISC. The RISC targets mRNAs in a sequence-specific manner, either to induce mRNA cleavage through the intrinsic activity of the Argonaute protein or to abrogate protein synthesis by a mechanism that is still under investigation. We describe here, in details, a method for the affinity chromatography of the let-7 RISC starting from extracts of the nematode Caenorhabditis elegans. Our method exploits the sequence specificity of the RISC and makes use of biotinylated and 2'-O-methylated oligonucleotides to trap and pull-down small RNAs and their associated proteins. Importantly, this technique may easily be adapted to target other small RNAs expressed in different cell types or model organisms. This method provides a useful strategy to identify the proteins associated with the RISC, and hence gain insight in the functions of small RNAs.

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)0.002
metaresearch head score (Gemma)0.001
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: none
Teacher disagreement score0.295
Threshold uncertainty score0.880

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.040
GPT teacher head0.390
Teacher spread0.350 · 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

Citations23
Published2011
Admission routes2
Has abstractyes

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