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Record W2144646507 · doi:10.1091/mbc.e12-12-0892

Hsp90 cochaperones p23 and FKBP4 physically interact with hAgo2 and activate RNA interference–mediated silencing in mammalian cells

2013· article· en· W2144646507 on OpenAlexafffund
Justin M. Pare, Paul LaPointe, Tom C. Hobman

Bibliographic record

VenueMolecular Biology of the Cell · 2013
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicHeat shock proteins research
Canadian institutionsUniversity of Alberta
FundersCanadian Institutes of Health ResearchAlberta InnovatesAlberta Cancer Foundation
KeywordsArgonauteRNA interferenceBiologyRasiRNARNA-induced silencing complexCell biologyGene silencingChaperone (clinical)RNA silencingSmall interfering RNAPiwi-interacting RNASmall RNAHsp90RNATrans-acting siRNAGeneticsHeat shock proteinGene

Abstract

fetched live from OpenAlex

Argonaute proteins and small RNAs together form the RNA-induced silencing complex (RISC), the central effector of RNA interference (RNAi). The molecular chaperone Hsp90 is required for the critical step of loading small RNAs onto Argonaute proteins. Here we show that the Hsp90 cochaperones Cdc37, Aha1, FKBP4, and p23 are required for efficient RNAi. Whereas FKBP4 and p23 form a stable complex with hAgo2, the function of Cdc37 in RNAi appears to be indirect and may indicate that two or more Hsp90 complexes are involved. Our data also suggest that p23 and FKBP4 interact with hAgo2 before small RNA loading and that RISC loading takes place in the cytoplasm rather than in association with RNA granules. Given the requirement for p23 and FKBP4 for efficient RNAi and that these cochaperones bind to hAgo2, we predict that loading of hAgo2 is analogous to Hsp90-mediated steroid hormone receptor activation. To this end, we outline a model in which FKBP4, p23, and Aha1 cooperatively regulate the progression of hAgo2 through the chaperone cycle. Finally, we propose that hAgo2 and RNAi can serve as a robust model system for continued investigation into the Hsp90 chaperone cycle.

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 score0.002
Threshold uncertainty score0.524

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 head0.006
GPT teacher head0.237
Teacher spread0.231 · 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

Citations40
Published2013
Admission routes2
Has abstractyes

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