Hsp90 Regulates the Function of Argonaute 2 and Its Recruitment to Stress Granules and P-Bodies
Bibliographic record
Abstract
Argonaute proteins are effectors of RNA interference that function in the context of cytoplasmic ribonucleoprotein complexes to regulate gene expression. Processing bodies (PBs) and stress granules (SGs) are the two main types of ribonucleoprotein complexes with which Argonautes are associated. Targeting of Argonautes to these structures seems to be regulated by different factors. In the present study, we show that heat-shock protein (Hsp) 90 activity is required for efficient targeting of hAgo2 to PBs and SGs. Furthermore, pharmacological inhibition of Hsp90 was associated with reduced microRNA- and short interfering RNA-dependent gene silencing. Neither Dicer nor its cofactor TAR RNA binding protein (TRBP) associates with PBs or SGs, but interestingly, protein activator of the double-stranded RNA-activated protein kinase (PACT), another Dicer cofactor, is recruited to SGs. Formation of PBs and recruitment of hAgo2 to SGs were not dependent upon PACT (or TRBP) expression. Together, our data suggest that Hsp90 is a critical modulator of Argonaute function. Moreover, we propose that Ago2 and PACT form a complex that functions at the level of SGs.
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".