Molecular Composition of Staufen2-Containing Ribonucleoproteins in Embryonic Rat Brain
Bibliographic record
Abstract
Messenger ribonucleoprotein particles (mRNPs) are used to transport mRNAs along neuronal dendrites to their site of translation. Numerous mRNA-binding and regulatory proteins within mRNPs finely regulate the fate of bound-mRNAs. Their specific combination defines different types of mRNPs that in turn are related to specific synaptic functions. One of these mRNA-binding proteins, Staufen2 (Stau2), was shown to transport dendritic mRNAs along microtubules. Its knockdown expression in neurons was shown to change spine morphology and synaptic functions. To further understand the molecular mechanisms by which Stau2 modulates synaptic function in neurons, it is important to identify and characterize protein co-factors that regulate the fate of Stau2-containing mRNPs. To this end, a proteomic approach was used to identify co-immunoprecipitated proteins in Staufen2-containing mRNPs isolated from embryonic rat brains. The proteomic approach identified mRNA-binding proteins (PABPC1, hnRNP H1, YB1 and hsc70), proteins of the cytoskeleton (alpha- and beta-tubulin) and RUFY3 a poorly characterized protein. While PABPC1 and YB1 associate with Stau2-containing mRNPs through RNAs, hsc70 is directly bound to Stau2 and this interaction is regulated by ATP. PABPC1 and YB1 proteins formed puncta in dendrites of embryonic rat hippocampal neurons. However, they poorly co-localized with Stau2 in the large dendritic complexes suggesting that they are rather components of Stau2-containing mRNA particles. All together, these results represent a further step in the characterization of Stau2-containing mRNPs in neurons and provide new tools to study and understand how Stau2-containing mRNPs are transported, translationally silenced during transport and/or locally expressed according to cell needs.
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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 | 0.001 | 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".