Characterization of an RNA Granule from Developing Brain
Bibliographic record
Abstract
In brain, mRNAs are transported from the cell body to the processes, allowing for local protein translation at sites distant from the nucleus. Using subcellular fractionation, we isolated a fraction from rat embryonic day 18 brains enriched for structures that resemble amorphous collections of ribosomes. This fraction was enriched for the mRNA encoding beta-actin, an mRNA that is transported in dendrites and axons of developing neurons. Abundant protein components of this fraction, determined by tandem mass spectrometry, include ribosomal proteins, RNA-binding proteins, microtubule-associated proteins (including the motor protein dynein), and several proteins described only as potential open reading frames. The conjunction of RNA-binding proteins, transported mRNA, ribosomal machinery, and transporting motor proteins defines these structures as RNA granules. Expression of a subset of the identified proteins in cultured hippocampal neurons confirmed that proteins identified in the proteomics were present in neurites associated with ribosomes and mRNAs. Moreover many of the expressed proteins co-localized together. Time lapse video microscopy indicated that complexes containing one of these proteins, the DEAD box 3 helicase, migrated in dendrites of hippocampal neurons at the same speed as that reported for RNA granules. Although the speed of the granules was unchanged by activity or the neurotrophin brain-derived neurotrophic factor, brain-derived neurotrophic factor, but not activity, increased the proportion of moving granules. These studies define the isolation and composition of RNA granules expressed in developing brain.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".