Control of Selective mRNA Translation in Neuronal Subcellular Compartments in Health and Disease
Bibliographic record
Abstract
In multiple cell types, mRNAs are transported to subcellular compartments, where local translation enables rapid, spatially localized, and specific responses to external stimuli. Mounting evidence has uncovered important roles played by local translation in vivo in axon survival, axon regeneration, and neural wiring, as well as strong links between dysregulation of local translation and neurologic disorders. Omic studies have revealed that >1000 mRNAs are present and can be selectively locally translated in the presynaptic and postsynaptic compartments from development to adulthood in vivo . A large proportion of the locally translated mRNAs is specifically upregulated or downregulated in response to distinct extracellular signals. Given that the local translatome is large, selectively translated, and cue-specifically remodeled, a fundamental question concerns how selective translation is achieved locally. Here, we review the emerging regulatory mechanisms of local selective translation in neuronal subcellular compartments, their mRNA targets, and their orchestration. We discuss mechanisms of local selective translation that remain unexplored. Finally, we describe clinical implications and potential therapeutic strategies in light of the latest advances in gene therapy.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".