Translation stalling in neurons: a critical mechanism for timely protein delivery to distal cellular processes
Bibliographic record
Abstract
Neurons require local protein synthesis at synapses to control their proteome in response to local inputs. Work over the past two decades has revealed that neurons can use a specialized mechanism to transfer mRNAs and ribosomes to local sites in addition to canonical mechanisms used in many cell types. Neurons initiate translation on the ribosomes in the cellular soma, pause the process, and then package these stalled ribosomes into structures known as 'neuronal RNA granules' that are transported to synapses. This review provides an overview of recent studies that characterize these ribosomes/granules biochemically and structurally. These studies provide novel insights into the unique and specialized characteristics of neuronal ribosomes that facilitate this distinct transport mechanism. Many questions remain, including the influence of mRNA sequences on the stalling process and how ribosomes in the granules avoid the physiological responses that, in other cells, recycle ribosomal subunits upon stalling. Many neurodevelopmental disorders, such as autism and intellectual disability, occur when local translation is disrupted in neurons. Understanding mechanisms underlying the stalling of neuronal ribosomes, their transport to processes, and their reactivation may enable novel therapies for neurodevelopmental diseases.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".