An Emerging Role of m6A in Memory: A Case for Translational Priming
Bibliographic record
Abstract
Investigation into the role of methylation of the adenosine base (m6A) of RNA has only recently begun, but it quickly became apparent that m6A is able to control and fine-tune many aspects of mRNA, from splicing to translation. The ability of m6A to regulate translation distally, away from traditional sites near the nucleus, quickly caught the eye of neuroscientists because of implications for selective protein translation at synapses. Work in the brain has demonstrated how m6A is functionally required for many neuronal functions, but two in particular are covered at length here: The role of m6A in 1) neuron development; and 2) memory formation. The purpose of this review is not to cover all data about m6A in the brain. Instead, this review will focus on connecting mechanisms of m6A function in neuron development, with m6A's known function in memory formation. We will introduce the concept of "translational priming" and discuss how current data fit into this model, then speculate how m6A-mediated translational priming during memory consolidation can regulate learning and memory locally at the synapse.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| 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".