Neuronal activity triggers widespread changes in RNA stability
Bibliographic record
Abstract
ABSTRACT Neuronal activity shapes brain development and refines synaptic connectivity in part through dynamic changes in gene expression. While activity-regulated transcriptional programs have been extensively characterized, the holistic effects of neuronal activity on the full RNA life cycle remain relatively unexplored. Here, we show that neuronal activity influences multiple stages of RNA metabolism in vitro and in vivo . Among these, RNA stability emerges as a previously underappreciated regulator of gene expression, exerting a stronger influence than transcription on total RNA levels for ∼15% of activity-dependent genes. We go on to profile 3′UTR mRNA motifs that are sufficient to modulate activity-dependent mRNA stability and employ machine learning to identify the neuronal-specific RNA-binding protein HuD as a key regulator of activity-dependent mRNA stabilization. We demonstrate that HuD shapes activity-dependent mRNA abundance of hundreds of transcripts in both soma and distal neuronal processes and that neuronal activity drives the reorganization of HuD-interacting proteins, thereby stabilizing HuD-bound mRNAs and directing them into translationally active granules. Finally, we find that many variants associated with autism spectrum disorder (ASD) and other neurodevelopmental disorders disrupt or promote aberrant activity-dependent changes in mRNA stability. These findings reveal mRNA stability as a widespread mechanism of stimulus-responsive gene regulation in neurons with direct implications for the understanding of neurodevelopmental disorders.
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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 | 0.000 | 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".