The RNA‐binding protein HuD: a regulator of neuronal differentiation, maintenance and plasticity
Bibliographic record
Abstract
mRNA stability is increasingly recognized as being essential for controlling the expression of a wide variety of transcripts during neuronal development and synaptic plasticity. In this context, the role of AU-rich elements (ARE) contained within the 3' untranslated region (UTR) of transcripts has now emerged as key because of their high incidence in a large number of cellular mRNAs. This important regulatory element is known to significantly modulate the longevity of mRNAs by interacting with available stabilizing or destabilizing RNA-binding proteins (RBP). Thus, in parallel with the emergence of ARE, RBP are also gaining recognition for their pivotal role in regulating expression of a variety of mRNAs. In the nervous system, the member of the Hu family of ARE-binding proteins known as HuD, has recently been implicated in multiple aspects of neuronal function including the commitment and differentiation of neuronal precursors as well as synaptic remodeling in mature neurons. Through its ability to interact with ARE and stabilize multiple transcripts, HuD has now emerged as an important regulator of mRNA expression in neurons. The present review is designed to provide a comprehensive and updated view of HuD as an RBP in the nervous system. Additionally, we highlight the role of HuD in multiple aspects of a neuron's life from early differentiation to changes in mature neurons during learning paradigms and in response to injury and regeneration. Finally, we describe the current state of knowledge concerning the molecular and cellular events regulating the expression and activity of HuD in neurons.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".