Characterization of Multiple Exon 1 Variants in Mammalian HuD mRNA and Neuron-Specific Transcriptional Control via Neurogenin 2
Bibliographic record
Abstract
The RBP (RNA-binding protein) and Hu/ELAV family member HuD regulates mRNA metabolism of genes directly or indirectly involved in neuronal differentiation, learning and memory, and several neurological diseases. Given the important functions of HuD in a variety of processes, we set out to determine the mechanisms that promote HuD mRNA expression in neurons using a mouse model. Through several complementary approaches, we determined that the abundance of HuD mRNA is predominantly under transcriptional control in developing neurons. Bioinformatic and 5'RACE (rapid amplification of cDNA ends) analyses of the 5' genomic flanking region identified eight conserved HuD leader exons (E1s), two of which are novel. Expression of all E1 variants was determined in mouse embryonic (E14.5) and adult brains. Sequential deletion of the 5' regulatory region upstream of the predominantly expressed E1c variant revealed a well conserved 400 bp DNA region that contains five E-boxes and is capable of directing HuD expression specifically in neurons. Using EMSA (electrophoretic mobility shift assay), ChIP (chromatin immunoprecipitation), and 5' regulatory region deletion and mutation analysis, we found that two of these E-boxes are targets of Neurogenin 2 (Ngn2) and that this mechanism is important for HuD mRNA induction. Together, our findings reveal that transcriptional regulation of HuD involves the use of alternate leader exons and Ngn2 mediates neuron-specific mRNA expression. To our knowledge, this is the first study to identify molecular events that positively regulate HuD mRNA expression.
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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".