In vivo regulation of amyloid precursor protein neuronal splicing by microRNAs
Bibliographic record
Abstract
The β-amyloid peptide that accumulate in Alzheimer's disease (AD) brain derive from proteolytic processing of the amyloid precursor protein (APP). Recent evidence suggest that microRNAs (miRNAs) participate in the post-transcriptional regulation of APP expression. Because gene dosage effects of the APP gene can cause genetic AD, dysregulation of the miRNA network could contribute significantly to disease. Here, we present evidence that, besides APP expression regulation, miRNAs are equally involved in the regulation of neuronal APP mRNA alternative splicing. Lack of miRNAs in post-mitotic neurons in vivo is associated with APP exons 7 and 8 inclusion, while ectopic expression of miR-124, an abundant neuronal-specific miRNA, reversed these effects in cultured neurons. Similar results were obtained by depletion of endogenous polypyrimidine tract binding protein 1 (PTBP1) in cells, a recognized miR-124 target gene. Furthermore, PTBP1 levels correlate with the presence of APP exons 7 and 8, while PTBP2 levels correlate with the skipping of these exons during neuronal differentiation. Finally, we show that miR-124 is down-regulated in AD brain. In sum, our results suggest that specific miRNAs are involved in the fine-tuning of APP alternative splicing in neurons. Since abnormal neuronal splicing of APP affects β-amyloid peptide production, these results could contribute to the understanding of the implication of miRNAs in brain health and disease.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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".