Bibliographic record
Abstract
It is well established that changes in the regulation of tau expression and/or metabolism are involved in the development of Alzheimer's disease and related tauopathies. Despite intensive investigation, however, little is know about the molecular mechanisms that participate in the transcriptional and post-transcriptional regulation of endogenous tau, especially in neurons. We have recently shown that microRNAs, and in particular miR-132, play an important role in the regulation of tau exon 10 alternative splicing by modulating neuronal polypyrimidine-tract binding protein 2 (PTBP2). Interestingly, miR-132 is also predicted to target the 3' untranslated region (3'UTR) of tau, suggesting that tau expression is directly regulated by microRNAs. We used mouse neuroblastoma 2A (N 2 A) cells as biological model. These cells were transfected with either miR-132 mimics or inhibitors. Endogenous tau expression was assessed by Western blot and quantitative RT-PCR. These experiments were complemented with luciferase reporter assays. As hypothesized, overexpression of miR-132 decreased neuronal tau protein and mRNA levels, while the opposite effects were observed in response to the inhibition of miR-132. We could confirm the direct interaction between miR-132 and the murine tau 3'UTR. Interestingly, PTBP1 and 2 directly affected tau at the transcriptional level. Our results strongly suggest that miR-132 modulates tau metabolism through direct and indirect pathways. Further experiments are underway to understand the network between miR-132 and its targets, including tau.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".