MicroRNA-132 loss is associated with tau exon 10 inclusion in progressive supranuclear palsy
Bibliographic record
Abstract
Tauopathies represent a large class of neurological and movement disorders characterized by abnormal intracellular deposits of the microtubule-associated protein tau. It is now well established that mis-splicing of tau exon 10, causing an imbalance between three-repeat (3R) and four-repeat (4R) tau isoforms, can cause disease; however, the underlying mechanisms affecting tau splicing in neurons remain poorly understood. The small noncoding microRNAs (miRNAs), known for their critical role in posttranscriptional gene expression regulation, are increasingly acknowledged as important regulators of alternative splicing. Here, we identified a number of brain miRNAs, including miR-124, miR-9, miR-132 and miR-137, which regulate 4R:3R-tau ratios in neuronal cells. Analysis of miRNA expression profiles from sporadic progressive supranuclear palsy (PSP) patients, a major 4R-tau tauopathy, showed that miR-132 is specifically down-regulated in disease. We demonstrate that miR-132 directly targets the neuronal splicing factor polypyrimidine tract-binding protein 2 (PTBP2), which protein levels were increased in PSP patients. miR-132 overexpression or PTBP2 knockdown similarly affected endogenous 4R:3R-tau ratios in neuronal cells. Finally, we provide evidence that miR-132 is inversely correlated with PTBP2 during post-natal brain development at the time when 4R-tau becomes expressed. Taken together, these results suggest that changes in the miR-132/PTBP2 pathway could contribute to the abnormal splicing of tau exon 10 in the brain, and sheds light into the potential role played by miRNAs in a subset of tauopathies.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".