Advances and Challenges in Understanding MicroRNA Function in Tauopathies: A Case Study of miR-132/212
Bibliographic record
Abstract
In the past decade, several groups have reported that microRNAs (miRNAs) can participate in the regulation of tau protein at different levels, including its expression, alternative splicing, phosphorylation, and aggregation. These observations are significant, since the abnormal regulation and deposition of tau is associated with nearly 30 neurodegenerative disorders. Interestingly, miRNA profiles go awry in tauopathies such as Alzheimer’s disease, progressive supranuclear palsy, and frontotemporal dementia. Understanding the role and impact of miRNAs on tau biology could therefore provide important insights into disease risk, diagnostics, and perhaps therapeutics. In this Perspective article, we discuss recent advances in miRNA research related to tau. While proof-of-principle studies hold promise, physiological validation remains limited. To help fill this gap, we describe herein a pure tauopathy mouse model deficient for the miR-132/212 cluster. This miRNA family is strongly downregulated in human tauopathies and shown to regulate tau in vitro and in vivo. No significant differences in survival, motor deficits or body weight were observed in PS19 mice lacking miR-132/212. Age-specific effects were seen on tau expression and phosphorylation but not aggregation. Moreover, various miR-132/212 targets previously implicated in tau modulation were unaffected (GSK-3beta, Foxo3a, Mapk1, p300) or, unexpectedly, reduced (Mapk3, Foxo1, p300, Calpain 2) in miR-132/212-deficient PS19 mice. These observations highlight the challenges of miRNA research in living models, and current limitations of transgenic tau mouse models lacking functional miRNA binding sites. Based on these findings, we finally recommend different strategies to better understand the role of miRNAs in tau physiology and pathology.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".