MicroRNAs and the Regulation of Tau Metabolism
Bibliographic record
Abstract
Abnormal regulation of tau phosphorylation and/or alternative splicing is associated with the development of a large (>20) group of neurodegenerative disorders collectively known as tauopathies, the most common being Alzheimer's disease. Despite intensive research, little is known about the molecular mechanisms that participate in the transcriptional and posttranscriptional regulation of endogenous tau, especially in neurons. Recently, we showed that mice lacking Dicer in the forebrain displayed progressive neurodegeneration accompanied by disease-like changes in tau phosphorylation and splicing. Dicer is a key enzyme in the biogenesis of microRNAs (miRNAs), small noncoding RNAs that function as part of the RNA-induced silencing complex (RISC) to repress gene expression at the posttranscriptional level. We identified miR-16 and miR-132 as putative endogenous modulators of neuronal tau phosphorylation and tau exon 10 splicing, respectively. Interestingly, these miRNAs have been implicated in cell survival and function, whereas changes in miR-16/132 levels correlate with tau pathology in human neurodegenerative disorders. Thus, understanding how miRNA networks influence tau metabolism and possibly other biological systems might provide important clues into the molecular causes of tauopathies, particularly the more common but less understood sporadic forms.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".