Beneficial effects of miR-132/212 deficiency in the zQ175 mouse model of Huntington’s disease
Bibliographic record
Abstract
Huntington's disease (HD) is a rare genetic neurodegenerative disorder caused by an expansion of CAG repeats in the Huntingtin (HTT) gene. One hypothesis suggests that the mutant HTT gene contributes to HD neuropathology through transcriptional dysregulation involving microRNAs (miRNAs). In particular, the miR-132/212 cluster is strongly diminished in the HD brain. This study explores the effects of miR-132/212 deficiency specifically in adult HD zQ175 mice. The absence of miR-132/212 did not impact body weight, body temperature, or survival rates. Surprisingly, miR-132/212 loss seemed to alleviate, in part, the effects on endogenous Htt expression, HTT inclusions, and neuronal integrity in HD zQ175 mice. Additionally, miR-132/212 depletion led to age-dependent improvements in certain motor functions. Transcriptomic analysis revealed alterations in HD-related networks in WT- and HD zQ175-miR-132/212-deficient mice, including significant overlap in BDNF and Creb1 signaling pathways. Interestingly, however, a higher number of miR-132/212 gene targets was observed in HD zQ175 mice lacking the miR-132/212 cluster, especially in the striatum. These findings suggest a nuanced interplay between miR-132/212 expression and HD pathogenesis, providing potential insights into therapeutic interventions. Further investigation is needed to fully understand the underlying mechanisms and therapeutic potential of modulating miR-132/212 expression during HD progression.
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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) | 0.001 | 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 | 0.001 |
| 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".