Selective preservation of MeCP2 in catecholaminergic cells is sufficient to improve the behavioral phenotype of male and female Mecp2-deficient mice
Bibliographic record
Abstract
Rett syndrome (RTT) is a neurodevelopmental disorder caused primarily by mutations of the X-linked MECP2 gene. Although the loss of MeCP2 function affects many neural systems, impairments of catecholaminergic function have been hypothesized to underlie several of the cardinal behavioral deficits of RTT patients and Mecp2-deficient mice. Although recent Mecp2 reactivation studies indicate that RTT may be a reversible condition, it remains unclear whether specifically preserving Mecp2 function within a specific system will be sufficient to convey beneficial effects. Here, we test whether the selective preservation of Mecp2 within catecholaminergic cells will improve the phenotype of Mecp2-deficient mice. Our results show that this targeted preservation of Mecp2 significantly improves the lifespan, phenotypic severity and cortical epileptiform discharge activity of both male and female Mecp2-deficient mice. Further, we found that the catecholaminergic preservation of Mecp2 also improves the ambulatory rate, rearing activity, motor coordination, anxiety and nest-building performances of Mecp2-deficient mice of each gender. Interestingly, our results also revealed a gender-specific improvement, as specific cortical and hippocampal electroencephalographic abnormalities were significantly improved in male, but not female, rescue mice. Collectively, these results support the role of the catecholaminergic system in the pathogenesis of RTT and provide proof-of-principle that restoring MeCP2 function within this specific system could represent a treatment strategy for RTT.
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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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".