MeCP2 Is an Epigenetic Factor That Links DNA Methylation with Brain Metabolism
Bibliographic record
Abstract
DNA methylation, one of the most well-studied epigenetic modifications, is involved in a wide spectrum of biological processes. Epigenetic mechanisms control cellular morphology and function. Such regulatory mechanisms involve histone modifications, chromatin remodeling, DNA methylation, non-coding regulatory RNA molecules, and RNA modifications. One of the most well-studied epigenetic modifications is DNA methylation that plays key roles in development, health, and disease. Our brain is probably the most complex part of our body, with a high level of DNA methylation. A key protein that binds to different types of methylated DNA in the brain is the methyl-CpG binding protein 2 (MeCP2). MeCP2 acts in a dose-dependent manner and its abnormally high or low expression level, deregulation, and/or genetic mutations lead to neurodevelopmental disorders and aberrant brain function. Recently, some of MeCP2-associated neurodevelopmental disorders have emerged as neurometabolic disorders, suggesting a role for MeCP2 in brain metabolism. Of note, MECP2 loss-of-function mutation in Rett Syndrome is reported to cause impairment of glucose and cholesterol metabolism in human patients and/or mouse models of disease. The purpose of this review is to outline the metabolic abnormalities in MeCP2-associated neurodevelopmental disorders that currently have no available cure. We aim to provide an updated overview into the role of metabolic defects associated with MeCP2-mediated cellular function for consideration of future therapeutic strategies.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".