Bibliographic record
Abstract
Methyl CpG Binding Protein 2 (MeCP2) is an epigenetic regulator capable of recognizing and binding to methylated DNA. Mutations in MECP2 are the primary cause of Rett Syndrome (RTT) and MECP2 Duplication Syndrome (MDS). RTT is a neurodevelopmental disorder that mainly affects young females. MDS on the other hand is 100% penetrant in males and is rarely reported in females. The two disorders, although caused by extremely different etiologies, exhibit many similarities in their phenotypes including but not limited to autistic features, learning impairments and seizures. However, the molecular basis of this phenotypic similarity remains unknown. No cure has been identified to date for RTT and MDS. Alternative splicing of Mecp2/MECP2 leads to the generation of two isoforms, MeCP2E1 and MeCP2E2. Limited knowledge exists on the expression patterns and function of the two isoforms. In this thesis, I have attempted to address this knowledge gap by taking part in the validation of custom-made MeCP2 isoform-specific antibodies that are capable of differentially recognizing MeCP2E1 and MeCP2E2. Using the custom-made MeCP2E1-specific antibody, I also demonstrate that MeCP2E1 is expressed at much higher levels in neurons, as compared to astrocytes. My studies into the functional role of MeCP2 isoforms in neurons suggest that overexpression of both MECP2E1 and MECP2E2 leads to reduced rRNA levels in neurons. The potential role of MeCP2 as a negative regulator of neuronal rRNA biogenesis is further corroborated by direct binding of MeCP2 to the rDNA promoter, specifically the methylated fraction of rDNAs. Preliminary evidence from my studies suggests that MECP2 duplication in mice leads to brain region-specific alterations in rRNA levels, specifically in the cerebellum. Thus, the data presented in this thesis addresses two important knowledge gaps in the field of MeCP2 research: the higher levels of MeCP2E1 in neurons compared to astrocytes and the molecular consequences of MECP2E1 and MECP2E2 overexpression in neurons.
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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 | 0.000 | 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) | 0.002 | 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".