Differential brain region‐specific expression of MeCP2 and BDNF in Rett Syndrome patients: a distinct grey‐white matter variation
Bibliographic record
Abstract
INTRODUCTION AND OBJECTIVES: Rett Syndrome (RTT) is a neurodevelopmental disorder caused by Methyl CpG Binding Protein 2 (MECP2) gene mutations. Previous studies of MeCP2 in the human brain showed variable and inconsistent mosaic-pattern immunolabelling, which has been interpreted as a reflection of activation-state variability. We aimed to study post mortem MeCP2 and BDNF (MeCP2 target) degradation and brain region-specific detection in relation to RTT pathophysiology. METHODS: We investigated MeCP2 and BDNF stabilities in non-RTT human brains by immunohistochemical labelling and compared them in three brain regions of RTT and controls. RESULTS: In surgically excised samples of human hippocampus and cerebellum, MeCP2 was universally detected. There was no significantly obvious difference between males and females. However, post mortem delay in autopsy samples had substantial influence on MeCP2 detection. Immunohistochemistry studies in RTT patients showed lower MeCP2 detection in glial cells of the white matter. Glial fibrillary acidic protein (GFAP) expression was also reduced in RTT brain samples without obvious change in myelin basic protein (MBP). Neurons did not show any noticeable decrease in MeCP2 detection. BDNF immunohistochemical detection showed an astroglial/endothelial pattern without noticeable difference between RTT and controls. CONCLUSIONS: Our findings indicate that MeCP2 protein is widely expressed in mature human brain cells at all ages. However, our data points towards a possible white matter abnormality in RTT and highlights the importance of studying human RTT brain tissues in parallel with research on animal and cell models of 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".