Enhanced dense core granule function and adrenal hypersecretion in a mouse model of Rett syndrome
Bibliographic record
Abstract
Rett syndrome (RTT) is a progressive developmental disorder resulting from loss-of-function mutations in the gene encoding methyl-CpG-binding protein 2 (MeCP2), a transcription regulatory protein. The RTT phenotype is complex and includes severe cardiorespiratory abnormalities, dysautonomia and behavioral symptoms of elevated stress. These findings have been attributed to an apparent hyperactivity of the sympathetic nervous system due to defects in brainstem development; however, the possibility that the peripheral sympathoadrenal axis itself is abnormal has not been explored. The present study demonstrates that the adrenal medulla and sympathetic ganglia of Mecp2 null mice exhibit markedly reduced catecholamine content compared with wild-type controls. Despite this, null animals exhibit significantly higher plasma epinephrine levels, suggesting enhanced secretory granule function in adrenal chromaffin cells. Indeed, we find that Mecp2 null chromaffin cells exhibit a cell autonomous hypersecretory phenotype characterized by significant increases in the speed and size of individual secretory granule fusion events in response to electrical stimulation. These findings appear to indicate accelerated formation and enhanced dilation of the secretory granule fusion pore, resulting in elevated catecholamine release. Our data therefore highlight abnormal catecholamine function in the sympathoadrenal axis as a potential source of autonomic dysfunction in RTT. These findings may help to explain the apparent 'overactivity' of the sympathetic nervous system reported in patients with RTT.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".