Regulable neural progenitor-specific <i>Tsc1</i> loss yields giant cells with organellar dysfunction in a model of tuberous sclerosis complex
Bibliographic record
Abstract
Tuberous sclerosis complex (TSC) is a multiorgan genetic disease in which brain involvement causes epilepsy, intellectual disability, and autism. The hallmark pathological finding in TSC is the cerebral cortical tuber and its unique constituent, giant cells. However, an animal model that replicates giant cells has not yet been described. Here, we report that mosaic induction of Tsc1 loss in neural progenitor cells in Tsc1(cc) Nestin-rtTA(+) TetOp-cre(+) embryos by doxycycline leads to multiple neurological symptoms, including severe epilepsy and premature death. Strikingly, Tsc1-null neural progenitor cells develop into highly enlarged giant cells with enlarged vacuoles. We found that the vacuolated giant cells had multiple signs of organelle dysfunction, including markedly increased mitochondria, aberrant lysosomes, and elevated cellular stress. We found similar vacuolated giant cells in human tuber specimens. Postnatal rapamycin treatment completely reversed these phenotypes and rescued the mutants from epilepsy and premature death, despite prenatal onset of Tsc1 loss and mTOR complex 1 activation in the developing brain. This TSC brain model provides insights into the pathogenesis and organelle dysfunction of giant cells, as well as epilepsy control in patients with TSC.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".