Neuronal CTGF/CCN2 negatively regulates myelination in a mouse model of tuberous sclerosis complex
Bibliographic record
Abstract
Disruption of myelination during development has been implicated in a range of neurodevelopmental disorders including tuberous sclerosis complex (TSC). TSC patients with autism display impairments in white matter integrity. Similarly, mice lacking neuronal Tsc1 have a hypomyelination phenotype. However, the mechanisms that underlie these phenotypes remain unknown. In this study, we demonstrate that neuronal TSC1/2 orchestrates a program of oligodendrocyte maturation through the regulated secretion of connective tissue growth factor (CTGF). We characterize oligodendrocyte maturation both in vitro and in vivo. We find that neuron-specific Tsc1 deletion results in an increase in CTGF secretion that non–cell autonomously stunts oligodendrocyte development and decreases the total number of oligodendrocytes. Genetic deletion of CTGF from neurons, in turn, mitigates the TSC-dependent hypomyelination phenotype. These results show that the mechanistic target of rapamycin (mTOR) pathway in neurons regulates CTGF production and secretion, revealing a paracrine mechanism by which neuronal signaling regulates oligodendrocyte maturation and myelination in TSC. This study highlights the role of mTOR-dependent signaling between neuronal and nonneuronal cells in the regulation of myelin and identifies an additional therapeutic avenue for this disease.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".