Molecular and developmental deficits in Smith-Magenis syndrome human stem cell-derived cortical neural models
Bibliographic record
Abstract
Smith-Magenis syndrome (SMS) is a genomic disorder caused by the deletion of a chromosomal region at 17p11.2. Individuals with SMS are frequently diagnosed with autism and have profound cortical deficits, including reduced cortex volume, mild ventriculomegaly, and epilepsy. Here, we developed human induced pluripotent stem cell (hiPSC)-derived neuronal models to understand how del(17)p11.2 affects cortical development. Hi-C experiments identified local fusion and global reorganization of topological domains, as well as genome-wide miswiring of chromatin three-dimensional (3D) interactions in SMS hiPSCs and 3D cortical organoids. Single-nucleus RNA sequencing of SMS cortical organoids identified neuropsychiatric disease-enriched transcriptional signatures and dysregulation of genes involved in catabolic and biosynthetic pathways, cell-cycle processes, and neuronal signaling. SMS cortical organoids displayed reduced growth, enlarged ventricles, impaired cell-cycle progression, and accelerated neuronal maturation. Through the use of a complementary hiPSC-derived 2D cortical neuronal model, we report that SMS cortical neurons exhibited accelerated dendritic growth, followed by neuronal hyperexcitability associated with reduced potassium conductance. Our study demonstrates that del(17)p11.2 disrupts multiple steps of human cortical development, from chromatin wiring, transcriptional regulation, cell-cycle progression, and morphological maturation to neurophysiological properties, and hiPSC-derived models recapitulate key neuroanatomical and neurophysiological features of SMS.
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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 | 0.000 | 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".