Reciprocal and non-reciprocal effects of clinically relevant SETBP1 protein dosage changes
Bibliographic record
Abstract
Many genes in the human genome encode proteins that are dosage sensitive, meaning they require protein levels within a narrow range to properly execute function. To investigate if clinically relevant variation in protein levels impacts the same downstream pathways in human disease, we generated cell models of two SETBP1 syndromes: Schinzel-Giedion Syndrome (SGS) and SETBP1 haploinsufficiency disease (SHD), where SGS is caused by too much protein, and SHD is caused by not enough SETBP1. Using patient and sex-matched healthy first-degree relatives from both SGS and SHD SETBP1 cases, we assessed how SETBP1 protein dosage affects downstream pathways in human forebrain progenitor cells. We find that extremes of SETBP1 protein dose reciprocally influence important signalling molecules such as AKT, suggesting that the SETBP1 protein operates within a narrow dosage range and that extreme doses are detrimental. We identified SETBP1 nuclear bodies as interacting with the nuclear lamina and suggest that SETBP1 may organize higher order chromatin structure via links to the nuclear envelope. SETBP1 protein doses may exert significant influence on global gene expression patterns via these SETBP1 nuclear bodies. This work provides evidence for the importance of SETBP1 protein dose in human brain development, with implications for two neurodevelopmental disorders.
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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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".