Multi-omics on truncating <i>ASXL1</i> mutations in Bohring Opitz syndrome identify dysregulation of canonical and non-canonical Wnt signaling
Bibliographic record
Abstract
ABSTRACT ASXL1 ( Additional sex combs-like 1 ) plays key roles in epigenetic regulation of early developmental gene expression. De novo truncating mutations in ASXL1 cause Bohring-Opitz syndrome (BOS, OMIM #605039), a rare neurodevelopmental condition characterized by severe intellectual disabilities, characteristic facial features, hypertrichosis, increased risk of Wilms tumor, and variable congenital anomalies including heart defects and severe skeletal defects giving rise to a typical ‘BOS posture’. These BOS-causing ASXL1 variants are also high-prevalence somatic driver mutations in acute myeloid leukemia (AML). We use primary cells from BOS individuals (n = 18) and controls (n = 49) to dissect gene regulatory changes caused by ASXL1 mutations using comprehensive multi-omics assays for chromatin accessibility (ATAC-seq), DNA methylation, histone methylation binding, and transcriptome in peripheral blood and skin fibroblasts. Our data shows that regardless of cell type, ASXL1 mutations drive strong cross-tissue effects that disrupt multiple layers of the epigenome. The data showed a broad activation of canonical Wnt signaling at the transcriptional and protein levels and upregulation of VANGL2 , a planar cell polarity pathway protein that acts through non-canonical Wnt signaling to direct tissue patterning and cell migration. This multi-omics approach identifies the core impact of ASXL1 mutations and therapeutic targets for BOS and myeloid leukemias. Brief summary Germline ASXL1 mutations that cause Bohring Optiz syndrome disrupt the epigenome and dysregulate gene expression resulting in activation of canonical and non-canonical Wnt signaling pathways.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".