Characterization of aberrant splicing of von Willebrand factor in von Willebrand disease: an underrecognized mechanism
Bibliographic record
Abstract
Approximately 10% of von Willebrand factor (VWF) gene mutations are thought to alter messenger RNA (mRNA) splicing through disruption of consensus splice sites. This mechanism is likely underrecognized and affected by mutations outside consensus splice sites. During VWF synthesis, splicing abnormalities lead to qualitative defects or quantitative deficiencies in VWF. This study investigated the pathologic mechanism acting in 3 von Willebrand disease (VWD) families with putative splicing mutations using patient-derived blood outgrowth endothelial cells (BOECs) and a heterologous human embryonic kidney (HEK 293(T)) cell model. The exonic mutation c.3538G>A causes 3 in-frame splicing variants (23del, 26del, and 23/26del) which cannot bind platelets, blood coagulation factor VIII, or collagen, causing VWD through dominant-negative intracellular retention of coexpressed wild-type (WT) VWF, and increased trafficking to lysosomes. Individuals heterozygous for the c.5842+1G>C mutation produce exon 33 skipping, exons 33-34 skipping, and WT VWF transcripts. Pathogenic intracellular retention of VWF lacking exons 33-34 causes their VWD. The branch site mutation c.6599-20A>T causes type 1 VWD through mRNA degradation of exon 38 skipping transcripts. Splicing ratios of aberrant transcripts and coexpressed WT were altered in the BOECs with exposure to shear stress. This study provides evidence of mutations outside consensus splice sites disrupting splicing and introduces the concept that VWF splicing is affected by shear stress on endothelial cells.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".