Functional characterization of variants found in Japanese patients with hereditary hemorrhagic telangiectasia
Bibliographic record
Abstract
Hereditary hemorrhagic telangiectasia (HHT) is an autosomal dominant form of vascular dysplasia. Genetic diagnosis is made by identifying loss-of-function variants in genes, such as ENG and ACVRL1. However, the causal mechanisms of various variants of unknown significance remains unclear. In this study, we analyzed 12 Japanese patients from 11 families who were clinically diagnosed with HHT. Sequencing analysis identified 11 distinct variants in ACVRL1 and ENG. Three of the 11 were truncating variants, leading to a definitive diagnosis, whereas the remaining eight were splice-site and missense variants that required functional analyses. In silico splicing analyses demonstrated that three variants, c.526-3C > G and c.598C > G in ACVRL1, and c.690-1G > A in ENG, caused aberrant splicing, as confirmed by a minigene assay. The five remaining missense variants were p.Arg67Gln, p.Ile256Asn, p.Leu285Pro, and p.Pro424Leu in ACVRL and p.Pro165His in ENG. Nanoluciferase-based bioluminescence analyses demonstrated that these ACVRL1 variants impaired cell membrane trafficking, resulting in the loss of bone morphogenetic protein 9 (BMP9) signal transduction. In contrast, the ENG mutation impaired BMP9 signaling despite normal cell membrane expression. The updated functional analysis methods performed in this study will facilitate effective genetic testing and appropriate medical care for patients with HHT.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".