Bibliographic record
Abstract
Objective: A recent report demonstrated that somatic loss of function variations in the HHT causative genes, ENG and ACVRL1, are associated with skin telangiectasias (Snellings et al., 2019).Here, we investigated the genetic mechanisms driving nasal telangiectasia formation in HHT patients.Methods: DNA was extracted from 15 fresh/frozen nasal telangiectasia, 4 dermal telangiectasia, and 10 control tissue biopsies resected from nine unrelated individuals with HHT.DNA was evaluated using a 736 vascular malformation and cancer next-generation sequencing (NGS) gene panel down to 1% somatic mosaicism.Spatial single cell transcriptomic data from nasal telangiectasia and control tissues using the 10 9 Genomics Visium platform was also examined.Results: Heterozygous germline mutations were identified in all tissue specimens.Telangiectasia tissues from 5/9 cases had a pathogenic somatic mutation ranging from 1.03-1.96% in the same gene that had the germline mutation.Four of fifteen (26.7%) nasal telangiectasia and one of four (25%) dermal telangiectasia had a detectable somatic second hit.Surprisingly, additional low level somatic mutations in other genes were identified in several telangiectasias.Spatial single cell sequencing data revealed differential expression patterns in nasal telangiectasia tissues versus controls.Conclusions: This is the first report that nasal telangiectasia in HHT are caused by very low level somatic second hit mutations.Our data is consistent with previous reports and suggest that bi-allelic loss of ENG and ACVRL1 is required for the development of vascular malformation lesions observed in HHT.Further spatial single cell sequencing data analysis may reveal additional mechanistic insights required for telangiectasia formation.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.430 | 0.262 |
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".