Altered endothelial gene expression associated with hereditary haemorrhagic telangiectasia
Bibliographic record
Abstract
BACKGROUND: Mutations in endoglin (ENG) and activin receptor-like kinase 1 (ALK-1 or ACVRL1) genes are the underlying basis of hereditary haemorrhagic telangiectasia (HHT) types 1 and 2, respectively. Both genes belong to the transforming growth factor-beta (TGF-beta) receptors superfamily and are expressed in endothelial cells. The current model for HHT is that ENG or ALK-1 haplo-insufficiency affects angiogenesis and predisposes to vascular dysplasia and arteriovenous malformations. MATERIALS AND METHODS: Using microarray technology, we compared human umbilical vein endothelial cells (HUVEC) from newborns with ENG or ALK-1 mutations to control cells to search for gene profiles associated with early stages of the disease. Real-time polymerase chain reaction and Western blot analysis were used to validate a subset of the modulated genes and functionally related genes. RESULTS: Our results indicate that HHT endothelial cells in vitro display several gene expression disturbances, including genes associated with the activation phase of angiogenesis, with cell guidance and intercellular connections, and also with the TGF-beta pathway. Hierarchical clustering using modulated genes enables discrimination between affected and non-affected samples. CONCLUSION: HHT HUVECs display gene modulations which can suggest that ENG and ALK-1 haplo-insufficiency induces compensatory regulatory mechanisms at the expression levels.
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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) | 0.001 | 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".