RNF213 Loss-of-Function Promotes Angiogenesis of Cerebral Microvascular Endothelial Cells in a Cellular State Dependent Manner
Bibliographic record
Abstract
Enhanced and aberrant angiogenesis is one of the main features of Moyamoya disease (MMD) pathogenesis. The ring finger protein 213 (RNF213) and the variant p.R4810K have been linked with higher risks of MMD and intracranial arterial occlusion development in east Asian populations. The role of RNF213 in diverse aspects of the angiogenic process, such as proliferation, migration and capillary-like formation, is well-known but has been difficult to model in vitro. To evaluate the effect of the RNF213 MMD-associated gene on the angiogenic activity, we have generated RNF213 knockout in human cerebral microvascular endothelial cells (hCMEC/D3-RNF213−/−) using the CRISPR-Cas9 system. Matrigel-based assay and a tri-dimensional (3D) vascularized model using the self-assembly approach of tissue engineering were used to assess the formation of capillary-like structures. Quite interestingly, this innovative in vitro model of MMD recapitulated, for the first time, disease-associated pathophysiological features such as significant increase in angiogenesis in confluent endothelial cells devoid of RNF213 expression. These cells, grown to confluence, also showed a pro-angiogenic signature, i.e., increased secretion of soluble pro-angiogenic factors, that could be eventually used as biomarkers. Interestingly, we demonstrated that that these MMD-associated phenotypes are dependent of the cellular state, as only noted in confluent cells and not in proliferative RNF213-deficient 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 | 0.001 |
| 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".