Systematic pharmacological screens uncover novel pathways involved in cerebral cavernous malformations
Bibliographic record
Abstract
Cerebral cavernous malformations (CCMs) are vascular lesions in the central nervous system causing strokes and seizures which currently can only be treated through neurosurgery. The disease arises through changes in the regulatory networks of endothelial cells that must be comprehensively understood to develop alternative, non‐invasive pharmacological therapies. Here, we present the results of several unbiased small‐molecule suppression screens in which we applied a total of 5,268 unique substances to CCM mutant worm, zebrafish, mouse, or human endothelial cells. We used a systems biology‐based target prediction tool to integrate the results with the whole‐transcriptome profile of zebrafish CCM2 mutants, revealing signaling pathways relevant to the disease and potential targets for small‐molecule‐based therapies. We found indirubin‐3‐monoxime to alleviate the lesion burden in murine preclinical models of CCM2 and CCM3 and suppress the loss‐of‐CCM phenotypes in human endothelial cells. Our multi‐organism‐based approach reveals new components of the CCM regulatory network and foreshadows novel small‐molecule‐based therapeutic applications for suppressing this devastating disease in patients. Currently, the only treatment for cerebral cavernous malformation (CCM) vasculature lesions is surgery. This study by Otten et al establishes a multi‐organismal pharmacological approach to suppress the formation of new lesions or to regress existing ones. Currently, the only treatment for cerebral cavernous malformation (CCM) vasculature lesions is surgery. This study by Otten et al establishes a multi‐organismal pharmacological approach to suppress the formation of new lesions or to regress existing ones.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".