Impaired retinoic acid signaling in cerebral cavernous malformations.

The capillary-venous pathology cerebral cavernous malformation (CCM) is caused by loss of CCM1/Krev interaction trapped protein 1 (KRIT1), CCM2/MGC4607, or CCM3/PDCD10 in some endothelial cells. Mutations of CCM genes within the brain vasculature can lead to recurrent cerebral hemorrhages. Pharmacological treatment options are urgently needed when lesions are located in deeply-seated and in-operable regions of the central nervous system.

Novel Chronic Mouse Model of Cerebral Cavernous Malformations.

Background and Purpose- Cerebral cavernous malformations (CCMs) are vascular malformations of the brain that lead to cerebral hemorrhages. A pharmacological treatment is needed especially for patients with nonoperable deep-seated lesions. We and others obtained CCM mouse models that were useful for mechanistic studies and rapid trials testing the preventive effects of candidate drugs.

Systematic pharmacological screens uncover novel pathways involved in cerebral cavernous malformations.

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 mutant worm, zebrafish, mouse, or human endothelial cells.

The CCM1-CCM2 complex controls complementary functions of ROCK1 and ROCK2 that are required for endothelial integrity.

Endothelial integrity relies on a mechanical crosstalk between intercellular and cell-matrix interactions. This crosstalk is compromised in hemorrhagic vascular lesions of patients carrying loss-of-function mutations in cerebral cavernous malformation (CCM) genes. RhoA/ROCK-dependent cytoskeletal remodeling is central to the disease, as it causes unbalanced cell adhesion towards increased cell-extracellular matrix adhesions and destabilized cell-cell junctions.

Network-based analysis of omics data: the LEAN method.

Motivation: Most computational approaches for the analysis of omics data in the context of interaction networks have very long running times, provide single or partial, often heuristic, solutions and/or contain user-tuneable parameters.

Results: We introduce local enrichment analysis (LEAN) for the identification of dysregulated subnetworks from genome-wide omics datasets. By substituting the common subnetwork model with a simpler local subnetwork model, LEAN allows exact, parameter-free, efficient and exhaustive identification of local subnetworks that are statistically dysregulated, and directly implicates single genes for follow-up experiments.