Reducing Jagged 1 and 2 levels prevents cerebral arteriovenous malformations in matrix Gla protein deficiency.

Cerebral arteriovenous malformations (AVMs) are common vascular malformations, which may result in hemorrhagic strokes and neurological deficits. Bone morphogenetic protein (BMP) and Notch signaling are both involved in the development of cerebral AVMs, but the cross-talk between the two signaling pathways is poorly understood. Here, we show that deficiency of matrix Gla protein (MGP), a BMP inhibitor, causes induction of Notch ligands, dysregulation of endothelial differentiation, and the development of cerebral AVMs in MGP null (Mgp(-/-)) mice. Increased BMP activity due to the lack of MGP induces expression of the activin receptor-like kinase 1, a BMP type I receptor, in cerebrovascular endothelium. Subsequent activation of activin receptor-like kinase 1 enhances expression of Notch ligands Jagged 1 and 2, which increases Notch activity and alters the expression of Ephrin B2 and Ephrin receptor B4, arterial and venous endothelial markers, respectively. Reducing the expression of Jagged 1 and 2 in the Mgp(-/-) mice by crossing them with Jagged 1 or 2 deficient mice reduces Notch activity, normalizes endothelial differentiation, and prevents cerebral AVMs, but not pulmonary or renal AVMs. Our results suggest that Notch signaling mediates and can modulate changes in BMP signaling that lead to cerebral AVMs.

A role for the endothelium in vascular calcification.

RATIONALE: Vascular calcification is a regulated process that involves osteoprogenitor cells and frequently complicates common vascular disease, such as atherosclerosis and diabetic vasculopathy. However, it is not clear whether the vascular endothelium has a role in contributing osteoprogenitor cells to the calcific lesions. OBJECTIVE: To determine whether the vascular endothelium contributes osteoprogenitor cells to vascular calcification. METHODS AND RESULTS: In this study, we use 2 mouse models of vascular calcification, mice with gene deletion of matrix Gla protein, a bone morphogenetic protein (BMP)-inhibitor, and Ins2Akita/+ mice, a diabetes model. We show that enhanced BMP signaling in both types of mice stimulates the vascular endothelium to contribute osteoprogenitor cells to the vascular calcification. The enhanced BMP signaling results in endothelial-mesenchymal transitions and the emergence of multipotent cells, followed by osteoinduction. Endothelial markers colocalize with multipotent and osteogenic markers in calcified arteries by immunostaining and fluorescence-activated cell sorting. Lineage tracing using Tie2-Gfp transgenic mice supports an endothelial origin of the osteogenic cells. Enhancement of matrix Gla protein expression in Ins2Akita/+ mice, as mediated by an Mgp transgene, limits the generation of multipotent cells. Moreover, matrix Gla protein-depleted human aortic endothelial cells in vitro acquire multipotency rendering the cells susceptible to osteoinduction by BMP and high glucose. CONCLUSIONS: Our data suggest that the endothelium is a source of osteoprogenitor cells in vascular calcification that occurs in disorders with high BMP activation, such as deficiency of BMP-inhibitors and diabetes mellitus.

Crossveinless 2 regulates bone morphogenetic protein 9 in human and mouse vascular endothelium.

The importance of morphogenetic proteins (BMPs) and their antagonists in vascular development is increasingly being recognized. BMP-4 is essential for angiogenesis and is antagonized by matrix Gla protein (MGP) and crossveinless 2 (CV2), both induced by the activin receptor like-kinase 1 (ALK1) when stimulated by BMP-9. In this study, however, we show that CV2 preferentially binds and inhibits BMP-9 thereby providing strong feedback inhibition for BMP-9/ALK1 signaling rather than for BMP-4/ALK2 signaling. CV2 disrupts complex formation involving ALK2, ALK1, BMP-4, and BMP-9 required for the induction of both BMP antagonists. It also limits VEGF expression, proliferation, and tube formation in ALK1-expressing endothelial cells. In vivo, CV2 deficiency translates into a dysregulation of vascular BMP signaling, resulting in an abnormal endothelium with increased endothelial cellularity and expression of lineage markers for mature endothelial cells. Thus, mutual regulation by BMP-9 and CV2 is essential in regulating the development of the vascular endothelium.

Accuracy of Asthma Computable Phenotypes to Identify Pediatric Asthma at an Academic Institution.

OBJECTIVES: Asthma is a heterogenous condition with significant diagnostic complexity, including variations in symptoms and temporal criteria. The disease can be difficult for clinicians to diagnose accurately. Properly identifying asthma patients from the electronic health record is consequently challenging as current algorithms (computable phenotypes) rely on diagnostic codes (e.g., International Classification of Disease, ICD) in addition to other criteria (e.g., inhaler medications)-but presume an accurate diagnosis. As such, there is no universally accepted or rigorously tested computable phenotype for asthma. METHODS: We compared two established asthma computable phenotypes: the Chicago Area Patient-Outcomes Research Network (CAPriCORN) and Phenotype KnowledgeBase (PheKB). We established a large-scale, consensus gold standard (n = 1,365) from the University of California, Los Angeles Health System's clinical data warehouse for patients 5 to 17 years old. Results were manually reviewed and predictive performance (positive predictive value [PPV], sensitivity/specificity, F1-score) determined. We then examined the classification errors to gain insight for future algorithm optimizations. RESULTS: As applied to our final cohort of 1,365 expert-defined gold standard patients, the CAPriCORN algorithms performed with a balanced PPV = 95.8% (95% CI: 94.4-97.2%), sensitivity = 85.7% (95% CI: 83.9-87.5%), and harmonized F1 = 90.4% (95% CI: 89.2-91.7%). The PheKB algorithm was performed with a balanced PPV = 83.1% (95% CI: 80.5-85.7%), sensitivity = 69.4% (95% CI: 66.3-72.5%), and F1 = 75.4% (95% CI: 73.1-77.8%). Four categories of errors were identified related to method limitations, disease definition, human error, and design implementation. CONCLUSION: The performance of the CAPriCORN and PheKB algorithms was lower than previously reported as applied to pediatric data (PPV = 97.7 and 96%, respectively). There is room to improve the performance of current methods, including targeted use of natural language processing and clinical feature engineering.