Long-wavelength traveling waves of vasomotion modulate the perfusion of cortex.

Brain arterioles are active, multicellular complexes whose diameters oscillate at ∼ 0.1 Hz. We assess the physiological impact and spatiotemporal dynamics of vaso-oscillations in the awake mouse.

A lone spike in blood glucose can enhance the thrombo-inflammatory response in cortical venules.

How transient hyperglycemia contributes to cerebro-vascular disease has been a challenge to study under controlled physiological conditions. We use amplified, ultrashort laser-pulses to physically disrupt brain-venule endothelium at targeted locations. This vessel disruption is performed in conjunction with transient hyperglycemia from a single injection of metabolically active -glucose into healthy mice.

Abortive intussusceptive angiogenesis causes multi-cavernous vascular malformations.

Mosaic inactivation of in humans causes cerebral cavernous malformations (CCMs) containing adjacent dilated blood-filled multi-cavernous lesions. We used CRISPR-Cas9 mutagenesis to induce mosaic inactivation of zebrafish resulting in a novel lethal multi-cavernous lesion in the embryonic caudal venous plexus (CVP) caused by obstruction of blood flow by intraluminal pillars. These pillars mimic those that mediate intussusceptive angiogenesis; however, in contrast to the normal process, the pillars failed to fuse to split the pre-existing vessel in two.

Endothelial struts enable the generation of large lumenized blood vessels de novo.

De novo blood vessel formation occurs through coalescence of endothelial cells (ECs) into a cord-like structure, followed by lumenization either through cell- or cord-hollowing. Vessels generated in this manner are restricted in diameter to one or two ECs, and these models fail to explain how vasculogenesis can form large-diameter vessels. Here, we describe a model for large vessel formation that does not require a cord-like structure or a hollowing step.

Metabolic Network Prediction of Drug Side Effects.

Drug side effects levy a massive cost on society through drug failures, morbidity, and mortality cases every year, and their early detection is critically important. Here, we describe the array of model-based phenotype predictors (AMPP), an approach that leverages medical informatics resources and a human genome-scale metabolic model (GSMM) to predict drug side effects. AMPP is substantially predictive (AUC > 0.