Characterization of Zebrafish von Willebrand Factor Reveals Conservation of Domain Structure, Multimerization, and Intracellular Storage

von Willebrand disease (VWD) is the most common inherited human bleeding disorder and is caused by quantitative or qualitative defects in von Willebrand factor (VWF). VWF is a secreted glycoprotein that circulates as large multimers. While reduced VWF is associated with bleeding, elevations in overall level or multimer size are implicated in thrombosis. The zebrafish is a powerful genetic model in which the hemostatic system is well conserved with mammals. The ability of this organism to generate thousands of offspring and its optical transparency make it unique and complementary to mammalian models of hemostasis. Previously, partial clones of zebrafish vwf have been identified, and some functional conservation has been demonstrated. In this paper we clone the complete zebrafish vwf cDNA and show that there is conservation of domain structure. Recombinant zebrafish Vwf forms large multimers and pseudo-Weibel-Palade bodies (WPBs) in cell culture. Larval expression is in the pharyngeal arches, yolk sac, and intestinal epithelium. These results provide a foundation for continued study of zebrafish Vwf that may further our understanding of the mechanisms of VWD

Biomarkers of coagulation, endothelial function, and fibrinolysis in critically ill patients with COVID-19: A single-center prospective longitudinal study

Background: Immunothrombosis and coagulopathy in the lung microvasculature may lead to lung injury and disease progression in coronavirus disease 2019 (COVID-19). We aim to identify biomarkers of coagulation, endothelial function, and fibrinolysis that are associated with disease severity and may have prognostic potential. Methods: We performed a single-center prospective study of 14 adult COVID-19(+) intensive care unit patients who were age- and sex-matched to 14 COVID-19(−) intensive care unit patients, and healthy controls. Daily blood draws, clinical data, and patient characteristics were collected. Baseline values for 10 biomarkers of interest were compared between the three groups, and visualized using Fisher\u27s linear discriminant function. Linear repeated-measures mixed models were used to screen biomarkers for associations with mortality. Selected biomarkers were further explored and entered into an unsupervised longitudinal clustering machine learning algorithm to identify trends and targets that may be used for future predictive modelling efforts. Results: Elevated D-dimer was the strongest contributor in distinguishing COVID-19 status; however, D-dimer was not associated with survival. Variable selection identified clot lysis time, and antigen levels of soluble thrombomodulin (sTM), plasminogen activator inhibitor-1 (PAI-1), and plasminogen as biomarkers associated with death. Longitudinal multivariate k-means clustering on these biomarkers alone identified two clusters of COVID-19(+) patients: low (30%) and high (100%) mortality groups. Biomarker trajectories that characterized the high mortality cluster were higher clot lysis times (inhibited fibrinolysis), higher sTM and PAI-1 levels, and lower plasminogen levels. Conclusions: Longitudinal trajectories of clot lysis time, sTM, PAI-1, and plasminogen may have predictive ability for mortality in COVID-19