A rabbit model of spontaneous thrombosis induced by lipopolysaccharide

Role of Neutrophils and NETs in Animal Models of Thrombosis

Thrombosis is one of the major causes of mortality worldwide. Notably, it is not only implicated in cardiovascular diseases, such as myocardial infarction (MI), stroke, and pulmonary embolism (PE), but also in cancers. Understanding the cellular and molecular mechanisms involved in platelet thrombus formation is a major challenge for scientists today. For this purpose, new imaging technologies (such as confocal intravital microscopy, electron microscopy, holotomography, etc.) coupled with animal models of thrombosis (mouse, rat, rabbit, etc.) allow a better overview of this complex physiopathological process. Each of the cellular components is known to participate, including the subendothelial matrix, the endothelium, platelets, circulating cells, and, notably, neutrophils. Initially known as immune cells, neutrophils have been considered to be part of the landscape of thrombosis for more than a decade. They participate in this biological process through their expression of tissue factor (TF) and protein disulfide isomerase (PDI). Moreover, highly activated neutrophils are described as being able to release their DNA and thus form chromatin networks known as “neutrophil extracellular traps” (NETs). Initially, described as “dead sacrifices for a good cause” that prevent the dissemination of bacteria in the body, NETs have also been studied in several human pathologies, such as cardiovascular and respiratory diseases. Many articles suggest that they are involved in platelet thrombus formation and the activation of the coagulation cascade. This review presents the models of thrombosis in which neutrophils and NETs are involved and describes their mechanisms of action. We have even highlighted the medical diagnostic advances related to this research.

A previously unreported impact of a PLA2G7 gene polymorphism on the plasma levels of lipoprotein-associated phospholipase A2 activity and mass

Lipoprotein-associated phospholipase A2 (Lp-PLA₂) levels are associated with the development of atherosclerosis. We aimed to assess the genetic determinants of Lp-PLA₂ activity and mass by genotyping multiple polymorphisms in PLA2G7, the gene encoding Lp-PLA₂, among 1258 participants from the Chinese Multi-provincial Cohort Study-Beijing Project. The Sequenom MassARRAY system, Taqman assay and direct sequencing were adopted. For the first time, the rs13218408 polymorphism was found to be significantly associated with reduced Lp-PLA₂ levels. We also confirmed the significant association of previously validated polymorphisms (rs1421378, rs1805018, rs16874954 and rs2216465), even after adjusting for traditional cardiovascular risk factors and for Bonferroni correction. Percentages of variance attributable to rs13218408 were 7.2% for activity and 13.3% for mass, and were secondary to those of rs16874954 (8.1% for activity and 16.9% for mass). A significant joint effect of rs13218408 and rs16874954 was observed on Lp-PLA₂ activity (P = 0.058) and mass (P = 0.003), with their minor alleles together linking to the largest reduction in Lp-PLA₂ levels (37.8% reduction in activity and 41.6% reduction in mass). Taken together, our findings show a significant association of a PLA2G7 polymorphism with Lp-PLA₂ levels, which was previously unreported in any population. The functionality of this genetic variation deserves further investigations.

A Novel Prioritization Method in Identifying Recurrent Venous Thromboembolism-Related Genes

Identifying the genes involved in venous thromboembolism (VTE) recurrence is important not only for understanding the pathogenesis but also for discovering the therapeutic targets. We proposed a novel prioritization method called Function-Interaction-Pearson (FIP) by creating gene-disease similarity scores to prioritize candidate genes underling VTE. The scores were calculated by integrating and optimizing three types of resources including gene expression, gene ontology and protein-protein interaction. As a result, 124 out of top 200 prioritized candidate genes had been confirmed in literature, among which there were 34 antithrombotic drug targets. Compared with two well-known gene prioritization tools Endeavour and ToppNet, FIP was shown to have better performance. The approach provides a valuable alternative for drug targets discovery and disease therapy.