Background

The mechanisms governing the correlation between exposure to ultrafine particles and the increased incidence of cardiovascular disease remain unknown. Ultrafine particles appear to cross the pulmonary epithelial barrier into the bloodstream, raising the possibility of direct contact with the vascular endothelium.

Objectives

Because endothelial inflammation is critical for the development of cardiovascular pathology, we hypothesized that direct exposure of human aortic endothelial cells (HAECs) to ultrafine particles induces an inflammatory response and that this response depends on particle composition.

Methods

To test the hypothesis, we incubated HAECs for 1-8 hr with different concentrations (0.001-50 mug/mL) of iron oxide (Fe(2)O(3)), yttrium oxide (Y(2)O(3)), and zinc oxide (ZnO) nanoparticles and subsequently measured mRNA and protein levels of the three inflammatory markers intra-cellular cell adhesion molecule-1, interleukin-8, and monocyte chemotactic protein-1. We also determined nanoparticle interactions with HAECs using inductively coupled plasma mass spectrometry and transmission electron microscopy.

Results

Our data indicate that nanoparticle delivery to the HAEC surface and uptake within the cells correlate directly with particle concentration in the cell culture medium. All three types of nanoparticles are internalized into HAECs and are often found within intracellular vesicles. Fe(2)O(3) nanoparticles fail to provoke an inflammatory response in HAECs at any of the concentrations tested; however, Y(2)O(3) and ZnO nanoparticles elicit a pronounced inflammatory response above a threshold concentration of 10 mug/mL. At the highest concentration, ZnO nanoparticles are cytotoxic and lead to considerable cell death.

Conclusions

These results demonstrate that inflammation in HAECs following acute exposure to metal oxide nanoparticles depends on particle composition.

Stochastic simulation is a key tool in population genetics, since the models involved are often analytically intractable and simulation is usually the only way of obtaining ground-truth data to evaluate inferences. Because of this, a large number of specialized simulation programs have been developed, each filling a particular niche, but with largely overlapping functionality and a substantial duplication of effort. Here, we introduce msprime version 1.0, which efficiently implements ancestry and mutation simulations based on the succinct tree sequence data structure and the tskit library. We summarize msprime's many features, and show that its performance is excellent, often many times faster and more memory efficient than specialized alternatives. These high-performance features have been thoroughly tested and validated, and built using a collaborative, open source development model, which reduces duplication of effort and promotes software quality via community engagement.