Endogenous Nitric Oxide Can Enhance Oxidative Stress Caused by Air Pollutants and Explain Higher Susceptibility of Individuals with Inflammatory Disorders.

Air pollution causes morbidity and excess mortality. In the epithelial lining fluid of the respiratory tract, air pollutants trigger a chemical reaction sequence that causes the formation of noxious hydroxyl radicals that drive oxidative stress. For hitherto unknown reasons, individuals with pre-existing inflammatory disorders are particularly susceptible to air pollution. Through detailed multiphase chemical kinetic analysis, we show that the commonly elevated concentrations of endogenous nitric oxide in diseased individuals can increase the production of hydroxyl radicals via peroxynitrite formation. Our findings offer a molecular rationale of how adverse health effects and oxidative stress caused by air pollutants may be exacerbated by inflammatory disorders.

A numerical compass for experiment design in chemical kinetics and molecular property estimation.

Kinetic process models are widely applied in science and engineering, including atmospheric, physiological and technical chemistry, reactor design, or process optimization. These models rely on numerous kinetic parameters such as reaction rate, diffusion or partitioning coefficients. Determining these properties by experiments can be challenging, especially for multiphase systems, and researchers often face the task of intuitively selecting experimental conditions to obtain insightful results. We developed a numerical compass (NC) method that integrates computational models, global optimization, ensemble methods, and machine learning to identify experimental conditions with the greatest potential to constrain model parameters. The approach is based on the quantification of model output variance in an ensemble of solutions that agree with experimental data. The utility of the NC method is demonstrated for the parameters of a multi-layer model describing the heterogeneous ozonolysis of oleic acid aerosols. We show how neural network surrogate models of the multiphase chemical reaction system can be used to accelerate the application of the NC for a comprehensive mapping and analysis of experimental conditions. The NC can also be applied for uncertainty quantification of quantitative structure-activity relationship (QSAR) models. We show that the uncertainty calculated for molecules that are used to extend training data correlates with the reduction of QSAR model error. The code is openly available as the Julia package KineticCompass.

Oligomerization and tyrosine nitration enhance the allergenic potential of the birch and grass pollen allergens Bet v 1 and Phl p 5.

Protein modifications such as oligomerization and tyrosine nitration alter the immune response to allergens and may contribute to the increasing prevalence of allergic diseases. In this mini-review, we summarize and discuss relevant findings for the major birch and grass pollen allergens Bet v 1 and Phl p 5 modified with tetranitromethane (laboratory studies), peroxynitrite (physiological processes), and ozone and nitrogen dioxide (environmental conditions). We focus on tyrosine nitration and the formation of protein dimers and higher oligomers via dityrosine cross-linking and the immunological effects studied.