reactive nitrogen
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Abstract

Abstract Protein tyrosine nitration (PTN), in which tyrosine (Tyr) residues on proteins are converted into 3-nitrotyrosine (NT), is one of the post-translational modifications mediated by reactive nitrogen species (RNS). Many recent studies have reported that PTN contributed to signaling systems by altering the structures and/or functions of proteins. This study aimed to investigate connections between PTN and the inhibitory effect of nitrite-derived RNS on fermentation ability using the yeast Saccharomyces cerevisiae. The results indicated that RNS inhibited the ethanol production of yeast cells with increased intracellular pyruvate content. We also found that RNS decreased the activities of pyruvate decarboxylase (PDC) as a critical enzyme involved in ethanol production. Our proteomic analysis revealed that the main PDC isozyme Pdc1 underwent the PTN modification at Tyr38, Tyr157, and Tyr344. The biochemical analysis using the recombinant purified Pdc1 enzyme indicated that PTN at Tyr157 or Tyr344 significantly reduced the Pdc1 activity. Interestingly, the substitution of Tyr157 or Tyr344 to phenylalanine, which is no longer converted into NT, recovered the ethanol production under the RNS treatment conditions. These findings suggest that nitrite impairs the fermentation ability of yeast by inhibiting the Pdc1 activity via its PTN modification at Tyr157 and Tyr344 of Pdc1.

Peroxynitrite (ONOO–) is a potent reactive nitrogen species that plays a critical mediator in liver injury elicited by drugs such as acetaminophen (APAP). At a therapeutic dosage, most APAP is...

Abstract. Nitrous acid (HONO), one of the reactive nitrogen oxides (NOy), plays an important role in the formation of ozone (O3) and fine aerosols (PM2.5) in the urban atmosphere. In this study, a simulation model of Reactive Nitrogen species using Deep neural network model (RND) was constructed to calculate the HONO mixing ratios through a deep learning technique using measured variables. A Python-based Deep Neural Network (DNN) was trained, validated, and tested with HONO measurement data obtained in Seoul during the warm months from 2016 to 2019. A k-fold cross validation and test results confirmed the performance of RND v1.0 with an Index Of Agreement (IOA) of 0.79 ~ 0.89 and a Mean Absolute Error (MAE) of 0.21 ~ 0.31 ppbv. The RNDV1.0 adequately represents the main characteristics of HONO and thus, RND v1.0 is proposed as a supplementary model for calculating the HONO mixing ratio in a high- NOx environment.