l-Arginine, as an essential amino acid, is a potential substitute for treating COPD via regulation of ROS/NLRP3/NF-κB signaling pathway

Abstract Backgrounds Chronic obstructive pulmonary disease (COPD) is a frequent and common disease in clinical respiratory medicine and its mechanism is unclear. The purpose of this study was to find the new biomarkers of COPD and elucidate its role in the pathogenesis of COPD. Analysis of metabolites in plasma of COPD patients were performed by ultra-high performance liquid chromatography (UPLC) and quadrupole time-of-flight mass spectrometry (TOF–MS). The differential metabolites were analyzed and identified by multivariate analysis between COPD patients and healthy people. The role and mechanisms of the differential biomarkers in COPD were verified with COPD rats, arginosuccinate synthetase 1 (ASS-l) KO mice and bronchial epithelial cells (BECs). Meanwhile, whether the differential biomarkers can be the potential treatment targets for COPD was also investigated. 85 differentials metabolites were identified between COPD patients and healthy people by metabonomic. Results l-Arginine (LA) was the most obvious differential metabolite among the 85 metabolites. Compare with healthy people, the level of LA was markedly decreased in serum of COPD patients. It was found that LA had protective effects on COPD with in vivo and in vitro experiments. Silencing Ass-1, which regulates LA metabolism, and α-methy-dl-aspartic (NHLA), an Ass-1 inhibitor, canceled the protective effect of LA on COPD. The mechanism of LA in COPD was related to the inhibition of ROS/NLRP3/NF-κB signaling pathway. It was also found that exogenous LA significantly improved COPD via regulation of ROS/NLRP3/NF-κB signaling pathway. l-Arginine (LA) as a key metabolic marker is identified in COPD patients and has a protective effect on COPD via regulation of ROS/NLRP3/NF-κB signaling pathway. Conclusion LA may be a novel target for the treatment of COPD and also a potential substitute for treating COPD

Construction of compatible and additive individual tree biomass models for Pinus tabulaeformis in China

Current biomass models for Chinese pine (Pinus tabulaeformis) fail to accurately estimate biomass in large geographic regions because they were: usually based on limited sample trees on local sites; incompatible with stem volume; and not additive among components and total biomass. This study was based on mensuration data of individual tree biomass from large samples. The purpose was to construct compatible and additive biomass models using error-in-variable simultaneous equations and dummy variable modeling approach. This approach could ensure compatibility of aboveground biomass model with biomass conversion factor (BCF) and stem volume model, and compatibility of belowground biomass model with root-to-shoot ratio (RSR) model. Also, stem, branch and foliage biomass models were additive to aboveground biomass model. Results showed that mean prediction errors (MPEâ s) of the developed one- and two-variable aboveground biomass models were less than 4%, the MPEâ s of three components (stem, branch, and foliage) and belowground biomass were less than 10%. Furthermore, climate impact on above- and below-ground biomass were analyzed. Aboveground biomass was related to mean annual temperature (MAT), while belowground biomass had no significant relationship with either MAT or mean annual precipitation (MAP). The developed models provide a good basis for estimating biomass of Chinese pine forests.The accepted manuscript in pdf format is listed with the files at the bottom of this page. The presentation of the authors' names and (or) special characters in the title of the manuscript may differ slightly between what is listed on this page and what is listed in the pdf file of the accepted manuscript; that in the pdf file of the accepted manuscript is what was submitted by the author