Background and Objective Asthma as a chronic heterogeneous disease seriously affects the quality of life. Incorrect identification for its clinical phenotypes lead to a huge waste of medical resources. Metabolomic technique as a novel approach to explore the pathogenesis of diseases have not been used to study asthma based on their clear defined inflammatory phenotypes. This study is aimed to distinguish the divergent metabolic profile in different asthma phenotypes and clarify the pathogenesis of them. Methods Participants including eosinophilic asthmatics (EA, n=13), noneosinophilic asthmatics (NEA, n=16), and healthy controls (HC, n=15) were enrolled. A global profile of untargeted serum metabolomics was identified with Ultra Performance Liquid Chromatography–Mass Spectrometry technique. Results Multivariate analysis was performed and showed a clear distinction between EA, NEA, and HC. A total of 18 different metabolites were recognized between the three groups based on OPLS-DA model and involved in 10 perturbed metabolic pathways. Glycerophospholipid metabolism, retinol metabolism, and sphingolipid metabolism were identified as the most significant changed three pathways (impact > 0.1 and -log(P) > 4) between the phenotypes. Conclusions We showed that the different inflammatory phenotypes of asthma involve the immune regulation, energy, and nutrients metabolism. The clarified metabolic profile contributes to understanding the pathophysiology of asthma phenotypes and optimizing the therapeutic strategy against asthma heterogeneity.

Aiming at further systematically comparing the similarities and differences of the chemical components in ginseng of different ages, especially comparing the younger or the older and mountain-cultivated ginseng (MCG), 4, 5, 6-year-old cultivated ginseng (CG) and 12, 20-year-old MCG were chosen as the analytical samples in the present study. The combination of UPLC-QTOF-MS E , UNIFI platform and multivariate statistical analysis were developed to profile CGs and MCGs. By the screening analysis based on UNIFI, 126 chemical components with various structural types were characterized or tentatively identified from all the CG and MCG samples for the first time. The results showed that all the CG and MCG samples had the similar chemical composition, but there were significant differences in the contents of markers. By the metabolomic analysis based on multivariate statistical analysis, it was shown that CG 4-6 years , MCG 12 years and MCG 20 years samples were obviously divided into three different groups, and a total of 17 potential age-dependent markers enabling differentiation among the three groups of samples were discovered. For differentiation from other two kinds of samples, there were four robust makers such as α-linolenic acid, 9-octadecenoic acid, linoleic acid and panaxydol for CG 4-6 years , five robust makers including ginsenoside Re 1 , -Re 2 , -Rs 1 , malonylginsenoside Rb 2 and isomer of malonylginsenoside Rb 1 for MCG 20 years , and two robust makers, 24-hydroxyoleanolic acid and palmitoleic acid, for MCG 12 years were discovered, respectively. The proposed approach could be applied to directly distinguish MCG root ages, which is an important criterion for evaluating the quality of MCG. The results will provide the data for the further study on the chemical constituents of MCG.Molecules 2019, 24, 33 2 of 24 distinct [4,5], and more aged ginseng is usually of higher economic value. In an investigation of the characteristic components for distinguishing CG (4-7-year of age) and MCG (with 15-years of growth), 12 compounds, including ginsenoside Ra 3 /isomer, gypenoside XVII, quinquenoside R 1 , ginsenoside Ra 7 , notoginsenoside Fe, ginsenoside Ra 2 , ginsenoside Rs 6 /Rs 7 , malonyl ginsenoside Rc, malonyl ginsenoside Rb 1 , malonyl ginsenoside Rb 2 , palmitoleic acid, and ethyl linoleate were regarded as the characteristic chemical markers for the discrimination [6]. Recently, a UPLC/QTOF-MS-based metabolomics approach was applied to the global metabolite profiling of MCG leaf samples aged from 6 to 18 years, and the authors claimed that the approach could also be applied to discriminate MCG root ages indirectly [7]. It is undoubted that the developed method can be used as a standard protocol for discriminating and predicting MCG leaf ages directly, but there might be some inaccuracy and uncertainty when discriminating MCG root ages indirectly.In the past decades, some analytical methods focusing on ginsenosides had been used to distinguish MCG from CG, such as thin layer chromatography (TLC), or high p...

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