Zhi-Shu Tang1, Yan-Ru Liu1, Yang Lv1, Jin-Ao Duan2, Shi-Zhong Chen3, Jing Sun1, Zhong-Xing Song1, Xiao-Min Wu4, Li Liu5. 1. Shaanxi Research Centre on Discovery & Innovation of New Medicine, Shaanxi Collaborative Innovation Center of Chinese Medicinal Resources Industrialization, Shaanxi University of Chinese Medicine, 712083 Xianyang, PR China. 2. Key Laboratory for High Technology Research of TCM Formulae and Jiangsu Collaborative Innovation Center of Chinese Medicinal Resources Industrialization, Nanjing University of Chinese Medicine, 210023 Nanjing, PR China. 3. School of Pharmaceutical Sciences, Peking University, 100191 Beijing, PR China. 4. Shaanxi Institute of Zoology, Northwest Institute of Endangered Zoological Species, 710032 Xi'an, PR China. 5. Shaanxi Research Centre on Discovery & Innovation of New Medicine, Shaanxi Collaborative Innovation Center of Chinese Medicinal Resources Industrialization, Shaanxi University of Chinese Medicine, 712083 Xianyang, PR China. Electronic address: 1501011@sntcm.edu.cn.
Abstract
BACKGROUND: Common farming environmental elements, such as longitude, latitude, and altitude, and physiological conditions, such as age and body weight, are thought to influence medicinal animal homeostasis and material quality by altering endocrine functions for primary and secondary metabolite formation. However, the currently available methods for evaluating complex components of traditional Chinese animal medicines have insufficient sensitivity and specificity. PURPOSE: Characterizing the primary/secondary metabolomes of medicinal animals is essential for understanding their material basis, controlling product quality, and reflecting on distribution interactions. Therefore, this study aimed to screen ecological- and physiological-related metabolites in captive Moschus berezovskii throughout the collection period based on the quality marker (Q-marker) concept. STUDY DESIGN AND METHODS: Fifty-one musk deer samples from 12 different distribution farms ranging in age from 2 to 11 years were enrolled. Differentially expressed musk metabolites were assessed via chromatography-tandem mass spectrometry technologies. A metabolome that mapped connections among these factors was established using chemometric and topological calculations. RESULTS: Statistical analysis revealed that muscone, cis-9-hexadecenal, antioxidant 2264, prasterone-3-sulfate, androstan-17-one, and 1,2-benzenedicarboxylic acid showed significantly altered expression. Partial least squares (PLS) regression analysis of qualified data for these 6 secondary metabolites (active components) demonstrated that age is the most important factor underlying the varying levels of muscone, androstan-17-one and 1,2-benzenedicarboxylic acid. Furthermore, weight was the most important factor for cis-9-hexadecenal, longitude was important for antioxidant 2264, latitude was important for prasterone-3-sulfate, and altitude was important for antioxidant 2264, androstan-17-one and 1,2-benzenedicarboxylic. Metabolite analysis within the MetaboAnalyst (MetPA) suite showed that 18 candidate biomarker metabolites were screened, including allantoin, glycine, serine, creatine, alanine, taurine, lactate, 2-oxoglutarate (2-OG), fumarate, proline, xanthine, cytosine, carnitine, arginine, threonine, aspartate, and urea. Metabolic network analysis showed 4 important pathways that were involved: arginine and proline metabolism, the urea cycle, aspartate metabolism, and glycine, serine and threonine metabolism. CONCLUSION: Using this combined metabolomic and chemometric approach, this study was successful in screening Q-markers for musk quality control and provided new insights into correlations among "ecological & physiological factors→Q-markers→metabolites", which potentially provides crucial information for musk breeding and material quality control.
BACKGROUND: Common farming environmental elements, such as longitude, latitude, and altitude, and physiological conditions, such as age and body weight, are thought to influence medicinal animal homeostasis and material quality by altering endocrine functions for primary and secondary metabolite formation. However, the currently available methods for evaluating complex components of traditional Chinese animal medicines have insufficient sensitivity and specificity. PURPOSE: Characterizing the primary/secondary metabolomes of medicinal animals is essential for understanding their material basis, controlling product quality, and reflecting on distribution interactions. Therefore, this study aimed to screen ecological- and physiological-related metabolites in captive Moschus berezovskii throughout the collection period based on the quality marker (Q-marker) concept. STUDY DESIGN AND METHODS: Fifty-one musk deer samples from 12 different distribution farms ranging in age from 2 to 11 years were enrolled. Differentially expressed musk metabolites were assessed via chromatography-tandem mass spectrometry technologies. A metabolome that mapped connections among these factors was established using chemometric and topological calculations. RESULTS: Statistical analysis revealed that muscone, cis-9-hexadecenal, antioxidant 2264, prasterone-3-sulfate, androstan-17-one, and 1,2-benzenedicarboxylic acid showed significantly altered expression. Partial least squares (PLS) regression analysis of qualified data for these 6 secondary metabolites (active components) demonstrated that age is the most important factor underlying the varying levels of muscone, androstan-17-one and 1,2-benzenedicarboxylic acid. Furthermore, weight was the most important factor for cis-9-hexadecenal, longitude was important for antioxidant 2264, latitude was important for prasterone-3-sulfate, and altitude was important for antioxidant 2264, androstan-17-one and 1,2-benzenedicarboxylic. Metabolite analysis within the MetaboAnalyst (MetPA) suite showed that 18 candidate biomarker metabolites were screened, including allantoin, glycine, serine, creatine, alanine, taurine, lactate, 2-oxoglutarate (2-OG), fumarate, proline, xanthine, cytosine, carnitine, arginine, threonine, aspartate, and urea. Metabolic network analysis showed 4 important pathways that were involved: arginine and proline metabolism, the urea cycle, aspartate metabolism, and glycine, serine and threonine metabolism. CONCLUSION: Using this combined metabolomic and chemometric approach, this study was successful in screening Q-markers for musk quality control and provided new insights into correlations among "ecological & physiological factors→Q-markers→metabolites", which potentially provides crucial information for musk breeding and material quality control.