scholarly journals Extensive Metabolic Profiles of Leaves and Stems from the Medicinal Plant Dendrobium officinale Kimura et Migo

Metabolites ◽  
2019 ◽  
Vol 9 (10) ◽  
pp. 215 ◽  
Author(s):  
Cao ◽  
Ji ◽  
Li ◽  
Lu ◽  
Tian ◽  
...  

Dendrobium officinale Kimura et Migo is a commercially and pharmacologically highly prized species widely used in Western Asian countries. In contrast to the extensive genomic and transcriptomic resources generated in this medicinal species, detailed metabolomic data are still missing. Herein, using the widely targeted metabolomics approach, we detect 649 diverse metabolites in leaf and stem samples of D. officinale. The majority of these metabolites were organic acids, amino acids and their derivatives, nucleotides and their derivatives, and flavones. Though both organs contain similar metabolites, the metabolite profiles were quantitatively different. Stems, the organs preferentially exploited for herbal medicine, contained larger concentrations of many more metabolites than leaves. However, leaves contained higher levels of polyphenols and lipids. Overall, this study reports extensive metabolic data from leaves and stems of D. officinale, providing useful information that supports ongoing genomic research and discovery of bioactive compounds.

2020 ◽  
Vol 36 (Supplement_2) ◽  
pp. i787-i794
Author(s):  
Gian Marco Messa ◽  
Francesco Napolitano ◽  
Sarah H. Elsea ◽  
Diego di Bernardo ◽  
Xin Gao

Abstract Motivation Untargeted metabolomic approaches hold a great promise as a diagnostic tool for inborn errors of metabolisms (IEMs) in the near future. However, the complexity of the involved data makes its application difficult and time consuming. Computational approaches, such as metabolic network simulations and machine learning, could significantly help to exploit metabolomic data to aid the diagnostic process. While the former suffers from limited predictive accuracy, the latter is normally able to generalize only to IEMs for which sufficient data are available. Here, we propose a hybrid approach that exploits the best of both worlds by building a mapping between simulated and real metabolic data through a novel method based on Siamese neural networks (SNN). Results The proposed SNN model is able to perform disease prioritization for the metabolic profiles of IEM patients even for diseases that it was not trained to identify. To the best of our knowledge, this has not been attempted before. The developed model is able to significantly outperform a baseline model that relies on metabolic simulations only. The prioritization performances demonstrate the feasibility of the method, suggesting that the integration of metabolic models and data could significantly aid the IEM diagnosis process in the near future. Availability and implementation Metabolic datasets used in this study are publicly available from the cited sources. The original data produced in this study, including the trained models and the simulated metabolic profiles, are also publicly available (Messa et al., 2020).


Life ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 544
Author(s):  
Jianfei Gao ◽  
Kangning Xiong ◽  
Wei Zhou ◽  
Weijie Li

Black tiger (Kadsura coccinea (Lem.)) has been reported to hold enormous pharmaceutical potential. The fruit and rhizome of black tiger are highly exploited in the pharmaceutical and other industries. However, the most important organs from the plant such as the leaf and stem are considered biowastes mainly because a comprehensive metabolite profile has not been reported in these organs. Knowledge of the metabolic landscape of the unexploited black tiger organs could help identify and isolate important compounds with pharmaceutical and nutritional values for a better valorization of the species. In this study, we used a widely targeted metabolomics approach to profile the metabolomes of the K. coccinea leaf (KL) and stem (KS) and compared them with the root (KR). We identified 642, 650 and 619 diverse metabolites in KL, KS and KR, respectively. A total of 555 metabolites were mutually detected among the three organs, indicating that the leaf and stem organs may also hold potential for medicinal, nutritional and industrial applications. Most of the differentially accumulated metabolites between organs were enriched in flavone and flavonol biosynthesis, phenylpropanoid biosynthesis, arginine and proline metabolism, arginine biosynthesis, tyrosine metabolism and 2-oxocarboxylic acid metabolism pathways. In addition, several important organ-specific metabolites were detected in K. coccinea. In conclusion, we provide extensive metabolic information to stimulate black tiger leaf and stem valorization in human healthcare and food.


Metabolites ◽  
2020 ◽  
Vol 10 (2) ◽  
pp. 46 ◽  
Author(s):  
Yajun Wang ◽  
Xiaojie Liang ◽  
Yuekun Li ◽  
Yunfang Fan ◽  
Yanlong Li ◽  
...  

This study aimed at assessing the climatic factors influencing the wolfberry fruit morphology, and the composition of its nutritious metabolites. The cultivar Ningqi1, widely grown in Northwest China was collected from three typical ecological growing counties with contrasting climatic conditions: Ningxia Zhongning (NF), Xinjiang Jinghe (XF) and Qinghai Nomuhong (QF). During the ripening period, 45 fruits from different plantations at each location were sampled. A total of 393 metabolites were detected in all samples through the widely targeted metabolomics approach and grouped into 19 known classes. Fruits from QF were the biggest followed by those from XF and NF. The altitude, relative humidity and light intensity had negative and strong correlations with most of the metabolites, suggesting that growing wolfberry in very high altitudes and under high light intensity is detrimental for the fruit nutritional quality. Soil moisture content is highly and negatively correlated with vitamins, organic acids and carbohydrates while moderately and positively correlated with other classes of metabolites. In contrast, air and soil temperatures exhibited positive correlation with majority of the metabolites. Overall, our results suggest high soil and air temperatures, low altitude and light intensity and moderate soil moisture, as the suitable conditions to produce Lycium fruits with high content of nutritious metabolites.


Metabolites ◽  
2020 ◽  
Vol 10 (11) ◽  
pp. 472
Author(s):  
Patrick Pann ◽  
Martin Hrabě de Angelis ◽  
Cornelia Prehn ◽  
Jerzy Adamski

A large part of metabolomics research relies on experiments involving mouse models, which are usually 6 to 20 weeks of age. However, in this age range mice undergo dramatic developmental changes. Even small age differences may lead to different metabolomes, which in turn could increase inter-sample variability and impair the reproducibility and comparability of metabolomics results. In order to learn more about the variability of the murine plasma metabolome, we analyzed male and female C57BL/6J, C57BL/6NTac, 129S1/SvImJ, and C3HeB/FeJ mice at 6, 10, 14, and 20 weeks of age, using targeted metabolomics (BIOCRATES AbsoluteIDQ™ p150 Kit). Our analysis revealed high variability of the murine plasma metabolome during adolescence and early adulthood. A general age range with minimal variability, and thus a stable metabolome, could not be identified. Age-related metabolomic changes as well as the metabolite profiles at specific ages differed markedly between mouse strains. This observation illustrates the fact that the developmental timing in mice is strain specific. We therefore stress the importance of deliberate strain choice, as well as consistency and precise documentation of animal age, in metabolomics studies.


2020 ◽  
Vol 323 ◽  
pp. 126822 ◽  
Author(s):  
Shicheng Zou ◽  
Jincheng Wu ◽  
Muhammad Qasim Shahid ◽  
Yehua He ◽  
Shunquan Lin ◽  
...  

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