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      Chemotype classification and biomarker screening of male Eucommia ulmoides Oliv. flower core collections using UPLC-QTOF/MS-based non-targeted metabolomics

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          Abstract

          Background

          In the Chinese health care industry, male Eucommia ulmoides Oliv. flowers are newly approved as a raw material of functional food. Core collections have been constructed from conserved germplasm resources based on phenotypic traits and molecular markers. However, little is known about these collections’ phytochemical properties. This study explored the chemical composition of male E. ulmoides flowers, in order to provide guidance in the quality control, sustainable cultivation, and directional breeding of this tree species.

          Methods

          We assessed the male flowers from 22 core collections using ultra-performance liquid chromatography and quadrupole time-of-flight mass spectrometry (UPLC-QTOF/MS) non-targeted metabolomics, and analyzed them using multivariate statistical methods including principal component analysis (PCA), hierarchical cluster analysis (HCA), and orthogonal partial least squares discriminant analysis (OPLS-DA).

          Results

          We annotated a total of 451 and 325 metabolites in ESI+ and ESI− modes, respectively, by aligning the mass fragments of the secondary mass spectra with those in the database. Four chemotypes were well established using the ESI+ metabolomics data. Of the 29 screened biomarkers, 21, 6, 19, and 5 markers corresponded to chemotypes I, II, III, and IV, respectively. More than half of the markers belonged to flavonoid and amino acid derivative classes.

          Conclusion

          Non-targeted metabolomics is a suitable approach to the chemotype classification and biomarker screening of male E. ulmoides flower core collections. We first evaluated the metabolite profiles and compositional variations of male E. ulmoides flowers in representative core collections before establishing possible chemotypes and significant biomarkers denoting the variations. We used genetic variations to infer the metabolite compositional variations of male E. ulmoides flower core collections instead of using the geographical origins of the germplasm resources. The newly proposed biomarkers sufficiently classified the chemotypes to be applied for germplasm resource evaluation.

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          Most cited references38

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          MS-DIAL: Data Independent MS/MS Deconvolution for Comprehensive Metabolome Analysis

          Data-independent acquisition (DIA) in liquid chromatography tandem mass spectrometry (LC-MS/MS) provides more comprehensive untargeted acquisition of molecular data. Here we provide an open-source software pipeline, MS-DIAL, to demonstrate how DIA improves simultaneous identification and quantification of small molecules by mass spectral deconvolution. For reversed phase LC-MS/MS, our program with an enriched LipidBlast library identified total 1,023 lipid compounds from nine algal strains to highlight their chemotaxonomic relationships.
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            CAMERA: an integrated strategy for compound spectra extraction and annotation of liquid chromatography/mass spectrometry data sets.

            Liquid chromatography coupled to mass spectrometry is routinely used for metabolomics experiments. In contrast to the fairly routine and automated data acquisition steps, subsequent compound annotation and identification require extensive manual analysis and thus form a major bottleneck in data interpretation. Here we present CAMERA, a Bioconductor package integrating algorithms to extract compound spectra, annotate isotope and adduct peaks, and propose the accurate compound mass even in highly complex data. To evaluate the algorithms, we compared the annotation of CAMERA against a manually defined annotation for a mixture of known compounds spiked into a complex matrix at different concentrations. CAMERA successfully extracted accurate masses for 89.7% and 90.3% of the annotatable compounds in positive and negative ion modes, respectively. Furthermore, we present a novel annotation approach that combines spectral information of data acquired in opposite ion modes to further improve the annotation rate. We demonstrate the utility of CAMERA in two different, easily adoptable plant metabolomics experiments, where the application of CAMERA drastically reduced the amount of manual analysis. © 2011 American Chemical Society
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              Reflections on univariate and multivariate analysis of metabolomics data

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                Author and article information

                Contributors
                Journal
                PeerJ
                PeerJ
                PeerJ
                PeerJ
                PeerJ
                PeerJ Inc. (San Diego, USA )
                2167-8359
                21 August 2020
                2020
                : 8
                : e9786
                Affiliations
                [1 ]Paulownia Research & Development Center of China, National Forestry and Grassland Administration , Zhengzhou, Henan, China
                [2 ]Key Laboratory of Non-timber Forest Germplasm Enhancement & Utilization of State Forestry and Grassland Administration , Zhengzhou, Henan, China
                [3 ]Non-timber Forestry Research & Development Center, Chinese Academy of Forestry , Zhengzhou, Henan, China
                Article
                9786
                10.7717/peerj.9786
                7444510
                4619c4ac-5537-4cf3-9e5e-7e69f215aebb
                © 2020 Liu et al.

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ) and either DOI or URL of the article must be cited.

                History
                : 18 February 2020
                : 31 July 2020
                Funding
                Funded by: Fundamental Research Funds for Central Public-interest Scientific Institution
                Award ID: CAFYBB2018QA008
                This research was funded by the Fundamental Research Funds for Central Public-interest Scientific Institution (CAFYBB2018QA008). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Agricultural Science
                Plant Science
                Metabolic Sciences
                Forestry

                eucommia ulmoides oliv.,chemotype classification,biomarker screening,non-targeted metabolomics,opls-da

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