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      Influences of chemotype and parental genotype on metabolic fingerprints of tansy plants uncovered by predictive metabolomics

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          Abstract

          Intraspecific plant chemodiversity shapes plant-environment interactions. Within species, chemotypes can be defined according to variation in dominant specialised metabolites belonging to certain classes. Different ecological functions could be assigned to these distinct chemotypes. However, the roles of other metabolic variation and the parental origin (or genotype) of the chemotypes remain poorly explored. Here, we first compared the capacity of terpenoid profiles and metabolic fingerprints to distinguish five chemotypes of common tansy ( Tanacetum vulgare) and depict metabolic differences. Metabolic fingerprints captured higher variation in metabolites while preserving the ability to define chemotypes. These differences might influence plant performance and interactions with the environment. Next, to characterise the influence of the maternal origin on chemodiversity, we performed variation partitioning and generalised linear modelling. Our findings revealed that maternal origin was a higher source of chemical variation than chemotype. Predictive metabolomics unveiled 184 markers predicting maternal origin with 89% accuracy. These markers included, among others, phenolics, whose functions in plant-environment interactions are well established. Hence, these findings place parental genotype at the forefront of intraspecific chemodiversity. We recommend considering this factor when comparing the ecology of various chemotypes. Additionally, the combined inclusion of inherited variation in main terpenoids and other metabolites in computational models may help connect chemodiversity and evolutionary principles.

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          Regularization Paths for Generalized Linear Models via Coordinate Descent

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            FactoMineR: AnRPackage for Multivariate Analysis

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              MetaboAnalyst 5.0: narrowing the gap between raw spectra and functional insights

              Since its first release over a decade ago, the MetaboAnalyst web-based platform has become widely used for comprehensive metabolomics data analysis and interpretation. Here we introduce MetaboAnalyst version 5.0, aiming to narrow the gap from raw data to functional insights for global metabolomics based on high-resolution mass spectrometry (HRMS). Three modules have been developed to help achieve this goal, including: (i) a LC–MS Spectra Processing module which offers an easy-to-use pipeline that can perform automated parameter optimization and resumable analysis to significantly lower the barriers to LC-MS1 spectra processing; (ii) a Functional Analysis module which expands the previous MS Peaks to Pathways module to allow users to intuitively select any peak groups of interest and evaluate their enrichment of potential functions as defined by metabolic pathways and metabolite sets; (iii) a Functional Meta-Analysis module to combine multiple global metabolomics datasets obtained under complementary conditions or from similar studies to arrive at comprehensive functional insights. There are many other new functions including weighted joint-pathway analysis, data-driven network analysis, batch effect correction, merging technical replicates, improved compound name matching, etc. The web interface, graphics and underlying codebase have also been refactored to improve performance and user experience. At the end of an analysis session, users can now easily switch to other compatible modules for a more streamlined data analysis. MetaboAnalyst 5.0 is freely available at https://www.metaboanalyst.ca . Graphical Abstract From raw data to statistical and functional insights using MetaboAnalyst 5.0.
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                Author and article information

                Contributors
                thomas.dussarrat@uni-bielefeld.de
                caroline.mueller@uni-bielefeld.de
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                19 July 2023
                19 July 2023
                2023
                : 13
                : 11645
                Affiliations
                [1 ]GRID grid.7491.b, ISNI 0000 0001 0944 9128, Department of Chemical Ecology, , Bielefeld University, ; Universitätsstr. 25, 33615 Bielefeld, Germany
                [2 ]CTL GmbH Bielefeld, Krackser Straße 12, 33659 Bielefeld, Germany
                Article
                38790
                10.1038/s41598-023-38790-7
                10356770
                37468576
                3a80f8a4-b4cb-4c45-8df2-eb8109926e62
                © The Author(s) 2023

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 23 February 2023
                : 14 July 2023
                Funding
                Funded by: German Research Foundation (DFG)
                Award ID: MU 1829/28-1
                Award Recipient :
                Funded by: Universität Bielefeld (3146)
                Categories
                Article
                Custom metadata
                © Springer Nature Limited 2023

                Uncategorized
                chemical ecology,metabolomics
                Uncategorized
                chemical ecology, metabolomics

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