1. Data sharing in PredRet for accurate prediction of retention time: Application to plant food bioactive compounds
- Author
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Mark Philo, Kateřina Valentová, Ana Rodriguez-Mateos, Pierre Micheau, F. A. T. Barberán, Ville M. Koistinen, Pedro Mena, Gözde Gürdeniz, Marynka Ulaszewska, Sonia de Pascual-Teresa, Christof Van Poucke, László Abrankó, Stéphanie Durand, Conceição Almeida, Dorrain Yanwen Low, Dilip K. Rai, Wiesław Wiczkowski, Letizia Bresciani, Jan Stanstrup, Cristina Andres-Lacueva, Gesine Schmidt, Andreia Bento da Silva, Lars O. Dragsted, Senem Kamiloglu, Claudine Manach, Raúl González-Domínguez, Lucie Petrásková, Kati Hanhineva, Maria Rosário Bronze, Fulvio Mattivi, Esra Capanoglu, Rocío García-Villalba, European Cooperation in Science and Technology, European Commission, European Research Council, Nanyang Technological University, Prime Minister's Office Singapore, Agence Nationale de la Recherche (France), Czech Science Foundation, Universidade Nova de Lisboa, Fundação para a Ciência e a Tecnologia (Portugal), Lantmännen Research Foundation, Academy of Finland, University of Eastern Finland, Instituto de Salud Carlos III, Ministerio de Economía y Competitividad (España), Generalitat de Catalunya, Agencia Estatal de Investigación (España), Ministerio de Ciencia, Innovación y Universidades (España), Institución Catalana de Investigación y Estudios Avanzados, Consejo Superior de Investigaciones Científicas (España), Fundación Séneca, Gobierno de la Región de Murcia, Norwegian Institute of Food, Fisheries and Aquaculture Research, Carlsberg Foundation, Hungarian Academy of Sciences, Unité de Nutrition Humaine (UNH), Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université Clermont Auvergne (UCA), Institute of Public Health and Clinical Nutrition, University of Turku, Szent István University, King‘s College London, Universidade Nova de Lisboa = NOVA University Lisbon (NOVA), University Institute of Lisbon (ISCTE), Research Institute for Agricultural, Fisheries and Food (ILVO), University of Barcelona, CIBER de Fragilidad y Envejecimiento Saludable, Teagasc Ashtown Food Research Centre (Teagasc), Istanbul Technical University (ITÜ), Centro de Edafologia y Biologia aplicada del Segura (CEBAS - CSIC), Consejo Superior de Investigaciones Científicas [Madrid] (CSIC), Taif University (TU), Partenaires INRAE, Fdn Edmund Mach, IASMA Research and Innovation Centre, University of Trento [Trento], Norwegian Institute of Food,Fisheries and Aquaculture Research (NOFIMA), IT University of Copenhagen (ITU), Institute of Microbiology of the Czech Academy of Sciences [Prague, Czech Republic] (MBU / CAS), Czech Academy of Sciences [Prague] (CAS), Università degli studi di Parma = University of Parma (UNIPR), Department of Nutrition, Exercise and Sports [Copenhagen], Faculty of Science [Copenhagen], University of Copenhagen = Københavns Universitet (UCPH)-University of Copenhagen = Københavns Universitet (UCPH), Quadram Institute Bioscience [Norwich, U.K.] (QIB), Biotechnology and Biological Sciences Research Council (BBSRC), Fondazione Edmund Mach - Edmund Mach Foundation [Italie] (FEM), Department of Food & Drugs, Institute of Food Science, Technology and Nutrition (ICTAN-CSIC), Plateforme Exploration du Métabolisme (PFEM), Institut National de la Recherche Agronomique (INRA)-Université Blaise Pascal - Clermont-Ferrand 2 (UBP)-MetaboHUB-Clermont, MetaboHUB-MetaboHUB, and Institute of Animal Reproduction and Food Research of Polish Academy of Sciences
- Subjects
Mean squared prediction error ,Computational biology ,Plant foods ,01 natural sciences ,Plant food bioactive compounds ,Analytical Chemistry ,0404 agricultural biotechnology ,Metabolomics ,[CHIM.ANAL]Chemical Sciences/Analytical chemistry ,UHPLC ,Faculty of Science ,Metabolites ,liquid chromatography ,Settore CHIM/10 - CHIMICA DEGLI ALIMENTI ,2. Zero hunger ,Predicted retention time ,010401 analytical chemistry ,External validation ,Metabolomikk ,04 agricultural and veterinary sciences ,General Medicine ,NUTRITION&DIETETICS ,040401 food science ,0104 chemical sciences ,Data sharing ,Untargeted metabolomics ,Predicted retention time, metabolomics, plant food bioactive compounds, metabolites, data sharing, PredRet, liquid chromatography, HPLC, UHPLC ,HPLC ,PredRet ,[SDV.AEN]Life Sciences [q-bio]/Food and Nutrition ,Retention time ,Food Science - Abstract
Prediction of retention times (RTs) is increasingly considered in untargeted metabolomics to complement MS/MS matching for annotation of unidentified peaks. We tested the performance of PredRet (http://predret.org/) to predict RTs for plant food bioactive metabolites in a data sharing initiative containing entry sets of 29–103 compounds (totalling 467 compounds, >30 families) across 24 chromatographic systems (CSs). Between 27 and 667 predictions were obtained with a median prediction error of 0.03–0.76 min and interval width of 0.33–8.78 min. An external validation test of eight CSs showed high prediction accuracy. RT prediction was dependent on shape and type of LC gradient, and number of commonly measured compounds. Our study highlights PredRet’s accuracy and ability to transpose RT data acquired from one CS to another CS. We recommend extensive RT data sharing in PredRet by the community interested in plant food bioactive metabolites to achieve a powerful community-driven open-access tool for metabolomics annotation., The authors acknowledge networking support by the European Cooperation in Science and Technology (COST) Action FA 1403 POSITIVe (Interindividual Variation in Response to Consumption of Plant Food Bioactives and Determinants Involved). Dorrain Low has received the support of EU H2020 in the framework of the FP7-Marie Curie-COFUND People Programme, through award of the AgreenSkills+ Fellowship (grant number 609398) and Nanyang Technological University, Singapore, through award of the Presidential Postdoctoral Fellowship (grant number 001991-00001). The MetaboHUB French infrastructure has supported the INRAE platform (PFEM, MetaboHUB-Clermont) involved in this work (grant number ANR-INBS-0010). Kateřina Valentová and Lucie Petrásková acknowledge financial support from the Czech Science Foundation (grant number 19-00043S). The Faculty of Pharmacy of Lisbon University acknowledges FUNDAÇÃO PARA A CIÊNCIA E TECNOLOGIA and PORTUGAL 2020 for financial support of the Portuguese Mass Spectrometry Network (grant number LISBOA-01-0145-FEDER-402-022125). Kati Hanhineva and Ville Koistinen have received funding from the Academy of Finland (grant numbers 277986 and 312550), Lantmännen Foundation and EU H2020 FP7-Marie Curie-COFUND MoRE Programme (grant number 754412). Biocenter Finland has financially supported the LC-MS metabolomics unit of University of Eastern Finland. Cristina Andres-Lacueva and Raúl González-Domínguez thank CIBERFES and ISCIII projects AC19/00111 and AC19/00096 (co-funded by FEDER Program from EU, “A way to make Europe”), Generalitat de Catalunya’s Agency AGAUR (grant number 2017SGR1546), “Juan de la Cierva” program from MINECO (grant number IJC2019-041867-I) and ICREA Academia award 2018. Francisco A. Tomás-Barberán has received financial support from the Spanish National Research program (grant numbers AGL-2015-73107-EXP/AEI, CSIC 201870E014) and Fundación Seneca Región de Murcia (grant number 19900/GERM/15). Gesine Schmidt acknowledges support through the Norwegian Agriculture and Food Industry Research Funds (grant number 262300). Lars Dragsted and Jan Stanstrup thank the Carlsberg Foundation for a Semper Ardens grant to support this work. László Abrankó acknowledges the Hungarian Academy of Sciences for the János Bolyai Scholarship, and support of the EU and ESF co-financed project of SZIU (grant number EFOP-3.6.3-VEKOP-16-2017-00005). Sonia de Pascual-Teresa thanks the Spanish MINECO for financial support (grant number AGL2016-76832-R). Dilip K. Rai acknowledges Teagasc for the financial support through the Walsh Fellowship (grant number 2016038).
- Published
- 2021
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