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.
| Original language | English |
|---|---|
| Article number | 129757 |
| Journal | Food Chemistry |
| Volume | 357 |
| DOIs | |
| Publication status | Published - 30 Sept 2021 |
Keywords
- Data sharing
- Metabolites
- Metabolomics
- Plant food bioactive compounds
- Predicted retention time
- UHPLC
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In: Food Chemistry, Vol. 357, 129757, 30.09.2021.
Research output: Contribution to journal › Article › peer-review
TY - JOUR
T1 - Data sharing in PredRet for accurate prediction of retention time
T2 - Application to plant food bioactive compounds
AU - Low, Dorrain Yanwen
AU - Micheau, Pierre
AU - Koistinen, Ville Mikael
AU - Hanhineva, Kati
AU - Abrankó, László
AU - Rodriguez-Mateos, Ana
AU - da Silva, Andreia Bento
AU - van Poucke, Christof
AU - Almeida, Conceição
AU - Andres-Lacueva, Cristina
AU - Rai, Dilip K.
AU - Capanoglu, Esra
AU - Tomás Barberán, Francisco A.
AU - Mattivi, Fulvio
AU - Schmidt, Gesine
AU - Gürdeniz, Gözde
AU - Valentová, Kateřina
AU - Bresciani, Letizia
AU - Petrásková, Lucie
AU - Dragsted, Lars Ove
AU - Philo, Mark
AU - Ulaszewska, Marynka
AU - Mena, Pedro
AU - González-Domínguez, Raúl
AU - Garcia-Villalba, Rocío
AU - Kamiloglu, Senem
AU - de Pascual-Teresa, Sonia
AU - Durand, Stéphanie
AU - Wiczkowski, Wieslaw
AU - Bronze, Maria Rosário
AU - Stanstrup, Jan
AU - Manach, Claudine
N1 - Funding Information: 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). Funding Information: 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). Kate?ina Valentov? and Lucie Petr?skov? acknowledge financial support from the Czech Science Foundation (grant number 19-00043S). The Faculty of Pharmacy of Lisbon University acknowledges FUNDA??O PARA A CI?NCIA 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), Lantm?nnen 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 Ra?l Gonz?lez-Dom?nguez 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. Tom?s-Barber?n has received financial support from the Spanish National Research program (grant numbers AGL-2015-73107-EXP/AEI, CSIC 201870E014) and Fundaci?n Seneca Regi?n 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. L?szl? Abrank? acknowledges the Hungarian Academy of Sciences for the J?nos 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). Publisher Copyright: © 2021 The Author(s)
PY - 2021/9/30
Y1 - 2021/9/30
N2 - 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.
AB - 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.
KW - Data sharing
KW - Metabolites
KW - Metabolomics
KW - Plant food bioactive compounds
KW - Predicted retention time
KW - UHPLC
UR - http://www.scopus.com/inward/record.url?scp=85104344822&partnerID=8YFLogxK
U2 - 10.1016/j.foodchem.2021.129757
DO - 10.1016/j.foodchem.2021.129757
M3 - Article
AN - SCOPUS:85104344822
SN - 0308-8146
VL - 357
JO - Food Chemistry
JF - Food Chemistry
M1 - 129757
ER -