Data sharing in PredRet for accurate prediction of retention time: Application to plant food bioactive compounds

Dorrain Yanwen Low; Pierre Micheau; Ville Mikael Koistinen; Kati Hanhineva; László Abrankó; Ana Rodriguez-Mateos; Andreia Bento da Silva; Christof van Poucke; Conceição Almeida; Cristina Andres-Lacueva; Dilip K. Rai; Esra Capanoglu; Francisco A. Tomás Barberán; Fulvio Mattivi; Gesine Schmidt; Gözde Gürdeniz; Kateřina Valentová; Letizia Bresciani; Lucie Petrásková; Lars Ove Dragsted; Mark Philo; Marynka Ulaszewska; Pedro Mena; Raúl González-Domínguez; Rocío Garcia-Villalba; Senem Kamiloglu; Sonia de Pascual-Teresa; Stéphanie Durand; Wieslaw Wiczkowski; Maria Rosário Bronze; Jan Stanstrup; Claudine Manach

doi:10.1016/j.foodchem.2021.129757

Data sharing in PredRet for accurate prediction of retention time: Application to plant food bioactive compounds

Dorrain Yanwen Low^*, Pierre Micheau, Ville Mikael Koistinen, Kati Hanhineva, László Abrankó, Ana Rodriguez-Mateos, Andreia Bento da Silva, Christof van Poucke, Conceição Almeida, Cristina Andres-Lacueva, Dilip K. Rai, Esra Capanoglu, Francisco A. Tomás Barberán, Fulvio Mattivi, Gesine Schmidt, Gözde Gürdeniz, Kateřina Valentová, Letizia Bresciani, Lucie Petrásková, Lars Ove DragstedMark Philo, Marynka Ulaszewska, Pedro Mena, Raúl González-Domínguez, Rocío Garcia-Villalba, Senem Kamiloglu, Sonia de Pascual-Teresa, Stéphanie Durand, Wieslaw Wiczkowski, Maria Rosário Bronze, Jan Stanstrup, Claudine Manach

^*Corresponding author for this work

Department of Food Engineering

Research output: Contribution to journal › Article › peer-review

11 Citations (Scopus)

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	https://doi.org/10.1016/j.foodchem.2021.129757
Publication status	Published - 30 Sept 2021

Bibliographical note

Publisher Copyright:
© 2021 The Author(s)

Funding

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). 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).

Funders	Funder number
EU H2020 FP7-Marie Curie-COFUND
FP7-Marie Curie-COFUND
Fundaci?n Seneca Regi?n de Murcia
Fundación Seneca Región de Murcia	19900/GERM/15
Generalitat de Catalunya's Agency AGAUR
Lantm?nnen Foundation
Lantmännen Foundation
Nanyang Technological University, Singapore
Norwegian Agriculture and Food Industry Research Funds	262300
Portuguese Mass Spectrometry Network	LISBOA-01-0145-FEDER-402-022125
Spanish National Research program	CSIC 201870E014, AGL-2015-73107-EXP/AEI
Itä-Suomen Yliopisto
Horizon 2020 Framework Programme
Seventh Framework Programme	754412, 609398
Centro de Investigación Biomédica en Red Fragilidad y Envejecimiento Saludable
European Commission
European Cooperation in Science and Technology
Nanyang Technological University	001991-00001, ANR-INBS-0010
Grantová Agentura České Republiky	19-00043S
Academy of Finland	277986, 312550
Carlsbergfondet
Generalitat de Catalunya	2017SGR1546
Ministerio de Economía y Competitividad	IJC2019-041867-I
Institució Catalana de Recerca i Estudis Avançats
Magyar Tudományos Akadémia
Instituto de Salud Carlos III	AC19/00096, AC19/00111
European Social Fund
Szent István Egyetem	AGL2016-76832-R, EFOP-3.6.3-VEKOP-16-2017-00005, 2016038

Keywords

Data sharing
Metabolites
Metabolomics
Plant food bioactive compounds
Predicted retention time
UHPLC

Access to Document

10.1016/j.foodchem.2021.129757

Cite this

Low, D. Y., Micheau, P., Koistinen, V. M., Hanhineva, K., Abrankó, L., Rodriguez-Mateos, A., da Silva, A. B., van Poucke, C., Almeida, C., Andres-Lacueva, C., Rai, D. K., Capanoglu, E., Tomás Barberán, F. A., Mattivi, F., Schmidt, G., Gürdeniz, G., Valentová, K., Bresciani, L., Petrásková, L., ... Manach, C. (2021). Data sharing in PredRet for accurate prediction of retention time: Application to plant food bioactive compounds. Food Chemistry, 357, Article 129757. https://doi.org/10.1016/j.foodchem.2021.129757

@article{5eef41819c57436495fc11ba8f6a48fa,

title = "Data sharing in PredRet for accurate prediction of retention time: Application to plant food bioactive compounds",

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.",

keywords = "Data sharing, Metabolites, Metabolomics, Plant food bioactive compounds, Predicted retention time, UHPLC",

author = "Low, {Dorrain Yanwen} and Pierre Micheau and Koistinen, {Ville Mikael} and Kati Hanhineva and L{\'a}szl{\'o} Abrank{\'o} and Ana Rodriguez-Mateos and {da Silva}, {Andreia Bento} and {van Poucke}, Christof and Concei{\c c}{\~a}o Almeida and Cristina Andres-Lacueva and Rai, {Dilip K.} and Esra Capanoglu and {Tom{\'a}s Barber{\'a}n}, {Francisco A.} and Fulvio Mattivi and Gesine Schmidt and G{\"o}zde G{\"u}rdeniz and Kate{\v r}ina Valentov{\'a} and Letizia Bresciani and Lucie Petr{\'a}skov{\'a} and Dragsted, {Lars Ove} and Mark Philo and Marynka Ulaszewska and Pedro Mena and Ra{\'u}l Gonz{\'a}lez-Dom{\'i}nguez and Roc{\'i}o Garcia-Villalba and Senem Kamiloglu and {de Pascual-Teresa}, Sonia and St{\'e}phanie Durand and Wieslaw Wiczkowski and Bronze, {Maria Ros{\'a}rio} and Jan Stanstrup and Claudine Manach",

note = "Publisher Copyright: {\textcopyright} 2021 The Author(s)",

year = "2021",

month = sep,

day = "30",

doi = "10.1016/j.foodchem.2021.129757",

language = "English",

volume = "357",

journal = "Food Chemistry",

issn = "0308-8146",

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Low, DY, Micheau, P, Koistinen, VM, Hanhineva, K, Abrankó, L, Rodriguez-Mateos, A, da Silva, AB, van Poucke, C, Almeida, C, Andres-Lacueva, C, Rai, DK, Capanoglu, E, Tomás Barberán, FA, Mattivi, F, Schmidt, G, Gürdeniz, G, Valentová, K, Bresciani, L, Petrásková, L, Dragsted, LO, Philo, M, Ulaszewska, M, Mena, P, González-Domínguez, R, Garcia-Villalba, R, Kamiloglu, S, de Pascual-Teresa, S, Durand, S, Wiczkowski, W, Bronze, MR, Stanstrup, J & Manach, C 2021, 'Data sharing in PredRet for accurate prediction of retention time: Application to plant food bioactive compounds', Food Chemistry, vol. 357, 129757. https://doi.org/10.1016/j.foodchem.2021.129757

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

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

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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 -

Data sharing in PredRet for accurate prediction of retention time: Application to plant food bioactive compounds

Abstract

Bibliographical note

Funding

Keywords

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