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Integration of Phenomics and Metabolomics Datasets Reveals Different Mode of Action of Biostimulants Based on Protein Hydrolysates in Lactuca sativa L. and Solanum lycopersicum L. Under Salinity

Plant phenomics is becoming a common tool employed to characterize the mode of action of biostimulants. A combination of this technique with other omics such as metabolomics can offer a deeper understanding of a biostimulant effect in planta. However, the most challenging part then is the data analy...

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Autores principales: Sorrentino, Mirella, Panzarová, Klára, Spyroglou, Ioannis, Spíchal, Lukáš, Buffagni, Valentina, Ganugi, Paola, Rouphael, Youssef, Colla, Giuseppe, Lucini, Luigi, De Diego, Nuria
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8851396/
https://www.ncbi.nlm.nih.gov/pubmed/35185959
http://dx.doi.org/10.3389/fpls.2021.808711
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author Sorrentino, Mirella
Panzarová, Klára
Spyroglou, Ioannis
Spíchal, Lukáš
Buffagni, Valentina
Ganugi, Paola
Rouphael, Youssef
Colla, Giuseppe
Lucini, Luigi
De Diego, Nuria
author_facet Sorrentino, Mirella
Panzarová, Klára
Spyroglou, Ioannis
Spíchal, Lukáš
Buffagni, Valentina
Ganugi, Paola
Rouphael, Youssef
Colla, Giuseppe
Lucini, Luigi
De Diego, Nuria
author_sort Sorrentino, Mirella
collection PubMed
description Plant phenomics is becoming a common tool employed to characterize the mode of action of biostimulants. A combination of this technique with other omics such as metabolomics can offer a deeper understanding of a biostimulant effect in planta. However, the most challenging part then is the data analysis and the interpretation of the omics datasets. In this work, we present an example of how different tools, based on multivariate statistical analysis, can help to simplify the omics data and extract the relevant information. We demonstrate this by studying the effect of protein hydrolysate (PH)-based biostimulants derived from different natural sources in lettuce and tomato plants grown in controlled conditions and under salinity. The biostimulants induced different phenotypic and metabolomic responses in both crops. In general, they improved growth and photosynthesis performance under control and salt stress conditions, with better performance in lettuce. To identify the most significant traits for each treatment, a random forest classifier was used. Using this approach, we found out that, in lettuce, biomass-related parameters were the most relevant traits to evaluate the biostimulant mode of action, with a better response mainly connected to plant hormone regulation. However, in tomatoes, the relevant traits were related to chlorophyll fluorescence parameters in combination with certain antistress metabolites that benefit the electron transport chain, such as 4-hydroxycoumarin and vitamin K1 (phylloquinone). Altogether, we show that to go further in the understanding of the use of biostimulants as plant growth promotors and/or stress alleviators, it is highly beneficial to integrate more advanced statistical tools to deal with the huge datasets obtained from the -omics to extract the relevant information.
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spelling pubmed-88513962022-02-18 Integration of Phenomics and Metabolomics Datasets Reveals Different Mode of Action of Biostimulants Based on Protein Hydrolysates in Lactuca sativa L. and Solanum lycopersicum L. Under Salinity Sorrentino, Mirella Panzarová, Klára Spyroglou, Ioannis Spíchal, Lukáš Buffagni, Valentina Ganugi, Paola Rouphael, Youssef Colla, Giuseppe Lucini, Luigi De Diego, Nuria Front Plant Sci Plant Science Plant phenomics is becoming a common tool employed to characterize the mode of action of biostimulants. A combination of this technique with other omics such as metabolomics can offer a deeper understanding of a biostimulant effect in planta. However, the most challenging part then is the data analysis and the interpretation of the omics datasets. In this work, we present an example of how different tools, based on multivariate statistical analysis, can help to simplify the omics data and extract the relevant information. We demonstrate this by studying the effect of protein hydrolysate (PH)-based biostimulants derived from different natural sources in lettuce and tomato plants grown in controlled conditions and under salinity. The biostimulants induced different phenotypic and metabolomic responses in both crops. In general, they improved growth and photosynthesis performance under control and salt stress conditions, with better performance in lettuce. To identify the most significant traits for each treatment, a random forest classifier was used. Using this approach, we found out that, in lettuce, biomass-related parameters were the most relevant traits to evaluate the biostimulant mode of action, with a better response mainly connected to plant hormone regulation. However, in tomatoes, the relevant traits were related to chlorophyll fluorescence parameters in combination with certain antistress metabolites that benefit the electron transport chain, such as 4-hydroxycoumarin and vitamin K1 (phylloquinone). Altogether, we show that to go further in the understanding of the use of biostimulants as plant growth promotors and/or stress alleviators, it is highly beneficial to integrate more advanced statistical tools to deal with the huge datasets obtained from the -omics to extract the relevant information. Frontiers Media S.A. 2022-02-03 /pmc/articles/PMC8851396/ /pubmed/35185959 http://dx.doi.org/10.3389/fpls.2021.808711 Text en Copyright © 2022 Sorrentino, Panzarová, Spyroglou, Spíchal, Buffagni, Ganugi, Rouphael, Colla, Lucini and De Diego. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Plant Science
Sorrentino, Mirella
Panzarová, Klára
Spyroglou, Ioannis
Spíchal, Lukáš
Buffagni, Valentina
Ganugi, Paola
Rouphael, Youssef
Colla, Giuseppe
Lucini, Luigi
De Diego, Nuria
Integration of Phenomics and Metabolomics Datasets Reveals Different Mode of Action of Biostimulants Based on Protein Hydrolysates in Lactuca sativa L. and Solanum lycopersicum L. Under Salinity
title Integration of Phenomics and Metabolomics Datasets Reveals Different Mode of Action of Biostimulants Based on Protein Hydrolysates in Lactuca sativa L. and Solanum lycopersicum L. Under Salinity
title_full Integration of Phenomics and Metabolomics Datasets Reveals Different Mode of Action of Biostimulants Based on Protein Hydrolysates in Lactuca sativa L. and Solanum lycopersicum L. Under Salinity
title_fullStr Integration of Phenomics and Metabolomics Datasets Reveals Different Mode of Action of Biostimulants Based on Protein Hydrolysates in Lactuca sativa L. and Solanum lycopersicum L. Under Salinity
title_full_unstemmed Integration of Phenomics and Metabolomics Datasets Reveals Different Mode of Action of Biostimulants Based on Protein Hydrolysates in Lactuca sativa L. and Solanum lycopersicum L. Under Salinity
title_short Integration of Phenomics and Metabolomics Datasets Reveals Different Mode of Action of Biostimulants Based on Protein Hydrolysates in Lactuca sativa L. and Solanum lycopersicum L. Under Salinity
title_sort integration of phenomics and metabolomics datasets reveals different mode of action of biostimulants based on protein hydrolysates in lactuca sativa l. and solanum lycopersicum l. under salinity
topic Plant Science
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8851396/
https://www.ncbi.nlm.nih.gov/pubmed/35185959
http://dx.doi.org/10.3389/fpls.2021.808711
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