Cargando…

Using machine learning to link the influence of transferred Agrobacterium rhizogenes genes to the hormone profile and morphological traits in Centella asiatica hairy roots

Hairy roots are made after the integration of a small set of genes from Agrobacterium rhizogenes in the plant genome. Little is known about how this small set is linked to their hormone profile, which determines development, morphology, and levels of secondary metabolite production. We used C. asiat...

Descripción completa

Detalles Bibliográficos
Autores principales: Alcalde, Miguel Angel, Müller, Maren, Munné-Bosch, Sergi, Landín, Mariana, Gallego, Pedro Pablo, Bonfill, Mercedes, Palazon, Javier, Hidalgo-Martinez, Diego
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9479193/
https://www.ncbi.nlm.nih.gov/pubmed/36119596
http://dx.doi.org/10.3389/fpls.2022.1001023
_version_ 1784790734220558336
author Alcalde, Miguel Angel
Müller, Maren
Munné-Bosch, Sergi
Landín, Mariana
Gallego, Pedro Pablo
Bonfill, Mercedes
Palazon, Javier
Hidalgo-Martinez, Diego
author_facet Alcalde, Miguel Angel
Müller, Maren
Munné-Bosch, Sergi
Landín, Mariana
Gallego, Pedro Pablo
Bonfill, Mercedes
Palazon, Javier
Hidalgo-Martinez, Diego
author_sort Alcalde, Miguel Angel
collection PubMed
description Hairy roots are made after the integration of a small set of genes from Agrobacterium rhizogenes in the plant genome. Little is known about how this small set is linked to their hormone profile, which determines development, morphology, and levels of secondary metabolite production. We used C. asiatica hairy root line cultures to determine the putative links between the rol and aux gene expressions with morphological traits, a hormone profile, and centelloside production. The results obtained after 14 and 28 days of culture were processed via multivariate analysis and machine-learning processes such as random forest, supported vector machines, linear discriminant analysis, and neural networks. This allowed us to obtain models capable of discriminating highly productive root lines from their levels of genetic expression (rol and aux genes) or from their hormone profile. In total, 12 hormones were evaluated, resulting in 10 being satisfactorily detected. Within this set of hormones, abscisic acid (ABA) and cytokinin isopentenyl adenosine (IPA) were found to be critical in defining the morphological traits and centelloside content. The results showed that IPA brings more benefits to the biotechnological platform. Additionally, we determined the degree of influence of each of the evaluated genes on the individual hormone profile, finding that aux1 has a significant influence on the IPA profile, while the rol genes are closely linked to the ABA profile. Finally, we effectively verified the gene influence on these two specific hormones through feeding experiments that aimed to reverse the effect on root morphology and centelloside content.
format Online
Article
Text
id pubmed-9479193
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher Frontiers Media S.A.
record_format MEDLINE/PubMed
spelling pubmed-94791932022-09-17 Using machine learning to link the influence of transferred Agrobacterium rhizogenes genes to the hormone profile and morphological traits in Centella asiatica hairy roots Alcalde, Miguel Angel Müller, Maren Munné-Bosch, Sergi Landín, Mariana Gallego, Pedro Pablo Bonfill, Mercedes Palazon, Javier Hidalgo-Martinez, Diego Front Plant Sci Plant Science Hairy roots are made after the integration of a small set of genes from Agrobacterium rhizogenes in the plant genome. Little is known about how this small set is linked to their hormone profile, which determines development, morphology, and levels of secondary metabolite production. We used C. asiatica hairy root line cultures to determine the putative links between the rol and aux gene expressions with morphological traits, a hormone profile, and centelloside production. The results obtained after 14 and 28 days of culture were processed via multivariate analysis and machine-learning processes such as random forest, supported vector machines, linear discriminant analysis, and neural networks. This allowed us to obtain models capable of discriminating highly productive root lines from their levels of genetic expression (rol and aux genes) or from their hormone profile. In total, 12 hormones were evaluated, resulting in 10 being satisfactorily detected. Within this set of hormones, abscisic acid (ABA) and cytokinin isopentenyl adenosine (IPA) were found to be critical in defining the morphological traits and centelloside content. The results showed that IPA brings more benefits to the biotechnological platform. Additionally, we determined the degree of influence of each of the evaluated genes on the individual hormone profile, finding that aux1 has a significant influence on the IPA profile, while the rol genes are closely linked to the ABA profile. Finally, we effectively verified the gene influence on these two specific hormones through feeding experiments that aimed to reverse the effect on root morphology and centelloside content. Frontiers Media S.A. 2022-09-02 /pmc/articles/PMC9479193/ /pubmed/36119596 http://dx.doi.org/10.3389/fpls.2022.1001023 Text en Copyright © 2022 Alcalde, Müller, Munné-Bosch, Landín, Gallego, Bonfill, Palazon and Hidalgo-Martinez. 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
Alcalde, Miguel Angel
Müller, Maren
Munné-Bosch, Sergi
Landín, Mariana
Gallego, Pedro Pablo
Bonfill, Mercedes
Palazon, Javier
Hidalgo-Martinez, Diego
Using machine learning to link the influence of transferred Agrobacterium rhizogenes genes to the hormone profile and morphological traits in Centella asiatica hairy roots
title Using machine learning to link the influence of transferred Agrobacterium rhizogenes genes to the hormone profile and morphological traits in Centella asiatica hairy roots
title_full Using machine learning to link the influence of transferred Agrobacterium rhizogenes genes to the hormone profile and morphological traits in Centella asiatica hairy roots
title_fullStr Using machine learning to link the influence of transferred Agrobacterium rhizogenes genes to the hormone profile and morphological traits in Centella asiatica hairy roots
title_full_unstemmed Using machine learning to link the influence of transferred Agrobacterium rhizogenes genes to the hormone profile and morphological traits in Centella asiatica hairy roots
title_short Using machine learning to link the influence of transferred Agrobacterium rhizogenes genes to the hormone profile and morphological traits in Centella asiatica hairy roots
title_sort using machine learning to link the influence of transferred agrobacterium rhizogenes genes to the hormone profile and morphological traits in centella asiatica hairy roots
topic Plant Science
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9479193/
https://www.ncbi.nlm.nih.gov/pubmed/36119596
http://dx.doi.org/10.3389/fpls.2022.1001023
work_keys_str_mv AT alcaldemiguelangel usingmachinelearningtolinktheinfluenceoftransferredagrobacteriumrhizogenesgenestothehormoneprofileandmorphologicaltraitsincentellaasiaticahairyroots
AT mullermaren usingmachinelearningtolinktheinfluenceoftransferredagrobacteriumrhizogenesgenestothehormoneprofileandmorphologicaltraitsincentellaasiaticahairyroots
AT munneboschsergi usingmachinelearningtolinktheinfluenceoftransferredagrobacteriumrhizogenesgenestothehormoneprofileandmorphologicaltraitsincentellaasiaticahairyroots
AT landinmariana usingmachinelearningtolinktheinfluenceoftransferredagrobacteriumrhizogenesgenestothehormoneprofileandmorphologicaltraitsincentellaasiaticahairyroots
AT gallegopedropablo usingmachinelearningtolinktheinfluenceoftransferredagrobacteriumrhizogenesgenestothehormoneprofileandmorphologicaltraitsincentellaasiaticahairyroots
AT bonfillmercedes usingmachinelearningtolinktheinfluenceoftransferredagrobacteriumrhizogenesgenestothehormoneprofileandmorphologicaltraitsincentellaasiaticahairyroots
AT palazonjavier usingmachinelearningtolinktheinfluenceoftransferredagrobacteriumrhizogenesgenestothehormoneprofileandmorphologicaltraitsincentellaasiaticahairyroots
AT hidalgomartinezdiego usingmachinelearningtolinktheinfluenceoftransferredagrobacteriumrhizogenesgenestothehormoneprofileandmorphologicaltraitsincentellaasiaticahairyroots