Deciphering herbivory-induced gene-to-metabolite dynamics in nicotiana attenuata tissues using a multifactorial approach

• Verfasst von: Gulati, Jyotasana [VerfasserIn]
• Verfasst von: Gaquerel, Emmanuel [VerfasserIn]
• Titel: Deciphering herbivory-induced gene-to-metabolite dynamics in nicotiana attenuata tissues using a multifactorial approach
• Verf.angabe: Jyotasana Gulati, Sang-Gyu Kim, Ian T. Baldwin, and Emmanuel Gaquerel
• Umfang: 18 S.
• Fussnoten: Gesehen am 17.05.2017
• Titel Quelle: Enthalten in: Plant physiology
• Jahr Quelle: 2013
• Band/Heft Quelle: 162(2013), 2, S. 1042-1059
• ISSN Quelle: 1532-2548
• DOI: doi:10.1104/pp.113.217588
• Datenträger: Online-Ressource
• Sprache: eng
• K10plus-PPN: 1558748806

Abstract

In response to biotic stresses, such as herbivore attack, plants reorganize their transcriptomes and reconfigure their physiologies not only in attacked tissues but throughout the plant. These whole-organismic reconfigurations are coordinated by a poorly understood network of signal transduction cascades. To explore tissue-based interdependencies in the resistance of Nicotiana attenuata to insect attack, we conducted time-series transcriptome and metabolome profiling of herbivory-elicited source leaves and unelicited sink leaves and roots. To probe the multidimensionality of these molecular responses, we designed a novel approach of combining an extended self-organizing maps-based dimensionality reduction method with bootstrap-based nonparametric analysis of variance models to identify the onset and context of signaling and metabolic pathway activations. We illustrate the value of this analysis by revisiting dynamic changes in the expression of regulatory and structural genesof the oxylipin pathway and by studying nonlinearities in gene-metabolite associations involved in the acyclic diterpene glucoside pathway after selectively extracting modules based on their dynamic response patterns. This novel dimensionality reduction approach is broadly applicable to capture the dynamic rewiring of gene and metabolite networks in experimental design with multiple factors.