Interplay between secondary metabolites and plant hormones in silver nitrate-elicited Arabidopsis thaliana plants.

Plants produce a myriad of specialized compounds in response to threats such as pathogens or pests and different abiotic factors. The stress-related induction of specialized metabolites can be mimicked using silver nitrate (AgNO3) as an elicitor, which application in conservation agriculture has gained interest. In Arabidopsis thaliana, AgNO3 triggers the accumulation of indole glucosinolates (IGs) and the phytoalexin camalexin as well as pheylpropanoid-derived defensive metabolites such as coumaroylagmatins and scopoletin through a yet unknown mechanism. In this work, the role of jasmonic (JA) and salicylic acid (SA) signaling in the AgNO3-triggered specialized metabolite production was investigated. To attain this objective, AgNO3, MeJA and SA were applied to A. thaliana lines impaired in JA or SA signaling, or affected in the endogenous levels of IGs and AGs. Metabolomics data indicated that AgNO3 elicitation required an intact JA and SA signaling to elicit the metabolic response, although mutants impaired in hormone signaling retained certain capacity to induce specialized metabolites. In turn, plants overproducing or abolishing IGs production had also an altered hormonal signaling response, both in the accumulation of signaling molecules and the molecular response mechanisms (ORA59, PDF1.2, VSP2 and PR1 gene expression), which pointed out to a crosstalk between defense hormones and specialized metabolites. The present work provides evidence of a crosstalk mechanism between JA and SA underlying AgNO3 defense metabolite elicitation in A. thaliana. In this mechanism, IGs would act as retrograde feedback signals dampening the hormonal response; hence, expanding the signaling molecule concept.

Jasmonic acid catabolism in Arabidopsis defence against mites.

Jasmonates are essential modulators of plant defences but the role of JA-derivatives has been scarcely studied, particularly in the plant-pest interplay. To deepen into the JA catabolism and its impact on plant responses to spider mite infestation, we selected the Arabidopsis JAO2 gene as a key element involved in the first step of the JA-catabolic route. JAO2 is responsible for the hydroxylation of JA into 12-OH-JA, contributes to attenuate JA and JA-Ile content and consequently, determines the formation of other JA-catabolites. JAO2 was up-regulated in Arabidopsis by mite infestation. Mites also induced JA-derivative accumulation in plants. In jao2 mutant lines, and in the triple mutant jaoT (jao2-1, jao3-1, jao4-2), mite feeding produced less leaf damage, minor callose deposition and lower mite fecundity rates than in Col-0 plants. The impairment of JA oxidation in jao2 lines not only diminished the 12-OH-JA levels but turned off further sulfation as shown the significant reduction of 12-HSO4-JA form. Thus, JAO2 acts as a negative modulator of defences to spider mites mediated by changes in the generation of JA catabolic molecules, and the consequent production of defensive metabolites such as glucosinolates or camalexin.

From Classical to Modern Computational Approaches to Identify Key Genetic Regulatory Components in Plant Biology.

The selection of plant genotypes with improved productivity and tolerance to environmental constraints has always been a major concern in plant breeding. Classical approaches based on the generation of variability and selection of better phenotypes from large variant collections have improved their efficacy and processivity due to the implementation of molecular biology techniques, particularly genomics, Next Generation Sequencing and other omics such as proteomics and metabolomics. In this regard, the identification of interesting variants before they develop the phenotype trait of interest with molecular markers has advanced the breeding process of new varieties. Moreover, the correlation of phenotype or biochemical traits with gene expression or protein abundance has boosted the identification of potential new regulators of the traits of interest, using a relatively low number of variants. These important breakthrough technologies, built on top of classical approaches, will be improved in the future by including the spatial variable, allowing the identification of gene(s) involved in key processes at the tissue and cell levels.