Metabolomics to Exploit the Primed Immune System of Tomato Fruit

Abstract

Tomato is a major crop suffering substantial yield losses from diseases, as fruit decay at a postharvest level can claim up to 50% of the total production worldwide. Due to the environmental risks of fungicides, there is an increasing interest in exploiting plant immunity through priming, which is an adaptive strategy that improves plant defensive capacity by stimulating induced mechanisms. Broad-spectrum defence priming can be triggered by the compound ß-aminobutyric acid (BABA). In tomato plants, BABA induces resistance against various fungal and bacterial pathogens and different methods of application result in durable protection. Here, we demonstrate that the treatment of tomato plants with BABA resulted in a durable induced resistance in tomato fruit against Botrytis cinerea, Phytophthora infestans and Pseudomonas syringae. Targeted and untargeted metabolomics were used to investigate the metabolic regulations that underpin the priming of tomato fruit against pathogenic microbes that present different infection strategies. Metabolomic analyses revealed major changes after BABA treatment and after inoculation. Remarkably, primed responses seemed specific to the type of infection, rather than showing a common fingerprint of BABA-induced priming. Furthermore, top-down modelling from the detected metabolic markers allowed for the accurate prediction of the measured resistance to fruit pathogens and demonstrated that soluble sugars are essential to predict resistance to fruit pathogens. Altogether, our results demonstrate that metabolomics is particularly insightful for a better understanding of defence priming in fruit. Further experiments are underway in order to identify key metabolites that mediate broad-spectrum BABA-induced priming in tomato fruit.

Unravelling Plant Responses to Stress—The Importance of Targeted and Untargeted Metabolomics

“…A recent study reports the metabolic setting of plant–virus interactions in a natural habitat and its predictive link to susceptibility, unravelling new insight into plant–virus interactions and providing attractive antimicrobial targets [ 163 ]. Likewise, machine learning-based predictive metabolomics is particularly promising, to reveal and validate metabolic predictors under biotic stress [ 164 ]. Future bioinformatics developments for metabolomics will reduce human intervention and increase data analysis throughput, thanks to robotized assays, for instance [ 157 , 164 ].…”

Metabolic Profile Discriminates and Predicts Arabidopsis Susceptibility to Virus under Field Conditions

“…Some compounds as VIP 324;184 and VIP 433;482, that presented high fold-change in resistant accessions compared to susceptible ones are of particular interest. Preliminary putative annotation of major metabolic markers was performed using RT, accurate m / z detected by high-resolution MS and MS2 fragments as described previously [ 60 , 61 ]. The resulting predicted molecular formula were screened through chemical databases (HMDB, METLIN, MassBank) to match putative metabolite identification.…”

Induced Resistance by Ascorbate Oxidation Involves Potentiating of the Phenylpropanoid Pathway and Improved Rice Tolerance to Parasitic Nematodes

“…Upon this challenge, the plant effectively mounts a faster and/or stronger defense response resulting in reduced disease and/or stress tolerance ( Mauch-Mani et al, 2017 ). However, potential changes in plant development and plant growth should be monitored upon IR activation, as excessive activation of plant defense can have fitness costs ( van Hulten et al, 2006 ; Wu et al, 2010 ; Koen et al, 2014 ; Luna et al, 2014 , 2020 ; Cohen et al, 2016 ; Yassin et al, 2021 ).…”