Natural variation in wild tomato trichomes; selecting metabolites that contribute to insect resistance using a random forest approach
Open Access
- 2 July 2021
- journal article
- research article
- Published by Springer Science and Business Media LLC in BMC Plant Biology
- Vol. 21 (1), 1-19
- https://doi.org/10.1186/s12870-021-03070-x
Abstract
BackgroundPlant-produced specialised metabolites are a powerful part of a plant's first line of defence against herbivorous insects, bacteria and fungi. Wild ancestors of present-day cultivated tomato produce a plethora of acylsugars in their type-I/IV trichomes and volatiles in their type-VI trichomes that have a potential role in plant resistance against insects. However, metabolic profiles are often complex mixtures making identification of the functionally interesting metabolites challenging. Here, we aimed to identify specialised metabolites from a wide range of wild tomato genotypes that could explain resistance to vector insects whitefly (Bemisia tabaci) and Western flower thrips (Frankliniella occidentalis). We evaluated plant resistance, determined trichome density and obtained metabolite profiles of the glandular trichomes by LC-MS (acylsugars) and GC-MS (volatiles). Using a customised Random Forest learning algorithm, we determined the contribution of specific specialised metabolites to the resistance phenotypes observed.ResultsThe selected wild tomato accessions showed different levels of resistance to both whiteflies and thrips. Accessions resistant to one insect can be susceptible to another. Glandular trichome density is not necessarily a good predictor for plant resistance although the density of type-I/IV trichomes, related to the production of acylsugars, appears to correlate with whitefly resistance. For type VI-trichomes, however, it seems resistance is determined by the specific content of the glands. There is a strong qualitative and quantitative variation in the metabolite profiles between different accessions, even when they are from the same species. Out of 76 acylsugars found, the random forest algorithm linked two acylsugars (S3:15 and S3:21) to whitefly resistance, but none to thrips resistance. Out of 86 volatiles detected, the sesquiterpene alpha -humulene was linked to whitefly susceptible accessions instead. The algorithm did not link any specific metabolite to resistance against thrips, but monoterpenes alpha -phellandrene, alpha -terpinene and beta -phellandrene/D-limonene were significantly associated with susceptible tomato accessions.ConclusionsWhiteflies and thrips are distinctly targeted by certain specialised metabolites found in wild tomatoes. The machine learning approach presented helped to identify features with efficacy toward the insect species studied. These acylsugar metabolites can be targets for breeding efforts towards the selection of insect-resistant cultivars.This publication has 107 references indexed in Scilit:
- Identification and QTL mapping of whitefly resistance components in Solanum galapagenseTheoretical and Applied Genetics, 2013
- Improved herbivore resistance in cultivated tomato with the sesquiterpene biosynthetic pathway from a wild relativeProceedings of the National Academy of Sciences of the United States of America, 2012
- Striking Natural Diversity in Glandular Trichome Acylsugar Composition Is Shaped by Variation at the Acyltransferase2 Locus in the Wild Tomato Solanum habrochaitesPlant Physiology, 2012
- Identification of a BAHD acetyltransferase that produces protective acyl sugars in tomato trichomesProceedings of the National Academy of Sciences of the United States of America, 2012
- Evolution of TPS20‐related terpene synthases influences chemical diversity in the glandular trichomes of the wild tomato relative Solanum habrochaitesThe Plant Journal, 2012
- Comparative Functional Genomic Analysis ofSolanumGlandular Trichome TypesPlant Physiology, 2010
- The Tomato odorless-2 Mutant Is Defective in Trichome-Based Production of Diverse Specialized Metabolites and Broad-Spectrum Resistance to Insect HerbivoresPlant Physiology, 2010
- Enhancement of Plant Metabolite Fingerprinting by Machine LearningPlant Physiology, 2010
- Behavioural aspects influencing plant virus transmission by homopteran insectsVirus Research, 2009
- Insect host location: a volatile situationTrends in Plant Science, 2005