title: Navigating natural variation in herbivory-induced secondary metabolism in coyote tobacco populations using MS/MS structural analysis.
abstract: Natural variation can be extremely useful in unraveling the determinants of phenotypic trait evolution but has rarely been analyzed with unbiased metabolic profiling to understand how its effects are organized at the level of biochemical pathways. Native populations of Nicotiana attenuata, a wild tobacco species, have been shown to be highly genetically diverse for traits important for their interactions with insects. To resolve the chemodiversity existing in these populations, we developed a metabolomics and computational pipeline to annotate leaf metabolic responses to Manduca sexta herbivory. We selected seeds from 43 accessions of different populations from the southwestern United States--including the well-characterized Utah 30th generation inbred accession--and grew 183 plants in the glasshouse for standardized herbivory elicitation. Metabolic profiles were generated from elicited leaves of each plant using a high-throughput ultra HPLC (UHPLC)-quadrupole TOFMS (qTOFMS) method, processed to systematically infer covariation patterns among biochemically related metabolites, as well as unknown ones, and finally assembled to map natural variation. Navigating this map revealed metabolic branch-specific variations that surprisingly only partly overlapped with jasmonate accumulation polymorphisms and deviated from canonical jasmonate signaling. Fragmentation analysis via indiscriminant tandem mass spectrometry (idMS/MS) was conducted with 10 accessions that spanned a large proportion of the variance found in the complete accession dataset, and compound spectra were computationally assembled into spectral similarity networks. The biological information captured by this networking approach facilitates the mining of the mass spectral data of unknowns with high natural variation, as demonstrated by the annotation of a strongly herbivory-inducible phenolic derivative, and can guide pathway analysis.
authors.affiliation: Department of Molecular Ecology, Max Planck Institute for Chemical Ecology, D-07745 Jena, Germany;
authors.forename: Dapeng
authors.initials: D
authors.lastname: Li
authors.affiliation: Department of Molecular Ecology, Max Planck Institute for Chemical Ecology, D-07745 Jena, Germany;
authors.forename: Ian T
authors.initials: IT
authors.lastname: Baldwin
authors.affiliation: Department of Molecular Ecology, Max Planck Institute for Chemical Ecology, D-07745 Jena, Germany; Centre for Organismal Studies, University of Heidelberg, 69120 Heidelberg, Germany emmanuel.gaquerel@cos.uni-heidelberg.de.
authors.forename: Emmanuel
authors.initials: E
authors.lastname: Gaquerel
mesh_terms: D000818:Animals; D002851:Chromatography, High Pressure Liquid; D016000:Cluster Analysis; D003517:Cyclopentanes / Q000378:metabolism; D014644:Genetic Variation; D005843:Geography; D060434:Herbivory; D007313:Insecta; D055432:Metabolomics; D054883:Oxylipins / Q000378:metabolism; D010641:Phenotype; D018515:Plant Leaves / Q000378:metabolism*; D010940:Plant Proteins / Q000378:metabolism; D064210:Secondary Metabolism; D016415:Sequence Alignment; D013045:Species Specificity; D053719:Tandem Mass Spectrometry; D014026:Nicotiana / Q000235:genetics*
chemical_list: D003517:Cyclopentanes; D054883:Oxylipins; D010940:Plant Proteins; C011006:jasmonic acid
keywords: mass spectrometry; metabolomics; natural variation; plant–insect interactions