Navigating natural variation in herbivory-induced secondary metabolism in coyote tobacco populations using MS/MS structural analysis

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.Elucidating the structure of metabolites underlying complex traits and the factors that maintain their variation in natural populations are important challenges in plant ecological studies (1). Many studies have notably shown that stress-responsive pathways that produce secondary metabolites are sporadically found across different plant taxa with extensive diversification (2). This important diversification suggests that particular metabolic systems have been recruited through natural selection when the set of compounds that they produce address specific ecological needs. Interactions with insects are important selection pressures that have sculpted plant metabolism, and many plant metabolites protect against herbivore attack and physical damage (3–5). The timely production of particular secondary metabolites in response to insect attack benefits plants by decreasing the costs of constitutive metabolite production. Trade-offs between defense metabolite productions and the intrinsic growth-related functions of central metabolic pathways likely provide important selection pressures that maintain the extensive metabolic polymorphisms commonly observed in natural populations.Gene discovery strategies exploiting natural variation in quantitative traits, including metabolite levels, have been extensively used in combination with genetic approaches (6–12). Analytical approaches applied in this research field are frequently focused on the quantification of individual or small families of compounds. Procedures such as liquid chromatography-mass spectrometry (LC-MS) and NMR have notably been used with both model and crop species to identify the genetic architecture of metabolic traits using quantitative trait locus mapping approaches (reviewed in ref. 13). Such approaches have been very successful in addressing genomic regions responsible for glucosinolate accumulation in Arabidopsis and related species (10, 14–16). Compared with modern sequencing and proteomics technologies, the profiling of entire plant metabolomes is, however, technically unfeasible with the existing analytical platforms, and, as a consequence, the analysis of metabolite natural variation has frequently been biased to secondary metabolite classes, for which a priori knowledge exists regarding their biological function, or to well-mapped parts of primary metabolism associated with energy and growth processes (17–19).Another critical aspect for exploiting natural variation in metabolism lies in the identification of unknown metabolites that exhibit significant associations with a phenotype of interest. Nontargeted approaches for rapidly collecting repertoires of tandem mass spectrometry (MS/MS) data can be extremely powerful in capturing the metabolic diversity expected to occur in natural populations (20). Indiscriminant or shotgun MS/MS strategies with high-resolution MS detectors offer many advantages in terms of rapidity and scale of analysis. Pipelines have been recently established to analyze such data (20). However, querying MS/MS data from the analysis of secondary metabolites from public databases is frequently unsuccessful because few standards are available for these compounds (21). An alternative is the use of comparative spectral analysis applied to experimental MS/MS datasets (22). This approach, termed molecular networking, is relatively new and aims at creating a map of mass spectral structural space in which molecules with related MS/MS spectra cluster together. Here, we combine the rapidly generated MS/MS data for all mass signals detected and molecular network construction in the analysis of the metabolic composition of natural plant populations.We applied our MS method to the natural variation in secondary metabolic profiles observed in accessions of the coyote tobacco, Nicotiana attenuata. This annual, native to the Great Basin Desert in the United States, primarily occurs in large ephemeral populations in post-fire habitats and smaller persistent populations found in washes (23). Dormant seeds of this species germinate from long-lived seed banks in sagebrush and pinyon-juniper ecosystems when fires pyrolize the litter layer, removing germination inhibitors and saturating the soils with smoke-derived germination cues (24, 25). This particular germination behavior affects the genetic structure of ephemeral monocultures produced by this species and results in relatively high within-population variation. N. attenuata populations represent a primary food source for insects that colonize the ecosystem after fires, and a vast array of genes and dependent metabolic pathways underlying resistance traits to native herbivores have been functionally characterized in this species. Among the major compound classes that contribute to the antiherbivore defense mechanisms of this plant is nicotine, a neurotoxin that functions synergistically with antidigestive plant proteins (26, 27), phenolic derivatives that exhibit strong tissue-specific responses to insect herbivory (28, 29), and 17-hydroxygeranyllinalool diterpene glycosides (HGL-DTGs) (30).Several studies have analyzed, with a high degree of spatial and temporal resolution, some of the metabolomic reconfigurations that are activated in plant tissues during biotic stresses (for a review, see ref. 31), including the attack of insects (32–34); but few of these studies have explored qualitative and quantitative variations of these metabolic adjustments across native populations. To systematically explore natural diversity patterns in the metabolic response to Manduca sexta herbivory of different N. attenuata populations, we conducted a glasshouse-based high-throughput MS-based metabolomics approach on 183 plants derived from seeds collected in Utah, Nevada, Arizona, and California. We then optimized an analytical and computational pipeline to assemble MS/MS data collected in a nontargeted manner and established mass spectral maps using a bioinformatics method to visualize metabolic branch-specific natural variation effects and annotate metabolites of interest.