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 Statesincluding 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.plant-insect interactions | metabolomics | mass spectrometry | natural variation E lucidating 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 prov...

Illuminating a plant’s tissue-specific metabolic diversity using computational metabolomics and information theory

“…In recent years, the idMS/MS technique, sometimes referred to as shotgun or broad-scale MS/MS, has gained considerable interest as an exploratory method for metabolomics measurements. In a previous study, we showed that idMS/MS can be efficiently implemented to most qTOF instruments by running replicated measurements of the same sample using idMS/MS at different CID voltages to maximize fragment coverage (16). Furthermore, because the idMS/MS method has the disadvantage of being uninformative about precursor-to-fragment relationships, we optimized a computational pipeline based on cross-sample correlation calculations to perform fragment relationship assignments with high confidence (16).…”

“…In a previous study, we showed that idMS/MS can be efficiently implemented to most qTOF instruments by running replicated measurements of the same sample using idMS/MS at different CID voltages to maximize fragment coverage (16). Furthermore, because the idMS/MS method has the disadvantage of being uninformative about precursor-to-fragment relationships, we optimized a computational pipeline based on cross-sample correlation calculations to perform fragment relationship assignments with high confidence (16). Here, we improved the previous computational pipeline for exploiting cross-tissue metabolic variations to gain statistical power in precursor-to-fragment assignments.…”

“…The MS/MS molecular network method pioneered by Dorrestein and coworkers (21) circumvents the limitation of spectral databases via the analysis of within-dataset MS/MS similarities to accelerate hypothesis generation about the identity of unknown MS/MS (21). This approach also has the advantage of being amenable to the visualization of putative biochemical relationships among metabolites corresponding to highly similar MS/ MS spectra (16). In a recent study, we improved the scoring and classification of MS/MS similarities for plant secondary metabolites notably by the implementation of a biclustering method that detects possible compound familial groupings according to fragment and neutral loss (NL)-based similarities (16).…”

“…This approach also has the advantage of being amenable to the visualization of putative biochemical relationships among metabolites corresponding to highly similar MS/ MS spectra (16). In a recent study, we improved the scoring and classification of MS/MS similarities for plant secondary metabolites notably by the implementation of a biclustering method that detects possible compound familial groupings according to fragment and neutral loss (NL)-based similarities (16). Applying this method to the total pool of idMS/MSs from the present study resulted in the formation of nine modules within which idMS/MSs are expected to share high structural similarities (Fig.…”

Natural variation of plant metabolism: genetic mechanisms, interpretive caveats, evolutionary and mechanistic insights.

“…These include the advent of rapid metabolomic platforms allowing the quantification of hundreds to thousands of metabolites in as many different genotypes (Fiehn, 2001;Meyer et al, 2007;Fiehn et al, 2008). In combination with the ability to sequence and measure the transcriptome of all of these same lines, there is a massive influx of studies reporting on the identification of causal genes controlling the variation in metabolites in numerous species, from crop plants like maize (Zea mays), rice (Oryza sativa), and tomato (Solanum lycopersicum) to models like Arabidopsis (Arabidopsis thaliana) and ecological models like Boechera stricta and Nicotiana attenuata (Fu and Xue, 2010;Hartings et al, 2011;Li et al, 2011Li et al, , 2015Kausch et al, 2012;Prasad et al, 2012;Matsuba et al, 2013;Chang et al, 2015;Yan et al, 2015). These studies provide new insights into the mechanistic and evolutionary structures that influence how plant metabolism functions within a broader context.…”