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Navigating natural variation in herbivory-induced secondary metabolism in coyote tobacco populations using MS/MS structural analysis
Wednesday, 2015/07/29 | 07:57:45

Dapeng Li, Ian T. Baldwin, and Emmanuel Gaquerel

 

Significance

The study of natural variation has profoundly advanced our understanding of plants’ phenotypic trait evolution. The analysis of intraspecific variations in metabolism, however, has lagged behind and frequently been biased toward central metabolism. To redress this bias, we present a metabolomics case study of leaf secondary metabolites of wild tobacco ecotypes subjected to simulated insect herbivory in which mass spectral maps are constructed. Navigating these maps revealed metabolic branch-specific variations and allowed the annotation of unknown metabolites of likely ecological importance. Although the profiling of entire plant metabolomes remains technically challenging due to their structural complexity, we predict that the workflow described here provides a significant advance in our ability to rapidly explore small molecules that mediate functionally important phenotypes.

 

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.

 

See: http://www.pnas.org/content/112/30/E4147.abstract.html?etoc

PNAS July 28, 2015 vol. 112 no. 30 E4147-E4155

 

Fig. 1.  Herbivory-induced metabolic profiles of N. attenuata populations exhibit extensive intra- and interaccession quantitative variations. (A) The location of the 43 accession seed collection sites in Utah, Nevada, Arizona, and California. A close-up for the collection sites in Utah is presented. Full GPS coordinates are provided in SI Appendix, Table S1. Colors were arbitrarily given to accessions to highlight accessions collected within the same large geographic region. (B) Classification of accession according to intraaccession (x axis) and interaccession (y axis) Euclidean distances calculated for the complete metabolic profile. As expected, metabolomes of replicated plants from U30, a Utah accession inbred for 30 generations, show low variations. Plants from seeds collected in California and Arizona exhibit higher inter- than intraaccession variations. (C) Scatter plots of Euclidean distances calculated individual sample pairs, demonstrating no clear relationship between geographical distance and metabolic profile variations. Each dot represents one sample pair, and its coordinates correspond to the geographic distance (x axis) and Euclidean distance of the metabolic profiles for this sample pair. Closely related individuals exhibit high quantitative metabolic variation when grown under glasshouse conditions, and no correlation is detected between the geographic and Euclidean distances.

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