Illuminating a plant's tissue specific metabolic diversity using computational metabolomics and information theory
Significance Population geneticists have educated molecular biologists in how to harness the statistical power of variance arising from interindividual natural variation to elucidate gene function in plants. The metabolic differences among tissues within a plant provide another source of variance that can be harnessed in the quest to understand gene function. We combine the power of information theory statistics and computational metabolomics to parse metabolic diversity within an ecological model plant, Nicotiana attenuata, to reveal intriguing patterns of metabolic specialization in floral limb and anthers, the responsible mechanisms of which we parse further by detecting and silencing the expression of two UDP glycosyltransferases involved in floral flavonoid metabolism. The workflow defines a framework for future evolutionary studies on plant tissue metabolic specialization. Secondary metabolite diversity is considered an important fitness determinant for plants' biotic and abiotic interactions in nature. This diversity can be examined in two dimensions. The first one considers metabolite diversity across plant species. A second way of looking at this diversity is by considering the tissue specific localization of pathways underlying secondary metabolism within a plant. Although these cross tissue metabolite variations are increasingly regarded as important readouts of tissue level gene function and regulatory processes, they have rarely been comprehensively explored by nontargeted metabolomics. As such, important questions have remained superficially addressed. For instance, which tissues exhibit prevalent signatures of metabolic specialization? Reciprocally, which metabolites contribute most to this tissue specialization in contrast to those metabolites exhibiting housekeeping characteristics? Here, we explore tissue level metabolic specialization in Nicotiana attenuata, an ecological model with rich secondary metabolism, by combining tissue wide nontargeted mass spectral data acquisition, information theory analysis, and tandem MS (MS/MS) molecular networks. This analysis was conducted for two different methanolic extracts of 14 tissues and deconvoluted 895 nonredundant MS/MS spectra. Using information theory analysis, anthers were found to harbor the most specialized metabolome, and most unique metabolites of anthers and other tissues were annotated through MS/MS molecular networks. Tissue metabolite association maps were used to predict tissue specific gene functions. Predictions for the function of two UDP glycosyltransferases in flavonoid metabolism were confirmed by virus induced gene silencing. The present workflow allows biologists to amortize the vast amount of data produced by modern MS instrumentation in their quest to understand gene function.
Proceedings of the National Academy of Sciences