Exploring the diversity of natural products in plants requires efficient methods to gain sufficient structural information to rapidly discriminate known compounds from novel or closely related ones. This process can be extremely challenging when analyzing profiles of transgenic/mutant plants in which natural product biosynthetic pathways are manipulated. The task is even more difficult when the objective is to follow plant metabolites as they move through trophic cascades, as plants are eaten by herbivores, and herbivores by predators. Workflows that combine statistical data mining and automatic compound identification routines are therefore needed.
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Here, we present a software solution for the rapid and efficient screening of metabolites from wild tobacco plants transformed to alter the expression of several glycosyltransferase genes. These genes are part of the biosynthetic pathway leading to defensive 17-hydroxygeranyllinallool diterpene glycosides (HGL-DTGs). Furthermore we analyze post-ingestive modifications of these metabolites within the tobacco hornworm Manduca sexta. Our results illustrate the power of this software-based workflow for gene discovery in the HGL-DTG pathway in tobacco and for analyzing the consequences, in both the plant and insect, of manipulating this pathway.
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