An Effective Strategy for Exploring Unknown Metabolic Pathways by Genome Mining

Plants allocate an estimated 15-25% of their proteome to specialized metabolic pathways that remain largely uncharacterized. Here, we describe a genome mining strategy for exploring such unknown pathways and demonstrate this approach for triterpenoids by functionally characterizing three cytochrome P450s from Arabidopsis thaliana. Building on proven methods for characterizing oxidosqualene cyclases, we heterologously expressed in yeast known cyclases with candidate P450s chosen from gene clustering and microarray coexpression patterns. The yeast cultures produced mg/L amounts of plant metabolites in vivo without the complex phytochemical background of plant extracts. Despite this simplification, the product multiplicity and novelty overwhelmed analytical efforts by MS methods. HSQC analysis overcame this problem. Side-by-side HSQC comparisons of crude P450 extracts against a control resolved even minor P450 products among similar to 100 other yeast metabolites spanning a dynamic range of >10 000:1. HSQC and GC-MS then jointly guided purification and structure determination by classical NMR methods. Including our present results for P450 oxidation of thalianol, arabidiol, and marneral, the metabolic fate for most of the major triterpene synthase products in Arabidopsis is now at least partially known.