A main bottleneck in our gene discovery studies is that the identity of most metabolites is unknown. We can only discover the function of new genes encoding metabolic steps if we know the identity of the differentially accumulating compounds, e.g. in reverse genetics studies where we aim at identifying the substrate for an enzyme. One major objective in the group is to systematically identify the main secondary metabolites in maize stems and leaves, the focal tissues in our reverse genetic analyses. To this end, we use the CSPP algorithm that was developed in the group and that is used to characterize unknown compounds (Morreel et al., 2014). The algorithm searches for peak pairs that differ by a mass that corresponds to an enzymatic reaction. If this is done for all peaks in a chromatogram, and for the most prominent reactions that take place in metabolism, self-propagating networks are generated where each node is a metabolite and each edge a metabolic conversion. At the same time, the algorithm also predicts tentative biosynthetic pathways.
Our expertise in metabolite profiling of secondary metabolites has allowed to establish a VIB Metabolomics Core facility.
CSPP network of metabolites from maize.