Building a metabolite database for S. lycopersicum

During our research into the differences in metabolic profiles and genetic expression between water stress and control in Solanum lycopersicum we encountered a major bottleneck: we identified numerous metabolites that require additional annotation efforts in order to characterize them. Our research goal is to maximize annotation of untargeted metabolic analysis for S. lycopersicum using both metabolic and genomic analysis. That annotation could lead to novel discoveries of metabolic pathways, metabolite annotation, and by the nature of the analysis; novel gene annotation. To reach this goal we applied several approaches:

I) Networks are used as a visual tool to help us make inferences about indirect connections that aren’t as obvious. In the case untargeted metabolomics, we can use highly curated and experiment-based networks to annotate metabolites. This can be done by the clustering of co-regulated metabolites, their connection to annotated pathways, and their connection to genes that are involved in metabolic pathways.

II) Correlation and connectivity between metabolite activity and gene expression can open us a new avenue of annotation where we can use gene annotation to characterize metabolites and the other way around. To support that we can merge highly curated networks that provide additional information regarding nodes within the system - both genes and metabolites.

III) Isotope labeling as a means for expanding our knowledge. If we can get compound information, however small, for our metabolites, it will narrow down or even disclose the identity of the metabolite, especially when combined with the other approaches.