A data-integrative modeling approach accurately characterizes the effects of mutations on Arabidopsis lipid metabolism.

Collections of insertional mutants have been instrumental for characterizing the functional relevance of genes in different model organisms, including Arabidopsis (Arabidopsis thaliana). However, mutations may often result in subtle phenotypes, rendering it difficult to pinpoint the function of a knocked-out gene. Here, we present a data-integrative modeling approach that enables predicting the effects of mutations on metabolic traits and plant growth. To test the approach, we gathered lipidomics data and physiological read-outs for a set of 64 Arabidopsis lines with mutations in lipid metabolism. Use of flux sums as a proxy for metabolite concentrations allowed us to integrate the relative abundance of lipids and facilitated accurate predictions of growth and biochemical phenotype in approximately 73% and 76% of the mutants, respectively, for which phenotypic data were available. Likewise, we showed that this approach can pinpoint alterations in metabolic pathways related to silent mutations. Therefore, our study paves the way for coupling model-driven characterization of mutant lines from different mutagenesis approaches with metabolomic technologies, as well as for validating knowledge structured in large-scale metabolic networks of plants and other species.