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

ABSTRACT: 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 appr

SUBMITTER: Correa Cordoba S 

PROVIDER: S-EPMC11823118 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature