Correa Córdoba Sandra, Burgos Asdrúbal, Cuadros-Inostroza Álvaro, Xu Ke, Brotman Yariv, Nikoloski Zoran
Bioinformatics Department, Institute of Biochemistry and Biology, University of Potsdam, Potsdam 14476, Germany.
Systems Biology and Mathematical Modelling, Max Planck Institute of Molecular Plant Physiology, Potsdam 14476, Germany.
Plant Physiol. 2025 Feb 7;197(2). doi: 10.1093/plphys/kiae615.
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.
插入突变体文库对于表征不同模式生物（包括拟南芥）中基因的功能相关性具有重要作用。然而，突变往往会导致细微的表型，使得难以确定敲除基因的功能。在此，我们提出一种数据整合建模方法，该方法能够预测突变对代谢性状和植物生长的影响。为了测试该方法，我们收集了一组64个在脂质代谢方面存在突变的拟南芥株系的脂质组学数据和生理读数。使用通量总和作为代谢物浓度的替代指标，使我们能够整合脂质的相对丰度，并分别在约73%和76%有表型数据的突变体中促进了对生长和生化表型的准确预测。同样，我们表明该方法能够确定与沉默突变相关的代谢途径的改变。因此，我们的研究为将不同诱变方法产生的突变体系的模型驱动表征与代谢组学技术相结合，以及验证植物和其他物种大规模代谢网络中的知识结构铺平了道路。
