Metabolic reconstruction of Setaria italica: A systems biology approach for integrating tissue-specific omics and pathway analysis of bioenergy grasses

摘要

The urgent need for major gains in industrial crops productivity and in biofuel production from bioenergy grasses have reinforced attention on understanding C photosynthesis. Systems biology studies of C model plants may reveal important features of C metabolism. Here we chose foxtail millet (, as a C model plant and developed protocols to perform systems biology studies. As part of the systems approach, we have developed and used a genome-scale metabolic reconstruction in combination with the use of multi-omics technologies to gain more insights into the metabolism of . mRNA, protein, and metabolite abundances, were measured in mature and immature stem/leaf phytomers, and the multi-omics data were integrated into the metabolic reconstruction framework to capture key metabolic features in different developmental stages of the plant. RNA-Seq reads were mapped to the resulting for 83% coverage of the protein coding genes of . Besides revealing similarities and differences in central metabolism of mature and immature tissues, transcriptome analysis indicates significant gene expression of two malic enzyme isoforms (NADP- ME and NAD-ME). Although much greater expression levels of NADP-ME genes are observed and confirmed by the correspondent protein abundances in the samples, the expression of multiple genes combined to the significant abundance of metabolites that participates in C metabolism of NAD-ME and NADP-ME subtypes suggest that may use mixed decarboxylation modes of C photosynthetic pathways under different plant developmental stages. The overall analysis also indicates different levels of regulation in mature and immature tissues in carbon fixation, glycolysis, TCA cycle, amino acids, fatty acids, lignin, and cellulose syntheses. Altogether, the multi-omics analysis reveals different biological entities and their interrelation and regulation over plant development. With this study, we demonstrated that this systems approach is powerful enough to complement the functional metabolic annotation of bioenergy grasses.