Metabolic engineering of astaxanthin biosynthesis in maize endosperm and characterization of a prototype high oil hybrid
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Maize was genetically engineered for the biosynthesis of the high value carotenoid astaxanthin in the kernel endosperm. Introduction of a β-carotene hydroxylase and a β-carotene ketolase into a white maize genetic background extended the carotenoid pathway to astaxanthin. Simultaneously, phytoene synthase, the controlling enzyme of carotenogenesis, was over-expressed for enhanced carotenoid production and lycopene ε-cyclase was knocked-down to direct more precursors into the β-branch of the extended ketocarotenoid pathway which ends with astaxanthin. This astaxanthin-accumulating transgenic line was crossed into a high oil- maize genotype in order to increase the storage capacity for lipophilic astaxanthin. The high oil astaxanthin hybrid was compared to its astaxanthin producing parent. We report an in depth metabolomic and proteomic analysis which revealed major up- or down- regulation of genes involved in primary metabolism. Specifically, amino acid biosynthesis and the citric acid cycle which compete with the synthesis or utilization of pyruvate and glyceraldehyde 3-phosphate, the precursors for carotenogenesis, were down-regulated. Nevertheless, principal component analysis demonstrated that this compositional change is within the range of the two wild type parents used to generate the high oil producing astaxanthin hybrid.
KeywordsAstaxanthin Genetically engineered carotenoid biosynthesis GM maize Metabolomics Transcriptomics
Funding through the Plant KBBE project CaroMaize is gratefully acknowledged. Further support to PC was by the Ministerio de Economia y Competitividad, Spain (BIO2014-54441-P, BIO2011-22525) and a European Research Council Advanced Grant (BIOFORCE); PROGRAMA ESTATAL DE INVESTIGACIÓN CIENTÍFICA Y TÉCNICA DE EXCELENCIA, Spain (BIO2015-71703-REDT). PDF and LP are grateful for funding from the EU FP7 project DISCO grant number 613513. We thank Sys2Diag team (CNRS, France) for statistical analyses of proteomic data and particularly Nicolas Salvetat and Franck Molina.
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