Unraveling lipid metabolism in maize with time-resolved multi-omics data

Francisco de Abreu e Lima, Kun Li, Weiwei Wen, Jianbing Yan, Zoran Nikoloski*, Lothar Willmitzer, Yariv Brotman

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Maize is the cereal crop with the highest production worldwide, and its oil is a key energy resource. Improving the quantity and quality of maize oil requires a better understanding of lipid metabolism. To predict the function of maize genes involved in lipid biosynthesis, we assembled transcriptomic and lipidomic data sets from leaves of B73 and the high-oil line By804 in two distinct time-series experiments. The integrative analysis based on high-dimensional regularized regression yielded lipid–transcript associations indirectly validated by Gene Ontology and promoter motif enrichment analyses. The co-localization of lipid-transcript associations using the genetic mapping of lipid traits in leaves and seedlings of a B73 × By804 recombinant inbred line population uncovered 323 genes involved in the metabolism of phospholipids, galactolipids, sulfolipids and glycerolipids. The resulting association network further supported the involvement of 50 gene candidates in modulating levels of representatives from multiple acyl-lipid classes. Therefore, the proposed approach provides high-confidence candidates for experimental testing in maize and model plant species.

Original languageEnglish
Pages (from-to)1102-1115
Number of pages14
JournalPlant Journal
Volume93
Issue number6
DOIs
StatePublished - Mar 2018
Externally publishedYes

Funding

FundersFunder number
National Basic Research Program of China (973 Program)2016YFD0101003

    Keywords

    • GFLASSO
    • lipid metabolism
    • omics
    • QTL
    • Zea mays

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