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Combined Transcriptome and Lipidomic Analyses of Lipid Biosynthesis in Macadamia ternifolia Nuts.

Rui ShiHaidong BaiBiao LiCan LiuZhiping YingZhi XiongWenlin Wang
Published in: Life (Basel, Switzerland) (2021)
Macadamia nuts are considered a high-quality oil crop worldwide. To date, the lipid diversity and the genetic factors that mediate storage lipid biosynthesis in Macadamia ternifolia are poorly known. Here, we performed a comprehensive transcriptomic and lipidomic data analysis to understand the mechanism of lipid biosynthesis by using young, medium-aged, and mature fruit kernels. Our lipidomic analysis showed that the M. ternifolia kernel was a rich source of unsaturated fatty acids. Moreover, different species of triacylglycerols, diacylglycerol, ceramides, phosphatidylethanolamine, and phosphatidic acid had altered accumulations during the developmental stages. The transcriptome analysis revealed a large percentage of differently expressed genes during the different stages of macadamia growth. Most of the genes with significant differential expression performed functional activity of oxidoreductase and were enriched in the secondary metabolite pathway. The integration of lipidomic and transcriptomic data allowed for the identification of glycerol-3-phosphate acyltransferase, diacylglycerol kinase, phosphatidylinositols, nonspecific phospholipase C, pyruvate kinase 2, 3-ketoacyl-acyl carrier protein reductase, and linoleate 9S-lipoxygenase as putative candidate genes involved in lipid biosynthesis, storage, and oil quality. Our study found comprehensive datasets of lipidomic and transcriptomic changes in the developing kernel of M . ternifolia . In addition, the identification of candidate genes provides essential prerequisites to understand the molecular mechanism of lipid biosynthesis in the kernel of M . ternifolia .
Keyphrases
  • fatty acid
  • data analysis
  • single cell
  • rna seq
  • genome wide
  • cell wall
  • bioinformatics analysis
  • gene expression
  • dna methylation
  • machine learning
  • electronic health record
  • transcription factor