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Integrative analyses of metabolome and genome-wide transcriptome reveal the regulatory network governing flavor formation in kiwifruit (Actinidia chinensis).

Ruochen WangPeng ShuChi ZhangJunlin ZhangYa ChenYaoxin ZhangKui DuYue XieMingzhang LiTao MaYang ZhangZheng-Guo LiDonald GriersonJulien PirrelloKunsong ChenMondher BouzayenBo ZhangMingchun Liu
Published in: The New phytologist (2021)
Soluble sugars, organic acids and volatiles are important components that determine unique fruit flavor and consumer preferences. However, the metabolic dynamics and underlying regulatory networks that modulate overall flavor formation during fruit development and ripening remain largely unknown for most fruit species. In this study, by integrating flavor-associated metabolism and transcriptome data from 12 fruit developmental and ripening stages of Actinidia chinensis cv Hongyang, we generated a global map of changes in the flavor-related metabolites throughout development and ripening of kiwifruit. Using this dataset, we constructed complex regulatory networks allowing to identify key structural genes and transcription factors that regulate the metabolism of soluble sugars, organic acids and important volatiles in kiwifruit. Moreover, our study revealed the regulatory mechanism involving key transcription factors regulating flavor metabolism. The modulation of flavor metabolism by the identified key transcription factors was confirmed in different kiwifruit species providing the proof of concept that our dataset provides a suitable tool for clarification of the regulatory factors controlling flavor biosynthetic pathways that have not been previously illuminated. Overall, in addition to providing new insight into the metabolic regulation of flavor during fruit development and ripening, the outcome of our study establishes a foundation for flavor improvement in kiwifruit.
Keyphrases
  • transcription factor
  • genome wide
  • gene expression
  • dna methylation
  • ms ms
  • healthcare
  • genome wide identification
  • machine learning
  • electronic health record
  • rna seq
  • big data