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Integrated Metabolo-transcriptomics Reveals the Defense Response of Homogentisic Acid in Wheat against Puccinia striiformis f. sp. tritici .

Saifei LiuLiyang XieJiaxuan SuBinnian TianAnfei FangYang YuChaowei BiYuheng Yang
Published in: Journal of agricultural and food chemistry (2022)
Stripe rust is a widespread and harmful wheat disease caused by Puccinia striiformis f. sp. tritici ( Pst ) worldwide. Targeted metabolome and transcriptomics analyses of CYR23 infected leaves were performed to identify the differential metabolites and differentially expressed genes related to wheat disease resistance. We observed upregulation of 33 metabolites involved in the primary and secondary metabolism, especially for homogentisic acid (HGA), p -coumaroylagmatine, and saccharopine. These three metabolites were mainly involved in the phenylpropanoid metabolic pathway, hydroxycinnamic acid amides pathway, and saccharopine pathway. Combined with transcriptome data on non-compatible interaction, the synthesis-related genes of these three differential metabolites were all upregulated significantly. The gene regulatory network involved in response to Pst infection was constructed, which revealed that several transcription factor families including WRKYs, MYBs, and bZIPs were identified as potentially hubs in wheat resistance response against Pst . An in vitro test showed that HGA effectively inhibited the germination of stripe rust fungus urediniospores and reduced the occurrence of wheat stripe rust. The results of gene silencing and overexpression of HGA synthesis-related gene 4-hydroxyphenylpyruvate dioxygenase proved that HGA was involved in wheat disease resistance. These results provided a further understanding of the disease resistance of wheat and indicated that HGA can be developed as a potential agent against Pst .
Keyphrases
  • ms ms
  • single cell
  • transcription factor
  • genome wide
  • cell proliferation
  • risk assessment
  • rna seq
  • signaling pathway
  • dna methylation
  • drug delivery
  • big data
  • cancer therapy
  • deep learning
  • essential oil
  • genome wide analysis