Comparative transcriptome and metabolome analyses reveal the mechanism of silicon to improve stem rust resistance in oat (Avena sativa L.)

Silicon (Si) has been demonstrated to enhance oat resistance to stem rust, caused by Puccinia graminis f. sp. avenae (Pga). However, the molecular mechanisms underlying Si-mediated resistance against Pga remain poorly characterized. To address this, we performed transcriptomic and metabolomic analyses on oat plants treated with or without Si and inoculated with Pga. Our results showed that Si treatment increased the activities of catalase (CAT), peroxidase (POD), and superoxide dismutase (SOD) under Pga infection, thereby inhibiting reactive oxygen species (ROS) accumulation. Transcriptomic analysis identified 143 differentially expressed genes (55 upregulated, 88 downregulated) in Si-treated plants. Most of these genes were associated with diterpenoid biosynthesis, zeatin biosynthesis, and phenylpropanoid biosynthesis. Metabolomic profiling revealed 69 significantly enriched metabolites, including carbohydrates, organic acids, amino acids, and secondary metabolites. Based on KEGG database annotation, these metabolites were primarily involved in arginine biosynthesis; alanine, aspartate, and glutamate metabolism; cyanoamino acid metabolism; aminoacyl-tRNA biosynthesis; pyrimidine metabolism; and purine metabolism. Integrative analysis of transcriptome and metabolome data indicated that Si treatment significantly altered key metabolic pathways in oat leaves, including tryptophan metabolism, glyoxylate and dicarboxylate metabolism, porphyrin metabolism, and chlorophyll metabolism. Collectively, these findings provide novel molecular insights into Si-mediated enhancement of oat resistance to stem rust.

• Publication year

  2025

• Venue

  Scientific Reports

• Publication date

  2025-11-11

• Fields of study

  Biology, Agricultural and Food Sciences, Medicine, Environmental Science

• Identifiers
  DOI 10.1038/s41598-025-21482-9PMID 41219239PMCID 12606092
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar, PubMed

• No claims are published for this paper.

• No concepts are published for this paper.

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