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Main conclusion <p>This study links rice leaf metabolome to yield traits, identifying 13 key metabolites through computational metabolomics. These enable early prediction of high-yield varieties, enhancing screening strategies in crop breeding.</p> Abstract <p>Metabolites serve as dynamic indicators of plant phenotype, linking genotype and environment through metabolomics profiling. Here, we used a computational metabolomics approach to correlate leaf metabolites with yield traits in four indica rice varieties. Dani Gora, with the highest yield, showed distinct phenotypic and metabolic profiles compared to Njavera N96. Analysis of robust non-redundant mass features revealed maximal ‘metabotype’ and trait differences between these two varieties. Dani Gora displayed higher central metabolism diversity, while Njavera N96 showed elevated specialization in secondary metabolism. Comparative pathway impact analysis identified 14 central metabolites, especially involved in six metabolic pathways, significantly enriched and positively correlated with the yield parameters. Machine learning (Random Forest) and fold change analysis finally validated 13 key metabolites predictive of yield traits. This framework demonstrates how leaf metabolite classifiers can enable early, high-throughput screening for high-yield rice varieties, offering a tool for accelerating rice breeding strategies.</p>

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Phenotype-driven leaf deep metabolomics framework depicts key metabolisms and metabolites associated with yield traits in rice

  • 期刊论文

  • 发表日期：

    2025年12月10日

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  • Manjima Mohanan,
  • Anish Kundu

摘要

Main conclusion

This study links rice leaf metabolome to yield traits, identifying 13 key metabolites through computational metabolomics. These enable early prediction of high-yield varieties, enhancing screening strategies in crop breeding.

Abstract

Metabolites serve as dynamic indicators of plant phenotype, linking genotype and environment through metabolomics profiling. Here, we used a computational metabolomics approach to correlate leaf metabolites with yield traits in four indica rice varieties. Dani Gora, with the highest yield, showed distinct phenotypic and metabolic profiles compared to Njavera N96. Analysis of robust non-redundant mass features revealed maximal ‘metabotype’ and trait differences between these two varieties. Dani Gora displayed higher central metabolism diversity, while Njavera N96 showed elevated specialization in secondary metabolism. Comparative pathway impact analysis identified 14 central metabolites, especially involved in six metabolic pathways, significantly enriched and positively correlated with the yield parameters. Machine learning (Random Forest) and fold change analysis finally validated 13 key metabolites predictive of yield traits. This framework demonstrates how leaf metabolite classifiers can enable early, high-throughput screening for high-yield rice varieties, offering a tool for accelerating rice breeding strategies.