Prediction of Germination in Aged Seeds and Identification of New Seed Viability Biomarkers Using NMR Metabolomics.

Abstract

The fast evaluation of seed performance is crucial for the agricultural industry. In this work, we apply NMR to identify specific metabolites that are related to the germination capacity of seeds. As our results show, NMR is a fast method with great potential to discover new accumulated metabolites during seed ageing and to predict the germination of a seed batch. In an initial study, we compared the metabolomic profile of Arabidopsis fresh and naturally aged seeds applying Partial Least Square Discriminant Analysis (OPLS-DA) and identified several sugars, amino acids, lactate, and methyl-nicotinate (MeNA), among others, as differentially accumulated metabolites in aged versus fresh seeds. Furthermore, we used our NMR metabolomics data to predict seed viability. A multivariate Partial Least Squares regression (PLS) analysis showed a direct correlation between the metabolomic profile and the seed germination rate, which allows for the prediction of seed germination. We then applied the same approach to natural and artificially aged wheat seeds, where we identified samples with high (91%) and low (0%) germination with 0.92 accuracy for artificially aged seeds and 0.80 accuracy for naturally aged seeds. In addition, we found a decrease in glucose and an increase in the dimethylamine content in wheat aged seeds, like in Arabidopsis. MeNA, a metabolite accumulated in aged Arabidopsis seeds but not statistically relevant in wheat, inhibited germination in both species via an ABA-independent mechanism involving the repression of the transcription of PARP3 and ERF72 genes in both species.

Plant phenotyping relevance

NMRメタボロミクスを用いて種子の発芽能力・生存性を予測し、複数作物で精度を評価しているため、植物状態の取得・推定法が中心です。

Code and data availability

The article describes NMR metabolomics of aged Arabidopsis and wheat seeds with PLS/OPLS-DA models, but no public phenotype dataset, NMR raw data, analysis code, or trained model deposit is mentioned. Supporting information (Table S1/S2) contains metabolite annotations only, with no authors' public URL. No qualifying,

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