Nuclear magnetic resonance metabolomics of iron deficiency in soybean leaves

Marta R. M. Lima, Sílvia O. Diaz, Inês Lamego, Michael A. Grusak, Marta W. Vasconcelos*, Ana M. Gil

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

22 Citations (Scopus)


Iron (Fe) deficiency is an important agricultural concern that leads to lower yields and crop quality. A better understanding of the condition at the metabolome level could contribute to the design of strategies to ameliorate Fe-deficiency problems. Fe-sufficient and Fe-deficient soybean leaf extracts and whole leaves were analyzed by liquid 1H nuclear magnetic resonance (NMR) and high-resolution magic-angle spinning NMR spectroscopy, respectively. Overall, 30 compounds were measurable and identifiable (comprising amino and organic acids, fatty acids, carbohydrates, alcohols, polyphenols, and others), along with 22 additional spin systems (still unassigned). Thus, metabolite differences between treatment conditions could be evaluated for different compound families simultaneously. Statistically relevant metabolite changes upon Fe deficiency included higher levels of alanine, asparagine/aspartate, threonine, valine, GABA, acetate, choline, ethanolamine, hypoxanthine, trigonelline, and polyphenols and lower levels of citrate, malate, ethanol, methanol, chlorogenate, and 3-methyl-2-oxovalerate. The data indicate that the main metabolic impacts of Fe deficiency in soybean include enhanced tricarboxylic acid cycle activity, enhanced activation of oxidative stress protection mechanisms and enhanced amino acid accumulation. Metabolites showing accumulation differences in Fe-starved but visually asymptomatic leaves could serve as biomarkers for early detection of Fe-deficiency stress.
Original languageEnglish
Pages (from-to)3075-3087
Number of pages13
JournalJournal of Proteome Research
Issue number6
Publication statusPublished - 6 Jun 2014


  • Chlorosis
  • Fe deficiency
  • Glycine max (soybean)
  • High-resolution magic-angle spinning (HRMAS)
  • Metabolomics
  • Multivariate analysis
  • Nuclear magnetic resonance (NMR)


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