Metabolic origin of δ¹⁵N values in nitrogenous compounds from Brassica napus L. leaves

Nitrogen isotope composition (δ¹⁵N) in plant organic matter is currently used as a natural tracer of nitrogen acquisition efficiency. However, the δ¹⁵N value of whole leaf material does not properly reflect the way in which N is assimilated because isotope fractionations along metabolic reactions may cause substantial differences among leaf compounds. In other words, any change in metabolic composition or allocation pattern may cause undesirable variability in leaf δ¹⁵N. Here, we investigated the δ¹⁵N in different leaf fractions and individual metabolites from rapeseed (Brassica napus) leaves. We show that there were substantial differences in δ¹⁵N between nitrogenous compounds (up to 30‰) and the content in (¹⁵N enriched) nitrate had a clear influence on leaf δ¹⁵N. Using a simple steady-state model of day metabolism, we suggest that the δ¹⁵N value in major amino acids was mostly explained by isotope fractionation associated with isotope effects on enzyme-catalysed reactions in primary nitrogen metabolism. δ¹⁵N values were further influenced by light versus dark conditions and the probable occurrence of alternative biosynthetic pathways. We conclude that both biochemical pathways (that fractionate between isotopes) and nitrogen sources (used for amino acid production) should be considered when interpreting the δ¹⁵N value of leaf nitrogenous compounds. Still, little is known on the natural isotope composition (δ¹⁵N) in individual metabolites and on ¹⁴N/¹⁵N isotope fractionations in plant metabolism. This lack of knowledge impedes our current understanding of ¹⁵N-distribution among plant tissues and of relationships between environmental conditions and whole-plant δ¹⁵N values. In the present paper, we carried out δ¹⁵N analyses on several leaf fractions and individual metabolites by taking advantage of quite recent isotope ratio mass spectrometry techniques. Our study further compares δ¹⁵N values obtained in the dark with that in the light, and observed values with that predicted with a metabolic model.