Pathway Elucidation in Duckweed by Means of Dual Isotope Labeling and Liquid Chromatography-Mass Spectrometry

Liron Feldberg, Department of Plant & Environmental Sciences, Weizmann Institute of Science, Rehovot, 7610001, Israel
Dong Yonghui,, Department Of Plant & Environmental Sciences, Weizmann Institute Of Science, Rehovot, 7610001, Israel
Uwe Heinig, Department Of Plant & Environmental Sciences, Weizmann Institute Of Science, Rehovot, 7610001, Israel
Ilana Rogachev, Department Of Plant & Environmental Sciences, Weizmann Institute Of Science, Rehovot, 7610001, Israel
Asaph Aharoni, Department Of Plant & Environmental Sciences, Weizmann Institute Of Science, Rehovot, 7610001, Israel

Duckweeds (Lemna spp.) are free-floating macrophytes that grow rapidly by clonal propagation. The morphology, growth habits and sensitivity to a wide range of xenobiotics make Lemna species an excellent bioassay organism for evaluation of phytotoxicity. However, despite its great value, relatively limited information is available regarding its metabolism, particularly with respect to secondary metabolites. Here we used DLEMMA (Dual Labeling of Metabolites for Metabolome Analysis), a metabolomics tool previously developed in our lab, for the detection and identification of tyrosine-derived metabolites and investigation of metabolic pathways in Lemna (i.e. Spirodela polyrhiza). This tool enables tracking a particular metabolic pathway by enriching the concentrations of intermediate metabolites by means of labelled precursor feeding. Labeling patterns extracted from MS spectra assist in structure elucidation. In this study, Lemna plants were fed with two different stable isotope labeled tyrosine (Tyr D₄ and Tyr¹³C₉¹⁵N). The metabolites derived from the fed precursor were detected as three co-eluting peaks (unlabeled and two differently labeled metabolites) in the UPLC-QTOF-MS chromatogram. An in-house generated script was further used to filter tyrosine-derived masses. Subsequently, interrogation of public metabolite databases, labeling pattern filtering and analysis of  MS/MS fragmentation patterns allowed the identification of more than 40 tyrosine-derived metabolites. Based on the identified metabolites, a putative tyrosine derived metabolic network was proposed.