Integrating transcriptomic techniques and k-means clustering in metabolomics to identify markers of abiotic and biotic stress in Medicago truncatula

• Published in: Metabolomics Vol. 14; no. 10; pp. 126 - 12
• Main Authors: Dickinson, Elizabeth, Rusilowicz, Martin J., Dickinson, Michael, Charlton, Adrian J., Bechtold, Ulrike, Mullineaux, Philip M., Wilson, Julie
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.10.2018; Springer Nature B.V
• Subjects: Biochemistry; Biomedical and Life Sciences; Biomedicine; Cell Biology; Citric acid; Cluster Analysis; Developmental Biology; Discriminant analysis; Disease Resistance - genetics; Drought; Food security; Food Supply; Least-Squares Analysis; Leaves; Legumes; Life Sciences; Liquid chromatography; Malic acid; Mass spectroscopy; Medicago truncatula; Medicago truncatula - genetics; Medicago truncatula - metabolism; Metabolism; Metabolites; Metabolomics; Molecular Medicine; Phosphoric acid; Principal Component Analysis; Principal components analysis; Propagation; Roots; Stress, Physiological - genetics; Sucrose; Time series; Transcriptome; Metabolomics; Biotic stress; Drought; Clustering; Medicago truncatula
• ISSN: 1573-3882; 1573-3890
• DOI: 10.1007/s11306-018-1424-y

Introduction Nitrogen-fixing legumes are invaluable crops, but are sensitive to physical and biological stresses. Whilst drought and infection from the soil-borne pathogen Fusarium oxysporum have been studied individually, their combined effects have not been widely investigated. Objectives We aimed to determine the effect of combined stress using methods usually associated with transcriptomics to detect metabolic differences between treatment groups that could not be identified by more traditional means, such as principal component analysis and partial least squares discriminant analysis. Methods Liquid chromatography-high resolution mass spectrometry data from the root and leaves of model legume Medicago truncatula were analysed using Gaussian Process 2-Sample Test, k -means cluster analysis and temporal clustering by affinity propagation. Results Metabolic differences were detected: we identified known stress markers, including changes in concentration for sucrose and citric acid, and showed that combined stress can exacerbate the effect of drought. Changes in roots were found to be smaller than those in leaves, but differences due to Fusarium infection were identified. The transfer of sucrose from leaves to roots can be seen in the time series using transcriptomic techniques with the metabolomics time series. Other metabolite concentrations that change as a result of treatment include phosphoric acid, malic acid and tetrahydroxychalcone. Conclusions Probing metabolomic data with transcriptomic tools provides new insights and could help to identify resilient plant varieties, thereby increasing future crop yield and improving food security.