Evaluation of changes induced in rice metabolome by Cd and Cu exposure using LC-MS and XCMS and MCR-ALS data analysis strategies

MERITXELL NAVARRO; JOAQUIM JAUMOT; ALEJANDRO G. GARCÍA REIRIZ; ROMÀ TAULER

ANALYTICAL AND BIOANALYTICAL CHEMISTRY

SPRINGER HEIDELBERG

Lugar: HEIDELBERG; Año: 2015 vol. 407 p. 8835 - 8847

1618-2642

XCMS is a data analysis approach very popular among the metabolomics community for feature detection. Alternatively, multivariate curve resolution by alternating least squares (MCR-ALS) has been recently proposed as a different approach to detect potential biomarkers in untargeted metabolomics studies. In this work, these two approaches are compared by means of their application to a metabolomics data set of rice (Oryza sativa japonica nipponbare) exposed to heavy metal stress. Different chemometric tools have been used to evaluate the influence of three experimental factors on the rice metabolomics: tissue sample (root or leaf), metal of treatment (Cd or Cu) and metal concentration in irrigation water during the treatment (0, 10, 50 1000 µM). Statistical significance of the factors and their interactions was evaluated applying ANOVA-simultaneous component analysis (ASCA) to total ion current (TIC) chromatograms and peak areas of the obtained metabolites. Principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) allowed the evaluation of sample differentiation according to the considered factors and, also, identification of the most discriminant metabolites for each factor. Results were the same for XCMS and MCR-ALS strategies in all the considered cases.