Statistics
Five replicates were done for all conditions for ionomics, isotopes, metabolomics and four replicates were done for proteomics. Supervised multivariate analysis of omics data was carried out by orthogonal projection on latent structure (OPLS) (Bylesjö et al. , 2007) with Simca (Umetrics), using K, Ca and putrescine as predicted Y variables and metabolites (or proteins) as predicting X variables. The absence of statistical outliers was first checked using a principal component analysis (PCA) to verify that no data point was outside the 99% confidence Hotelling region. The goodness of the OPLS model was appreciated using the determination coefficient R² and the predictive power was quantified by the cross-validated determination coefficient, Q². The significance of the statistical OPLS model was tested using a χ² comparison with a random model (average ± random error), and the associated P -value (P CV-ANOVA) is reported (Eriksson et al. , 2008). Best discriminating features were identified using volcano plots whereby the logarithm of theP -value obtained in univariate analysis (two-way ANOVA; factors used: K x Ca or K x putrescine) was plotted against the rescaled loading (pcorr) obtained in the OPLS. In such a representation, best biomarkers have both maximal –log(P ) and pcorr values. Univariate analysis of statistical classes in bar plots was performed using a two-way ANOVA (Fisher statistics), with a threshold of P as indicated in figure legends. When all conditions were compared at once, a one-way ANOVA was conducted.