Statistical analysis
All data from targeted analyses were tested for normality
(Kolmogorov–Smirnov test) and for homogeneity of variance (Leven median
test). Thereafter, three-way ANOVA was used to investigate the effect of
treatments (control, drought, heat, and drought+heat), root type
(primary and seminal), root zones (apical and sub-apical) and their
interactions. The means were separated by the Tukey’s honest significant
difference (HSD) test (p<0.05).
The metabolomics-based analyses were performed according to García Pérezet al. (2021). Briefly, raw mass features were elaborated using
the software Agilent Mass Profiler Professional B.12.06 for
normalization and baselining, and then the multivariate unsupervised
hierarchical cluster analysis (HCA) was performed (Euclidean distance,
Ward’s linkage rule) to describe similarities and dissimilarities among
treatments from a fold-change based heat map. A Volcano analysis
(α = 0.05; fold change, FC > 1.2) was performed to identify
compounds varying significantly between treatments and control. In
addition, supervised modeling by Orthogonal Projection to Latent
Structure Discriminant Analysis (OPLS-DA) was performed using SIMCA 16
software tool (Umetrics, Sweden). The Variable Importance in Projection
(VIP) analysis was performed to further identify markers responsible of
the discrimination. The obtained multivariate model was then
cross-validated by Cross Validation-Analysis of Variance (CV-ANOVA,
α < 0.01) and its fitness and prediction were evaluated by R2Y
and Q2Y parameters, respectively.