Chronic nutrient inputs alter root exudate Composition and Morphology：A case study in an alpine meadow of the Qinghai-Tibetan Plateau

The effects of nutrient supplementation treatments on root traits, root exudation rate and its’ stoichiometry were evaluated using one-way analysis of variance (ANOVA). SPSS 28.0 (IBM Corp. Armonk, NY, USA) was used for statistical analyses with a significance level of 0.05. For the standardized data after extraction of root exudates and metabolome pretreatment, the relative abundance of metabolites in the Super Class was calculated first, and then the content of metabolites in root exudates under N and P supplementation treatments were statistically analyzed using bar plots. The Shannon index was calculated based on the relative abundance of metabolites during data processing. Principal Coordinates Analysis (PCoA) was performed using the Bray-Curtis dissimilarity matrix through the R vegan package, followed by visualization using the R “ggplot2” package. Permutational Multivariate Analysis of Variance (PERMANOVA) implemented in the R “vegan” package was used to test for significant differences in root exudate profiles among different treatments. Partial Least Squares Discriminant Analysis (PLS-DA) was performed to obtain the variable importance in projection (VIP) values of each metabolite. The statistical significance of each metabolite between the CK and treatments groups was calculated based on the Steudent’s t-test as implemented in R v.4.1.2 (R Core Team, 2021), and the fold change (FC) of each metabolite between the two groups was calculated. Finally, metabolites with P < 0.05, VIP > 1, and either FC ≥ 1.5 or  ≤ 0.667 were identified as differential metabolites. Then OPLS-DA was used to analyze and filter orthogonal variables that were not related to categorical variables, and the projected importance of variables (VIP value) was calculated. After that, univariate statistical analysis combined with student’s t-test was used to verify the differences between different treatments to achieve the screening of differential metabolites, and the volcano plot of differential metabolites was drawn. Metabolites with VIP>1 and P-value<0.05 were selected as metabolites with significant differences to evaluate the effects of metabolite changes on organisms. The metabolome analysis process is completed based on the MetaboAnalyst R package of R software.