Simca p 13 software package

The metabolites detected by multimetabonomics analyses were relatively quantified. After normalization by the internal standard A (GC-MS) or B (HPLC-QTOF/MS), the peak areas were weighted followed by volume for urine and weight for kidney. To compare the difference in each group, the SIMCA-P 13 Software package (Umetrics, Umeå, Sweden) was used for multivariate statistical analysis. Principal components analysis (PCA), an unsupervised method, was used to explicate the overall distribution of all samples. Partial least squares discriminant analysis (PLS-DA), a supervised method, was used to confirm the general separation of groups. Orthogonal partial least squares discriminant analysis (OPLS-DA), an extension of PLS-DA, was used to distinguish between two groups and identify the differential metabolites. In addition, metabolomics pathway analysis based on differential metabolites was performed with MetaboAnalyst (http://www.metaboanalyst.ca), which is a website tool including the KEGG (http://www.genome.jp/kegg/) and HMDB (http://www.hmdb.ca/) databases.

Descriptive statistics were calculated. For each set of experiments (ie, assays for cell cytotoxicity/functionality and RT-analysis), values were reported as means±standard deviation (SD). The results were analyzed by using the Mann-Whitney rank-sum test and statistical significance was defined as p<0.05. All statistical analyses were performed with SPSS Statistic 20 software (SPSS Inc., Chicago, IL). Proteomics data were analyzed by both multivariate and univariate approaches. The normalized spectral counts for each identified proteins were submitted to SIMCA-P13 software package (Umetrics, Umea, Sweden) for multivariate data analysis. Variables were scaled using Pareto scaling and data were analyzed by orthogonal partial least-squares discriminant analysis (OPLS-DA). S-plots were calculated to visualize the relationship between covariance and correlation within the OPLS-DA results. Variables that showed significant contribution to discrimination between groups and a significant change in their expression (Mann-Whitney-Wilcoxon test, p<0.05), were accepted as significantly modulated proteins upon treatment and submitted to network analysis.

Photosynthetic pigments, metal elements, and inorganic anions were statistically analyzed using SPSS 13. All data were presented as average, along with the standard error (SE), of five biological replicates. Significant difference between old and young leaves at same stress condition was determined by T-test. Metabolites were identified by searching FiehnLib (GC-TOF), a commercial EI-MS library (Kind et al., 2009 (link)). The resulting three-dimensional data, namely, peak number, sample name, and normalized peak area, were run in SIMCA-P 13 software package (Umetrics, Umea, Sweden) and subjected to principal component analysis (PCA) and orthogonal projections to latent structure-discriminant analysis (OPLS-DA). Non-commercial databases, including Kyoto Encyclopedia of Genes and Genomes (KEGG)¹, were utilized to search for metabolite pathways. Format data were uploaded in the MetaboAnalyst website ² for further analysis (Paz and Martinez-Ramos, 2003 (link)).