Additional file 3: Table S1. of Genetic manipulation of putrescine biosynthesis reprograms the cellular transcriptome and the metabolome

Summary of changes in expression of genes involved in polyamine metabolism. For abbreviations see Fig. 1. Sequences for AS, CARB, DAO, NAGK, NAGPR, NAOD, NAOGAcT, ODC, OTC and SPDS were not represented on the microarray. Table S2. Functional clustering of gene models showing significant (P ≤ 0.05) differences (≥2 fold) between the HP and the control cell lines on both day 3 and day 5. Table S3. Functional clustering of gene models showing significant (P ≤ 0.05) differences (≥2 fold) between the HP and the control cell lines on day 3 only. Table S4. Functional clustering of gene models showing significant (P ≤ 0.05) differences (≥2 fold) between the HP and the control cell lines on day 5 only. Table S5. List of metabolites that were positively identified in poplar control and HP cell lines. ND = not detectable. Values that are significantly different (P ≤ 0.05) in the HP cells from the corresponding control cells on a given day are marked in bold. (DOCX 103 kb

Additional file 1: Figure S1. of Genetic manipulation of putrescine biosynthesis reprograms the cellular transcriptome and the metabolome

The pathway for the biosynthesis of polyamines and related metabolites starting from the assimilation of nitrogen (Adapted from Majumdar et al. 2016). Figure S2. (A, B) - Quality control scatter plots showing expression level for data that passed CV and dye-swap tests. Red spots indicate data that passed statistical analysis for differential expression between the control and the HP cells. Figure S3. The loading plots (S-plot) of the OPLS-DA results for the control and HP cell extracts on days 2 (A), 4 (B), and 6 (C). In the S-plot, each point represents a single metabolite (marker). The x-axis shows the variable contributions. The farther away a data point is from the 0 value, the more it contributes to sample variance. The y-axis shows the sample correlations within the same sample group. The farther away a metabolite is from the 0 value, the better is its correlation from injection to injection. As a result, the metabolites on both ends of the S-shaped curve represent the leading contributing ions from each sample group. The OPLS-DA is a multivariate analysis model which separates the systematic variation in X into two parts, one that is linearly related (and therefore predictive) to Y and one that is orthogonal to Y (unrelated); the Y-predictive/related part represents the between-class variation, the Y-orthogonal (ToPo) part constitutes the within-class variation. (DOCX 1653 kb

Additional file 2: of Genetic manipulation of putrescine biosynthesis reprograms the cellular transcriptome and the metabolome

Methods for RNA extraction, cDNA preparation and labeling, microarray hybridization and processing, and metabolomic analysis. Supplemental data Microarrays: http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE79420 . Metabolomic Data: https://mynotebook.labarchives.com/share/ulav72/MjIuMXwxNzEzMTkvMTcvVHJlZU5vZGUvMzg1Mzg2MTkxNHw1Ni4x . (PDF 451 kb