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Nov 30, 2023
Correlating transcriptomic data that has a large number of differentially expressed genes with differential metabolites detected by metabolomics can pinpoint key genes, metabolites, and metabolic pathways that are closely associated with internal changes in the system, and thereby explain biological problems in a more holistic approach.
To better understand the mechanisms of Se tolerance in Cardamine enshiensis, the authors constructed the 443Mb genome for C. enshiensis and performed RNA-seq and Widely-Targeted Metabolomics on seedlings treated with water (control) or 400uM sodium selenite.
Mechanisms of Se tolerance and hyperaccumulation in C. enshiensis
In total, 29,671 differentially expressed genes were identified and a total of 558 metabolites were identified in the leaf tissue of C. enshiensis, of which 127 were differential metabolites. The authors found 10 flavone-related metabolites were altered between the two groups, indicating that flavones play a pivotal role in Se tolerance. KEGG analyses showed the significantly enriched pathways were associated with the biosynthesis of secondary metabolites and flavone/flavonoid/flavonol compounds. A Pearson’s correlation coefficient threshold of r > 0.8 was used to identify the metabolites that were significantly correlated with each gene and the results revealed that 175 transcripts were highly correlated (R2 > 0.96) with tricetin O-malonylhexoside and amentoflavone.
Sample Type | Minimum requirement per sample | Storage and transportation |
Whole blood | 2ml | For LCMS Snap freeze in liquid nitrogen. Store in -80C. Ship with dry ice. For RNAseq Prepare whole blood with 3x Trizol Resuspend cells with 1ml TRIzol for every 5x106 cells. Snap freeze materials in liquid nitrogen and store all materials in -80C |
Tissue | 300mg | |
Cultured Cells | 1 x 107 cells | |
Plant materials | 3g |
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