Comprehensive transcriptome analyses correlated with untargeted metabolome reveal differentially expressed pathways in response to cell wall alterations
This research provides new insights into plant response to cell wall perturbations through correlation of transcriptome and metabolome datasets obtained from transgenic plants expressing cell wall-modifying enzymes.
Plants respond to changes in their cell walls in order to protect themselves from pathogens and other stresses. Cell wall modifications in Arabidopsis thaliana have profound effects on gene expression and defense response, but the cell signaling mechanisms underlying these responses are not well understood. Three transgenic Arabidopsis lines, two with reduced cell wall acetylation (AnAXE and AnRAE) and one with reduced feruloylation (AnFAE), were used in this study to investigate the plant responses to cell wall modifications. RNA-Seq in combination with untargeted metabolome was employed to assess differential gene expression and metabolite abundance. RNA-Seq results were correlated with metabolite abundances to determine the pathways involved in response to cell wall modifications introduced in each line. The resulting pathway enrichments revealed the deacetylation events in AnAXE and AnRAE plants induced similar responses, notably, upregulation of aromatic amino acid biosynthesis and changes in regulation of primary metabolic pathways that supply substrates to specialized metabolism, particularly those related to defense responses. In contrast, genes and metabolites of lipid biosynthetic pathways and peroxidases involved in lignin polymerization were downregulated in AnFAE plants. These results elucidate how primary metabolism responds to extracellular stimuli. Combining the transcriptomics and metabolomics datasets increased the power of pathway prediction, and demonstrated the complexity of pathways involved in cell wall-mediated signaling.
KeywordsArabidopsis thaliana Cell wall signaling Metabolomics Systems biology Transcriptomics
This research was in part supported by the NSF-MCB (National Science Foundation) (Grant #1121163 to OAZ) and USDA-NIFA (National Institute of Food and Agriculture) (Grant #2015-07802 to OAZ). The authors thank Zebulun Arendsee (Iowa State University) for his assistance in bioinformatics analysis.
NTR conducted all analyses of transcriptome and metabolome datasets, and wrote the paper. OZ and LL designed the experiments and wrote the paper. XZ cleaned and mapped reads, and applied statistical analysis to transcriptome data. HC and XL conducted metabolite analysis. MH and ESW developed and maintain the PMR database and statistical analyses and contributed to the paper.
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