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Transcript and Metabolite Profiling for the Evaluation of Tobacco Tree and Poplar as Feedstock for the Bio-based Industry
Published on: May 16, 2014
Transcriptome and Metabolome Analyses of Short-Term Responses of Populus talassica × Populus euphratica to Leaf
Mengxu Su1, Zhanjiang Han1, Ying Liu1
1State Key Laboratory Incubation Base for Conservation and Utilization of Bio-Resource in Tarim Basin, College of Life Science and Technology, Tarim University, Alar 843300, China.
Abstract:
After being subjected to mechanical damage, plants trigger changes in primary and secondary metabolites to enhance their resistance or defenses. However, there are limited studies on the joint use of transcriptomics and metabolomics in investigating leaf damage-related defense mechanisms and their regulation in woody plants. This study investigated the leaf damage defense mechanisms of Populus talassica × Populus euphratica at the molecular level using transcriptome and secondary metabolome analyses. In total, 4078 differentially expressed genes (DEGs; 1207 up-regulated and 2871 down-regulated) and 30 differential secondary metabolites (DSMs; 21 up-regulated and nine down-regulated) were identified from a transcriptome analysis of controls (CK) and CL75-treated leaves after 24 h. Plant-pathogen interactions and the MAPK signaling pathway were important defense pathways that synergized in the early stages of leaf damage in P. talassica × P. euphratica. There were 44 DEGs enriched in the KEGG pathways that encoded 21 WRKY transcription factors. Flavonoid genes were the most abundant. They were mainly enriched in the flavone and flavonol biosynthesis and flavonoid biosynthesis pathways. Sakuranetin and pinocembrin were most frequently associated with the differential metabolites and may be the main flavonoids involved in responding to leaf damage in P. talassica × P. euphratica. This study has far-reaching theoretical and practical significance for understanding the response strategies of P. talassica × P. euphratica to leaf damage and for achieving sustainable management and accurate predictions of artificial forests.
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