Gene Discovery of Characteristic Metabolic Pathways in the Tea Plant (Camellia sinensis) Using ‘Omics’-based Network Approaches: A Future Perspective
- 1Anhui Agricultural University, China
- 2University of Georgia, United States
- 3Rutgers University–Newark, Rutgers University, The State University of New Jersey, United States
Characteristic secondary metabolites, including flavonoids, theanine and caffeine, in the tea plant (Camellia sinensis) are the primary sources of the rich flavors, fresh taste, and health benefits of tea. The decoding of genes involved in these characteristic components is still significantly lagging, which lays an obstacle for applied genetic improvement and metabolic engineering. With the popularity of high-throughout transcriptomics and metabolomics, ‘omics’-based network approaches, such as gene co-expression network and gene-to-metabolite network, have emerged as powerful tools for gene discovery of plant-specialized (secondary) metabolism. Thus, it is pivotal to summarize and introduce such system-based strategies in facilitating gene identification of characteristic metabolic pathways in the tea plant (or other plants). In this review, we describe recent advances in transcriptomics and metabolomics for transcript and metabolite profiling, and highlight ‘omics’-based network strategies using successful examples in model and non-model plants. Further, we summarize recent progress in ‘omics’ analysis for gene identification of characteristic metabolites in the tea plant. Limitations of the current strategies are discussed by comparison with ‘omics’-based network approaches. Finally, we demonstrate the potential of introducing such network strategies in the tea plant, with a prospects ending for a promising network discovery of characteristic metabolite genes in the tea plant.
Keywords: Tea plant (Camellia sinensis), characteristic metabolic pathway, plant-specialized metabolite, Transcriptomics, Metabolomics, Gene discovery, Network approach
Received: 15 Oct 2017;
Accepted: 29 Mar 2018.
Edited by:Yi Zhao, Institute of Computing Technology,C.A.S., China
Reviewed by:Haibo Liu, University of Massachusetts Medical School, United States
Wei-Lun Hung, University of Florida, United States
Copyright: © 2018 Zhang, Zhang, Tai, Wang, ho and wan. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Prof. xiao-chun wan, Anhui Agricultural University, Hefei, China, firstname.lastname@example.org