Abstract

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.

Highlights

  • The tea plant (Camellia sinensis) in the family Theaceae is an important commercial crop that is extensively cultivated in Asian, African, Latin American, and Oceanian countries (ITC, 2014)

  • As to the tea plant focused in this review, apart from the high complexity in its genome, characteristic metabolic pathways in this crop have their intrinsic features different from other plant species

  • As an ammonium-tolerant and perennial plant species, the tea plant is different from other plants in nitrogen metabolism, which systematically governs the three characteristic metabolic pathways and makes them particular (Britto and Kronzucker, 2002)

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Summary

INTRODUCTION

The tea plant (Camellia sinensis) in the family Theaceae is an important commercial crop that is extensively cultivated in Asian, African, Latin American, and Oceanian countries (ITC, 2014). In the past few decades, parallel and integrated analysis of the multi-‘omics’ datasets in a network fashion, such as gene co-expression network and gene-to-metabolite network, has become efficient ways to identify genes underling specialized metabolism in the model Arabidopsis thaliana and other non-model plants (Oksman-Caldentey et al, 2004; Hirai, 2009) Such gene discovery strategies are based on a simple assumption that genes involved in a specialized metabolic pathway are coordinately regulated under a shared regulatory system, using the ‘guilt-by-association’ principle (Oliver, 2000; Saito et al, 2008). We introduce recent advances in transcriptomics and metabolomics for transcript and metabolite profiling, and highlight different ‘omics’-based network strategies for the gene discovery in plant-specialized metabolism using successful examples that are applied in the model Arabidopsis thaliana and non-model plants (e.g., tomato, wheat). A SURVEY OF ‘OMICS’-BASED NETWORK STRATEGIES FOR THE IDENTIFICATION OF SPECIALIZED METABOLITE GENES IN PLANTS

Introduction to Biological Network
LIMITATIONS
Findings
CONCLUSION AND FUTURE PROSPECTS
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