Differential Gene Expression between Leaf and Rhizome in Atractylodes lancea: A Comparative Transcriptome Analysis.

Qianqian Huang,Kun Yu,Xiao Huang,Juan Deng,Yanwen Liu,Hegang Liu,Bisheng Huang

doi:10.3389/fpls.2016.00348

Abstract

The rhizome of Atractylodes lancea is extensively used in the practice of Traditional Chinese Medicine because of its broad pharmacological activities. This study was designed to characterize the transcriptome profiling of the rhizome and leaf of Atractylodes lancea in an attempt to uncover the molecular mechanisms regulating rhizome formation and growth. Over 270 million clean reads were assembled into 92,366 unigenes, 58% of which are homologous with sequences in public protein databases (NR, Swiss-Prot, GO, and KEGG). Analysis of expression levels showed that genes involved in photosynthesis, stress response, and translation were the most abundant transcripts in the leaf, while transcripts involved in stress response, transcription regulation, translation, and metabolism were dominant in the rhizome. Tissue-specific gene analysis identified distinct gene families active in the leaf and rhizome. Differential gene expression analysis revealed a clear difference in gene expression pattern, identifying 1518 up-regulated genes and 3464 down-regulated genes in the rhizome compared with the leaf, including a series of genes related to signal transduction, primary and secondary metabolism. Transcription factor (TF) analysis identified 42 TF families, with 67 and 60 TFs up-regulated in the rhizome and leaf, respectively. A total of 104 unigenes were identified as candidates for regulating rhizome formation and development. These data offer an overview of the gene expression pattern of the rhizome and leaf and provide essential information for future studies on the molecular mechanisms of controlling rhizome formation and growth. The extensive transcriptome data generated in this study will be a valuable resource for further functional genomics studies of A. lancea.

Highlights

IntroductionRhizomatous plants comprise a large group, and many of them contribute ecosystem services (e.g., prevention of soil erosion) or have high economic value (e.g., ginger) or significant medicinal uses, such as Paris polyphylla and other rhizomatous medicinal plants (Glover et al, 2010; Yu et al, 2013)
Rhizomatous plants comprise a large group, and many of them contribute ecosystem services or have high economic value or significant medicinal uses, such as Paris polyphylla and other rhizomatous medicinal plants (Glover et al, 2010; Yu et al, 2013)
To obtain a comprehensive overview of the A. lancea transcriptome, RNAseq libraries were constructed from leaves and rhizomes and sequenced using Illumina pairedend sequencing technology

Summary

Introduction

Rhizomatous plants comprise a large group, and many of them contribute ecosystem services (e.g., prevention of soil erosion) or have high economic value (e.g., ginger) or significant medicinal uses, such as Paris polyphylla and other rhizomatous medicinal plants (Glover et al, 2010; Yu et al, 2013). The relationship of growth and development between leaf and rhizome and the molecular mechanisms underlying rhizome formation are largely not understood, due to the complexity of the developmental connections and the physiological coordination between the two organs. Developed functional genomics approaches offer an efficient way to dissect complex physiological processes (Baginsky et al, 2010). The large scale of genomic and transcriptomic data have greatly enhanced our understanding of plant growth and development, especially in model plants, such as Arabidopsis and rice. RNA sequencing (RNA-Seq), has been widely used to obtain transcriptome data, profile global gene expression, and identify novel genes in both model and non-model plant species, including Arabidopsis (Begara-Morales et al, 2014), rice (Wakasa et al, 2014), Salvia miltiorrhiza (Gao et al, 2014), and Medicago truncatula (Cabeza et al, 2014). With advances in sequencing technology, RNA sequencing has become an effective and powerful tool for transcriptome analysis, especially in nonmodel species where limited genetic and genomic resources are available (Dillies et al, 2013)

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