Discovery of putative capsaicin biosynthetic genes by RNA-Seq and digital gene expression analysis of pepper

被引:0
|
作者
Zi-Xin Zhang
Shu-Niu Zhao
Gao-Feng Liu
Zu-Mei Huang
Zhen-Mu Cao
Shan-Han Cheng
Shi-Sen Lin
机构
[1] Key Laboratory of Protection and Development Utilization,
[2] Tropical Crop Germplasm Resources (Hainan University),undefined
[3] Ministry of Education,undefined
[4] Haikou 570228,undefined
[5] China ,undefined
[6] Tropical Crops Genetic Resources Institute,undefined
[7] Chinese Academy of Tropical Agricultural Sciences/Key Laboratory of Crop Gene Resources and Germplasm Enhancement in Southern China,undefined
[8] Ministry of Agriculture,undefined
[9] College of Horticulture and Landscape,undefined
[10] Hainan University,undefined
[11] College of Horticulture,undefined
[12] Nanjing Agricultural University,undefined
来源
关键词
D O I
暂无
中图分类号
学科分类号
摘要
The Indian pepper ‘Guijiangwang’ (Capsicum frutescens L.), one of the world’s hottest chili peppers, is rich in capsaicinoids. The accumulation of the alkaloid capsaicin and its analogs in the epidermal cells of the placenta contribute to the pungency of Capsicum fruits. To identify putative genes involved in capsaicin biosynthesis, RNA-Seq was used to analyze the pepper’s expression profiles over five developmental stages. Five cDNA libraries were constructed from the total RNA of placental tissue and sequenced using an Illumina HiSeq 2000. More than 19 million clean reads were obtained from each library, and greater than 50% of the reads were assignable to reference genes. Digital gene expression (DGE) profile analysis using Solexa sequencing was performed at five fruit developmental stages and resulted in the identification of 135 genes of known function; their expression patterns were compared to the capsaicin accumulation pattern. Ten genes of known function were identified as most likely to be involved in regulating capsaicin synthesis. Additionally, 20 new candidate genes were identified related to capsaicin synthesis. We use a combination of RNA-Seq and DGE analyses to contribute to the understanding of the biosynthetic regulatory mechanism(s) of secondary metabolites in a nonmodel plant and to identify candidate enzyme-encoding genes.
引用
收藏
相关论文
共 50 条
  • [31] Parametric analysis of RNA-seq expression data
    Konishi, Tomokazu
    GENES TO CELLS, 2016, 21 (06) : 639 - 647
  • [32] Characterizing short read sequencing for gene discovery and RNA-Seq analysis in Crassostrea gigas
    Gavery, Mackenzie R.
    Roberts, Steven B.
    COMPARATIVE BIOCHEMISTRY AND PHYSIOLOGY D-GENOMICS & PROTEOMICS, 2012, 7 (02): : 94 - 99
  • [33] The Utility of Shallow RNA-Seq for Documenting Differential Gene Expression in Genes with High and Low Levels of Expression
    Atallah, Joel
    Plachetzki, David C.
    Jasper, W. Cameron
    Johnson, Brian R.
    PLOS ONE, 2013, 8 (12):
  • [34] The RNA-Seq approach to studying the expression of mosquito mitochondrial genes
    Neira-Oviedo, M.
    Tsyganov-Bodounov, A.
    Lycett, G. J.
    Kokoza, V.
    Raikhel, A. S.
    Krzywinski, J.
    INSECT MOLECULAR BIOLOGY, 2011, 20 (02) : 141 - 152
  • [35] Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks
    Cole Trapnell
    Adam Roberts
    Loyal Goff
    Geo Pertea
    Daehwan Kim
    David R Kelley
    Harold Pimentel
    Steven L Salzberg
    John L Rinn
    Lior Pachter
    Nature Protocols, 2012, 7 : 562 - 578
  • [36] Dynamic Analysis of Gene Expression in Rice Superior and Inferior Grains by RNA-Seq
    Sun, Hongzheng
    Peng, Ting
    Zhao, Yafan
    Du, Yanxiu
    Zhang, Jing
    Li, Junzhou
    Xin, Zeyu
    Zhao, Quanzhi
    PLOS ONE, 2015, 10 (09):
  • [37] A probabilistic approach for automated discovery of perturbed genes using expression data from microarray or RNA-Seq
    Sundaramurthy, Gopinath
    Eghbalnia, Hamid R.
    COMPUTERS IN BIOLOGY AND MEDICINE, 2015, 67 : 29 - 40
  • [38] Comprehensive evaluation of differential gene expression analysis methods for RNA-seq data
    Franck Rapaport
    Raya Khanin
    Yupu Liang
    Mono Pirun
    Azra Krek
    Paul Zumbo
    Christopher E Mason
    Nicholas D Socci
    Doron Betel
    Genome Biology, 14
  • [39] Empirical Bayes Analysis of RNA-seq Data for Detection of Gene Expression Heterosis
    Jarad Niemi
    Eric Mittman
    Will Landau
    Dan Nettleton
    Journal of Agricultural, Biological, and Environmental Statistics, 2015, 20 : 614 - 628
  • [40] Advantages of CEMiTool for gene co-expression analysis of RNA-seq data
    Cheng, Chew Weng
    Beech, David J.
    Wheatcroft, Stephen B.
    COMPUTERS IN BIOLOGY AND MEDICINE, 2020, 125