Genome-Wide Constitutively Expressed Gene Analysis and New Reference Gene Selection Based on Transcriptome Data: A Case Study from Poplar/Canker Disease Interaction.

Jiaping Zhao,Xianchong Wan,Fan Yang,Yanli Wang,Barbara Lachenbruch,Jiange Wang,Jinxia Feng

doi:10.3389/fpls.2017.01876

Abstract

A number of transcriptome datasets for differential expression (DE) genes have been widely used for understanding organismal biology, but these datasets also contain untapped information that can be used to develop more precise analytical tools. With the use of transcriptome data generated from poplar/canker disease interaction system, we describe a methodology to identify candidate reference genes from high-throughput sequencing data. This methodology will improve the accuracy of RT-qPCR and will lead to better standards for the normalization of expression data. Expression stability analysis from xylem and phloem of Populus bejingensis inoculated with the fungal canker pathogen Botryosphaeria dothidea revealed that 729 poplar transcripts (1.11%) were stably expressed, at a threshold level of coefficient of variance (CV) of FPKM < 20% and maximum fold change (MFC) of FPKM < 2.0. Expression stability and bioinformatics analysis suggested that commonly used house-keeping (HK) genes were not the most appropriate internal controls: 70 of the 72 commonly used HK genes were not stably expressed, 45 of the 72 produced multiple isoform transcripts, and some of their reported primers produced unspecific amplicons in PCR amplification. RT-qPCR analysis to compare and evaluate the expression stability of 10 commonly used poplar HK genes and 20 of the 729 newly-identified stably expressed transcripts showed that some of the newly-identified genes (such as SSU_S8e, LSU_L5e, and 20S_PSU) had higher stability ranking than most of commonly used HK genes. Based on these results, we recommend a pipeline for deriving reference genes from transcriptome data. An appropriate candidate gene should have a unique transcript, constitutive expression, CV value of expression < 20% (or possibly 30%) and MFC value of expression <2, and an expression level of 50–1,000 units. Lastly, when four of the newly identified HK genes were used in the normalization of expression data for 20 differential expressed genes, expression analysis gave similar values to Cufflinks output. The methods described here provide an alternative pathway for the normalization of transcriptome data, a process that is essential for integrating analyses of transcriptome data across environments, laboratories, sequencing platforms, and species.

Highlights

With the development of new generation high-throughput sequencing technology, terabytes (TB) or more of sequencing data are being generated daily from different species, tissues, cells, or environments
We synthesized the biological information from previous studies on 72 commonly used plant HK genes, including gene names, transcripts names, locus in the chromosome, primer sequences, and the gene’s basic or putative function (Table S1.1)
This study showed through both transcriptome analysis and RT-qPCR that four of the genes coding for ribosomes are stably expressed in poplar branches: SSU_S8e and SSU_S4e, which code for the small subunit S8e and S4e proteins, respectively; 18S_RNA, which codes for the ribosomal gene 18S rRNA; and LSU_L5e, which codes for the large subunit L5e protein

Summary

Introduction

With the development of new generation high-throughput sequencing technology, terabytes (TB) or more of sequencing data are being generated daily from different species, tissues, cells, or environments. These data are uploaded to public platforms, research centers and laboratories. HK genes are constitutive genes that are transcribed at a relatively constant level in different cells, stages or environments. They are implicated in many basic cellular processes and functions, such as protein synthesis and regulation, translation, and transcription. HK genes are useful reference points for comparative gene expression analysis (Czechowski et al, 2005); Actin, EF1α, tubulin, UBQ, 5.8S rRNA, and 18S rRNA are examples of HK genes commonly used to normalize gene expression data

Methods

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Discussion

Conclusion