Elsevier

Plant Physiology and Biochemistry

Volume 70, September 2013, Pages 304-310
Plant Physiology and Biochemistry

Research article
Validation of reference genes for quantitative real-time PCR during Chinese wolfberry fruit development

https://doi.org/10.1016/j.plaphy.2013.05.038Get rights and content

Highlights

  • Lycium barbarum L., a woody bush, is an ornamental and medicinal plant.

  • No report has so far described the selection of reference genes in Lycium barbarum L.

  • We identified reliable reference genes for normalization of qPCR data.

  • Different reference genes should be selected at the different fruit stages.

  • These results provide a guideline on gene expression in L. barbarum L.

Abstract

Lycium barbarum L., a woody bush that grows in Eurasia and North Africa, is an ornamental and medicinal plant. Its fruits have been used for centuries in China as a traditional herbal medicine and as a valuable nourishing tonic. There has been no report describing the selection of reference genes for stringent normalization for quantitative PCR (qPCR) in L. barbarum. The present study identified reliable reference genes for normalization of qPCR data in L. barbarum during fruit development from among eight candidate genes (GAPDH, TEF G, EF 1a, UBQ, TUB a, SAMS, EF2 and Hsp80) using the geNorm and NormFinder statistical algorithms. The results showed that the best-ranked references genes differed across the samples. A combination of GAPDH and EF1a would be appropriate as a reference panel for normalizing gene expression data across fruit developmental stages. A combination of EF 1a and SAMS would be appropriate as a reference panel for normalizing gene expression data at the stage A tested, whereas the combination of TUB a, and TEF G was the most suitable for stage B. EF2 and Hsp80 exhibited the most stable expression under stage C and stage D. NormFinder ranking of reference gene candidates was slightly different from that determined by geNorm. These results provide guidelines for the selection of reference genes under different development stages and also represent a foundation for more accurate and widespread use of qRT-PCR in L. barbarum gene analysis.

Introduction

Quantitative real-time reverse transcription PCR (qRT-PCR) is a very powerful and accurate technique for quantifying transcript expression levels in the study of biological and molecular mechanisms [1], [2], [3]. qRT-PCR has become the first choice in quantitative gene expression studies [4]. The accuracy of qRT-PCR is affected by a number of variables, such as initial sample quantity, RNA recovery and integrity, enzymatic efficiency in cDNA synthesis and PCR amplification, and the overall transcriptional activity of the tissues or cells analyzed [5]. Normalizing gene expression to the expression of an internal control or reference gene, whose expression is presumed to be constitutive, is normally used to control these variables [6].

An ideal reference gene is one that is stably expressed within the samples to be compared, regardless of tissue differences, experimental conditions or treatments [7], [8]. ‘Housekeeping’ genes are often supposed to have a steady expression pattern, and have been used extensively as reference genes [1]. However, many reports have shown that the expression levels of internal standards, including some housekeeping genes, alter considerably in response to alterations in experimental condition, for example, actin (ACT) [9], polyubiquitin (UBQ) [10] and 18S ribosomal RNA (18S) [11].

Gutierrez et al. (2008) [12] found high variability in the relative expression of ACT and UBQ during various developmental stages in Arabidopsis. The reason for these variations in expression may be that the reference genes not only participate in basic cell metabolism, but also take part in other cellular process [13]. For 18S rRNA, its high abundance compared with target mRNA transcripts makes it difficult to subtract accurately the baseline value in RT-qPCR data analysis [14], and also makes it necessary to dilute the cDNA samples prior to real-time analysis, thus risking dilution errors [15]. Furthermore, 18S rRNA cannot be used as a reference gene when reverse transcription reaction is carried out using oligo-dT primers or only mRNA is used as the template [16].

Various statistical algorithms, such as geNorm [14] and NormFinder [17], have been developed to evaluate reference gene expression stability and to choose the most appropriate reference gene under different experimental conditions [9], [16]. These algorithms require statistical analysis of the variation in each gene, by either single-factor analysis of variance [18] or by comparison of the mean variation in each gene relative to the mean variation of other genes in and/or between data sets [17], [19], [20]. In geNorm [14], the stability of each gene is calculated on the basis of the pairwise variation between that gene and all other genes tested. The genes with the lowest pairwise variation are the most stable, and geNorm identifies these genes by increasingly eliminating less stable genes from the analysis. The NormFinder algorithm hinges on a statistical and mathematical model that not only estimates the overall expression variation of a candidate gene, but also considers the variation between chosen subgroups [21]. Using these procedures, an increasing number of reports have evaluated reference genes in plants, including reports in Arabidopsis thaliana [8], [16], [22], [23] wheat, sugarcane, grape, barley, soybean [23], [24], [25], [26], [27], [28], [29], tomato [20], potato [9], poplar [18], [30], tobacco [5], flax [21], lychee, cucumber, chicory and perennial ryegrass [31], [32], [33], [34].

Lycium barbarum L. is a perennial bush common to most areas of China, Europe, and the Mediterranean region. Its fruits have been used for centuries in China as a traditional herbal medicine and as a valuable nourishing tonic (Committee of Chinese Pharmacopoeia 1990). L. barbarum fruits contain active compounds, L. barbarum polysaccharides, which have been shown to reduce cancer risk and delay senescence, moisturize the lungs and improve vision [35]. The absence of genetic and genomic information for this species limits its current utility. Its fruit development is a complex process, involving major changes in fruit metabolism [36]. Biochemical processes occur in a well-defined order under the control of a series of polysaccharide biosynthesis-related genes. Understanding the expression patterns of some key genes will help to illuminate the mechanisms involved in these processes in fruit and improve fruit quality. For a better understanding of L. barbarum fruit development, expression patterns of multiple stress-responsive genes at multiple time points are required.

The aim of this research was to select and evaluate the stability of eight reference genes for normalization across a large set of biological samples representing L. barbarum fruit developmental stages. Statistical algorithms implemented in geNorm [14] and NormFinder [17] were used. These results are valuable for future research on gene expression in L. barbarum.

Section snippets

Results

“Ningqi No 1”, a wolfberry (L. barbarum) cultivar was used for the analyses. We harvested fruits representing four developmental stages, from young fruits to fully mature fruits (Fig. 1).

To identify the best reference genes for studies of L. barbarum gene expression, an RT-qPCR assay, based on SYBR green detection, was designed for the transcription profiling of the eight genes (GAPDH, TEF G, EF 1a, UBQ, TUB a, SAMS, EF2 and Hsp80 (Table 1)). The specificity of the amplifications was confirmed

Discussion

Gene expression analysis has become increasingly important in furthering our understanding of the metabolic and signaling pathways that underlie developmental and cellular processes. Real-time RT-PCR is an important technique for the quantitative analysis of gene expression. The accuracy of real-time RT-PCR is strongly influenced by the stability of the internal reference genes used for data normalization. The importance of reference genes for gene expression studies and the need to validate

Conclusions

The present study tested the suitability of eight reference genes across 32 L. barbarum samples with two commonly used analysis programs and confirmed that different suitable reference genes should be selected according to the different L. barbarum fruit development stages. A combination of GAPDH and EF1a would be appropriate as a reference panel for normalizing gene expression data across fruit developmental stages. UBQ was the least stable among the reference genes. According to the geNorm

Plant materials

Tissues of Ningqi No 1, a wolfberry (L. barbarum) cultivar, were sampled from 5-year-old trees growing in the National Germplasm Orchard of the Academy of Agriculture and forestry Science, Yinchuan, Ningxia, China. Fruit at different developmental stages were sampled at 7, 19, 25 and 34 days after anthesis over the growing season. Fruits were classified into four developmental stages: young fruit (stage A, after anthesis 7 days), over-elongated fruit (stage B, after anthesis 19 days), immature

Acknowledgments

This study was supported by grants from the National Natural Science Foundation of China (31060043) and (31260065), the Natural Science Fund of Ningxia Province (NZ1009).

References (51)

  • E.R. Marino et al.

    Selection of internal control genes for quantitative real-time RT-PCR studies during tomato development process

    BMC Plant Biol.

    (2008)
  • G.W. Schmidt et al.

    Stable internal reference genes for normalization of real-time RT-PCR in tobacco (Nicotiana tabacum) during development and abiotic stress

    Mol. Genet. Genomics

    (2010)
  • P. Fernandez et al.

    Comparison of predictive methods and biological validation for qPCR reference genes in sunflower leaf senescence transcript analysis

    Plant Cell Rep.

    (2011)
  • M. Selim et al.

    Identification of suitable reference genes for real-time RT-PCR normalization in the grapevine-downy mildew pathosystem

    Plant Cell Rep.

    (2012)
  • B.R. Kim et al.

    Normalization of reverse transcription quantitative-PCR with housekeeping genes in rice

    J. Exp. Bot.

    (2003)
  • N. Nicot et al.

    Housekeeping gene selection for real-time RT-PCR normalization in potato during biotic and abiotic stress

    J. Exp. Bot.

    (2005)
  • L. Gutierrez et al.

    Towards a systematic validation of references in realtime RT-PCR

    Plant Cell

    (2008)
  • R. Singh et al.

    Sequence-specific binding of transfer RNA by glyceraldehyde-3-phosphate dehydrogenase

    Science

    (1993)
  • J. Vandesompele et al.

    Accurate normalization of real-time quantitative RT-PCR data by geometric averaging of multiple internal control genes

    Genome Biol.

    (2002)
  • G.W. Takle et al.

    Evaluation of reference genes for real-time RT-PCR expression studies in the plant pathogen pectobacterium atrosepticum

    BMC Plant Biol.

    (2007)
  • C.L. Andersen et al.

    Normalization of real-time quantitative reverse transcription-PCR data: a model-based variance estimation approach to identify genes suited for normalization, applied to bladder and colon cancer data sets

    Cancer Res.

    (2004)
  • A.M. Brunner et al.

    Validating internal controls for quantitative plant gene expression studies

    BMC Plant Biol.

    (2004)
  • M.W. Pfaffl

    Quantification strategies in real-time PCR

  • M. Expósito-Rodríguez et al.

    Selection of internal control genes for quantitative real-time RT-PCR studies during tomato development process

    BMC Plant Biol.

    (2008)
  • R. Huis et al.

    Selection of reference genes for quantitative gene expression normalization in flax (Linum usitatissimum L.)

    BMC Plant Biol.

    (2010)
  • Cited by (30)

    View all citing articles on Scopus
    View full text