Skip to main content
Download PDF

Selection and evaluation of reference genes for qRT-PCR analysis in Euscaphis konishii Hayata based on transcriptome data

Plant Methods volume 14, Article number: 42 (2018) Cite this article

Abstract

Background

Quantitative real-time reverse transcription-polymerase chain reaction has been widely used in gene expression analysis, however, to have reliable and accurate results, reference genes are necessary to normalize gene expression under different experimental conditions. Several reliable reference genes have been reported in plants of Traditional Chinese Medicine, but none have been identified for Euscaphis konishii Hayata.

Results

In this study, 12 candidate reference genes, including 3 common housekeeping genes and 9 novel genes based on E. konishii Hayata transcriptome data were selected and analyzed in different tissues (root, branch, leaf, capsule and seed), capsule and seed development stages. Expression stability was calculated using geNorm and NormFinder, the minimal number of reference genes required for accurate normalization was calculated by Vn/Vn + 1 using geNorm. EkEEF-5A-1 and EkADF2 were the two most stable reference genes for all samples, while EkGSTU1 and EkGAPDH were the most stable reference genes for tissue samples. For seed development stages, EkGAPDH and EkEEF-5A-1 were the most stable genes, whereas EkGSTU1 and EkGAPDH were identified as the two most stable genes in the capsule development stages. Two reference genes were sufficient to normalize gene expression across all sample sets.

Conclusion

Results of this study revealed that suitable reference genes should be selected for different experimental samples, and not all the common reference genes are suitable for different tissue samples and/or experimental conditions. In this study, we present the first data of reference genes selection for E. konishii Hayata based on transcriptome data, our data will facilitate further studies in molecular biology and gene function on E. konishii Hayata and other closely related species.

Background

Quantitative real-time reverse transcription-polymerase chain reaction (qRT-PCR) has become one of the most powerful tools to study gene expression due to its high sensitivity, accuracy and specificity [1]. However, to get accurate and reliable results, a reference gene is necessary to normalize gene expression and avoid errors caused by different experimental procedure, such as sample amounts, quality and quantity of RNA, efficiency of enzymatic reaction and PCR efficiency [2, 3].

Most of the commonly used reference genes are housekeeping genes, such as actin (ACT), tubulin (TUB), polyubiquitin (BUQ), elongation factor 1-α (EF1-α), glyceraldehyde-3-phosphate dehydrogenase (GAPDH) and ribosomal RNAs (18S rRNA or 28S rRNA). However, some data showed that expression levels of these housekeeping genes can vary considerably under different experimental conditions [4, 5], and also, in non-model plant species, usually the used reference genes are identified by the orthologous sequence of common housekeeping genes reported in model plant species due to the lack of genetic and sequence genome information [6]. Consequently, the unsuitable use of traditional housekeeping genes as reference gene in non-model plants can cause bias. Therefore, it is important to select proper reference genes according to experimental conditions [7]. Moreover, statistical software, including geNorm, BestKeeper, NormFinder and RefFinder, have been widely used as efficient tools to evaluate gene expression stability for qRT-PCR normalization [8,9,10]. Reference gene validation has been done in many plant species, such as banana [11], peach [12], soybean [13], amorphophallus [14], Jatropha curcas [15], Isatis indigotica Fort. [16], Achyranthes bidentata Blume [17], Kentucky bluegrass [18], Salix matsudana [19], Rhododendron molle G. Don [20], Sapium sebiferum [21], Petroselinum crispum [22], Lilium spp. [23], Hibiscus cannabinus L. [24] and Dendrobium officinale [25].

Euscaphis is a member of the family Staphyleaceae, which has two species in China: E. japonica Dippel and E. konishii Hayata. Euscaphis has been widely used in traditional Chinese medicine. Several chemical compounds have been isolated from Euscaphis, such as triterpene compounds [26,27,28,29], phenolic acid compounds [30, 31], flavonoid compounds [27, 31] and others [31,32,33], however, no molecular and gene expression data has been reported in Euscaphis.

Twelve genes (EkUBC, EkF-ACP, EkARP7, EkEF2, EkACT, EkGAPDH, EkEEF-5A-1, EkADF2, EkTUB, EkPLAC8, EkLPP, EkGSTU1) were selected as candidate genes according to transcriptome data from our lab (Liang et al., College of Forestry, Fujian Agriculture and Forestry University) (unpublished data), and their expression stability was evaluated by qRT-PCR across different experimental conditions: including five tissues (root, branch, seed, leaf and capsule), six different developmental stages of seed and six different development stages of capsule. Their expression stability was calculated using geNorm and NormFinder. Additionally, in order to validate our results, the expression levels of EkCAD1 in different tissues were normalized by the most and least stable genes.

Methods

Plant material

Euscaphis konishii Hayata tissues were collected from Fujian Agriculture and Forestry University, Fujian Province, China. Tissues (leaf, capsule, seed, root and branch) were collected on November 15th 2016, and six developmental stages of capsule and seed were collected once every 15 days after formation. All samples were harvested, washed and surface dried and then frozen in liquid nitrogen and immediately stored at − 80 °C until required for further analyzes. Three biological replicates for each sample were used for RNA extraction.

RNA isolation and cDNA synthesis

Total RNA was extracted from each sample using the RNAprepPure Plant Kit DP441 (Tiangen Biothch CO., LTD, Beijing, China), according to the manufacturer’s instructions. RNA was treated with DNase I (Tiangen, Beijing, China) to eliminate DNA contamination. RNA quality was determined by 1.2% agarose gel electrophoresis. The concentration and purity of total RNA was determined using a NanoDrop 2000c Spectrophotometer (Thermo Scientific, US). The A260/A280 ratio of total RNA between 1.90 and 2.10 was considered to meet the required quality for further experiments. First-strand of cDNA was synthesized using the First Strand cDNA Synthesis Kit (Roche, Switzerland) using 1.0 μg of total RNA in a 20 μL reaction volume according to the manufacturer’s protocols.

Selection of candidate reference genes and primer design

Based on transcriptome sequencing data from our laboratory, 12 reference genes were selected to normalize and validate qRT-PCR experiments by screening for genes with relatively stable expression (based on their RPKM and fold change values), including nine novel genes and three common housekeeping genes. Their sequence/alignment/phylogenetic data are shown in Additional files 1 and 2. Forward and reverse primers of all candidate reference genes were designed using Primer Premier 5.0 with the following parameters: Tm values ranging from 50 to 70 °C, GC percent of 45–50%, primer lengths of 18–25 bp and product length of 90–200 bp. All primers were synthesized by Sangon Biotech Co., Ltd (Shanghai, China). Primer details are shown in Table 1.

Table 1 Primers used for qRT-PCR normalization
Full size table

qRT-PCR analysis for each candidate reference gene was performed on a 7500 Fast ABI Real-time PCR system (Applied Biosystems, US) using FastStart Universal SYBR Green Master (Roche, Switzerland). A 20 μL reaction mixture contained: 10 μL 2 × SYBR Green Master, 0.4 μL forward primer (10 μM), 0.4 μL reverse primer (10 μM), 2 μL cDNA and 7.2 μL dd H2O in a 96-well plates. The amplification conditions were as follows: 50 °C for 2 min, 95 °C for 10 min, 40 cycles of 95 °C for 15 s and 60 °C for 30 s. Melting curve was analyzed to determine primer specificity.

All samples were analyzed in three biological and technical replicates. Serial tenfold dilutions of cDNA template were used to generate slope of the standard curve to calculate amplification efficiency and correlation coefficient of each candidate reference gene.

Data analysis

NormFinder and geNorm were used to analyze the stability of the 12 candidate reference genes under different conditions. Expression levels of each reference gene were shown by Cq values. Before using the two softwares, the raw Cq values was used to calculate relative quantities by the equation: Q = 2−(sampleCq-mimCq). The values of stability (M) and pairwise variation (V) between genes was generated by geNorm, the lower M value is the gene expression is more stable [8, 34, 35]. Furthermore, the normalization factor generated by computing the pairwise variation of the two normalization factor was used to determine the most suitable numbers of reference genes with a cut-off value of 0.15 [17]. NormFinder was used to evaluate the stability of candidate genes by intra- and inter- group variations. The more stable reference gene will have lower stability value and inter- and intra-group variation.

Validation of the candidate reference genes

In order to verify the results of our experiments, the most stable and unstable reference genes were selected to validate the expression of the E. konishii Cinnamyl alcohol dehydrogenase 1 (EkCAD1) gene in different tissue samples (root, branch, capsule, seed and leaf). CAD1 belongs to CAD family, which catalyzes the reduction of p-coumaricaldehyde, coniferyl aldehyde and sinapyl aldehyde to their alcohol derivatives which are then polymerized into lignin [36], CAD is one of the most used genes to manipulate to obtain plants with low lignin content [37]. qRT-PCR experimental method was the same as described above, and the relative expression level was calculated by 2−ΔΔct method [12]. Data from three biological replicates were analyzed using analysis of variance (ANOVA) followed by Student’s t test (P < 0.05).

Results

Primer specificity and PCR amplification efficiency

A total of 12 candidate reference genes, including three common housekeeping genes and nine novel genes from transcriptome sequencing data of E. konishii were selected for qRT-PCR normalization. The details of gene names, abbreviation, accession number, primer sequence, primers Tm, product length, amplification efficiency and correlation coefficient are shown in Table 1. The specificity for each primer set was validated by melting curve. For all primer sets the melting curve showed a single amplification peak (Additional file 3). qRT-PCR efficiency for all 12 candidate reference genes ranged from 97.89% for EkUBC to 103.21% for EkACT, and correlation coefficients varied from 0.9795 to 0.9999 (Table 1).

Cq values of candidate reference genes

Cq values for all 12 reference genes are shown in Fig. 1. The Cq values varied from 15.812 (EkF-ACP) to 30.121 (EkACT) across all samples, and mean Cq ranged from 18.0575 (EkF-ACP) to 25.6685 (EkACT). Moreover, EkACT expression levels were the most variable with 8.905 Cq, while EkGAPDH showed the least variable levels with 2.609 Cq. Since gene expression levels are negatively correlated to Cq values, EkF-ACP had high expression and EkACT with low expression.

Fig. 1
figure 1

Cq values of the twelve candidate reference genes. The lines across the box indicate median values, boxes depict 25/75 percentiles. Whisker caps indicate the minimum and maximum values

Full size image

Expression stability of candidate reference genes

Expression stability of the 12 reference genes was analyzed by geNorm and NormFinder. Samples were divided into three different experimental groups: (1) five tissues (root, leaf, branch, seed and capsule), (2) six seed developmental stages and (3) six capsule developmental stages.

geNorm analysis

Gene expression stability was determined by M-value in geNorm analysis, the lower the M value is, the more gene expression stability. For the tissue group the two most stable genes were EkGSTU1 and EkGAPDH with the lowest M value, and EkTUB was the most unstable gene. In the seed group EkEEF-5A-1 and EkGAPDH were the two most stable genes through all the different developmental stages, and EkLPP was the most unstable gene. Finally, in the capsule group EkGAPDH was the most stable gene, followed by EkGSTU1, and EkF-ACP and EkUBC were the least stable genes (Table 2; Fig. 2). For all sample sets EkADF2 and EkEEF-5A-1 were the most stable genes, and EkF-ACP and EkUBC were the least stable. The minimum number of genes required for normalization in all the different groups was calculated by geNorm. The V2/3 values for all different experimental groups were below the cut-off value of 0.15 (0.143 of all samples, 0.11 for tissues samples, 0.101 for seed development stages and 0.135 for capsule development stages), which indicate that two reference genes are enough to normalize gene expression data (Fig. 3).

Table 2 Gene expression stability across sample sets calculated by geNorm
Full size table
Fig. 2
figure 2

Average expression stability (M-value) of 12 candidate genes calculated by geNorm and ranking of the candidate reference genes in different experimental group. Tissues: five tissues sample sets; DSS: seed development stages; DSC: capsule development stages. Total: all samples

Full size image
Fig. 3
figure 3

Optimal number of reference genes in different experimental groups using the geNorm. Pairwise variation (Vn/Vn + 1) analysis between normalization factors (NFn and NFn + 1) to calculate the number of reference genes in each experimental group. Tissues: five tissues sample sets; DSS: seed development stages; DSC: capsule development stages. Total: all samples

Full size image

NormFinder analysis

Expression stability values analyzed by NormFinder are shown in Table 3. For tissue group, EkGSTU1 and EkGAPDH were the most stable reference genes, and EkTUB was the least stable gene, same as shown by geNorm analysis. In the seed group EkEEF-5A-1 and EkGAPDH were the most stable reference genes, while EkARP7 was the least stable gene. In the capsule group, EkGAPDH and EkGSTU1 got the top rank, while EkUBC and EkF-ACP were ranked at the lowest. In general, the ranking was same as geNorm analysis (Table 3).

Table 3 Gene expression stability across sample sets calculated by NormFinder
Full size table

EkCAD1 expression and validation of EkGSTU1 and EkGAPDH

In order to verify the reliability of the selected reference genes, expression profiles of EkCAD1 gene was determined in different tissues. Relative expression levels were normalized using the two most stable reference genes (EkGSTU1 and EkGAPDH) and the least stable reference gene (EkTUB).

EkCAD1 showed similar expression levels when single or a combination of reference genes (EkGSTU1 and EkGAPDH) were used to normalize the expression. EkCAD1 expression was up regulated in all the tissues except in seed. However, when EkTUB was used for normalization (unstable gene), relative expression profile of EkCAD1 was different when compared when the normalization expression was done using the two most stable reference genes identified in our study (EkGSTU1 and EkGAPDH) (Fig. 4). Our results suggest that the expression patterns of target genes are differed when normalized by different reference genes.

Fig. 4
figure 4

Relative expression of EkCAD1 in different tissues. EkGSTU1, EkGAPDH and EkGSTU1 + EkGAPDH were used as one or two most stable reference genes, EkTUB was used as the least stable reference gene. Data are represented as mean ± SD, different words indicate significant difference of the expression of the target gene based on three biological replications (P < 0.05, t test; n  =  3)

Full size image

Discussion

qRT-PCR is one of the most commonly used technique to determine gene expression in plants. To ensure the accuracy and reliability of the results, a suitable reference gene is necessary for data normalization. Conventionally, some housekeeping genes such as ACT, GAPDH, TUB, have been used as reference genes, however, no single gene can be used for all plant species, experimental conditions and/or tissues. Therefore, it is required to select proper reference gene(s) for certain species under different conditions rather than using common reference genes.

The development of high-throughput sequencing technology provides a more efficient approach to study plant molecular biology, and it has been widely used in plant genomes [38,39,40,41,42,43], plant transcriptome [44,45,46,47], plant ncRNA [48,49,50], moreover, the generation of large-scale gene segments and gene expression data by sequencing provides a new resource for the identification of reference genes, especially in non-model species [51,52,53]. Therefore, transcriptome data on E. konishii Hayata, available in our laboratory can be used as a tool to identify candidate reference genes. Asystematic study of 12 candidate reference genes in three conditions was carried in this paper, and their expression stability was calculated using geNorm and NormFinder.

ACT and TUB, the most widely used reference genes, did not show a good expression stability in E. konishii Hayata across all sample sets (Tables 2, 3). The phenomenon that expression levels of common reference genes varied in a large range has been reported in several papers [54, 55]. GAPDH, a common housekeeping gene also, has been widely used as reference gene in different species and experimental conditions [51, 56,57,58,59,60], in our experiments this gene was one of the two most stable genes in tissue sample set and capsule development stages, but it did not perform well in across all the sample and seed sets. The different performance of EkGAPDH in different experimental conditions in this study demonstrated that there is no single reference gene that can be used for all species or different experimental conditions [61,62,63,64,65].

In this study, EkGSTU1 (glutathione-S-transferase tau 1), which belongs to tau class of glutathione transferases (GSTs) [66], was the one of two most stable genes in tissues sample and capsule development stages. EkADF2 and EkEEF-5A-1 were the two most stable genes in total sample set, ADF (actin-depolymerizing factor) play important roles in several cellular processes that require cytoskeletal rearrangements, such as cell migration, chromosome introgression, cleavage plane orientation and furrow formation [67,68,69]. VvADF has been identified as candidate reference gene for grapevine during anthesis [6], rubber tree duration of latex flow [70] and TrADF3 was selected as reference gene in staminate and perfect flowers of T. rupestris [71].

It has been widely accepted that using combination of multiple reference genes to normalize gene expression can give more accurate and reliable expression patterns than using a single gene in qRT-PCR analysis [57]. Based on validation results of target gene expression, when EkGAPDH and EkGSTU1 were selected as reference genes for normalization either single or combination, the target gene EkCAD1 showed the similar expression pattern among different tissues, which indicated that the expression pattern of EkCAD1 was nearly identical when normalized with a single reference gene or two. Interestingly, in the tissue group, the combination of traditional housekeeping gene (EkGAPDH) and a novel identified reference gene (EkGSTU1) were identified as the most stable reference genes, suggesting that combination of traditional housekeeping genes and newly identified reference genes based on transcriptome data can be used as a good strategy for expression normalization of E. konishii Hayata genes.

Conclusion

In this study, we evaluated the expression stability of twelve candidate reference genes, including three traditional housekeeping genes and nine novel genes based on transcriptome data of E. konishii Hayata. Additionally, the expression pattern of target gene EkCAD1 was determined in different tissues to further verify the reliability of the identified stable reference genes. This study shows the first data for reference genes validation on E. konishii Hayata. Our study will contribute in future studies of gene expression in E. konishii Hayata and related species.

References

  1. Bustin SA. Quantification of mRNA using real-time reverse transcription PCR (RT-PCR): trends and problems. J Mol Endocrinol. 2002;29(1):23–39.

    Article  PubMed  CAS  Google Scholar 

  2. Die JV, Roman B, Nadal S, Gonzalez-Verdejo CI. Evaluation of candidate reference genes for expression studies in Pisum sativum under different experimental conditions. Planta. 2010;232(1):145–53.

    Article  PubMed  CAS  Google Scholar 

  3. Schmidt GW, Delaney SK. Stable internal reference genes for normalization of real-time RT-PCR in tobacco (Nicotiana tabacum) during development and abiotic stress. Mol Genet Genom. 2010;283(3):233–41.

    Article  CAS  Google Scholar 

  4. Bustin SA, Nolan T. Pitfalls of quantitative real-time reverse-transcription polymerase chain reaction. J Biomol Tech. 2004;15(3):155–66.

    PubMed  PubMed Central  Google Scholar 

  5. Gutierrez L, Mauriat M, Guenin S, Pelloux J, Lefebvre JF, Louvet R, Rusterucci C, Moritz T, Guerineau F, Bellini C, et al. The lack of a systematic validation of reference genes: a serious pitfall undervalued in reverse transcription-polymerase chain reaction (RT-PCR) analysis in plants. Plant Biotechnol J. 2008;6(6):609–18.

    Article  PubMed  CAS  Google Scholar 

  6. Gonzalez-Aguero M, Garcia-Rojas M, Di Genova A, Correa J, Maass A, Orellana A, Hinrichsen P. Identification of two putative reference genes from grapevine suitable for gene expression analysis in berry and related tissues derived from RNA-Seq data. BMC Genom. 2013;14:878.

    Article  CAS  Google Scholar 

  7. Jian B, Liu B, Bi Y, Hou W, Wu C, Han T. Validation of internal control for gene expression study in soybean by quantitative real-time PCR. BMC Mol Biol. 2008;9:59.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  8. Vandesompele J, De Preter K, Pattyn F, Poppe B, Van Roy N, De Paepe A, Speleman F. Accurate normalization of real-time quantitative RT-PCR data by geometric averaging of multiple internal control genes. Genome Biol. 2002;3(7):Research0034.

    Article  PubMed  PubMed Central  Google Scholar 

  9. Andersen CL, Jensen JL, Orntoft TF. 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. Can Res. 2004;64(15):5245–50.

    Article  CAS  Google Scholar 

  10. Pfaffl MW, Tichopad A, Prgomet C, Neuvians TP. Determination of stable housekeeping genes, differentially regulated target genes and sample integrity: BestKeeper–Excel-based tool using pair-wise correlations. Biotechnol Lett. 2004;26(6):509–15.

    Article  PubMed  CAS  Google Scholar 

  11. Chen L, Zhong HY, Kuang JF, Li JG, Lu WJ, Chen JY. Validation of reference genes for RT-qPCR studies of gene expression in banana fruit under different experimental conditions. Planta. 2011;234(2):377–90.

    Article  PubMed  CAS  Google Scholar 

  12. Tong Z, Gao Z, Wang F, Zhou J, Zhang Z. Selection of reliable reference genes for gene expression studies in peach using real-time PCR. BMC Mol Biol. 2009;10:71.

    Article  PubMed  PubMed Central  Google Scholar 

  13. Gao M, Liu Y, Ma X, Shuai Q, Gai J, Li Y. Evaluation of reference genes for normalization of gene expression using quantitative RT-PCR under aluminum, cadmium, and heat stresses in soybean. PLoS ONE. 2017;12(1):e0168965.

    Article  PubMed  PubMed Central  Google Scholar 

  14. Wang K, Niu Y, Wang Q, Liu H, Jin Y, Zhang S. Cloning and evaluation of reference genes for quantitative real-time PCR analysis in Amorphophallus. PeerJ. 2017;5:e3260.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  15. Karuppaiya P, Yan XX, Liao W, Wu J, Chen F, Tang L. Correction: identification and validation of superior reference gene for gene expression normalization via RT-qPCR in staminate and pistillate flowers of Jatropha curcas—a biodiesel plant. PLoS ONE. 2017;12(5):e0177039.

    Article  PubMed  PubMed Central  Google Scholar 

  16. Li T, Wang J, Lu M, Zhang T, Qu X, Wang Z. Selection and validation of appropriate reference genes for qRT-PCR analysis in Isatis indigotica Fort. Front Plant Sci. 2017;8:1139.

    Article  PubMed  PubMed Central  Google Scholar 

  17. Li J, Han X, Wang C, Qi W, Zhang W, Tang L, Zhao X. Validation of suitable reference genes for RT-qPCR data in Achyranthes bidentata Blume under different experimental conditions. Front Plant Sci. 2017;8:776.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  18. Niu K, Shi Y, Ma H. Selection of candidate reference genes for gene expression analysis in Kentucky Bluegrass (Poa pratensis L.) under abiotic stress. Front Plant Sci. 2017;8:193.

    PubMed  PubMed Central  Google Scholar 

  19. Zhang Y, Han X, Chen S, Zheng L, He X, Liu M, Qiao G, Wang Y, Zhuo R. Selection of suitable reference genes for quantitative real-time PCR gene expression analysis in Salix matsudana under different abiotic stresses. Sci Rep. 2017;7:40290.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  20. Xiao Z, Sun X, Liu X, Li C, He L, Chen S, Su J. Selection of reliable reference genes for gene expression studies on Rhododendron molle G. Don. Front Plant Sci. 2016;7:1547.

    PubMed  PubMed Central  Google Scholar 

  21. Chen X, Mao Y, Huang S, Ni J, Lu W, Hou J, Wang Y, Zhao W, Li M, Wang Q, et al. Selection of suitable reference genes for quantitative real-time PCR in Sapium sebiferum. Front Plant Sci. 2017;8:637.

    Article  PubMed  PubMed Central  Google Scholar 

  22. Li MY, Song X, Wang F, Xiong AS. Suitable reference genes for accurate gene expression analysis in parsley (Petroselinum crispum) for abiotic stresses and hormone stimuli. Front Plant Sci. 2016;7:1481.

    PubMed  PubMed Central  CAS  Google Scholar 

  23. Xu L, Xu H, Cao Y, Yang P, Feng Y, Tang Y, Yuan S, Ming J. Validation of reference genes for quantitative real-time PCR during bicolor tepal development in asiatic hybrid lilies (Lilium spp.). Front Plant Sci. 2017;8:669.

    Article  PubMed  PubMed Central  Google Scholar 

  24. Niu X, Chen M, Huang X, Chen H, Tao A, Xu J, Qi J. Reference gene selection for qRT-PCR normalization analysis in kenaf (Hibiscus cannabinus L.) under abiotic stress and hormonal stimuli. Front Plant Sci. 2017;8:771.

    Article  PubMed  PubMed Central  Google Scholar 

  25. An H, Zhu Q, Pei W, Fan J, Liang Y, Cui Y, Lv N, Wang W. Whole-transcriptome selection and evaluation of internal reference genes for expression analysis in protocorm development of Dendrobium officinale Kimura et Migo. PLoS ONE. 2016;11(11):e0163478.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  26. Takahashi K, Kawaguchi S, Nishimura K, Kubota K, Tanabe Y. Studies on constituents of medicinal plants. XIII. Constituents of the pericarps of the capsules of Euscaphis japonica Pax. Chem Pharm Bull. 1974;22(3):650–3.

    Article  CAS  Google Scholar 

  27. Cheng JJ, Zhang LJ, Cheng HL, Chiou CT, Lee IJ, Kuo YH. Cytotoxic hexacyclic triterpene acids from Euscaphis japonica. J Nat Prod. 2010;73(10):1655–8.

    Article  PubMed  CAS  Google Scholar 

  28. Li YC, Tian K, Sun LJ, Long H, Li LJ, Wu ZZ. A new hexacyclic triterpene acid from the roots of Euscaphis japonica and its inhibitory activity on triglyceride accumulation. Fitoterapia. 2016;109:261–5.

    Article  PubMed  CAS  Google Scholar 

  29. Lee MK, Lee KY, Jeon HY, Sung SH, Kim YC. Antifibrotic activity of triterpenoids from the aerial parts of Euscaphis japonica on hepatic stellate cells. J Enzyme Inhib Med Chem. 2009;24(6):1276–9.

    Article  PubMed  CAS  Google Scholar 

  30. Maeda H, Matsuo Y, Tanaka T, Kouno I. Euscaphinin, a new ellagitannin dimer from Euscaphis japonica (THUNB.) KANITZ. Chem Pharm Bull. 2009;57(4):421–3.

    Article  CAS  Google Scholar 

  31. Lee MK, Jeon HY, Lee KY, Kim SH, Ma CJ, Sung SH, Lee HS, Park MJ, Kim YC. Inhibitory constituents of Euscaphis japonica on lipopolysaccharide-induced nitric oxide production in BV2 microglia. Planta Med. 2007;73(8):782–6.

    Article  PubMed  CAS  Google Scholar 

  32. Takeda Y, Okada Y, Masuda T, Hirata E, Shinzato T, Takushi A, Yu Q, Otsuka H. New megastigmane and tetraketide from the leaves of Euscaphis japonica. Chem Pharm Bull. 2000;48(5):752–4.

    Article  CAS  Google Scholar 

  33. Takeda Y, Okada Y, Masuda T, Hirata E, Takushi A, Otsuka H. Euscapholide and its glucoside from leaves of Euscaphis japonica. Phytochemistry. 1998;49(8):2565–8.

    Article  CAS  Google Scholar 

  34. Guo Y, Chen JX, Yang S, Fu XP, Zhang Z, Chen KH, Huang Y, Li Y, Xie Y, Mao YM. Selection of reliable reference genes for gene expression study in nasopharyngeal carcinoma. Acta Pharmacol Sin. 2010;31(11):1487–94.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  35. Han X, Lu M, Chen Y, Zhan Z, Cui Q, Wang Y. Selection of reliable reference genes for gene expression studies using real-time PCR in tung tree during seed development. PLoS ONE. 2012;7(8):e43084.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  36. Vanholme R, Demedts B, Morreel K, Ralph J, Boerjan W. Lignin biosynthesis and structure. Plant Physiol. 2010;153(3):895–905.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  37. Vanholme R, Morreel K, Darrah C, Oyarce P, Grabber JH, Ralph J, Boerjan W. Metabolic engineering of novel lignin in biomass crops. New Phytol. 2012;196(4):978–1000.

    Article  PubMed  CAS  Google Scholar 

  38. Velasco R, Zharkikh A, Affourtit J, Dhingra A, Cestaro A, Kalyanaraman A, Fontana P, Bhatnagar SK, Troggio M, Pruss D, et al. The genome of the domesticated apple (Malus × domestica Borkh.). Nat Genet. 2010;42(10):833–9.

    Article  PubMed  CAS  Google Scholar 

  39. Parkin IA, Koh C, Tang H, Robinson SJ, Kagale S, Clarke WE, Town CD, Nixon J, Krishnakumar V, Bidwell SL, et al. Transcriptome and methylome profiling reveals relics of genome dominance in the mesopolyploid Brassica oleracea. Genome Biol. 2014;15(6):R77.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  40. Chapman JA, Mascher M, Buluc A, Barry K, Georganas E, Session A, Strnadova V, Jenkins J, Sehgal S, Oliker L, et al. A whole-genome shotgun approach for assembling and anchoring the hexaploid bread wheat genome. Genome Biol. 2015;16:26.

    Article  PubMed  PubMed Central  Google Scholar 

  41. Harper AL, Trick M, He Z, Clissold L, Fellgett A, Griffiths S, Bancroft I. Genome distribution of differential homoeologue contributions to leaf gene expression in bread wheat. Plant Biotechnol J. 2016;14(5):1207–14.

    Article  PubMed  CAS  Google Scholar 

  42. Guan R, Zhao Y, Zhang H, Fan G, Liu X, Zhou W, Shi C, Wang J, Liu W, Liang X, et al. Draft genome of the living fossil Ginkgo biloba. GigaScience. 2016;5(1):49.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  43. Lin Y, Min J, Lai R, Wu Z, Chen Y, Yu L, Cheng C, Jin Y, Tian Q, Liu Q, et al. Genome-wide sequencing of longan (Dimocarpus longan Lour.) provides insights into molecular basis of its polyphenol-rich characteristics. GigaScience. 2017;6(5):1–14.

    Article  PubMed  PubMed Central  Google Scholar 

  44. Zhan X, Yang L, Wang D, Zhu JK, Lang Z. De novo assembly and analysis of the transcriptome of Ocimum americanum var. pilosum under cold stress. BMC Genom. 2016;17:209.

    Article  CAS  Google Scholar 

  45. Evangelistella C, Valentini A, Ludovisi R, Firrincieli A, Fabbrini F, Scalabrin S, Cattonaro F, Morgante M, Mugnozza GS, Keurentjes JJB, et al. De novo assembly, functional annotation, and analysis of the giant reed (Arundo donax L.) leaf transcriptome provide tools for the development of a biofuel feedstock. Biotechnol Biofuels. 2017;10:138.

    Article  PubMed  PubMed Central  Google Scholar 

  46. Abdeen A, Schnell J, Miki B. Transcriptome analysis reveals absence of unintended effects in drought-tolerant transgenic plants overexpressing the transcription factor ABF3. BMC Genom. 2010;11:69.

    Article  CAS  Google Scholar 

  47. Galla G, Vogel H, Sharbel TF, Barcaccia G. De novo sequencing of the Hypericum perforatum L. flower transcriptome to identify potential genes that are related to plant reproduction sensu lato. BMC Genom. 2015;16:254.

    Article  CAS  Google Scholar 

  48. Sunkar R, Zhou X, Zheng Y, Zhang W, Zhu JK. Identification of novel and candidate miRNAs in rice by high throughput sequencing. BMC Plant Biol. 2008;8:25.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  49. Nobuta K, Lu C, Shrivastava R, Pillay M, De Paoli E, Accerbi M, Arteaga-Vazquez M, Sidorenko L, Jeong DH, Yen Y, et al. Distinct size distribution of endogeneous siRNAs in maize: evidence from deep sequencing in the mop1-1 mutant. Proc Natl Acad Sci USA. 2008;105(39):14958–63.

    Article  PubMed  PubMed Central  Google Scholar 

  50. Liu TT, Zhu D, Chen W, Deng W, He H, He G, Bai B, Qi Y, Chen R, Deng XW. A global identification and analysis of small nucleolar RNAs and possible intermediate-sized non-coding RNAs in Oryza sativa. Mol Plant. 2013;6(3):830–46.

    Article  PubMed  CAS  Google Scholar 

  51. Zhuang H, Fu Y, He W, Wang L, Wei Y. Selection of appropriate reference genes for quantitative real-time PCR in Oxytropis ochrocephala Bunge using transcriptome datasets under abiotic stress treatments. In: Frontiers in plant science. vol. 6, 2015/07/16 edn; 2015:475.

  52. Wang H, Zhang X, Liu Q, Liu X, Ding S. Selection and evaluation of new reference genes for RT-qPCR analysis in Epinephelus akaara based on transcriptome data. PLoS ONE. 2017;12(2):e0171646.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  53. Demidenko NV, Logacheva MD, Penin AA. Selection and validation of reference genes for quantitative real-time PCR in buckwheat (Fagopyrum esculentum) based on transcriptome sequence data. PLoS ONE. 2011;6(5):e19434.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  54. Ma R, Xu S, Zhao Y, Xia B, Wang R. Selection and validation of appropriate reference genes for quantitative real-time PCR analysis of gene expression in Lycoris aurea. Front Plant Sci. 2016;7:536.

    PubMed  PubMed Central  Google Scholar 

  55. Ma S, Niu H, Liu C, Zhang J, Hou C, Wang D. Expression stabilities of candidate reference genes for RT-qPCR under different stress conditions in soybean. PLoS ONE. 2013;8(10):e75271.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  56. Monteiro F, Sebastiana M, Pais MS, Figueiredo A. Reference gene selection and validation for the early responses to downy mildew infection in susceptible and resistant Vitis vinifera cultivars. PLoS ONE. 2013;8(9):e72998.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  57. Guo J, Ling H, Wu Q, Xu L, Que Y. The choice of reference genes for assessing gene expression in sugarcane under salinity and drought stresses. Sci Rep. 2014;4:7042.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  58. Tian C, Jiang Q, Wang F, Wang GL, Xu ZS, Xiong AS. Selection of suitable reference genes for qPCR normalization under abiotic stresses and hormone stimuli in carrot leaves. PLoS ONE. 2015;10(2):e0117569.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  59. Cao J, Wang L, Lan H. Validation of reference genes for quantitative RT-PCR normalization in Suaeda aralocaspica, an annual halophyte with heteromorphism and C4 pathway without Kranz anatomy. PeerJ. 2016;4:e1697.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  60. Wan D, Wan Y, Yang Q, Zou B, Ren W, Ding Y, Wang Z, Wang R, Wang K, Hou X. Selection of reference genes for qRT-PCR analysis of gene expression in Stipa grandis during environmental stresses. PLoS ONE. 2017;12(1):e0169465.

    Article  PubMed  PubMed Central  Google Scholar 

  61. Warzybok A, Migocka M. Reliable reference genes for normalization of gene expression in cucumber grown under different nitrogen nutrition. PLoS ONE. 2013;8(9):e72887.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  62. Ray DL, Johnson JC. Validation of reference genes for gene expression analysis in olive (Olea europaea) mesocarp tissue by quantitative real-time RT-PCR. BMC Res Notes. 2014;7:304.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  63. Gimeno J, Eattock N, Van Deynze A, Blumwald E. Selection and validation of reference genes for gene expression analysis in switchgrass (Panicum virgatum) using quantitative real-time RT-PCR. PLoS ONE. 2014;9(3):e91474.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  64. Qi S, Yang L, Wen X, Hong Y, Song X, Zhang M, Dai S. Reference gene selection for RT-qPCR analysis of flower development in Chrysanthemum morifolium and Chrysanthemum lavandulifolium. Front Plant Sci. 2016;7:287.

    PubMed  PubMed Central  Google Scholar 

  65. Zhao Y, Luo J, Xu S, Wang W, Liu T, Han C, Chen Y, Kong L. Selection of reference genes for gene expression normalization in Peucedanum praeruptorum Dunn under abiotic stresses, hormone treatments and different tissues. PLoS ONE. 2016;11(3):e0152356.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  66. Lan T, Yang ZL, Yang X, Liu YJ, Wang XR, Zeng QY. Extensive functional diversification of the Populus glutathione S-transferase supergene family. Plant Cell. 2009;21(12):3749–66.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  67. Lenart P, Bacher CP, Daigle N, Hand AR, Eils R, Terasaki M, Ellenberg J. A contractile nuclear actin network drives chromosome congression in oocytes. Nature. 2005;436(7052):812–8.

    Article  PubMed  CAS  Google Scholar 

  68. Insall RH, Machesky LM. Actin dynamics at the leading edge: from simple machinery to complex networks. Dev Cell. 2009;17(3):310–22.

    Article  PubMed  CAS  Google Scholar 

  69. Kuure S, Cebrian C, Machingo Q, Lu BC, Chi X, Hyink D, D’Agati V, Gurniak C, Witke W, Costantini F. Actin depolymerizing factors cofilin1 and destrin are required for ureteric bud branching morphogenesis. PLoS Genet. 2010;6(10):e1001176.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  70. Chao J, Yang S, Chen Y, Tian WM. Evaluation of reference genes for quantitative real-time PCR analysis of the gene expression in laticifers on the basis of latex flow in rubber tree (Hevea brasiliensis Muell. Arg.). Front Plant Sci. 2016;7:1149.

    Article  PubMed  PubMed Central  Google Scholar 

  71. Li W, Zhang L, Zhang Y, Wang G, Song D, Zhang Y. Selection and validation of appropriate reference genes for quantitative real-time PCR normalization in staminate and perfect flowers of andromonoecious Taihangia rupestris. Front Plant Sci. 2017;8:729.

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Authors’ contributions

WXL, XXZ, CLR, SQW and SQZ designed the experiments, LJW, XYY, WHS and HD selected the material, WXL, LJW, WHS and WH performed the experiments, WXL, XYY, HD, PFL, LN and WH analyzed the data, WXL, SQZ wrote the paper. All authors read and approved the final manuscript.

Competing interests

The authors declare that they have no competing interests.

Availability of data and materials

The datasets supporting the conclusions and description of a complete protocol are included within the article.

Consent for publication

All authors have consented for publication.

Ethics approval and consent to participate

Not applicable.

Funding

This work was supported by the National Science Foundation of China Projects (Grant No. 31700292), the Special fund for science and technology innovation of Fujian Agriculture and Forestry University (Project Nos. CXZX2016072, CXZX2016073, CXZX2016074).

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Author information

Authors and Affiliations

  1. College of Forestry, Fujian Agriculture and Forestry University, Fuzhou, China

    Wenxian Liang, Xiaoxing Zou, Lingjiao Wu, Weihong Sun, Xueyuan Yuan, Songqing Wu, Pengfei Li, Hui Ding & Shuangquan Zou

  2. Fujian Colleges and Universities Engineering Research Institute of Conservation and Utilization of Natural Bioresources, Fujian Agriculture and Forestry University, Fuzhou, China

    Wenxian Liang, Xiaoxing Zou, Lingjiao Wu, Weihong Sun, Xueyuan Yuan, Songqing Wu, Pengfei Li, Hui Ding, Lin Ni, Wei Huang & Shuangquan Zou

  3. Department of Microbiology and Molecular Genetics, University of California, Irvine, USA

    Rebeca Carballar-Lejarazú

  4. College of Plant Protection, Fujian Agriculture and Forestry University, Fuzhou, China

    Lin Ni

  5. College of Life Sciences, Fujian Agriculture and Forestry University, Fuzhou, China

    Wei Huang

Authors
  1. Wenxian Liang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xiaoxing Zou
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Rebeca Carballar-Lejarazú
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Lingjiao Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Weihong Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Xueyuan Yuan
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Songqing Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Pengfei Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Hui Ding
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Lin Ni
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Wei Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Shuangquan Zou
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Shuangquan Zou.

Additional files

Additional file 1.

Sequences of 12 candidate genes and 1 validation gene.

Additional file 2.

Alignment and phylogenetic tree of 12 candidate genes and 1 validation gene.

Additional file 3.

Melting curves for the 12 candidate reference genes.

Rights and permissions

Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Liang, W., Zou, X., Carballar-Lejarazú, R. et al. Selection and evaluation of reference genes for qRT-PCR analysis in Euscaphis konishii Hayata based on transcriptome data. Plant Methods 14, 42 (2018). https://doi.org/10.1186/s13007-018-0311-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s13007-018-0311-x

Keywords

Plant Methods

ISSN: 1746-4811