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 experimen‑ tal 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 tran‑ scriptome data, our data will facilitate further studies in molecular biology and gene function on E. konishii Hayata and other closely related species. Keywords: Euscaphis konishii Hayata, Reference gene, qRT‑ PCR, Transcriptome, Gene expression, Normalization, EkCAD1 gene Background amounts, quality and quantity of RNA, efficiency of enzy - Quantitative real-time reverse transcription-polymerase matic reaction and PCR efficiency [ 2, 3]. chain reaction (qRT-PCR) has become one of the most Most of the commonly used reference genes are house- powerful tools to study gene expression due to its high keeping genes, such as actin (ACT), t ubulin (TUB), sensitivity, accuracy and specificity [ 1]. However, to get polyubiquitin (BUQ), elongation factor 1-α (EF1-α), accurate and reliable results, a reference gene is necessary glyceraldehyde-3-phosphate dehydrogenase (GAPDH) to normalize gene expression and avoid errors caused and ribosomal RNAs (18S rRNA or 28S rRNA). How- by different experimental procedure, such as sample ever, some data showed that expression levels of these housekeeping genes can vary considerably under dif- ferent experimental conditions [4, 5], and also, in non- *Correspondence: email@example.com model plant species, usually the used reference genes College of Forestry, Fujian Agriculture and Forestry University, Fuzhou, China are identified by the orthologous sequence of common Full list of author information is available at the end of the article © The Author(s) 2018. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creat iveco mmons .org/licen ses/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://creat iveco mmons .org/ publi cdoma in/zero/1.0/) applies to the data made available in this article, unless otherwise stated. Liang et al. Plant Methods (2018) 14:42 Page 2 of 9 housekeeping genes reported in model plant species due required for further analyzes. Three biological replicates to the lack of genetic and sequence genome information for each sample were used for RNA extraction. . Consequently, the unsuitable use of traditional house- keeping genes as reference gene in non-model plants can cause bias. Therefore, it is important to select proper ref - RNA isolation and cDNA synthesis erence genes according to experimental conditions . Total RNA was extracted from each sample using the Moreover, statistical software, including geNorm, Best- RNAprepPure Plant Kit DP441 (Tiangen Biothch CO., Keeper, NormFinder and RefFinder, have been widely LTD, Beijing, China), according to the manufacturer’s used as efficient tools to evaluate gene expression stabil - instructions. RNA was treated with DNase I (Tiangen, ity for qRT-PCR normalization [8–10]. Reference gene Beijing, China) to eliminate DNA contamination. RNA validation has been done in many plant species, such quality was determined by 1.2% agarose gel electropho- as banana , peach , soybean , amorphophal- resis. The concentration and purity of total RNA was lus , Jatropha curcas , Isatis indigotica Fort. , determined using a NanoDrop 2000c Spectrophotom- Achyranthes bidentata Blume , Kentucky bluegrass eter (Thermo Scientific, US). The A /A ratio of total 260 280 , Salix matsudana , Rhododendron molle G. Don RNA between 1.90 and 2.10 was considered to meet the , Sapium sebiferum , Petroselinum crispum , required quality for further experiments. First-strand of Lilium spp. , Hibiscus cannabinus L.  and Dend- cDNA was synthesized using the First Strand cDNA Syn- robium officinale . thesis Kit (Roche, Switzerland) using 1.0 μg of total RNA Euscaphis is a member of the family Staphyleaceae, in a 20 μL reaction volume according to the manufactur- which has two species in China: E. japonica Dippel and er’s protocols. E. konishii Hayata. Euscaphis has been widely used in tra- ditional Chinese medicine. Several chemical compounds have been isolated from Euscaphis, such as triterpene Selection of candidate reference genes and primer design compounds [26–29], phenolic acid compounds [30, 31], Based on transcriptome sequencing data from our labo- flavonoid compounds [27, 31] and others [31–33], how- ratory, 12 reference genes were selected to normalize and ever, no molecular and gene expression data has been validate qRT-PCR experiments by screening for genes reported in Euscaphis. with relatively stable expression (based on their RPKM Twelve genes (EkUBC, EkF-ACP, EkARP7, EkEF2, and fold change values), including nine novel genes and EkACT , EkGAPDH, EkEEF-5A-1, EkADF2, EkTUB, three common housekeeping genes. Their sequence/ EkPLAC8, EkLPP, EkGSTU1) were selected as candi- alignment/phylogenetic data are shown in Additional date genes according to transcriptome data from our lab files 1 and 2. Forward and reverse primers of all candi- (Liang et al., College of Forestry, Fujian Agriculture and date reference genes were designed using Primer Premier Forestry University) (unpublished data), and their expres- 5.0 with the following parameters: Tm values ranging sion stability was evaluated by qRT-PCR across differ - from 50 to 70 °C, GC percent of 45–50%, primer lengths ent experimental conditions: including five tissues (root, of 18–25 bp and product length of 90–200 bp. All prim- branch, seed, leaf and capsule), six different developmen - ers were synthesized by Sangon Biotech Co., Ltd (Shang- tal stages of seed and six different development stages of hai, China). Primer details are shown in Table 1. capsule. Their expression stability was calculated using qRT-PCR analysis for each candidate reference gene geNorm and NormFinder. Additionally, in order to vali- was performed on a 7500 Fast ABI Real-time PCR system date our results, the expression levels of EkCAD1 in (Applied Biosystems, US) using FastStart Universal SYBR different tissues were normalized by the most and least Green Master (Roche, Switzerland). A 20 μL reaction stable genes. 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 H O in a 96-well plates. The Methods 2 amplification conditions were as follows: 50 °C for 2 min, Plant material 95 °C for 10 min, 40 cycles of 95 °C for 15 s and 60 °C Euscaphis konishii Hayata tissues were collected from for 30 s. Melting curve was analyzed to determine primer Fujian Agriculture and Forestry University, Fujian Prov- specificity. ince, China. Tissues (leaf, capsule, seed, root and branch) All samples were analyzed in three biological and tech- were collected on November 15th 2016, and six devel- nical replicates. Serial tenfold dilutions of cDNA tem- opmental stages of capsule and seed were collected plate were used to generate slope of the standard curve once every 15 days after formation. All samples were to calculate amplification efficiency and correlation coef - harvested, washed and surface dried and then frozen in ficient of each candidate reference gene. liquid nitrogen and immediately stored at − 80 °C until Liang et al. Plant Methods (2018) 14:42 Page 3 of 9 Table 1 Primers used for qRT-PCR normalization Gene abbreviation Gene name Primer sequence (5′–3′) Amplicon Primers Tm (°C) E (%) R length (bp) EkUBC E. konishii Ubiquitin‑ conjugating For: TCT GCA GGT CCT TCA ATT CC 100 54.8/54.8 97.89 0.9998 enzyme E2‑17 kDa Rev: CGC AAA CCC TAG AGA GAG TAAG EkF‑ACP E. konishii F‑actin capping protein alpha For: CCA GTA ACT CGC ACC CTA TTT 96 54.44/54.56 99.59 0.9994 subunit Rev: TCA CTG TCA CTT TCC GAT TCC EkARP7 E. konishii Actin‑related protein 7For: CCT TCA TTA CCC ATC TCC CATC 100 55.03/53.41 99.35 0.9878 Rev: CTA ATG AAT CCT CGT ATG ACT GGA T EkEF2 E. konishii Elongation factor 2For: GAG AGC GAC AAG GGA ATG AG 108 55.7/54.8 100.09 0.9997 Rev: TAT TAC TGA TGG TGC GCT GG EkACT E. konishii ActinFor: CAT TGT GAG CAA CTG GGA TG 125 54.01/54.21 103.21 0.9998 Rev: GAT TAG CCT TCG GGT TGA GA EkGAPDH E. konishii Glyceraldehyde‑3‑phosphate For: TGG CTT TCC GTG TTC CTA CT 113 56.14/57.12 101.1 0.9795 dehydrogenase Rev: TCC CTC TGA CTC CTC CTT GA EkEEF‑5A‑1 E. konishii Eukaryotic elongation factor For: TCC GAC ATA GCT CCG ATT CA 101 55.42/55.4 98.46 0.9991 5A‑1 Rev: GAA GAG ACG GAG AGG AGA GATT EkADF2 E. konishii Actin‑ depolymerizing factor 2For: CCG AAG AGA ATG TCC AGA AGAG 98 54.97/54.48 99.89 0.9998 Rev: GTC CTT TGA GCT CGC ATA GAT EkTUB E. konishii β‑ TubulinFor: AAA GAT GAG CAC CAA GGA GGT 108 56.18/55.60 98.69 0.9879 Rev: TCA CAC ACG CTG GAT TTC AC EkPLAC8 E. konishii PLAC8 family protein isoform For: GGG AAT CGG AGG TAA AGA TCAA 102 54/54 99.00 0.9822 Rev: TGG ATC TGA AGA AAT GGG AGAC EkLPP E. konishii Lonprotease‑2‑like proteinFor: TTG GCC TCA TCT ATT GCT ACTG 98 54.3/55.4 101.00 0.9931 Rev: GTT CTC CTG TGC CCT CTA ATG EkGSTU1 E. konishii Glutathione‑S‑transferase For: GCC CTC ATC CCA AAC ATA CT 113 54.6/54 98.99 0.9999 tau 1 Rev: GAG ATT GTT TGC AGC GAA TAGG EkCAD1 E. konishii Cinnamyl alcohol dehydro‑For: GTG GGC TTT CCG TCA GTG TA 123 59.97/59.97 99.23 0.9969 genase 1 Rev: GGT CGG AGT TGG AGC TAT CG Data analysis stability of candidate genes by intra- and inter- group NormFinder and geNorm were used to analyze the sta- variations. The more stable reference gene will have bility of the 12 candidate reference genes under different lower stability value and inter- and intra-group variation. conditions. Expression levels of each reference gene were shown by Cq values. Before using the two softwares, the Validation of the candidate reference genes raw Cq values was used to calculate relative quantities by In order to verify the results of our experiments, the −(sampleCq-mimCq) the equation: Q = 2 . The values of stability most stable and unstable reference genes were selected (M) and pairwise variation (V) between genes was gener- to validate the expression of the E. konishii Cinnamyl ated by geNorm, the lower M value is the gene expression alcohol dehydrogenase 1 (EkCAD1) gene in different tis - is more stable [8, 34, 35]. Furthermore, the normalization sue samples (root, branch, capsule, seed and leaf ). CAD1 factor generated by computing the pairwise variation of belongs to CAD family, which catalyzes the reduction of the two normalization factor was used to determine the p-coumaricaldehyde, coniferyl aldehyde and sinapyl alde- most suitable numbers of reference genes with a cut-off hyde to their alcohol derivatives which are then polymer- value of 0.15 . NormFinder was used to evaluate the ized into lignin , CAD is one of the most used genes Liang et al. Plant Methods (2018) 14:42 Page 4 of 9 Expression stability of candidate reference genes to manipulate to obtain plants with low lignin content Expression stability of the 12 reference genes was ana- . qRT-PCR experimental method was the same as lyzed by geNorm and NormFinder. Samples were divided described above, and the relative expression level was −ΔΔct into three different experimental groups: (1) five tissues calculated by 2 method . Data from three bio- (root, leaf, branch, seed and capsule), (2) six seed devel- logical replicates were analyzed using analysis of variance opmental stages and (3) six capsule developmental stages. (ANOVA) followed by Student’s t test (P < 0.05). geNorm analysis Gene expression stability was determined by M-value in Results geNorm analysis, the lower the M value is, the more gene Primer specificity and PCR amplification efficiency expression stability. For the tissue group the two most sta- A total of 12 candidate reference genes, including three ble genes were EkGSTU1 and EkGAPDH with the lowest common housekeeping genes and nine novel genes M value, and EkTUB was the most unstable gene. In the from transcriptome sequencing data of E. konishii were seed group EkEEF-5A-1 and EkGAPDH were the two most selected for qRT-PCR normalization. The details of gene stable genes through all the different developmental stages, names, abbreviation, accession number, primer sequence, and EkLPP was the most unstable gene. Finally, in the cap- primers Tm, product length, amplification efficiency and sule group EkGAPDH was the most stable gene, followed correlation coefficient are shown in Table 1. The specific - by EkGSTU1, and EkF-ACP and EkUBC were the least ity for each primer set was validated by melting curve. stable genes (Table 2; Fig. 2). For all sample sets EkADF2 For all primer sets the melting curve showed a single and EkEEF-5A-1 were the most stable genes, and EkF-ACP amplification peak (Additional file 3). qRT-PCR efficiency and EkUBC were the least stable. The minimum number of for all 12 candidate reference genes ranged from 97.89% genes required for normalization in all the different groups for EkUBC to 103.21% for EkACT, and c orrelation coef- was calculated by geNorm. The V2/3 values for all differ - ficients varied from 0.9795 to 0.9999 (Table 1). ent 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 devel- Cq values of candidate reference genes opment stages), which indicate that two reference genes Cq values for all 12 reference genes are shown in Fig. 1. are enough to normalize gene expression data (Fig. 3). The Cq values varied from 15.812 (EkF -ACP) to 30.121 (EkACT) ac ross all samples, and mean Cq ranged from NormFinder analysis 18.0575 (EkF-ACP) to 25.6685 (EkACT). M oreover, Expression stability values analyzed by NormFinder EkACT expression levels were the most variable with are shown in Table 3. For tissue group, EkGSTU1 and 8.905 Cq, while EkGAPDH showed the least variable EkGAPDH were the most stable reference genes, and 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. Table 2 Gene expression stability across sample sets calculated by geNorm Gene name Different Seed Capsule Total tissues development development stages stages EkUBC 0.412 (5) 0.369 (3) 1.023 (12) 0.491 (7) EkF‑ACP 0.568 (8) 1.201 (10) 0.911 (11) 0.428 (6) EkARP7 0.390 (4) 1.065 (9) 0.398 (3) 0.251 (3) EkEF2 0.599 (9) 0.890 (8) 0.753 (8) 0.655 (8) EkACT 0.498 (7) 0.729 (7) 0.646 (7) 1.698 (12) EkGAPDH 0.315 (2) 0.283 (2) 0.254 (1) 0.858 (10) EkEEF‑5A‑1 0.752 (11) 0.231 (1) 0.568 (5) 0.159 (2) EkADF2 0.629 (10) 0.649 (6) 0.792 (9) 0.134 (1) EkTUB 1.198 (12) 1.216 (11) 0.599 (6) 1.421 (11) Fig. 1 Cq values of the twelve candidate reference genes. The EkPLAC8 0.469 (6) 0.534 (5) 0.412 (4) 0.699 (9) lines across the box indicate median values, boxes depict 25/75 EkLPP 0.348 (3) 1.368 (12) 0.855 (10) 0.284 (4) percentiles. Whisker caps indicate the minimum and maximum values EkGSTU1 0.269 (1) 0.412 (4) 0.289 (2) 0.344 (5) Liang et al. Plant Methods (2018) 14:42 Page 5 of 9 Fig. 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 Fig. 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 Liang et al. Plant Methods (2018) 14:42 Page 6 of 9 Table 3 Gene expression stability across sample sets calculated by NormFinder Gene name Different Seed Capsule Total tissues development development stages stages EkUBC 0.268 (6) 0.239 (3) 0.391 (11) 0.274 (8) EkF‑ACP 0.331 (7) 0.392 (10) 0.414 (12) 0.201 (6) EkARP7 0.256 (3) 0.601 (12) 0.178 (3) 0.103 (4) EkEF2 0.367 (9) 0.521 (11) 0.369 (8) 0.348 (9) EkACT 0.546 (11) 0.379 (9) 0.365 (7) 1.495 (12) Fig. 4 Relative expression of EkCAD1 in different tissues. EkGSTU1, EkGAPDH 0.240 (2) 0.171 (2) 0.102 (1) 0.493 (10) EkGAPDH and EkGSTU1 + EkGAPDH were used as one or two most EkEEF‑5A‑1 0.338 (8) 0.153 (1) 0.295 (6) 0.090 (2) stable reference genes, EkTUB was used as the least stable reference EkADF2 0.458 (10) 0.358 (8) 0.384 (10) 0.035 (1) gene. Data are represented as mean ± SD, different words indicate EkTUB 0.806 (12) 0.349 (7) 0.286 (5) 1.131 (11) significant difference of the expression of the target gene based on three biological replications (P < 0.05, t test; n = 3) EkPLAC8 0.261 (5) 0.273 (4) 0.251 (4) 0.259 (7) EkLPP 0.256 (3) 0.302 (6) 0.371 (9) 0.102 (3) EkGSTU1 0.165 (1) 0.285 (5) 0.116 (2) 0.159 (5) gene is necessary for data normalization. Conventionally, EkTUB was the least stable gene, same as shown by some housekeeping genes such as ACT , GAPDH, TUB, geNorm analysis. In the seed group EkEEF-5A-1 and have been used as reference genes, however, no single EkGAPDH were the most stable reference genes, while gene can be used for all plant species, experimental con- EkARP7 was the least stable gene. In the capsule group, ditions and/or tissues. Therefore, it is required to select EkGAPDH and EkGSTU1 got the top rank, while proper reference gene(s) for certain species under dif- EkUBC and EkF-ACP were ranked at the lowest. In gen- ferent conditions rather than using common reference eral, the ranking was same as geNorm analysis (Table 3). genes. The development of high-throughput sequencing tech - nology provides a more efficient approach to study plant EkCAD1 expression and validation of EkGSTU1 molecular biology, and it has been widely used in plant and EkGAPDH genomes [38–43], plant transcriptome [44–47], plant In order to verify the reliability of the selected refer- ncRNA [48–50], moreover, the generation of large-scale ence genes, expression profiles of EkC AD1 gene was gene segments and gene expression data by sequencing determined in different tissues. Relative expression provides a new resource for the identification of refer - levels were normalized using the two most stable ref- ence genes, especially in non-model species [51–53]. erence genes (EkGSTU1 and EkGAPDH) and the least Therefore, transcriptome data on E. konishii Hayata, stable reference gene (EkTUB). available in our laboratory can be used as a tool to iden- EkC AD1 showed similar expression levels when sin- tify candidate reference genes. Asystematic study of 12 gle or a combination of reference genes (EkGSTU1 and candidate reference genes in three conditions was carried EkGAPDH) were used to normalize the expression. in this paper, and their expression stability was calculated EkC AD1 expression was up regulated in all the tissues using geNorm and NormFinder. except in seed. However, when EkTUB was used for ACT and TUB, the most widely used reference genes, normalization (unstable gene), relative expression pro- did not show a good expression stability in E. konishii file of EkC AD1 was different when compared when the Hayata across all sample sets (Tables 2, 3). The phenom - normalization expression was done using the two most enon that expression levels of common reference genes stable reference genes identified in our study (EkG- varied in a large range has been reported in several papers STU1 and EkGAPDH) (Fig. 4). Our results suggest that [54, 55]. GAPDH, a common housekeeping gene also, has the expression patterns of target genes are differed been widely used as reference gene in different species when normalized by different reference genes. and experimental conditions [51, 56–60], in our experi- ments this gene was one of the two most stable genes in Discussion tissue sample set and capsule development stages, but it qRT-PCR is one of the most commonly used technique did not perform well in across all the sample and seed to determine gene expression in plants. To ensure the sets. The different performance of EkGAPDH in different accuracy and reliability of the results, a suitable reference experimental conditions in this study demonstrated that Liang et al. Plant Methods (2018) 14:42 Page 7 of 9 Authors’ contributions there is no single reference gene that can be used for all WXL, XXZ, CLR, SQW and SQZ designed the experiments, LJW, XYY, WHS and species or different experimental conditions [61–65]. HD selected the material, WXL, LJW, WHS and WH performed the experiments, In this study, EkGSTU1 (glutathione-S-transferase tau WXL, XYY, HD, PFL, LN and WH analyzed the data, WXL, SQZ wrote the paper. All authors read and approved the final manuscript. 1), which belongs to tau class of glutathione transferases (GSTs) , was the one of two most stable genes in tis- Author details sues sample and capsule development stages. EkADF2 College of Forestry, Fujian Agriculture and Forestry University, Fuzhou, China. Fujian Colleges and Universities Engineering Research Institute of Conserva‑ and EkEEF-5A-1 were the two most stable genes in total tion and Utilization of Natural Bioresources, Fujian Agriculture and Forestry sample set, ADF (actin-depolymerizing factor) play University, Fuzhou, China. Department of Microbiology and Molecular important roles in several cellular processes that require Genetics, University of California, Irvine, USA. College of Plant Protection, Fujian Agriculture and Forestry University, Fuzhou, China. College of Life Sci‑ cytoskeletal rearrangements, such as cell migration, ences, Fujian Agriculture and Forestry University, Fuzhou, China. chromosome introgression, cleavage plane orientation and furrow formation [67–69]. VvADF has been identi- Competing interests The authors declare that they have no competing interests. fied as candidate reference gene for grapevine during anthesis , rubber tree duration of latex flow  and Availability of data and materials TrADF3 was selected as reference gene in staminate and The datasets supporting the conclusions and description of a complete proto‑ col are included within the article. perfect flowers of T. rupestris . It has been widely accepted that using combination of Consent for publication multiple reference genes to normalize gene expression can All authors have consented for publication. give more accurate and reliable expression patterns than Ethics approval and consent to participate using a single gene in qRT-PCR analysis . Based on val- Not applicable. idation results of target gene expression, when EkGAPDH Funding and EkGSTU1 were selected as reference genes for nor- This work was supported by the National Science Foundation of China malization either single or combination, the target gene Projects (Grant No. 31700292), the Special fund for science and technol‑ EkCAD1 showed the similar expression pattern among dif- ogy innovation of Fujian Agriculture and Forestry University (Project Nos. CXZX2016072, CXZX2016073, CXZX2016074). ferent tissues, which indicated that the expression pattern of EkCAD1 was nearly identical when normalized with a Publisher’s Note single reference gene or two. Interestingly, in the tissue Springer Nature remains neutral with regard to jurisdictional claims in pub‑ group, the combination of traditional housekeeping gene lished maps and institutional affiliations. (EkGAPDH) and a novel identified reference gene (EkG - Received: 25 October 2017 Accepted: 29 May 2018 STU1) were identified as the most stable reference genes, suggesting that combination of traditional housekeeping genes and newly identified reference genes based on tran - scriptome data can be used as a good strategy for expres- References sion normalization of E. konishii Hayata genes. 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. 2. Die JV, Roman B, Nadal S, Gonzalez‑ Verdejo CI. Evaluation of candidate reference genes for expression studies in Pisum sativum under different Conclusion experimental conditions. Planta. 2010;232(1):145–53. In this study, we evaluated the expression stability of 3. Schmidt GW, Delaney SK. Stable internal reference genes for normaliza‑ tion of real‑time RT ‑PCR in tobacco (Nicotiana tabacum) during develop ‑ twelve candidate reference genes, including three tradi- ment and abiotic stress. Mol Genet Genom. 2010;283(3):233–41. tional housekeeping genes and nine novel genes based 4. Bustin SA, Nolan T. Pitfalls of quantitative real‑time reverse ‑transcription on transcriptome data of E. konishii Hayata. Additionally, polymerase chain reaction. J Biomol Tech. 2004;15(3):155–66. 5. Gutierrez L, Mauriat M, Guenin S, Pelloux J, Lefebvre JF, Louvet R, the expression pattern of target gene EkCAD1 was deter- Rusterucci C, Moritz T, Guerineau F, Bellini C, et al. The lack of a systematic mined in different tissues to further verify the reliability validation of reference genes: a serious pitfall undervalued in reverse of the identified stable reference genes. This study shows transcription‑polymerase chain reaction (RT ‑PCR) analysis in plants. Plant Biotechnol J. 2008;6(6):609–18. the first data for reference genes validation on E. konishii 6. Gonzalez‑Aguero M, Garcia‑Rojas M, Di Genova A, Correa J, Maass A, Hayata. Our study will contribute in future studies of Orellana A, Hinrichsen P. Identification of two putative reference genes gene expression in E. konishii Hayata and related species. from grapevine suitable for gene expression analysis in berry and related tissues derived from RNA‑Seq data. BMC Genom. 2013;14:878. Additional files 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. Additional file 1. Sequences of 12 candidate genes and 1 validation 8. Vandesompele J, De Preter K, Pattyn F, Poppe B, Van Roy N, De Paepe A, gene. Speleman F. Accurate normalization of real‑time quantitative RT ‑PCR data by geometric averaging of multiple internal control genes. Genome Biol. Additional file 2. Alignment and phylogenetic tree of 12 candidate 2002;3(7):Research0034. genes and 1 validation gene. Additional file 3. Melting curves for the 12 candidate reference genes. Liang et al. Plant Methods (2018) 14:42 Page 8 of 9 9. Andersen CL, Jensen JL, Orntoft TF. Normalization of real‑time quantita‑ 30. Maeda H, Matsuo Y, Tanaka T, Kouno I. Euscaphinin, a new ellagitan‑ tive reverse transcription‑PCR data: a model‑based variance estimation nin dimer from Euscaphis japonica ( THUNB.) KANITZ. Chem Pharm Bull. approach to identify genes suited for normalization, applied to bladder 2009;57(4):421–3. and colon cancer data sets. Can Res. 2004;64(15):5245–50. 31. Lee MK, Jeon HY, Lee KY, Kim SH, Ma CJ, Sung SH, Lee HS, Park MJ, Kim 10. Pfaffl MW, Tichopad A, Prgomet C, Neuvians TP. Determination of stable YC. Inhibitory constituents of Euscaphis japonica on lipopolysaccha‑ housekeeping genes, differentially regulated target genes and sample ride‑induced nitric oxide production in BV2 microglia. Planta Med. integrity: BestKeeper–Excel‑based tool using pair ‑ wise correlations. 2007;73(8):782–6. Biotechnol Lett. 2004;26(6):509–15. 32. Takeda Y, Okada Y, Masuda T, Hirata E, Shinzato T, Takushi A, Yu Q, Otsuka 11. Chen L, Zhong HY, Kuang JF, Li JG, Lu WJ, Chen JY. Validation of refer‑ H. New megastigmane and tetraketide from the leaves of Euscaphis ence genes for RT‑ qPCR studies of gene expression in banana fruit japonica. Chem Pharm Bull. 2000;48(5):752–4. under different experimental conditions. Planta. 2011;234(2):377–90. 33. Takeda Y, Okada Y, Masuda T, Hirata E, Takushi A, Otsuka H. Euscapholide 12. Tong Z, Gao Z, Wang F, Zhou J, Zhang Z. Selection of reliable reference and its glucoside from leaves of Euscaphis japonica. Phytochemistry. genes for gene expression studies in peach using real‑time PCR. BMC 1998;49(8):2565–8. Mol Biol. 2009;10:71. 34. Guo Y, Chen JX, Yang S, Fu XP, Zhang Z, Chen KH, Huang Y, Li Y, Xie Y, Mao 13. Gao M, Liu Y, Ma X, Shuai Q, Gai J, Li Y. Evaluation of reference genes YM. Selection of reliable reference genes for gene expression study in for normalization of gene expression using quantitative RT‑PCR nasopharyngeal carcinoma. Acta Pharmacol Sin. 2010;31(11):1487–94. under aluminum, cadmium, and heat stresses in soybean. PLoS ONE. 35. Han X, Lu M, Chen Y, Zhan Z, Cui Q, Wang Y. Selection of reliable reference 2017;12(1):e0168965. genes for gene expression studies using real‑time PCR in tung tree dur ‑ 14. Wang K, Niu Y, Wang Q, Liu H, Jin Y, Zhang S. Cloning and evaluation ing seed development. PLoS ONE. 2012;7(8):e43084. of reference genes for quantitative real‑time PCR analysis in Amor ‑ 36. Vanholme R, Demedts B, Morreel K, Ralph J, Boerjan W. Lignin biosynthe‑ phophallus. PeerJ. 2017;5:e3260. sis and structure. Plant Physiol. 2010;153(3):895–905. 15. Karuppaiya P, Yan XX, Liao W, Wu J, Chen F, Tang L. Correction: identi‑ 37. Vanholme R, Morreel K, Darrah C, Oyarce P, Grabber JH, Ralph J, Boerjan fication and validation of superior reference gene for gene expres‑ W. Metabolic engineering of novel lignin in biomass crops. New Phytol. sion normalization via RT‑ qPCR in staminate and pistillate flowers of 2012;196(4):978–1000. Jatropha curcas—a biodiesel plant. PLoS ONE. 2017;12(5):e0177039. 38. Velasco R, Zharkikh A, Aour ff tit J, Dhingra A, Cestaro A, Kalyanaraman 16. Li T, Wang J, Lu M, Zhang T, Qu X, Wang Z. Selection and validation of A, Fontana P, Bhatnagar SK, Troggio M, Pruss D, et al. The genome appropriate reference genes for qRT‑PCR analysis in Isatis indigotica of the domesticated apple (Malus × domestica Borkh.). Nat Genet. Fort. Front Plant Sci. 2017;8:1139. 2010;42(10):833–9. 17. Li J, Han X, Wang C, Qi W, Zhang W, Tang L, Zhao X. Validation of suit‑ 39. Parkin IA, Koh C, Tang H, Robinson SJ, Kagale S, Clarke WE, Town CD, able reference genes for RT‑ qPCR data in Achyranthes bidentata Blume Nixon J, Krishnakumar V, Bidwell SL, et al. Transcriptome and methylome under different experimental conditions. Front Plant Sci. 2017;8:776. profiling reveals relics of genome dominance in the mesopolyploid Bras - 18. Niu K, Shi Y, Ma H. Selection of candidate reference genes for gene sica oleracea. Genome Biol. 2014;15(6):R77. expression analysis in Kentucky Bluegrass (Poa pratensis L.) under 40. Chapman JA, Mascher M, Buluc A, Barry K, Georganas E, Session A, abiotic stress. Front Plant Sci. 2017;8:193. Strnadova V, Jenkins J, Sehgal S, Oliker L, et al. A whole‑ genome shotgun 19. Zhang Y, Han X, Chen S, Zheng L, He X, Liu M, Qiao G, Wang Y, Zhuo approach for assembling and anchoring the hexaploid bread wheat R. Selection of suitable reference genes for quantitative real‑time PCR genome. Genome Biol. 2015;16:26. gene expression analysis in Salix matsudana under different abiotic 41. Harper AL, Trick M, He Z, Clissold L, Fellgett A, Griffiths S, Bancroft I. stresses. Sci Rep. 2017;7:40290. Genome distribution of differential homoeologue contributions to leaf 20. Xiao Z, Sun X, Liu X, Li C, He L, Chen S, Su J. Selection of reliable refer‑ gene expression in bread wheat. Plant Biotechnol J. 2016;14(5):1207–14. ence genes for gene expression studies on Rhododendron molle G. 42. Guan R, Zhao Y, Zhang H, Fan G, Liu X, Zhou W, Shi C, Wang J, Liu W, Liang Don. Front Plant Sci. 2016;7:1547. X, et al. Draft genome of the living fossil Ginkgo biloba. GigaScience. 21. Chen X, Mao Y, Huang S, Ni J, Lu W, Hou J, Wang Y, Zhao W, Li M, Wang 2016;5(1):49. Q, et al. Selection of suitable reference genes for quantitative real‑time 43. Lin Y, Min J, Lai R, Wu Z, Chen Y, Yu L, Cheng C, Jin Y, Tian Q, Liu Q, et al. PCR in Sapium sebiferum. Front Plant Sci. 2017;8:637. Genome‑ wide sequencing of longan (Dimocarpus longan Lour.) provides 22. Li MY, Song X, Wang F, Xiong AS. Suitable reference genes for accurate insights into molecular basis of its polyphenol‑rich characteristics. GigaS‑ gene expression analysis in parsley (Petroselinum crispum) for abiotic cience. 2017;6(5):1–14. stresses and hormone stimuli. Front Plant Sci. 2016;7:1481. 44. Zhan X, Yang L, Wang D, Zhu JK, Lang Z. De novo assembly and analysis 23. Xu L, Xu H, Cao Y, Yang P, Feng Y, Tang Y, Yuan S, Ming J. Validation of of the transcriptome of Ocimum americanum var. pilosum under cold reference genes for quantitative real‑time PCR during bicolor tepal stress. BMC Genom. 2016;17:209. development in asiatic hybrid lilies (Lilium spp.). Front Plant Sci. 45. Evangelistella C, Valentini A, Ludovisi R, Firrincieli A, Fabbrini F, Scalabrin 2017;8:669. S, Cattonaro F, Morgante M, Mugnozza GS, Keurentjes JJB, et al. De novo 24. Niu X, Chen M, Huang X, Chen H, Tao A, Xu J, Qi J. Reference gene assembly, functional annotation, and analysis of the giant reed (Arundo selection for qRT‑PCR normalization analysis in kenaf (Hibiscus can- donax L.) leaf transcriptome provide tools for the development of a nabinus L.) under abiotic stress and hormonal stimuli. Front Plant Sci. biofuel feedstock. Biotechnol Biofuels. 2017;10:138. 2017;8:771. 46. Abdeen A, Schnell J, Miki B. Transcriptome analysis reveals absence of 25. An H, Zhu Q, Pei W, Fan J, Liang Y, Cui Y, Lv N, Wang W. Whole‑transcrip ‑ unintended effects in drought ‑tolerant transgenic plants overexpressing tome selection and evaluation of internal reference genes for expression the transcription factor ABF3. BMC Genom. 2010;11:69. analysis in protocorm development of Dendrobium officinale Kimura et 47. Galla G, Vogel H, Sharbel TF, Barcaccia G. De novo sequencing of the Migo. PLoS ONE. 2016;11(11):e0163478. Hypericum perforatum L. flower transcriptome to identify potential 26. Takahashi K, Kawaguchi S, Nishimura K, Kubota K, Tanabe Y. Studies on genes that are related to plant reproduction sensu lato. BMC Genom. constituents of medicinal plants. XIII. Constituents of the pericarps of the 2015;16:254. capsules of Euscaphis japonica Pax. Chem Pharm Bull. 1974;22(3):650–3. 48. Sunkar R, Zhou X, Zheng Y, Zhang W, Zhu JK. Identification of novel and 27. Cheng JJ, Zhang LJ, Cheng HL, Chiou CT, Lee IJ, Kuo YH. Cytotoxic candidate miRNAs in rice by high throughput sequencing. BMC Plant hexacyclic triterpene acids from Euscaphis japonica. J Nat Prod. Biol. 2008;8:25. 2010;73(10):1655–8. 49. Nobuta K, Lu C, Shrivastava R, Pillay M, De Paoli E, Accerbi M, Arteaga‑ 28. Li YC, Tian K, Sun LJ, Long H, Li LJ, Wu ZZ. A new hexacyclic triterpene Vazquez M, Sidorenko L, Jeong DH, Yen Y, et al. Distinct size distribution acid from the roots of Euscaphis japonica and its inhibitory activity on of endogeneous siRNAs in maize: evidence from deep sequencing in the triglyceride accumulation. Fitoterapia. 2016;109:261–5. mop1‑1 mutant. Proc Natl Acad Sci USA. 2008;105(39):14958–63. 29. Lee MK, Lee KY, Jeon HY, Sung SH, Kim YC. Antifibrotic activity of triterpe ‑ 50. Liu TT, Zhu D, Chen W, Deng W, He H, He G, Bai B, Qi Y, Chen R, Deng noids from the aerial parts of Euscaphis japonica on hepatic stellate cells. J XW. A global identification and analysis of small nucleolar RNAs and Enzyme Inhib Med Chem. 2009;24(6):1276–9. Liang et al. Plant Methods (2018) 14:42 Page 9 of 9 possible intermediate‑sized non‑ coding RNAs in Oryza sativa. Mol Plant. 62. Ray DL, Johnson JC. Validation of reference genes for gene expression 2013;6(3):830–46. analysis in olive (Olea europaea) mesocarp tissue by quantitative real‑time 51. Zhuang H, Fu Y, He W, Wang L, Wei Y. Selection of appropriate reference RT‑PCR. BMC Res Notes. 2014;7:304. genes for quantitative real‑time PCR in Oxytropis ochrocephala Bunge 63. Gimeno J, Eattock N, Van Deynze A, Blumwald E. Selection and valida‑ using transcriptome datasets under abiotic stress treatments. In: Frontiers tion of reference genes for gene expression analysis in switchgrass in plant science. vol. 6, 2015/07/16 edn; 2015:475. (Panicum virgatum) using quantitative real‑time RT ‑PCR. PLoS ONE. 52. Wang H, Zhang X, Liu Q, Liu X, Ding S. Selection and evaluation of new 2014;9(3):e91474. reference genes for RT‑ qPCR analysis in Epinephelus akaara based on 64. Qi S, Yang L, Wen X, Hong Y, Song X, Zhang M, Dai S. Reference gene transcriptome data. PLoS ONE. 2017;12(2):e0171646. selection for RT‑ qPCR analysis of flower development in Chrysanthe - 53. Demidenko NV, Logacheva MD, Penin AA. Selection and validation of mum morifolium and Chrysanthemum lavandulifolium. Front Plant Sci. reference genes for quantitative real‑time PCR in buckwheat (Fag - 2016;7:287. opyrum esculentum) based on transcriptome sequence data. PLoS ONE. 65. Zhao Y, Luo J, Xu S, Wang W, Liu T, Han C, Chen Y, Kong L. Selection of 2011;6(5):e19434. reference genes for gene expression normalization in Peucedanum 54. Ma R, Xu S, Zhao Y, Xia B, Wang R. Selection and validation of appropriate praeruptorum Dunn under abiotic stresses, hormone treatments and dif‑ reference genes for quantitative real‑time PCR analysis of gene expres‑ ferent tissues. PLoS ONE. 2016;11(3):e0152356. sion in Lycoris aurea. Front Plant Sci. 2016;7:536. 66. Lan T, Yang ZL, Yang X, Liu YJ, Wang XR, Zeng QY. Extensive functional 55. Ma S, Niu H, Liu C, Zhang J, Hou C, Wang D. Expression stabilities of diversification of the Populus glutathione S‑transferase supergene family. candidate reference genes for RT‑ qPCR under different stress conditions Plant Cell. 2009;21(12):3749–66. in soybean. PLoS ONE. 2013;8(10):e75271. 67. Lenart P, Bacher CP, Daigle N, Hand AR, Eils R, Terasaki M, Ellenberg J. A 56. Monteiro F, Sebastiana M, Pais MS, Figueiredo A. Reference gene selection contractile nuclear actin network drives chromosome congression in and validation for the early responses to downy mildew infection in sus‑ oocytes. Nature. 2005;436(7052):812–8. ceptible and resistant Vitis vinifera cultivars. PLoS ONE. 2013;8(9):e72998. 68. Insall RH, Machesky LM. Actin dynamics at the leading edge: from simple 57. Guo J, Ling H, Wu Q, Xu L, Que Y. The choice of reference genes for assess‑ machinery to complex networks. Dev Cell. 2009;17(3):310–22. ing gene expression in sugarcane under salinity and drought stresses. Sci 69. Kuure S, Cebrian C, Machingo Q, Lu BC, Chi X, Hyink D, D’Agati V, Gurniak Rep. 2014;4:7042. C, Witke W, Costantini F. Actin depolymerizing factors cofilin1 and destrin 58. Tian C, Jiang Q, Wang F, Wang GL, Xu ZS, Xiong AS. Selection of suitable are required for ureteric bud branching morphogenesis. PLoS Genet. reference genes for qPCR normalization under abiotic stresses and hor‑ 2010;6(10):e1001176. mone stimuli in carrot leaves. PLoS ONE. 2015;10(2):e0117569. 70. Chao J, Yang S, Chen Y, Tian WM. Evaluation of reference genes for quan‑ 59. Cao J, Wang L, Lan H. Validation of reference genes for quantitative titative real‑time PCR analysis of the gene expression in laticifers on the RT‑PCR normalization in Suaeda aralocaspica, an annual halophyte basis of latex flow in rubber tree (Hevea brasiliensis Muell. Arg.). Front Plant with heteromorphism and C4 pathway without Kranz anatomy. PeerJ. Sci. 2016;7:1149. 2016;4:e1697. 71. Li W, Zhang L, Zhang Y, Wang G, Song D, Zhang Y. Selection and valida‑ 60. Wan D, Wan Y, Yang Q, Zou B, Ren W, Ding Y, Wang Z, Wang R, Wang tion of appropriate reference genes for quantitative real‑time PCR K, Hou X. Selection of reference genes for qRT‑PCR analysis of gene normalization in staminate and perfect flowers of andromonoecious expression in Stipa grandis during environmental stresses. PLoS ONE. Taihangia rupestris. Front Plant Sci. 2017;8:729. 2017;12(1):e0169465. 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. Ready to submit your research ? Choose BMC and benefit from: fast, convenient online submission thorough peer review by experienced researchers in your ﬁeld rapid publication on acceptance support for research data, including large and complex data types • gold Open Access which fosters wider collaboration and increased citations maximum visibility for your research: over 100M website views per year At BMC, research is always in progress. Learn more biomedcentral.com/submissions
Plant Methods – Springer Journals
Published: Jun 4, 2018
It’s your single place to instantly
discover and read the research
that matters to you.
Enjoy affordable access to
over 18 million articles from more than
15,000 peer-reviewed journals.
All for just $49/month
Query the DeepDyve database, plus search all of PubMed and Google Scholar seamlessly
Save any article or search result from DeepDyve, PubMed, and Google Scholar... all in one place.
Get unlimited, online access to over 18 million full-text articles from more than 15,000 scientific journals.
Read from thousands of the leading scholarly journals from SpringerNature, Elsevier, Wiley-Blackwell, Oxford University Press and more.
All the latest content is available, no embargo periods.
“Hi guys, I cannot tell you how much I love this resource. Incredible. I really believe you've hit the nail on the head with this site in regards to solving the research-purchase issue.”Daniel C.
“Whoa! It’s like Spotify but for academic articles.”@Phil_Robichaud
“I must say, @deepdyve is a fabulous solution to the independent researcher's problem of #access to #information.”@deepthiw
“My last article couldn't be possible without the platform @deepdyve that makes journal papers cheaper.”@JoseServera