Selection of Suitable Reference Genes for RT-qPCR Normalization under Abiotic Stresses and Hormone Stimulation in Persimmon (Diospyros kaki Thunb)

Peihong Wang; Aisheng Xiong; Zhihong Gao; Xinyi Yu; Man Li; Yingjun Hou; Chao Sun; Shenchun Qu

doi:10.1371/journal.pone.0160885

Abstract

The success of quantitative real-time reverse transcription polymerase chain reaction (RT-qPCR) to quantify gene expression depends on the stability of the reference genes used for data normalization. To date, systematic screening for reference genes in persimmon (Diospyros kaki Thunb) has never been reported. In this study, 13 candidate reference genes were cloned from 'Nantongxiaofangshi' using information available in the transcriptome database. Their expression stability was assessed by geNorm and NormFinder algorithms under abiotic stress and hormone stimulation. Our results showed that the most suitable reference genes across all samples were UBC and GAPDH, and not the commonly used persimmon reference gene ACT. In addition, UBC combined with RPII or TUA were found to be appropriate for the "abiotic stress" group and α-TUB combined with PP2A were found to be appropriate for the "hormone stimuli" group. For further validation, the transcript level of the DkDREB2C homologue under heat stress was studied with the selected genes (CYP, GAPDH, TUA, UBC, α-TUB, and EF1-α). The results suggested that it is necessary to choose appropriate reference genes according to the test materials or experimental conditions. Our study will be useful for future studies on gene expression in persimmon.

Citation: Wang P, Xiong A, Gao Z, Yu X, Li M, Hou Y, et al. (2016) Selection of Suitable Reference Genes for RT-qPCR Normalization under Abiotic Stresses and Hormone Stimulation in Persimmon (Diospyros kaki Thunb). PLoS ONE 11(8): e0160885. https://doi.org/10.1371/journal.pone.0160885

Editor: Prasanta K. Subudhi, Louisiana State University College of Agriculture, UNITED STATES

Received: April 20, 2016; Accepted: July 23, 2016; Published: August 11, 2016

Copyright: © 2016 Wang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the paper and its Supporting Information files.

Funding: The research was supported by the National Public Welfare Industry (Agriculture) Project Special Scientific Research Funds (201203047). The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

Introduction

Persimmon (Diospyros kaki Thunb.), which is prevalent worldwide, originated in Eastern Asia and was mainly cultivated in China, Korea, and Japan [1]. Due to lack of genetic or genomic information, previous studies on persimmon mainly focused on its diversity and phylogeny [2]. Currently, with the development of genomic technologies, molecular biology studies on persimmon have gained attention and provide further understanding of complex biological mechanisms in persimmon. For example, sequence-specific amplification polymorphism (SSAP) was applied to reveal the genes associated with deastringency in persimmon [3]; genome-wide transcriptome analysis was performed to identify the primary genes involved in proanthocyanidin (PA) biosynthesis in persimmon [2]. For these molecular biological studies, gene expression analysis is an effective and widely used approach commonly performed by relying on methods such as Northern blotting, microarray analysis, and quantitative real-time reverse transcription polymerase chain reaction (RT-qPCR).

Rapidity, sensitivity, specificity, and quantification are relevant features of RT-qPCR [4,5] that make it the preferred method for gene expression analyses. However, when performing relative quantitative experiments, RNA quantity, quality, and processing may influence the accuracy of the results [6]. To ensure the accuracy of RT-qPCR among different samples, the choice of one or more verified reference genes is particularly critical [7–9]. The most commonly used RT-qPCR reference genes are housekeeping genes, which act as a basic component of the organelle skeleton or participate in the basic biochemical metabolism of the organism. Thus, it is commonly believed that these genes are not regulated or influenced by environmental and growth factors. These genes include: ACT (actin), GAPDH (glyceraldehyde-3-phosphate dehydrogenase), β-TUB (beta tubulin), α-TUB (alpha tubulin), UBQ (polyubiquitin), 18S rRNA (18S ribosomal RNA), etc. [10,11]. However, several studies have indicated that their expression levels do not remain relatively stable under different conditions in different species [12–16]. In other words, the so-called constant expression of these genes is only constant under certain conditions or species. It is thus clear that screening appropriate reference genes according to the test materials or experimental conditions is important. In recent years, related studies have increased in non-model species, such as Oenanthe javanica [17], Caragana intermedia [8], carrot [18], watermelon [19], pepper [20], oil palm [21], celery [22], and Heterosigma akashiwo [23]. To date, no systematic screening for reference genes has been reported in persimmon.

In this study, 13 candidate reference genes (ACT, α-TUB, β-TUB, UBC, CYP, RPL13, PP2A, GAPDH, EF1-α, F-box, RPII, TUA, and SAND) were selected because of their stable expression as evidenced in previous studies [17,24–26]. Their sequences were obtained from the transcriptome sequencing database of persimmon. Their stabilities in persimmon were analyzed by two algorithms (geNorm and NormFinder) under abiotic stresses (heat, cold and salt) and hormone stimulation, including gibberellin, salicylic acid and abscisic acid. Furthermore, the reliability of the identified reference genes was verified by assessing the expression level of the DkDREB2C gene under heat treatment. This work aims to select suitable reference genes for future gene expression studies under abiotic stresses and hormone stimulation in persimmon.

Materials and Methods

Plant Materials and Treatments

`Nantongxiaofangshi' (D. kaki Thunb.) is a good persimmon cultivar suitable for dwarfing and closely spaced planting due to weak apical dominance and no obvious trunk [27]. In this study, virus-free clonal persimmon (D. kaki Thunb. cv. Nantongxiaofangshi) microplants were obtained from the College of Horticulture, Nanjing Agricultural University. Seedlings were grown on MS propagation medium with 1 mg/L ZT and 0.1 mg/l IAA (pH = 6.8–7.0) at a constant temperature of 25°C and a 16:8 h light:dark cycle with an illumination of 4100 lx. There were three biological experimental replicates per treatment. The control group was grown in normal medium and conditions as stated above.

For the hormone treatments, 100 μM gibberellins (GA treatment) [28], 100 μM salicylic acid (SA treatment) [29], or 100 μM abscisic acid (ABA treatment) [30] was added to the media. Then, three-week-old seedlings on the normal medium were transplanted into the medium with hormone for a week. Salt treatment (150 mM NaCl) was applied in the same way [31,32]. Temperature treatments included placing three-week-old tissue culture seedlings in light incubators at 4°C (cold treatment) or 40°C (heat treatment) for 6 h. Leaves were collected from both the treated samples and the control group. The samples were immediately immersed in liquid nitrogen and stored at −80°C for further use.

RNA Extraction and cDNA Synthesis

Frozen samples were ground to a fine powder in liquid nitrogen and total RNA was extracted using Plant Total RNA Isolation Kit Plus (Foregene, Chengdu, China). RNA integrity was checked by 1% agarose gel electrophoresis and RNA quality was measured by Nanodrop ND 1000 spectrophotometer (Nanodrop Technologies Inc, Delaware, USA). Samples with 28S/18S ribosomal RNA between 1.5 and 2.0 and an absorbance ratio OD₂₆₀/₂₈₀ between 1.9 and 2.2 were used for subsequent experiments. Approximately 1,000 ng total RNA was used for cDNA synthesis using the PrimeScript RT reagent Kit with gDNA Eraser (TaKaRa, Dalian, China). The cDNA was diluted with nuclease-free water before RT-qPCR.

Selection of Candidate Reference Genes

In our study, the stability of 13 candidate reference genes was evaluated. Among these genes, ACT is commonly used in persimmon [2,3,33–35] and others have been validated as good reference genes in other crops [8,17–21,26,36,37], including α-TUB, β-TUB, UBC, CYP, RPL13, PP2A, GAPDH, EF1-α, F-box, RPII, TUA, and SAND. Based on the transcriptome sequencing data of ‘Nantongxiaofangshi’ leaves (unpublished data), potential homologues of the 13 reference genes were cloned and identified. Meanwhile, the corresponding target amplicon of each gene was sequenced (Biogene, Nanjing, China). Details were presented in Table 1, S1 Table and S3–S15 Figs.

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Table 1. Primer information of reference genes in persimmon (qPCR).

https://doi.org/10.1371/journal.pone.0160885.t001

PCR Primer Design and Amplification Efficiency Testing

Using the BioEdit Sequence Alignment v 7.0.9 software, the potential homolog sequences of these genes were aligned and edited. The primers for RT-qPCR were designed using the Beacon Designer 7.0 software and the primers for cloning were designed online (NCBI/Primer-Blast). The PCR products of the expected size were sequenced to confirm the specificity of these genes. For all primer pairs used for RT-qPCR, the optimal annealing temperature and amplification specificity were separately tested by gradient PCR and 1.5% gel electrophoresis. The efficiency (E) for these primer sets, which amplified a single, specific product, was estimated by standard curve analysis of five-fold diluted series (1, 5, 5², 5³, 5⁴, and 5⁵ × dilution) with the following equation: E = [10^(-1/slope)-1]×100%, where the slope is the standard curve slope. The information about the specificity-validated primers was summarized in Table 1.

Quantitative Real-Time PCR Assay

RT-qPCR reactions were performed by using an ABI 7300 Real-Time PCR System (Applied Biosystems, USA) with SYBR Premix Ex Taq^™ (TaKaRa, Dalian, China). The 20 μL reaction system consists of 10 μL SYBR Green I Mix, 1 μL diluted cDNA template (140 ng/uL), 8.6 μL ddH₂O, and 0.2 μL each primer. The following cycling conditions were used: an initial denaturation step of 95°C (30 s), followed by 40 cycles of 95°C (5 s) and 60°C (30 s). Dissociation curves were generated from 60°C to 95°C to verify primer specificity with the presence of a single peak (Fig 1). Meanwhile, there were three technical and biological replicates, as well as a no-template control in all assays. All RT-qPCR reactions were carried out in accordance with the Minimum Information for Publication of Quantitative Real-Time PCR Experiments (MIQE) guidelines [38].

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Fig 1. Melting curves of the 13 candidate reference genes.

In order: ACT, α-TUB, β-TUB, UBC, CYP, RPL13, PP2A, GAPDH, EF1-α, F-box, RPII, TUA, and SAND.

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Data Analysis

Two different types of statistical algorithms, geNorm [39] and NormFinder [40], were employed to rank the expression stability of 13 candidate reference genes under the different experimental conditions. The quantification cycle (Cq) values from the RT-qPCR were listed in S2 Table. Before the raw Cq values were input into the software mentioned above, all Cq values were converted to relative quantities by using the formula: 2^{- ΔCq}, in which ΔCq = the corresponding Cq value—minimum Cq.

In geNorm, the reference gene expression stability measurement (M) value is automatically calculated. The expression ratio of two ideal internal control genes is identical in a given sample set, which is considered as the principle of the program. Regardless of the co-regulation of the control genes, a combination of two housekeeping genes with the most stable expression level in the tested samples was verified by stepwise exclusion of the least stable gene with the highest M value [39]. Normfinder enables avoidance of misinterpretations caused by artificial selection of co-regulated genes [18]. Relying on intra- and inter-group variations, this program ranks all candidate reference genes and combines both results to yield a stability value for each candidate reference gene [40].

Results

Primer Specificity and Amplification Efficiencies

The specificity of each primer pair was verified by a single band, which had the expected size in agarose gel electrophoresis (S1 Fig). The absence of both signals in the no-template controls and primer dimers further confirmed the specificity of the primers (Fig 1). The amplification efficiency (E) of the 13 candidate reference genes ranged from 95.9%-108.5% (Table 1), which was calculated from the standard curves with good linear relationships (R² varying from 0.990–0.996) (S2 Fig). Both E and R² were within the commonly reported range for RT-qPCR.

Expression Profiling of Candidate Reference Genes

The expression level of the 13 candidate genes was determined as quantification cycle (Cq) values (S2 Table). The variations of their transcript levels were clearly shown by the box-plot (Fig 2). As shown in Fig 2A, GAPDH exhibited the highest expression level with lower Cq values (ranging from 15.76 to 22.62, S2 Table) than the other genes, whereas ACT and F-box had higher Cq values (ranging from 21.66 to 31.55 and from 20.86 to 31.70, respectively, S2 Table). Furthermore, there were many outliers (mild and extreme) for each gene across all samples, which affected the judgment of their expression stability. To directly observe the distribution of these outliers, a distribution diagram of each gene in a single treatment and the control group was drawn (Fig 2B). The Cq values of all genes under salt treatment were obviously higher than in the other five treatments. That is, all of the tested reference genes showed a low expression level in salt treatment. CYP was the least variable gene among the remaining samples. However, EF1-α and F-box were the most variable genes.

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Fig 2. Expression levels of the candidate reference genes in persimmon.

A: Cq values for the reference genes across all samples. B: Cq values of each reference gene in the single treatment and control group (CK). Boxes depict Cq values, including the median values (lines across the boxes), Q1 (one-quarter, lower outline), Q3 (three-quarters, upper outline), and whiskers. The whiskers are set at Q3+1.5IQR (interquartile range) and Q1-1.5IQR. Mild outliers (°) represent the values outside this range but within Q3+3IQR and Q1-3IQR. Extreme outliers (*) represent the values outside Q3+3IQR or Q1-3IQR.

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Expression Stability Analyses

After a simple comparison of the raw Cq values, geNorm and NormFinder were used to further evaluate the stability of the candidate reference genes. In addition, we sorted six different treatment sets into three groups: "abiotic stress" (heat, cold and salt), "hormone stimuli" (SA, GA and ABA) and "total" (samples in all treatments). For both the single stress treatments and groups, 9 evaluation patterns were generated.

a) geNorm analysis.

geNorm generated the ranks of the selected reference genes based on the expression stability value M, which were shown in Fig 3. By graphing the results, it was not difficult to determine that all of the genes performed well in both individual stress conditions and multiple stresses, with M values less than the default limit (1.5).

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Fig 3. Ranking of the expression stability of the 13 reference genes in geNorm.

The tested genes were ranked according to their expression stability by stepwise exclusion of the gene with the highest M value. The gene with the highest M value is on the left and is the least stable. Conversely, the gene on the right is the most stable.

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In heat treatment, CYP and GAPDH were the most stable genes and CYP was also one of the top-ranked genes in SA treatment. The other best gene in SA treatment was UBC, which ranked as one of the top two under NaCl treatment, abiotic stress, and total. In cold treatment, RPII and TUA ranked in the top two. TUA and SAND were the highest-ranked genes in ABA treatment. α-TUB and PP2A were the best-performing genes in the “hormone stimuli” group. Among all treatments and groups, EF1-α, F-box, RPL13, and ACT showed relative instability, especially EF1-α and F-box.

In addition, we determined the optimal number of reference genes using geNorm with the calculation of pairwise variations (V_n/n+1) between the sequential normalization factors (NF_n and NF_n+1, n ≥ 2) [39]. As Fig 4 shows, except for the “total” group, the inclusion of the third gene was not required for the remaining groups and treatments with a low V_2/3 value, which was below the cut-off value (0.15) [39]. In other words, two reference genes would be sufficient to normalize gene expression under these conditions. However, four genes were needed for the ''total'' group (V_4/5 = 0.136).

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Fig 4. Determination of the optimal number of reference genes required for effective normalization.

By using geNorm, the optimal number of reference genes in each sample was determined by an analysis of pairwise variation (V_n/n+1) between the normalization factors (NF_n and NF_{n +1}).

https://doi.org/10.1371/journal.pone.0160885.g004

b) NormFinder analysis.

NormFinder was used to perform an independent assessment of these reference genes. The rankings were listed in Tables 2 and 3. According to NormFinder, except for ABA stress, UBC performed well with a ranking in the top three under the remaining individual stress conditions and three groups. For individual experimental treatments, UBC was the most stable gene for the heat, cold and SA treatments; α-TUB was the best reference gene for the ABA and GA stresses; and PP2A ranked first in the NaCl treatment. Considering all the conditions together, UBC was the most stable genes according to both software algorithms. EF1-α, F-box, ACT, and RPL13 were low-ranking genes in most single stresses and groups, which was consistent with the result from geNorm analysis. Thus, these genes might not be suitable for use as reference genes.

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Table 2. Gene expression stability under individual treatment ranked by NormFinder software.

https://doi.org/10.1371/journal.pone.0160885.t002

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Table 3. Gene expression stability under multiple stresses ranked by NormFinder software.

https://doi.org/10.1371/journal.pone.0160885.t003

Reference Gene Validation

In Arabidopsis, DREB2C was induced by heat stress and might function as a late regulator [41,42]. To evaluate the reliability of the selected reference genes, DkDREB2C was cloned from persimmon, and the expression profiles of the DkDREB2C gene were calculated in persimmon under heat treatment by using the four most genes CYP, GAPDH, TUA, and UBC in individually and in group (Fig 5). The result showed that there were similar expression patterns: the expression levels of DkDREB2C gradually increased at 1 h, 3 h, and 6 h. In contrast, when using the least stable genes separately or a combination of α-TUB and EF1-α as reference genes for normalization, the expression patterns of DkDREB2C increased at 1 h, decreased at 3 h, and then increased again at 6 h (Fig 5).

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Fig 5. Relative quantification of DkDREB2C gene expression using validated reference genes for normalization under heat treatment in persimmon.

https://doi.org/10.1371/journal.pone.0160885.g005

Discussion

In plant molecular biological research, RT-qPCR is used to quantify the gene expression levels due to its high sensitivity and specificity. Accurate measurement of gene expression with such a method relies on the stability of reference gene expression. However, the expression of commonly used reference genes could vary considerably in response to various factors [12,43]. This implies that the stability of the reference genes should be evaluated according to the test materials or experimental conditions. For persimmons, genomic information is limited in comparison to apple, pear, peach, citrus, and grape [2]. Thus, the 13 reference genes in our study were cloned based on transcriptome sequencing data (unpublished data).

In this study, we conducted a systematic evaluation of stability of 13 widely used genes under different abiotic stresses and hormone stimuli in persimmon. Before the implementation of RT-qPCR, we optimized the reaction system by performing standard curve analysis of five-fold diluted series (1, 5, 5², 5³, 5⁴, and 5⁵ × dilution) to ensure the accuracy and repeatability of the subsequent test sample results. Using RT-qPCR, expression levels of 13 genes were determined as quantification cycle (Cq) values. Previous studies showed that the Cq values of reference genes within an acceptable range were regarded as reliable [17,20]. Here, most Cq values ranged from 16 to 30, which indicated that these genes could be used as candidate reference genes in this study.

To assess reference gene expression patterns more accurately, we performed two different statistical approaches (geNorm and NormFinder) [39,40]. Not surprisingly, the two programs gave slightly different results in the ranking of the most stably expressed candidate genes (S3 and S4 Tables), which may be due to the differences between the approaches [44,45]. For instance, under heat treatment, geNorm had the top four genes, in order, as CYP, GAPDH, TUA, and UBC, while NormFinder provided the opposite sorting. Under cold treatment, RPII and TUA were selected as the most stably expressed by geNorm, but were ranked sixth and fourth, respectively, by NormFinder. α-TUB emerged as the most stably expressed gene using NormFinder in the GA and ABA treatments, but it was ranked seventh and fifth by geNorm, respectively. However, both analysis programs produced the same unstable genes (F-box, EF1-α, ACT, and RPL13), which were ranked within the last five in all samples. In recent studies, RPL13 performed poorly in peach [36], and the widely used persimmon reference gene ACT has been noted to show variable expression in soybean [46], C. intermedia [8], watermelon [19] and peach [36]. F-box and EF1-α were found to be unsuitable reference genes across all samples in oil palm [21], which is consistent with the results in our study, but in opposition to the results for cucumber [47]. In addition, EF1-α was unsuitable in melon [48], but suitable in wheat [49] and carrot [18]. Taken together, there is no complete unanimity of results with regards to the stabilities of these genes in different plant species, but it is clear that these genes should not be recommended as reference genes for normalization in persimmon. Altogether, these data suggested species-specific characteristics of reference genes [50].

When determining the best reference genes available for RT-qPCR normalization, we chose the common top-ranked genes from the two algorithms. Except for the "total" group, the optimal number of reference genes was the same for the remaining groups and each individual stress, as evidenced by the pairwise variation V_2/3 value below the cut-off value (0.15). Based on the ranking orders, we could choose any two of the four genes (CYP, GAPDH, TUA, and UBC) as a suitable combination in the heat treatment, while any two of the three genes (TUA, UBC, and CYP) would be sufficient for cold stress. Similarly, UBC combined with TUA or PP2A would be marked as a suitable combination for NaCl stress; RPL13 and PP2A were selected for GA stress; TUA and SAND were selected for ABA stress; and UBC combined with CYP or α-TUB would be suitable for SA stress. Similarly, we recommended UBC combined with RPII or TUA as the best combination under "abiotic stress", while α-TUB and PP2A were most stable reference genes in the "hormone stimuli". For "total", we recommended UBC and GAPDH as the best combination considering that the proposed 0.15 value is not a strict cut-off [39]. Our results indicated that no suitable genes can be used as references under a variety of conditions, as noted in other previous studies [8,17,18,21,36].

Among these stable reference genes, UBC displayed the maximum stability for almost all single stresses and groups in the present study, and good performance of UBC as a traditional reference gene has been shown in Arabidopsis [26]. Like UBC, PP2A performed well across most individual treatments, and good stability of PP2A was reported in other plants [8,17,19]. GAPDH, a classical housekeeping genes, had very poor expression stability in oil palm [21], peach [36], banana [51] and wheat [49]; on the contrary, its expression was stable in pepper [20] and carrot [18], which is consistent with the results of our study. Furthermore, CYP and TUA were unsuitable in cucumber [47]. RPII was the most suitable reference genes in peach [36]. These discrepancies about the most and least stable genes between different plant species emphasizes the importance of evaluating the expression stability of reference genes under specific experimental conditions and specific species.

Finally, further validation of the identified reference genes was performed by normalizing the relative expression of DKDREB2C. DREB2C is one of the Arabidopsis class 2 DREBs (dehydration responsive element binding), which plays a critical role in improving plant resistance [52]. Its expression pattern under heat stress differed from DREB2A that was rapidly and transiently induced and peaked within 1 h [53]. In our study, when the four most stable genes were used separately or any two of them as a group, the transcript level of DkDREB2C increased with the extension of treatment time, peaked at 6 h, and decreased between 6 h and 12 h. This was slightly different from Arabidopsis, in which its expression was observed at 3 h after heat treatment, and then continued to increase for 12 h or 24 h [41,42]. We suspect that this might be due to different experimental materials. When using the least stable genes separately or in combination, we found obvious differences. The transcript level of DkDREB2C peaked at 1 h and 6 h. These results further suggested that CYP, GAPDH, TUA, and UBC are suitable for RT-qPCR under heat treatment in persimmon, while α-TUB and EF1-α were not suitable. With this validation, we found that an inappropriate reference gene might affect the normalization results.

Conclusions

In conclusion, 13 candidate reference genes were first evaluated under different treatments (heat, cold, and salt, GA, SA, and ABA) in persimmon. As a result of the analysis, we identified suitable persimmon reference genes under specific experimental conditions. Among them, UBC and GAPDH could be considered the most suitable reference genes across all samples, whereas the commonly used persimmon reference gene ACT as well as genes F-box, EF1-α, and RPL13 could not be recommended as an optimal reference gene for normalization of persimmon gene expression data. We concluded that the use of an unsuitable reference gene might yield misleading results. In other words, it is essential to choose proper reference genes for RT-qPCR analysis to obtain accurate and reliable data depending on both the test materials and experimental conditions.

Supporting Information

S1 Fig. Amplification specificity of the 13 reference genes in qPCR.

Amplification fragments were separated by 1.5% agarose gel electrophoresis.

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S2 Fig. Standard curves of the 13 candidate reference genes.

The amplification efficiency (E) of each reference gene was calculated from the standard curve with the following equation: E = [10^(-1/slope)-1]×100%. C0 represents the initial copy number of cDNA.

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S3 Fig. Nucleic acid and deduced amino acid sequences of ACT from persimmon.

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S4 Fig. Nucleic acid and deduced amino acid sequences of α-TUB from persimmon.

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S5 Fig. Nucleic acid and deduced amino acid sequences of β-TUB from persimmon.

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S6 Fig. Nucleic acid and deduced amino acid sequences of UBC from persimmon.

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S7 Fig. Nucleic acid and deduced amino acid sequences of CYP from persimmon.

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S8 Fig. Nucleic acid and deduced amino acid sequences of RPL13 from persimmon.

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S9 Fig. Nucleic acid and deduced amino acid sequences of PP2A from persimmon.

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S10 Fig. Nucleic acid and deduced amino acid sequences of GAPDH from persimmon.

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S11 Fig. Nucleic acid and deduced amino acid sequences of EF1-α from persimmon.

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S12 Fig. Nucleic acid and deduced amino acid sequences of F-box from persimmon.

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S13 Fig. Nucleic acid and deduced amino acid sequences of RPII from persimmon.

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S14 Fig. Nucleic acid and deduced amino acid sequences of TUA from persimmon.

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S15 Fig. Nucleic acid and deduced amino acid sequences of SAND from persimmon.

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S1 Table. Primer sequences of reference genes and DkDREB2C in persimmon (clone).

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S2 Table. Raw Cq values in persimmon.

Plants were submitted to the following treatments: heat, cold, salt, salicylic acid (SA), gibberellins (GA), and abscisic acid (ABA); CK: samples without any treatment.

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S3 Table. Gene expression stability under multiple stresses ranked by geNorm and NormFinder.

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S4 Table. Gene expression stability under individual stress ranked by geNorm and NormFinder.

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Author Contributions

Conceptualization: SCQ ASX ZHG PHW.
Formal analysis: PHW.
Methodology: PHW XYY ML YJH CS.
Software: PHW XYY.
Validation: PHW.
Writing - original draft: PHW.
Writing - review & editing: PHW ASX SCQ ZHG.

References

1. Yamada M, Giordani E, Yonemori K (2012) Persimmon. Fruit Breeding: Springer US. pp. 663–693.
2. Luo C, Zhang QL, Luo ZR (2014) Genome-wide transcriptome analysis of Chinese pollination-constant nonastringent persimmon fruit treated with ethanol. BMC Genomics 15: 112. pmid:24507483
- View Article
- PubMed/NCBI
- Google Scholar
3. Guan CF, Chen L, Chen WX, Mo RL, Zhang QL, Du XY, et al. (2015) SSAP analysis reveals candidate genes associated with deastringency in persimmon (Diospyros kaki Thunb.) treated with 40°C water. Tree Genet Genom 11: 20.
- View Article
- Google Scholar
4. Gachon C, Mingam A, Charrier B (2004) Real-time PCR: what relevance to plant studies? J Exp Bot 55: 1445–1454. pmid:15208338
- View Article
- PubMed/NCBI
- Google Scholar
5. Huggett J, Dheda K, Bustin S, Zumla A (2005) Real-time RT-PCR normalisation; strategies and considerations. Genes Immun 6: 279–284. pmid:15815687
- View Article
- PubMed/NCBI
- Google Scholar
6. Pfaffl MW (2001) A new mathematical model for relative quantification in real-time RT-PCR. Nucleic Acids Res 29: e45. pmid:11328886
- View Article
- PubMed/NCBI
- Google Scholar
7. Quackenbush J (2002) Microarray data normalization and transformation. Nat Genet 32: 496–501. pmid:12454644
- View Article
- PubMed/NCBI
- Google Scholar
8. Zhu JF, Zhang LF, Li WF, Han SY, Yang WH, Qi LW (2013) Reference gene selection for quantitative real-time PCR normalization in Caragana intermedia under different abiotic stress conditions. PLoS ONE 8: e53196. pmid:23301042
- View Article
- PubMed/NCBI
- Google Scholar
9. Chang E, Shi SQ, Liu JF, Cheng TL, Xue L, Yang XY, et al. (2012) Selection of reference genes for quantitative gene expression studies in Platycladus orientalis (Cupressaceae) using real-time PCR. PLoS ONE 7: e33278. pmid:22479379
- View Article
- PubMed/NCBI
- Google Scholar
10. Kozera B, Rapacz M (2013) Reference genes in real-time PCR. J Appl Genet 54: 391–406. pmid:24078518
- View Article
- PubMed/NCBI
- Google Scholar
11. Radonić A, Thulke S, Mackay IM, Landt O, Siegert W, Nitsche A (2004) Guideline to reference gene selection for quantitative real-time PCR. Biochem Biophys Res Commun 313: 856–862. pmid:14706621
- View Article
- PubMed/NCBI
- Google Scholar
12. Thellin O, Zorzi W, Lakaye B, De Borman B, Coumans B, Hennen G, et al. (1999) Housekeeping genes as internal standards: use and limits. J Biotechnol 75: 291–295. pmid:10617337
- View Article
- PubMed/NCBI
- Google Scholar
13. Kim BR, Nam HY, Kim SU, Kim SI, Chang YJ (2003) Normalization of reverse transcription quantitative-PCR with housekeeping genes in rice. Biotechnol Lett 25:1869–1872. pmid:14677714
- View Article
- PubMed/NCBI
- Google Scholar
14. Suzuki T, Higgins PJ, Crawford DR (2000) Control selection for RNA quantitation. Biotechniques 29: 332–337. pmid:10948434
- View Article
- PubMed/NCBI
- Google Scholar
15. Bustin SA (2002) Quantification of mRNA using real-time reverse transcription PCR (RT-PCR): trends and problems. J Mol Endocrinol 29: 23–29. pmid:12200227
- View Article
- PubMed/NCBI
- Google Scholar
16. Lee PD, Sladek R, Greenwood CM, Hudson TJ (2002) Control genes and variability: absence of ubiquitous reference transcripts in diverse mammalian expression studies. Genome Res 12: 292–297. pmid:11827948
- View Article
- PubMed/NCBI
- Google Scholar
17. Jiang Q, Wang F, Li MY, Ma J, Tan GF, Xiong AS (2014) Selection of suitable reference genes for qPCR normalization under abiotic stresses in Oenanthe javanica (BI.) DC. PLoS ONE 9: e92262. pmid:24651080
- View Article
- PubMed/NCBI
- Google Scholar
18. Tian C, Jiang Q, Wang F, Wang GL, Xu ZS, Xiong AS (2015) Selection of suitable reference genes for qPCR normalization under abiotic stresses and hormone stimuli in carrot leaves. PLoS ONE 10: e117569.
- View Article
- Google Scholar
19. Kong QS, Yuan JX, Gao LY, Zhao S, Jiang W, Huang Y, et al. (2014) Identification of suitable reference genes for gene expression normalization in qRT-PCR analysis in watermelon. PLoS ONE 9: e90612. pmid:24587403
- View Article
- PubMed/NCBI
- Google Scholar
20. Wan HJ, Yuan W, Ruan MY, Ye QJ, Wang RQ, Li ZM, et al. (2011) Identification of reference genes for reverse transcription quantitative real-time PCR normalization in pepper (Capsicum annuum L.). Biochem Biophys Res Commun 416: 24–30. pmid:22086175
- View Article
- PubMed/NCBI
- Google Scholar
21. Yeap W, Loo JM, Wong YC, Kulaveerasingam H (2014) Evaluation of suitable reference genes for qRT-PCR gene expression normalization in reproductive, vegetative tissues and during fruit development in oil palm. Plant Cell Tissue Organ Cult 116: 55–66.
- View Article
- Google Scholar
22. Li MY, Wang F, Jiang Q, Wang GL, Tian C, Xiong AS (2016) Validation and comparison of reference genes for qPCR normalization of celery (Apium graveolens) at different development stages. Front Plant Sci 7:313. pmid:27014330
- View Article
- PubMed/NCBI
- Google Scholar
23. Ji NJ, Li L, Lin LX, Lin SJ (2015) Screening for suitable reference genes for quantitative real-time PCR in Heterosigma akashiwo (Raphidophyceae). PLoS ONE 10: e132183.
- View Article
- Google Scholar
24. Brunner AM, Yakovlev IA, Strauss SH (2004) Validating internal controls for quantitative plant gene expression studies. BMC Plant Biol 4: 14. pmid:15317655
- View Article
- PubMed/NCBI
- Google Scholar
25. Hong SY, Seo PJ, Yang MS, Xiang F, Park CM (2008) Exploring valid reference genes for gene expression studies in Brachypodium distachyon by real-time PCR. BMC Plant Biol 8: 112. pmid:18992143
- View Article
- PubMed/NCBI
- Google Scholar
26. Czechowski T (2005) Genome-wide identification and testing of superior reference genes for transcript normalization in Arabidopsis. Plant Physiol 139: 5–17. pmid:16166256
- View Article
- PubMed/NCBI
- Google Scholar
27. Tu X, Zhang Z, Zhang S, Luo C, Chen X, Qu SC (2013) A dwarf persimmon cultivar, 'Nantong-xiaofangshi'. International Society for Horticultural Science (ISHS), Leuven, Belgium. pp. 139–144.
28. Qi XX, Wu J, Wang LF, Li LT, Cao YF, Tian LM, et al. (2013) Identifying the candidate genes involved in the calyx abscission process of 'Kuerlexiangli' (Pyrus sinkiangensis Yu) by digital transcript abundance measurements. BMC Genomics 14: 727. pmid:24152304
- View Article
- PubMed/NCBI
- Google Scholar
29. Senaratna T, Touchell D, Bunn E, Dixon K. (2000) Acetyl salicylic acid (Aspirin) and salicylic acid induce multiple stress tolerance in bean and tomato plants. Plant Growth Regul 30: 157–161.
- View Article
- Google Scholar
30. Chan ZL (2012) Expression profiling of ABA pathway transcripts indicates crosstalk between abiotic and biotic stress responses in Arabidopsis. Genomics 100: 110–115. pmid:22709556
- View Article
- PubMed/NCBI
- Google Scholar
31. Lopez-Delgado H, Dat JF, Foyer CH, Scott IM (1998) Induction of thermotolerance in potato microplants by acetylsalicylic acid and H₂O₂. J Exp Bot 49: 713–720.
- View Article
- Google Scholar
32. Watad AA, Swartzberg D, Bressan RA, Izhar S, Hasegawa PM (1991) Stability of salt tolerance at the cell level after regeneration of plants from a salt tolerant tobacco cell line. Physiol Plant 83: 307–313.
- View Article
- Google Scholar
33. Zhang ZK, Fu RS, Huber DJ, Rao JP, Chang XX, Hu MJ, et al. (2012) Expression of expansin gene (CDK-Exp3) and its modulation by exogenous gibberellic acid during ripening and softening of persimmon fruit. Hortscience 47: 378–381.
- View Article
- Google Scholar
34. Akagi T, Takeda Y, Yonemori K, Ikegami A, Kono A, Yamada M, et al. (2010) Quantitative genotyping for the astringency locus in hexaploid persimmon cultivars using quantitative real-time PCR. J Am Soc Hortic Sci 135: 59–66.
- View Article
- Google Scholar
35. Min T, Yin XR, Shi YN, Luo ZR, Yao YC, Grierson D, et al. (2012) Ethylene-responsive transcription factors interact with promoters of ADH and PDC involved in persimmon (Diospyros kaki) fruit de-astringency. J Exp Bot 63: 695–709.
- View Article
- Google Scholar
36. Tong ZG, Gao ZH, Wang F, Zhou J, Zhang Z (2009) Selection of reliable reference genes for gene expression studies in peach using real-time PCR. BMC Mol Biol 10: 71. pmid:19619301
- View Article
- PubMed/NCBI
- Google Scholar
37. Imai T, Ubi BE, Saito T, Moriguchi T(2014) Evaluation of reference genes for accurate normalization of gene expression for real time- quantitative PCR in Pyrus pyrifolia using different tissue samples and seasonal conditions. PLoS ONE 9: e86492. pmid:24466117
- View Article
- PubMed/NCBI
- Google Scholar
38. Bustin SA, Benes V, Garson JA, Hellemans J, Huggett J, Kubista M, et al. (2009) The MIQE guidelines: minimum information for publication of quantitative real-time PCR experiments. Clin Chem 55: 611–622. pmid:19246619
- View Article
- PubMed/NCBI
- Google Scholar
39. Vandesompele J, De Preter K, Pattyn F, Poppe B, Van Roy N, De Paepe A, et al. (2002) Accurate normalization of real-time quantitative RT-PCR data by geometric averaging of multiple internal control genes. Genome Biol 3: H34.
- View Article
- Google Scholar
40. Andersen CL, Jensen JL, Ørntoft TF (2004) 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 64: 5245–5250. pmid:15289330
- View Article
- PubMed/NCBI
- Google Scholar
41. Chen H, Hwang JE, Lim CJ, Kim DY, Lee SY, Lim CO (2010) Arabidopsis DREB2C functions as a transcriptional activator of HsfA3 during the heat stress response. Biochem Biophys Res Commun 401: 238–244. pmid:20849812
- View Article
- PubMed/NCBI
- Google Scholar
42. Lim CJ, Hwang JE, Chen H, Hong JK, Yang KA, Choi MS, et al. (2007) Over-expression of the Arabidopsis DRE/CRT-binding transcription factor DREB2C enhances thermotolerance. Biochem Biophys Res Commun 362: 431–436. pmid:17716623
- View Article
- PubMed/NCBI
- Google Scholar
43. Bustin SA (2000) Absolute quantification of mRNA using real-time reverse transcription polymerase chain reaction assays. J Mol Endocrinol 25: 169–193. pmid:11013345
- View Article
- PubMed/NCBI
- Google Scholar
44. Ransbotyn V, R T (2006) Housekeeping gene selection for quantitative real-time PCR assays in the seagrass Zostera marina subjected to heat stress. Limnology and Oceanography: Methods 4: 367–373.
- View Article
- Google Scholar
45. Mafra V, Kubo KS, Alves-Ferreira M, Ribeiro-Alves M, Stuart RM, Boava LP, et al. (2012) Reference genes for accurate transcript normalization in citrus genotypes under different experimental conditions. PLoS ONE 7: e31263. pmid:22347455
- View Article
- PubMed/NCBI
- Google Scholar
46. Libault M, Thibivilliers S, Bilgin DD, Radwan O, Benitez M, Clough SJ et al. (2008) Identification of four soybean reference genes for gene expression normalization. Plant Genome 1: 44–54.
- View Article
- Google Scholar
47. Migocka M, Papierniak A (2011) Identification of suitable reference genes for studying gene expression in cucumber plants subjected to abiotic stress and growth regulators. Mol Breed 28: 343–357.
- View Article
- Google Scholar
48. Sestili S, Sebastiani MS, Belisario A, Ficcadenti N (2014) Reference gene selection for gene expression analysis in melon infected by Fusarium oxysporum f.sp. melonis. J Plant Biochem Biotechnol 23: 238–248.
- View Article
- Google Scholar
49. Long XY, Wang JR, Ouellet T, Rocheleau H, Wei YM, Pu ZE, et al. (2010) Genome-wide identification and evaluation of novel internal control genes for Q-PCR based transcript normalization in wheat. Plant Mol Biol 74: 307–311. pmid:20658259
- View Article
- PubMed/NCBI
- Google Scholar
50. Le DT, Donavan LA, Babu V, Yasuko W, Chien VH, Nishiyama R, et al. (2012) Evaluation of candidate reference genes for normalization of quantitative RT-PCR in soybean tissues under various abiotic stress conditions. PLoS ONE 7: e46487. pmid:23029532
- View Article
- PubMed/NCBI
- Google Scholar
51. Chen L, Zhong HY, Kuang JF, Li JG, Lu WJ,Chen JY (2011) Validation of reference genes for RT-qPCR studies of gene expression in banana fruit under different experimental conditions. Planta 234: 377–390. pmid:21505864
- View Article
- PubMed/NCBI
- Google Scholar
52. Lata C, Prasad M (2011) Role of DREBs in regulation of abiotic stress responses in plants. J Exp Bot 62: 4731–4748. pmid:21737415
- View Article
- PubMed/NCBI
- Google Scholar
53. Sakuma Y, Maruyama K, Qin F, Osakabe Y, Shinozaki K, et al. (2006) Dual function of an Arabidopsis transcription factor DREB2A in water-stress-responsive and heat-stress-responsive gene expression. Proc Natl Acad Sci USA 103: 18822–18827. pmid:17030801
- View Article
- PubMed/NCBI
- Google Scholar

[ref1] 1. Yamada M, Giordani E, Yonemori K (2012) Persimmon. Fruit Breeding: Springer US. pp. 663–693.

[ref2] 2. Luo C, Zhang QL, Luo ZR (2014) Genome-wide transcriptome analysis of Chinese pollination-constant nonastringent persimmon fruit treated with ethanol. BMC Genomics 15: 112. pmid:24507483
View Article
PubMed/NCBI
Google Scholar

[3] View Article

[4] PubMed/NCBI

[5] Google Scholar

[ref3] 3. Guan CF, Chen L, Chen WX, Mo RL, Zhang QL, Du XY, et al. (2015) SSAP analysis reveals candidate genes associated with deastringency in persimmon (Diospyros kaki Thunb.) treated with 40°C water. Tree Genet Genom 11: 20.
View Article
Google Scholar

[7] View Article

[8] Google Scholar

[ref4] 4. Gachon C, Mingam A, Charrier B (2004) Real-time PCR: what relevance to plant studies? J Exp Bot 55: 1445–1454. pmid:15208338
View Article
PubMed/NCBI
Google Scholar

[10] View Article

[11] PubMed/NCBI

[12] Google Scholar

[ref5] 5. Huggett J, Dheda K, Bustin S, Zumla A (2005) Real-time RT-PCR normalisation; strategies and considerations. Genes Immun 6: 279–284. pmid:15815687
View Article
PubMed/NCBI
Google Scholar

[14] View Article

[15] PubMed/NCBI

[16] Google Scholar

[ref6] 6. Pfaffl MW (2001) A new mathematical model for relative quantification in real-time RT-PCR. Nucleic Acids Res 29: e45. pmid:11328886
View Article
PubMed/NCBI
Google Scholar

[18] View Article

[19] PubMed/NCBI

[20] Google Scholar

[ref7] 7. Quackenbush J (2002) Microarray data normalization and transformation. Nat Genet 32: 496–501. pmid:12454644
View Article
PubMed/NCBI
Google Scholar

[22] View Article

[23] PubMed/NCBI

[24] Google Scholar

[ref8] 8. Zhu JF, Zhang LF, Li WF, Han SY, Yang WH, Qi LW (2013) Reference gene selection for quantitative real-time PCR normalization in Caragana intermedia under different abiotic stress conditions. PLoS ONE 8: e53196. pmid:23301042
View Article
PubMed/NCBI
Google Scholar

[26] View Article

[27] PubMed/NCBI

[28] Google Scholar

[ref9] 9. Chang E, Shi SQ, Liu JF, Cheng TL, Xue L, Yang XY, et al. (2012) Selection of reference genes for quantitative gene expression studies in Platycladus orientalis (Cupressaceae) using real-time PCR. PLoS ONE 7: e33278. pmid:22479379
View Article
PubMed/NCBI
Google Scholar

[30] View Article

[31] PubMed/NCBI

[32] Google Scholar

[ref10] 10. Kozera B, Rapacz M (2013) Reference genes in real-time PCR. J Appl Genet 54: 391–406. pmid:24078518
View Article
PubMed/NCBI
Google Scholar

[34] View Article

[35] PubMed/NCBI

[36] Google Scholar

[ref11] 11. Radonić A, Thulke S, Mackay IM, Landt O, Siegert W, Nitsche A (2004) Guideline to reference gene selection for quantitative real-time PCR. Biochem Biophys Res Commun 313: 856–862. pmid:14706621
View Article
PubMed/NCBI
Google Scholar

[38] View Article

[39] PubMed/NCBI

[40] Google Scholar

[ref12] 12. Thellin O, Zorzi W, Lakaye B, De Borman B, Coumans B, Hennen G, et al. (1999) Housekeeping genes as internal standards: use and limits. J Biotechnol 75: 291–295. pmid:10617337
View Article
PubMed/NCBI
Google Scholar

[42] View Article

[43] PubMed/NCBI

[44] Google Scholar

[ref13] 13. Kim BR, Nam HY, Kim SU, Kim SI, Chang YJ (2003) Normalization of reverse transcription quantitative-PCR with housekeeping genes in rice. Biotechnol Lett 25:1869–1872. pmid:14677714
View Article
PubMed/NCBI
Google Scholar

[46] View Article

[47] PubMed/NCBI

[48] Google Scholar

[ref14] 14. Suzuki T, Higgins PJ, Crawford DR (2000) Control selection for RNA quantitation. Biotechniques 29: 332–337. pmid:10948434
View Article
PubMed/NCBI
Google Scholar

[50] View Article

[51] PubMed/NCBI

[52] Google Scholar

[ref15] 15. Bustin SA (2002) Quantification of mRNA using real-time reverse transcription PCR (RT-PCR): trends and problems. J Mol Endocrinol 29: 23–29. pmid:12200227
View Article
PubMed/NCBI
Google Scholar

[54] View Article

[55] PubMed/NCBI

[56] Google Scholar

[ref16] 16. Lee PD, Sladek R, Greenwood CM, Hudson TJ (2002) Control genes and variability: absence of ubiquitous reference transcripts in diverse mammalian expression studies. Genome Res 12: 292–297. pmid:11827948
View Article
PubMed/NCBI
Google Scholar

[58] View Article

[59] PubMed/NCBI

[60] Google Scholar

[ref17] 17. Jiang Q, Wang F, Li MY, Ma J, Tan GF, Xiong AS (2014) Selection of suitable reference genes for qPCR normalization under abiotic stresses in Oenanthe javanica (BI.) DC. PLoS ONE 9: e92262. pmid:24651080
View Article
PubMed/NCBI
Google Scholar

[62] View Article

[63] PubMed/NCBI

[64] Google Scholar

[ref18] 18. Tian C, Jiang Q, Wang F, Wang GL, Xu ZS, Xiong AS (2015) Selection of suitable reference genes for qPCR normalization under abiotic stresses and hormone stimuli in carrot leaves. PLoS ONE 10: e117569.
View Article
Google Scholar

[66] View Article

[67] Google Scholar

[ref19] 19. Kong QS, Yuan JX, Gao LY, Zhao S, Jiang W, Huang Y, et al. (2014) Identification of suitable reference genes for gene expression normalization in qRT-PCR analysis in watermelon. PLoS ONE 9: e90612. pmid:24587403
View Article
PubMed/NCBI
Google Scholar

[69] View Article

[70] PubMed/NCBI

[71] Google Scholar

[ref20] 20. Wan HJ, Yuan W, Ruan MY, Ye QJ, Wang RQ, Li ZM, et al. (2011) Identification of reference genes for reverse transcription quantitative real-time PCR normalization in pepper (Capsicum annuum L.). Biochem Biophys Res Commun 416: 24–30. pmid:22086175
View Article
PubMed/NCBI
Google Scholar

[73] View Article

[74] PubMed/NCBI

[75] Google Scholar

[ref21] 21. Yeap W, Loo JM, Wong YC, Kulaveerasingam H (2014) Evaluation of suitable reference genes for qRT-PCR gene expression normalization in reproductive, vegetative tissues and during fruit development in oil palm. Plant Cell Tissue Organ Cult 116: 55–66.
View Article
Google Scholar

[77] View Article

[78] Google Scholar

[ref22] 22. Li MY, Wang F, Jiang Q, Wang GL, Tian C, Xiong AS (2016) Validation and comparison of reference genes for qPCR normalization of celery (Apium graveolens) at different development stages. Front Plant Sci 7:313. pmid:27014330
View Article
PubMed/NCBI
Google Scholar

[80] View Article

[81] PubMed/NCBI

[82] Google Scholar

[ref23] 23. Ji NJ, Li L, Lin LX, Lin SJ (2015) Screening for suitable reference genes for quantitative real-time PCR in Heterosigma akashiwo (Raphidophyceae). PLoS ONE 10: e132183.
View Article
Google Scholar

[84] View Article

[85] Google Scholar

[ref24] 24. Brunner AM, Yakovlev IA, Strauss SH (2004) Validating internal controls for quantitative plant gene expression studies. BMC Plant Biol 4: 14. pmid:15317655
View Article
PubMed/NCBI
Google Scholar

[87] View Article

[88] PubMed/NCBI

[89] Google Scholar

[ref25] 25. Hong SY, Seo PJ, Yang MS, Xiang F, Park CM (2008) Exploring valid reference genes for gene expression studies in Brachypodium distachyon by real-time PCR. BMC Plant Biol 8: 112. pmid:18992143
View Article
PubMed/NCBI
Google Scholar

[91] View Article

[92] PubMed/NCBI

[93] Google Scholar

[ref26] 26. Czechowski T (2005) Genome-wide identification and testing of superior reference genes for transcript normalization in Arabidopsis. Plant Physiol 139: 5–17. pmid:16166256
View Article
PubMed/NCBI
Google Scholar

[95] View Article

[96] PubMed/NCBI

[97] Google Scholar

[ref27] 27. Tu X, Zhang Z, Zhang S, Luo C, Chen X, Qu SC (2013) A dwarf persimmon cultivar, 'Nantong-xiaofangshi'. International Society for Horticultural Science (ISHS), Leuven, Belgium. pp. 139–144.

[ref28] 28. Qi XX, Wu J, Wang LF, Li LT, Cao YF, Tian LM, et al. (2013) Identifying the candidate genes involved in the calyx abscission process of 'Kuerlexiangli' (Pyrus sinkiangensis Yu) by digital transcript abundance measurements. BMC Genomics 14: 727. pmid:24152304
View Article
PubMed/NCBI
Google Scholar

[100] View Article

[101] PubMed/NCBI

[102] Google Scholar

[ref29] 29. Senaratna T, Touchell D, Bunn E, Dixon K. (2000) Acetyl salicylic acid (Aspirin) and salicylic acid induce multiple stress tolerance in bean and tomato plants. Plant Growth Regul 30: 157–161.
View Article
Google Scholar

[104] View Article

[105] Google Scholar

[ref30] 30. Chan ZL (2012) Expression profiling of ABA pathway transcripts indicates crosstalk between abiotic and biotic stress responses in Arabidopsis. Genomics 100: 110–115. pmid:22709556
View Article
PubMed/NCBI
Google Scholar

[107] View Article

[108] PubMed/NCBI

[109] Google Scholar

[ref31] 31. Lopez-Delgado H, Dat JF, Foyer CH, Scott IM (1998) Induction of thermotolerance in potato microplants by acetylsalicylic acid and H₂O₂. J Exp Bot 49: 713–720.
View Article
Google Scholar

[111] View Article

[112] Google Scholar

[ref32] 32. Watad AA, Swartzberg D, Bressan RA, Izhar S, Hasegawa PM (1991) Stability of salt tolerance at the cell level after regeneration of plants from a salt tolerant tobacco cell line. Physiol Plant 83: 307–313.
View Article
Google Scholar

[114] View Article

[115] Google Scholar

[ref33] 33. Zhang ZK, Fu RS, Huber DJ, Rao JP, Chang XX, Hu MJ, et al. (2012) Expression of expansin gene (CDK-Exp3) and its modulation by exogenous gibberellic acid during ripening and softening of persimmon fruit. Hortscience 47: 378–381.
View Article
Google Scholar

[117] View Article

[118] Google Scholar

[ref34] 34. Akagi T, Takeda Y, Yonemori K, Ikegami A, Kono A, Yamada M, et al. (2010) Quantitative genotyping for the astringency locus in hexaploid persimmon cultivars using quantitative real-time PCR. J Am Soc Hortic Sci 135: 59–66.
View Article
Google Scholar

[120] View Article

[121] Google Scholar

[ref35] 35. Min T, Yin XR, Shi YN, Luo ZR, Yao YC, Grierson D, et al. (2012) Ethylene-responsive transcription factors interact with promoters of ADH and PDC involved in persimmon (Diospyros kaki) fruit de-astringency. J Exp Bot 63: 695–709.
View Article
Google Scholar

[123] View Article

[124] Google Scholar

[ref36] 36. Tong ZG, Gao ZH, Wang F, Zhou J, Zhang Z (2009) Selection of reliable reference genes for gene expression studies in peach using real-time PCR. BMC Mol Biol 10: 71. pmid:19619301
View Article
PubMed/NCBI
Google Scholar

[126] View Article

[127] PubMed/NCBI

[128] Google Scholar

[ref37] 37. Imai T, Ubi BE, Saito T, Moriguchi T(2014) Evaluation of reference genes for accurate normalization of gene expression for real time- quantitative PCR in Pyrus pyrifolia using different tissue samples and seasonal conditions. PLoS ONE 9: e86492. pmid:24466117
View Article
PubMed/NCBI
Google Scholar

[130] View Article

[131] PubMed/NCBI

[132] Google Scholar

[ref38] 38. Bustin SA, Benes V, Garson JA, Hellemans J, Huggett J, Kubista M, et al. (2009) The MIQE guidelines: minimum information for publication of quantitative real-time PCR experiments. Clin Chem 55: 611–622. pmid:19246619
View Article
PubMed/NCBI
Google Scholar

[134] View Article

[135] PubMed/NCBI

[136] Google Scholar

[ref39] 39. Vandesompele J, De Preter K, Pattyn F, Poppe B, Van Roy N, De Paepe A, et al. (2002) Accurate normalization of real-time quantitative RT-PCR data by geometric averaging of multiple internal control genes. Genome Biol 3: H34.
View Article
Google Scholar

[138] View Article

[139] Google Scholar

[ref40] 40. Andersen CL, Jensen JL, Ørntoft TF (2004) 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 64: 5245–5250. pmid:15289330
View Article
PubMed/NCBI
Google Scholar

[141] View Article

[142] PubMed/NCBI

[143] Google Scholar

[ref41] 41. Chen H, Hwang JE, Lim CJ, Kim DY, Lee SY, Lim CO (2010) Arabidopsis DREB2C functions as a transcriptional activator of HsfA3 during the heat stress response. Biochem Biophys Res Commun 401: 238–244. pmid:20849812
View Article
PubMed/NCBI
Google Scholar

[145] View Article

[146] PubMed/NCBI

[147] Google Scholar

[ref42] 42. Lim CJ, Hwang JE, Chen H, Hong JK, Yang KA, Choi MS, et al. (2007) Over-expression of the Arabidopsis DRE/CRT-binding transcription factor DREB2C enhances thermotolerance. Biochem Biophys Res Commun 362: 431–436. pmid:17716623
View Article
PubMed/NCBI
Google Scholar

[149] View Article

[150] PubMed/NCBI

[151] Google Scholar

[ref43] 43. Bustin SA (2000) Absolute quantification of mRNA using real-time reverse transcription polymerase chain reaction assays. J Mol Endocrinol 25: 169–193. pmid:11013345
View Article
PubMed/NCBI
Google Scholar

[153] View Article

[154] PubMed/NCBI

[155] Google Scholar

[ref44] 44. Ransbotyn V, R T (2006) Housekeeping gene selection for quantitative real-time PCR assays in the seagrass Zostera marina subjected to heat stress. Limnology and Oceanography: Methods 4: 367–373.
View Article
Google Scholar

[157] View Article

[158] Google Scholar

[ref45] 45. Mafra V, Kubo KS, Alves-Ferreira M, Ribeiro-Alves M, Stuart RM, Boava LP, et al. (2012) Reference genes for accurate transcript normalization in citrus genotypes under different experimental conditions. PLoS ONE 7: e31263. pmid:22347455
View Article
PubMed/NCBI
Google Scholar

[160] View Article

[161] PubMed/NCBI

[162] Google Scholar

[ref46] 46. Libault M, Thibivilliers S, Bilgin DD, Radwan O, Benitez M, Clough SJ et al. (2008) Identification of four soybean reference genes for gene expression normalization. Plant Genome 1: 44–54.
View Article
Google Scholar

[164] View Article

[165] Google Scholar

[ref47] 47. Migocka M, Papierniak A (2011) Identification of suitable reference genes for studying gene expression in cucumber plants subjected to abiotic stress and growth regulators. Mol Breed 28: 343–357.
View Article
Google Scholar

[167] View Article

[168] Google Scholar

[ref48] 48. Sestili S, Sebastiani MS, Belisario A, Ficcadenti N (2014) Reference gene selection for gene expression analysis in melon infected by Fusarium oxysporum f.sp. melonis. J Plant Biochem Biotechnol 23: 238–248.
View Article
Google Scholar

[170] View Article

[171] Google Scholar

[ref49] 49. Long XY, Wang JR, Ouellet T, Rocheleau H, Wei YM, Pu ZE, et al. (2010) Genome-wide identification and evaluation of novel internal control genes for Q-PCR based transcript normalization in wheat. Plant Mol Biol 74: 307–311. pmid:20658259
View Article
PubMed/NCBI
Google Scholar

[173] View Article

[174] PubMed/NCBI

[175] Google Scholar

[ref50] 50. Le DT, Donavan LA, Babu V, Yasuko W, Chien VH, Nishiyama R, et al. (2012) Evaluation of candidate reference genes for normalization of quantitative RT-PCR in soybean tissues under various abiotic stress conditions. PLoS ONE 7: e46487. pmid:23029532
View Article
PubMed/NCBI
Google Scholar

[177] View Article

[178] PubMed/NCBI

[179] Google Scholar

[ref51] 51. Chen L, Zhong HY, Kuang JF, Li JG, Lu WJ,Chen JY (2011) Validation of reference genes for RT-qPCR studies of gene expression in banana fruit under different experimental conditions. Planta 234: 377–390. pmid:21505864
View Article
PubMed/NCBI
Google Scholar

[181] View Article

[182] PubMed/NCBI

[183] Google Scholar

[ref52] 52. Lata C, Prasad M (2011) Role of DREBs in regulation of abiotic stress responses in plants. J Exp Bot 62: 4731–4748. pmid:21737415
View Article
PubMed/NCBI
Google Scholar

[185] View Article

[186] PubMed/NCBI

[187] Google Scholar

[ref53] 53. Sakuma Y, Maruyama K, Qin F, Osakabe Y, Shinozaki K, et al. (2006) Dual function of an Arabidopsis transcription factor DREB2A in water-stress-responsive and heat-stress-responsive gene expression. Proc Natl Acad Sci USA 103: 18822–18827. pmid:17030801
View Article
PubMed/NCBI
Google Scholar

[189] View Article

[190] PubMed/NCBI

[191] Google Scholar

Figures

Abstract

Introduction

Materials and Methods

Plant Materials and Treatments

RNA Extraction and cDNA Synthesis

Selection of Candidate Reference Genes

PCR Primer Design and Amplification Efficiency Testing

Quantitative Real-Time PCR Assay

Data Analysis

Results

Primer Specificity and Amplification Efficiencies

Expression Profiling of Candidate Reference Genes

Expression Stability Analyses

a) geNorm analysis.

b) NormFinder analysis.

Reference Gene Validation

Discussion

Conclusions

Supporting Information

S1 Fig. Amplification specificity of the 13 reference genes in qPCR.

S2 Fig. Standard curves of the 13 candidate reference genes.

S3 Fig. Nucleic acid and deduced amino acid sequences of ACT from persimmon.

S4 Fig. Nucleic acid and deduced amino acid sequences of α-TUB from persimmon.

S5 Fig. Nucleic acid and deduced amino acid sequences of β-TUB from persimmon.

S6 Fig. Nucleic acid and deduced amino acid sequences of UBC from persimmon.

S7 Fig. Nucleic acid and deduced amino acid sequences of CYP from persimmon.

S8 Fig. Nucleic acid and deduced amino acid sequences of RPL13 from persimmon.

S9 Fig. Nucleic acid and deduced amino acid sequences of PP2A from persimmon.

S10 Fig. Nucleic acid and deduced amino acid sequences of GAPDH from persimmon.

S11 Fig. Nucleic acid and deduced amino acid sequences of EF1-α from persimmon.

S12 Fig. Nucleic acid and deduced amino acid sequences of F-box from persimmon.

S13 Fig. Nucleic acid and deduced amino acid sequences of RPII from persimmon.

S14 Fig. Nucleic acid and deduced amino acid sequences of TUA from persimmon.

S15 Fig. Nucleic acid and deduced amino acid sequences of SAND from persimmon.

S1 Table. Primer sequences of reference genes and DkDREB2C in persimmon (clone).

S2 Table. Raw Cq values in persimmon.

S3 Table. Gene expression stability under multiple stresses ranked by geNorm and NormFinder.

S4 Table. Gene expression stability under individual stress ranked by geNorm and NormFinder.

Author Contributions

References