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Article

Screening and Validation of Appropriate Reference Genes for Real-Time Quantitative PCR under PEG, NaCl and ZnSO4 Treatments in Broussonetia papyrifera

by
Mengdi Chen
1,
Zhengbo Wang
1,
Ziyuan Hao
1,
Hongying Li
1,*,
Qi Feng
1,
Xue Yang
1,
Xiaojiao Han
2,* and
Xiping Zhao
1
1
College of Horticulture and Plant Protection, Henan University of Science and Technology, Luoyang 471023, China
2
Key Laboratory of Tree Breeding of Zhejiang Province, Research Institute of Subtropical Forestry, Chinese Academy of Forestry, Hangzhou 311400, China
*
Authors to whom correspondence should be addressed.
Int. J. Mol. Sci. 2023, 24(20), 15087; https://doi.org/10.3390/ijms242015087
Submission received: 12 September 2023 / Revised: 4 October 2023 / Accepted: 7 October 2023 / Published: 11 October 2023
(This article belongs to the Special Issue Research on Plant Genomics and Breeding 2.0)
Browse Figures
Versions Notes

Abstract

:
Real-time quantitative PCR (RT-qPCR) has a high sensitivity and strong specificity, and is widely used in the analysis of gene expression. Selecting appropriate internal reference genes is the key to accurately analyzing the expression changes of target genes by RT-qPCR. To find out the most suitable internal reference genes for studying the gene expression in Broussonetia papyrifera under abiotic stresses (including drought, salt, and ZnSO4 treatments), seven different tissues of B. papyrifera, as well as the roots, stems, and leaves of B. papyrifera under the abiotic stresses were used as test materials, and 15 candidate internal reference genes were screened based on the transcriptome data via RT-qPCR. Then, the expression stability of the candidate genes was comprehensively evaluated through the software geNorm (v3.5), NormFinder (v0.953), BestKeeper (v1.0), and RefFinder. The best internal reference genes and their combinations were screened out according to the analysis results. rRNA and Actin were the best reference genes under drought stress. Under salt stress, DOUB, HSP, NADH, and rRNA were the most stable reference genes. Under heavy metal stress, HSP and NADH were the most suitable reference genes. EIF3 and Actin were the most suitable internal reference genes in the different tissues of B. papyrifera. In addition, HSP, rRNA, NADH, and UBC were the most suitable internal reference genes for the abiotic stresses and the different tissues of B. papyrifera. The expression patterns of DREB and POD were analyzed by using the selected stable and unstable reference genes. This further verified the reliability of the screened internal reference genes. This study lays the foundation for the functional analysis and regulatory mechanism research of genes in B. papyrifera.

1. Introduction

Broussonetia papyrifera is a deciduous tree of the genus Broussonetia in the Moraceae family. It is distributed in most parts of China and Southeast Asia. It is a typical native tree species and a pioneer plant [1]. B. papyrifera has the advantages of easy reproduction, strong stress resistance, and fast growth, and is widely used in the fields of feed, papermaking, and vegetation restoration [2,3]. Moreover, B. papyrifera has medicinal values, as well as flavonoids, polyphenols, and fructose contents that are much higher than those of other plants [4]. Flavonoid derivatives in B. papyrifera have inhibitory effects on cancer cells [5], and polyphenols can inhibit coronavirus proteases [6]. Generally speaking, B. papyrifera is a woody plant with great potential for development, combining economic value and excellent resistance. At the same time, due to the characteristics of its growing environment, the B. papyrifera also has a strong ability to tolerate a variety of unfavorable environments, such as drought, salt, and heavy metals [7,8,9]. Currently, research on B. papyrifera focuses on their breeding [10], physiological characteristics [11], medicinal [12], and pasture values [13], but less research has been performed on the molecular mechanisms of their stress tolerance. As a pioneer tree species widely cultivated in harsh environments, stress resistance is a hotspot in molecular biology research in B. papyrifera [14]. Therefore, it is important to carry out research on the molecular mechanism of B. papyrifera for resistance breeding and the genetic improvement of B. papyrifera.
Real-time quantitative PCR (RT-qPCR) has many advantages, such as convenience, strong specificity, and high sensitivity, and is an effective means to study gene functions [15]. However, the accuracy of RT-qPCR is affected by various factors, such as RNA integrity, cDNA quality, sample dilution factor, and experimental operations [16]. In the study of the expression levels and regulation mechanisms of plant functional genes, the optimization of internal reference genes is the key and the basis for correcting and normalizing the expression of the functional genes [17]. Therefore, the introduction of appropriate internal reference genes is crucial for the normalized analysis of target gene expression [18]. An ideal internal reference gene should be the gene that can be stably expressed in cells and whose expression level is almost not disturbed by the external environment. It is generally the housekeeping gene that maintains the basic life activities of cells, such as Actin, 18s Ribosome RNA, Tublin and Ubiquitin, etc. [19,20]. However, many studies have proved that the transcription levels of the housekeeping genes may change with different species, tissues, and organs [21,22,23]. Therefore, appropriate internal reference genes should be selected according to specific experimental conditions to reduce experimental errors. However, there has been no report on the screening of the internal reference genes in B. papyrifera. This limits the research on the regulation mechanisms of gene expression in B. papyrifera under adversity stresses.
In this study, 15 candidate internal reference genes (NADH, L13, EIF3, HIS, Actin, PP2A, DOUB, UBE2, UBC, PTB, rRNA, GAPDH, HSP, RPL8, and TUA) were selected based on the transcriptome data in B. papyrifera. The RT-qPCR technology, and the geNorm (Version 3.5) [24], NormFinder (version 0.953) [25], and BestKeeper (version 1.0) [26] software were used to analyze the expression stability of the candidate internal reference genes under the abiotic stresses (i.e., drought stress, salt stress, and heavy metal stress) and in different tissues. In addition, the online analysis tool RefFinder [27] was used to comprehensively evaluate the results obtained by the above software. Then, the selected internal reference genes were verified with target genes DREB and POD. This study is the first to screen and verify the internal reference genes used for RT-qPCR normalization in B. papyrifera under the abiotic stresses and the different tissues, which lays the foundation for gene expression analysis in B. papyrifera.

2. Results

2.1. Determination of Primer Specificity and Amplification Efficiency

The PCR amplifications were performed using equal amounts of the mixed cDNA as templates (Figure 1). The target fragments were unique and bright for all the primers, without primer dimers and non-specific amplifications. The band sizes were in line with the expected values. The primer specificity was verified by the RT-qPCR technology (Figure 2). The melting curves of individual genes showed a single melting peak. This indicates that those primers can perform specific amplifications. After calculation, the amplification efficiency of each candidate internal reference gene was between 90.26–117.99%, and the correlation coefficient (R2) was between 0.987–0.999 (Table 1). Therefore, those primers achieved good specificity and efficiency for amplifying the candidate genes, suggesting that the candidate genes can be used in subsequent experiments.

2.2. Ct Values of the Internal Reference Genes

The cycle threshold (Ct) value is inversely proportional to the gene expression level. A low Ct value reflects a high gene expression level. In the analysis of the box plot (Figure 3), the Ct values of most of the genes were between 22 and 28, indicating moderate expression abundances. As it can be seen in the box plot, the GAPDH gene has a broad range of Ct values (20.30–33.24), indicating a low gene expression stability. However, the Actin gene has a narrow range of Ct values (23.90–26.59), indicating a high gene expression stability. Therefore, different internal reference genes have different expression levels under the abiotic stresses and in the different tissues of B. papyrifera. According to the range of Ct values, the expression level of the Actin gene was stable. Therefore, the Actin gene was the best candidate internal reference gene.

2.3. geNorm Analysis

The M value of the expression stability of each candidate internal reference gene under the drought stress and the different tissues was calculated by the geNorm program. The program takes M = 1.5 as the critical value. The smaller the value of M, the more stable the internal reference gene [24]. The geNorm analyses are shown in Figure 4. The M values of each candidate internal reference gene under the abiotic stresses and in the different tissues were lower than 1.5, suggesting that the expression level of each candidate internal reference gene was stable. The expression levels of the Actin and EIF3 were stable under the drought stress. The DOUB and HSP were the most stable reference genes under the salt stress. The expression levels of the NADH and HSP were the most stable under the heavy metal stress. However, among the different tissues of B. papyrifera, the PP2A and EIF3 were the most stable genes among the 15 reference genes. Comprehensive analysis of the expression levels of the 15 candidate internal reference genes in all the samples showed that the M values of the other genes were less than 1.5, except for the GAPDH, whose M values were greater than 1.5. The order of expression stability from high to low is HSP = rRNA > NADH > PP2A > Actin > UBC > DOUB > L13 > PTB > HIS > TUA > EIF3 > RPL8 > UBE2 > GAPDH. Therefore, the HSP and rRNA genes are the most stable internal reference genes in all the samples, and the GAPDH was the least stable one.
Determining the optimal number of internal reference genes can reduce the bias and fluctuation caused by a single internal reference gene. In Figure 5, the V2/3 of B. papyrifera were 0.130, 0148, and 0.094 under the drought stress, under the heavy metal stress, and in the different tissues, respectively, all of which were less than 0.15. This shows that the results of selecting two internal reference genes were stable and reliable, and thus there was no need to select more than two reference genes. Under the salt stress and all samples, the coefficients of variation were V2/3 (0.208) and V2/3 (0.191), both of which were higher than the critical value 0.15. Both V4/5 (0.135) and V4/5 (0.148) were less than 0.15. This indicates that the samples under the salt stress and all samples need to introduce four internal reference genes for correction to keep the results stable and reliable.

2.4. NormFinder Analysis

NormFinder needs to convert the Ct values of the genes into relative expressions before analyzing the data. Then, the stability of the candidate internal reference genes was sorted based on variance analysis. A smaller expression stability value of the candidate internal reference gene indicates a more stable expression [25]. The NormFinder analysis results are shown in Table 2. Under the drought stress and the salt stress, the most stable internal reference gene was the DOUB, and the least stable gene was the GAPDH. Under the heavy metal stress, the most stable internal reference gene was the HSP. In the different tissues, the rRNA has a relatively stable expression. The ranking of the gene expression stability in all samples from high to low was: rRNA (0.338) > HSP (0.383) > NADH (0.495) > PP2A (0.691) > UBC (0.738) > Actin (0.751) > DOUB (0.753) > PTB (0.792) > HIS (0.966) > L13 (1.028) > TUA (1.090) > EIF3 (1.277) > RPL8 (1.598) > UBE2 (1.652) > GAPDH (2.786). Therefore, the rRNA (0.338) was the most stable gene in all samples, and the GAPDH (2.786) was the least stable gene.

2.5. BestKeeper Analysis

The Bestkeeper mainly evaluates the stability of genes by comparing the SD values among Ct values of the candidate internal reference genes [26]. The analysis results of the Bestkeeper are shown in Table 3. Under the drought stress, the UBC was the most stable reference gene, and the GAPDH was the least stable gene. Under the salt stress, the HSP was the most stable reference gene, and the GAPDH was the least stable gene. Under the heavy metal stress, the UBC was the most stable reference gene, and the UBE2 was the least stable gene. In addition, the HSP was the most stable reference gene, and the UBE2 was the least stable gene in the different tissues. In all samples, the ranking of the gene expression stability from high to low was: HSP (0.518) > UBC (0.535) > NADH (0.607) > DOUB (0.615) > Actin (0.643) > PP2A (0.647) > rRNA (0.672) > TUA (0.886) > L13 (0.913) > PTB (0.917) > HIS (1.018) > EIF3 (1.023) > RPL8 (1.353) > UBE2 (1.477) > GAPDH (2.123). It indicates that the HSP (0.518) was the most stable gene in all samples, and the GAPDH (2.123) was the least stable gene.

2.6. RefFinder Analysis

To avoid the error caused by the evaluation program of a single internal reference gene, the online analysis tool RefFinder was used to calculate the geometric mean of the gene expression stability rankings of the above three programs (geNorm, NormFinder, and Bestkeeper). The smaller the geometric mean, the more stable the gene expression [27]. From Table 4, under the drought stress, the rRNA and Actin were the most stable internal reference genes, and the GAPDH was the least stable gene. Under the salt stress, the DOUB and HSP were the most stable internal reference genes, and the GAPDH was the least stable gene. Under the heavy metal stress, the HSP and NADH were the most stable internal reference genes, and the UBE2 was the least stable gene. In the different tissues, the EIF3 and Actin were the most stable internal reference genes, and the UBE2 was the least stable gene. The ranking of the gene expression stability in all samples was: HSP (1.414) > rRNA (1.627) > NADH (3) > UBC (3.761) > PP2A (5.091) > Actin (5.477) > DOUB (5.856) > PTB (8.459) > L13 (9.24) > HIS (9.975) > TUA (10.158) > EIF3 (12) > RPL8 (13) > UBE2 (14) > GAPDH (15). Among them, the HSP and rRNA were identified as the most stable internal reference genes in all samples, and the GAPDH was the least stable gene in all samples.

2.7. Verification of the Expression Stability of the Internal Reference Genes

To verify the reliability of the selected internal reference genes, the most stable and least stable internal reference genes screened by the ReFinder program were used as normalization factors. Then, the DREB and POD gene expression levels were independently validated. As shown in Figure 6, there were great differences in the DREB and POD expression levels obtained by using different internal reference genes. When the selected stable genes were used alone or in combination as normalized internal reference genes, the relative expression patterns of the DREB and POD genes showed a similar trend. On the contrary, when relatively unstable genes were used for relative quantification, the relative expression levels of the DREB and POD were quite different.
Under the drought stress, when the most stable internal reference genes rRNA, Actin, and their combination (rRNA + Actin) were used, the expression levels of the DREB and POD generally showed a trend of increasing first and then decreasing. The DREB and POD expression level reached the peak at 24 h, and 12 h, respectively. However, when the least stable gene GAPDH was used for calculation, the expression levels of the DREB and POD showed an overall trend of first increasing, then decreasing, followed by increasing. Under the salt stress, when the most stable internal reference genes DOUB, HSP, NADH, rRNA, and their combination (DOUB + HSP + NADH + rRNA) were used as candidate internal reference genes, the relative expression of the DREB showed an overall trend of first increasing, and reached the peak at 12 h. The relative expression of the POD decreased first and then increased, and reached a peak at 72 h, with its expression remaining at a low level. When calculated with the unstable gene GAPDH, the DREB reached the peak at 72 h. Although the POD gene expression also reached its peak at 72 h, its expression remained at a high level. Under the heavy metal stress, when the stable internal reference genes HSP, NADH, and their combination (HSP + NADH) were used, the relative expression levels of the DREB and POD both reached their peaks at 72 h. When using the least stable internal reference gene UBE2, the DREB and POD reached their peaks at 48 h. Therefore, the selection of internal reference genes has a great impact on the expression levels of the target genes. Appropriate internal reference genes are conducive to obtaining accurate RT-qPCR results. Using unstable reference genes can lead to unreliable results.

3. Discussion

With the development of molecular biology research, the study of key genes controlling plant stress tolerance and its molecular stress tolerance mechanism will provide important information for plant breeding [28]. The RT-qPCR is one of the main methods for analyzing gene expression levels and regulatory patterns [29]. Selection of internal reference genes with stable expression levels is the key to accurately analyzing the target gene expression [30]. The screening of the internal reference genes in combination with the plant transcriptome database is one of the most effective methods for the research in non-model plants [31]. It has been applied in various plants such as Malpighia emarginata [32], Sinocalycanthus chinensisb [33], and Oryza sativab [34]. Therefore, through the transcriptome database, this study screened a batch of stable candidate internal reference genes, NADH, L13, EIF3, HIS, Actin, PP2A, DOUB, UBE2, UBC, PTB, rRNA, GAPDH, HSP, RPL8, and TUA. Then, their expression stability under abiotic stresses (i.e., drought stress, salt stress, and heavy metal stress) and in seven different tissues were studied.
Due to the differences in the operational logic and statistical methods used in each program, the ranking of the expression stability of the internal reference genes were slightly different among the three programs. For example, under the drought stress, the Actin and EIF3 genes are the most suitable reference genes verified by geNorm software. The DOUB and rRNA genes are the most stable internal reference genes analyzed by the Normfinder software. The UBC and rRNA genes are the most stable reference genes verified by the Bestkeeper. This phenomenon also appeared in Carya illinoinensis [35], Passiflora edulis [36], Forsythia suspensa [37], etc. Therefore, in order to avoid the one-sidedness of the analysis caused by a single piece of software, scholars usually choose the RefFinder as the comprehensive analysis program for internal reference gene stability. It is widely used in reference gene screening studies [38,39]. In this study, RefFinder was used to comprehensively evaluate the results of the above three kinds of software to determine the ranking of the expression stability of the candidate internal reference genes. However, accurate RT-qPCR analysis results cannot be obtained with only one single internal reference gene. Therefore, the geNorm is often used to determine the optimal number of reference genes under abiotic stresses and in different tissues. This can determine the most appropriate combination of internal reference genes for different experimental samples.
The DREB is a transcription factor unique to plants. Under adversity stresses, the DREB interacts with the DRE/CRT (dehydration response element) cis-element in the promoter region of the stress resistance genes, regulating the expression of a series of downstream genes (including DRE/CRT elements), and enhancing the resistance of plants to stresses [40,41]. The DREB gene can be induced to up-regulate its expression under the adversity stresses in Musa acuminata [42], Glycine max [43], and Ricinus communis [44]. The POD gene is a functional gene of antioxidant enzymes, and up-regulating its expression can help plants resist external damages when they encounter abiotic stresses [45]. This has been verified in Phytophtora capsici [46], Ipomoea batatas [47], Tamarix hispida [48], etc. Therefore, the DREB and POD genes can be used to verify the reliability of the screened reference genes. This study screened the most stable internal reference genes and their combinations under drought stress, salt stress, and heavy metal stress. Furthermore, the expression patterns of the DREB and POD genes in B. papyrifera under abiotic stresses were analyzed with the least stable genes as reference genes. When normalizing the gene expression levels with genes of stable expression, the expression patterns of the stress-responsive genes DREB and POD were consistent. However, when the unstable gene was used as a reference gene, the DREB and POD gene expression levels were significantly different. This further verified the accuracy of the screened internal reference genes. In summary, the selection of suitable internal reference genes is the key to analyzing the expression changes of target genes.

4. Materials and Methods

4.1. Materials

The seeds of B. papyrifera were collected from Yanji Town, Shuyang County, Suqian City, Jiangsu Province (N34.16560, E118.58537) in China. The seeds were soaked in 1600 mg L−1 Gibberellin A3 (GA3) solution (Coolaber, Beijing, China) for 24 h, rinsed with distilled water 2–3 times, and then sowed in a mixed substrate of peat soil (Pindstrup Mosebrug A/S, Ryomgaard, Denmark) and vermiculite (Guangdong Chenxing Agriculture Co., Ltd., Guangzhou, China). Then, they were cultured in a light incubator (LHP-300H, Changzhou Putian Instrument Manufacturing Co., Ltd., Changzhou, China) (at a temperature of 30 °C, with a humidity between 60% and 70%, a light intensity of 800 μmol m−2 s−1, and a photoperiod of 12 h light/12 h dark). After six months of culture, samples were collected from seven different tissues (i.e., terminal bud, young leaf, petiole, old leaf, phloem, xylem, and root). We selected seedlings of B. papyrifera with good growth and uniform size, carefully peeled off the nutrient matrix, placed them in the 1/2 Hoagland nutrient solution (Phygene Biotechnology Co., Ltd., Fuzhou, China) for one week, and then applied the abiotic stresses on the seedlings. The drought stress was simulated by a 30 g/L PEG-6000 solution (Tianjin Kermel Chemical Reagent Co., Ltd., Tianjin, China). The salt stress was carried out with a 300 mmol L−1 NaCl solution (Tianjin Kermel Chemical Reagent Co., Ltd., Tianjin, China). The heavy metal stress was applied by a 500 μmol L−1 ZnSO4·7H2O solution (Tianjin Bodi Chemicals Co., Ltd., Tianjin, China). Then, at 0 h (CK), 6 h, 12 h, 24 h, 48 h, and 72 h after the treatment, the roots, stems, and leaves were cut off and used as samples. Finally, the samples were frozen in liquid nitrogen immediately, and stored at −80 °C in an ultra-low temperature freezer (DW-HL668, Zhongke Meiling Cryogenics Co., Ltd., Hefei, China) until use. All the experiments were repeated three times.

4.2. Extraction and Detection of RNA

The RNA prep Pure Polysaccharide Polyphenol Plant Total RNA Extraction Kit (Cat. #DP441, Tiangen Biotech (Beijing) Co., Ltd., Beijing, China) was used to extract RNA according to the manufacturer’s instructions. In addition, 1% agarose gel (Biosharp Life Sciences, Anhui, China) electrophoresis was used to test the integrity of the RNA. Then, the concentration and purity of RNA were tested using an ultra-micro ultraviolet-visible spectrophotometer (NanoDrop2000) (NanoDrop2000, Thermo Fisher Scientific, Waltham, MA, USA).

4.3. Synthesis of cDNA

The RNase-free water was used to dilute the RNA to a concentration of 200 ng/μL, and the concentrations of the RNA were the same for all samples. To achieve a higher efficiency of synthesis, the RNA templates were incubated at 65 °C for 5 min, and then the samples were placed on ice for 2 min. According to the instructions of the M5 Super qPCR RT Kit (Cat. #MF012, Mei5 Biotechnology Co., Ltd., Beijing, China), the PCR reaction system was configured as follows: 4 μL 5 × M5 RT Super Mix and 2 μg RNA template were blended to a total volume of 20 µL with RNase-free water (Cat. #CD4381, Phygene, Biotechnology Co., Ltd., Fuzhou, China). The operations were performed on ice. Samples were reverse-transcribed using a gradient PCR amplification instrument from Bio-Rad (T100TM Thermal Cycler, Bio-Rad, Hercules, CA, USA). The PCR reaction process was as follows: first incubated at 37 °C for 15 min, then incubated at 50 °C for 5 min, and finally heated at 96 °C for 5 min to deactivate the enzyme. After the reaction, the reverse-transcribed cDNA was stored at −20 °C for subsequent experiments.

4.4. Screening of Candidate Internal Reference Genes and Designing of Primers

According to the common internal reference genes in other plants in the existing literature, 15 candidate internal reference genes were selected according to the transcriptome database of B. papyrifera, namely: NADH, L13, EIF3, HIS, Actin, PP2A, DOUB, UBE2, UBC, PTB, rRNA, GAPDH, HSP, RPL8, and TUA. Primer 3web (http://primer3.ut.ee/, accessed on 1 June 2022) was used to design primers. The principles of primer design included: the length of the PCR amplification product between 100 bp and 300 bp, the primer length between 18 bp and 25 bp, the annealing temperatures between 50 °C and 60 °C, and the GC base content between 45% and 55%. It is necessary to avoid the occurrence of hairpin structures and primer-dimer mismatches as much as possible. In addition, the NCBI Primer-BLAST (https://www.ncbi.nlm.nih.gov/tools/primer-blast/index.cgi?LINK_LOC=BlastHome, accessed on 1 June 2022) was utilized to test the primer specificity. Primers were synthesized by General Biology (Anhui) Co., Ltd., Chuzhou, China.

4.5. RT-qPCR Reaction Conditions

The RT-qPCR used the SYBR green dye method, and the following PCR reaction system was created according to the instructions of the 2 × M5 HiPer SYBR Premix EsTaq kit (Cat. #MF787, Mei5 Biotechnology Co., Ltd., Beijing, China): 1 μL cDNA template, 0.2 μL both forward and reverse primers (10 μmol L−1), 3.6 μL ddH2O and 5 μL 2 × M5 HiPer SYBR Premix EsTaq. These operations were performed three times for all samples. The samples were amplified using the CFX96 RT-qPCR instrument from Bio-Rad (CFX96 Real-time System, Bio-Rad, Hercules, CA, USA). The PCR reaction programs consisted of pre-denaturation at 95 °C for 30 s, denaturation at 95 °C for 5 s, then annealing at 60 °C for 30 s, for 39 cycles (the melting curve was from 65 °C to 95 °C, increasing by 0.5 °C for each cycle, and lasting for 0.05 s to reach the melting temperature). The fluorescence signals were collected.

4.6. Detection of Primer Specificity and Amplification Efficiency

The cDNA samples were mixed in equal amounts and diluted three times with ddH2O as a template for the ordinary PCR amplification to test the specificity of the primers. Normal PCR amplification was performed according to the TaKaRa Taq (Cat. #R001A, TaKaRa, Kyoto, Japan) kit. The reaction system comprised 14.3 μL ddH2O, 2 μL 10 × PCR Buffer (Mg2+ plus), 0.1 μL TaKaRa Taq (5 U μL−1), 1.6 μL dNTP Mixture (2.5 mmol L−1), 1.5 μL cDNA, 0.25 μL upstream primers, and 0.25 μL downstream primers (10 μmol L−1). The reaction procedures consisted of 95 °C for 2 min; 98 °C for 10 s, 60 °C for 30 s, 72 °C for 30 s, 30 cycles, and 72 °C for 5 min at the end. After the reaction, the primer specificity was tested with a 1% agarose gel. The cDNA of all the samples were mixed in appropriate amounts and diluted into six gradients (1/3, 1/9, 1/27, 1/81, 1/243, and 1/729). These were then used as the templates to perform the RT-qPCR amplifications for a standard curve. In addition, the primer amplification efficiency was calculated by the formula:
E% = (3−1/slope − 1) × 100%

4.7. The Stability of Candidate Internal Reference Genes

The Ct of the 15 candidate genes in B. papyrifera was obtained via the RT-qPCR. The original Ct values were sorted out by using the software Microsoft Excel 2016, and three programs (geNorm [24], NormFinder [25], BestKeeper [26]) and the online analysis tool RefFinder [27] were operated to comprehensively evaluate the expression stability of the 15 candidate internal reference genes. Finally, the best internal reference genes in B. papyrifera under the abiotic stresses and in the various tissues were screened out.

4.8. Verification of the Expression Stability of the Internal Reference Genes

The genes in response to adversity stresses, i.e., DREB and POD, were selected to verify the stability of the screened internal reference genes. The cDNAs of the leaves of B. papyrifera under the drought stress, the stems of B. papyrifera under the salt stress, and the roots of B. papyrifera under the heavy metal stress were used as templates. By the RT-qPCR technology, the best candidate internal reference genes and their combinations were used as normalization factors, and the unstable internal reference genes were used for comparisons. Then, the relative expressions of the DREB and POD genes of B. papyrifera under the abiotic stresses were analyzed using the 2−ΔΔCT method. The experiments were repeated three times. The reaction system and procedures were as described in Section 4.5.

5. Conclusions

In this study, 15 candidate internal reference genes were selected based on the transcriptome database of B. papyrifera, and their expression levels under abiotic stresses and in seven different tissues were studied. We used the programs geNorm, NormFinder, BestKeeper, and RefFinder to evaluate the expression stability of the candidate internal reference genes. Then, the accuracy of the screened reference genes was verified by the stress-responsive genes DREB and POD. This study provides a few reliable internal reference genes for the analysis of target gene expression in B. papyrifera under abiotic stresses and in the different tissues. This research lays the foundation for the study of the stress resistance and regulatory mechanisms in B. papyrifera, and the discovery of its important functional genes.

Author Contributions

Conceptualization, H.L. and X.H.; methodology, H.L. and M.C.; software, Z.W.; validation, Q.F. and X.Y.; formal analysis, Z.W., Z.H. and M.C.; investigation, Q.F.; resources, H.L.; data curation, M.C.; writing—original draft preparation, M.C.; writing—review and editing, M.C.; visualization, Z.H.; supervision, X.H.; project administration, H.L. and X.H.; funding acquisition, X.Z. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the National Natural Science Foundation of China (No. 32171701).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Acknowledgments

We thank Jianwei Ni for his generosity in sharing the transcriptome data and providing his experience in the cultivation of B. papyrifera seedlings. We also thank Enying Liu for language edit.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. Agarose gel electrophoresis of the conventional PCR products of the candidate internal reference genes in B. papyrifera.
Figure 1. Agarose gel electrophoresis of the conventional PCR products of the candidate internal reference genes in B. papyrifera.
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Figure 2. Melting curves of the candidate reference genes in B. papyrifera.
Figure 2. Melting curves of the candidate reference genes in B. papyrifera.
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Figure 3. Box plot of the Ct values of the 15 candidate reference genes in B. papyrifera. The box represents the concentrated range of Ct values. The horizontal line in the middle of the box represents the median value, and the black square represents the average value. The upper and lower edges of the box represent the upper and lower quartiles, respectively. The upper and lower ends of the box represent the maximum and minimum values of the gene, respectively.
Figure 3. Box plot of the Ct values of the 15 candidate reference genes in B. papyrifera. The box represents the concentrated range of Ct values. The horizontal line in the middle of the box represents the median value, and the black square represents the average value. The upper and lower edges of the box represent the upper and lower quartiles, respectively. The upper and lower ends of the box represent the maximum and minimum values of the gene, respectively.
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Figure 4. Average expression stability of the 15 candidate reference genes in B. papyrifera analyzed by the geNorm program. (A) Drought stress, (B) salt stress, (C) heavy metal stress, (D) different tissues, (E) all samples.
Figure 4. Average expression stability of the 15 candidate reference genes in B. papyrifera analyzed by the geNorm program. (A) Drought stress, (B) salt stress, (C) heavy metal stress, (D) different tissues, (E) all samples.
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Figure 5. Analysis of the paired variation of the 15 candidate internal reference genes in B. papyrifera by geNorm program. The optimal number of candidate reference genes required for accurate normalization was determined by paired variation Vn/(n+1). The threshold value of Vn/(n+1) was 0.15. When Vn/(n+1) is less than 0.15, n genes can ensure stable and reliable results.
Figure 5. Analysis of the paired variation of the 15 candidate internal reference genes in B. papyrifera by geNorm program. The optimal number of candidate reference genes required for accurate normalization was determined by paired variation Vn/(n+1). The threshold value of Vn/(n+1) was 0.15. When Vn/(n+1) is less than 0.15, n genes can ensure stable and reliable results.
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Figure 6. Using DREB and POD to verify the expression stability of internal reference genes screened under the abiotic stress in B. papyrifera. (A,D): Drought stress (leaves); (B,E): Salt stress (stalks); (C,F): Heavy metal stress (roots). The error bars indicate the standard deviations (SDs).
Figure 6. Using DREB and POD to verify the expression stability of internal reference genes screened under the abiotic stress in B. papyrifera. (A,D): Drought stress (leaves); (B,E): Salt stress (stalks); (C,F): Heavy metal stress (roots). The error bars indicate the standard deviations (SDs).
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Table 1. Primer sequence and amplification efficiency of the 15 candidate reference genes.
Table 1. Primer sequence and amplification efficiency of the 15 candidate reference genes.
GenePrimer IDPrimer sequence (5′-3′)Amplicon Size (Bp)Efficiency (%)R2
NADHNADH-FGGACAGGTGGAAGATCGTCTG11197.180.987
NADH-RGGAATCTTCAGAACCCCGGAA
L13L13-FTGCCAGCCCTAACTTTCATGT12692.170.999
L13-RAGACCCGGAGAAGAATTGCTC
EIF3EIF3-FGTCCACATCATTCGAAGCAGC130106.310.997
EIF3-RGATCTATGAAGTGCCTGCGGA
HISHIS-FTGGCCTTGCATTCTCCAGTAG11898.870.996
HIS-RGACAAGCTGCGAGAGTGGTAT
ActinActin-FTACGCATTGAAGACCCTCCAC14890.260.998
Actin-RTGGCCACACTTGCTTAGACAA
PP2APP2A-FTCCTTTTGCGAGTCGATGGAA119117.990.988
PP2A-RCTTTGACGTTTGAAGCGAGCA
DOUBDOUB-FCCTGATCTTCGCCGGAAAACA19497.480.999
DOUB-RTGGAGAGGGTTGAAGAGAGCT
UBE2UBE2-FTCTCTGCTTACGGACCCAAAC14492.290.998
UBE2-RGAGGAGGAGCTATTGGGCCTA
UBCUBC-FAGCATTACTTTCCGCTCCACA11991.440.995
UBC-RTGGCGAAAGTTTCTGTCCAGT
PTBPTB-FCTGGAAACCTGCTGCCTTTTC15196.290.999
PTB-RATTGAGGGTGTAGAAGCTGGC
rRNArRNA-FCAGGTTTCGATGTTGGGGAGA19695.560.999
rRNA-RCCAGCTTCCGAGAACATTCCT
GAPDHGAPDH-FCCATGGAAGGACTTGGGGATC15690.430.995
GAPDH-RGTTCACTCCCACCACGTATGT
HSPHSP-FCCAGCGCTGATGTTAGATTGC17492.660.993
HSP-RTTGCCATCAGAGCCTTTTCCT
RPL8RPL8-FTGATCACCGACATCATCCACG18590.550.992
RPL8-RTCTGATCGGAAGGACATTGCC
TUATUA-FTCGAAAGGCCAACATACACCA17596.590.997
TUA-RGAGATGACAGGGGCATACGAG
PODPOD-FCTCCTGTGACCTCAACTGCAA13691.710.987
POD-RGAGTTGAACCATGGCGCAAAT
DREBDREB-FTAAACCAGCTCACCCAATCCC27490.990.989
DREB-RCGGTTCTTGGGGAGTCTGATC
Table 2. Expression stability of the reference genes in B. papyrifera calculated by NormFinder.
Table 2. Expression stability of the reference genes in B. papyrifera calculated by NormFinder.
RankDrought StressSalt StressHeavy Metal StressDifferent TissuesAll Samples
GeneStabilityGeneStabilityGeneStabilityGeneStabilityGeneStability
1DOUB0.152DOUB0.172HSP0.359rRNA0.051rRNA0.338
2rRNA0.162HSP0.396rRNA0.362Actin0.222HSP0.383
3Actin0.394NADH0.540NADH0.401EIF30.229NADH0.495
4HSP0.429rRNA0.569UBC0.513TUA0.343PP2A0.691
5UBC0.436PP2A0.630DOUB0.707DOUB0.346UBC0.738
6PTB0.520HIS0.635HIS0.745PP2A0.383Actin0.751
7EIF30.547UBC0.654Actin0.805PTB0.383DOUB0.753
8TUA0.586L130.720PP2A0.907HIS0.456PTB0.792
9PP2A0.596Actin0.742PTB0.969NADH0.484HIS0.966
10NADH0.616PTB0.817L130.992HSP0.493L131.028
11RPL80.729TUA1.133RPL81.265UBC0.503TUA1.090
12HIS0.917EIF31.461TUA1.400L130.557EIF31.277
13L130.998UBE21.791GAPDH1.511RPL80.836RPL81.598
14UBE21.090RPL81.924EIF31.672GAPDH1.178UBE21.652
15GAPDH4.183GAPDH2.216UBE21.921UBE21.727GAPDH2.786
Table 3. Analysis of the expression stability by Bestkeeper and the ranking of the internal reference genes in B. papyrifera.
Table 3. Analysis of the expression stability by Bestkeeper and the ranking of the internal reference genes in B. papyrifera.
RankDrought StressSalt StressHeavy Metal StressDifferent TissuesAll Samples
GeneStabilityGeneStabilityGeneStabilityGeneStabilityGeneStability
1UBC0.398HSP0.453UBC0.382HSP0.215HSP0.518
2rRNA0.465NADH0.49NADH0.416RPL80.216UBC0.535
3HSP0.481DOUB0.521HSP0.439DOUB0.256NADH0.607
4DOUB0.513UBC0.561rRNA0.493UBC0.475DOUB0.615
5PP2A0.515HIS0.596DOUB0.524NADH0.489Actin0.643
6Actin0.530PP2A0.609Actin0.607Actin0.514PP2A0.647
7EIF30.557rRNA0.723PP2A0.645PP2A0.566rRNA0.672
8PTB0.634L130.729HIS0.741L130.588TUA0.886
9RPL80.639Actin0.772L130.768EIF30.622L130.913
10TUA0.671TUA0.784PTB0.866TUA0.641PTB0.917
11NADH0.727PTB0.871RPL80.972rRNA0.650HIS1.018
12UBE20.844EIF31.336TUA1.088HIS0.651EIF31.023
13L130.928UBE21.356GAPDH1.137PTB0.819RPL81.353
14HIS0.982RPL81.551EIF31.303GAPDH1.213UBE21.477
15GAPDH3.496GAPDH1.554UBE21.593UBE21.619GAPDH2.123
Table 4. Ranking of the expression stability of the reference genes in B. papyrifera by RefFinder.
Table 4. Ranking of the expression stability of the reference genes in B. papyrifera by RefFinder.
RankDrought StressSalt StressHeavy Metal StressDifferent TissuesAll Samples
GeneStabilityGeneStabilityGeneStabilityGeneStabilityGeneStability
1rRNA2.115DOUB1.316HSP1.316EIF32.28HSP1.414
2Actin2.449HSP1.414NADH2.06Actin3.13rRNA1.627
3UBC2.590NADH3.31rRNA2.632PP2A3.742NADH3
4DOUB3.130rRNA3.984UBC2.828rRNA4.031UBC3.761
5EIF33.956HIS4.949DOUB5.477DOUB4.787PP2A5.091
6HSP4.899PP2A5.733HIS5.886HSP5.477Actin5.477
7PP2A6.160UBC6.293Actin6.735NADH6.344DOUB5.856
8PTB7.416L137.737PP2A7.737UBC6.477PTB8.459
9NADH9.124Actin9PTB9.487L137.502L139.24
10TUA9.685PTB10.241L139.487TUA7.933HIS9.975
11RPL810.215TUA10.741RPL811RPL88.142TUA10.158
12HIS12.471EIF312TUA12.243PTB8.596EIF312
13L1313UBE213GAPDH13.243HIS10.843RPL813
14UBE213.471RPL814EIF313.741GAPDH14UBE214
15GAPDH15GAPDH15UBE215UBE215GAPDH15
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Chen, M.; Wang, Z.; Hao, Z.; Li, H.; Feng, Q.; Yang, X.; Han, X.; Zhao, X. Screening and Validation of Appropriate Reference Genes for Real-Time Quantitative PCR under PEG, NaCl and ZnSO4 Treatments in Broussonetia papyrifera. Int. J. Mol. Sci. 2023, 24, 15087. https://doi.org/10.3390/ijms242015087

AMA Style

Chen M, Wang Z, Hao Z, Li H, Feng Q, Yang X, Han X, Zhao X. Screening and Validation of Appropriate Reference Genes for Real-Time Quantitative PCR under PEG, NaCl and ZnSO4 Treatments in Broussonetia papyrifera. International Journal of Molecular Sciences. 2023; 24(20):15087. https://doi.org/10.3390/ijms242015087

Chicago/Turabian Style

Chen, Mengdi, Zhengbo Wang, Ziyuan Hao, Hongying Li, Qi Feng, Xue Yang, Xiaojiao Han, and Xiping Zhao. 2023. "Screening and Validation of Appropriate Reference Genes for Real-Time Quantitative PCR under PEG, NaCl and ZnSO4 Treatments in Broussonetia papyrifera" International Journal of Molecular Sciences 24, no. 20: 15087. https://doi.org/10.3390/ijms242015087

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