Next Article in Journal
Epigenetic and Proteomic Biomarkers of Elevated Alcohol Use Predict Epigenetic Aging and Cell-Type variation Better Than Self-Report
Next Article in Special Issue
Variations in Growth and Photosynthetic Traits of Polyploid Poplar Hybrids and Clones in Northeast China
Previous Article in Journal
A Novel Transcriptome Approach to the Investigation of the Molecular Pathology of Vitreous and Retinal Detachment
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Genes
Volume 13
Issue 10
10.3390/genes13101887
genes-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Reference Gene Selection for qPCR Analysis in Schima superba under Abiotic Stress

by
Jun Yao
1,†,
Gang Zhu
2,†,
Dongcheng Liang
1,
Boxiang He
1,
Yingli Wang
1,
Yanling Cai
1 and
Qian Zhang
1,*
1
Guangdong Provincial Key Laboratory of Silviculture Protection and Utilization, Guangdong Academy of Forestry, Guangzhou 510520, China
2
College of Horticulture and Landscape Architecture, Zhongkai University of Agriculture and Engineering, Guangzhou 510225, China
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Genes 2022, 13(10), 1887; https://doi.org/10.3390/genes13101887
Submission received: 6 September 2022 / Revised: 11 October 2022 / Accepted: 14 October 2022 / Published: 18 October 2022
(This article belongs to the Special Issue Feature Papers in Genes & Environments)
Browse Figures
Review Reports Versions Notes

Abstract

:
Quantitative real-time PCR (qPCR) is an indispensable technique for gene expression analysis in modern molecular biology. The selection and evaluation of suitable reference genes is a prerequisite for accurate gene expression analysis. Schima superba is a valuable tree species that is environmentally adaptable and highly fire-resistant. In this study, 12 candidate reference genes were selected to check their stability of gene expression in different tissues under abiotic stresses: cold stress, salt stress, and drought stress by ΔCt, geNorm, NormFinder, BestKeeper, and RefFinder. The results indicated that AP-2 was the most stably expressed overall and for the cold stress and drought stress. eIF-5α gene expression was the most stable under the salt stress treatment, while UBQ expression was the most stable across mature leaves, shoots, stems, and roots. In contrast, UBC20, GAPDH, and TUB were the least stably expressed genes tested. This study delivers valid reference genes in S. superba under the different experimental conditions, providing an important resource for the subsequent elucidation of the abiotic stress adaptation mechanisms and genes with biological importance.

1. Introduction

To date, quantitative real-time PCR (qPCR) has been widely used in many fields, such as agriculture, genetics, microbiology, and medical gene expression detection and molecular technology for quantitative research, based on its significant advantages in sensitivity, accuracy, specificity, and operability [1,2]. However, the accuracy of qPCR results is affected by the number of initial templates, RNA quality, reverse transcription efficiency, amplification efficiency, and reference genes [3]. In addition, the selection of reference genes is an important factor affecting the stability of qPCR results. In general, gene expression can be standardized or quantified by using one or more stable reference genes. Ideally, the internal reference genes should be stably expressed under different tissues, developmental stages, and abiotic stresses of the plant. However, many studies have demonstrated that plants lack a common reference gene. The suitability of specific reference genes depends on the special tissue or the experimental conditions [4]. Therefore, selecting and validating reference genes for specific experimental conditions is a necessary prerequisite for the reliability of qPCR results [5].
S. superba is a large evergreen broad-leaved tree in the Theaceae (Supplementary Materials Figure S1). Its timber is excellent, solid, and tight [6]. S. superba is the main fire prevention forest belt construction tree in the eastern subtropical region of China due to its high water content of fresh leaves, high fire point, and an oil content of only 6% [7]. At the same time, it is classified as a valuable tree species because of its high economic value [8]. S. superba is highly adaptable and can grow under unfavorable conditions such as acidic soil, barren mountains, and arid areas [9]. However, the mechanism regulating the adaptation of S. superba to abiotic stresses has not been reported. Elucidating the abiotic stress mechanism of S. superba and detecting the expression of related genes are important for the selection and breeding of superior varieties. The changes in gene expression levels induced by abiotic stresses such as drought, salinity, and cold can be complex and multifaceted, often affecting the expression levels of stable reference genes under other experimental conditions. To date, reference genes have been reported in S. superba [10], but these genes have only been validated under normal experimental conditions. Many stable reference genes under abiotic stresses have been reported from different plants, including rice [11], luffa [12], and sorghum [13]. The stable expression of reference genes in S. superba under abiotic conditions has not been reported. Therefore, the main objective of this study was to identify reference genes that exhibit high expression stability under various abiotic stress conditions in S. superba to facilitate subsequent studies on abiotic stress mechanisms and to provide an important research basis for the selection and breeding of superior varieties.

2. Materials and Methods

2.1. Plant Materials and Treatments

Plant materials were collected from the nursery of the Guangdong Academy of Forestry. Seedlings were selected from 1–2 years of age and cultivated in artificial climate incubators with a 16-h artificial light–8-h dark cycle and 65–75% relative humidity. For the cold stress treatment, seedlings were grown at 16 °C, and 25 °C was selected for the other stress treatments. For salt stress treatment, seedlings were treated with 200 mM NaCl. For the drought stress treatment, seedlings were watered with the 20% PEG 6000 solution. Leaves were then collected at 0, 1, 3, 6, 9, 12, 24 h, and 3 d after the treatments. Plant tissues were collected from mature leaves, shoots, stems, and roots. Each experiment was completed with three replicates and immediately frozen in liquid nitrogen and stored at −80 °C until use.

2.2. Reference Gene Selection and Primer Design

Candidate genes were selected based on pre-lab transcriptomic data from our laboratory (unpublished) and conventional reference genes. The primers for qPCR were designed using primer 5.0 (Table S1) and synthesized by Tsingke Biotechnology Co., Ltd. (Beijing, China).

2.3. qPCR Analysis

The total RNA was extracted using the RNAprep Pure Plant Plus Kit (polysaccharides and polyphenolics-rich) (Code No. DP441, TIANGEN, Beijing, China). RNA quality and concentration were assessed by 1% agarose gel electrophoresis and the BioDrop nucleic acid protein analyzer. The RNA samples with a 260/280 nm absorbance ratio of 1.8–2.0 were used to synthesize the first strand cDNA with the PrimeScript™ RT reagent Kit with gDNA Eraser (Perfect Real Time) (Code No. RR047A, TAKARA, Beijing, China) for further analyses.
qPCR follows the guidelines of the Biomarker 2 × SYBR Green Fast qPCR Mix (Code No. RK02001, BioMarker, Beijing, China): 8.0 μL of Nuclease-free water, 10 μL of 2 × SYBR Green Fast qPCR MIX, 0.5 μL of forward and reverse primers (10 μM), and 1 μL of diluted cDNA. qPCR was conducted using BIO-RAD CFX Connect (Bio-Rad Laboratories, Hercules, California, CA, USA) with the following cycling conditions: initial denaturation at 95 °C for 3 min followed by 40 cycles of 95 °C for 5 s, 60 °C for 30 s, and 72 °C for 20 s. The relative quantification in each sample was determined. A blank control with double-distilled water as a template was also analyzed, and three independent biological replicates and three technical repetitions were performed for each of the quantitative PCR experiments. The cDNA samples were diluted in a 10-fold gradient to measure the threshold cycle (Ct) (10,000-fold template concentration was too low for some candidate genes to determine the exact Ct value), and the standard curve was plotted using Excel with the horizontal coordinate as the dilution and the vertical coordinate as the mean Ct value. The linear slope K and correlation coefficient R2 of the candidate reference genes were analyzed, as well as the amplification efficiency E, calculated as E = (10−1/K−1) × 100%.

2.4. Statistical Data Analysis

The qPCR cycle threshold (Ct) value was recorded for each candidate reference gene under different treatments. The ΔCt [14], geNorm [15], NormFinder [16], BestKeeper [17], and RefFinder [18] algorithms were used to assess the expression stability of candidate reference genes. The Bestkeeper and ΔCt algorithms assess the stability of candidate reference genes based on Ct values, whereas the geNorm and NormFinder algorithms are based on the 2−ΔCt values obtained from the Ct value transformation. Finally, the reference genes were ranked based on the geometric mean (GM) values calculated by RefFinder.

3. Results

3.1. Candidate Reference Genes and PCR Amplification

The genes selected as potential reference genes were actin (Actin), glyceraldehyde-3-phosphate dehydrogenase (GAPDH), ubiquitin (UBQ), ubiquitin-conjugating enzyme (UBC), α tubulin (TUA), eukaryotic initiation factor 5A (eIF5α), ribosomal protein L17 (RPL17), elongation factor-1α (EF1α), AP-2 complex subunit mu-like (AP-2), UDP-galactose transporter (UDP), tubulin β chain (TUB), GIIaglucosidase II α-subunit (GIIα). These genes were selected as stable reference genes in other species [19,20,21,22,23,24,25].
The primer specificities were confirmed by 1% gel electrophoresis and melting curve analyses. All primers amplified a single amplicon of the expected size (Figure S2). The candidate reference genes were analyzed and the solubilization temperatures of the candidate reference genes’ melting curves were consistent, while the candidate reference genes showed a single specific peak (Figure S3). The results indicated that the 12 primer pairs were highly specific.
The general amplification efficiency should be in the range of 90–120%. The highest amplification efficiency of the 12 candidate reference genes was 123.13% and the lowest was 92.35%. The correlation coefficients R2 of all candidate genes were above 0.98 (Table 1). Therefore, the linearity and amplification efficiency of the candidate reference genes were largely satisfied by qPCR analysis.

3.2. Ct Values of Candidate Reference Genes

The Ct values of the candidate reference genes ranged from 21.20 to 34.61 under different experimental treatments, and the Ct values were inversely proportional to the gene expression abundance. As shown in Figure 1, The box plots reflect the differences in the distribution of Ct values among the different candidate reference genes, and the dispersion degree indicates the stability of genes. A lower dispersion degree indicates more stable gene expression with the same experimental sample. UBQ has the highest concentration trend, and TUB has the lowest concentration trend.

3.3. Stability of Candidate Reference Genes

The ΔCt algorithm is based on the mean standard deviation (mSD). The level of the mSD value indicates the stability of the internal reference gene, and the smaller the mSD value indicates the higher the stability of the internal reference gene. In this study, the most stably expressed genes were different under different experimental conditions (Figure 2). For all the experimental conditions, AP-2, UBC4, and UBQ were the most stably expressed genes. Under cold stress, AP-2, eIF-5α, and UDP were the most stably expressed genes. For the salt stress, eIF-5α, AP-2, and UBC4 were the most stably expressed genes. For the drought stress, eIF-5α, Actin, and UBC4 were the most stably expressed genes. For the different tissues, UBQ, EF1α, and UBC4 were the most stably expressed genes. GAPDH, UBC20, and TUB showed unstable expression under all experimental conditions.
The geNorm algorithm ranks the stability of reference gene expression based on the calculation of the average M value. Larger M values for candidate reference genes indicate lower stability (M value threshold of 1.5). In this study, the most stably expressed genes were different under different experimental conditions (Figure 3). For all the experimental conditions, AP-2 and Actin expression were the most stable with an M value of 0.38. For the cold stress, AP-2 and eIF-5α expression were the most stable with an M value of 0.33. For the salt stress, AP-2 and eIF-5α expression were the most stable with an M value of 0.34. For the drought stress, AP-2 and Actin expression were the most stable with an M value of 0.2. For the different tissues, UBQ and EF1α expression were the most stable with an M value of 0.13. Consistent with the ΔCt algorithm, GAPDH, UBC20, and TUB expressions showed the most unstable performance.
The pairwise variations value (V) is the value of pairwise variations of the standardized factor. The default threshold value of V is 0.15. If the value of Vn/Vn + 1 is less than 0.15, then n is the optimal number of internal genes, and if the value of Vn/Vn + 1 is greater than 0.15, then n + 1 is the optimal number of reference genes. As shown in Figure 4, for all the experimental conditions, V2/V3 values were equal to 0.15 and V3/V4 were equal to 0.11, less than 0.15, so three reference genes are sufficient for normalizing gene expression data. In cold stress, salt stress, drought stress, and different tissues, V2/V3 was less than 0.15, demonstrating that two reference genes were sufficient to normalize the expression of the target gene.
NormFinder assesses expression stability (S value) by the variance method. Lower S values correspond to higher gene expression stability. As shown in Table 2, for all the experimental conditions, AP-2 expression was the most stable with an S value of 0.087. Similarly, the stability of AP-2 expression was highest under the experimental conditions of drought and salt stress. Under cold stress, UDP has the best stability. For the different tissues, the expression of UBQ and EF1α was equally stable, whereas TUB was the least stably expressed reference gene.
The BestKeeper algorithm is used to assess the stability of the expression of the reference genes by calculating the standard deviation (SD) and the variation deviation (CV). High SD and CV values indicate low gene stability. As shown in Table 3, in all samples, the three most stably expressed genes were UBQ, EF1α, and UBC4. In cold stress, the most stably expressed genes were UBQ, EF1α, and eIF-5α. In drought stress, the most stably expressed genes were UBQ, UBC4, and AP-2. In salt stress, the most stably expressed genes were UBQ, eIF-5α, and RPL17. In different tissues, the most stably expressed genes were RPL17, GIIα, and eIF-5α. It is noteworthy that the RPL17 gene was assessed to be more stable in the BestKeeper algorithm and less stable in the NormFinder algorithm under different tissue and salt stress experimental conditions.
The results of the ΔCt, geNorm, NormFinder, and BestKeeper algorithms were combined and ranked comprehensively using RefFinder (Figure 5). The results showed that AP-2 was the most stably expressed reference gene in all samples, also under cold stress and drought stress experimental conditions. For the salt stress, eIF-5α expression was the most stable, while UBQ expression was the most stable in different tissues. In contrast, UBC20, GAPDH, and TUB were the least stably expressed genes.

4. Discussion

The stability of reference genes can directly affect the accuracy and stability of qPCR results [26]. The selection of an appropriate reference gene is the key to target gene expression studies. However, plants lack a common reference gene, and the reference genes are relatively stable only under specific tissue or experimental conditions. The same reference genes show different stability in different species. For example, Actin showed the best stability in Prunus persica [27] and Pitaya [28], but the worst in Citrus sinensis [29] and Populus [30], and EF1α had superior stability in blueberry [31], but not in apple [32]. The stability of the same reference gene also differs in different experimental conditions and tissues, and the expression of the reference gene is influenced by biotic and abiotic factors. For example, EF1α showed the highest stability in ‘Xiacui’ samples while being the most unstable gene among all samples of Prunus persica [27]. Gene expression patterns in plants are more complex and diverse under abiotic stress. Therefore, it is necessary to verify the stability of reference genes under different experimental conditions. Meanwhile, the selection of suitable candidate genes also has an impact on the results. The published studies on the reference genes of S. superba mainly focus on the stability of the expression of reference genes in different tissues under normal experimental conditions [10], and the candidate reference genes such as Actin, eIF5α, GAPDH, and TUB were selected, and their comprehensive evaluation of expression stability was similar to this study. Actin is more stable than eIF5α, while the worst stability was found in GAPDH and TUB. Unlike in the present study, the common candidate genes UBQ and EF1α were selected among the candidate genes, which had superior stability in different tissues. This study also investigated the expression levels of more complex abiotic stress conditions.
In this study, the stability of 12 candidate internal reference genes (Actin, AP-2, EF1α, eIF-5α, GAPDH, GIIα, RPL17, TUB, UBC20, UBC4, UBQ, UDP) was analyzed under four experimental conditions: cold stress, drought stress, salt stress, and different tissues. The gene expression stability was evaluated by the ΔCt, geNorm, NormFinder, BestKeeper, and RefFinder algorithms. The data showed that AP-2, UBQ, and Actin were the most stable internal reference genes of all samples, and Actin’s performance in different tissues of plants is similar to Yang’s study [10]. It is worth noting that AP-2 performs well in all three types of abiotic stresses and its expression is, relatively, not the most stable in different tissues, which may indicate that AP-2 is able to maintain stable expression, especially under complex abiotic stresses. UBQ is able to maintain a relatively stable expression, especially in different organizations. In contrast, Actin expression stability fluctuates under different experimental treatments, phenotypes are relatively unstable under cold treatments, and phenotypes are not optimally stable under other treatments. For the salt stress, eIF-5α expression was the most stable, while UBQ expression was the most stable in different tissues. These stably expressed genes are also used as suitable reference genes in other plants. AP-2 (adaptor protein-2 complex) was identified as a potential reference gene due to stability in Arabidopsis [33] and grapevine [34]. The expression of AP-2 was also stable under abiotic stresses in Clerodendrum trichotomum [23] and Sedum alfredii [35]. eIF-5α (eukaryotic translation initiation factor 5A) exhibited highly stable expression by microarray analysis of Arabidopsis [33]. Stable expression has also been demonstrated in some species, such as Tree Peony [25] and Metasequoia [36]. UBQ (ubiquitin family 6) is found in all eukaryotes and is highly conserved in the amino acid sequence. Gene expression levels are well stabilized in different tissues, such as Chinese prickly ash [37] and Populus [38]. A ubiquitin tag is reported to mark particular proteins for proteolytic elimination, but it can also have nonproteolytic functions [39]. Thus, its wide range of functions leads to the variable expression of UBQ in different plants, such as Passiflora edulis [40]. Other candidate genes: Actin [19], EF1α [20], GAPDH [41,42], GIIα [21], RPL17 [43], TUB [44], UBC [22], and UDP [24] were selected as stable reference genes in other species, but not the most stable in this study. Normalization of the expression data of a target gene using one or more stably expressed reference genes is generally performed. Applying multiple genes may increase the accuracy and reliability of data normalization to some extent [45]. However, combined with the geNorm analysis results in this study, we believe that the use of two reference genes can better ensure the stability of the experiment and the accuracy of the results. The stably expressed reference genes obtained in this study will contribute to the study of gene expression levels in S. superba under abiotic stress, facilitate the study of abiotic stress mechanisms, and help discover new genes and signaling networks used by S. superba to cope with these challenges, which is essential for the development of new varieties with enhanced tolerance to stress.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/genes13101887/s1, Figure S1: Normal appearance and samples under drought stress conditions of Schima superba. Figure S2: Agarose gel electrophoresis analysis of primer specificity (1. TUB, 2. UBC4, 3. GAPDH, 4. UBQ, 5. UDP, 6. GIIa, 7. UBC20, 8. AP-2, 9. eIF-5a, 10. EF1a, 11. RPL17, 12. Actin). Figure S3: Melting curve analysis of twelve reference genes. Table S1: Primer design of reference genes.

Author Contributions

J.Y.: investigation, data curation, visualization, writing—original draft preparation. G.Z.: investigation, data curation, visualization, writing—original draft preparation. D.L.: investigation, data curation, visualization. B.H.: investigation, visualization, data curation. Y.W.: investigation, visualization. Y.C.: visualization, validation. Q.Z.: software, validation. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Key Technologies R&D Program of Guangdong Province (grant numbers 2020B020215002).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Kou, X.; Zhang, L.; Yang, S.; Li, G.; Ye, J. Selection and validation of reference genes for quantitative RT-PCR analysis in peach fruit under different experimental conditions. Sci. Hortic. 2017, 225, 195–203. [Google Scholar] [CrossRef]
  2. Huggett, J.; Dheda, K.; Bustin, S.; Zumla, A. Real-time RT-PCR normalisation; strategies and considerations. Genes Immun. 2005, 6, 279–284. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  3. Nolan, T.; Hands, R.E.; Bustin, S.A. Quantification of mRNA using real-time RT-PCR. Nat. Protoc. 2006, 1, 1559–1582. [Google Scholar] [CrossRef] [PubMed]
  4. Ma, R.; Xu, S.; Zhao, Y.; Xia, B.; Wang, R. Selection and validation of appropriate reference genes for quantitative real-time PCR analysis of gene expression in Lycoris aurea. Front. Plant Sci. 2016, 7, 536. [Google Scholar] [CrossRef] [Green Version]
  5. Xiao, Z.; Sun, X.; Liu, X.; Li, C.; He, L.; Chen, S.; Su, J. Selection of reliable reference genes for gene expression studies on Rhododendron molle G. Don. Front. Plant Sci. 2016, 7, 1547. [Google Scholar] [CrossRef] [Green Version]
  6. Wei, C.; Qin, W.; Zhang, D.; Yan, L.; Wang, F. The Growth Rhythm of Schima Superba Plantation in Nanning, Guangxi. For. Environ. Sci. 2020, 36, 48–54. [Google Scholar]
  7. Zhang, P.; Zhou, Z.; Jin, G.; Wu, Y.; Fan, H. Provenance Differences for Fire-assistant and Fire-resistant Chemical Components in Fresh Leaf of Schima superba. For. Res. 2005, 18, 80. [Google Scholar]
  8. Li, Z.; Li, B.; Qi, C.; Yu, X.; Wu, Y. Studies on important of valuable wood species and its development strategy. Zhongnan Linye Keji Daxue Xuebao 2013, 32, 1–8. [Google Scholar]
  9. Huang, Y.; Zhuang, X. South China Native Tree Nursery Technology; China Forestry Publishing House: Beijing, China, 2007; pp. 341–342. [Google Scholar]
  10. Yang, Z.; Zhang, R.; Zhou, Z. Identification and validation of reference genes for gene expression analysis in Schima superba. Genes 2021, 12, 732. [Google Scholar] [CrossRef]
  11. Pabuayon, I.M.; Yamamoto, N.; Trinidad, J.L.; Longkumer, T.; Raorane, M.L.; Kohli, A. Reference genes for accurate gene expression analyses across different tissues, developmental stages and genotypes in rice for drought tolerance. Rice 2016, 9, 1–8. [Google Scholar] [CrossRef] [Green Version]
  12. Chen, M.D.; Wang, B.; Li, Y.P.; Zeng, M.J.; Liu, J.T.; Ye, X.R.; Zhu, H.S.; Wen, Q.F. Reference gene selection for qRT-PCR analyses of luffa (Luffa cylindrica) plants under abiotic stress conditions. Sci. Rep. 2021, 11, 1–12. [Google Scholar] [CrossRef] [PubMed]
  13. Sudhakar Reddy, P.; Srinivas Reddy, D.; Sivasakthi, K.; Bhatnagar-Mathur, P.; Vadez, V.; Sharma, K.K. Evaluation of sorghum [Sorghum bicolor (L.)] reference genes in various tissues and under abiotic stress conditions for quantitative real-time PCR data normalization. Front. Plant Sci. 2016, 7, 529. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  14. Silver, N.; Best, S.; Jiang, J.; Thein, S.L. Selection of housekeeping genes for gene expression studies in human reticulocytes using real-time PCR. BMC Mol. Biol. 2006, 7, 1–9. [Google Scholar] [CrossRef]
  15. Vandesompele, J.; De Preter, K.; Pattyn, F.; Poppe, B.; Van Roy, N.; De Paepe, A.; Speleman, F. Accurate normalization of real-time quantitative RT-PCR data by geometric averaging of multiple internal control genes. Genome Biol. 2002, 3, 1–12. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  16. Andersen, C.L.; Jensen, J.L.; Ørntoft, T.F. Normalization of real-time quantitative reverse transcription-PCR data: A model-based variance estimation approach to identify genes suited for normalization, applied to bladder and colon cancer data sets. Cancer Res. 2004, 64, 5245–5250. [Google Scholar] [CrossRef] [Green Version]
  17. Pfaffl, M.W.; Tichopad, A.; Prgomet, C.; Neuvians, T.P. Determination of stable housekeeping genes, differentially regulated target genes and sample integrity: BestKeeper–Excel-based tool using pair-wise correlations. Biotechnol. Lett. 2004, 26, 509–515. [Google Scholar] [CrossRef]
  18. Xie, F.; Xiao, P.; Chen, D.; Xu, L.; Zhang, B. miRDeepFinder: A miRNA analysis tool for deep sequencing of plant small RNAs. Plant Mol. Biol. 2012, 80, 75–84. [Google Scholar] [CrossRef]
  19. Li, Q.F.; Sun, S.S.; Yuan, D.Y.; Yu, H.X.; Gu, M.H.; Liu, Q.Q. Validation of candidate reference genes for the accurate normalization of real-time quantitative RT-PCR data in rice during seed development. Plant Mol. Biol. Rep. 2010, 28, 49–57. [Google Scholar] [CrossRef]
  20. Wan, H.; Zhao, Z.; Qian, C.; Sui, Y.; Malik, A.A.; Chen, J. Selection of appropriate reference genes for gene expression studies by quantitative real-time polymerase chain reaction in cucumber. Anal. Biochem. 2010, 399, 257–261. [Google Scholar] [CrossRef]
  21. Wang, H.L.; Chen, J.; Tian, Q.; Wang, S.; Xia, X.; Yin, W. Identification and validation of reference genes for Populus euphratica gene expression analysis during abiotic stresses by quantitative real-time PCR. Physiol. Plant. 2014, 152, 529–545. [Google Scholar] [CrossRef]
  22. Wang, Y.; Zhang, Y.; Liu, Q.; Tong, H.; Zhang, T.; Gu, C.; Liu, L.; Huang, S.; Yuan, H. Selection and validation of appropriate reference genes for RT-qPCR analysis of flowering stages and different genotypes of Iris germanica L. Sci. Rep. 2021, 11, 1–10. [Google Scholar]
  23. Hua, Y.J.; Yue, Y.Z.; Yang, X.L.; He, Q. Selection of Reference Genes for Real-time Quantitative PCR in Clerodendrum trichotomum Thunb. Leaves. For. Res. 2022, 35, 193–201. [Google Scholar]
  24. Liu, M.; Wang, Q.; Xie, H.; Liu, S.; Wang, S.; Zhang, H.; Zhao, Y. UDP and NTF2 are the most consistently expressed genes in Panax ginseng roots at different growth stages. Mol. Med. Rep. 2017, 15, 4382–4390. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  25. Zhou, L.; Shi, Q.; Wang, Y.; Li, K.; Zheng, B.; Miao, K. Evaluation of candidate reference genes for quantitative gene expression studies in tree peony. J. Am. Soc. Hortic. Sci. 2016, 141, 99–111. [Google Scholar] [CrossRef]
  26. Hossain, M.; Ahmed, R.; Haque, M.; Alam, M.; Islam, M. Identification and validation of reference genes for real-time quantitative RT-PCR analysis in jute. BMC Mol. Biol. 2019, 20, 1–13. [Google Scholar] [CrossRef]
  27. You, S.; Cao, K.; Chen, C.; Li, Y.; Wu, J.; Zhu, G.; Fang, W.; Wang, X.; Wang, L. Selection and validation reference genes for qRT-PCR normalization in different cultivars during fruit ripening and softening of peach (Prunus persica). Sci. Rep. 2021, 11, 1–13. [Google Scholar] [CrossRef]
  28. Chen, C.; Wu, J.; Hua, Q.; Tel-Zur, N.; Xie, F.; Zhang, Z.; Chen, J.; Zhang, R.; Hu, G.; Zhao, J. Identification of reliable reference genes for quantitative real-time PCR normalization in pitaya. Plant Methods 2019, 15, 1–12. [Google Scholar] [CrossRef] [Green Version]
  29. Pinheiro, T.; Nishimura, D.; De Nadai, F.; Figueira, A.; Latado, R. Selection of reference genes for expression analyses of red-fleshed sweet orange (Citrus sinensis). Genet. Mol. Res. 2015, 14, 18440–18451. [Google Scholar] [CrossRef]
  30. Xu, M.; Zhang, B.; Su, X.; Zhang, S.; Huang, M. Reference gene selection for quantitative real-time polymerase chain reaction in Populus. Anal. Biochem. 2011, 408, 337–339. [Google Scholar] [CrossRef]
  31. Deng, Y.; Li, Y.; Sun, H. Selection of reference genes for RT-qPCR normalization in blueberry (Vaccinium corymbosum× angustifolium) under various abiotic stresses. FEBS Open Bio 2020, 10, 1418–1435. [Google Scholar] [CrossRef]
  32. Zhu, L.; Yang, C.; You, Y.; Liang, W.; Wang, N.; Fengwang, M.; Li, C. Validation of reference genes for qRT-PCR analysis in peel and flesh of six apple cultivars (Malus domestica) at diverse stages of fruit development. Sci. Hortic. 2019, 244, 165–171. [Google Scholar] [CrossRef]
  33. Czechowski, T.; Stitt, M.; Altmann, T.; Udvardi, M.K.; Scheible, W.-R. Genome-wide identification and testing of superior reference genes for transcript normalization in Arabidopsis. Plant Physiol. 2005, 139, 5–17. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  34. Tashiro, R.M.; Philips, J.G.; Winefield, C.S. Identification of suitable grapevine reference genes for qRT-PCR derived from heterologous species. Mol. Genet. Genom. 2016, 291, 483–492. [Google Scholar] [CrossRef] [PubMed]
  35. Sang, J.; Han, X.; Liu, M.; Qiao, G.; Jiang, J.; Zhuo, R. Selection and validation of reference genes for real-time quantitative PCR in hyperaccumulating ecotype of Sedum alfredii under different heavy metals stresses. PLoS ONE 2013, 8, e82927. [Google Scholar] [CrossRef] [PubMed]
  36. Wang, J.J.; Han, S.; Yin, W.; Xia, X.; Liu, C. Comparison of reliable reference genes following different hormone treatments by various algorithms for qRT-PCR analysis of Metasequoia. Int. J. Mol. Sci. 2018, 20, 34. [Google Scholar] [CrossRef]
  37. Fei, X.; Shi, Q.; Yang, T.; Fei, Z.; Wei, A. Expression stabilities of ten candidate reference genes for RT-qPCR in Zanthoxylum bungeanum Maxim. Molecules 2018, 23, 802. [Google Scholar]
  38. Shen, Z.; Sun, J.; Yao, J.; Wang, S.; Ding, M.; Zhang, H.; Qian, Z.; Zhao, N.; Sa, G.; Zhao, R. High rates of virus-induced gene silencing by tobacco rattle virus in Populus. Tree Physiol. 2015, 35, 1016–1029. [Google Scholar] [CrossRef] [Green Version]
  39. Hochstrasser, M. Evolution and function of ubiquitin-like protein-conjugation systems. Nat. Cell Biol. 2000, 2, E153–E157. [Google Scholar] [CrossRef]
  40. Wu, Y.; Tian, Q.; Huang, W.; Liu, J.; Xia, X.; Yang, X.; Mou, H. Identification and evaluation of reference genes for quantitative real-time PCR analysis in Passiflora edulis under stem rot condition. Mol. Biol. Rep. 2020, 47, 2951–2962. [Google Scholar]
  41. Iskandar, H.M.; Simpson, R.S.; Casu, R.E.; Bonnett, G.D.; Maclean, D.J.; Manners, J.M. Comparison of reference genes for quantitative real-time polymerase chain reaction analysis of gene expression in sugarcane. Plant Mol. Biol. Rep. 2004, 22, 325–337. [Google Scholar] [CrossRef]
  42. Ye, Y.; Lu, Y.; Wang, G.; Liu, Y.; Zhang, Y.; Tang, H. Stable reference gene selection for qRT-PCR normalization in strawberry (Fragaria × ananassa) leaves under different stress and light-quality conditions. Horticulturae 2021, 7, 452. [Google Scholar] [CrossRef]
  43. Niu, K.; Shi, Y.; Ma, H. Selection of candidate reference genes for gene expression analysis in Kentucky bluegrass (Poa pratensis L.) under abiotic stress. Front. Plant Sci. 2017, 8, 193. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  44. Zhou, L.; Niu, J.; Quan, S. Identification of appropriate reference genes for RT-qPCR analysis in Juglans regia L. PLoS ONE 2018, 13, e0209424. [Google Scholar] [CrossRef]
  45. De Jonge, H.J.; Fehrmann, R.S.; De Bont, E.S.; Hofstra, R.M.; Gerbens, F.; Kamps, W.A.; De Vries, E.G.; Van Der Zee, A.G.; Te Meerman, G.J.; Ter Elst, A. Evidence based selection of housekeeping genes. PLoS ONE 2007, 2, e898. [Google Scholar] [CrossRef] [PubMed]
Genes 13 01887 g001 550
Figure 1. Distribution of Ct values among the 12 candidate reference genes.
Figure 1. Distribution of Ct values among the 12 candidate reference genes.
Genes 13 01887 g001
Genes 13 01887 g002 550
Figure 2. ΔCt stability analysis of 12 candidate internal reference genes.
Figure 2. ΔCt stability analysis of 12 candidate internal reference genes.
Genes 13 01887 g002
Genes 13 01887 g003 550
Figure 3. Expression stability of twelve reference genes under different conditions based on a geNorm analysis.
Figure 3. Expression stability of twelve reference genes under different conditions based on a geNorm analysis.
Genes 13 01887 g003
Genes 13 01887 g004 550
Figure 4. Pairwise variations value under different conditions based on a geNorm analysis.
Figure 4. Pairwise variations value under different conditions based on a geNorm analysis.
Genes 13 01887 g004
Genes 13 01887 g005 550
Figure 5. Comprehensive ranking of stability.
Figure 5. Comprehensive ranking of stability.
Genes 13 01887 g005
Table
Table 1. Amplification efficiency and correlation coefficient of candidate reference genes.
Table 1. Amplification efficiency and correlation coefficient of candidate reference genes.
Reference GenesSlope (K)R2Amplification Efficiency (E)
UBQ−2.86690.9987123.13%
AP−2−2.93750.9875118.99%
Gllα−2.95700.9943117.86%
UBC4−2.98760.9942116.13%
elF−5α−3.10230.9938110.06%
TUB−3.12140.9950109.11%
GAPDH−3.12130.9996109.11%
RPL17−3.12700.9985108.83%
UDP−3.18450.9833106.07%
EF1α−3.18630.9998105.99%
Actin−3.48850.995393.49%
UBC20−3.52010.993092.35%
Table
Table 2. Stability analysis of candidate reference genes based on NormFinder algorithm.
Table 2. Stability analysis of candidate reference genes based on NormFinder algorithm.
RankAll SamplesColdDroughtSaltTissue
GeneSVGeneSVGeneSVGeneSVGeneSV
1AP-20.087UDP0.171AP-20.069AP-20.218UBQ0.066
2UDP0.251AP-20.183Actin0.086UDP0.228EF1α0.066
3UBC40.305eIF-5α0.221UBC40.176eIF-5α0.273UBC40.067
4Actin0.330EF1α0.309eIF-5α0.233UBC40.392Actin0.182
5UBQ0.368UBC40.323UDP0.292Actin0.421AP-20.237
6eIF-5α0.421GIIα0.398UBQ0.297UBQ0.513UDP0.271
7GIIα0.477UBQ0.399GIIα0.462GIIα0.522eIF-5α0.307
8EF1α0.551Actin0.466EF1α0.518EF1α0.612GIIα0.635
9RPL170.762GAPDH0.671UBC200.579RPL170.727RPL170.668
10UBC201.223RPL170.678GAPDH0.694UBC200.837UBC200.953
11GAPDH1.246UBC201.254RPL170.755GAPDH1.175GAPDH1.176
12TUB2.448TUB1.641TUB2.861TUB2.741TUB1.760
Table
Table 3. Stability analysis of candidate reference genes based on BestKeeper algorithm.
Table 3. Stability analysis of candidate reference genes based on BestKeeper algorithm.
RankAll SamplesColdDroughtSaltTissue
GeneSDCVGeneSDCVGeneSDCVGeneSDCVGeneSDCV
1UBQ0.311.20UBQ0.140.53UBQ0.160.65UBQ0.210.80RPL170.401.64
2EF1α0.451.70EF1α0.311.14UBC40.260.98eIF-5α0.271.17GIIα0.672.54
3UBC40.481.78eIF-5α0.421.94AP-20.341.19RPL170.331.38eIF-5α0.683.06
4Actin0.502.19UDP0.491.87eIF-5α0.341.56Actin0.351.54EF1α0.722.66
5AP-20.511.79UBC40.491.80Actin0.341.51AP-20.371.30GAPDH0.732.88
6GIIα0.531.99AP-20.511.80EF1α0.361.36GIIα0.401.46UBQ0.783.01
7RPL170.542.26Actin0.542.39GIIα0.391.43UBC40.421.54UBC40.782.90
8eIF-5α0.582.62GIIα0.582.18GAPDH0.441.72EF1α0.471.77Actin0.944.14
9UDP0.602.28RPL170.763.19UDP0.471.76UDP0.672.51AP-20.953.30
10GAPDH1.023.86GAPDH0.802.93RPL170.502.07GAPDH0.943.47UDP1.043.89
11UBC201.184.10TUB1.033.77UBC200.652.19UBC200.973.33UBC201.114.03
12TUB1.976.90UBC201.284.55TUB2.338.05TUB2.257.87TUB1.956.48
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Yao, J.; Zhu, G.; Liang, D.; He, B.; Wang, Y.; Cai, Y.; Zhang, Q. Reference Gene Selection for qPCR Analysis in Schima superba under Abiotic Stress. Genes 2022, 13, 1887. https://doi.org/10.3390/genes13101887

AMA Style

Yao J, Zhu G, Liang D, He B, Wang Y, Cai Y, Zhang Q. Reference Gene Selection for qPCR Analysis in Schima superba under Abiotic Stress. Genes. 2022; 13(10):1887. https://doi.org/10.3390/genes13101887

Chicago/Turabian Style

Yao, Jun, Gang Zhu, Dongcheng Liang, Boxiang He, Yingli Wang, Yanling Cai, and Qian Zhang. 2022. "Reference Gene Selection for qPCR Analysis in Schima superba under Abiotic Stress" Genes 13, no. 10: 1887. https://doi.org/10.3390/genes13101887

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop