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Article

Selection and Validation of Reference Genes for qRT-PCR Analysis of Gene Expression in Tropaeolum majus (Nasturtium)

by
Qing Tang
1,†,
Guang-Can Zhou
2,†,
Si-Jie Liu
1,
Wen Li
3,4,
Yi-Lei Wang
2,
Gao-Ying Xu
2,
Teng-Fei Li
2,
Guo-Qing Meng
2,* and
Jia-Yu Xue
1,*
1
College of Horticulture, Academy for Advanced Interdisciplinary Studies, Nanjing Agricultural University, Nanjing 210095, China
2
College of Agricultural and Biological Engineering, Heze University, Heze 274015, China
3
School of Life Sciences, Jiangsu Normal University, Xuzhou 221131, China
4
Jiangsu Xuzhou Sweetpotato Research Center, Chinese Agricultural Academy of Sciences, Xuzhou 221131, China
*
Authors to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Horticulturae 2023, 9(11), 1176; https://doi.org/10.3390/horticulturae9111176
Submission received: 7 September 2023 / Revised: 18 October 2023 / Accepted: 23 October 2023 / Published: 27 October 2023
(This article belongs to the Section Genetics, Genomics, Breeding, and Biotechnology (G2B2))
Browse Figures
Versions Notes

Abstract

:
Tropaeolum majus (nasturtium) is an important ornamental and medicinal plant due to its colorful flowers, shield-shaped leaves, and richness in mineral elements and bioactive compounds. However, the key genes related to these important biological traits, as well as their expression patterns and functions, remain obscure. In this study, to choose appropriate reference genes for quantitative real-time reverse transcription polymerase chain reaction (qRT-PCR) analysis, we screened 14 candidate genes from the transcriptome of T. majus and evaluated their expression stability. Through evaluation with four commonly used algorithms (geNorm, NormFinder, BestKeeper, and RefFinder), EXP1, EXP2, and TUB6 were found to be the most stably expressed genes among different organs, while EXP1 combined with CYP2 was identified as the optimal reference gene combination for seeds at different development stages. For all the tested samples, EXP1, EXP2, CYP2, and ACT2 were the most suitable reference genes. Moreover, the target gene KCS11 involved in very-long-chain fatty acid biosynthesis was employed to confirm the most and least stable reference genes in different organs, seeds at different development stages, and all the tested samples. The expression profiles of KCS11 were similar, with minor differences based on the analysis of different stable reference genes (either alone or in combination), while the expression profiles were diverse and the relative expression level was overestimated when using the least stable ones. These results suggest that the appropriate selection of reference genes is critical for the normalization of gene expression. Furthermore, the reference genes screened in this study will greatly improve the accuracy of the qRT-PCR quantification of candidate genes involved in the many biological characteristics of nasturtium.

1. Introduction

The quantitative real-time reverse transcription polymerase chain reaction (qRT-PCR) technique has been widely used for transcript quantification due to its high accuracy, sensitivity, specificity, low cost, and high-throughput characteristics [1,2,3,4,5,6,7]. Gene expression can be significantly influenced by a series of steps in an experimental procedure, such as the initial material amount, the quality of mRNA templates, reverse-transcription efficiency, and cDNA quality [1,8,9,10]. For accurate qRT-PCR results, it is essential to select one or more stably expressed genes as internal reference genes for the evaluation of gene expression levels [10,11,12]. Housekeeping genes, such as GAPDH, ACT, TUB, 18S rRNA, EF-1α, UBC, and PP2AA3, are regarded as stably expressed genes in all tissues at various developmental stages influencing basic cellular components and maintaining basal cellular functions and have been widely used to normalize the qRT-PCR data of plants and animals [6,13,14,15,16,17].
However, numerous studies have shown that the transcript levels of these housekeeping genes vary considerably in response to variable experimental conditions, different tissue types, and/or across taxa [6,7,14,18,19,20,21]; thus, none of the commonly developed genes reported so far can act as a universal reference. Hence, it is necessary to select a set of appropriate reference genes for the specific set of chosen experimental conditions and tissue types [13,22,23,24,25]. The use of proper reference genes for normalization in the relative quantification of gene expression can allow one to obtain valid results and correct conclusions from qRT-PCR analysis [1,19,26].
Tropaeolum majus (nasturtium) is an annual garden plant in the genus Tropaeolum and the family Tropaeolaceae native to South and Central America with shield-shaped leaves and pungent, edible yellow, orange, or red spurred flowers (www.iplant.cn/foc/ accessed on 10 February 2023). It is also cultivated as a medicinal plant due to its richness in mineral elements and bioactive compounds [27]. Additionally, it is a source of elongase involved in the synthesis of very-long-chain fatty acids, and this plant specifically contains significant amounts of erucic acid (70–75% of its total fatty acid content) in its seeds [28]. Mietkiewska et al. found a fatty acid elongase gene (nasturtium KCS11) in nasturtium embryos using the degenerate primer PCR approach, and seed-specific expression resulted in an up to eight-fold increase in erucic acid proportions in transgenic Arabidopsis seed oil [29]. The investigation of gene expression profiling related to the above-described important biological characteristics via qRT-PCR can help to advance our understanding of the molecular mechanisms regulating these characteristics in nasturtium. In a previous study, ACTIN was used as a positive control in a qRT-PCR analysis of the candidate genes involved in adaxial–abaxial differential growth during spur formation in T. longifolium [30]. However, there has not been a systematic selection of reference genes for qRT-PCR analysis in nasturtium so far.
Benefitting from high-throughput sequencing technology, growing plant transcriptome datasets have provided potential resources for the identification of appropriate reference genes [6,7,31,32,33]. In this study, 14 candidate reference genes (TUA4, TUB6, ROC3, CYP2, GAPC2, YLS8, PTB1, TIP41, EXP1, EXP2, ACT2, PP2AA3, EF1-α, and UBC9) were assembled from the nasturtium seed transcriptome database (Table 1). qRT-PCR analysis was used to assess the expression profiles of these candidate genes in different organs and seeds across different nasturtium developmental stages. Four statistical algorithms, namely, NormFinder, geNorm, BestKeeper, and RefFinder, were utilized to evaluate the stability of these putative reference genes. Moreover, the nasturtium fatty acid elongase gene KCS11 was used to validate the stability of the exploited reference genes. The aims of this study were to evaluate the expression stability of 14 candidate reference genes and select the most stable reference genes expressed in different organs (roots, stem, leaves, and flowers) and seeds in different developmental stages in nasturtium. Additionally, we assessed the expression patterns of nasturtium KCS11 in different organs and seed developmental stages of nasturtium. The results provide a reference for the further expression profiling of functional genes and facilitate studies on the mechanisms regulating erucic acid synthesis in nasturtium.

2. Materials and Methods

2.1. Plant Material, Candidate Reference Gene Isolation, and Primer Design

The plant materials were grown in a greenhouse under natural light conditions at the Institute of Botany, Jiangsu Province and Chinese Academy of Sciences (32.06° N, 118.84° E). Four organ samples—taken from the roots, stem, leaves, and flowers (just after blooming)—and seeds at different developmental stages were collected from the same plant. Flowers were hand-pollinated, and the seeds were classified into four developmental stages: 7, 14, 21, and 28 days after pollination (dap). All samples were collected from three individuals in each organ or seed developmental stage, frozen using liquid nitrogen, and then stored at −80 °C prior to use. Homologs for the ACT2, GAPC2, YLS8, ROC3, CYP2, TIP41, PTB1, EXP1, EXP2, PP2AA3, TUA4, TUB6, EF1-α, and UBC9 genes from nasturtium were isolated from a reference transcriptome based on mixed nasturtium seeds at different developmental stages. RNA-seq experiments were conducted using a TruSeq mRNA library construction kit (Illumina, San Diego, CA, USA) and sequenced using the Illumina NextSeq 500 platform, and 150 bp paired-end reads were read using a product developed by the sequencing company Personalbio (Shanghai, China). The transcriptome was assembled de novo using Trinity (r20140717, k-mer 25 bp) [34]. The search for homolog genes was performed using BLASTN on the assembled transcriptome, using ACT2, GAPC2, YLS8, ROC3, CYP2, TIP41, PTB1, EXP1, EXP2, PP2AA3, TUA4, TUB6, EF1-α, and UBC9 gene sequences from Arabidopsis thaliana as queries. Raw sequence data were deposited at the NCBI Sequence Read Archive (SRR25909845). Specific primers were designed based on these candidate reference genes sequences (Table S1) using Primer-BLAST (http://www.ncbi.nlm.nih.gov/tools accessed on 5 September 2021) with the following parameters: primer lengths of 20–25 bp, amplicon lengths of 110–250 bp, GC content of 40–55%, and melting temperature (Tm) in a range of 55–60 °C.

2.2. Total RNA Extraction, cDNA Synthesis, and Quantitative Real-Time PCR Analysis

Total RNA was isolated from roots, stems, leaves, flowers (just after blooming), and seeds at different developmental stages using an RNAprep Pure Plant Kit (Tiangen Biotech, Beijing, China) and quantified using a Nanodrop 2000 spectrophotometer (Thermo Scientific, Waltham, MA, USA). Isolated RNA was treated with DNase I (Ambion, Foster City, CA, USA). Only the RNA samples with absorption ratios of A260/230 higher than 1.8 and A260/280 = 1.8–2.2 were used for cDNA synthesis. A total of 1 μg per sample was used as a template for cDNA synthesis using the PrimeScriptTM RT Reagent Kit with Oligo dT primers (TaKaRa Bio Inc., Dalian, China). The cDNA was diluted five-fold with nuclease-free water and stored at −20 °C for qRT-PCR analysis.
qRT-PCR was performed in a 96-well plate using a QuantStudioTM 3 Flex System (Thermo Fisher Scientific, Waltham, MA, USA). The qRT-PCR reaction mix amounted to 20 µL in total and contained 10 μL of TB Green Premix (TaKaRa Bio Inc., Dalian, China), 2 µL of diluted cDNA, 0.4 μL of upstream and downstream primers (10 µmol/L), and ddH2O, adding up to its final volume. The qRT-PCR reaction procedures were as follows: 95 °C for 5 min and 40 cycles of 95 °C for 15 s and 60 °C for 30 s.
Melting curve analysis and PCR products run on a 1.5% agarose gel were used to verify the specificity of each primer pair. The cycle threshold (Ct) was measured automatically and used to define the expression level of each reference gene. The correlation coefficients (R2) and slope were calculated from a standard curve based on a five-fold serial dilution of all sample cDNA templates, and the equation (% Efficiency = (10[−1/slope] − 1) × 100%) was used to calculate the mean amplification efficiency (E) for each primer pair [35,36].

2.3. Statistical Analysis of Candidate Reference Genes

GeNorm [18], NormFinder [37], BestKeeper [38], and RefFinder [39] were used to statistically assess the stability of the 14 selected reference genes across all experimental subsets. The qRT-PCR data of all tested samples were collated and summarized using Excel 2010 software. The stability of each reference gene was analyzed in strict adherence to the algorithm principles stipulated by the software programs. As recommended by GeNorm, a reference gene with an M value below 1.5 was considered stably expressed, and a lower M value represented more stable expression [18]. NormFinder was used to calculate the stability value (SV) of reference genes in evaluating expression variation, and a lower SV indicates higher stability [37]. For BestKeeper, the Ct values of these 14 candidate reference genes were used to calculate the coefficient of variance (CV) and the standard deviation (SD). The lowest CV ± SD value represents the most stable gene, and genes with an SD of less than 1 were considered acceptable [38,40,41]. Finally, a comprehensive analysis integrating M values from GeNorm, SV from NormFinder, CV and SD from BestKeeper, and delta Ct values was performed in RefFinder to generate a comprehensive, stable ranking of the candidate reference genes.

2.4. Validation of Selected Reference Genes

To confirm the utility of the candidate reference genes in the final standardization results, the relative expression levels of the nasturtium KCS11 gene in different organs and in seeds at different developmental stages were analyzed using the most stable and unstable reference genes or a combination of stable reference genes after normalization via RefFinder across all experimental sets. The KCS11 sequences were obtained from the transcriptome data (Table S1). The qRT-PCR amplification conditions of the target gene were the same as those described above. The 2−ΔΔCT method was used to calculate the fold change in relative gene expression [42].

3. Results

3.1. Selection of Candidate Reference Genes and PCR Amplification

Fourteen genes (ACT2, GAPC2, YLS8, ROC3, CYP2, TIP41, PTB1, EXP1, EXP2, PP2AA3, TUA4, TUB6, EF1-α, and UBC9) were selected as candidate reference genes. All of these genes were retrieved from the transcriptomic data of nasturtium via BLASTX using Arabidopsis orthologous as queries. The primer sequences and PCR amplification characteristics of the 14 candidate reference genes are described in Table 1. Agarose gel electrophoresis and melting curve analysis were used to evaluate the primer specificities, and the results showed that all the target reference genes had single-peak melting curves and a single amplicon with the expected size (Figure 1). The amplification product lengths ranged from 134 bp for TUA4 to 241 bp for TIP41. For each primer pair, the amplification efficiencies ranged from 95.79% for TUA4 to 119.79% for UBC9, while the correlation coefficients (R2) ranged from 0.9908 for YLS8 to 1.0000 for EF1-α (Table 1).

3.2. Expression Profiles of Candidate Reference Genes

The expression profiles of these 14 reference genes in the roots, stems, leaves, and flowers and in four seed developmental stages were examined using their mean cycle threshold (Ct) values. The mean Ct values of the 14 reference genes considering all the samples varied from 17 to 23 (Figure 2). Among these reference genes, EF1-α exhibited the highest expression levels, with the lowest average Ct value, namely, 17.37 ± 1.21 (mean ± SD), followed by ROC3 (18.02 ± 0.75), GAPC2 (18.68 ± 0.76), ACT2 (18.92 ± 1.02), UBC9 (18.97 ± 1.03), TUA4 (20.06 ± 2.84), TUB6 (20.1 ± 1.87), and YLS8 (20.48 ± 1.3). EXP1 had the lowest expression level of all the tested genes, with mean Ct values of 23.08 ± 0.81, followed by TIP41 (22.9 ± 2.05), PTB1 (22.8 ± 1.75), PP2AA3 (22.62 ± 1.25), CYP2 (22.25 ± 0.8), and EXP2 (21.67 ± 0.78) (Figure 2). Moreover, the standard deviations (SD) of the Ct values ranged from 0.75 to 2.84. The reference genes with a lower SD of Ct values indicate higher stability. ROC3 (18.02 ± 0.75) showed the smallest variation in gene expression, while TUA4 (20.06 ± 2.84) showed the most variable expression levels (Figure 2).

3.3. Expression Stability of Candidate Reference Genes

To obtain the optimal reference genes for qRT-PCR analysis in the investigation of different organs and seeds in different development stages, we divided these tested samples into three groups: different organs, seeds at different developmental stages, and all samples. Four statistical algorithms (geNorm, NormFinder, BestKeeper, and RefFinder) were used to further assess the stability of the 14 reference genes. As shown in Figure 3, all these tested candidate reference genes were considered stable genes on the basis of their M values being less than 1.5 when assessed using geNorm. CYP2 and EXP2 were recommended as the most stable reference genes in the seeds at different developmental stages and all samples, while EXP1 and EXP2 were identified as the most stable reference genes in different organs. In contrast, TUA4 in the seeds at different developmental stages and all samples, and PTB1 in different organs with the highest M values, were considered to be the most unstable reference genes (Figure 3). Additionally, the pairwise variation (Vn/Vn+1) between the normalization factors was calculated using the geNorm program, and the optimal number of reference genes for accurate normalization was determined (Figure 4). A ratio of V2/3 in the seeds at different development stages less than 0.15 suggests that at least two reference genes should be used in the qRT-PCR analysis. For the different organs and all samples, V3/4 and V4/5 were 0.119 and 0.126, indicating that three and four internal reference genes, respectively, were required to obtain accurate results (Figure 4). The stability values (SVs) of the candidate reference genes were determined using Normfinder (Table 2). A lower SV indicates higher stability. For all three sample groups, EXP1 was deemed the most stable reference gene, followed by EXP2, ROC3, and ACT2 in different organs, seeds at different developmental stages, and all samples, respectively. Similar to geNorm, TUA4 in seeds at different developmental stages and all samples and PTB1 in different organs were the least stable genes.
According to the criteria of the BestKeeper program, TUB6 (2.39 ± 0.44) was identified as the most stable gene for normalization in different organs (Table 3). In the seeds of the different development stages group, UBC9 (2.23 ± 0.42) had the lowest CV ± SD values, showing remarkably stable expression. In all samples, EXP2 (2.94 ± 0.64) was considered the most stable reference gene, followed by CYP2 (3.06 ± 0.68) and EXP1 (3.11 ± 0.72), while TUA4 (11.72 ± 2.35) was the most unstable reference gene (Table 3).
Due to the different principles of the geNorm, NormFinder, and BestKeeper algorithms, the results for the most stable reference genes were not completely consistent. Therefore, a comprehensive evaluation of gene expression stability for each candidate gene was performed using RefFinder to calculate the geometric mean of the three algorithms’ corresponding rankings (Table 4). This comprehensive analysis indicated that EXP1 was the most stable gene in different organs, in seeds at different developmental stages, and in all the tested samples. Conversely, the traditional housekeeping gene TUA4 was unstably expressed in different organs and in seeds at different developmental stages in nasturtium.

3.4. Validation of the Selected Reference Genes

To examine the reliability of the reference genes for normalization, the relative expression patterns of nasturtium KCS11, which is related to erucic acid biosynthesis [29], were evaluated in different organs, in seeds at different developmental stages, and in all the samples. The most stable reference genes (EXP1, EXP2, TUB6, and EXP1/EXP2/TUB6 for different organs; EXP1, CYP2, and EXP1/CYP2 for seeds at different development stages; and EXP1, EXP2, CYP2, ACT2, and EXP1/EXP2/ CYP2/ACT2 for all samples) were selected in accordance with the pairwise variation (Vn/Vn+1) using geNorm to normalize KCS11 gene expression in the three group samples. In addition, the least stable reference genes (PTB1 for different organs, TIP41 for seeds at different development stages, and TUA4 for all samples) were used to normalize the target gene expression profiles for a comparative analysis.
KCS11 was expressed in the roots, stems, leaves, flowers, and seeds (7 dap) but at different levels (Figure 5A). According to the analysis of different stable reference genes, either alone (EXP1, EXP2, or TUB6) or in combination (EXP1/EXP2/TUB6), the expression patterns of KCS11 were similar, with minor differences, and the results showed that KCS11 was relatively highly expressed in leaves and flowers and weakly expressed in stems and seeds (7 dap). However, the expression patterns of KCS11 were observed to be diverse in different organs, especially in stems, leaves, and flowers, when using the least stable gene, PTB1, for normalization. Although the expression level of KCS11 was higher in the leaves and flowers, its relative expression was overestimated (Figure 5A). When the different stable reference gene(s), either alone (EXP1 or CYP2) or in combination (EXP1/CYP2), were used for normalization in seeds at different development stages, the KCS11 expression patterns were similar, with minor differences (Figure 5B). KCS11 transcripts were significantly increased from 7 to 14 dap and then successively decreased at 21 and 28 dap. When the least stable gene, TIP41, was used for normalization, the expression patterns and transcript levels were very different. The expression levels increased, peaked at 21 dap, and then decreased. Based on the suggestions of RefFinder and the geNorm pairwise variation analysis, a single (EXP1, EXP2, CYP2, or ACT2) reference gene and a combination (EXP1/EXP2/CYP2/ACT2) of four reference genes were selected to normalize KCS11 gene expression in all samples. For either a single reference gene or a combination of stable reference genes, the expression patterns of KCS11 were similar in all the tested samples (Figure 5C). The expression levels were remarkably lower in roots, stems, leaves, flowers, and seeds (7 dap), while they increased, peaked at 14 dap, and then decreased at 21 and 28 dap. However, when TUA4 was regarded as the reference gene, the KCS11 expression patterns were diverse, and the relative expression level was overestimated (Figure 5C). Overall, our results suggest that these genes’ expression patterns are closely correlated with the stability of the reference genes.

4. Discussion

The qRT-PCR technique is widely used in gene expression analysis due to its rapid and high-throughput properties [26]. To obtain reliable quantification results in qRT-PCR analysis, it is essential to select and validate suitable reference genes for data normalization. Ideally, reference genes should be expressed at a constant level regardless of the various organs, tissue types, or developmental stages. In this study, 14 candidate reference genes were selected based on the transcriptome data of mixed individual developing nasturtium seeds, and their expression stability was measured in different nasturtium organs and in nasturtium seeds at different development stages. The PPI network made in the STRING database v11.5 with high confidence (0.700) using housekeeping genes (gene names following Arabidopsis gene ID names) was analyzed in the present work. The analysis revealed that in 14 species, some of the genes were used as a reference (https://version-11-5.string-db.org/cgi/network?networkId=bFxkb046DcDI, accessed on 10 October 2023). Due to the different principles of evaluating gene stability of the geNorm, NormFinder, and BestKeeper algorithms, the stability orders of 14 candidate reference genes were not completely identical (Figure 3, Table 2 and Table 3). For example, in different organs, TUB6 was selected as the most stable reference gene by BestKeeper, while it was ranked third and fifth in NormFinder and geNorm, respectively. In all the tested samples, EXP2 was ranked first in geNorm and BestKeeper, while it was ranked fourth in NormFinder. A similar phenomenon has been reported for many other plants, such as L. aurea, H. salicornia, C. capsularis, and two Chrysanthemum species [6,41,43,44]. For determining the stable reference genes for nasturtium, RefFinder, a widely used comprehensive tool for screening appropriate reference genes in plants [6,7,44,45,46,47,48,49,50,51], was employed to determine the final overall ranking of candidate reference genes according to the geometric mean of the weights of each gene calculated using the different algorithms [39]. Based on the RefFinder analysis, EXP1 was marked as the most stable reference gene for normalization in different nasturtium organs and seeds at different development stages (Table 4). Moreover, we noticed that ACT2 displayed relative expression stability and ranked fourth in the RefFinder analysis (Table 4), and it was used as a positive control gene in Tropaeolum longifolium [30]. Thus, a better accuracy for each candidate gene was acquired by integrating the results of the three algorithms used in this study, and it is necessary to utilize more than two algorithms for evaluating the stability of reference genes.
Increasing evidence has shown that the expression levels of classic housekeeping genes vary greatly among species, organs, tissues, specific conditions, and developmental stages [6,7,13,45,52,53,54,55,56,57,58,59,60]. In this study, the expression stability of commonly used housekeeping genes, such as ACT2, GAPC2, UBC9, TUA4, EF-1α, and TUB6, displayed remarkably differential expression patterns in nasturtium organs and seeds at different developmental stages. Of these selected housekeeping genes, ACT2 ranked fourth among all tested samples. GAPC2 and UBC9 were the most stable reference genes for flower development in Paeonia suffruticosa and Jatropha curcas [52], while they ranked in medium positions in the present study. Moreover, UBC9 exhibited strong stability in L. aurea, Corchorus olitorius, and Platycladus orientalis [6,40,43]. EF1-α showed a less stable expression pattern, with a lower ranking order in RefFinder. EF1-α was also identified as unstable across the tissues and organs of tomato at various developmental stages [61], but it was highly stable in rice tissues [62]. TUA4 was identified as the most unstable in all the tested nasturtium samples. The lower stability of TUA4 in nasturtium was consistent with the results regarding L. aurea and Taihangia rupestris flowers [6,32]. Additionally, the qRT-PCR validation results showed that the KCS11 expression profiles were strongly biased when the least stable gene TUA4 was used as a reference for all the tested samples (Figure 4). These results indicate that it is necessary to select suitable reference genes to normalize the transcription levels of target genes under a given experimental condition.
Recently, several new reference genes have been developed in qRT-PCR analysis for non-model plants in addition to the classic ones [45,54,63,64,65,66]. In this study, eight newly developed reference genes, ROC3, CYP2, EXP1, YLS8, EXP2, PP2AA3, PTB1, and TIP41, were tested. As shown in Table 4, EXP1 ranked ahead of EXP2 and was judged to be the most stable gene across all the sample groups. High expression stability of EXP1 has been reported in the developing stages of anthers and pollen in rice [67] and in most experimental subsets, including tissue samples and all samples in L. aurea [6]. CYPs (Cyclophilins) belong to a large family of proteins with peptidyl-prolyl cis-trans isomerase activity relevant to a variety of biological processes and are widely expressed in bacteria, fungi, animals, and plants [68]. CYP is the most stable reference gene for different tissues in Isatis indigotica [69]. In L. aurea, ROC3 and CYP2 have also been selected as the most stable reference genes for methyl jasmonate treatments and cold stress, respectively [6]. In this study, ROC3 and CYP2 were more stable in the seeds at different developmental stages than in different organs and all the tested samples. PP2AA3 and YLS8 have also been reported to be stable reference genes in Setaria viridis, Corchorus olitorius, Oenanthe javanica, peanut, Humulus lupulus, and Arabidopsis [36,70,71,72,73,74] while also being less stably expressed in L. aurea [6] and our tested samples. The stability results of PTB1 and TIP41 were similar to those of PP2AA3 and YLS8, although they displayed a relatively stable expression profile in some plants and experimental conditions [6,41,75,76,77,78,79,80]. They were the least stable reference genes in the different organs and seeds of nasturtium at different developmental stages. However, TIP41 was identified as the second most stable gene in organs only via geNorm software (Figure 3; Table 2, Table 3 and Table 4). This phenomenon was similar to that regarding PP2AA3 in some L. aurea experimental conditions [6] and could be attributed to the fact that TIP41 plays a role in the regulation of the TOR signaling pathway and indirectly activates PP2A phosphatase via interaction with its suppressor TAP46 [54].
It is widely acknowledged that more accurate expression quantification results can be obtained by using multiple reference genes as opposed to a single one in qRT-PCR analysis [61,81]. Based on the pairwise variation analysis and validation of target gene expression, EXP1/EXP2/TUB6 for different organs, EXP1 combined with CYP2 for seeds at different development stages, and EXP1/EXP2/CYP2/ACT2 for all samples were chosen as suitable reference genes in the qRT-PCR analysis for nasturtium (Figure 5). The results indicated that the gene expression level of KCS11 was relatively low in roots, stems, leaves, flowers, and seeds (7 dap), while it increased, peaked at 14 dap, and then decreased at 21 and 28 dap (Figure 5). However, PTB1, TIP41, and TUA4 were validated as reference genes for the normalization of KCS11, and their expression patterns and levels were misestimated (Figure 5). Therefore, it is crucial to screen the appropriate reference genes for the normalization of target gene expression via qRT-PCR for nasturtium. In addition, previous studies showed that nasturtium KCS11 is involved in erucic acid biosynthesis and expressed in seeds [28,29], and the expression level of KCS11 increasing and peaking at 14 dap might be in line with erucic acid accumulation. A similar expression pattern of KCS11 in the developing nasturtium seeds was found by analyzing the Jensen’s RNA-Seq data [82], where the expression of KCS11 reached the highest level at 16 DAP (Table S2).

5. Conclusions

Here, we investigated the expression stability of 14 candidate reference genes in different organs and seeds at different developmental stages in nasturtium using four statistical algorithms. Additionally, the expression pattern of the target gene nasturtium KCS11 was determined in different organs and seeds at different development stages to further confirm the reliability of the identified stable genes. EXP1, EXP2, and TUB6 were the most stably expressed across different organs. EXP1 and CYP2 expression were the most stable for the seeds at different development stages, and EXP1, EXP2, CYP2, and ACT2 were the best genes for all the tested samples. The findings presented in this study could facilitate functional genomic studies on many biological characteristics of nasturtium.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/horticulturae9111176/s1, Table S1: Sequences of the 14 candidate reference genes assembled from the nasturtium seed transcriptome; Table S2: Results of nasturtium KCS11 expression level analysis using Jensen’s RNA-Seq data.

Author Contributions

Resources, G.-C.Z. and J.-Y.X.; methodology, Q.T., W.L. and G.-Y.X.; data curation, Y.-L.W.; software, S.-J.L. and T.-F.L.; writing—original draft preparation, Q.T. and S.-J.L.; writing—review and editing, G.-C.Z. and J.-Y.X.; visualization, G.-C.Z.; supervision, G.-Q.M.; project administration, G.-C.Z. and J.-Y.X. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Doctoral Fund (No. XY19BS15) of Heze University (provided to G.C.Z.), and Cooperative Innovation Center of High-efficiency Circular Eco-Agriculture in Southwest of Shandong Province.

Data Availability Statement

Sequences of the 14 candidate reference genes assembled from the nasturtium seed transcriptome. Results of nasturtium KCS11 expression level analysis using Jensen’s RNA-Seq data.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Bustin, S. Quantification of Mrna Using Real-Time Reverse Transcription PCR (RT-PCR): Trends and Problems. J. Mol. Endocrinol. 2002, 29, 23–39. [Google Scholar] [CrossRef]
  2. Aleksandar, R.; Thulke, S.; Mackay, I.M.; Landt, O.; Siegert, W.; Nitsche, A. Guideline to Reference Gene Selection for Quantitative Real-Time PCR. Biochem. Biophys. Res. Commun. 2004, 313, 856–862. [Google Scholar]
  3. Camila, C.; Scheible, W.-R.; Mueller-Roeber, B.; Ruzicic, S. A Quantitative RT-PCR Platform for High-Throughput Expression Profiling of 2500 Rice Transcription Factors. Plant Methods 2007, 3, 7. [Google Scholar]
  4. Vanguilder, H.D.; Vrana, K.E.; Freeman, W.M. Twenty-Five Years of Quantitative PCR for Gene Expression Analysis. Biotechniques 2008, 44, 619–626. [Google Scholar] [CrossRef] [PubMed]
  5. Claire, G.; Mingam, A.; Charrier, B. Real-Time PCR: What Relevance to Plant Studies? J. Exp. Bot. 2004, 55, 1445–1454. [Google Scholar]
  6. Ma, R.; Sheng, X.; Yucheng, Z.; Bing, X.; Ren, W. 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]
  7. Liu, Y.P.; Zhang, Y.; Liu, F.; Liu, T.; Chen, J.Y.; Fu, G.; Zheng, C.Y.; Su, D.D.; Wang, Y.N.; Zhou, H.K.; et al. Establishment of Reference (Housekeeping) Genes via Quantitative Real-Time PCR for Investigation of the Genomic Basis of Abiotic Stress Resistance in Psammochloa villosa (Poaceae). J. Plant Physiol. 2022, 268, 153575. [Google Scholar] [CrossRef]
  8. Huggett, J.K.; Bustin, D.S.; Zumla, A. Real-Time RT-PCR Normalization; Strategies and Considerations. Genes Immun. 2005, 6, 279–284. [Google Scholar] [CrossRef]
  9. 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]
  10. 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]
  11. Bustin, S.A.; Benes, V.; Nolan, T.; Pfaffl, M.W. Quantitative Real-Time RT-PCR—A Perspective. J. Mol. Endocrinol. 2005, 34, 597–601. [Google Scholar] [CrossRef] [PubMed]
  12. Thellin, O.; ElMoualij, B.; Heinen, E.; Zorzi, W. A Decade of Improvements in Quantification of Gene Expression and Internal Standard Selection. Biotechnol. Adv. 2009, 27, 323–333. [Google Scholar] [CrossRef] [PubMed]
  13. Thellin, O.; Zorzi, W.; Lakaye, B.; De Borman, B.; Coumans, B.; Hennen, G.; Grisar, T.; Igout, A.; Heinen, E. Housekeeping Genes as Internal Standards: Use and Limits. J. Biotechnol. 1999, 75, 291–295. [Google Scholar] [CrossRef] [PubMed]
  14. Kozera, B.; Rapacz, M. Reference Genes in Real-Time PCR. J. Appl. Genet. 2013, 54, 391–406. [Google Scholar] [CrossRef]
  15. Monteiro, F.; Sebastiana, M.; Pais, M.S.; Figueiredo, A. Reference Gene Selection and Validation for the Early Responses to Downy Mildew Infection in Susceptible and Resistant Vitis vinifera Cultivars. PLoS ONE 2013, 8, e72998. [Google Scholar] [CrossRef]
  16. Kiarash, J.G.; Wilde, H.D.; Amirmahani, F.; Moemeni, M.M.; Zaboli, M.; Nazari, M.; Moosavi, S.S.; Jamalvandi, M. Selection and Validation of Reference Genes for Normalization of QRT-PCR Gene Expression in Wheat (Triticum durum L.) under Drought and Salt Stresses. J. Genet. 2018, 97, 1433–1444. [Google Scholar] [CrossRef]
  17. Chapman, J.R.; Waldenström, J. With Reference to Reference Genes: A Systematic Review of Endogenous Controls in Gene Expression Studies. PLoS ONE 2015, 10, e0141853. [Google Scholar] [CrossRef]
  18. 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, Research0034. [Google Scholar] [CrossRef]
  19. Bustin, S.A.; Benes, V.; Garson, J.A.; Hellemans, J.; Huggett, J.; Kubista, M.; Mueller, R.; Nolan, T.; Pfaffl, M.W.; Shipley, G.L.; et al. The Miqe Guidelines: Minimum Information for Publication of Quantitative Real-Time PCR Experiments. Clin. Chem. 2009, 55, 611–622. [Google Scholar] [CrossRef]
  20. Hu, R.; Fan, C.; Li, H.; Zhang, Q.; Fu, Y.F. Evaluation of Putative Reference Genes for Gene Expression Normalization in Soybean by Quantitative Real-Time RT-PCR. BMC Mol. Biol. 2009, 10, 93. [Google Scholar] [CrossRef]
  21. Zhang, Y.; Han, X.; Chen, S.; Zheng, L.; He, X.; Liu, M.; Qiao, G.; Wang, Y.; Zhuo, R. Selection of Suitable Reference Genes for Quantitative Real-Time PCR Gene Expression Analysis in Salix matsudana under Different Abiotic Stresses. Sci. Rep. 2017, 7, 40290. [Google Scholar] [CrossRef]
  22. Suzuki, T.; Higgins, P.J.; Crawford, D.R. Control Selection for RNA Quantitation. Biotechniques 2000, 29, 332–337. [Google Scholar] [CrossRef]
  23. Ruan, W.; Lai, M. Actin, a Reliable Marker of Internal Control? Clin. Chim. Acta 2007, 385, 1–5. [Google Scholar] [CrossRef]
  24. Selvey, S.; Thompson, E.W.; Matthaei, K.; Lea, R.A.; Irving, M.G.; Griffiths, L.R. Beta-Actin--an Unsuitable Internal Control for RT-PCR. Mol. Cell. Probes 2001, 15, 307–311. [Google Scholar] [CrossRef] [PubMed]
  25. Thorrez, L.; Van Deun, K.; Tranchevent, L.C.; Van Lommel, L.; Engelen, K.; Marchal, K.; Moreau, Y.; Van Mechelen, I.; Schuit, F. Using Ribosomal Protein Genes as Reference: A Tale of Caution. PLoS ONE 2008, 3, e1854. [Google Scholar] [CrossRef] [PubMed]
  26. Gutierrez, L.; Mauriat, M.; Pelloux, J.; Bellini, C.; Van Wuytswinkel, O. Towards a Systematic Validation of References in Real-Time RT-PCR. Plant Cell 2008, 20, 1734–1735. [Google Scholar] [CrossRef] [PubMed]
  27. Jakubczyk, K.; Janda, K.; Watychowicz, K.; Łukasiak, J.; Wolska, J. Garden Nasturtium (Tropaeolum majus L.)—A Source of Mineral Elements and Bioactive Compounds. Rocz. Panstw. Zakl. Hig. 2018, 69, 119–126. [Google Scholar] [PubMed]
  28. Pollard, M.R.; Stumpf, P.K. Long Chain (C(20) and C(22)) Fatty Acid Biosynthesis in Developing Seeds of Tropaeolum majus: An in Vivo Study. Plant Physiol. 1980, 66, 641–648. [Google Scholar] [CrossRef]
  29. Mietkiewska, E.; Giblin, E.M.; Wang, S.; Barton, D.L.; Dirpaul, J.; Brost, J.M.; Katavic, V.; Taylor, D.C. Seed-Specific Heterologous Expression of a Nasturtium Fae Gene in Arabidopsis Results in a Dramatic Increase in the Proportion of Erucic Acid. Plant Physiol. 2004, 136, 2665–2675. [Google Scholar] [CrossRef]
  30. Sebastián, M.-S.; Favio, G.; Juan, F.A.; Natalia, P.-M. Molecular Framework Underlying Floral Bilateral Symmetry and Nectar Spur Development in Tropaeolum, an Atypical Member of the Brassicales. Am. J. Bot. 2021, 108, 1315–1330. [Google Scholar]
  31. Zhuang, H.; Fu, Y.; He, W.; Wang, L.; Wei, Y. Selection of Appropriate Reference Genes for Quantitative Real-Time PCR in Oxytropis ochrocephala Bunge Using Transcriptome Datasets under Abiotic Stress Treatments. Front. Plant Sci. 2015, 6, 475. [Google Scholar] [CrossRef] [PubMed]
  32. Li, W.; Zhang, L.; Zhang, Y.; Wang, G.; Song, D.; Zhang, Y. Selection and Validation of Appropriate Reference Genes for Quantitative Real-Time PCR Normalization in Staminate and Perfect Flowers of Andromonoecious Taihangia rupestris. Front. Plant Sci. 2017, 8, 729. [Google Scholar] [CrossRef]
  33. Demidenko, N.V.; Logacheva, M.D.; Penin, A.A. Selection and Validation of Reference Genes for Quantitative Real-Time PCR in Buckwheat (Fagopyrum esculentum) Based on Transcriptome Sequence Data. PLoS ONE 2011, 6, e19434. [Google Scholar] [CrossRef] [PubMed]
  34. Haas, B.J.; Papanicolaou, A.; Yassour, M.; Grabherr, M.; Blood, P.D.; Bowden, J.; Couger, M.B.; Eccles, D.; Li, B.; Lieber, M.; et al. De Novo Transcript Sequence Reconstruction from RNA-Seq Using the Trinity Platform for Reference Generation and Analysis. Nat. Protoc. 2013, 8, 1494–1512. [Google Scholar] [CrossRef] [PubMed]
  35. Ginzinger, D.G. Gene Quantification Using Real-Time Quantitative PCR: An Emerging Technology Hits the Mainstream. Exp. Hematol. 2002, 30, 503–512. [Google Scholar] [CrossRef]
  36. Jiang, Q.; Wang, F.; Li, M.Y.; Ma, J.; Tan, G.F.; Xiong, A.S. Selection of Suitable Reference Genes for QPCR Normalization under Abiotic Stresses in Oenanthe javanica (Bi.) Dc. PLoS ONE 2014, 9, e92262. [Google Scholar] [CrossRef] [PubMed]
  37. 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]
  38. 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]
  39. 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]
  40. Chang, E.; Shi, S.; Liu, J.; Cheng, T.; Xue, L.; Yang, X.; Yang, W.; Lan, Q.; Jiang, Z. Selection of Reference Genes for Quantitative Gene Expression Studies in Platycladus orientalis (Cupressaceae) Using Real-Time PCR. PLoS ONE 2012, 7, e33278. [Google Scholar] [CrossRef]
  41. Xiao, X.; Ma, J.; Wang, J.; Wu, X.; Li, P.; Yao, Y. Validation of Suitable Reference Genes for Gene Expression Analysis in the Halophyte salicornia Europaea by Real-Time Quantitative PCR. Front. Plant Sci. 2014, 5, 788. [Google Scholar] [CrossRef] [PubMed]
  42. Livak, K.J.; Schmittgen, T.D. Analysis of Relative Gene Expression Data Using Real-Time Quantitative PCR and the 2(-Delta Delta C (T)) Method. Methods A Companion Methods Enzymol. 2001, 25, 402–408. [Google Scholar] [CrossRef] [PubMed]
  43. Niu, X.; Qi, J.; Zhang, G.; Xu, J.; Tao, A.; Fang, P.; Su, J. Selection of Reliable Reference Genes for Quantitative Real-Time PCR Gene Expression Analysis in Jute (Corchorus capsularis) under Stress Treatments. Front. Plant Sci. 2015, 6, 848. [Google Scholar] [CrossRef]
  44. Qi, S.; Yang, L.; Wen, X.; Hong, Y.; Song, X.; Zhang, M.; Dai, S. Reference Gene Selection for RT-PCR Analysis of Flower Development in Chrysanthemum morifolium and Chrysanthemum lavandulifolium. Front. Plant Sci. 2016, 7, 287. [Google Scholar] [CrossRef]
  45. Zhu, J.; Zhang, L.; Li, W.; Han, S.; Yang, W.; Qi, L. Reference Gene Selection for Quantitative Real-Time PCR Normalization in Caragana intermedia under Different Abiotic Stress Conditions. PLoS ONE 2013, 8, e53196. [Google Scholar] [CrossRef] [PubMed]
  46. Huang, L.; Yan, H.; Jiang, X.; Zhang, Y.; Zhang, X.; Ji, Y.; Zeng, B.; Xu, B.; Yin, G.; Lee, S.; et al. Reference Gene Selection for Quantitative Real-Time Reverse-Transcriptase PCR in Orchard grass Subjected to Various Abiotic Stresses. Gene 2014, 553, 158–165. [Google Scholar] [CrossRef] [PubMed]
  47. Huang, T.; Long, J.; Liu, S.W.; Yang, Z.W.; Zhu, Q.J.; Zhao, X.L.; Peng, C. Selection and Validation of Reference Genes for Mrna Expression by Quantitative Real-Time PCR Analysis in Neolamarckia cadamba. Sci. Rep. 2018, 8, 9311. [Google Scholar] [CrossRef]
  48. Galli, V.; Borowski, J.M.; Perin, E.C.; Rda, S.M.; Labonde, J.; Idos, S.P.; Silva, S.D.; Rombaldi, C.V. Validation of Reference Genes for Accurate Normalization of Gene Expression for Real Time-Quantitative PCR in Strawberry Fruits Using Different Cultivars and Osmotic Stresses. Gene 2015, 554, 205–214. [Google Scholar] [CrossRef]
  49. Duan, M.; Wang, J.; Zhang, X.; Yang, H.; Wang, H.; Qiu, Y.; Song, J.; Guo, Y.; Li, X. Identification of Optimal Reference Genes for Expression Analysis in Radish (Raphanus sativus L.) and Its Relatives Based on Expression Stability. Front. Plant Sci. 2017, 8, 1605. [Google Scholar] [CrossRef]
  50. Tang, X.; Zhang, N.; Si, H.; Calderón-Urrea, A. Selection and Validation of Reference Genes for RT-QPCR Analysis in Potato under Abiotic Stress. Plant Methods 2017, 13, 85. [Google Scholar] [CrossRef]
  51. Ho-Youn, K.; Saha, P.; Farcuh, M.; Li, B.; Sadka, A.; Blumwald, E. RNA-Seq Analysis of Spatiotemporal Gene Expression Patterns During Fruit Development Revealed Reference Genes for Transcript Normalization in Plums. Plant Mol. Biol. Report. 2015, 33, 1634–1649. [Google Scholar]
  52. Li, J.; Han, J.; Hu, Y.; Yang, J. Selection of Reference Genes for Quantitative Real-Time PCR during Flower Development in Tree Peony (Paeonia suffruticosa Andr.). Front. Plant Sci. 2016, 7, 516. [Google Scholar] [CrossRef] [PubMed]
  53. 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]
  54. 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]
  55. Nicot, N.; Hausman, J.F.; Hoffmann, L.; Evers, D. Housekeeping Gene Selection for Real-Time RT-PCR Normalization in Potato during Biotic and Abiotic Stress. J. Exp. Bot. 2005, 56, 2907–2914. [Google Scholar] [CrossRef]
  56. Jian, B.; Liu, B.; Bi, Y.; Hou, W.; Wu, C.; Han, T. Validation of Internal Control for Gene Expression Study in Soybean by Quantitative Real-Time PCR. BMC Mol. Biol. 2008, 9, 59. [Google Scholar] [CrossRef]
  57. Die, J.V.; Román, B.; Nadal, S.; González-Verdejo, C.I. Evaluation of Candidate Reference Genes for Expression Studies in Pisum sativum under Different Experimental Conditions. Planta 2010, 232, 145–153. [Google Scholar] [CrossRef]
  58. Yang, Y.; Hou, S.; Cui, G.; Chen, S.; Wei, J.; Huang, L. Characterization of Reference Genes for Quantitative Real-Time PCR Analysis in Various Tissues of Salvia miltiorrhiza. Mol. Biol. Rep. 2010, 37, 507–513. [Google Scholar] [CrossRef]
  59. Lilly, S.T.; Drummond, R.S.; Pearson, M.N.; MacDiarmid, R.M. Identification and Validation of Reference Genes for Normalization of Transcripts from Virus-Infected Arabidopsis thaliana. Mol. Plant Microbe Interact. 2011, 24, 294–304. [Google Scholar] [CrossRef]
  60. Li, X.S.; Yang, H.L.; Zhang, D.Y.; Zhang, Y.M.; Wood, A.J. Reference Gene Selection in the Desert Plant Eremosparton Songoricum. Int. J. Mol. Sci. 2012, 13, 6944–6963. [Google Scholar] [CrossRef]
  61. Expósito-Rodríguez, M.; Borges, A.A.; Borges-Pérez, A.; Pérez, J.A. Selection of Internal Control Genes for Quantitative Real-Time RT-PCR Studies during Tomato Development Process. BMC Plant Biol. 2008, 8, 131. [Google Scholar] [CrossRef] [PubMed]
  62. Jain, M.; Nijhawan, A.; Tyagi, A.K.; Khurana, J.P. Validation of Housekeeping Genes as Internal Control for Studying Gene Expression in Rice by Quantitative Real-Time PCR. Biochem. Biophys. Res. Commun. 2006, 345, 646–651. [Google Scholar] [CrossRef] [PubMed]
  63. Narsai, R.; Ivanova, A.; Ng, S.; Whelan, J. Defining Reference Genes in Oryza sativa Using Organ, Development, Biotic and Abiotic Transcriptome Datasets. BMC Plant Biol. 2010, 10, 56. [Google Scholar] [CrossRef] [PubMed]
  64. González-Agüero, M.; García-Rojas, M.; Di Genova, A.; Correa, J.; Maass, A.; Orellana, A.; Hinrichsen, P. Identification of Two Putative Reference Genes from Grapevine Suitable for Gene Expression Analysis in Berry and Related Tissues Derived from RNA-Seq Data. BMC Genom. 2013, 14, 878. [Google Scholar] [CrossRef]
  65. Libault, M.; Thibivilliers, S.; Bilgin, D.D.; Radwan, O.; Benitez, M.; Clough, S.J.; Stacey, G. Identification of Four Soybean Reference Genes for Gene Expression Normalization. Plant Genome 2008, 1, 44–54. [Google Scholar] [CrossRef]
  66. Løvdal, T.; Lillo, C. Reference Gene Selection for Quantitative Real-Time PCR Normalization in Tomato Subjected to Nitrogen, Cold, and Light Stress. Anal. Biochem. 2009, 387, 238–242. [Google Scholar] [CrossRef]
  67. Ji, Y.; Tu, P.; Wang, K.; Gao, F.; Yang, W.; Zhu, Y.; Li, S. Defining Reference Genes for Quantitative Real-Time PCR Analysis of Anther Development in Rice. Acta Biochim. Biophys. Sin. 2014, 46, 305–312. [Google Scholar] [CrossRef]
  68. Olejnik, P.; Mądrzak, C.J.; Nuc, K. Cyclophilins and Their Functions in Abiotic Stress and Plant-Microbe Interactions. Biomolecules 2021, 11, 1390. [Google Scholar] [CrossRef]
  69. Qu, R.; Miao, Y.; Cui, Y.; Cao, Y.; Zhou, Y.; Tang, X. Selection of Reference Genes for the Quantitative Real-Time PCR Normalization of Gene Expression in Isatis indigotica Fortune. BMC Mol. Biol. 2019, 20, 9. [Google Scholar] [CrossRef]
  70. Nguyen, D.Q.; Eamens, A.L.; Grof, C.P.L. Reference Gene Identification for Reliable Normalisation of Quantitative RT-PCR Data in Setaria viridis. Plant Methods 2018, 14, 24. [Google Scholar] [CrossRef]
  71. Hossain, M.S.; Ahmed, R.; Haque, M.S.; Alam, M.M.; Islam, M.S. Identification and Validation of Reference Genes for Real-Time Quantitative RT-PCR Analysis in Jute. BMC Mol. Biol. 2019, 20, 13. [Google Scholar] [CrossRef] [PubMed]
  72. Morgante, C.V.; Guimarães, P.M.; Martins, A.C.; Araújo, A.C.; Leal-Bertioli, S.C.; Bertioli, D.J.; Brasileiro, A.C. Reference Genes for Quantitative Reverse Transcription-Polymerase Chain Reaction Expression Studies in Wild and Cultivated Peanut. BMC Res. Notes 2011, 4, 339. [Google Scholar] [CrossRef] [PubMed]
  73. Han, B.; Yang, Z.; Samma, M.K.; Wang, R.; Shen, W. Systematic Validation of Candidate Reference Genes for QRT-PCR Normalization under Iron Deficiency in Arabidopsis. Biometals 2013, 26, 403–413. [Google Scholar] [CrossRef] [PubMed]
  74. Štajner, N.; Cregeen, S.; Javornik, B. Evaluation of Reference Genes for RT-QPCR Expression Studies in Hop (Humulus lupulus L.) During Infection with Vascular Pathogen Verticillium Albo-Atrum. PLoS ONE 2013, 8, e68228. [Google Scholar] [CrossRef]
  75. Zhao, Y.; Luo, J.; Xu, S.; Wang, W.; Liu, T.; Han, C.; Chen, Y.; Kong, L. Selection of Reference Genes for Gene Expression Normalization in Peucedanum praeruptorum Dunn under Abiotic Stresses, Hormone Treatments and Different Tissues. PLoS ONE 2016, 11, e0152356. [Google Scholar] [CrossRef]
  76. Delporte, M.; Legrand, G.; Hilbert, J.L.; Gagneul, D. Selection and Validation of Reference Genes for Quantitative Real-Time PCR Analysis of Gene Expression in Cichorium intybus. Front. Plant Sci. 2015, 6, 651. [Google Scholar] [CrossRef]
  77. Fan, C.; Ma, J.; Guo, Q.; Li, X.; Wang, H.; Lu, M. Selection of Reference Genes for Quantitative Real-Time PCR in Bamboo (Phyllostachys edulis). PLoS ONE 2013, 8, e56573. [Google Scholar] [CrossRef]
  78. Tjasa, R.; Stajner, N.; Bandelj, D.; Javornik, B.; Jakse, J. Validation of Candidate Reference Genes in RT-QPCR Studies of Developing Olive Fruit and Expression Analysis of Four Genes Involved in Fatty Acids Metabolism. Mol. Breed. 2013, 32, 211–222. [Google Scholar]
  79. Chandna, R.; Augustine, R.; Bisht, N.C. Evaluation of Candidate Reference Genes for Gene Expression Normalization in Brassica juncea Using Real Time Quantitative RT-PCR. PLoS ONE 2012, 7, e36918. [Google Scholar] [CrossRef]
  80. de Carvalho, K.; Filho, J.C.B.; Santos, T.B.D.; de Souza, S.G.; Vieira, L.G.; Pereira, L.F.; Domingues, D.S. Nitrogen Starvation, Salt and Heat Stress in Coffee (Coffea arabica L.): Identification and Validation of New Genes for QPCR Normalization. Mol. Biotechnol. 2013, 53, 315–325. [Google Scholar] [CrossRef]
  81. Reid, K.E.; Olsson, N.; Schlosser, J.; Peng, F.; Lund, S.T. An Optimized Grapevine RNA Isolation Procedure and Statistical Determination of Reference Genes for Real-Time RT-PCR during Berry Development. BMC Plant Biol. 2006, 6, 27. [Google Scholar] [CrossRef] [PubMed]
  82. Jensen, J.K.; Schultink, A.; Keegstra, K.; Wilkerson, C.G.; Pauly, M. RNA-Seq Analysis of Developing Nasturtium Seeds (Tropaeolum majus): Identification and Characterization of an Additional Galactosyltransferase Involved in Xyloglucan Biosynthesis. Mol. Plant 2012, 5, 984–992. [Google Scholar] [CrossRef] [PubMed]
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Figure 1. Agarose gel electrophoresis of PCR products and melting curves. (A) Agarose gel (1.5%) electrophoresis indicating the amplification of a single PCR product of the expected size for 14 candidate reference genes and the KCS11 gene in nasturtium (lines 1–15: TUA4, TUB6, ROC3, CYP2, EF1-α, EXP1, EXP2, PP2AA3, PTB1, TIP41, UBC9, YLS8, ACT2, GAPC2, and KCS11). M represents a DL2000 DNA marker. (B) Melting curves for the 15 genes showing single peaks.
Figure 1. Agarose gel electrophoresis of PCR products and melting curves. (A) Agarose gel (1.5%) electrophoresis indicating the amplification of a single PCR product of the expected size for 14 candidate reference genes and the KCS11 gene in nasturtium (lines 1–15: TUA4, TUB6, ROC3, CYP2, EF1-α, EXP1, EXP2, PP2AA3, PTB1, TIP41, UBC9, YLS8, ACT2, GAPC2, and KCS11). M represents a DL2000 DNA marker. (B) Melting curves for the 15 genes showing single peaks.
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Figure 2. Distribution of Ct values of candidate reference genes across all samples. The Ct distribution boxes indicate the 25th and 75th percentiles. The whiskers represent the maximum and minimum values, while the lines and the squares within the boxes indicate the median and mean, respectively. The asterisk represents the outliers.
Figure 2. Distribution of Ct values of candidate reference genes across all samples. The Ct distribution boxes indicate the 25th and 75th percentiles. The whiskers represent the maximum and minimum values, while the lines and the squares within the boxes indicate the median and mean, respectively. The asterisk represents the outliers.
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Figure 3. Expression stability (M) values of the 14 candidate reference genes determined using geNorm software v 1.0. The lowest M value represents the most stable gene.
Figure 3. Expression stability (M) values of the 14 candidate reference genes determined using geNorm software v 1.0. The lowest M value represents the most stable gene.
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Figure 4. Determination of optimal number of reference genes for normalization via pairwise variation analysis using GeNorm software. Vn/Vn+1 values were used to determine the optimal number of reference genes.
Figure 4. Determination of optimal number of reference genes for normalization via pairwise variation analysis using GeNorm software. Vn/Vn+1 values were used to determine the optimal number of reference genes.
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Figure 5. KCS11 expression patterns normalized using an unstable gene and the most stable genes in different organs (A), seeds at different development stages (B), and all tested samples (C).
Figure 5. KCS11 expression patterns normalized using an unstable gene and the most stable genes in different organs (A), seeds at different development stages (B), and all tested samples (C).
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Table 1. Information on candidate reference genes in nasturtium.
Table 1. Information on candidate reference genes in nasturtium.
Gene NameGene SymbolArabidopsis
Homologue 		Primer Sequence (Forward/Reverse)Size (bp)Efficiency (%)R2
Actin-2ACT2AT3G18780CAGACCGTATGAGCAAGGAGA17898.070.9979
ATTGATGGACCAGACTCGTCG
Glyceraldehyde-3-phosphate dehydrogenase GAPC2GAPC2AT1G13440GGCTGCTATCAAGGAGGAGTCTG203115.200.9913
GATCAACCACCCGGGTACTGT
Tubulin alpha-4 chainTUA4AT1G04820GCATTGGTATGTTGGTGAGGGT13495.790.9942
ACTCATCCCCATCATCATCTTCC
Tubulin beta-6 chainTUB6AT5G12250ATGTTCAGGAGGGTGAGTGA168104.130.9996
CTCATCAGCGGTAGCATCTTG
Peptidyl-prolyl cis-trans isomerase CYP19-1 ROC3ROC3AT2G16600CTCAGTTCTTCGTCTGTACCGAG21398.840.9985
AGAAGCGCAGATCCTCCACT
Cyclophilin-like peptidyl-prolyl cis-trans isomerase 2CYP2AT4G33060CAGCTCAGTTGAAGTTTGTGCC23599.890.9964
CAGCCTCATCCAGGTTATAGAACTC
GTP binding Elongation factor EFTu/EF1AEF1-αAT1G07920ATCTCCAAGGATGGGCAGAC168108.641.0000
TCCAACCTTCTTCAGGTAGGAAG
Expressed protein 1 (Dimethylallyl, adenosine tRNA methylthiotransferase)EXP1AT4G33380CTGAGTTTGGACGACGTGAAG173105.260.9979
CCAGCTTTCTTCTTCCTCATCG
Expressed protein 2 (S-adenosyl-L-methionine-dependent methyltransferases)EXP2AT2G32170CCGAGTATGGATGCTATTCTCCAG203105.540.9939
GATTGACTGCTCATTGTGAAGTCC
Serine/threonine-protein phosphatase PP2AA3PP2AA3AT1G13320GCCTGAAGATTGTGTTGCTCAC19797.970.9959
CTGCTATTCGTACTTCAGCCTCA
Polypyrimidine tract-binding protein 1PTB1AT3G01150TGCTGTCACTGTGGATGTGC142106.440.9974
TGCGTCTCTAGCAACTGCTG
Type 2a phosphatase activator tip41TIP41AT4G34270CGCAAACGCTCCATTCTCACTTC24198.150.9944
CCTGCTGAGAAGGTTTGCATCTG
Ubiquitin-conjugating enzyme 9UBC9AT4G27960TTGCTCTCAATCTGCTCGCTG175119.790.9978
TCCTATCGCAACCATCATAGCG
Mitosisprotein YLS8YLS8AT5G08290TGATTGGGATGAAACATGCATGC146110.540.9908
TTGTGGATGGATCGTACAGCTC
3-ketoacyl-CoA synthase 11KCS11AT2G26640AGGAAGACTCGGACGGGAAGAT115100.360.9982
GACATGGGTAGAACAAGTGGTCCG
Table 2. Stability of candidate reference genes according to NormFinder software v 1.0.
Table 2. Stability of candidate reference genes according to NormFinder software v 1.0.
Ranking OrderOrgansSeeds in Different Developmental StagesAll Samples
Gene NameStability ValueGene NameStability ValueGene NameStability Value
1EXP10.175EXP10.155EXP10.070
2EXP20.268ROC30.158ACT20.262
3TUB60.300ACT20.257PP2AA30.375
4ACT20.346CYP20.399EXP20.376
5UBC90.422EF1-α0.416CYP20.443
6PP2AA30.504PP2AA30.424ROC30.499
7CYP20.505EXP20.434GAPC20.553
8GAPC20.535GAPC20.463EF1-α0.554
9TIP410.576YLS80.643UBC90.658
10ROC30.642UBC90.714YLS80.722
11YLS80.687PTB10.941TUB61.053
12EF1-α0.699TUB60.987PTB11.091
13TUA40.859TIP411.179TIP411.099
14PTB10.921TUA41.481TUA41.686
Table 3. Stability of candidate reference genes according to Bestkeeper software v 1.0.
Table 3. Stability of candidate reference genes according to Bestkeeper software v 1.0.
Ranking OrderOrgansSeeds in Different Developmental StagesAll Samples
Gene NameStability ValueGene NameStability ValueGene NameStability Value
1TUB62.39 ± 0.44UBC92.23 ± 0.42EXP22.94 ± 0.64
2ROC32.91 ± 0.52CYP22.26 ± 0.51CYP23.06 ± 0.68
3EXP13.20 ± 0.73EXP22.37 ± 0.52EXP13.11 ± 0.72
4EXP23.23 ± 0.69GAPC22.40 ± 0.45GAPC23.28 ± 0.61
5GAPC23.65 ± 0.68YLS82.50 ± 0.51ROC33.47 ± 0.63
6EF1-α3.85 ± 0.65ACT22.62 ± 0.51UBC94.08 ± 0.77
7CYP24.00 ± 0.88EXP12.89 ± 0.67ACT24.26 ± 0.81
8UBC94.54 ± 0.88PTB13.57 ± 0.79YLS84.55 ± 0.93
9ACT24.84 ± 0.90ROC33.68 ± 0.66PP2AA35.15 ± 1.17
10TIP414.88 ± 1.13PP2AA34.66 ± 1.06PTB15.95 ± 1.36
11PP2AA35.57 ± 1.23EF1-α5.71 ± 1.03EF1-α6.05 ± 1.05
12YLS85.96 ± 1.24TUB66.05 ± 1.29TIP416.80 ± 1.56
13PTB16.11 ± 1.44TUA47.53 ± 1.67TUB68.39 ± 1.69
14TUA47.97 ± 1.43TIP419.04 ± 2.03TUA411.72 ± 2.35
Table 4. Stability of candidate reference genes according to RefFinder.
Table 4. Stability of candidate reference genes according to RefFinder.
Ranking OrderOrgansSeeds in Different Developmental StagesAll Samples
Gene NameStability ValueGene NameStability ValueGene NameStability Value
1EXP11.57EXP12.21EXP11.97
2EXP22.11CYP22.83EXP22.45
3TUB62.71ROC33.44CYP22.99
4ACT26.00ACT23.66ACT23.60
5PP2AA36.31EXP24.12GAPC23.96
6ROC36.32UBC95.01ROC34.12
7UBC96.47GAPC25.24PP2AA35.69
8GAPC26.51YLS85.38UBC97.67
9CYP26.65PP2AA37.93EF1-α8.74
10TIP417.02EF1-α8.80YLS89.46
11EF1-α8.30PTB110.46PTB111.24
12YLS811.49TUB612.00TUB612.22
13TUA413.00TUA413.00TIP4112.49
14PTB114.00TIP4114.00TUA414.00
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MDPI and ACS Style

Tang, Q.; Zhou, G.-C.; Liu, S.-J.; Li, W.; Wang, Y.-L.; Xu, G.-Y.; Li, T.-F.; Meng, G.-Q.; Xue, J.-Y. Selection and Validation of Reference Genes for qRT-PCR Analysis of Gene Expression in Tropaeolum majus (Nasturtium). Horticulturae 2023, 9, 1176. https://doi.org/10.3390/horticulturae9111176

AMA Style

Tang Q, Zhou G-C, Liu S-J, Li W, Wang Y-L, Xu G-Y, Li T-F, Meng G-Q, Xue J-Y. Selection and Validation of Reference Genes for qRT-PCR Analysis of Gene Expression in Tropaeolum majus (Nasturtium). Horticulturae. 2023; 9(11):1176. https://doi.org/10.3390/horticulturae9111176

Chicago/Turabian Style

Tang, Qing, Guang-Can Zhou, Si-Jie Liu, Wen Li, Yi-Lei Wang, Gao-Ying Xu, Teng-Fei Li, Guo-Qing Meng, and Jia-Yu Xue. 2023. "Selection and Validation of Reference Genes for qRT-PCR Analysis of Gene Expression in Tropaeolum majus (Nasturtium)" Horticulturae 9, no. 11: 1176. https://doi.org/10.3390/horticulturae9111176

APA Style

Tang, Q., Zhou, G. -C., Liu, S. -J., Li, W., Wang, Y. -L., Xu, G. -Y., Li, T. -F., Meng, G. -Q., & Xue, J. -Y. (2023). Selection and Validation of Reference Genes for qRT-PCR Analysis of Gene Expression in Tropaeolum majus (Nasturtium). Horticulturae, 9(11), 1176. https://doi.org/10.3390/horticulturae9111176

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