Next Article in Journal
Virtual New Nitrogen, Phosphorus, Water Input, and Greenhouse Gas Emission Indicators for the Potatoes Consumed in China
Previous Article in Journal
Influence of Different Irrigation Water Qualities and Irrigation Techniques on the Soil Attributes and Bacterial Community Structure
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Agronomy
Volume 12
Issue 12
10.3390/agronomy12123171
agronomy-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
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Selection and Evaluation of Reference Genes for Quantitative Real-Time PCR in Tomato (Solanum lycopersicum L.) Inoculated with Oidium neolycopersici

by
Shengyi Bai
1,
Xiaomin Wang
1,2,3,*,
Meng Guo
1,2,3,
Guoxin Cheng
1,2,3,
Abid Khan
4,
Wenkong Yao
1,2,3,
Yanming Gao
1,2,3 and
Jianshe Li
1,2,3
1
School of Agriculture, Ningxia University, Yinchuan 750021, China
2
Ningxia Modern Facility Horticulture Engineering Technology Research Center, Ningxia University, Yinchuan 750021, China
3
Key Laboratory of Modern Molecular Breeding for Dominant and Special Crops in Ningxia, Ningxia University, Yinchuan 750021, China
4
Department of Horticulture, The University of Haripur, Haripur 22620, Pakistan
*
Author to whom correspondence should be addressed.
Agronomy 2022, 12(12), 3171; https://doi.org/10.3390/agronomy12123171
Submission received: 3 November 2022 / Revised: 30 November 2022 / Accepted: 13 December 2022 / Published: 14 December 2022
(This article belongs to the Section Crop Breeding and Genetics)
Browse Figures
Review Reports Versions Notes

Abstract

:
In order to screen out the most stable reference genes in tomatoes under powdery mildew (PM) stress and study the expression of related genes in the interaction between tomato and PM more accurately, this study will provide a calibration basis for the expression of related functional genes. In this study, the expression stabilities of eight tomato candidate reference genes of EF1α, L33, Act, Ubi, GAPDH, UK, CAC and TIP41 in susceptible tomato and resistant tomatoes under PM stress were ranked using four different computation programs, including geNorm, Normfinder, BestKeeper and the comparative ∆CT method. Then RefFinder was used to analyze the ranking results of four kinds of software comprehensively. Finally, the selected reference genes were validated by the target gene SlMLO1. The results of geNorm showed that the normalization of qRT-PCR using two reference genes could meet the requirements. The comprehensive analysis of RefFinder showed that the most stable reference genes were Act and EF1α for both tomato varieties. The combination of Act and GAPDH was most stable in susceptible tomato ‘MM’. The combination of Act and EF1α was most stable in resistant tomato ‘62579′. Generally, the Act was the most stable reference gene in the two tomato varieties under PM stress. This study will lay a foundation for the related functional gene expression research in tomatoes under PM stress.

1. Introduction

Tomato (Solanum lycopersicum L.) is one of the most important economic vegetable crops in the world [1]. It is not only a nutritious daily vegetable but also a new type of fruit with a special flavor. In recent years, molecular biology methods have been used to study the related gene functions of tomatoes, especially the application of gene editing technology in tomato genetic improvement and breeding is the research hotspot [2].
Powdery mildew (PM) is a serious obligate parasitic fungal disease of tomatoes. Powdery mildew pathogen can attack over 60 species in 13 plant families, including Solanaceae, which can cause serious yield reduction in tomatoes [3,4]. Spraying a large amount of fungicides may not only pollute the environment but also seriously threaten the safety of agricultural products [5]. Therefore, exploring tomato PM resistance-related functional genes, studying their expression profiles induced by PM, and breeding excellent disease-resistant varieties have important theoretical and practical value in the prevention and control of tomato PM [6,7].
Quantitative real-time PCR (qRT-PCR) is widely used in the quantitative analysis of target genes [8], which can accurately analyze the gene expression differences of plants in different periods and after different treatments [9,10]. In studying the relative expression profile of genes, many factors affect the accuracy of quantitative analysis results. In order to obtain accurate, scientific and reasonable data, one or more genes with stable expression are needed as reference genes for correction, thereby reducing the error in the test process [11,12]. The stability of reference genes is important for the quantitative results analysis [13], but not all candidate reference genes are suitable for use under any experimental conditions [14,15]. However, some reference genes in tomatoes, such as Actin (Act) [16] and Elongation factor 1α (EF1α) [17] genes, have been reported to study gene expression regulation. The most suitable reference genes selected under different biotic and abiotic stress conditions are different [18,19]. Recent studies have shown that the selection of the reference genes varies with changes in the experimental conditions [20,21]. Therefore, before the qRT-PCR experiment, it is necessary to screen out reference genes that can stably express and are not affected by experimental treatment to ensure the objectivity and accuracy of the quantitative detection results of target genes.
Tomato is a classic model plant for studying fruit development, axis branching, compound leaf formation and disease resistance. It is also the main object of domestication and genetic improvement [22]. It is of great significance for a tomato to analyze the differential expression of various functional genes at different growth stages or under specific environmental factors, and the high stability of the reference genes is crucial for the analysis of the expression profile [23,24]. In tomatoes, the selection of the most stable reference genes under PM stress has not been reported yet. Therefore, in this study, geNorm, NormFinder, BestKeeper, the comparative ∆CT method and RefFinder were used to evaluate the stability of eight candidate reference genes, namely EF1α, 50S ribosomal protein L33 (L33), Act, Ubiquitin (Ubi), Glyceraldehyde 3-phosphate dehydrogenase (GAPDH), Uridylate kinase (UK), Clathrin adaptor complexes medium subunit (CAC) and TIP 41 (TAP42-Interacting protein) (TIP41), in the PM infection process of tomato leaves, aiming to lay a foundation for the later study on the expression of PM resistance related functional genes in tomato.

2. Materials and Methods

2.1. Plant and Fungal Materials

The susceptible tomato variety ‘Moneymaker’ (MM, susceptible to PM) and the resistant tomato inbred line ‘62579′ (resistant to PM) were provided by Ningxia University and Ningxia Jufeng Seed and Seedling Co., LTD. Tomato PM pathogen, O. neolycopersici, was isolated and purified by Vegetable Group from School of Agriculture, Ningxia University [25].

2.2. Inoculation with O. neolycopersici

The seeds of tomato varieties ‘MM’ and ‘62579′ were germinated, and then they were planted in the aperture disks with substrate when 1 mm buds were observed in the tips of the seeds. When the plants grew to the four-leaf stage, they were brought back to the laboratory. The well-grown seedlings were selected and transplanted into pots. The leaves of seedlings at the four-leafed stage were inoculated with O. neolycopersici.
The purified O. neolycopersici was prepared into 106/mL spore suspension, which was inoculated artificially by leaf brushing method, and moisturized for 24 h post-inoculation (hpi). Leaves were collected at 0 hpi, 24 hpi, 48 hpi and 120 hpi, directly frozen in liquid nitrogen and stored at −80 °C for further analysis. There were three biological replicates at each time point after inoculation. Each biological replicate contained three plants, and the first true leaf was collected from each plant.

2.3. RNA Isolation and cDNA Synthesis

One biological replicate, that is, three leaves, was used to extract one RNA sample. Total RNA was extracted from the above leaf samples, respectively, and treated with DNase, according to the manufacturer’s instructions of RNAprep pure Plant Kit (TIANGEN, Beijing, China). First-strand cDNA was synthesized from 1 µg total RNA using HiScript® III 1st Strand cDNA Synthesis Kit (Vazyme, Nanjing, Jiangsu, China).

2.4. Candidate Reference Genes Selection

Eight commonly used reference genes, EF1α, L33, Act, Ubi, GAPDH, UK, CAC and TIP41, were used as candidate reference genes for the qRT-PCR analysis. The SlMLO1 gene was used to validate the selected reference gene. The qRT-PCR primer sequences of eight candidate reference genes and SlMLO1 gene are shown in Table 1. The primer bases were synthesized by Shaanxi Aoke Technology Co., Ltd (Xian, China).

2.5. Specificity Detection of Candidate Reference Genes Primers and qRT-PCR

The specificity of amplification fragments of eight reference genes was detected by ordinary PCR using 0 hpi ‘MM’ tomato cDNA as a template. Each reaction mixture, containing 1 μL of cDNA, 5 μL of 2 × Taq Master Mix, 1 μL of each primer, and 2 μL of ddH2O at a final volume of 10 μL, was subjected to the following PCR conditions: 4 min at 94 °C (pre-denaturation); 35 cycles of 30 s at 94 °C (denaturation), 30 s at 58 °C (annealing), and 30 s at 72 °C (extension); and a final step of 10 min at 72 °C for extension. The amplification products were detected by 1.5% agarose gel electrophoresis.
The tomato cDNA of each treatment was used as the template, and the Ultra SYBR Mixture enzyme (CWBIO, Beijing, China) was used for real-time fluorescence quantitative analysis by using a Qtower2.0 quantitative PCR instrument (Analytik Jena, Jena, Thuringia, Germany) with three technical replicates. Each reaction mixture contains 1 μL of cDNA, 15 μL of UltraSYBR Mixture, 1 μL of each primer and 12 μL of ddH2O at a final volume of 30 μL. The reaction procedure was set according to the kit instructions.

2.6. Data Analysis and Stability Evaluation of Candidate Reference Genes

The Ct value is an important parameter reflecting peak value of gene expression and is negatively correlated with gene expression peak. The Ct values of eight candidate reference genes in tomatoes were summarized. The Ct values of each sample were processed via Microsoft Excel 2007 and calculated by 2−ΔΔCT method [31]. The distribution of the qRT-PCR Ct values of the eight candidate reference genes was evaluated by Origin Pro2017 software. The stability of candidate reference genes was evaluated by geNorm [9], NormFinder [32], BestKeeper [33] software and comparative ∆CT method [34], respectively. RefFinder was used to comprehensively sort out the results of the four analysis software and evaluate the stability of candidate genes [35].
For geNorm, the M value was negatively correlated with the stability of the gene, and the candidate reference genes with M > 1.5 were not used as reference genes. The paired variation value (V) can determine the number of reference genes, and the paired difference value (Vn/n + 1) of the reference genes was analyzed by geNorm Software. When Vn/n + 1 > 0.15, then the n + 1 gene needs to be introduced, and when the Vn/n + 1 < 0.15, then no new reference gene is needed [36]. NormFinder algorithm ranked the stability of candidate reference genes based on variance analysis. In the expression stability analysis (S) of eight candidate reference genes by NormFinder Software was similar to the geNorm, where S value was negatively correlated with gene stability [37]. According to the gene correlation analysis of candidate genes, the BestKeeper Software can obtain the standard deviation (SD), coefficient of variation (CV), and Pearson correlation coefficient (r) of pairing between each gene by calculation and then determine the best reference gene by evaluating these values. The SD and CV of Ct values were negatively correlated with the stability of genes. The smaller the SD and CV values, the better the stability of the genes. Genes with SD values less than 1 were better as candidate reference genes. The r value was positively correlated with gene stability, i.e., the higher the r value, the more stable the reference gene [38]. Similar to geNorm and NormFinder, the average analysis of the STDEV of eight candidate reference genes by the comparative ∆CT method was negatively correlated with gene stability [39].

2.7. Reference Gene Validation

In plants, the MLO gene family members have been confirmed to be involved in the process of PM infection in host plants, which are susceptible to PM [40]. In tomatoes, SlMLO1 gene was confirmed to be a very important PM-related gene and a major negative regulator of disease resistance [41]. To further validate the reliability of candidate reference genes expression in tomato inoculated with O. neolycopersici, the expression level of SlMLO1 in response to PM was normalized by the most stable and unstable reference genes in ‘MM’ and ‘62579′. The expression profiles of SlMLO1 were normalized using each most stable reference genes, all the most stable reference genes (the average Ct values were used) and unstable reference genes, respectively, and the expression trends of SlMLO1 were compared.

3. Results

3.1. RNA Quality Detection and Reference Gene Primer Specificity Analysis

The total RNA extracted from tomato leaves at different time points after stress treatment was detected by electrophoresis. The results showed that the extracted RNA had good integrity (Supplementary Figure S1). The A260/280 and A260/230 of RNA were 1.9~2.1 and 2.1~2.3, respectively. The ‘MM’ tomato cDNA at 0 hpi was used as the template for PCR of eight candidate reference genes. The electrophoresis detection results showed that the products were amplified well, and the fragment size of each candidate reference gene ranged from 100 bp to 250 bp. The characteristic of the amplified products was consistent with our expectation: single and bright bands, as well as no primer dimer, were observed among these products. Melting curves showed a single peak, indicating a single amplified product for all target transcripts. It shows that eight pairs of primers for candidate reference genes could be used for the next experiments (Figure 1).

3.2. Expression Level Analysis of Candidate Reference Genes

The Ct values of eight candidate reference genes in tomatoes were summarized, and the expression abundance of these genes was evaluated by OriginPro2017 software. The smaller the Ct value, the higher the expression abundance [30]. Data in Figure 2 showed that the Ct values of the eight candidate reference genes are between 20 and 31, with moderate expression abundance. The Ubi gene possessed the highest expression peak due to the lowest Ct value observed among the eight candidate reference genes. The Ct values of TIP41 and UK were relatively large, and the average Ct values were 28.12 and 27.76, respectively. The expression peaks of the two genes were relatively low. The Ct values of EF1α, L33, Act, GAPDH and CAC were in the middle range, and peaks of the gene expression ranged between Ubi and TIP41.

3.3. Expression Stability of Candidate Reference Genes in Tomato after Powdery Mildew Infection

3.3.1. geNorm Analysis

The relative expression levels of eight candidate reference genes in two tomato varieties were statistically analyzed by geNorm Software, and the expression stability value (M) of each reference gene was calculated. Figure 3 showed that the M values of the eight candidate reference genes were less than 1.5 and the V2/3 values were less than 0.15, indicating that the two genes could meet the requirements of the reference gene combination for tomato under PM stress, and there is no need to introduce the third gene for correction. In the entire dataset of ‘MM’ and ‘62579′, the M values of the eight candidate reference genes ranked from low to high as EF1α = L33 < Ubi < Act < CAC < UK < GAPDH < TIP41, and EF1α and L33 genes (M value was 0.275) had the best stability. However, when analyzed separately according to the variety subdataset, the results changed. In the ‘MM’ subdataset analysis, the M values of EF1α and Act genes were the lowest (M value was 0.243); that is, EF1α and Act were the most stable reference genes of susceptible tomato ‘MM’ under PM stress. In the ‘62579′ subdataset analysis, although the ranking of M values of eight genes changed compared with the analysis results of the entire dataset, the most stable reference genes of resistant tomato ‘62579′ screened under PM stress were EF1α and L33 (M value was 0.310), which were consistent with the results of the entire dataset of ‘MM’ and ‘62579′.

3.3.2. NormFinder Analysis

It can be seen from Table 2 that in the analysis of the entire dataset of ‘MM’ and ‘62579′, the S values of the eight reference genes ranked from low to high as Act < EF1α < L33 < Ubi < CAC < GAPDH < UK < TIP41, and the stability of Act gene was the best (S value was 0.279). The results changed in the analysis of the ‘MM’ subdataset, and the S value of the GAPDH gene (S value was 0.133) was the lowest, i.e., the most stable reference gene of susceptible tomato ‘MM’ under PM stress was GAPDH. In the ‘62579′ subdataset analysis, although the S values of the eight genes changed relative to the analysis results of the entire dataset, the S value of the Act gene (S value was 0.223) was still the lowest; that is, the most stable reference gene of the resistant tomato ‘62579′ under PM stress was Act. The analysis result of NormFinder Software is inconsistent with geNorm Software, which is due to different algorithms.

3.3.3. BestKeeper Analysis

Table 3 demonstrates that in the analysis of the entire dataset and the ‘MM’ subdataset, the SD values of the eight putative reference genes were all less than 1; however, the SD value of the TIP41 gene in the ‘62579′ subdataset was greater than 1, indicating that the TIP41 gene could not be used as a reference gene for resistant tomato ‘62579′ variety under PM stress. By evaluating the SD, CV and r values of eight candidate reference genes, the stability ranking of eight candidate reference genes in three datasets was obtained. Although the stability ranking of eight candidate reference genes in three datasets was inconsistent, the most stable reference genes in three datasets were the Act gene. This indicates that the Act gene is the most stable one in both susceptible and resistant tomato varieties under PM stress. This inconsistency with the results of the first two software is mainly due to different software algorithms.

3.3.4. The Comparative ∆CT Method Analysis

The relative expression levels of eight candidate reference genes in two tomato varieties were statistically analyzed by the comparative ∆CT method, and the average STDEV values of each reference gene were calculated. By evaluating the average STDEV values of eight candidate reference genes, it can be seen from Table 4 that the stability ranking of eight candidate reference genes in three datasets was obtained. Although the stability ranking of eight candidate reference genes in three datasets was inconsistent, the most stable reference genes in three datasets were Act. This indicates that Act is the most stable one in both susceptible and resistant tomato varieties under PM stress. This inconsistency with the results of the BestKeeper Software is due to different algorithms.

3.4. Comprehensive Analysis of the Candidate Reference Genes of Tomato under Powdery Mildew Stress

Since geNorm, NormFinder, BestKeeper and the ∆CT method are based on different algorithms and analysis programs, the results of the four software analyses cannot be completely consistent. Figure 3 shows that V2/3 is less than 0.15, i.e., in order to accurately and reliably quantify the test results, the two genes can meet the requirements as a reference gene combination of tomato under PM stress. In order to select the stable reference genes of tomato used under PM stress, the expression stability of eight candidate reference genes was comprehensively ranked by RefFinder Software.
It can be seen from Table 5 that in the analysis of the entire dataset of ‘MM’ and ‘62579′, the geomean of ranking values of Act and EF1α were smaller (1.41 and 2.00, respectively). Therefore, the relatively stable reference genes of tomatoes under PM stress were Act and EF1α. Table 6 showed that in the analysis of the ‘MM’ subdataset, the geomean of ranking values of Act and GAPDH were the smallest (1.19 and 2.21, respectively), i.e., the expression of these two genes was the most stable, so the most stable reference genes for susceptible tomato ‘MM’ under PM stress were Act and GAPDH. It can be seen from Table 7 that in the analysis of the ‘62579′ subdataset, the geomean of ranking values of Act and EF1α were smaller (1.41 and 1.68, respectively), so the relatively stable reference genes for resistant tomato ‘62579′ under PM stress were Act and EF1α.
The comprehensive analysis results showed that under PM stress, the most stable reference genes of tomatoes selected from different datasets were different. According to the experimental needs, the best reference genes should be flexibly selected to correct the quantitative results of the target genes.

3.5. Reference Gene Validation

The comprehensive analysis results of Table 6 and Table 7 showed that the expression of Act and GAPDH in susceptible tomato ‘MM’ was stable, and the expression of UK was the most unstable. In resistant tomato ‘62579′, the expression of Act and EF1α was stable, and the expression of TIP41 was the most unstable. According to the above analysis results, two candidate reference genes with the best stability and one candidate reference gene with the worst stability were selected as calibration reference genes to analyze the relative expression level of the SlMLO1 gene in different materials under PM stress for validation.
The results showed that the expression level of SlMLO1 in both materials initially decreased at first and then increased under PM stress. When Act or GAPDH was used as the reference gene in ‘MM’ and Act or EF1α was used as the reference gene in ‘62579′, similar changes were observed. However, when UK and TIP41 were used as reference genes for calibration in ‘MM’ and ‘62579′ respectively, the expression level trend of the SlMLO1 under PM stress was obviously observed to be inconsistent with the actual situation (Figure 4), which was clearly caused by the low expression stability of UK and TIP41 in the samples, indicating that it was essential to select appropriate reference genes in qRT-PCR analysis. There was some bias in the expression of SlMLO1 normalized with Act and EF1α individually as reference genes in ANOVA (Figure 4E,F). In order to analyze the relative expression level of the SlMLO1 gene in different materials more accurately, the reference gene combination of Act and GAPDH was used in ‘MM’ to correct the expression level of the SlMLO1 gene (Figure 4C), which was consistent with the expression trends of using best reference genes individually (Figure 4A,B). Moreover, the reference gene combination of Act and EF1α was used in ‘62579’ to correct the expression level of the SlMLO1 gene (Figure 4G), which was consistent with the expression trends of using best reference genes individually (Figure 4E,F).

4. Discussion

The selection of appropriate reference genes is a very important preliminary stage in gene expression research because the selection of inappropriate reference genes (whose expression may be affected by exogenous treatment) may lead to errors in the results [11,42]; however, this issue has received little attention, while its importance seems to be not fully recognized. The most stable reference genes are different in various plant species. Each plant has its own most stable reference genes, and even the most stable reference genes screened in one plant may not be applicable to other plants of the same family. Studies have shown that Act is the most stable reference gene in tomato leaves under abiotic stress [43], but in potato leaves, whether it is under biotic stress or abiotic stress, Act is the most inappropriate reference gene [44]. In addition, under different stress treatments, even in the same tissue of the same plant, the most stable reference genes are not completely consistent. The expression of candidate reference genes may be different due to different types of exogenous treatments. Under biotic stress (such as virus infection, such as TSWV and ToMV), the expression of Act, CAC and EF1α were the most stable in tomato leaves [16,27]. Even under biotic stress, the most stable reference genes are different because of the different types of pathogens. For example, the most stable reference genes in tomato leaves infected by tobacco mosaic virus (TMV) were Act [28], while the most stable reference genes in tomato yellow leaf curl virus (TYLCV) were TUB and GAPDH [45]. This indicates that the ideal reference genes only exist relatively; therefore, to examine the expression of target genes by qRT-PCR, it is necessary to select the most stable reference genes according to different experimental treatments and tissue samples.
In studying the interaction between plants and pathogens, the use of inappropriate reference genes in the qRT-PCR analysis will lead to unreliable results of the target genes. A large number of studies have shown that different reference genes are used for normalization in the study of plant PM-related functional gene expression profiles. It has been reported that Act was used as a reference gene in the study of cucumber, tomato, bitter gourd, wheat and rubber tree [46,47,48,49,50]. EF1α was used as a reference gene for eggplant and petunia [51,52]. Tubulin was used as a reference gene for studying the mulberry [53], and 28SrRNA was used as a reference gene for studying the oat [54]. The reference genes used to investigate the functional gene expression profiles associated with tomato PM are also different. The Act, EF1α, L33 and GAPDH have been reported as reference genes for tomatoes [26,47,55,56,57]. In addition, BestKeeper Software was used to analyze the stability of L33, EF1α, Act and Ubi genes of susceptible tomato MM under PM stress [26]. The results showed that EF1α and L33 were relatively stable reference genes, which was inconsistent with our current results. It may be caused by the inconsistency of the number of software used and the sampling time after inoculation. Using 6 hpi and 10 hpi MM leaves as materials, Zheng et al. [26] analyzed the stability of four candidate reference genes using BestKeeper Software only. However, in this study, we used four different software to analyze the stability of eight candidate reference genes in resistant and susceptible materials at 0 h, 24 h, 48 h and 120 h after inoculation, and RefFinder was used for comprehensive analysis of the four rankings was more reliable and reasonable to determine the stability of candidate reference genes in tomato leaf tissue under PM stress, and provide a theoretical basis for the subsequent detection of changes in the expression of related functional genes in the interaction between tomato and PM. The results show that the results of gene expression stability ranking analyzed by these four software were not completely consistent, which was caused by different computing programs. By analysis of RefFinder, the comprehensive analysis results showed that the most stable reference gene of tomato was Act under the PM stress. Act and GAPDH were the most stable reference genes for ‘MM’ in susceptible tomatoes; the most stable reference genes for resistant tomato ‘62579′ were Act and EF1α. We also found that there was some bias by using a single reference gene. In this study, when using the best reference gene individually in the normalization of the SlMLO1 gene, similar expression trends were observed, but there was some bias in expression level. This should be caused by the different expression levels of the two reference genes, which is unavoidable. Therefore, it is more reliable to use the combination of all the best reference genes for normalization in the study of target gene expression profiles.
In addition, many studies have used the same reference gene in resistant and susceptible tomato materials [46,47,57]. Although the Act gene screened in this study was the most stable in both susceptible and resistant tomatoes, the expression of the more stable reference gene GAPDH in susceptible tomatoes was relatively unstable in resistant tomatoes, and the comprehensive ranking of candidate reference gene stability was inconsistent in susceptible and resistant tomatoes. Therefore, it is unreasonable to use the same reference gene in resistant and susceptible materials. In general, by comparing the analysis results obtained from different reference gene screening software, the most stable reference genes in the expression studies of different varieties and various treatments can be better determined, and the experimental errors caused by artificial blind selection of reference genes can be eliminated.
Tomato is a traditional model plant and one of the most important economic crops in the world. There are many varieties of tomatoes [26]. In the molecular breeding of tomato resistance to PM, it is of great significance to study the differential expression of tomato PM-related functional genes at different growth stages or in specific environments. The expression stability of the reference genes determines the authenticity of the target gene’s expression [58,59], but there is no such report on the systematic screening of reference genes in tomatoes under PM stress. Therefore, mining the most stable reference genes of tomatoes under PM stress will also be more helpful in revealing the internal law of the expression of tomato PM-related functional genes and lay the foundation for the molecular breeding of tomato resistance to PM.

5. Conclusions

This study analyzed and compared the expression stability of eight candidate reference genes in susceptible and resistant tomato varieties under PM stress. The results of geNorm showed that the normalization of qRT-PCR using two reference genes could meet the requirements of tomatoes under PM stress. It was more reliable and reasonable to select stable reference genes by RefFinder comprehensive analysis of the stability ranking values of candidate reference genes in the four software. Under PM stress, the stable reference genes for the susceptible tomato ‘MM’ were the combination of Act and GAPDH, while that for the resistant tomato ‘62579′ were the combination of Act and EF1α. Generally, the Act gene was the most stable reference gene in both tomato varieties under PM stress. This study provides a theoretical basis for the subsequent study on the expression changes of functional genes related to PM resistance or stress response in tomatoes and has a certain reference value for the analysis of the molecular mechanism of resistance or susceptibility of tomato response to PM stress.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/agronomy12123171/s1, Figure S1: Results of RNA integrity. (A) susceptible tomato ‘MM’. (B) resistant tomato ‘62579’.

Author Contributions

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

Funding

This research was funded by the National Natural Science Foundation of China (NSFC) project (31860561), Ningxia Hui Autonomous Region Agricultural Special and Dominant Industry Breeding Project (NXNYYZ20200101), Ningxia Hui Autonomous Region Key Research and Development Plan (Major) Key Project (2019BBF02022).

Data Availability Statement

Not applicable.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Bergougnoux, V. The history of tomato: From domestication to biopharming. Biotechnol. Adv. 2014, 32, 170–189. [Google Scholar] [CrossRef]
  2. Doudna Jennifer, A.; Charpentier, E. The new frontier of genome engineering with CRISPR-Cas9. Science 2014, 346, 1258096. [Google Scholar] [CrossRef] [PubMed]
  3. Jones, H.; Whipps, J.M.; Gurr, S.J. The tomato powdery mildew fungus Oidium neolycopersici. Mol. Plant Pathol. 2001, 2, 303–309. [Google Scholar] [CrossRef] [Green Version]
  4. Lebeda, A.; Mieslerová, B.; Jankovics, T.; Kiss, L.; Van, E.J. First detection of tomato powdery mildew caused by Oidium neolycopersici in South Africa. S. Afr. J. Bot. 2015, 99, 153–157. [Google Scholar] [CrossRef]
  5. Mamgain, A.; Biswas, M.K.; Dey, N. Potential role of chemical elicitors in induced systemic resistance for the effective management of Alternaria blight in mustard. J. Pharmacogn. Phytochem. 2019, 8, 2246–2250. [Google Scholar]
  6. Riaz, S.; Boursiquot, J.M.; Dangl, G.S.; Lacombe, T.; Laucou, V.; Tenscher, A.C.; Walker, M.A. Identification of mildew resistance in wild and cultivated Central Asian grape germplasm. BMC Plant Biol. 2013, 13, 149–169. [Google Scholar] [CrossRef] [Green Version]
  7. Zou, S.; Wang, H.; Li, Y.; Kong, Z.; Tang, D. The NB-LRR gene Pm60 confers powdery mildew resistance in wheat. New Phytol. 2017, 218, 298–309. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  8. 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, 1–11. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  9. Vandesompele, J.; Preter, K.D.; Pattyn, F.; Poppe, B.; Roy, N.V.; Paepe, A.D.; 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] [Green Version]
  10. Lu, Y.; Liao, J.; Guo, Z.; Zheng, H.; Lu, L.; Cai, Z.; Zeng, Z.; Ying, Z.; Chen, M. Selection and evaluation of potential reference genes for quantitative real-time PCR in Agaricus blazei based on transcriptome sequencing data. BioMed. Res. Int. 2021, 2021, 6661842. [Google Scholar] [CrossRef]
  11. Bustin, S.A. Quantification of mRNA using real-time reverse transcription PCR (RT-PCR): Trends and problems. J. Mol. Endocrinol. 2002, 29, 23–39. [Google Scholar] [CrossRef] [PubMed]
  12. Fan, L.; Chen, M.; Dong, B.; Wang, N.; Yu, Q.; Wang, X.; Xuan, L.; Wang, Y.; Zhang, S.; Shen, Y. Transcriptomic analysis of flower bud differentiation in Magnolia sinostellata. Genes 2018, 9, 212–226. [Google Scholar] [CrossRef] [Green Version]
  13. 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] [PubMed] [Green Version]
  14. Coker, J.S.; Davies, E. Selection of Candidate Housekeeping Controls in Tomato Plants Using EST Data. Biotechniques 2003, 35, 740–748. [Google Scholar] [CrossRef] [PubMed]
  15. Hu, R.; Fan, C.; Li, H.; Zhang, Q.; Fu, Y. Evaluation of putative reference genes for gene expression normalization in soybean by quantitative real-time RT-PCR. BMC Mol. Biol. 2009, 10, 93–104. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  16. Wieczorek, P.; Wrzesińska, B.; Obrępalska-Stęplowska, A. Assessment of reference gene stability influenced by extremely divergent disease symptoms in Solanum Lycopersicum L. J. Virol. Methods 2013, 194, 161–168. [Google Scholar] [CrossRef]
  17. Zheng, Z.; Nonomura, T.; Appiano, M.; Pavan, S.; Matsuda, Y.; Toyoda, H.; Wolters, A.A.; Visser, R.G.F.; Bai, Y. Loss of function in Mlo orthologs reduces susceptibility of pepper and tomato to powdery mildew disease caused by Leveillula taurica. PLoS ONE 2013, 8, e70723. [Google Scholar] [CrossRef]
  18. Jarošová, J.; Kundu, J.K. Validation of reference genes as internal control for studying viral infections in cereals by quantitative real-time RT-PCR. BMC Plant Biol. 2010, 10, 146–154. [Google Scholar] [CrossRef] [Green Version]
  19. Scholtz, J.J.; Visser, B. Reference gene selection for qPCR gene expression analysis of rust-infected wheat. Physiol. Mol. Plant Pathol. 2013, 81, 22–25. [Google Scholar] [CrossRef]
  20. Foss, D.L.; Baarsch, M.J.; Murtaugh, M.P. Regulation of hypoxanthine phosphoribosyltransferase, glyceraldehyde-3-phosphate dehydrogenase and β-actin mRNA expression in porcine immune cells and tissues. Anim. Biotechnol. 1998, 9, 67–78. [Google Scholar] [CrossRef]
  21. Schmittgen, T.D.; Zakrajsek, B.A. Effect of experimental treatment on housekeeping gene expression: Validation by real-time, quantitative RT-PCR. J. Biochem. Biophys. Methods 2000, 46, 69–81. [Google Scholar] [CrossRef]
  22. Du, M.; Zhou, M.; Deng, L.; Li, C.; Li, C. Current status and prospects on tomato molecular breeding—From gene cloning to cultivar improvement. Acta Hortic. Sin. 2017, 44, 581–600. (In Chinese) [Google Scholar] [CrossRef]
  23. Eissa, N.; Kermarrec, L.; Hussein, H.; Bernstein, C.N.; Ghia, J.E. Appropriateness of reference genes for normalizing messenger RNA in mouse 2,4-dinitrobenzene sulfonic acid (DNBS)-induced colitis using quantitative real time PCR. Sci. Rep. 2017, 7, 42427. [Google Scholar] [CrossRef] [Green Version]
  24. Albuquerque, G.M.R.; Fonseca, F.C.A.; Boiteux, L.S.; Borges, R.C.F.; Miller, R.N.G.; Lopes, C.A.; Souza, E.B.; Fonseca, M.E.N. Stability analysis of reference genes for RT-qPCR assays involving compatible and incompatible Ralstonia solanacearum-tomato ‘Hawaii 7996’ interactions. Sci. Rep. 2021, 11, 18719. [Google Scholar] [CrossRef] [PubMed]
  25. Zhou, P.; Wang, X.; Kong, W.; Wang, L.; Bai, S.; Li, X.; Guo, M.; Cheng, G. Identification of pathogen causing tomato powdery mildew in Yinchuan. J. Hunan Agric. Univ. (Nat. Sci.) 2022, 48, 196–200. (In Chinese) [Google Scholar] [CrossRef]
  26. Zheng, Z.; Appiano, M.; Pavan, S.; Bracuto, V.; Ricciardi, L.; Visser, R.G.F.; Wolters, A.A.; Bai, Y. Genome-wide study of the tomato SlMLO gene family and its functional characterization in response to the powdery mildew fungus Oidium neolycopersici. Front. Plant Sci. 2016, 7, 380. [Google Scholar] [CrossRef] [Green Version]
  27. Mascia, T.; Santovito, E.; Gallitelli, D.; Cillo, F. Evaluation of reference genes for quantitative reverse-transcription polymerase chain reaction normalization in infected tomato plants. Mol. Plant Pathol. 2010, 11, 805–816. [Google Scholar] [CrossRef]
  28. Zhang, N.; Wei, Z.; Du, L.; Qing, L.; Sun, X. Selection of reference genes in tobacco and tomato stressed by TMV or PVX. J. Southwest Univ. (Nat. Sci. Ed. ) 2016, 38, 13–18. (In Chinese) [Google Scholar] [CrossRef]
  29. 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, 131–142. [Google Scholar] [CrossRef] [Green Version]
  30. Lacerda, A.L.M.; Fonseca, L.N.; Blawid, R.; Boiteux, L.S.; Ribeiro, S.G.; Brasileiro, A.C.M. Reference gene selection for qPCR analysis in tomato-bipartite begomovirus interaction and validation in additional tomato-virus pathosystems. PLoS ONE 2015, 10, e0136820. [Google Scholar] [CrossRef] [Green Version]
  31. Jose, S.; Abbey, J.; Jaakola, L.; Percival, D. Selection and validation of reliable reference genes for gene expression studies from Monilinia vaccinii-corymbosi infected wild blueberry phenotypes. Sci. Rep. 2020, 10, 11688. [Google Scholar] [CrossRef] [PubMed]
  32. Andersen, C.L.; Jensen, J.L.; Rntoft, 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] [PubMed] [Green Version]
  33. 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] [PubMed]
  34. Hao, X.; Horvath, D.P.; Chao, W.S.; Yang, Y.; Wang, X.; Xiao, B. Identification and evaluation of reliable reference genes for quantitative real-time PCR analysis in tea plant (Camellia Sinensis (L.) O. Kuntze). Int. J. Mol. Sci. 2014, 15, 22155–22172. [Google Scholar] [CrossRef]
  35. Wang, J.; Liu, L.; Tang, Q.; Sun, K.; Zeng, L.; Wu, Z. Evaluation and selection of suitable qRT-PCR reference genes for light responses in Tea Plant (Camellia Sinensis). Sci. Hortic. 2020, 289, 110488. [Google Scholar] [CrossRef]
  36. Li, R.; Cui, K.; Xie, Q.; Xie, S.; Chen, X.; Zhuo, L.; Cao, A.; Shen, H.; Jin, X.; Wang, F.; et al. Selection of the reference genes for quantitative gene expression by RT-qPCR in the Desert Plant Stipagrostis pennata. Sci. Rep. 2021, 11, 21711. [Google Scholar] [CrossRef]
  37. Zhu, X.; Wang, B.; Wang, X.; Wei, X. Screening of stable internal reference gene of quinoa under hormone treatment and abiotic stress. Physiol. Mol. Biol. Plants 2021, 27, 2459–2470. [Google Scholar] [CrossRef]
  38. Yin, Z.; Xie, F.; Michalak, K.; Zhang, B.; Zimnoch-Guzowska, E. Reference gene selection for miRNA and mRNA normalization in potato in response to potato virus Y. Mol. Cell. Probes 2021, 55, 101691. [Google Scholar] [CrossRef] [PubMed]
  39. Jiang, X.; Xue, Y.; Zhou, H.; Li, S.; Zhang, Z.; Hou, R.; Ding, Y.; Hu, K. Evaluation of reference gene suitability for quantitative expression analysis by quantitative polymerase chain reaction in the mandibular condyle of sheep. Mol. Med. Rep. 2015, 12, 5633–5640. [Google Scholar] [CrossRef] [Green Version]
  40. Chen, Y.; Wang, Y.; Zhang, H. Genome-wide analysis of the mildew resistance locus o (MLO) gene family in tomato (Solanum Lycopersicum L.). Plant Omics J. 2014, 7, 87–93. [Google Scholar]
  41. Kim, D.; Jin, B.; Je, B.I.; Choi, Y.; Kim, B.S.; Jung, H.J.; Nou, I.S.; Park, Y. Development of DNA markers for Slmlo1.1, a new mutant allele of the powdery mildew resistance gene SlMlo1 in tomato (Solanum Lycopersicum). Genome 2018, 61, 703–712. [Google Scholar] [CrossRef] [Green Version]
  42. Bustin, S.A.; Benes, V.; Garson, J.A.; Hellemans, J.; Huggett, J.; Kubista, M.; Mueller, R.; Nolan, T.; Pfaffl, M.W.; Shipley, G.L. The MIQE guidelines: Minimum information for publication of quantitative real-time PCR experiments. Clin. Chem. 2009, 55, 611–622. [Google Scholar] [CrossRef]
  43. 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]
  44. 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] [PubMed]
  45. Jiang, J.; Wang, Y.; Zhao, L.; Zhou, R.; Li, Y.; Zhao, T.; Yu, W. Selection of tomato reference genes for qRT-PCR. Jiangsu J. Agric. Sci. 2017, 33, 389–396. (In Chinese) [Google Scholar] [CrossRef]
  46. Moradi, N.; Rahimian, H.; Dehestani, A.; Babaeizad, V. Cucumber response to Sphaerotheca fuliginea: Differences in antioxidant enzymes activity and pathogenesis-related gene expression in susceptible and resistant genotypes. J. Plant Mol. Breed. 2016, 4, 33–40. [Google Scholar] [CrossRef]
  47. Zhang, Y.; Xu, K.; Pei, D.; Yu, D.; Zhang, J.; Li, X.; Chen, G.; Yang, H.; Zhou, W.; Li, C. ShORR-1, a novel tomato gene, confers enhanced host resistance to Oidium neolycopersici. Front. Plant Sci. 2019, 10, 1400. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  48. Zhang, X.; Cui, J.; Zhang, C.; Yang, X.; Pan, H.; Du, H.; Ahmad, A.; Wu, T.; Yao, C. Genome-wide identification and analysis of the MLO gene family for candidate powdery mildew susceptibility factors in Momordica charantia. Sci. Hortic. 2022, 283, 110119. [Google Scholar] [CrossRef]
  49. Eissa, H.F.; Hassanien, S.E.; Ramadan, A.M.; El-Shamy, M.M.; Saleh, O.M.; Shokry, A.M.; Abdelsattar, M.; Morsy, Y.B.; El-Maghraby, M.A.; Alameldin, H.F.; et al. Developing Transgenic wheat to encounter rusts and powdery mildew by overexpressing barley Chi26 gene for fungal resistance. Plant Methods 2017, 13, 41–53. [Google Scholar] [CrossRef] [Green Version]
  50. Qin, B.; Wang, M.; He, H.; Xiao, H.; Zhang, Y.; Wang, L. Identification and characterization of a potential candidate Mlo gene conferring susceptibility to powdery mildew in rubber tree. Phytopathology 2019, 109, 1236–1245. [Google Scholar] [CrossRef] [PubMed]
  51. Bracuto, V.; Appiano, M.; Ricciardi, L.; Göl, D.; Visser, R.G.F.; Bai, Y.; Pavan, S. Functional characterization of the powdery mildew susceptibility gene SmMLO1 in eggplant (Solanum Melongena L.). Transgenic Res. 2017, 26, 323–330. [Google Scholar] [CrossRef]
  52. Jiang, P.; Chen, Y.; Wilde, H.D. Reduction of MLO1 expression in petunia increases resistance to powdery mildew. Sci. Hortic. 2016, 201, 225–229. [Google Scholar] [CrossRef]
  53. Reddy, R.A.; Dubey, H.; Vijayan, K.; Ponnuvel, K.M.; Mishra, R.K.; Suresh, K. Genome wide characterization revealed MnMLO2 and MnMLO6A as candidate genes involved in powdery mildew susceptibility in mulberry. Mol. Biol. Rep. 2020, 47, 2889–2900. [Google Scholar] [CrossRef]
  54. Reilly, A.; Gibriel, H.A.Y.; Karki, S.J.; Twamley, A.; Finnan, J.; Kildea, S.; Feechan, A. Genome-wide identification of the oat MLO family and identification of a candidate AsMLO associated with powdery mildew susceptibility. bioRxiv 2021, 1–31. [Google Scholar] [CrossRef]
  55. Huibers, R.P.; Loonen, A.E.H.M.; Gao, D.; Ackerveken, G.V.; Visser, R.G.F.; Bai, Y. Powdery mildew resistance in tomato by impairment of SlPMR4 and SlDMR1. PLoS ONE 2013, 8, e67467. [Google Scholar] [CrossRef] [Green Version]
  56. Appiano, M.; Pavan, S.; Catalano, D.; Zheng, Z.; Bai, Y. Identification of candidate MLO powdery mildew susceptibility genes in cultivated Solanaceae and functional characterization of tobacco NtMLO1. Transgenic Res. 2015, 24, 847–858. [Google Scholar] [CrossRef] [Green Version]
  57. Lian, Q.; Meng, Y.; Zhao, X.; Xu, Y.; Wang, Y.; Day, B.; Ma, Q. ShNPSN11, a vesicle-transport-related gene, confers disease resistance in tomato to Oidium neolycopersici. Biochem. J. 2020, 477, 3851–3866. [Google Scholar] [CrossRef]
  58. Tu, C.; Xu, P.; Han, R.; Luo, J.; Xu, L. Defining Suitable Reference Genes for qRT-PCR in Plagiodera versicolora (Coleoptera: Chrysomelidae) under Different Biotic or Abiotic Conditions. Agronomy 2022, 12, 1192. [Google Scholar] [CrossRef]
  59. Lin, Y.; Lai, Z. Reference gene selection for qPCR analysis during somatic embryogenesis in longan tree. Plant Sci. 2010, 178, 359–365. [Google Scholar] [CrossRef]
Agronomy 12 03171 g001 550
Figure 1. Agarose gel electrophoresis analysis of PCR products of eight reference genes in tomato. M. DL2000 maker, 1. EF1α, 2. L33, 3. Act, 4. Ubi, 5. GAPDH, 6. UK, 7. CAC, 8. TIP41.
Figure 1. Agarose gel electrophoresis analysis of PCR products of eight reference genes in tomato. M. DL2000 maker, 1. EF1α, 2. L33, 3. Act, 4. Ubi, 5. GAPDH, 6. UK, 7. CAC, 8. TIP41.
Agronomy 12 03171 g001
Agronomy 12 03171 g002 550
Figure 2. Distribution of Ct values of eight candidate reference genes in tomato. Black circles represent the maximum and minimum values.
Figure 2. Distribution of Ct values of eight candidate reference genes in tomato. Black circles represent the maximum and minimum values.
Agronomy 12 03171 g002
Agronomy 12 03171 g003 550
Figure 3. Analysis results of geNorm Software. (A) Expression stability of eight reference genes analyzed by geNorm. (B) geNorm analysis of eight reference genes matching variation values. The red line is a 0.15 threshold line.
Figure 3. Analysis results of geNorm Software. (A) Expression stability of eight reference genes analyzed by geNorm. (B) geNorm analysis of eight reference genes matching variation values. The red line is a 0.15 threshold line.
Agronomy 12 03171 g003
Agronomy 12 03171 g004 550
Figure 4. Relative expression normalization of SlMLO1 genes in ‘MM’ and ‘62579′. (A) Using Act as reference gene in ‘MM’. (B) Using GAPDH as reference gene in ‘MM’. (C) Using the reference gene combination of Act and GAPDH in ‘MM’. (D) Using UK as reference gene in ‘MM’. (E) Using Act as reference gene in ‘62579′. (F) Using EF1α as reference gene in ‘62579′. (G) Using the reference gene combination of Act and EF1α in ‘62579′. (H) Using TIP41 as reference gene in ‘62579′.
Figure 4. Relative expression normalization of SlMLO1 genes in ‘MM’ and ‘62579′. (A) Using Act as reference gene in ‘MM’. (B) Using GAPDH as reference gene in ‘MM’. (C) Using the reference gene combination of Act and GAPDH in ‘MM’. (D) Using UK as reference gene in ‘MM’. (E) Using Act as reference gene in ‘62579′. (F) Using EF1α as reference gene in ‘62579′. (G) Using the reference gene combination of Act and EF1α in ‘62579′. (H) Using TIP41 as reference gene in ‘62579′.
Agronomy 12 03171 g004
Table
Table 1. Primers of tomato candidate reference genes.
Table 1. Primers of tomato candidate reference genes.
OrdinalGene Name Accession
Number		Primer Sequence (5′-3′)Amplicon
(bp)		R2E (%)Reference
1EF1αX14449F: ACAGGCGTTCAGGTAAGGAA1200.99899.6[26]
R: GAGGGTATTCAGCAAAGGTCTC
2L33Q2MI79F: GGGAAGAGGCTGGGATACATC1380.99898.4[26]
R: AGGAGGCAAATTGGACTTGAAC
3ActNP_001317048.1F: GCTCCACCAGAGAGGAAATACAGT1070.998101.5[17]
R: CATACTCTGCCTTTGCAATCCA
4UbiXP_004248311F: GGACGGACGTACTCTAGCTGAT1340.996103.2[26]
R: AGCTTTCGACCTCAAGGGTA
5GAPDHU93208F: ACCACAAATTGCCTTGCTCCCTTG1100.99897.6[27]
R: ATCAACGGTCTTCTGAGTGGCTGT
6UKLOC101267587F: TGGTAAGGGCACCCAATGTGCTAA1140.990106.7[28]
R: ATCATCGTCCCATTCTCGGAACCA
7CACSGN-U314153F: CCTCCGTTGTGATGTAACTGG1730.993107.1[29]
R: ATTGGTGGAAAGTAACATCATCG
8TIP41SGN-U584254F: ATGGAGTTTTTGAGTCTTCTGC2350.994101.8[30]
R: GCTGCGTTTCTGGCTTAGG
9SlMLO1KU759512F: CTTTGGGCAGGCTAAAGATG1280.998102.1[26]
R: AATGCCTACGTCCAAACGAG
Table
Table 2. Analysis results of NormFinder Software.
Table 2. Analysis results of NormFinder Software.
Ranking Order‘MM’ and ‘62579′‘MM’‘62579′
Gene NameStability Value (S)Gene NameStability Value (S)Gene NameStability Value (S)
1Act0.279GAPDH0.133Act0.223
2EF1α0.395Act0.154EF1α0.356
3L330.397L330.275L330.426
4Ubi0.466CAC0.280Ubi0.528
5CAC0.520EF1α0.371UK0.625
6GAPDH0.540TIP410.377CAC0.670
7UK0.770Ubi0.385GAPDH0.774
8TIP411.118UK0.918TIP411.567
Table
Table 3. Expression stability of eight reference genes was analyzed by BestKeeper.
Table 3. Expression stability of eight reference genes was analyzed by BestKeeper.
DatasetRanking Order12345678
‘MM’ and ‘62579′Gene nameActGAPDHUbiEF1αCACL33UKTIP41
geo Mean [CP]24.32 26.07 21.60 22.73 26.95 23.81 27.75 28.09
min [CP]23.75 23.38 20.83 21.64 25.53 22.57 26.78 25.66
max [CP]24.92 27.14 23.07 23.74 28.43 25.18 29.39 31.32
std dev [±CP]0.23 0.41 0.48 0.52 0.58 0.62 0.72 0.83
CV [% CP]0.95 1.56 2.22 2.29 2.16 2.61 2.60 2.96
coeff. of corr. [r]0.768 0.673 0.707 0.794 0.732 0.866 0.527 0.560
p-value0.0010.0010.0010.0010.0010.0010.0080.004
‘MM’Gene nameActCACGAPDHUbiL33EF1αTIP41UK
geo Mean [CP]24.3027.2426.2421.6624.1423.0528.1927.84
min [CP]23.7526.8225.6921.2723.4722.5327.2126.78
max [CP]24.9228.0126.7322.3524.9423.6429.3329.39
std dev [±CP]0.230.230.230.300.320.350.390.77
CV [% CP]0.930.840.891.411.331.521.392.76
coeff. of corr. [r]0.8360.5210.8020.4700.7960.5850.7460.519
p-value0.0010.0820.0020.1230.0020.0460.0050.083
‘62579′Gene nameActEF1αGAPDHL33UbiUKCACTIP41
geo Mean [CP]24.34 22.41 25.90 23.48 21.54 27.65 26.65 28.00
min [CP]23.77 21.64 23.38 22.57 20.83 26.80 25.53 25.66
max [CP]24.83 23.74 27.14 25.18 23.07 29.08 28.43 31.32
std dev [±CP]0.240.530.590.62 0.640.660.771.29
CV [% CP]1.00 2.38 2.29 2.66 2.95 2.39 2.88 4.60
coeff. of corr. [r]0.9260.8240.6310.8620.7810.6100.7230.555
p-value0.0010.0010.0280.0010.0030.0350.0080.061
CP: equivalent terminology for Ct; std dev [±CP]: the standard deviation of the CP (SD); CV (%CP): the coefficient of variance expressed as a percentage of the CP level. coeff. of corr. [r]: the Pearson correlation coefficient; The correlation between each candidate reference gene and the BestKeeper index was calculated using the Pearson correlation coefficient [r] and the p-value.
Table
Table 4. Analysis results of the comparative ∆CT method.
Table 4. Analysis results of the comparative ∆CT method.
Ranking Order‘MM’ and ‘62579′‘MM’‘62579′
Gene NameAverage of STDEVGene NameAverage of STDEVGene NameAverage of STDEV
1Act0.673Act0.411Act0.763
2EF1α0.687GAPDH0.430EF1α0.768
3L330.690L330.457L330.789
4Ubi0.722EF1α0.490Ubi0.828
5CAC0.784Ubi0.509CAC0.950
6GAPDH0.796CAC0.517UK0.964
7UK0.960TIP410.562GAPDH1.031
8TIP411.223UK0.954TIP411.653
Table
Table 5. Ranking of reference gene stability in the entire dataset of ‘MM’ and ‘62579′.
Table 5. Ranking of reference gene stability in the entire dataset of ‘MM’ and ‘62579′.
Gene NamegeNormNormFinderBestKeeper∆CTGeomean of Ranking ValuesRefFinder
Act41111.41 1
EF1α12422.00 2
L3313632.71 3
Ubi34343.46 4
GAPDH56264.36 5
CAC65555.23 6
UK77777.00 7
TIP4188888.00 8
Table
Table 6. Ranking of stability of reference genes in ‘MM’ subdataset.
Table 6. Ranking of stability of reference genes in ‘MM’ subdataset.
Gene NamegeNormNormFinderBestKeeper∆CTGeomean of Ranking ValuesRefFinder
Act12111.19 1
GAPDH41322.21 2
EF1α15643.31 3
L3333533.41 4
CAC64264.12 5
Ubi57455.14 6
TIP4176776.74 7
UK88888.00 8
Table
Table 7. Ranking of reference gene stability in the ‘62579′ subdatasets.
Table 7. Ranking of reference gene stability in the ‘62579′ subdatasets.
Gene NamegeNormNormFinderBestKeeper∆CTGeomean of Ranking ValuesRefFinder
Act4111 1.411
EF1α1222 1.682
L331343 2.453
Ubi3454 3.944
GAPDH7737 5.665
CAC5675 5.696
UK6566 5.737
TIP418888 8.008
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Bai, S.; Wang, X.; Guo, M.; Cheng, G.; Khan, A.; Yao, W.; Gao, Y.; Li, J. Selection and Evaluation of Reference Genes for Quantitative Real-Time PCR in Tomato (Solanum lycopersicum L.) Inoculated with Oidium neolycopersici. Agronomy 2022, 12, 3171. https://doi.org/10.3390/agronomy12123171

AMA Style

Bai S, Wang X, Guo M, Cheng G, Khan A, Yao W, Gao Y, Li J. Selection and Evaluation of Reference Genes for Quantitative Real-Time PCR in Tomato (Solanum lycopersicum L.) Inoculated with Oidium neolycopersici. Agronomy. 2022; 12(12):3171. https://doi.org/10.3390/agronomy12123171

Chicago/Turabian Style

Bai, Shengyi, Xiaomin Wang, Meng Guo, Guoxin Cheng, Abid Khan, Wenkong Yao, Yanming Gao, and Jianshe Li. 2022. "Selection and Evaluation of Reference Genes for Quantitative Real-Time PCR in Tomato (Solanum lycopersicum L.) Inoculated with Oidium neolycopersici" Agronomy 12, no. 12: 3171. https://doi.org/10.3390/agronomy12123171

APA Style

Bai, S., Wang, X., Guo, M., Cheng, G., Khan, A., Yao, W., Gao, Y., & Li, J. (2022). Selection and Evaluation of Reference Genes for Quantitative Real-Time PCR in Tomato (Solanum lycopersicum L.) Inoculated with Oidium neolycopersici. Agronomy, 12(12), 3171. https://doi.org/10.3390/agronomy12123171

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