Identifying optimal reference genes for the normalization of microRNA expression in cucumber under viral stress

Chaoqiong Liang; Jianjun Hao; Yan Meng; Laixin Luo; Jianqiang Li

doi:10.1371/journal.pone.0194436

Abstract

Cucumber green mottle mosaic virus (CGMMV) is an economically important pathogen and causes significant reduction of both yield and quality of cucumber (Cucumis sativus). Currently, there were no satisfied strategies for controlling the disease. A better understanding of microRNA (miRNA) expression related to the regulation of plant-virus interactions and virus resistance would be of great assistance when developing control strategies for CGMMV. However, accurate expression analysis is highly dependent on robust and reliable reference gene used as an internal control for normalization of miRNA expression. Most commonly used reference genes involved in CGMMV-infected cucumber are not universally expressed depending on tissue types and stages of plant development. It is therefore crucial to identify suitable reference genes in investigating the role of miRNA expression. In this study, seven reference genes, including Actin, Tubulin, EF-1α, 18S rRNA, Ubiquitin, GAPDH and Cyclophilin, were evaluated for the most accurate results in analyses using reverse transcription-quantitative polymerase chain reaction (RT-qPCR). Gene expression was assayed on cucumber leaves, stems and roots that were collected at different days post inoculation with CGMMV. The expression data were analyzed using algorithms including delta-Ct, geNorm, NormFinder, and BestKeeper as well as the comparative tool RefFinder. The reference genes were subsequently validated using miR159. The results showed that EF-1α and GAPDH were the most reliable reference genes for normalizing miRNA expression in leaf, root and stem samples, while Ubiquitin and EF-1α were the most suitable combination overall.

Citation: Liang C, Hao J, Meng Y, Luo L, Li J (2018) Identifying optimal reference genes for the normalization of microRNA expression in cucumber under viral stress. PLoS ONE 13(3): e0194436. https://doi.org/10.1371/journal.pone.0194436

Editor: Youjun Zhang, Chinese Academy of Agricultural Sciences, CHINA

Received: October 24, 2017; Accepted: March 2, 2018; Published: March 15, 2018

Copyright: © 2018 Liang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

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

Funding: This research was supported by National Natural Science Foundation of China (Project No. 31371910), http://www.nsfc.gov.cn/. The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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

Introduction

Cucumber (Cucumis sativus) is an economically important member of the gourd family, Cucurbitaceae, that is cultivated throughout the world [1, 2]. Cucumber yield is significantly affected by a range of biotic and abiotic stresses, and viral diseases constitute a major constraint to both cucumber quality and yield [3]. Cucumber green mottle mosaic virus (CGMMV) is one of the important viral pathogens of cucumber. CGMMV seriously affects the host’s physiology, leading to sterile flowers and abnormal fruit [4], which often results in reduced yields and a lowered market value [5]. As a consequence, strict quarantine regulations have been imposed to prevent the spread of CGMMV between different regions of cultivation, and limit its negative effect on global cucumber production [6, 7].

It is now well known that microRNAs (miRNAs), a class of small, non-coding, single stranded RNA molecules that range in the length from 19 to 24 nucleotides [8], play a fundamental role in key biological processes in plants including responses to environmental stresses and host-pathogen interactions [9–11]. Recent research has demonstrated that miRNA expression can be used as an indicator of cucumber responses to viral infection, which provides a new insight into the complex regulatory networks and specific miRNA involved in the metabolic and signaling pathways underlying developmental and cellular processes in cucumber infected by viroid [12] or virus [13]. For example, Liu et al. [13] identified 8 novel, 23 known and 127 putative candidate miRNAs with altered expression in CGMMV-infected cucumber. However, accurate quantification of miRNA expression levels in response to viral infections requires reliable internal controls with highly stable expression. Using reference genes that can be used universally in a wide range of tissues can greatly improve the efficacy of this kind of analysis. Indeed, the selection of suitable reference genes is a pre-requisite for any expression study, in order to minimize experimental errors associated with different conditions and samples [14–17]. Constitutively expressed housekeeping genes are transcribed at a relatively constant level, and are usually selected for this purpose [18, 19], because their expression is generally assumed to be minimally affected by experimental conditions. However, recent studies have shown that housekeeping genes that are commonly used as reference genes may not be as consistently expressed as previously thought [20–23], and can vary according to plant species, tissue type, and environmental conditions [24]. The use of non-validated references genes can therefore result in inaccurate quantification of miRNA expression when comparing samples from different sources.

Reverse transcription-quantitative polymerase chain reaction (RT-qPCR) has become an extremely powerful tool for the detection and quantification of miRNA expression, exhibiting a high degree of sensitivity and specificity [25, 26]. Although suitable reference genes are well established in wheat [27], citrus [28], castor bean [29] and honeysuckle [30], there have been only three studies validating candidate reference genes for normalizing expression in cucumber. Furthermore, although these studies investigated a range of test conditions including abiotic stresses (nitrogen, salinity, drought, osmotic and oxidative stress, heat or cold), biotic stress (Pseudoperonospora cubensis) and hormone treatment (salicylic acid, methyl jasmonic acid and abscisic acid) [31–33], there have been no similar evaluations of suitable reference genes for the expression of miRNA in cucumber infected by viruses.

Given the increasing evidence that the expression of genes commonly used as internal controls for RT-qPCR studies can respond differently under different treatment conditions [34], the systematic evaluation of reference genes derived from genome-wide analyses is becoming an essential component of RT-qPCR analyses in order to improve the reliability of the results [19, 35, 36]. Many statistical algorithms have been developed to evaluate the expression stability of reference genes. For example, geNorm [34], NormFinder [37] and BestKeeper [38], which are all Visual Basic Applets for Microsoft Excel, have been used to determine the expression stability of candidate reference genes in a wide variety of plants including Arabidopsis [19, 39, 40], potato [41, 15], rice [42,43], citrus [28], soybean [44]and cucumber [31–33]. However, differences between the results generated by these algorithms have sometimes been observed [45, 46], indicating that a combination of several algorithms could provide a more accurate analysis.

Previous studies have identified a variety of reference genes for use in a range of cucumber tissues and organs at different developmental stages and under different biotic and abiotic stresses [31–33]. The most widely used internal controls include the genes encoding actin and tubulin, which are cytoskeletal proteins [18]; elongation factor 1-alpha (EF-1α), which facilitates translational elongation [47]; 18S ribosomal RNA (18S rRNA), a part of the ribosomal functional core [18]; ubiquitins, which are involved in the degradation of cellular proteins [48]; glyceraldehyde 3-phosphate dehydrogenase (GAPDH), which is involved in glycolysis [18]; and cyclophilin, a specific cytosolic binding protein for cyclosporin A [49]. However, there is currently little data regarding the expression stability of these reference genes in cucumber infected with viruses such as CGMMV. The object of the current study was therefore to investigate seven commonly used references genes including Actin, Tubulin, EF-1α, 18S rRNA, Ubiquitin, GAPDH and Cyclophilin to discover which are the most reliable internal controls for miRNA expression studies in a range of cucumber tissues infected with CGMMV.

Materials and methods

Plant materials and virus inoculant

CGMMV-free cucumber seeds cv. ‘Zhongnong 16’) [50] were obtained from the Institute of Vegetables and Flowers at the Chinese Academy of Agricultural Sciences (Beijing, China). The seed was sown in an insect-proof greenhouse in accordance with the protocol of a previous study [4]. CGMMV was originally collected from Zhejiang province, China, and was propagated and maintained in vivo on a cucumber host (Beijing Key Laboratory of Seed Disease Testing and Control, Beijing, China). Cucumber seedlings were artificially inoculated with either CGMMV sap (Treatment samples) or a Phosphate buffer/tween 80 mixture (PBST) (Control) at the three-true-leaf stage. Samples from the leaves, stems and roots of the test plants that were collected at 1, 14, 28 and 42 days post-inoculation (dpi) respectively, immediately frozen in liquid nitrogen and stored at -80°C until use.

Total RNA extraction and cDNA synthesis

Total RNAs were extracted from the cucumber samples using TRIzol^® Reagent (Ambion, USA) according to the protocol of the manufacturer, and its quantity and quality assessed using a NanoDrop 2000 (Thermo Scientific). RNA samples exhibiting an A_260/A280 ratio of 1.8–2.0 and an A_260/A230 ratio of 2.0–2.2 were selected for the subsequent analysis. cDNA was synthesized using the miRcute miRNA First-Strand cDNA Synthesis Kit (KR201, Tiangen Biotech, China) and stored at -20°C until use (http://dx.doi.org/10.17504/protocols.io.naxdafn).

Amplification of gene transcripts

The expression stability of seven reference genes, including Actin, Tubulin, EF-1α, 18S rRNA, Ubiquitin, GAPDH and Cyclophilin was evaluated in cucumber leaf, stem and root samples collected at different time points post inoculation with CGMMV. According to sequences of seven reference genes (S1 File), specific primers for RT-qPCR were designed using the Primer Premier 5.0 software, with the product size restricted to 127 to 193 base pairs (bp) and an optimal melting temperature (Tm) of 60°C (Table 1). The miR159 forward primer was designed using a csa-miR159 specific sequence, while the universal primer from the miRcute miRNA qPCR Detection Kit (SYBR Green) (FP401, Tiangen Biotech, China) was used as the reverse primer.

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Table 1. Primers used to amplify partial sequences of 7 reference genes and miR159 from cucumber samples.

https://doi.org/10.1371/journal.pone.0194436.t001

RT-qPCR was conducted using the miRcute miRNA qPCR Detection Kit (SYBR Green) (FP401, Tiangen Biotech, China) and 20 μL reaction mixtures containing 1 μL template cDNA (corresponding to 600 ng of starting total RNA), 10 μL 2× miRcute miRNA Premix (with SYBR & ROX), 0.4 μL of each primer (10 μM) and 8.2 μL RNase-free water. The RT-qPCR was processed using the Applied Biosystems 7500 Fast Real-Time PCR System (Life Technologies, USA) with the following program: 94°C for 2 min, followed by 40 cycles of 94°C for 20 s, 60°C for 34 s. Melting curves were generated at 95°C after the reaction had been terminated (S1 Fig). A set of seven 4-fold cDNA dilutions were used to create standard curves, and the correlation coefficient (R²) and PCR efficiency (E) determined using a linear regression model: E = (10^−1/slope −1)×100 [51] (S2 Fig). Each RT-qPCR reaction was performed in triplicate (3 technical replicates) for each sample (3 plants per sample). Non-template controls produced undetectable cycle threshold (Ct) values, while subsequent electrophoresis on 2.0% agarose gel stained with ethidium bromide confirmed the size of RT-qPCR products amplified from the treatment samples (S3 Fig).

Expression stability of reference genes

Expression stability of each reference gene was evaluated using four subsets of the samples, including leaf, stem, and root samples as well as a composite sample that incorporated the data from all of the individual samples to provide an overall assessment. The stability of the seven reference genes was initially evaluated individually using four different algorithms, including comparative delta-Ct (ΔCt) [52], geNorm version 3.5 [34], NormFinder version 20 [37] and BestKeeper version 1 [38], before the comparative web-based tool, RefFinder (http://leonxie.esy.es/RefFinder/) was used to collate the results from all four algorithms.

The comparative delta-Ct algorithm compares and ranks candidate reference genes by comparing the relative expression of ‘pairs of genes’ within each sample. If the ΔCt value of the two genes remains constant among different samples, it means that either both genes are stably expressed in those samples, or that the genes are co-regulated. However, if the ΔCt fluctuates, then one or both genes are variably expressed. The introduction of a third, fourth, or fifth gene into the comparisons provides increased fidelity indicating which pairs show the least variability, and hence which gene(s) has the most stable expression among test samples. These results can then be ranked or discarded. This process of elimination strategy allows a relatively large number of genes to be compared against one another according to their ΔCt values [52].

The geNorm algorithm also compares pairs of reference genes calculating expression stability measure M, which represents the average pairwise variation of each gene with the other candidate genes, and allows the selection of two optimized reference genes through the sequential exclusion of genes with lower expression stability [34]. The lower the M value, the higher the expression stability of a particular gene. In addition, geNorm provides analytical data regarding the optimal number of reference genes required to provide reliable normalization (V_n/n+1). According to Vandesompele et al. [34], a variation of < 0.15 indicates that an additional reference gene provides no significant improvement to the normalization factor, which means that the optimal number of reference genes has already been achieved and that the inclusion of the additional reference gene is not required.

In contrast, the NormFinder algorithm calculates and ranks the expression stability of each reference gene according to comparisons made between intra-group and inter-group variations of the candidate genes under evaluation [37].

Based on the output from geNorm and NormFinder, the two poorest performing reference genes were eliminated, and the rest five genes were investigated further for expression stability using BestKeeper [38], which estimates the most appropriate reference genes by computing geometric mean of a candidate cDNA based on its crossing point (CP) or threshold cycle (Ct) values. In addition, BestKeeper also takes into account the raw data from the RT-qPCR analysis. The algorithm calculates a pairwise correlation coefficient between each gene and the BestKeeper index (BI) and a standard deviation (SD) of the Ct-values for the whole data set. The gene with the highest correlation coefficient is considered to have the most stable expression [33, 38].

The data of expression stability was analyzed further using RefFinder, which can be used to confirm and integrate the output obtained from the comparative delta-Ct, geNorm, NormFinder and BestKeeper algorithms. RefFinder produces a final overall ranking of the reference genes under evaluation based on the geometric mean calculated from the weighting of every gene produced by each of the individual algorithms [53].

Validation of selected reference genes using miR159 expression

Previous research has shown that miR159 responds to the stress of both Turnip yellow mosaic virus (TYMV) and Turnip mosaic virus (TuMV) in Arabidopsis thaliana [54]. Given that our own research has shown that miR159 could also be important in the cucumber response to CGMMV [13], it was selected as the best candidate miRNA to assess the reference genes evaluated in the bioinformatics analysis. Both the highest ranked reference genes and combinations of the most and least stable genes were evaluated. In addition to miR159, 8 other miRNAs (miR169, miR172, miR838, miR854, miR2673, miR5637, miR5658 and csa-miRn6-3p) were also evaluated to further investigate which combination of reference genes performed best in a variety of circumstances (S4 Fig). Plants were sampled at 1, 14, 28 and 42 dpi. The ratio of relative expression of miR159 in CGMMV-infected samples were compared to unstressed control and differential expression assessed using the combination of different reference genes. The generated data was normalized using the mathematic model described in REST 2009 software (http://REST.gene-quantification.info/) [55]. The ratio of relative expression of target gene was computed, based on its real-time PCR efficiencies (E) and the Ct difference (Δ) of one treatment sample (average Ct value) versus one control (average Ct value) (ΔCt _{treatment—control}). For example, the expression ratio of miR159 was estimated using its average Ct value at 1, 14, 28 and 42 dpi in leaf tissues. The calculation formula (Equation 3.4) was as follows:

Results

Performance of primers and expression stability of reference genes in cucumber

Melting curve and agarose gel electrophoresis analysis showed that a specific fragment of the expected size and a single peak were observed, respectively, in reverse transcriptase-PCR (RT-PCR) and RT-qPCR amplifications (S1 and S3 Figs).

The RT-qPCR analysis revealed that the seven reference genes under evaluation displayed different levels of expression in cucumber (Fig 1). Although expression levels varied according to tissue type and the period of time post inoculation, most Ct values ranged from 21 to 36. Furthermore, the relative level of expression between the genes remained reasonably constant with 18S rRNA, which exhibited a low Ct value, being the most highly expressed, and Ubiquitin, which had a particularly high Ct value, and thus a low level of expression, being the lowest in expression. The most consistent expression levels were observed with EF-1α and GAPDH, which had similar patterns of expression in all the tissue types and varied by only 5 Ct points across different time points post inoculation.

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Fig 1. RT-qPCR cycle threshold values of seven reference genes in cucumber leaf, stem and root samples at different time points post-inoculation with CGMMV.

Error bars represent the mean of three technical replicates ± SD.

https://doi.org/10.1371/journal.pone.0194436.g001

Delta-Ct analysis

The delta-Ct analysis also revealed that the expression stability of the different reference genes, which was represented by the standard deviation (SD), varied. For example, EF-1α had the lowest SD in leaf and root samples, while GAPDH had the lowest SD in stem tissues. Contrary to expectation, 18S rRNA and Actin exhibited the highest SD values in nearly all the samples tested (Table 2). Taken together, these results indicated that EF-1α was the best reference gene for leaf and root tissues, while GAPDH was the best for stem tissues, and that 18S rRNA and Actin were not a good choice under any circumstances.

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Table 2. Expression stability of seven reference genes in cucumber analyzed using the delta-Ct algorithm.

https://doi.org/10.1371/journal.pone.0194436.t002

geNorm analysis

M values of the seven reference genes generated by geNorm produced similar results compared to delta-Ct analysis. In this case, Ubiquitin, Cyclophilin and EF-1α had the highest expression stability in leaves (Fig 2A), GAPDH, Cyclophilin and Ubiquitin in stems (Fig 2B), and Tubulin, Ubiquitin and Cyclophilin in roots (Fig 2C), while Ubiquitin and Cyclophilin were the most stable overall (Fig 2D). Meanwhile, Actin and 18S rRNA had the lowest expression stability in both stem (Fig 2B) and root tissues (Fig 2C), as well as the lowest stability overall (Fig 2D). Although all the seven references genes exhibited acceptable expression stabilities, the M values indicated that in comparison to Actin, Ubiquitin and Cyclophilin, which had relatively low values in all the tissues tested, would be better selections. The geNorm analysis also indicated that if Ubiquitin and Cyclophilin were used in conjunction, the addition of further reference genes would provide no improvement in fidelity, given that the V_2/3 value was already below the cut-off (V_n/n+1<0.15), and therefore that the combination of Ubiquitin and Cyclophilin would provide reliable normalization of miRNA expression in all CGMMV-infected cucumber samples (Fig 2E, 2F, 2G and 2H).

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Fig 2. GeNorm data for 5 cucumber reference genes in cucumber leaf, stem and root samples at different time points post-inoculation with CGMMV.

(A) to (D) Average expression stability values (M) of cucumber reference genes during stepwise exclusion of the least stable reference gene in different tissue samples. (A) leaf, (B) stem, (C) root, and (D) overall. (E) to (H) Optimal number of reference genes required for accurate normalization of expression based on pairwise variation (V_n/n+1) analysis of the normalization factors of the reference genes in different tissue samples. (E) leaf, (F) stem, (G) root, and (H) overall.

https://doi.org/10.1371/journal.pone.0194436.g002

NormFinder analysis

The NormFinder results indicated that EF-1α was the most stable reference gene in leaf and root tissues, while Cyclophilin was the most stable gene in stem tissues (Table 3). In addition, the NormFinder analysis also identified the most suitable reference gene pairings for different tissue samples. Under these circumstances, Tubulin and GAPDH were the best selection for leaf samples, EF-1α and Cyclophilin for stem samples, and EF-1α and GAPDH for root samples, while EF-1α and Ubiquitin were the best overall (Table 4).

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Table 3. Expression stability of seven reference genes in cucumber analyzed using the NormFinder algorithm.

https://doi.org/10.1371/journal.pone.0194436.t003

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Table 4. Most suitable combination of reference genes for normalizing expression in cucumber according to NormFinder analysis.

https://doi.org/10.1371/journal.pone.0194436.t004

BestKeeper analysis

The two least suitable genes from the geNorm and NormFinder analyses, Actin and 18S rRNA were excluded from the BestKeeper investigation, which revealed that of the five remaining reference genes, Ubiquitin had the highest Pearson coefficient and was therefore the most suitable reference gene regardless of tissue type (Table 5).

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Table 5. Expression stability of five reference genes in cucumber analyzed using the BestKeeper algorithm.

https://doi.org/10.1371/journal.pone.0194436.t005

RefFinder analysis

The RefFinder analysis compared the results generated by all of the aforementioned algorithms, and determined that EF-1α was the most suitable reference gene for leaf and root samples, GAPDH for stem samples, and Ubiquitin the most suitable overall (Table 6).

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Table 6. RefFinder ranking of seven reference genes in cucumber in descending order of expression stability from 1 to 7.

https://doi.org/10.1371/journal.pone.0194436.t006

Validation of selected reference genes by miR159 expression analysis

The effectiveness of the reference genes identified as the most stable, most suitable combination and least stable was evaluated in conjunction with miR159, using leaf, stem and root tissues under CGMMV stress. Data normalizations using the most stably expressed reference genes, EF-1α, Ubiquitin and GAPDH resulted in consistent miR159 expression patterns for leaf, stem, root and combined data samples (Fig 3). Similar results were observed when miR159 was compared to stable reference genes in combination, such as EF-1α + Ubiquitin (0.0078). However, when the least stably expressed reference genes (i.e., Actin and 18S rRNA) were used for data normalization, the expression level of miR159 was considerably biased. For example, higher levels of miR159 expression were observed in leaf samples when Actin and 18S rRNA (0.0433 relative expression ratio for Actin and 0.0668 for 18S rRNA), the least stable reference genes, were used as the internal controls compared to the most stable EF-1α (0.0048) and Ubiquitin (0.0202). This result indicated that the least stable genes reference Actin and 18S rRNA failed to standardize the expression data effectively. This suggested that the most stably expressed reference genes and most suitable combination showed similar levels of miRNA expression in comparison to the least stable reference genes. Therefore, combinations of stable genes could be utilized for normalization of miRNA under viral stress. Taken together, these results provided a strong evidence reinforcing the observation that the selection of suitable reference genes is critical for obtaining accurate measurements during miRNA expression studies.

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Fig 3. Expression profile of miR159 and validation of selected reference genes in different cucumber tissues infected with CGMMV.

Error bars represent the mean of three technical replicates ± SD.

https://doi.org/10.1371/journal.pone.0194436.g003

Discussion

We have evaluated and validated the expression stability of seven selected reference genes, including Actin, Tubulin, EF-1α, 18S rRNA, Ubiquitin, GAPDH and Cyclophilin, to determine the most suitable reference genes used in RT-qPCR analysis of miRNA in leaf, stem and root tissues of cucumber infected by CGMMV. The selection of reliable reference genes is a significant factor influencing the accuracy of comparative expression studies, particularly given that the expression stability of reference genes can vary according to different tissues [24, 31] and experimental conditions such as abiotic and biotic stresses [23]. We have determined that EF-1α was the most suitable reference gene for cucumber leaf and root tissues infected by CGMMV, GAPDH was the best for stem tissues, and Ubiquitin was the best for overall.

Several algorithms have been used for the analysis of expression data relating to the suitability of reference genes, including comparative delta-Ct (ΔCt), geNorm version 3.5, NormFinder version 20 and BestKeeper version 1. We found that these different algorithms selected different genes as the best reference genes, depending on plant tissues evaluated. For example, geNorm predicted that Ubiquitin and Cyclophilin were the best reference genes for leaves, GAPDH and Cyclophilin for stems, and Tubulin and Ubiquitin for roots, while NormFinder indicated that EF-1α was the most stable reference gene in leaves and roots, and Cyclophilin in stems; BestKeeper resulted in Ubiquitin as the most suitable reference gene overall. Such differences are common, and have been observed in many previous studies [31–33, 56–59]. Nonetheless the application of individual algorithms generates extremely useful data that when collated by comparative software such as RefFinder can provide a consensus regarding the expression stability of each reference gene evaluated. The application of this methodology in the current study indicated that Ubiquitinand EF-1α were the most suitable reference gene combination regardless of tissue types.

Our results were greatly in agreement on EF-1α with other studies. For example, EF-1α is an extremely common reference gene, and has previously been used to investigate abiotic stresses such as oxidative, heavy metal, salt, osmotic as well as plant growth regulator and biotic stress responses in cucumber [31, 32]. EF-1α is also considered as the best reference gene in other plant systems including citrus rootstock under drought stress [60] and castor bean under drought conditions [29], as well as in combination with Cyclophilin to study salt stress in potato, and with 18S rRNA to study late blight [41].

Actin and 18S rRNA are commonly used as reference genes to normalize the quantification of gene expression [20]. For example, they are selected for miRNA expression in sugarcane under cold stress [61], and in wheat infected by Puccinia graminis f. sp. tritici [62]. However, they were not the most appropriate reference genes in our study, as well as in many other reports. For example, although Actin is highly stable in cucumber under cold, heat, drought and salt stress [31], it is the least stably expressed gene in Arabidopsis [19], flax [58] and citrus [59]. Furthermore, Actin expression can be significantly affected by abiotic and biotic factors [32]. We found that neither Actin nor 18S rRNA exhibited a stable expression pattern, which in turn resulted in much higher values for miR159 expression in cucumber infected with CGMMV compared to EF-1α. We agreed with several other studies that Actin, 18S rRNA and GAPDH are not the most suitable reference genes [34, 63].

Both Tubulin and GAPDH have intermediate expression stability compared to the other reference genes in cucumber infected by CGMMV. Several studies have indicated that Tubulin is not suitable as a reference gene in cucumber exposed to heavy metals, oxidative, salt and osmotic stress [32] or at extremes of temperature [31], while similar observations have also been made in drought stressed citrus rootstock [60] and citrus exposed to biotic stress [59]. However, some studies have found that under certain circumstances, Tubulin can be used as a reference gene, for example, alpha tubulin is the most stable reference gene in soybean root tips in responding to Aluminum stress and in soybean roots exposed to heat stress [44], while beta tubulin was the most stable reference gene in wheat infected by Puccinia graminis f. sp. tritici [62].

We found that Cyclophilin was one of the most stably expressed reference genes in CGMMV-infected cucumber tissues, which was supported by the study of wheat flag leaves [64], but contraversal to other studies in cucumbers under abiotic or nitrogen stresses, as well as phytohormones [31–33]. We also found that Ubiquitin was a robust reference gene, and the most suitable gene for normalizing miRNA expression in cucumber infected by GGMMV. This is in agreement with Warzybok and Migocka, who examined the root and stem of cucumber in response to nitrogen impact [33].

The normalization of expression data can be improved by using combinations of reference genes instead of single genes [34, 65]. We identified three different sets of reference genes optimized to different tissue types, depending on plant tissues. Particularly, EF-1α and Ubiquitin were the best reference genes in leaf tissues, GAPDH and Cyclophilin in stems, and EF-1α and GAPDH in roots, while Ubiquitin and EF-1α were the best combination overall. The variation observed in these studies confirmed that the selection of references genes optimized for specific biological conditions is an important prerequisite for accurate and reliable normalization of RT-qPCR data. Indeed, the lack of specific reference genes or the use of inappropriate reference genes can result in large variations that skew the interpretation of expression studies.

miR159 belongs to a conserved miRNA family, and plays an important role in cucumber-CGMMV interactions and disease resistance [13]. Given that miR159 has also been implicated in other plant-virus interactions [54] and has great potential as a transgene that confers virus resistance, it was selected as the most suitable target gene to validate the reference genes evaluated in the current study.

The reference genes identified so far appear to be a significant breakthrough in accurately normalizing miRNA expression in virus-infected cucumber samples, and will be of great value when investigating the role of miRNA in the disease resistance and plant-virus interactions of cucumber.

Conclusions

The selection of reference genes for gene expression study is highly affected by plant tissues, plant toxanomy, and environmental factors and stresses such as pathogen and nitrogen stress. In the cucumber- CGMMV pathosystem, we have determined several individual and combinations of reference genes for the reliable normalization of miRNA expression. EF-1α was the most appropriate reference gene for leaf and root tissues, while GAPDH was the most suitable reference gene for stems. Ubiquitin and EF-1α was the most suitable combined references when comparing miRNA expression under a broad range of conditions. Our results could provide a useful foundation reference genes for miRNA expression studies in CGMMV-infected cucumber.

Supporting information

S1 Fig. Melting curves of seven candidate reference genes.

(A) to (G) Melting curves of Actin, Tubulin, EF-1α, 18S rRNA, Ubiquitin, GAPDH and Cyclophilin.

https://doi.org/10.1371/journal.pone.0194436.s001

(TIF)

S2 Fig. Standard curves of seven candidate reference genes.

https://doi.org/10.1371/journal.pone.0194436.s002

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S3 Fig. Electrophoresis of PCR products corresponding to seven candidate reference genes with expected sizes on an agarose gel.

(A) Amplification of reference genes with gradient annealing temperatures (from 52°C to 61°C) in RT-PCR. (B) Gel electrophoresis of RT-qPCR products of reference genes. M: DNA marker.

https://doi.org/10.1371/journal.pone.0194436.s003

(TIF)

S4 Fig. Expression profiles of miR169, miR172, miR838, miR854, miR2673, miR5637, miR5658 and csa-miRn6-3p and validation of selected reference genes in cucumber leaf tissues infected with CGMMV.

Error bars represent the mean of three technical replicates ± SD.

https://doi.org/10.1371/journal.pone.0194436.s004

(TIF)

S1 File. Sequences of candidate reference genes.

https://doi.org/10.1371/journal.pone.0194436.s005

(PDF)

Acknowledgments

We would like to thank Barbara Baker, from the Plant Gene Expression Center, United States Department of Agriculture, Agricultural Research Service, California, USA, for providing valuable comments during the preparation of the manuscript.

References

1. Pitrat M, Chauvet M, Foury C. Diversity, history and production of cultivated cucurbits. Acta Hortic. 1999; 492, 21–28.
- View Article
- Google Scholar
2. Huang SW, Li RQ, Zhang ZH, Li L, Gu XF, Fan W, et al. The genome of the cucumber, Cucumis sativus L. Nat Genet. 2009; 41(12): 1275–1281. pmid:19881527
- View Article
- PubMed/NCBI
- Google Scholar
3. Reingold V, Lachman O, Belausov E, Koren A, Mor N, Dombrovsky A. Epidemiological study of Cucumber green mottle mosaic virus in greenhouses enables reduction of disease damage in cucurbit production. Ann Appl Biol. 2016; 168(1): 29–40.
- View Article
- Google Scholar
4. Komuro Y. Cucumber green mottle mosaic virus on cucumber and watermelon and melon necrotic spot virus on muskmelon. Jap Agr Res Quart. 1971; 6: 41–45.
- View Article
- Google Scholar
5. Shim CK, Han KS, Lee JH, Bae DW, Kim DK, Kim HK. Isolation and characterization of watermelon isolate of Cucumber green mottle mosaic virus (CGMMV-HY1) from watermelon plants with severe mottle mosaic symptoms. Plant Pathol J. 2005; 21(2): 167–171.
- View Article
- Google Scholar
6. Lovisolo O. Virus and viroid diseases of cucurbits. Acta Hortic. 1981; 88: 33–82.
- View Article
- Google Scholar
7. Dombrovsky A, Tran-Nguyen LTT, Jones RAC. Cucumber green mottle mosaic virus: rapidly increasing global distribution, etiology, epidemiology, and management. Annu Rev Phytopathol. 2017; 55(1): 231–56. pmid:28590876
- View Article
- PubMed/NCBI
- Google Scholar
8. Reinhart BJ, Weinstein EG, Rhoades MW, Bartel B, Bartel DP. MicroRNAs in plants. Genes Dev. 2002; 16(13): 1616–1626. pmid:12101121
- View Article
- PubMed/NCBI
- Google Scholar
9. Bartel DP. MicroRNAs: genomics, biogenesis, mechanism, and function. Cell. 2004; 116(2): 281–297. pmid:14744438
- View Article
- PubMed/NCBI
- Google Scholar
10. Garcia D. A miRacle in plant development: role of microRNAs in cell differentiation and patterning. Semin Cell Dev Biol. 2008; 19(6): 586–595. pmid:18708151
- View Article
- PubMed/NCBI
- Google Scholar
11. Jones-Rhoades MW, Bartel DP. Computational identification of plant microRNAs and their targets, including a stress-induced miRNA. Mol. Cell. 2004; 14(6): 787–799. pmid:15200956
- View Article
- PubMed/NCBI
- Google Scholar
12. Martínez G, Forment J, Llave C, Pallás V, Gómez G. High-throughput sequencing, characterization and detection of new and conserved cucumber miRNAs. PLoS One. 2011; 6(5): e19523. pmid:21603611
- View Article
- PubMed/NCBI
- Google Scholar
13. Liu HW, Luo LX, Liang CQ, Jiang N, Liu PF, Li JQ. High-throughput sequencing identifies novel and conserved cucumber (Cucumis sativus L.) microRNAs in response to Cucumber green mottle mosaic virus infection. PLoS One. 2015; 10(6): e0129002. pmid:26076360
- View Article
- PubMed/NCBI
- Google Scholar
14. Dheda K, Huggett JF, Chang JS, Kim LU, Bustin SA, Johnson MA, et al. The implications of using an inappropriate reference gene for real-time reverse transcription PCR data normalization. Anal Biochem. 2005; 344(1): 141–143. pmid:16054107
- View Article
- PubMed/NCBI
- Google Scholar
15. Lopez-Pardo R, Ruiz de Galarreta JI, Ritter E. Selection of housekeeping genes for qRT-PCR analysis in potato tubers under cold stress. Mol Breed. 2013; 31(1): 39–45.
- View Article
- Google Scholar
16. Zhu JF, Zhang LF, Li WF, Han SY, Yang WH, Qi LW. Reference gene selection for quantitative real-time PCR normalization in Caragana intermedia under different abiotic stress conditions. PLoS One. 2013; 8(1): e53196. pmid:23301042
- View Article
- PubMed/NCBI
- Google Scholar
17. Yang Q, Yin JJ, Li G, Qi LW, Yang FY, Wang RG, et al. Reference gene selection for qRT-PCR in Caragana korshinskii Kom. under different stress conditions. Mol Biol Rep. 2014; 41(4): 2325–2334. pmid:24452712
- View Article
- PubMed/NCBI
- Google Scholar
18. Thellin O, Zorzi W, Lakaye B, de Borman B, Coumans B, Hennen G, et al. Housekeeping genes as internal standards: use and limits. J Biotechnol. 1999; 75(2–3): 291–295. pmid:10617337
- View Article
- PubMed/NCBI
- Google Scholar
19. Czechowski T, Stitt M, Altmann T, Udvardi MK, Scheible WR. Genome-wide identification and testing of superior reference genes for transcript normalization in Arabidopsis. Plant Physiol. 2005; 139(1): 5–17. pmid:16166256
- View Article
- PubMed/NCBI
- Google Scholar
20. Huggett J, Dheda K, Bustin S, Zumla A. Real-time RT-PCR normalisation; strategies and considerations. Genes Immun. 2005; 6(4): 279–284. pmid:15815687
- View Article
- PubMed/NCBI
- Google Scholar
21. Gutierrez L, Mauriat M, Guénin S, Pelloux J, Lefevre JF, Louvet R, et al. The lack of a systematic validation of reference genes: a serious pitfall undervalued in reverse transcription-polymerase chain reaction (RT-PCR) analysis in plants. Plant Biotechnol J. 2008; 6(6): 609–618. pmid:18433420
- View Article
- PubMed/NCBI
- Google Scholar
22. Die JV, Román B, Nadal S, González-Verdejo CI. Evaluation of candidate reference genes for expression studies in Pisum sativum under different experimental conditions. Planta. 2010; 232(1): 145–153. pmid:20379832
- View Article
- PubMed/NCBI
- Google Scholar
23. 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. pmid:28261247
- View Article
- PubMed/NCBI
- Google Scholar
24. Brunner AM, Yakovlev IA, Strauss SH. Validating internal controls for quantitative plant gene expression studies. BMC Plant Biol. 2004; 4: 14. pmid:15317655
- View Article
- PubMed/NCBI
- Google Scholar
25. Bustin SA, Benes V, Garson JA, Hellemans J, Huggett J, Kubista M, et al. The MIQE guidelines: minimum information for publication of quantitative real-time PCR experiments. Clin Chem. 2009; 55(4): 611–622. pmid:19246619
- View Article
- PubMed/NCBI
- Google Scholar
26. Bustin SA, Benes V, Nolan T, Pfaffl MW. Quantitative real-time RT-PCR—a perspective. J Mol Endocrinol. 2005; 34(3): 597–601. pmid:15956331
- View Article
- PubMed/NCBI
- Google Scholar
27. Feng H, Huang XL, Zhang Q, Wei GR, Wang XJ, Kang ZS. Selection of suitable inner reference genes for relative quantification expression of microRNA in wheat. Plant Physiol Biochem. 2012; 51: 116–122. pmid:22153247
- View Article
- PubMed/NCBI
- Google Scholar
28. Kou SJ, Wu XM, Liu Z, Liu YL, Xu Q, Guo WW. Selection and validation of suitable reference genes for miRNA expression normalization by quantitative RT-PCR in citrus somatic embryogenic and adult tissues. Plant Cell Rep. 2012; 31(2): 2151–2163. pmid:22865195
- View Article
- PubMed/NCBI
- Google Scholar
29. Cassol D, Cruz FP, Espindola K, Mangeon A, Müller C, Loureiro ME, et al. Identification of reference genes for quantitative RT-PCR analysis of microRNAs and mRNAs in castor bean (Ricinus communis L.) under drought stress. Plant Physiol Biochem. 2016; 106: 101–107. pmid:27156134
- View Article
- PubMed/NCBI
- Google Scholar
30. Wang YL, Liu J, Wang XM, Liu S, Wang GL, Zhou JH, et al. Validation of suitable reference genes for assessing gene expression of microRNAs in Lonicera japonica. Front Plant Sci. 2016; 7: 1101. pmid:27507983
- View Article
- PubMed/NCBI
- Google Scholar
31. Wan HJ, Zhao ZG, Qian CT, Sui YH, Malik AA, Chen JF. Selection of appropriate reference genes for gene expression studies by quantitative real-time polymerase chain reaction in cucumber. Anal Biochem. 2010; 399(2): 257–261. pmid:20005862
- View Article
- PubMed/NCBI
- Google Scholar
32. Migocka M, Papierniak A. Identification of suitable reference genes for studying gene expression in cucumber plants subjected to abiotic stress and growth regulators. Mol Breed. 2011; 28(3): 343–357.
- View Article
- Google Scholar
33. Warzybok A, Migocka M. Reliable reference genes for normalization of gene expression in cucumber grown under different nitrogen nutrition. PLoS One. 2013; 8(9): e72887. pmid:24058446
- View Article
- PubMed/NCBI
- Google Scholar
34. Vandesompele J, de Preter K, Pattyn F, Poppe B, Van-Roy N, de Paepe A, et al. Accurate normalization of real-time quantitative RT-PCR data by geometric averaging of multiple internal control genes. Genome Biol. 2002; 3(7): research0034. pmid:12184808
- View Article
- PubMed/NCBI
- Google Scholar
35. Faccioli P, Ciceri GP, Provero P, Stanca AM, Morcia C, Terzi V. A combined strategy of “in silico” transcriptome analysis and web search engine optimization allows an agile identification of reference genes suitable for normalization in gene expression studies. Plant Mol Biol. 2007; 63(5): 679–688. pmid:17143578
- View Article
- PubMed/NCBI
- Google Scholar
36. Paolacci AR, Tanzarella OA, Porceddu E, Ciaffi M. Identification and validation of reference genes for quantitative RT-PCR normalization in wheat. BMC Mol Biol. 2009; 10: 11. pmid:19232096
- View Article
- PubMed/NCBI
- Google Scholar
37. Andersen CL, Jensen JL, Orntoft TF. 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(15): 5245–5250. pmid:15289330
- View Article
- PubMed/NCBI
- Google Scholar
38. Pfaffl MW, Tichopad A, Prgomet C, Neuvians TP. Determination of stable housekeeping genes, differentially regulated target genes and sample integrity: BestKeeper-Excel-based tool using pair-wise correlations. Biotechnol Lett. 2004; 26(6): 509–515. pmid:15127793
- View Article
- PubMed/NCBI
- Google Scholar
39. Remans T, Smeets K, Opdenakker K, Mathijsen D, Vangronsveld J, Cuypers A. Normalisation of real-time RT-PCR gene expression measurements in Arabidopsis thaliana exposed to increased metal concentrations. Planta. 2008; 227(6): 1343–1349. pmid:18273637
- View Article
- PubMed/NCBI
- Google Scholar
40. Nardeli SM, Matta BP, Saad CF, Reinert F, Peixoto RS, Alves-Ferreira M. Early heat shock protein response and selection of reference genes in Arabidopsis thaliana seedlings subjected to marine fuel contamination. Water Air Soil Pollut. 2017; 228: 101.
- View Article
- Google Scholar
41. Nicot N, Hausman JF, 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(421): 2907–2914. pmid:16188960
- View Article
- PubMed/NCBI
- Google Scholar
42. Jain M, Nijhawan A, Tyagi AK, Khurana JP. Validation of housekeeping genes as internal control for studying gene expression in rice by quantitative realtime PCR. Biochem Biophys Res Commun. 2006; 345(2): 646–651. pmid:16690022
- View Article
- PubMed/NCBI
- Google Scholar
43. Li QF, Sun SSM, Yuan DY, Yu HX, Gu MH, Liu QQ. Validation of candidate reference genes for the accurate normalization of real-time quantitative RT-PCR data in rice during seed development. Plant Mol Biol Rep. 2010; 28(1): 49–57.
- View Article
- Google Scholar
44. Gao MM, Liu YP, Ma X, Shuai Q, Gai JY, Li Y. Evaluation of reference genes for normalization of gene expression using quantitative RT-PCR under Aluminum, Cadmium, and heat stresses in soybean. PLoS One. 2017; 12 (1): e0168965. pmid:28046130
- View Article
- PubMed/NCBI
- Google Scholar
45. Llanos A, Francois JM, Parrou JL. Tracking the best reference genes for RT-qPCR data normalization in filamentous fungi. BMC Genomics. 2015; 16: 71. pmid:25757610
- View Article
- PubMed/NCBI
- Google Scholar
46. Leal MF, Arliani GG, Astur DC, Franciozi CE, Debieux P, Andreoli CV, et al. Comprehensive selection of reference genes for expression studies in meniscus injury using quantitative real-time PCR. Gene. 2016; 584(1): 60–68. pmid:26968891
- View Article
- PubMed/NCBI
- Google Scholar
47. Frydenberg J, Poulsen K, Petersen AKB, Lund A, Ole F, Olesen OF. Isolation and characterization of the gene encoding EF-1αO, an elongation factor 1-α expressed during early development of Xenopus laevis. Gene. 1991; 109(2), 185–192. pmid:1765266
- View Article
- PubMed/NCBI
- Google Scholar
48. Smalle J, Vierstra RD. The ubiquitin 26S proteasome proteolytic pathway. Annu Rev Plant Biol. 2004; 55: 555–590. pmid:15377232
- View Article
- PubMed/NCBI
- Google Scholar
49. Handschumacher RE, Harding MW, Rice J, Drugge RJ, Speicher DW. Cyclophilin: a specific cytosolic binding protein for cyclosporin A. Science. 1984; 226(4674): 544–547. pmid:6238408
- View Article
- PubMed/NCBI
- Google Scholar
50. Gu XF, Zhang SP, Fang XJ, Li ZM, Xu CQ. A new cucumber hybrid with super quality, earliness and high yield ‘Zhongnong 16’. Acta Hortic Sinica. 2005; 32: 762. (in Chinese)
- View Article
- Google Scholar
51. Radonic A, Thulke S, Mackay IM, Landt O, Siegert W, Nitsche A. Guideline to reference gene selection for quantitative real-time PCR. Biochem Biophys Res Commun. 2004; 313(4): 856–862. pmid:14706621
- View Article
- PubMed/NCBI
- Google Scholar
52. Silver N, Best S, Jiang J, Thein SL. Selection of housekeeping genes for gene expression studies in human reticulocytes using real-time PCR. BMC Mol Biol. 2006; 7: 33. pmid:17026756
- View Article
- PubMed/NCBI
- Google Scholar
53. Karuppaiya P, Yan XX, Liao W, Wu J, Chen F, Tang L. Identification and validation of superior reference gene for gene expression normalization via RT-qPCR in staminate and pistillate flowers of Jatropha curcas-a biodiesel plant. PLoS One. 2017; 12(5): e0177039. pmid:28459843
- View Article
- PubMed/NCBI
- Google Scholar
54. Niu QW, Lin SS, Reyes JL, Chen KC, Wu HW, Yeh SD, et al. Expression of artificial microRNAs in transgenic Arabidopsis thaliana confers virus resistance. Nat Biotechnol. 2006; 24(11): 1420–1428. pmid:17057702
- View Article
- PubMed/NCBI
- Google Scholar
55. Pfaffl MW, Horgan GW, Dempfle L. Relative expression software tool (REST) for group-wise comparison and statistical analysis of relative expression results in real-time PCR. Nucleic Acids Res. 2002; 30(9), e36. pmid:11972351
- View Article
- PubMed/NCBI
- Google Scholar
56. Hong SY, Seo PJ, Yang MS, Xiang F, Park CM. Exploring valid reference genes for gene expression studies in Brachypodium distachyon by real-time PCR. BMC Plant Biol. 2008; 8: 112. pmid:18992143
- View Article
- PubMed/NCBI
- Google Scholar
57. Cruz F, Kalaoun S, Nobile P, Colombo C, Almeida J, Barros LMG, et al. Evaluation of coffee reference genes for relative expression studies by quantitative real-time RT-PCR. Mol Breed. 2009; 23(4): 607–616.
- View Article
- Google Scholar
58. Huis R, Hawkins S, Neutelings G. Selection of reference genes for quantitative gene expression normalization in flax (Linum usitatissimum L.). BMC Plant Biol. 2010; 10: 71. pmid:20403198
- View Article
- PubMed/NCBI
- Google Scholar
59. Mafra V, Kubo KS, Alves-Ferreira M, Ribeiro-Alves M, Stuart RM, Boava LP, et al. Reference genes for accurate transcript normalization in citrus genotypes under different experimental conditions. PLoS One. 2012; 7(2): e31263. pmid:22347455
- View Article
- PubMed/NCBI
- Google Scholar
60. Carvalho K, Campos MKF, Pereira LFP, Vieira LGE. Reference gene selection for real-time quantitative polymerase chain reaction normalization in “Swingle” citrumelo under drought stress. Anal Biochem. 2010; 402: 197–199. pmid:20363209
- View Article
- PubMed/NCBI
- Google Scholar
61. Yang YT, Zhang X, Chen Y, Guo JL, Ling H, Gao SW, et al. Selection of reference genes for normalization of microRNA expression by RT-qPCR in sugarcane buds under cold stress. Front Plant Sci. 2016; 7: 86. pmid:26904058
- View Article
- PubMed/NCBI
- Google Scholar
62. Scholtz JJ, Visser B. Reference gene selection for qPCR gene expression analysis of rust-infected wheat. Physiol Mol Plant Pathol. 2013; 81: 22–25.
- View Article
- Google Scholar
63. Zhu XY, Li XP, Chen WX, Chen JY, Lu WJ, Chen L, et al. Evaluation of new reference genes in papaya for accurate transcript normalization under different experimental conditions. PLoS One. 2012; 7(8): e44405. pmid:22952972
- View Article
- PubMed/NCBI
- Google Scholar
64. Tenea GN, Bota AP, Raposo FC, Maquet A. Reference genes for gene expression studies in wheat flag leaves grown under different farming conditions. BMC Res Notes. 2011; 4: 373. pmid:21951810
- View Article
- PubMed/NCBI
- Google Scholar
65. Janská A, Hodek J, Svoboda P, Zámcník J, Prášil IT, Vlasáková E, et al. The choice of reference gene set for assessing gene expression in barley (Hordeum vulgare L.) under low temperature and drought stress. Mol Genet Genomics. 2013; 288(11): 639–649. pmid:23979536
- View Article
- PubMed/NCBI
- Google Scholar

[ref1] 1. Pitrat M, Chauvet M, Foury C. Diversity, history and production of cultivated cucurbits. Acta Hortic. 1999; 492, 21–28.
View Article
Google Scholar

[2] View Article

[3] Google Scholar

[ref2] 2. Huang SW, Li RQ, Zhang ZH, Li L, Gu XF, Fan W, et al. The genome of the cucumber, Cucumis sativus L. Nat Genet. 2009; 41(12): 1275–1281. pmid:19881527
View Article
PubMed/NCBI
Google Scholar

[5] View Article

[6] PubMed/NCBI

[7] Google Scholar

[ref3] 3. Reingold V, Lachman O, Belausov E, Koren A, Mor N, Dombrovsky A. Epidemiological study of Cucumber green mottle mosaic virus in greenhouses enables reduction of disease damage in cucurbit production. Ann Appl Biol. 2016; 168(1): 29–40.
View Article
Google Scholar

[9] View Article

[10] Google Scholar

[ref4] 4. Komuro Y. Cucumber green mottle mosaic virus on cucumber and watermelon and melon necrotic spot virus on muskmelon. Jap Agr Res Quart. 1971; 6: 41–45.
View Article
Google Scholar

[12] View Article

[13] Google Scholar

[ref5] 5. Shim CK, Han KS, Lee JH, Bae DW, Kim DK, Kim HK. Isolation and characterization of watermelon isolate of Cucumber green mottle mosaic virus (CGMMV-HY1) from watermelon plants with severe mottle mosaic symptoms. Plant Pathol J. 2005; 21(2): 167–171.
View Article
Google Scholar

[15] View Article

[16] Google Scholar

[ref6] 6. Lovisolo O. Virus and viroid diseases of cucurbits. Acta Hortic. 1981; 88: 33–82.
View Article
Google Scholar

[18] View Article

[19] Google Scholar

[ref7] 7. Dombrovsky A, Tran-Nguyen LTT, Jones RAC. Cucumber green mottle mosaic virus: rapidly increasing global distribution, etiology, epidemiology, and management. Annu Rev Phytopathol. 2017; 55(1): 231–56. pmid:28590876
View Article
PubMed/NCBI
Google Scholar

[21] View Article

[22] PubMed/NCBI

[23] Google Scholar

[ref8] 8. Reinhart BJ, Weinstein EG, Rhoades MW, Bartel B, Bartel DP. MicroRNAs in plants. Genes Dev. 2002; 16(13): 1616–1626. pmid:12101121
View Article
PubMed/NCBI
Google Scholar

[25] View Article

[26] PubMed/NCBI

[27] Google Scholar

[ref9] 9. Bartel DP. MicroRNAs: genomics, biogenesis, mechanism, and function. Cell. 2004; 116(2): 281–297. pmid:14744438
View Article
PubMed/NCBI
Google Scholar

[29] View Article

[30] PubMed/NCBI

[31] Google Scholar

[ref10] 10. Garcia D. A miRacle in plant development: role of microRNAs in cell differentiation and patterning. Semin Cell Dev Biol. 2008; 19(6): 586–595. pmid:18708151
View Article
PubMed/NCBI
Google Scholar

[33] View Article

[34] PubMed/NCBI

[35] Google Scholar

[ref11] 11. Jones-Rhoades MW, Bartel DP. Computational identification of plant microRNAs and their targets, including a stress-induced miRNA. Mol. Cell. 2004; 14(6): 787–799. pmid:15200956
View Article
PubMed/NCBI
Google Scholar

[37] View Article

[38] PubMed/NCBI

[39] Google Scholar

[ref12] 12. Martínez G, Forment J, Llave C, Pallás V, Gómez G. High-throughput sequencing, characterization and detection of new and conserved cucumber miRNAs. PLoS One. 2011; 6(5): e19523. pmid:21603611
View Article
PubMed/NCBI
Google Scholar

[41] View Article

[42] PubMed/NCBI

[43] Google Scholar

[ref13] 13. Liu HW, Luo LX, Liang CQ, Jiang N, Liu PF, Li JQ. High-throughput sequencing identifies novel and conserved cucumber (Cucumis sativus L.) microRNAs in response to Cucumber green mottle mosaic virus infection. PLoS One. 2015; 10(6): e0129002. pmid:26076360
View Article
PubMed/NCBI
Google Scholar

[45] View Article

[46] PubMed/NCBI

[47] Google Scholar

[ref14] 14. Dheda K, Huggett JF, Chang JS, Kim LU, Bustin SA, Johnson MA, et al. The implications of using an inappropriate reference gene for real-time reverse transcription PCR data normalization. Anal Biochem. 2005; 344(1): 141–143. pmid:16054107
View Article
PubMed/NCBI
Google Scholar

[49] View Article

[50] PubMed/NCBI

[51] Google Scholar

[ref15] 15. Lopez-Pardo R, Ruiz de Galarreta JI, Ritter E. Selection of housekeeping genes for qRT-PCR analysis in potato tubers under cold stress. Mol Breed. 2013; 31(1): 39–45.
View Article
Google Scholar

[53] View Article

[54] Google Scholar

[ref16] 16. Zhu JF, Zhang LF, Li WF, Han SY, Yang WH, Qi LW. Reference gene selection for quantitative real-time PCR normalization in Caragana intermedia under different abiotic stress conditions. PLoS One. 2013; 8(1): e53196. pmid:23301042
View Article
PubMed/NCBI
Google Scholar

[56] View Article

[57] PubMed/NCBI

[58] Google Scholar

[ref17] 17. Yang Q, Yin JJ, Li G, Qi LW, Yang FY, Wang RG, et al. Reference gene selection for qRT-PCR in Caragana korshinskii Kom. under different stress conditions. Mol Biol Rep. 2014; 41(4): 2325–2334. pmid:24452712
View Article
PubMed/NCBI
Google Scholar

[60] View Article

[61] PubMed/NCBI

[62] Google Scholar

[ref18] 18. Thellin O, Zorzi W, Lakaye B, de Borman B, Coumans B, Hennen G, et al. Housekeeping genes as internal standards: use and limits. J Biotechnol. 1999; 75(2–3): 291–295. pmid:10617337
View Article
PubMed/NCBI
Google Scholar

[64] View Article

[65] PubMed/NCBI

[66] Google Scholar

[ref19] 19. Czechowski T, Stitt M, Altmann T, Udvardi MK, Scheible WR. Genome-wide identification and testing of superior reference genes for transcript normalization in Arabidopsis. Plant Physiol. 2005; 139(1): 5–17. pmid:16166256
View Article
PubMed/NCBI
Google Scholar

[68] View Article

[69] PubMed/NCBI

[70] Google Scholar

[ref20] 20. Huggett J, Dheda K, Bustin S, Zumla A. Real-time RT-PCR normalisation; strategies and considerations. Genes Immun. 2005; 6(4): 279–284. pmid:15815687
View Article
PubMed/NCBI
Google Scholar

[72] View Article

[73] PubMed/NCBI

[74] Google Scholar

[ref21] 21. Gutierrez L, Mauriat M, Guénin S, Pelloux J, Lefevre JF, Louvet R, et al. The lack of a systematic validation of reference genes: a serious pitfall undervalued in reverse transcription-polymerase chain reaction (RT-PCR) analysis in plants. Plant Biotechnol J. 2008; 6(6): 609–618. pmid:18433420
View Article
PubMed/NCBI
Google Scholar

[76] View Article

[77] PubMed/NCBI

[78] Google Scholar

[ref22] 22. Die JV, Román B, Nadal S, González-Verdejo CI. Evaluation of candidate reference genes for expression studies in Pisum sativum under different experimental conditions. Planta. 2010; 232(1): 145–153. pmid:20379832
View Article
PubMed/NCBI
Google Scholar

[80] View Article

[81] PubMed/NCBI

[82] Google Scholar

[ref23] 23. 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. pmid:28261247
View Article
PubMed/NCBI
Google Scholar

[84] View Article

[85] PubMed/NCBI

[86] Google Scholar

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

[88] View Article

[89] PubMed/NCBI

[90] Google Scholar

[ref25] 25. Bustin SA, Benes V, Garson JA, Hellemans J, Huggett J, Kubista M, et al. The MIQE guidelines: minimum information for publication of quantitative real-time PCR experiments. Clin Chem. 2009; 55(4): 611–622. pmid:19246619
View Article
PubMed/NCBI
Google Scholar

[92] View Article

[93] PubMed/NCBI

[94] Google Scholar

[ref26] 26. Bustin SA, Benes V, Nolan T, Pfaffl MW. Quantitative real-time RT-PCR—a perspective. J Mol Endocrinol. 2005; 34(3): 597–601. pmid:15956331
View Article
PubMed/NCBI
Google Scholar

[96] View Article

[97] PubMed/NCBI

[98] Google Scholar

[ref27] 27. Feng H, Huang XL, Zhang Q, Wei GR, Wang XJ, Kang ZS. Selection of suitable inner reference genes for relative quantification expression of microRNA in wheat. Plant Physiol Biochem. 2012; 51: 116–122. pmid:22153247
View Article
PubMed/NCBI
Google Scholar

[100] View Article

[101] PubMed/NCBI

[102] Google Scholar

[ref28] 28. Kou SJ, Wu XM, Liu Z, Liu YL, Xu Q, Guo WW. Selection and validation of suitable reference genes for miRNA expression normalization by quantitative RT-PCR in citrus somatic embryogenic and adult tissues. Plant Cell Rep. 2012; 31(2): 2151–2163. pmid:22865195
View Article
PubMed/NCBI
Google Scholar

[104] View Article

[105] PubMed/NCBI

[106] Google Scholar

[ref29] 29. Cassol D, Cruz FP, Espindola K, Mangeon A, Müller C, Loureiro ME, et al. Identification of reference genes for quantitative RT-PCR analysis of microRNAs and mRNAs in castor bean (Ricinus communis L.) under drought stress. Plant Physiol Biochem. 2016; 106: 101–107. pmid:27156134
View Article
PubMed/NCBI
Google Scholar

[108] View Article

[109] PubMed/NCBI

[110] Google Scholar

[ref30] 30. Wang YL, Liu J, Wang XM, Liu S, Wang GL, Zhou JH, et al. Validation of suitable reference genes for assessing gene expression of microRNAs in Lonicera japonica. Front Plant Sci. 2016; 7: 1101. pmid:27507983
View Article
PubMed/NCBI
Google Scholar

[112] View Article

[113] PubMed/NCBI

[114] Google Scholar

[ref31] 31. Wan HJ, Zhao ZG, Qian CT, Sui YH, Malik AA, Chen JF. Selection of appropriate reference genes for gene expression studies by quantitative real-time polymerase chain reaction in cucumber. Anal Biochem. 2010; 399(2): 257–261. pmid:20005862
View Article
PubMed/NCBI
Google Scholar

[116] View Article

[117] PubMed/NCBI

[118] Google Scholar

[ref32] 32. Migocka M, Papierniak A. Identification of suitable reference genes for studying gene expression in cucumber plants subjected to abiotic stress and growth regulators. Mol Breed. 2011; 28(3): 343–357.
View Article
Google Scholar

[120] View Article

[121] Google Scholar

[ref33] 33. Warzybok A, Migocka M. Reliable reference genes for normalization of gene expression in cucumber grown under different nitrogen nutrition. PLoS One. 2013; 8(9): e72887. pmid:24058446
View Article
PubMed/NCBI
Google Scholar

[123] View Article

[124] PubMed/NCBI

[125] Google Scholar

[ref34] 34. Vandesompele J, de Preter K, Pattyn F, Poppe B, Van-Roy N, de Paepe A, et al. Accurate normalization of real-time quantitative RT-PCR data by geometric averaging of multiple internal control genes. Genome Biol. 2002; 3(7): research0034. pmid:12184808
View Article
PubMed/NCBI
Google Scholar

[127] View Article

[128] PubMed/NCBI

[129] Google Scholar

[ref35] 35. Faccioli P, Ciceri GP, Provero P, Stanca AM, Morcia C, Terzi V. A combined strategy of “in silico” transcriptome analysis and web search engine optimization allows an agile identification of reference genes suitable for normalization in gene expression studies. Plant Mol Biol. 2007; 63(5): 679–688. pmid:17143578
View Article
PubMed/NCBI
Google Scholar

[131] View Article

[132] PubMed/NCBI

[133] Google Scholar

[ref36] 36. Paolacci AR, Tanzarella OA, Porceddu E, Ciaffi M. Identification and validation of reference genes for quantitative RT-PCR normalization in wheat. BMC Mol Biol. 2009; 10: 11. pmid:19232096
View Article
PubMed/NCBI
Google Scholar

[135] View Article

[136] PubMed/NCBI

[137] Google Scholar

[ref37] 37. Andersen CL, Jensen JL, Orntoft TF. 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(15): 5245–5250. pmid:15289330
View Article
PubMed/NCBI
Google Scholar

[139] View Article

[140] PubMed/NCBI

[141] Google Scholar

[ref38] 38. Pfaffl MW, Tichopad A, Prgomet C, Neuvians TP. Determination of stable housekeeping genes, differentially regulated target genes and sample integrity: BestKeeper-Excel-based tool using pair-wise correlations. Biotechnol Lett. 2004; 26(6): 509–515. pmid:15127793
View Article
PubMed/NCBI
Google Scholar

[143] View Article

[144] PubMed/NCBI

[145] Google Scholar

[ref39] 39. Remans T, Smeets K, Opdenakker K, Mathijsen D, Vangronsveld J, Cuypers A. Normalisation of real-time RT-PCR gene expression measurements in Arabidopsis thaliana exposed to increased metal concentrations. Planta. 2008; 227(6): 1343–1349. pmid:18273637
View Article
PubMed/NCBI
Google Scholar

[147] View Article

[148] PubMed/NCBI

[149] Google Scholar

[ref40] 40. Nardeli SM, Matta BP, Saad CF, Reinert F, Peixoto RS, Alves-Ferreira M. Early heat shock protein response and selection of reference genes in Arabidopsis thaliana seedlings subjected to marine fuel contamination. Water Air Soil Pollut. 2017; 228: 101.
View Article
Google Scholar

[151] View Article

[152] Google Scholar

[ref41] 41. Nicot N, Hausman JF, 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(421): 2907–2914. pmid:16188960
View Article
PubMed/NCBI
Google Scholar

[154] View Article

[155] PubMed/NCBI

[156] Google Scholar

[ref42] 42. Jain M, Nijhawan A, Tyagi AK, Khurana JP. Validation of housekeeping genes as internal control for studying gene expression in rice by quantitative realtime PCR. Biochem Biophys Res Commun. 2006; 345(2): 646–651. pmid:16690022
View Article
PubMed/NCBI
Google Scholar

[158] View Article

[159] PubMed/NCBI

[160] Google Scholar

[ref43] 43. Li QF, Sun SSM, Yuan DY, Yu HX, Gu MH, Liu QQ. Validation of candidate reference genes for the accurate normalization of real-time quantitative RT-PCR data in rice during seed development. Plant Mol Biol Rep. 2010; 28(1): 49–57.
View Article
Google Scholar

[162] View Article

[163] Google Scholar

[ref44] 44. Gao MM, Liu YP, Ma X, Shuai Q, Gai JY, Li Y. Evaluation of reference genes for normalization of gene expression using quantitative RT-PCR under Aluminum, Cadmium, and heat stresses in soybean. PLoS One. 2017; 12 (1): e0168965. pmid:28046130
View Article
PubMed/NCBI
Google Scholar

[165] View Article

[166] PubMed/NCBI

[167] Google Scholar

[ref45] 45. Llanos A, Francois JM, Parrou JL. Tracking the best reference genes for RT-qPCR data normalization in filamentous fungi. BMC Genomics. 2015; 16: 71. pmid:25757610
View Article
PubMed/NCBI
Google Scholar

[169] View Article

[170] PubMed/NCBI

[171] Google Scholar

[ref46] 46. Leal MF, Arliani GG, Astur DC, Franciozi CE, Debieux P, Andreoli CV, et al. Comprehensive selection of reference genes for expression studies in meniscus injury using quantitative real-time PCR. Gene. 2016; 584(1): 60–68. pmid:26968891
View Article
PubMed/NCBI
Google Scholar

[173] View Article

[174] PubMed/NCBI

[175] Google Scholar

[ref47] 47. Frydenberg J, Poulsen K, Petersen AKB, Lund A, Ole F, Olesen OF. Isolation and characterization of the gene encoding EF-1αO, an elongation factor 1-α expressed during early development of Xenopus laevis. Gene. 1991; 109(2), 185–192. pmid:1765266
View Article
PubMed/NCBI
Google Scholar

[177] View Article

[178] PubMed/NCBI

[179] Google Scholar

[ref48] 48. Smalle J, Vierstra RD. The ubiquitin 26S proteasome proteolytic pathway. Annu Rev Plant Biol. 2004; 55: 555–590. pmid:15377232
View Article
PubMed/NCBI
Google Scholar

[181] View Article

[182] PubMed/NCBI

[183] Google Scholar

[ref49] 49. Handschumacher RE, Harding MW, Rice J, Drugge RJ, Speicher DW. Cyclophilin: a specific cytosolic binding protein for cyclosporin A. Science. 1984; 226(4674): 544–547. pmid:6238408
View Article
PubMed/NCBI
Google Scholar

[185] View Article

[186] PubMed/NCBI

[187] Google Scholar

[ref50] 50. Gu XF, Zhang SP, Fang XJ, Li ZM, Xu CQ. A new cucumber hybrid with super quality, earliness and high yield ‘Zhongnong 16’. Acta Hortic Sinica. 2005; 32: 762. (in Chinese)
View Article
Google Scholar

[189] View Article

[190] Google Scholar

[ref51] 51. Radonic A, Thulke S, Mackay IM, Landt O, Siegert W, Nitsche A. Guideline to reference gene selection for quantitative real-time PCR. Biochem Biophys Res Commun. 2004; 313(4): 856–862. pmid:14706621
View Article
PubMed/NCBI
Google Scholar

[192] View Article

[193] PubMed/NCBI

[194] Google Scholar

[ref52] 52. Silver N, Best S, Jiang J, Thein SL. Selection of housekeeping genes for gene expression studies in human reticulocytes using real-time PCR. BMC Mol Biol. 2006; 7: 33. pmid:17026756
View Article
PubMed/NCBI
Google Scholar

[196] View Article

[197] PubMed/NCBI

[198] Google Scholar

[ref53] 53. Karuppaiya P, Yan XX, Liao W, Wu J, Chen F, Tang L. Identification and validation of superior reference gene for gene expression normalization via RT-qPCR in staminate and pistillate flowers of Jatropha curcas-a biodiesel plant. PLoS One. 2017; 12(5): e0177039. pmid:28459843
View Article
PubMed/NCBI
Google Scholar

[200] View Article

[201] PubMed/NCBI

[202] Google Scholar

[ref54] 54. Niu QW, Lin SS, Reyes JL, Chen KC, Wu HW, Yeh SD, et al. Expression of artificial microRNAs in transgenic Arabidopsis thaliana confers virus resistance. Nat Biotechnol. 2006; 24(11): 1420–1428. pmid:17057702
View Article
PubMed/NCBI
Google Scholar

[204] View Article

[205] PubMed/NCBI

[206] Google Scholar

[ref55] 55. Pfaffl MW, Horgan GW, Dempfle L. Relative expression software tool (REST) for group-wise comparison and statistical analysis of relative expression results in real-time PCR. Nucleic Acids Res. 2002; 30(9), e36. pmid:11972351
View Article
PubMed/NCBI
Google Scholar

[208] View Article

[209] PubMed/NCBI

[210] Google Scholar

[ref56] 56. Hong SY, Seo PJ, Yang MS, Xiang F, Park CM. Exploring valid reference genes for gene expression studies in Brachypodium distachyon by real-time PCR. BMC Plant Biol. 2008; 8: 112. pmid:18992143
View Article
PubMed/NCBI
Google Scholar

[212] View Article

[213] PubMed/NCBI

[214] Google Scholar

[ref57] 57. Cruz F, Kalaoun S, Nobile P, Colombo C, Almeida J, Barros LMG, et al. Evaluation of coffee reference genes for relative expression studies by quantitative real-time RT-PCR. Mol Breed. 2009; 23(4): 607–616.
View Article
Google Scholar

[216] View Article

[217] Google Scholar

[ref58] 58. Huis R, Hawkins S, Neutelings G. Selection of reference genes for quantitative gene expression normalization in flax (Linum usitatissimum L.). BMC Plant Biol. 2010; 10: 71. pmid:20403198
View Article
PubMed/NCBI
Google Scholar

[219] View Article

[220] PubMed/NCBI

[221] Google Scholar

[ref59] 59. Mafra V, Kubo KS, Alves-Ferreira M, Ribeiro-Alves M, Stuart RM, Boava LP, et al. Reference genes for accurate transcript normalization in citrus genotypes under different experimental conditions. PLoS One. 2012; 7(2): e31263. pmid:22347455
View Article
PubMed/NCBI
Google Scholar

[223] View Article

[224] PubMed/NCBI

[225] Google Scholar

[ref60] 60. Carvalho K, Campos MKF, Pereira LFP, Vieira LGE. Reference gene selection for real-time quantitative polymerase chain reaction normalization in “Swingle” citrumelo under drought stress. Anal Biochem. 2010; 402: 197–199. pmid:20363209
View Article
PubMed/NCBI
Google Scholar

[227] View Article

[228] PubMed/NCBI

[229] Google Scholar

[ref61] 61. Yang YT, Zhang X, Chen Y, Guo JL, Ling H, Gao SW, et al. Selection of reference genes for normalization of microRNA expression by RT-qPCR in sugarcane buds under cold stress. Front Plant Sci. 2016; 7: 86. pmid:26904058
View Article
PubMed/NCBI
Google Scholar

[231] View Article

[232] PubMed/NCBI

[233] Google Scholar

[ref62] 62. Scholtz JJ, Visser B. Reference gene selection for qPCR gene expression analysis of rust-infected wheat. Physiol Mol Plant Pathol. 2013; 81: 22–25.
View Article
Google Scholar

[235] View Article

[236] Google Scholar

[ref63] 63. Zhu XY, Li XP, Chen WX, Chen JY, Lu WJ, Chen L, et al. Evaluation of new reference genes in papaya for accurate transcript normalization under different experimental conditions. PLoS One. 2012; 7(8): e44405. pmid:22952972
View Article
PubMed/NCBI
Google Scholar

[238] View Article

[239] PubMed/NCBI

[240] Google Scholar

[ref64] 64. Tenea GN, Bota AP, Raposo FC, Maquet A. Reference genes for gene expression studies in wheat flag leaves grown under different farming conditions. BMC Res Notes. 2011; 4: 373. pmid:21951810
View Article
PubMed/NCBI
Google Scholar

[242] View Article

[243] PubMed/NCBI

[244] Google Scholar

[ref65] 65. Janská A, Hodek J, Svoboda P, Zámcník J, Prášil IT, Vlasáková E, et al. The choice of reference gene set for assessing gene expression in barley (Hordeum vulgare L.) under low temperature and drought stress. Mol Genet Genomics. 2013; 288(11): 639–649. pmid:23979536
View Article
PubMed/NCBI
Google Scholar

[246] View Article

[247] PubMed/NCBI

[248] Google Scholar

Figures

Abstract

Introduction

Materials and methods

Plant materials and virus inoculant

Total RNA extraction and cDNA synthesis

Amplification of gene transcripts

Expression stability of reference genes

Validation of selected reference genes using miR159 expression

Results

Performance of primers and expression stability of reference genes in cucumber

Delta-Ct analysis

geNorm analysis

NormFinder analysis

BestKeeper analysis

RefFinder analysis

Validation of selected reference genes by miR159 expression analysis

Discussion

Conclusions

Supporting information

S1 Fig. Melting curves of seven candidate reference genes.

S2 Fig. Standard curves of seven candidate reference genes.

S3 Fig. Electrophoresis of PCR products corresponding to seven candidate reference genes with expected sizes on an agarose gel.

S4 Fig. Expression profiles of miR169, miR172, miR838, miR854, miR2673, miR5637, miR5658 and csa-miRn6-3p and validation of selected reference genes in cucumber leaf tissues infected with CGMMV.

S1 File. Sequences of candidate reference genes.

Acknowledgments

References