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Evaluation of Reference Genes for Normalization of Gene Expression Using Quantitative RT-PCR under Aluminum, Cadmium, and Heat Stresses in Soybean

  • Mengmeng Gao ,

    Contributed equally to this work with: Mengmeng Gao, Yaping Liu

    Affiliation National Key Laboratory of Crop Genetics and Germplasm Enhancement, National Center for Soybean Improvement, Key Laboratory for Biology and Genetic Improvement of Soybean (General, Ministry of Agriculture), Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing, Jiangsu, China

  • Yaping Liu ,

    Contributed equally to this work with: Mengmeng Gao, Yaping Liu

    Affiliation National Key Laboratory of Crop Genetics and Germplasm Enhancement, National Center for Soybean Improvement, Key Laboratory for Biology and Genetic Improvement of Soybean (General, Ministry of Agriculture), Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing, Jiangsu, China

  • Xiao Ma,

    Affiliation National Key Laboratory of Crop Genetics and Germplasm Enhancement, National Center for Soybean Improvement, Key Laboratory for Biology and Genetic Improvement of Soybean (General, Ministry of Agriculture), Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing, Jiangsu, China

  • Qin Shuai,

    Affiliation National Key Laboratory of Crop Genetics and Germplasm Enhancement, National Center for Soybean Improvement, Key Laboratory for Biology and Genetic Improvement of Soybean (General, Ministry of Agriculture), Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing, Jiangsu, China

  • Junyi Gai,

    Affiliation National Key Laboratory of Crop Genetics and Germplasm Enhancement, National Center for Soybean Improvement, Key Laboratory for Biology and Genetic Improvement of Soybean (General, Ministry of Agriculture), Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing, Jiangsu, China

  • Yan Li

    yanli1@njau.edu.cn

    Affiliation National Key Laboratory of Crop Genetics and Germplasm Enhancement, National Center for Soybean Improvement, Key Laboratory for Biology and Genetic Improvement of Soybean (General, Ministry of Agriculture), Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing, Jiangsu, China

Evaluation of Reference Genes for Normalization of Gene Expression Using Quantitative RT-PCR under Aluminum, Cadmium, and Heat Stresses in Soybean

  • Mengmeng Gao, 
  • Yaping Liu, 
  • Xiao Ma, 
  • Qin Shuai, 
  • Junyi Gai, 
  • Yan Li
PLOS
x

Abstract

Quantitative reverse transcription polymerase chain reaction (qRT-PCR) is widely used to analyze the relative gene expression level, however, the accuracy of qRT-PCR is greatly affected by the stability of reference genes, which is tissue- and environment- dependent. Therefore, choosing the most stable reference gene in a specific tissue and environment is critical to interpret gene expression patterns. Aluminum (Al), cadmium (Cd), and heat stresses are three important abiotic factors limiting soybean (Glycine max) production in southern China. To identify the suitable reference genes for normalizing the expression levels of target genes by qRT-PCR in soybean response to Al, Cd and heat stresses, we studied the expression stability of ten commonly used housekeeping genes in soybean roots and leaves under these three abiotic stresses, using five approaches, BestKeeper, Delta Ct, geNorm, NormFinder and RefFinder. We found TUA4 is the most stable reference gene in soybean root tips under Al stress. Under Cd stress, Fbox and UKN2 are the most stable reference genes in roots and leaves, respectively, while 60S is the most suitable reference gene when analyzing both roots and leaves together. For heat stress, TUA4 and UKN2 are the most stable housekeeping genes in roots and leaves, respectively, and UKN2 is the best reference gene for analysis of roots and leaves together. To validate the reference genes, we quantified the relative expression levels of six target genes that were involved in soybean response to Al, Cd or heat stresses, respectively. The expression patterns of these target genes differed between using the most and least stable reference genes, suggesting the selection of a suitable reference gene is critical for gene expression studies.

Introduction

Quantitative reverse transcription polymerase chain reaction (qRT-PCR) is one of the most widely used techniques to detect the changes in gene expression [1], due to its relatively accurate quantification, high sensitivity and high throughput. The difference in the amount and quality of the template can affect the efficiency of the qRT-PCR reactions [2], therefore it is essential to normalize the expression level of the target gene by using a reference gene as an internal control. In general, an ideal reference gene should demonstrate a consistent expression level across all tested tissues or conditions [3].

Housekeeping genes are commonly used as the reference genes for qRT-PCR, such as 18S ribosomal RNA (18S rRNA), 25S ribosomal RNA (25S rRNA), β-actin (ACT11), cyclophilin (CYP2), elongation factor 1-alpha (ELF1A), glyceraldehyde-3-phosphate dehydrogenase (GAPDH), translation elongation factor (TEF), tubulin (TUB4) and polyubiquitin (UBQ) [4]. The housekeeping genes are involved in basic metabolic processes and important for normal cell growth, therefore their expression levels are thought to be stable [57]. However, many recent studies found that the expression levels of housekeeping genes may vary largely across different tissues, developmental stages, or experimental conditions [69]. Therefore, it is necessary to select stably expressed reference genes before they are utilized for normalizing the expression levels of target genes by qRT-PCR.

Soybean is an economically important crop worldwide, which provides important source of vegetable oil and proteins [10]. The production and quality of soybean can be affected by a variety of adverse environmental conditions, including abiotic and biotic stresses. Analyzing gene expression under stresses is important to select the candidate genes for soybean tolerance to various stresses. To date, several studies have been conducted to select stable reference genes in different tissues and under various environments in soybean, including different photoperiods [6], abscisic acid (ABA) treatment [8], as well as drought and salinity stresses [8,9], and the reference genes showed tissue- and stress-dependent [6,8,9].

Aluminum (Al) toxicity, cadmium (Cd) pollution, and heat stresses are three major adverse environments affecting agricultural production in southern China. Al toxicity is a major factor limiting crop production on acidic soils [11, 12]. The primary symptom of Al toxicity is the inhibition of root growth which later disrupts water and nutrient uptake by the roots [13]. Cd is a widespread toxic heavy metal pollutant in agricultural soils [14]. Cd can accumulate in human kidneys, leading to loss of calcium and osteoporosis [15]. Several studies found that Cd concentrations in soybean grains exceeded the maximum permissible levels in Japan [16], Argentina [17], and China [18], which poses potential threats to food safety. The damage of Cd toxicity to soybean includes inhibition of root and shoot growth, less water and nutrient uptake, chlorosis, and decreased yield [1821]. It has been found that warming increased Cd uptake and translocation in rice seedlings [22], which suggests Cd would be an important environmental stress in warmer regions and under future warmer climates. A recent study found that heat waves during early pod development in soybean caused significant yield loss [23]. Therefore, Al, Cd, and heat stresses are three major factors limiting soybean production especially under warmer climates. However, there are limited studies on gene expression analysis and selection of reference genes for qRT-PCR in soybean under Al, Cd, and heat stresses. In this study, we did a comprehensive analysis of ten commonly used reference genes in soybean to select the most stable ones for normalization of gene expression by qRT-PCR under Al, Cd, and heat stresses, which would be helpful to improve the accuracy of gene expression analysis under these important abiotic stresses.

Materials and Methods

Plant materials, growth conditions and stress treatments

The seeds of soybean (Glycine max [L.] Merr.) cultivar Kefeng-1 used in this study are provided by National Center for Soybean Improvement (Nanjing, China). The experiments were conducted in plant growth chambers with a 14 h/10 h (light /dark) cycle at 26°C / 24°C (light /dark) and 50–70% relative humidity. Soybean seeds were germinated in sterile sand for three days in dark. For Al toxicity treatment, 3-day-old seedlings were transferred to 0.5 mM CaCl2 (pH = 4.3) for one day, then to 0.5 mM CaCl2 solution (pH = 4.3) containing 25 μM AlCl3 (Al stress) or without Al (control). The primary root tips (0–1 cm) of ten seedlings were collected at 6, 12 and 24 h for Al stress and control, respectively [24]. For Cd and heat treatments, the soybean seedlings were transferred to containers filled with 1/2 Hoagland solution (pH = 5.8), which was replaced every three days. For Cd treatment, 14-day-old seedlings were treated with 100 μM CdCl2 (Cd stress) in 1/2 Hoagland solution or 1/2 Hoagland solution without Cd (control), and samples were collected after 3, 12 and 24 h, according to the previous studies [2526]. For heat stress, 14-day-old seedlings were maintained at 42°C for 1, 3 and 6 h as described previously [27]. The seedlings grown under normal conditions at the corresponding time points were used as control. For each biological replicate, the root tips (0–1 cm) from 10 individual plants were collected and pooled for each root sample, and the newest fully expanded trifoliolate leaves from three individual plants were harvested and pooled together for each leaf sample. Leaf and root samples were collected separately, frozen quickly in liquid nitrogen and stored at -80°C for RNA extraction. All experiments were conducted with three biological replications. There are 30 samples for each replication and 90 samples in total for this study. For Al stress, we collected 18 root samples (3 time points x 2 treatments x 3 replicates = 18). For Cd stress, we collected 18 root samples and 18 leaf samples, and 18 root samples and 18 leaf samples for heat stress as well.

RNA extraction and cDNA synthesis

Total RNA was isolated using RNAprep Pure Plant Kit (TianGen, China) according to the manufacturer’s instruction. Electrophoresis with 1% agarose gel was used to determine the integrity of total RNA. The quality and concentrations of RNA were measured by Infinite M200 (Tecan, Switzerland). Frist strand cDNA was synthesized by reverse transcription of 1 μg total RNA with PrimeScript RT reagent Kit with gDNA Eraser (Perfect Real Time) (TaKaRa, Japan), in a volume of 20 μl according to manufacturer’s protocol. All cDNA samples were stored at -20°C for later use.

qRT-PCR assays

Primers from published literature with good specificity and amplification efficiency were utilized in our study (S1 Table). All primers were synthesized by Invitrogen (Shanghai, China). Quantitative RT-PCR experiments were carried out using SYBR® Premix Ex Taq (Tli RNaseH Plus) (TaKaRa, Japan) on LightCycler 480 (Roche, Switzerland). The program of the qRT-PCR was 95°C for 5 min, followed by 40 cycles at 95°C for 10 s, 60°C for 15 s and 72°C for 15 s. Dissociation curves were obtained using a thermal melting profile performed after the last PCR cycle: 95°C decreases to 40°C at the speed of 5°C per second followed by a constant increase in the temperature between 60°C and 95°C. The MIQE (Minimum Information for Publication of Quantitative Real-Time PCR Experiments) guidelines proposed the use of quantification cycle (Cq) value over the threshold cycle (Ct) according to the RDML (Real-Time PCR Data Markup Language) data standard [2]. Cq value represents the number of cycles when the density of fluorescence meets the set threshold. Therefore, Cq values were used in this study. Each sample was tested in three technical replicates.

A series of 10-fold dilutions of cDNA templates (10–1,000 fold dilution) were made to generate standard curves, and the gene specific amplification efficiency for each primer pair in qRT-PCR was determined by the slope of the log-linear portion of the calibration curve. The gene specific PCR amplification efficiency (E) is calculated by using the equation: E (%) = (10−1/slope-1) × 100% [9]. The relative expression levels of target genes were calculated using the 2-ΔΔCT method [28].

Analysis of the stabilities of reference genes

The stabilities of reference genes were analyzed using software tools, including geNorm [29], BestKeeper [30], NormFinder [31], Delta Ct [32], and RefFinder [33], following the corresponding instructions. The geNorm program identifies the most stable reference genes based on the average pairwise variation of a reference gene with other housekeeping genes, and ranks the reference genes by their expression stability values (M). In general, the lower the M value, the higher is the expression stability of candidate genes [29]. BestKeeper determines the “optimal” reference genes on the basis of pair-wise correlation analysis of all pairs of candidate reference genes [30]. NormFinder calculates the overall variation of the candidate reference genes in all samples and also the variation of intra- and inter-groups [31]. Delta Ct compares the relative expression of pairs of candidate genes within each sample. If the ΔCt value between two reference genes does not change among different samples, it indicates either both genes have stable expression patterns or they are co-regulated among the samples, yet the different ΔCt value suggests that at least one of them is variably expressed. Then the third, fourth, or more genes are introduced into the comparisons to find out which pairs show less variability, and hence which gene has stable expression among the samples tested. Ultimately, an appropriate reference gene can be selected for a particular experimental system [32]. Finally, RefFinder generates a comprehensive ranking by calculating the geometric mean of each reference gene in the above four methods, in which the smaller the ranking, the more stable is the reference gene [3334].

Statistical analysis

Statistical analyses were performed using SPSS version 17.0 software. Differences in the relative expression levels of target genes between using the most and two least stable reference genes were analyzed based on Duncan’s multiple range test.

Results

Primer specificity and qRT-PCR amplification efficiency

In this study, we chose ten candidate reference genes in soybean whose stabilities have been previously tested under various conditions [6, 8, 9] but not under Al, Cd, or heat stresses. These ten candidate reference genes include 60s, ABC, ACT11, ACT2/7, CYP2, ELF1A, Fbox, TUA4, TUB4, and UKN2 [6, 8, 9]. In order to verify the performance of the candidate reference genes identified in this study, six target genes were selected for qRT-PCR to study their gene expression under Al, Cd and heat stresses, in which GmALMT1 [35] and GmARI1 [36] have been reported to play roles in soybean tolerance to Al toxicity, GmHMA13 and GmHMA19 were related to Cd stress [37], and GmGBP1 and GmHsfA1 were responsive to heat stress [27, 38]. The details of the ten reference genes and six target genes are listed in Table 1.

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Table 1. The ten candidate reference genes and six target genes used in this study.

https://doi.org/10.1371/journal.pone.0168965.t001

The specificity of the amplification was shown by the melting curves in qRT-PCR (S1 Fig). A single peak of each melting curve in the qRT-PCR experiments indicated good specificity of the primers. The qRT-PCR amplification efficiency (E) and correlation coefficient (R2) were calculated based on the slope of the calibration curves (Table 2). For the ten candidate reference genes and six target genes, the E values were 93.74% to 104.80% and R2 ranged from 0.9977 to 0.9999 (Table 2). The results showed that all 16 pairs of primers met the requirement of qRT-PCR experiments.

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Table 2. Amplification characteristics of the ten candidate reference genes and six target genes in qRT-PCR.

https://doi.org/10.1371/journal.pone.0168965.t002

Expression profiling of ten candidate reference genes

The transcript abundance of the ten candidate reference genes in all samples (including Al, Cd, and heat treatments and controls) was determined by the Cq values from qRT-PCR experiments (Fig 1). Cq is the number of cycles at which the fluorescence exceeds the detection threshold. During the PCR amplification, a cDNA sample with more abundance reaches the threshold at a lower Cq value, corresponding to a higher gene expression level. The Cq values of the ten candidate reference genes ranged from 16.88 to 28.53, with an average Cq value of 21.93. All of the Cq values were within the valid range of qRT-PCR, demonstrating their feasibility for further analysis. The mean Cq value of each candidate reference gene varied from 19.19 to 24.85 (Fig 1). ELF1A was the most abundant reference gene in this study, indicated by its mean Cq value of 19.19 (minimum mean Cq value in the set), whereas ABC was the least abundant reference gene (whose mean Cq value was 24.85).

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Fig 1. Distribution of quantification cycle (Cq) values for ten candidate reference genes in all samples.

Expression levels of the ten candidate reference genes were examined in all the samples in this study and shown by the boxplots. The solid line within each box represents the 50th percentile. The lower boundary and upper boundary of each box represents the 25th and 75th percentile, respectively. The line above and below the vertical dashed lines represents the maximum and minimum values, respectively. The circles represents potential outliers.

https://doi.org/10.1371/journal.pone.0168965.g001

Expression stability of the candidate reference genes in all samples

To identify the suitable reference genes for qRT-PCR that could be used in gene expression analysis under Al, Cd, and heat stresses, the expression profiles of the ten candidate reference genes in soybean roots and/or leaves across all experiments in this study were analyzed together using RefFinder, which gives a comprehensive evaluation of the reference genes based on the rankings made by the four algorithms, BestKeeper, Delta Ct, geNorm, and NormFinder. For leaf samples, the top five most stable reference genes identified by RefFinder were UKN2 > ACT11 > 60S > TUA4 > ACT2/7 (Table 3), which also showed low M values (the lowest five) by geNorm analysis (Fig 2A), implying their expression stability in leaves, and CYP2 was ranked as the most unstable gene by both RefFinder (Table 3) and geNorm (Fig 2A). For root samples, the top five most stably expressed genes were Fbox > ACT2/7 > CYP2 > ABC > UKN2 as shown by RefFinder (Table 3), which were also the five most stable genes identified by geNorm (Fig 2B). TUB4 was found as the least stable gene in roots by RefFinder (Table 3) and geNorm (Fig 2B). When samples of roots and leaves were analyzed together, UKN2 and 60S were identified as the most stably expressed reference genes by RefFinder (Table 3) and geNorm analysis (Fig 2C), whereas TUB4 was ranked as the least stable reference gene by RefFinder (Table 3).

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Fig 2. The expression stability of the ten soybean reference genes across all treatments in this study.

Y-axis represents the average expression stability (M) values analyzed by geNorm. (A) Leaf samples, (B) Root samples, (C) Leaf and root samples together.

https://doi.org/10.1371/journal.pone.0168965.g002

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Table 3. Rankings of the expression stability of ten candidate reference genes by RefFinder.

https://doi.org/10.1371/journal.pone.0168965.t003

Expression stability of the candidate reference genes under Al toxicity

Root is the primary tissue influenced by Al toxicity, which inhibits crop growth and yield in acid soils. Therefore we try to identify the most stable reference genes in soybean roots under Al toxicity. TUA4 was listed on the top of the ranking by RefFinder (Table 3), also by geNorm, BestKeeper and Delta Ct (S2 Fig, S2 Table). However, NormFinder found that ACT11 had a better performance than the other 9 candidate reference genes, followed by TUA4 and TUB4 (S2 Table). CYP2 was consistently evaluated as the least stable reference genes by RefFinder (Table 3), as well as Delta Ct, geNorm and NormFinder (S2 Fig, S2 Table). Therefore, when studying the relative expression of genes involved in soybean response to Al toxicity, TUA4 should be used as the reference gene while CYP2 should be avoided.

Expression stability of the candidate reference genes under Cd stress

Studies have demonstrated that Cd pollution in soil can have an adverse effect on soybean growth [39]. It is important to find stable reference genes for normalizing gene expression of target genes under Cd stress. In the leaves, the most stable reference genes were UKN2, ACT11 and 60S by RefFinder analysis (Table 3), and Fbox was the least stable one in the ranking (S3 Fig, S3 Table). But in the roots, Fbox was the most stable gene by RefFinder followed by ACT2/7 and 60S (Table 3), and TUB4 was consistently ranked as the least stable gene by all approaches (S3 Fig, S4 Table). When both leaves and roots were analyzed together, 60S, UKN2 and ACT2/7 were identified as the top three stable reference genes under Cd stress by RefFinder (Table 3) and the other four methods (S3 Fig, S5 Table). Meanwhile, Fbox and TUB4 were found to be the least stable ones by RefFinder (Table 3) and three methods including geNorm (S3 Fig, S5 Table). These results revealed that a reference gene might perform highly variable among different tissues.

Expression stability of the candidate reference genes under heat stress

Next we searched for the most suitable reference genes to be used for gene expression analysis under 42°C heat stress. UKN2 and 60S were the top two stably expressed genes in leaves under heat stress (Table 3 and S6 Table). If considering the roots under heat stress, TUA4 was the best one to be chosen as demonstrated by RefFinder, NormFinder, and Delta Ct (Table 3 and S7 Table), and ACT2/7 and ELF1A were the other two reference genes with good stability in roots (Table 3 and S7 Table). The four evaluation methods consistently showed that UKN2 was the most stable reference gene in both leaves and roots (Table 3 and S8 Table). The top two most stable genes in both roots and leaves were UKN2 and ACT2/7 ranked by both RefFinder (Table 3) and geNorm (S4 Fig).

Validation of the reference genes identified in this study

To validate the reference genes identified in this study, we analyzed the expression of six target genes in soybean, in which GmALMT1 and GmARI1 were considered to be involved in soybean response to Al toxicity [3536], GmHMA13 and GmHMA19 participated in soybean response to Cd stress [37], and GmGBP1 and GmHsfA1 were responsive to heat stress in soybean [27, 38]. As determined by RefFinder, we chose the most stable reference gene, TUA4, and two least stable genes, CYP2 and ABC, to normalize the expression levels of the two target genes under Al toxicity. As shown in Fig 3A, the relative expression level of GmALMT1 was up-regulated by 25 μM AlCl3 at 6 and 12 h when normalized by TUA4 gene, which was significantly different (P < 0.05 based on Duncan’s multiple range tests) with the relative expression levels when using CYP2 or ABC as the reference gene. Similar situation occurred in GmARI1 expression profile (Fig 3B). Compared with TUA4 as the reference gene, the relative expression levels of GmARI1 was significantly different (P < 0.05) with that when normalized by CYP2 or ABC, and the peak time point of GmARI1 expression level also shifted when using ABC as the reference gene (Fig 3B). Under Cd stress, the most stable reference gene is UKN2 and the two least stable reference genes are ELF1A and Fbox in soybean leaves, while Fbox, ELF1A and TUB4 were the most and two least stable reference genes in soybean roots. As shown in Fig 4, in both leaves and roots, the two target genes exhibited different expression profiles when using different reference genes. The relative expression levels of both target genes in soybean under Cd stress tend to be inflated when normalized by the least stable reference gene Fbox in leaves (Fig 4A and 4B) or TUB4 in roots (Fig 4C and 4D). Under heat stress, in soybean leaves, the relative expression levels of GmGBP1 was up-regulated when normalized by the most stable reference gene UKN2, which is significantly (P < 0.05) different compared with using the two least stable reference genes TUB4 and CYP2 at all time-points (Fig 5A). Similar result was found for the target gene GmHsfA1 (Fig 5B). In soybean roots, the most stable reference gene TUA4 and the two least stable reference genes 60S and Fbox were selected to normalize the expression levels of target genes under heat stress. The relative expression patterns of GmGBP1 and GmHsfA1 were different when normalized by TUA4 compared with the two least stable reference genes (Fig 5C and 5D). These results highlight that selection of a suitable reference gene is critical for gene expression studies.

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Fig 3. Relative expression levels of GmALMT1 (A) and GmARI1 (B) in soybean roots under 25 μM AlCl3 (pH4.3) treatment normalized by the most (TUA4) and two least (CYP2 and ABC) stable reference genes.

The relative expression levels of two target genes relative to the control samples at corresponding time points were calculated using the 2-ΔΔCT method. Bars represent the mean ± SE (standard error) from three replications. Different letters represent significant difference (P < 0.05) at each time point based on Duncan’s multiple range test.

https://doi.org/10.1371/journal.pone.0168965.g003

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Fig 4. Relative expression levels of GmHMA13 and GmHMA19 in soybean leaves and roots under 100 μM CdCl2 treatment normalized by the most (UKN2) and two least (ELF1A and Fbox) stable reference genes in leaves and the most (Fbox) and two least (ELF1A and TUB4) stable reference genes in roots.

(A) GmHMA13 in soybean leaves, (B) GmHMA19 in soybean leaves, (C) GmHMA13 in soybean roots, and (D) GmHMA19 in soybean roots. The relative expression levels of two target genes relative to the control samples at corresponding time points were calculated using the 2-ΔΔCT method. Bars represent the mean ± SE (standard error) from three replications. Different letters represent significant difference (P < 0.05) at each time point based on Duncan’s multiple range test.

https://doi.org/10.1371/journal.pone.0168965.g004

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Fig 5. Relative expression levels of GmGBP1 and GmHsfA1 in soybean leaves and roots under 42°C heat stress normalized by the most (UKN2) and two least (TUB4 and CYP2) stable reference genes in leaves and the most (TUA4) and two least (60S and Fbox) stable reference genes in roots.

(A) GmGBP1 in soybean leaves, (B) GmHsfA1 in soybean leaves, (C) GmGBP1 in soybean roots, and (D) GmHsfA1 in soybean roots. The relative expression levels of two target genes relative to the control samples at corresponding time points were calculated using the 2-ΔΔCT method. Bars represent the mean ± SE (standard error) from three replications. Different letters represent significant difference (P < 0.05) at each time point based on Duncan’s multiple range test.

https://doi.org/10.1371/journal.pone.0168965.g005

Discussion

Climate changes and human activities have generated many adverse environments for plants, such as acid rain, accumulation of heavy metals in soil, drought, high temperature, and salinity. Seeking for the molecular mechanisms and candidate genes are important to improve the abilities of plants to resist or tolerate these stresses. Analysis of gene expression patterns under abiotic stresses helps understanding the functions and regulation of genes. qRT-PCR is an efficient and most recognized method to evaluate the transcript abundance of genes. Many studies have demonstrated that the selection of suitable reference genes is critical to interpret the qRT-PCR results, and there is no reference gene showing stable expression in different tissues across all environments [6, 8, 9, 4042]. In soybean, the stable reference genes for study of gene expression under drought and salinity stresses have been reported [8, 9], but the suitable reference genes for study of gene expression under Al toxicity, Cd and heat stresses are needed to be explored.

In this study, ten candidate reference genes, 60S, ABC, ACT11, ACT2/7, CYP2, ELF1A, Fbox, TUA4, TUB4 and UKN2, were selected for normalization of gene expression under Al toxicity, Cd and heat stresses. The ten primer pairs for all candidate reference genes showed good amplification efficiencies of 93.74 ~ 104.80% (Table 2), and the average Cq value of each gene ranged from 19.19 to 24.85, indicating the qRT-PCR results are suitable for further analysis. Five approaches, geNorm, BestKeeper, NormFinder, delta Ct, and RefFinder, were employed to analyze the expression stability of these reference genes. Although the rank order of the reference genes was not identical when using different approaches, the overall most and least stable reference genes are consistent among several statistical analysis. The web-based tool RefFinder integrates the four statistical algorithms including geNorm, BestKeeper, NormFinder, and delta Ct, to rank the overall stability of candidate reference genes. Therefore, we selected the suitable reference genes mainly based on the output from RefFinder. When the samples from all three stresses (Al, Cd and heat stresses) were analyzed together, UKN2 was identified as the overall most stably expressed reference gene by RefFinder (Table 3). Under Al toxicity, TUA4 performed as the most stable reference gene in soybean roots, followed by ACT11, Fbox and UKN2. For Cd stress, 60S and UKN2 are the optimal reference genes when analyze the gene expression in both leaves and roots. And UKN2 also showed highest stability in both leaves and roots under 42°C heat stress. There is no report on selection of reference genes for Al or heat stress in soybean to date. A previous study showed that ACT3, PP2A, ELF1B and F-box were the most stable reference genes in soybean under Cd stress based on geNorm and NormFinder analysis [43], while 60S and UKN2, the most stable reference genes in soybean under Cd stress found in this study, were not evaluated in the previous report [43].

We also used six previously reported genes that are responsive to Al, Cd and heat stresses, respectively, to validate their relative expression using the most and least stable reference genes identified in our study. The results showed that the expression patterns of target genes differed between using the most and least stable reference genes (Figs 35), suggesting that selection of a suitable reference gene is critical for gene expression studies. This study provides a list of suitable reference genes for normalization of gene expression in soybean under Al toxicity, Cd and heat stresses, which would help to accurately analyze the gene expression patterns under these important abiotic stresses.

Supporting Information

S1 Fig. Melting curves of ten candidate reference genes and six target genes.

(A) to (J) represent ten candidate reference genes, 60S, ABC, ACT11, ACT2/7, CYP2, ELF1A, Fbox, TUA4, TUB4 and UKN2, respectively. (K) to (P) represent the six target genes, GmALMT1, GmARI1, GmHMA13, GmHMA19, GmGBP1 and GmHsfA1, respectively.

https://doi.org/10.1371/journal.pone.0168965.s001

(TIF)

S2 Fig. The expression stability of the ten reference genes in soybean roots under 25 μM AlCl3 (pH4.3) treatment.

Y-axis represents the average expression stability (M) values analyzed by geNorm.

https://doi.org/10.1371/journal.pone.0168965.s002

(TIF)

S3 Fig. The expression stability of the ten reference genes in soybean under 100 μM CdCl2 treatment.

Y-axis represents the average expression stability (M) values analyzed by geNorm. (A) Leaf samples, (B) Root samples, (C) Leaf and root samples together.

https://doi.org/10.1371/journal.pone.0168965.s003

(TIF)

S4 Fig. The expression stability of the ten reference genes in soybean leaves and roots under 42°C heat stress.

Y-axis represents the average expression stability (M) values analyzed by geNorm.

https://doi.org/10.1371/journal.pone.0168965.s004

(TIF)

S1 Table. Primers of ten candidate reference genes and six target genes for qRT-PCR in this study.

https://doi.org/10.1371/journal.pone.0168965.s005

(DOCX)

S2 Table. Rankings and expression stability values of ten candidate reference genes in soybean roots under 25 μM AlCl3 (pH4.3) treatment.

From top to the bottom represent the most stable to least stable gene.

https://doi.org/10.1371/journal.pone.0168965.s006

(DOCX)

S3 Table. Rankings and expression stability values of ten candidate reference genes in soybean leaves under 100 μM CdCl2 treatment.

From top to the bottom represent the most stable to least stable gene.

https://doi.org/10.1371/journal.pone.0168965.s007

(DOCX)

S4 Table. Rankings and expression stability values of ten candidate reference genes in soybean roots under 100 μM CdCl2 treatment.

From top to the bottom represent the most stable to least stable gene.

https://doi.org/10.1371/journal.pone.0168965.s008

(DOCX)

S5 Table. Rankings and expression stability values of ten candidate reference genes in soybean leaves and roots under 100 μM CdCl2 treatment.

From top to the bottom represent the most stable to least stable gene.

https://doi.org/10.1371/journal.pone.0168965.s009

(DOCX)

S6 Table. Rankings and expression stability values of ten candidate reference genes in soybean leaves under 42°C heat stress.

From top to the bottom represent the most stable to least stable gene.

https://doi.org/10.1371/journal.pone.0168965.s010

(DOCX)

S7 Table. Rankings and expression stability values of ten candidate reference genes in soybean roots under 42°C heat stress.

From top to the bottom represent the most stable to least stable gene.

https://doi.org/10.1371/journal.pone.0168965.s011

(DOCX)

S8 Table. Rankings and expression stability values of ten candidate reference genes in soybean leaves and roots under 42°C heat stress.

From top to the bottom represent the most stable to least stable gene.

https://doi.org/10.1371/journal.pone.0168965.s012

(DOCX)

Acknowledgments

The authors would like to thank the staff at the Jiangpu Experimental Station for their assistance with the production of soybean seeds.

Author Contributions

  1. Conceptualization: MG Y. Li.
  2. Data curation: MG Y. Liu Y. Li.
  3. Formal analysis: MG Y. Liu.
  4. Funding acquisition: JG Y. Li.
  5. Investigation: MG Y. Liu XM.
  6. Methodology: MG Y. Li.
  7. Project administration: Y. Li.
  8. Resources: JG Y. Li.
  9. Supervision: JG Y. Li.
  10. Validation: MG Y. Liu.
  11. Visualization: MG Y. Liu.
  12. Writing – original draft: MG Y. Liu Y. Li.
  13. Writing – review & editing: MG Y. Liu QS Y. Li.

References

  1. 1. Phillips MA, D'Auria JC, Luck K, Gershenzon J. Evaluation of Candidate Reference Genes for Real-Time Quantitative PCR of Plant Samples Using Purified cDNA as Template. Plant Mol Biol Report. 2009; 27: 407–416. pmid:24489433
  2. 2. 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: 611–622. pmid:19246619
  3. 3. Bustin SA. Quantification of mRNA using real-time reverse transcription PCR (RT-PCR): trends and problems. J Mol Endocrinol. 2002; 29(1): 23–39. pmid:12200227
  4. 4. Dheda K, Huggett JF, Bustin SA, Johnson MA, Rook G, Zumla A. Validation of housekeeping genes for normalizing RNA expression in real-time PCR. Biotechniques. 2004; 37(1): 112–119. pmid:15283208
  5. 5. Kim BR, Nam HY, Kim SU, Kim SI, Chang YJ. Normalization of reverse transcription quantitative-PCR with housekeeping genes in rice. Biotechnol Lett. 2003; 25(21): 1869–1872. pmid:14677714
  6. 6. Jian B, Liu B, Bi Y, Hou W, Wu C, Han T. Validation of internal control for gene expression study in soybean by quantitative real-time PCR. BMC Mol Biol. 2008; 9: 59. pmid:18573215
  7. 7. Hu R, Fan C, Li H, Zhang Q, Fu YF. Evaluation of putative reference genes for gene expression normalization in soybean by quantitative real-time RT-PCR. BMC Mol Biol. 2009; 10: 93. pmid:19785741
  8. 8. Le DT, Aldrich DL, Valliyodan B, Watanabe Y, Ha CV, Nishiyama R, et al. Evaluation of candidate reference genes for normalization of quantitative RT-PCR in soybean tissues under various abiotic stress conditions. PLoS ONE. 2012; 7(9): e46487. pmid:23029532
  9. 9. Ma S, Niu H, Liu C, Zhang J, Hou C, Wang D. Expression stabilities of candidate reference genes for RT-qPCR under different stress conditions in soybean. PLoS ONE. 2013; 8(10): e75271. pmid:24124481
  10. 10. Lu C, Napier JA, Clemente TE, Cahoon EB. New frontiers in oilseed biotechnology: meeting the global demand for vegetable oils for food, feed, biofuel, and industrial applications. Curr Opin Biotechnol. 2011; 22(2): 252–259. pmid:21144729
  11. 11. Kochian LV, Piñeros MA, Liu J, Magalhaes JV. Plant adaptation to acid soils: the molecular basis for crop aluminum resistance. Annu Rev Plant Biol. 2015; 66: 571–98. pmid:25621514
  12. 12. Ryan PR, Tyerman SD, Sasaki T, Furuichi T, Yamamoto Y, Zhang WH, et al. The identification of aluminium-resistance genes provides opportunities for enhancing crop production on acid soils. J Exp Bot. 2011; 62(1): 9–20. pmid:20847099
  13. 13. Horst WJ, Wang Y, Eticha D. The role of the root apoplast in aluminium-induced inhibition of root elongation and in aluminium resistance of plants: a review. Ann Bot. 2010; 106(1):185–97. pmid:20237112
  14. 14. Lu CA, Zhang JF, Jiang HM, Yang JC, Zhang JT, Wang JZ, et al. Assessment of soil contamination with Cd, Pb and Zn and source identification in the area around the Huludao Zinc Plant. J Hazard Mater. 2010; 182(1–3):743–748. pmid:20638790
  15. 15. Nawrot T, Geusens P, Nulens TS, Nemery B. Occupational cadmium exposure and calcium excretion, bone density, and osteoporosis in men. J Bone Miner Res. 2010; 25(6):1441–1445. pmid:20200937
  16. 16. MAFFJ. Survey of the cadmium contained in agricultural products. MAFFJ (Ministry of Agriculture, Forestry and Fisheries of Japan), Hokkaido. 2002.
  17. 17. Salazar MJ, Rodriguez JH, Nieto GL, Pignata ML. Effects of heavy metal concentrations (Cd, Zn and Pb) in agricultural soils near different emission sources on quality, accumulation and food safety in soybean [Glycine max (L.) Merrill]. J Hazard Mater. 2012; 233:244–253. pmid:22835772
  18. 18. Zhao Y, Fang X, Mu Y, Cheng Y, Ma Q, Nian H, et al. Metal pollution (Cd, Pb, Zn, and As) in agricultural soils and soybean, glycine max, in southern China. Bull Environ Contam Toxicol. 2014; 92(4):427–432. pmid:24519477
  19. 19. Xue Z, Gao H, Zhao S. Effects of cadmium on the photosynthetic activity in mature and young leaves of soybean plants. Environ Sci Pollut Res Int. 2014; 21(6):4656–64. pmid:24352551
  20. 20. Finger-Teixeira A, Ferrarese Mde L, Soares AR, da Silva D, Ferrarese-Filho O. Cadmium-induced lignification restricts soybean root growth. Ecotoxicol Environ Saf. 2010; 73 (8): 1959–64. pmid:20817298
  21. 21. Noriega GO, Balestrasse KB, Batlle A, Tomaro ML. Cadmium induced oxidative stress in soybean plants also by the accumulation of delta-aminolevulinic acid. Biometals. 2007; 20(6):841–51. pmid:17216352
  22. 22. Ge L, Cang L, Liu H, Zhou D. Effects of different warming patterns on the translocations of cadmium and copper in a soil-rice seedling system. Environ Sci Pollut Res Int. 2015; 22(20):15835–43. pmid:26036584
  23. 23. Siebers MH, Yendrek CR, Drag D, Locke AM, Rios Acosta L, Leakey AD, et al. Heat waves imposed during early pod development in soybean (Glycine max) cause significant yield loss despite a rapid recovery from oxidative stress. Glob Chang Biol. 2015; 21(8):3114–25. pmid:25845935
  24. 24. Liu JG, Li Y, Wang W, Gai JY and Li Y. Genome-wide analysis of MATE transporters and expression patterns of a subgroup of MATE genes in response to aluminum toxicity in soybean. BMC Genomics. 2016; 17:223. pmid:26968518
  25. 25. Hossain Z, Hajika M, Komatsu S. Comparative proteome analysis of high and low cadmium accumulating soybean under cadmium stress. Amino Acids. 2012; 43(6):2393–416. pmid:22588482
  26. 26. Marfa V, Marfa R, Alicia S. M, Hilda E. P, Fanny Z, Luisa M, et al. Cadmium induces two waves of reactive oxygen spscies in Glycine max (L.) roots. Plant, Cell and Environment. 2014; 37:1672–1687. pmid:24433233
  27. 27. Zhu B, Ye C, Lu H, Chen X, Chai G, Chen J, et al. Identification and characterization of a novel heat shock transcription factor gene, GmHsfA1, in soybeans (Glycine max). J Plant Res. 2006; 119(3):247–56. pmid:16570125
  28. 28. Livak KJ, Schmittgen TD. Analysis of Relative Gene Expression Data Using Real- Time Quantitative PCR and the 2−ΔΔCT method. Methods. 2001; 25(4): 402–408. pmid:11846609
  29. 29. 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.
  30. 30. 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. Biotechnology Letters. 2004; 26(6): 509–515. pmid:15127793
  31. 31. 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 Research. 2004; 64(15): 5245–5250. pmid:15289330
  32. 32. 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
  33. 33. Xie F, Xiao P, Chen D, Xu L, Zhang B. miRDeepFinder: a miRNA analyaia tool for deep sequencing of plant small RNAs. Plant Mol Biol. 2012; 80(1): 75–84.
  34. 34. Liu LL, Zhao H, Ma TF, Ge F, Chen CS, Zhang YP. Identification of valid reference genes for the normalization of RT-qPCR expression studies in human breast cancer cell lines treated with and without transient transfection. PLoS ONE. 2015; 10(1): e0117058. pmid:25617865
  35. 35. Liang C, Pineros MA, Tian J, Yao Z, Sun L, Liu J, et al. Low pH, aluminum, and phosphorus coordinately regulate malate exudation through GmALMT1 to improve soybean adaptation to acid soils. Plant Physiol. 2013; 161(3): 1347–1361. pmid:23341359
  36. 36. Zhang XL, Wang N, Chen P, Gao MM, Liu JG, Wang YF, et al. Overexpression of a Soybean Ariadne-Like Ubiquitin Ligase Gene GmARl1 Enhances Aluminum Tolerance in Arabidopsis[J]. PLoS ONE. 2014; 9(11):e111120. pmid:25364908
  37. 37. Fang X, Wang L, Deng X, Wang P, Ma Q, Nian H, et al. Genome-wide characterization of soybean P1B -ATPases gene family provides functional implications in cadmium responses. BMC Genomics. 2016; 17:376. pmid:27207280
  38. 38. Zhao L, Wang Z, Lu Q, Wang P, Li Y, Lv Q, et al. Overexpression of a GmGBP1 ortholog of soybean enhances the responses to flowering, stem elongation and heat tolerance in transgenic tobaccos. Plant Mol Biol. 2013; 82(3):279–99. pmid:23636865
  39. 39. Maejima Y, Makino T, Takano H, Kamiya T, Sekiya N, Itou T. Remediation of cadmium-contaminated paddy soils by washing with chemicals: effect of soil washing on cadmium uptake by soybean. Chemosphere. 2007; 67(4): 748–754. pmid:17141294
  40. 40. Hong SM, Bahn SC, Lyu A, Jung HS, Ahn JH. Identification and testing of superior reference genes for a starting pool of transcript normalization in Arabidopsis. Plant Cell Physiol. 2010; 51(10): 1694–1706. pmid:20798276
  41. 41. Kong Q, Yuan J, Gao L, Zhao S, Jiang W, Huang Y, et al. Identification of suitable reference genes for gene expression normalization in qRT-PCR analysis in watermelon. PLoS ONE. 2014; 9(2): e90612. pmid:24587403
  42. 42. Huang LK,Yan HD, Jiang XM, Yin GH, Zhang XQ, Qi X, et al. Identification of Candidate Reference Genes in Perennial Ryegrass for Quantitative RT-PCR under Various Abiotic Stress Conditions. PLoS ONE. 2014; 9(4): e9372.
  43. 43. Wang Y, Yu K, Poysa V, Shi C, Zhou Y. Selection of reference genes for normalization of qRT-PCR analysis of differentially expressed genes in soybean exposed to cadmium. Mol Biol Rep. 2012; 39(2):1585–94. pmid:21625860