Selection and validation of reference genes for quantitative gene expression analyses in black locust (Robinia pseudoacacia L.) using real-time quantitative PCR

Jinxing Wang; Manzar Abbas; Yanzhong Wen; Dongsheng Niu; Ling Wang; Yuhan Sun; Yun Li

doi:10.1371/journal.pone.0193076

Abstract

Black locust (Robinia pseudoacacia L.) is an easy to raise, fast growing, medium-sized deciduous tree species highly tolerant to harsh eco-conditions, i.e., drought and harsh winters, and it is widely adaptable to sandy, loamy, and marshy soils. The basis for this adaptability remains to be investigated at the transcriptomic level using real-time quantitative PCR (qPCR). Selection of a reliable gene for the normalization of qPCR data is important for obtaining accurate results in gene expression. The goal of this study was to identify an appropriate reference gene from 12 candidate genes for gene expression analysis in black locust exposed to various stressors such as abscisic acid (ABA), NaCl, polyethylene glycol (PEG) and varying temperatures. In GeNorm and NormFinder analyses, ACT (actin) and GAPDH (glyceraldehyde-3-phosphate dehydrogenase) gene expression were the most stable in all conditions except heat stress, but in BestKeeper analysis, GAPDH and helicase gene expression were the most stable under NaCl and heat stress. In contrast, ACT and GAPDH were highest under abscisic acid (ABA), GAPDH and βTUB (beta tubulin) under cold stress, and helicase and EF1α (elongation factor 1 alpha) under PEG stress. We found that the most stable reference gene combination for all conditions was ACT and GAPDH. Additionally, the expression pattern of NAC2 (a transcription factor) and BGL2 in different tissues and under different stress conditions was analyzed relative to ACT and GAPDH and UBQ (ubiquitin) the least stably expressed gene. NAC2 and BGL2 both had highest expression in flowers and pods under ABA stress at 48h. This study provides useful reference genes for future gene expression studies in black locust.

Citation: Wang J, Abbas M, Wen Y, Niu D, Wang L, Sun Y, et al. (2018) Selection and validation of reference genes for quantitative gene expression analyses in black locust (Robinia pseudoacacia L.) using real-time quantitative PCR. PLoS ONE 13(3): e0193076. https://doi.org/10.1371/journal.pone.0193076

Editor: J Joe Hull, USDA Agricultural Research Service, UNITED STATES

Received: February 25, 2017; Accepted: February 4, 2018; Published: March 12, 2018

Copyright: © 2018 Wang 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, tables and graphs file and its Supporting Information files.

Funding: The authors appreciate financial support from the Fundamental Research Funds for the Central Universities (2015ZCQ-SW-03), the project of National Nature Science Foundation of China (31400562, 31570677) State Forestry Administration of Science and Technology Development Center project (XPC-2015-3) and the State Forestry Administration of science and technology promotion project (No. [2015]-53).

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

Introduction

Black locust (Robinia pseudoacacia L.) is a monoclinous leguminous tree belonging to the Faboideae subfamily. It is widely cultivated in the southeastern United States, temperate Europe, and Asia [1] and is of ecological and economic value [2,3]. It is also used as forage, wood fiber, feed stock, lumber, fuel and in beekeeping [3,4,5]. Black locust was first introduced to China in 1877‒1878, and currently is extensively cultivated in many parts of the country [6]. In addition, black locust flowers can be used by honeybees to produce honey and royal jelly [5]. In the semi-arid Loess Plateau of China, it is also extensively being used for reforestation [7]. Consequently, black locust is an ecologically and economically valuable species.

Many biological techniques have been used to confirm the expression levels of genes, such as RNase protection assays (RPAs), Northern blotting, real-time quantitative PCR (qPCR), gene chips (DNA microarray), fluorescent in situ hybridization (FISH), serial analysis of gene expression (SAGE), tilling arrays, RNA sequencing, reporter genes, digital droplet PCR (ddPCR) and semi-quantitative PCR (semi-PCR). RNA sequencing and qPCR are considered the best methods due to their high throughput nature [8,9]. Northern blotting was the first RNA-based technique to allow measurement of differential gene expression. Northern blotting is time consuming and laborious, and most importantly, accurate quantification of expression levels is poor using this method. Another important gene expression technique is DNA microarray hybridization, which uses either cDNA or oligonucleotides. Microarray analysis has the ability to screen a large number of target genes, even under multiple stimuli [10]. Microarrays utilize plates spotted with DNA sequences representing each target gene. In this process, template cDNA from control and fluorophore-labeled samples are hybridized to the glass plate. The difference between fluorescent signal intensities represents differential gene expression; however, poor or weak signals generation can complicate gene expression analysis of low expression genes. Reporter genes such as lacZ, cat, luciferase, GUS, GFP and rfp can also be considered but they are time consuming and laborious, because they require genetic manipulation and successful transformation events.

The leading gene expression techniques for achieving authentic and reproducible results are RNA sequencing and qPCR [11]. They provides better results compared with other RNA quantification techniques because of its accurate quantification and high sensitivity [8,11,12]. As a result, the interpretation of differential gene expression patterns can lead to a better understanding of biological processes. Consequently, given the accuracy, high sensitivity, and reproducibility qPCR has become the standard method for in-depth studies of gene expression, alternative splicing events, validation of microarray data, and medical diagnostics [13–21]. Different non-specific factors present challenges for normalization of gene expression including differences in the nature and quantity of RNA, some of which can be controlled by establishing candidate reference genes [22,23].

In the past few years, ubiquitin (UBQ), elongation factor 1 alpha (EF1α) [24], and glyceraldehyde-3-phosphate dehydrogenase (GAPDH) [25], have been used frequently as internal controls for qPCR studies. Many studies revealed that these genes exhibit non-uniform and non-stable expression, highlighting the demand for better universal internal control genes [26,27]. With this goal in mind, a number of qPCR control genes have been reported in diverse plant species including tobacco [28], Arabidopsis [29,30], berry [31], poplar [32–34],eucalyptus [35,36], and teak [37]. To determine which reference gene is best for transcript normalization in a given subset of biological samples, statistical algorithms such as RefFinder, NormqPCR, RefGenes, OLIVER, geNorm, BestKeeper, and NormFinder have been developed (https://omictools.com/reference-genes-category) [38,39].

We used statistical and graphical methods to compare the expression stability of 12 candidate reference genes (Table 1) across a large set of organs i.e., root, stem, leaf, flower, pod and stress treatments (ABA, NaCl, PEG and variable temperature conditions). The expression stability of these genes was subsequently evaluated using geNorm [40], BestKeeper [38], and NormFinder [41]. Furthermore, the expression patterns of the target genes were investigated using the selected reference genes along with transcription factor NAC2 which is involved in protecting plants against abiotic stresses, flowering, senescence [41,42] and BGL2, which is also involved in flowering. This study provides the most extensive analysis of potential reference genes that can be applied to future gene expression studies in black locust.

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Table 1. Descriptions of the 12 candidate reference genes and two selected genes for validation of results.

https://doi.org/10.1371/journal.pone.0193076.t001

Materials and methods

Plant materials and treatments

From June to September 2016, we collected roots, leaves, flowers, pods and stems from 5 year old black locust grown in the Mijiabao Forest Farm, Yanqing District of Beijing, PR of China. Note, no specific permission or grant was required from authorities to collect samples for research purpose as this forest is open for research purposes. Seeds were grown in pots filled with soil mixture consisting of silt, perlite and Floraguard Substrat^® (3:1:5) and when the seedlings were two months old, they were pulled carefully and dipped in NaCl (200 mM), PEG-6000 (10%), or ABA (150 μM) for 12, 24 and 48h [43]. For the cold and heat treatments, plants were exposed to 4°C or 40°C by placing whole plants in chambers under a 14-h light/10-h dark photoperiod for 0, 12, 24, and 48 h. A total of 21 samples in triplicate were collected from different organs (roots, leaves, pods, and stems) at varying time points (0, 12, 24, and 48 h) and then immediately chilled in liquid nitrogen and stored at −80°C.

Total RNA extraction quality control and cDNA synthesis

For total RNA extraction, 2g samples from each tissue were collected, and immediately chilled in liquid nitrogen and grounded in a pre-chilled, autoclaved pestle and mortar. The RNeasy^® Plant Mini Kit (Qiagen, Hilden, Germany) was used for extraction of RNA from all samples. Total RNA was treated with RNase free DNase I (Promega, USA) to remove any contaminating DNA. The optical density (OD) of total RNA was determined on a Nanodrop ND-1000 spectrophotometer (Thermo Scientific, Waltham, MA, USA) at an absorbance ratio of 260/280 nm, with RNA samples ranging 1.9 to 2.2. For further analyses, an OD of 2.0 at an absorbance ratio of 260/230 nm was used [44]. RNA integrity was validated by separation of the RNA on a 1.5% (w/v) agarose gel containing ethidium bromide, followed by visualization under UV light in a gel documentation system. RNA samples with an OD of 1.5‒2.0 and clear bands representing 28S/18S rRNA without smearing on the agarose gel were used for cDNA synthesis. The first cDNA strand was synthesized from 1000 ng total RNA in a volume of 20 μl using the PrimeScript^™ RT reagent Kit with gDNA eraser (Perfect Real Time), (Clontech, Shiga, Japan), according to the manufacturer’s protocol. In the first step, genomic DNA (gDNA) was removed and the integrity of the RNA was verified by running samples on 1.2% agarose gel according to the protocol. In the second step, we used oligo-dT primers to perform reverse transcription. The quantity of cDNA was measured on a ND-1000 Nanodrop spectrophotometer at 260/280nm absorbance and quality was examined by electrophoresis using a 1.2% agarose gel. Newly synthesized cDNA was diluted 10-fold prior to determination of its quality and quantity and stored at -20°C until use for the qPCR study.

Primer design

Twelve reference genes were selected for primer design: SAMDC, 18SrRNA, ACT, EF1α, GAPDH, Helicase, UBQ, βTUB, CYP, SAND, CAC, and PP2A (Table 1). Black locust gene sequences were obtained by transcriptome sequencing in our laboratory. A total of 12 pairs of primers for candidate reference genes, along with two primer pairs for validation of results were designed from CDS sequences (with a maximum amplicon length of 293 bp, optimal Tm of 50‒60°C, GC percentage of 20‒80%, primer length 18‒23bp) using the bioinformatics tool Primer Premier 5.0 (Table 2).

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Table 2. Primers, amplicon sizes, and qPCR efficiency for the candidate internal control and results validation genes.

https://doi.org/10.1371/journal.pone.0193076.t002

qPCR analysis

Initially all primers were tested by qPCR using Premix Ex Taq^™ (TaKara) on an ABI 7500 (Applied Biosystems^®) real-time PCR system. The total volume of the reaction mixture was 20 μl containing 10 μl 2X SYBR Premix Ex Taq^™, 1 μl of the template cDNA reaction mixture, 0.5 μl of each primer (100 μM), 0.5 μl ROX Reference DyeII, and 7.5 μl double distilled H₂O (ddH₂O). The PCR protocol was as follows: pre-denaturation at 95°C for 30 s, denaturation at 95°C for 3 s, annealing and amplification at 58°C for 30 s, 33 cycles in total, and a 55‒95°C melt curve analysis. All reactions were performed in triplicate. The threshold cycles (C_q) were automatically calculated for each reaction, these values were then used to determine the mean C_q for each data set.

Statistical analysis

For adequate reference gene selection, three Microsoft Excel-based software programs, GeNorm (version 3.5) (http://medgen.ugent.be/*jvdesomp/genorm/), BestKeeper (version 1), and NormFinder (version 0.935), were used to analyze the stability of gene expression. The designer’s instructions and guidelines were followed for all three programs. To apply GeNorm and NormFinder for stable gene expression calculations, C_q values were transformed based on the data input format. The lowest C_q value, representing the highest expression level was used as control and given a value of 1. For GeNorm and NormFinder analyses, raw data were required to be transformed in standard input format, while for BestKeeper analysis, the only original C_q values were used. Standard curves were generated in Microsoft Excel the C_q values plotted against the logarithmic quantities of the known amounts of cDNA dilutions: 1, 1/5, 1/25, 1/125, 1/625, and 1/3125 using cDNA adjusted to 200 ng/μl as a start material. To determine the correlation coefficients (r² values), only C_q values less than 40 were considered. To determine amplification efficiencies (E) only C_q values less than 40 were used in the following formula E = [5^-(1/slope)-1]×100%. The efficiencies obtained in all qPCR assays were between 95% and 105%. To calculate the relative expression level of each gene, the 2^-△Cq method was applied [45]. While, △C_q was determined by subtracting the minimum C_q value from candidate reference genes C_q (△C_q = candidate reference gene C_q value–minimum C_q value). These data was further analyzed by GeNorm and BestKeeper software tools.

Analysis of variance using SPSS (http://xiazai.zol.com.cn/detail/44/437842.shtml) was performed to compare the data at a significance level of P<0.05. To calculate the expression stability values and to identify the most stably expressed reference genes, the Excel-based algorithm BestKeeper was inevitable [28]. For this, the standard deviation (SD), correlation coefficient (r) and coefficient of variance (CV) were estimated. A gene with a SD value >1 was treated as unstable. To identify the highest stably expressed genes, a BestKeeper Index based on the geometric means of the C_q values was calculated.

Results

Expression profiles of the reference genes

Of the 12 candidate reference genes, 9 (ACT, GAPDH, βTUB, UBQ, EF1a, helicase, PP2A, 18s rRNA, and SAMDC) are traditional control genes, while 3 (SAND, CAC, and CYP) are novel candidates (Table 1). The C_q values, representing the cycle at which significant PCR product was amplified, were calculated for each gene (Table 2). Generally, the C_q value was obtained in the mid-exponential phase of amplification (Fig 1). The C_q values of all 12 potential reference genes varied widely from 13 to 34 cycles. The highest expressed gene in all tissues was 18SrRNA, with a C_q value < 25.

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Fig 1. Distribution of the average C_q values of all 12 reference genes evaluated in black locust samples: 18SrRNA, ACT, CAC, CYP, EF1a, GAPDH, Helicase, PP2A, SAMDC, SAND, βTUB, and UBQ.

The linear line shows median, dots for extremes, boxes for 25%-75% C_q value and whiskers for 5%-95% respectively.

https://doi.org/10.1371/journal.pone.0193076.g001

geNorm analysis

To analyze and rank the reference genes in terms of expression stability, geNorm was used. C_q values were obtained for all samples and transformed into relative quantities using the △ C_q method. The stability value M was calculated for the average pairwise expression ratio. A gene with a low M value is considered stable, while a gene with a high M value is considered unstable (Fig 2). To identify potential reference genes, geNorm recommends selecting genes with an M value < 1.5. In combined analyses, helicase and ACT had the lowest M values (0.037) and thus the highest expression stability, followed by GAPDH and PP2A, whereas 18s rRNA and UBQ were less stable. When untreated tissues such as root, stem, leaf, flower and pods were analyzed, ACT and EF1a were most stably expressed, with a minimum M value of 0.01, followed by SAND and helicase, while SAMDC and TUB were least stable. Under ABA stress, ACT and GAPDH were most stable, TUB and EF1a moderately stable, and CAC and SAND the least stable. Under NaCl stress, ACT, helicase, and GAPDH were most stable, TUB and EF1a less stable, and UBQ and CYP the least stable. ACT, GAPDH, and PP2A were highly stable, helicase and EF1a were moderately stable, and 18s rRNA and UBQ the least stable under PEG treatment. Under heat stress (40°C), SAMDC, SAND, and GAPDH were relatively stable, ACT and PP2A less stable, and CYP and TUB were the least stable. Under cold stress (4°C), ACT, PP2A, and GAPDH were highly stable, TUB and EF1a were less stable, and 18s rRNA and SAND were the least stable. In conclusion, the most stable reference genes in all samples according to the geNorm analysis were ACT and GAPDH.

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Fig 2. Average expression stability values (M) of 12 candidate reference genes under different conditions and tissues calculated by geNorm.

https://doi.org/10.1371/journal.pone.0193076.g002

To minimize interpretation of qPCR expression analyses and to increase overall robustness of the analyses, the use of two internal reference genes is highly recommended [38]. To calculate the number of internal control genes needed, the following formula V_n/n+1<0.15 (n = 1,2,3…..) was developed [39]. According to the aforementioned formula, if V<0.15, then no inclusion of ≥n+1 reference genes should be considered. The data depicted in Fig 3 indicates that expanding the reference gene set beyond two control genes will not significantly impact the outcome. Surprisingly, NaCl treatment (D) had the greatest variation among all samples.

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Fig 3. Estimation of the required number of reference genes for normalization by pairwise variation (V) using geNorm.

https://doi.org/10.1371/journal.pone.0193076.g003

NormFinder analysis

NormFinder is another Microsoft Excel-based Visual Basic application that assigns stability values to single candidate reference genes. The NormFinder algorithm uses a model-based approach (M value) for estimation of gene expression variation among candidate genes. NormFinder takes into account intra- and intergroup variation for normalization factor calculations and prevents misinterpretations caused by artificial selection of co-regulated genes.

We used NormFinder to analyze the gene expression profiles of all genes evaluated (Table 3). Although the ranking order of the genes differed somewhat from the geNorm findings, no significant differences were observed. ACT, GAPDH, and helicase were stable candidate reference genes, with M values of 0.16, 0.17, and 0.17, respectively. In plants treated with chemicals ABA, PEG, and NaCl and different temperature conditions (cold and hot), the remaining nine genes (SAMDC, 18s rRNA, EF1α, UBQ, βTUB, CYP, SAND, CAC, and PP2A) exhibited large fluctuations in their expression profiles. Those genes that exhibited moderate gene expression in NormFinder exhibited slightly different expression stabilities relative to geNorm. The overall ranking of the candidate reference genes by NormFinder was ACT > GAPDH > helicase > EF1a > PP2A > SAMDC > βTUB > CYP> CAC > SAND >UBQ > 18s rRNA. The NormFinder results were extremely stable and consistent compared with the geNorm results.

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Table 3. Average expression stability values of the 12 reference genes in different organs, calculated by NormFinder.

https://doi.org/10.1371/journal.pone.0193076.t003

BestKeeper analysis

BestKeeper is another Microsoft Excel-based tool for stability analysis of candidate reference genes. With BestKeeper, the original average C_q values are used instead of transformed values. This algorithm compares the gene expression stability of all candidate reference genes overall. According to the BestKeeper analysis, ACT and GAPDH were highly stable, whereas CAC and SAND were the least stable under abscisic acid treatment. Helicase and EF1-α were the most stable, while SAMDC and CAC were the least stable in PEG-treated tissues. In NaCl-treated samples, the most stable genes were GAPDH and helicase, and the least stable were PP2A and SAND. GAPDH and helicase had high expression stabilities while CYP and TUB had the lowest expression stability under heat treatment. GAPDH and βTUB were the most stable, while CAC and SAND genes were the least stable under cold stress. ACT and GAPDH were highly stable in all tissues under all conditions (Table 4).

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Table 4. Ranking of 10 reference genes in order of their expression stability, as calculated by BestKeeper.

https://doi.org/10.1371/journal.pone.0193076.t004

Reference genes validation

To confirm expression stability of significantly normalized reference genes, it is crucial to calculate the relative expression levels of selected normalized genes. For this purpose, the expression level of the top two reference genes identified in this study, ACT and GAPDH, were compared along with the expression level of NAC2 (4A-F) and BGL2 (5A-F). Highest expression of NAC2 was observed on ABA treatment at 48 h (Fig 4B), while decreased expression levels were observed in response to cold treatment at 12 h (Fig 4A). In the case of BGL2, highest expression was observed following NaCl treatment at 48 h (Fig 5C), while decreased expression level was observed following PEG treatment at 48 h (Fig 5E).

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Fig 4.

Validation of NAC2 expression was assessed using individual reference genes (ACT, GAPDH or UBQ) under diverse abiotic conditions, (A) 4°C for 0, 12, 24 and 48h, (B) 40°C for 0, 12, 24 and 48h, (C) NaCl (200 mM), (D) ABA (150 mM), (E) PEG (100%), (F) Tissues (Root, Stem, Leaf, Flower and Pod).

https://doi.org/10.1371/journal.pone.0193076.g004

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Fig 5.

Validation of BGL2 expression was assessed using individual reference genes (ACT, GAPDH or UBQ) under diverse abiotic conditions, (A) 4°C for 0, 12, 24 and 48h, (B) 40°C for 0, 12, 24 and 48h, (C) NaCl (200 mM), (D) ABA (150 mM), (E) PEG (100%), (F) Tissues (Root, Stem, Leaf, Flower and Pod).

https://doi.org/10.1371/journal.pone.0193076.g005

There were no significant differences in the expression patterns of NAC2 and BGL2 using either ACT or GAPDH as the internal control (P<0.05), which shows that ACT and GAPDH were suitable reference genes. In contrast, use of the least stable candidate reference gene (UBQ) with both target genes resulted in expression profiles that were unstable in all stress conditions and tissues (Figs 4 and 5). Expression profile of NAC2 gene was significant when evaluated at P<0.05 (Fig 6).

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Fig 6. Expression profile of NAC2 gene in different tissues and in response to different stressors in Robinia pseudoacacia as investigated by qPCR with ACT and GAPDH used as dual reference genes.

https://doi.org/10.1371/journal.pone.0193076.g006

Discussion

Drought, salinity, heat, cold and other abiotic stress conditions have significant effect on black locust and other forest fauna. To improve resistance against these stress conditions and enhance plant productivity, it’s imperative to explore genes responsive to these stimuli. RNA sequencing and qPCR have been considered reliable techniques for the analysis of gene expression patterns. To obtain accurate results by qPCR, the PCR conditions and selection of genes used as internal controls must be optimized. The selection of an appropriate reference gene is a crucial prerequisite for gene expression analysis [46]. Internal control genes must retain expression stability, while the gene of interest exhibits varied in expression under the experimental condition.

Although UBQ showed highly stable expression in the model plant Arabidopsis [29,30], its expression varied across different developmental stages of rice and soybean [47,48]. The ribosomal RNA subunits 18S and 28S are commonly used internal control genes. While both genes are considered reference genes with extremely stable gene expression in all tissues of all organisms, their stability is controversial. We used mRNA for the RNA quantification studies, as mRNA free from other RNA residues gives satisfactory results. On the other hand, entire RNA contains variable amounts of 5S, 18S and 28S ribosomal RNA (rRNA) that may cause significant variations in results [38]. The most serious drawback of using these genes is their abundance compared with target transcripts. Because rRNA is not polyadenylated, these genes are not applicable when the template consists of cDNA reverse-transcribed from total RNA using oligo-dT primers. It is difficult to subtract the rRNA expression value from that of the gene of interest for accurate analysis of qPCR data. In short, for these reasons, 18S and 28S rRNA genes fail to replace other reference genes. There are no universal endogenous control genes available for gene expression profiling among all organisms. Thus, it is imperative to identify suitable reference genes as internal controls for accurate quantification of gene expression.

In this study, we selected and evaluated 12 genes from different functional classes and gene families as candidate reference genes i.e., SAMDC, 18s rRNA, EF1α, UBQ, βTUB, CYP, SAND, CAC, PP2A and helicase for black locust gene expression studies among different tissues and abiotic stress treatments. Because these genes already have been used for gene expression profiling of other organisms, we also considered these genes as candidate references. For example, ACT has been used for qPCR studies in Platycladus orientalis [43], TUB in Salix matsudana [49], UBQ in longan tree [24], and EF1β and GAPDH in Vernicia fordii [50]. We selected these genes from RNA sequencing data (PRJNA260115) and used qPCR together with statistical and graphical techniques (geNorm, BestKeeper and NormFinder) to determine the most stable internal control gene for qPCR studies.

On the basis of three algorithms analysis results, we found ACT and GAPDH with most stable gene expression among whole plants, as well as different tissues under both stress and normal conditions (Table 5). Actin (ACT) is a cytoskeleton structural protein involved in cell signaling while glyceradehde-3-phosphate dehydrogenase (GAPDH) plays pivotal role in energy yielding step during carbohydrates metabolism and involved in DNA repair [51,52]. These results suggest that our discovery is perfectly in agreement with previous studies, such as ACT has been ranked as best internal control for gene expression studies in Platycladus orientalis, Vernicia fordii and Pinus massoniana [43,50,53], whereas it’s expression stability is unstable in Glycine max [10]. Similarly, GAPDH retained perfectly stable expression in Vernicia fordii and Camellia sinensis [50,54] in different tissues and under various abiotic stress conditions, whereas it’s expression was compromised in Platycladus orientalis [43]. In order to meager errors, it is highly recommended to use more than one appropriate internal control genes in qPCR studies [38]. In this study we wittily identified two most stable internal control genes such as helicase and EF1α under PEG stress, GAPDH and βTUB under cold, GAPDH and helicase under NaCl and heat while ACT and GAPDH under all conditions except heat.

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Table 5. Ranking of the candidate reference genes according to their stability values using geNorm, NormFinder, and BestKeeper.

https://doi.org/10.1371/journal.pone.0193076.t005

Considering the importance we selected two genes, NAC2 and BGL2 as target genes for validation because both of these target genes are involved in flowering and abiotic stress responses. Target genes expression was very accurately measured when two selected reference genes ACT and GAPDH were tested along with targets. But, the expression of target genes was extremely unstable when its expression was calculated as compared with the least stable candidate reference gene UBQ (Figs 4 and 5). Our findings deliberately support previous discoveries that in order to normalization usage of two highly stable internal reference genes has significant importance. Moreover, two internal control gens are perfectly enough to analyze gene expression as has been done earlier in case of Salix matsudana [49] and Platycladus orientalis [43]. Therefore, ACT and GAPDH are the most stably expressed reference genes and are highly recommended in single or both as endogenous controls for gene expression studies under both normal and stress conditions. This study has important implications in obtaining more accurate results from expression analyses of black locust.

In conclusion, out of the 12 black locust genes evaluated, the two most reliable internal reference genes were ACT and GAPDH. All other candidate genes were not deemed suitable internal controls for black locust, because they exhibit high expression diversity. To obtain the most reliable results in gene expression studies of black locust, ACT and GAPDH are recommended as internal controls for qPCR analysis.

Supporting information

S1 Fig. Relative expression levels of the NAC2 in plants treated with Cold, ABA, NaCl, Heat, and PEG stresses.

https://doi.org/10.1371/journal.pone.0193076.s001

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S2 Fig. Relative expression levels of the NAC2 in different tissues.

https://doi.org/10.1371/journal.pone.0193076.s002

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S3 Fig. Relative expression levels of the BGL2 in plants treated with cold, ABA, NaCl, Heat, and PEG stresses.

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S4 Fig. Relative expression levels of the BGL2 in different tissues.

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S1 File. Selection of NAC2 for validation of results.

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S2 File. Selection of BGL2 for the validation of results.

https://doi.org/10.1371/journal.pone.0193076.s006

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S3 File. Validation of results by using NAC2 gene.

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S4 File. Validation of results by using BGL2 gene.

https://doi.org/10.1371/journal.pone.0193076.s008

(DOCX)

Acknowledgments

Wang JX and Abbas M contributed equally in this article, Yun Li is corresponding author. Substantially, we are very grateful to Prof. Dr. Yun Li for his advice and suggestions during whole research and proofreading of manuscript and also providing us lab facilities to conduct this research. We are also grateful to kind administration of Beijing Forestry University who provided us such an equipped and well organized platform to do research. We are tremendously thankful to kind administration of Mijiabao Forest Farm for granting permission and resources for sampling. We are also grateful to Textcheck Editing for improvement of our manuscript scientific language.

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Figures

Abstract

Introduction

Materials and methods

Plant materials and treatments

Total RNA extraction quality control and cDNA synthesis

Primer design

qPCR analysis

Statistical analysis

Results

Expression profiles of the reference genes

geNorm analysis

NormFinder analysis

BestKeeper analysis

Reference genes validation

Discussion

Supporting information

S1 Fig. Relative expression levels of the NAC2 in plants treated with Cold, ABA, NaCl, Heat, and PEG stresses.

S2 Fig. Relative expression levels of the NAC2 in different tissues.

S3 Fig. Relative expression levels of the BGL2 in plants treated with cold, ABA, NaCl, Heat, and PEG stresses.

S4 Fig. Relative expression levels of the BGL2 in different tissues.

S1 File. Selection of NAC2 for validation of results.

S2 File. Selection of BGL2 for the validation of results.

S3 File. Validation of results by using NAC2 gene.

S4 File. Validation of results by using BGL2 gene.

Acknowledgments

References