Bioenerg. Res. DOI 10.1007/s12155-014-9457-1

Evaluation of Candidate Reference Genes for Normalization of Quantitative RT-PCR in Switchgrass Under Various Abiotic Stress Conditions Linkai Huang & Haidong Yan & Xiaomei Jiang & Xinquan Zhang & Yunwei Zhang & Xiu Huang & Yu Zhang & Jiamin Miao & Bin Xu & Taylor Frazier & Bingyu Zhao

# Springer Science+Business Media New York 2014

Abstract Quantitative real-time reverse transcriptase PCR (qRT-PCR) is a sensitive and powerful technique for measuring differential gene expression; however, changes in gene expression induced by abiotic stresses are complex and multifaceted. Therefore, a set of stably expressed reference genes for data normalization is required. Switchgrass (Panicum virgatum L.) is a prime candidate crop for bioenergy production. The expression stability of reference genes in switchgrass, especially under different experimental conditions, is largely unknown. In order to identify the most suitable reference genes for abiotic stress studies in switchgrass, we evaluated 14 candidate genes for their expression stability under drought, high salinity, cold, heat, and waterlogging treatments using the Delta Ct, geNorm, BestKeeper, and NormFinder approaches. Validation of Linkai Huang and Haidong Yan contributed equally in the making of this paper. Electronic supplementary material The online version of this article (doi:10.1007/s12155-014-9457-1) contains supplementary material, which is available to authorized users. L. Huang : H. Yan : X. Jiang : X. Zhang (*) : X. Huang : Y. Zhang : J. Miao Department of Grassland Science, Animal Science and Technology College, Sichuan Agricultural University, 625014 Ya’an, Sichuan, China e-mail: [email protected] B. Xu College of Grassland Science, Nanjing Agricultural University, 210095 Nanjing, Jiangsu, China Y. Zhang Grassland Institute, China Agricultural University, Beijing, China T. Frazier : B. Zhao (*) Department of Horticulture, Virginia Tech, Blacksburg, VA 24060, USA e-mail: [email protected]

reference genes indicated that the best reference genes should be selected based on the stress treatment. Actin 2 (ACT2), carotenoid-binding protein 20 (CBP20), and Tubulin (TUB) were found to have the highest expression stability to study drought stress. 18S ribosomal RNA1 (18S rRNA1), ACT2, and TUB were the most stably expressed genes under salt stress. Ubiquitin-conjugating enzyme (UBC), TUB, and cyclophilin2 (CYP2) were the most suitable reference genes across cold treatments. Likewise, 18S rRNA1, UBC, and TUB were good reference genes for studying heat stress, while ACT2, 18S rRNA1, and ubiquitin3 (UBQ3) were the top three reference genes under waterlogging treatment. Considering that reference gene expression may vary across switchgrass tissues, ACT2 and 18S ribosomal RNA2 (18S rRNA2) were shown to be the most stably expressed genes in switchgrass leaves and roots, respectively. The highly ranked reference genes that were identified in this study were shown to be capable of detecting subtle differences in the expression rates of other genes. These differences may have been missed if a less suitable reference gene was used. Keywords Abiotic stress . qRT-PCR . Reference genes . Panicum virgatum L Abbreviations qRT-PCR Quantitative real-time reverse transcriptase PCR ACT2 Actin 2 CBP20 Carotenoid-binding protein 20 TUB Tubulin 18S rRNA1 18S ribosomal RNA1 UBC Ubiquitin-conjugating enzyme CYP2 Cyclophilin2 UBQ3 Ubiquitin3 EST Expression sequence tag EF1 Elongation factor 1

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CACS TIP41 EF1α PP2Acs ACTIN GAPDH UCE2 RNAp1 UBQ2 NAC 18S rRNA2 HK

Clathrin adaptor complex subunit Tonoplast intrinsic protein Elongation factor 1α Catalytic subunit of protein phosphatase 2A Actin 2 Glyceraldehyde-3-phosphate dehydrogenase Ubiquitin-conjugating enzyme 2 RNA polymerase I subunit Polyubiquitin NAC domain protein 18S ribosomal RNA2 Housekeeping function genes

Introduction Switchgrass (Panicum virgatum L.), a member of the family Poaceae and the genus Panicum, is a warm season C4 perennial grass that can produce remarkable biomass yields [1]. Therefore, switchgrass is considered a prime bioenergy crop for biofuel production [2, 3]. Arable lands that are suitable for growth are often times reserved solely for food crop production. Therefore, switchgrass crop establishment will be allocated to marginal lands. Sustainable and low-cost production [4, 5] of this crop will be critical for developing it as a biofuel [6, 7]. Marginal lands are often afflicted by various abiotic stresses such as drought, flooding, cold temperatures, and high salinity. Therefore, improving the abiotic stress resistance of switchgrass for growth on these suboptimal lands will be essential for achieving sustainable biomass production [8]. The physiological responses of switchgrass to various abiotic stresses have previously been reported [9–13]. However, the switchgrass gene networks underlying responses to these various abiotic stresses have not been intensively studied [14, 15]. Therefore, characterizing the genetic elements that control switchgrass adaptability to marginal lands, including those under various abiotic stresses, will facilitate the genetic improvement of switchgrass for use as a sustainable bioenergy crop. Identifying the expression patterns of different genes, especially under stress conditions, will allow us to associate the functions of these genes with certain biological processes. For example, gene expression profiling studies identified miR398 as a key regulator that controls plant response to various stresses [16–18]. Additionally, several transcription factors have also been identified as master regulators in response to abiotic stress [19, 20]. The orthologous sequences of these known stress-related genes can be identified from the switchgrass expressed sequence tag (EST) database [21, 22]. However, the expression patterns of these putative switchgrass stress-related genes have to be validated under stress conditions. Validation of stress-related genes in switchgrass is also

required for translating the knowledge learned from studies in model plant species, such as Arabidopsis and rice. Ultimately, the identification of switchgrass genes that play a fundamental role in abiotic stress tolerance will accelerate the production of elite switchgrass cultivars that are capable of producing copious amounts of biomass on marginal lands. Various methods have been developed to detect differential gene expression patterns, including quantitative real-time reverse transcriptase PCR (qRT-PCR), northern blots, microarrays, and next generation sequencing [23]. Among these methods, qRT-PCR is considered a convenient, cost-efficient, sensitive, and reliable technique for quantifying gene expression. qRT-PCR is often used to validate gene expression data obtained from microarrays or transcriptome sequencing analyses [24–26]. This technique has evolved rapidly with the development of new enzymes, chemistries, and instruments [27]. The process of qRT-PCR is similar to conventional PCR and uses forward and reverse primer sequences that target specific genes. In qRT-PCR, however, amplification of the PCR product can be observed in real time with two commonly used detection systems: (1) fluorescent dyes that detect the amount of doublestranded DNA amplified and (2) fluorophore-conjugated probes that target a segment of a particular gene and emit a fluorescence signal during gene amplification [28, 29]. Unlike DNA microarrays and northern blots, the use of gene-specific primers in qRT-PCR guarantees a high specificity of target gene amplification, and the use of fluorescence-based signal detection provides for high sensitivity of this method [30, 23]. Factors that affect the reliability of qRT-PCR include choosing appropriate reference genes, quality and quantity of extracted RNA, reverse transcription efficiencies, and PCR conditions. Selecting appropriate reference genes is the first step and is the prerequisite for more accurately detecting subtle expression differences of, and/or between, genes of interests [31]. Appropriate reference genes should maintain stable expression across various experimental conditions, such as plant developmental stages, tissue types, and responses to external stimuli, and should also have a range of expression similar to the target gene under various treatments [31–33]. Stably expressed reference genes can differ between plant species, between organs/tissues of the same plant, and between plant growth conditions (e.g., abiotic stresses). Some widely used reference genes often have varied expression levels, especially when plants are under stresses. For example, elongation factor 1 (EF1) of tomato was the chosen reference gene under cold stress treatment [34]. Other reference genes, however, such as clathrin adaptor complex subunit (CACS), F-box protein, and tonoplast intrinsic protein (TIP41) were more suitable for use in cucumber under other abiotic stress conditions [35]. The elongation factor 1α (EF1α) gene is stably expressed when plants are under aphid infestation but not under waterlogging conditions; on the contrary, the catalytic subunit of protein phosphatase 2A (PP2Acs) gene is the

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best reference gene under heat and waterlogging stresses but is not useful under aphid infestation [36]. In papaya (Carica papaya L.), many widely used reference genes, such as Actin 2 (ACTIN), and glyceraldehyde-3-phosphate dehydrogenase (GAPDH) were found to be differentially expressed under many experimental conditions [37]. Presently, few studies have focused on the comprehensive validation of switchgrass reference genes under various abiotic stresses [38]. In this study, we chose 14 switchgrass candidate reference genes and investigated their expression patterns in two types of tissues (leaf and root) under various abiotic stresses (drought, cold, heat, waterlogging, and salt). qRT-PCR was used to assess the value of these candidate reference genes as internal controls in gene expression studies.

RNA Isolation and cDNA Synthesis

Methods

Total RNAs were extracted from switchgrass tissue using the Total RNA Kit II. The possible DNA contamination in the RNA samples was removed by treatment with RNase-free DNase I (GBC, China) according to the manufacturer’s instruction. RNA concentration and purity were measured with a NanoDrop ND-1000 Micro-Volume UV-Vis Spectrophotometer (NanoDrop Technologies, Wilmington, DE, USA). RNAs with an absorbance ratio of 1.9:2.2 at OD260nm/ OD280nm and around 2.0 at OD260nm/OD230nm were used. The RNA integrity was also checked using 1 % agarose gels. Total RNA (0.8 μg) was used for reverse transcription with an iScript cDNA Synthesis Kit (Bio-Rad Laboratories Inc., Hercules, CA, USA) in a 20-μl reaction volume according to the manufacturer’s protocol. The complementary DNA (cDNA) obtained for each sample was diluted 1:20 with nuclease-free water and stored at −20 °C until qRT-PCR.

Plant Materials, Growth Conditions, and Treatments

Quantitative RT-PCR

Switchgrass cv. Alamo seeds were germinated and grown in 3-l pots containing 1,000 g soil (pH 5.46, 1.46 % organic qualitative content, 100.63 mg/kg N, 4.73 mg/kg P, and 338.24 mg/kg K). The switchgrass plants were grown in a growth chamber (model # MLR-353H, SANYO) at 25 °C with 100 μmol photons m−2 s−1 of white light on a 16/8-h light/dark regimen. The plants that were exposed to various stress conditions were grown as described below. For drought treatment, the control switchgrass plants were maintained at 80 % soil water content. In brief, the pots were maintained at 1 kg (0.97 kg potting medium weight plus 0.03 kg pot weight) by watering and weighing each pot every other day. The amount of water added to each pot was recorded. The switchgrass plants subjected to drought treatment were not watered for 15 days, and at the end of drought treatment, the leaf relative water content of drought-stressed plants was measured to be 10 %. For cold treatment, the control plants were maintained in a growth chamber as described above. The coldtreated plants were moved to a different growth chamber that was set at 5 °C for 10 days. All other parameters were set exactly the same as the control chamber. For heat treatment, the treated plants were moved to a growth chamber with the same settings except the temperature was adjusted to 37 °C for 7 days. For waterlogging treatment, both the control and the treated plants were maintained in the same growth chamber. The treated plants were flooded with water that was maintained at a level of 2.5 cm above the soil surface for 15 days. For salinity treatment, the treated plants were watered with 250 mmol/l NaCl for 12 days. For all controls and treatments, the leaf and root tissues were sampled separately at three different time points with three biological replicates for each expression analysis.

The 14 orthologous candidate reference gene sequences from Zea mays were used as “query” sequences to BLAST against the available switchgrass EST database in NCBI GenBank. All reference genes were named based on their similarity to the known Z. mays genes, and they shared sequence similarities of 89 to 94 %. Full-length consensus sequences from multiple cDNA alignments were used for primer design. Primer pairs were generated from two exons in the different reference genes using Primer 3 (http://frodo.wi.mit.edu/ primer3/) with the following parameters: melting temperature (Tm) at 58–62 °C (optimum Tm at 60 °C), PCR product size at 75–150 base pairs, primer sequences of 18 to 24 nucleotides in length (optimum length at 20 nucleotides), and a GC content of 45 to 55 %. In order to check the primers’ specificity, regular PCR was performed and the products were analyzed by electrophoresis on 2.0 % agarose gels. The gels were stained using GelRed (Biotium, USA) and visualized under UV light. qRT-PCR was carried out in 96-well blocks with a Bio-Rad CFX96 real-time PCR system (Bio-Rad, USA). The final reaction volume for all reactions was 20 μl, and each reaction contained the following components: 2 μl 1:20 diluted cDNA reaction mixture, 10 μl 2 × SYBR Premix Ex TaqTM (TaKaRa, Japan), 0.4 μl ROX Reference Dye II, 0.4 μl forward primer, 0.4 μl reverse primer, and 6.8 μl ddH2O. The reaction conditions (as recommended by the manufacturer) included an initial denaturation step at 95 °C for 30 s, followed by 40 cycles of denaturation at 95 °C for 5 s, and annealing/ extension at 60 °C for 34 s. The dissociation curve for each product was obtained by heating the amplicon from 60 °C to 95 °C. qRT-PCR reactions were carried out for three biological replicates per treatment with three technical replicates per

95.34 99.67 96.09 96.56 93.04 93.02 95.45 94.34 93.21 99.09 99.10 97.12 93.19 88.97 82 93 83 75 75 149 147 105 85 117 84 78 161 99 FL920273.1 GR876745.1 FL977617.1 GR879053.1 GR878775.1 FL856960.1 JQ425118.1 GR879761.1 FL942644.1 FL997951.1 AC243242

ID

Ubiquitin 1 Actin 2 Polyubiquitin Tubulin Glyceraldehyde-3-phosphate dehydrogenase Carotenoid-binding protein 20 Ubiquitin-conjugating enzyme 2 18S ribosomal RNA1 NAC domain protein Ubiquitin 3 Ubiquitin-conjugating enzyme Cyclophilin RNA polymerase I subunit 18S ribosomal RNA

The cycle threshold (Ct) values were obtained from each reaction using the 14 primer pairs. To reveal the differences

UBQ1 ACT2 UBQ2 TUB GAPDH CBP20 UCE2 18SrRNA1 NAC UBQ3 UBC CYP2 RNAp1 18SrRNA2

Ct Data Collection and Variations in Reference Genes

Function

A total of 14 candidate reference genes were selected for normalization of gene expression in switchgrass using qRTPCR. Gene name, accession number, gene description, primer sequences, amplicon length, Tm values, and correlation coefficients are listed in Table 1. The melting temperatures (Tm) of all PCR products ranged from 80.53 °C for CBP20 to 84.98 °C for UBQ3 (Table 1). Gene-specific amplification was confirmed by the presence of a single band of expected size for each primer pair using gel electrophoresis (Fig. 1a). Gene-specific amplification was also confirmed by singlepeak melting curves of the qRT-PCR products (Fig. 1b). No primer dimers or other PCR products were generated from nonspecific amplifications (Fig. 1a). Similarly, no nonspecific products were detected in negative controls. As shown in Table 1, the gene-specific PCR amplification efficiencies ranged between 86 and 101 %.

Gene name

Verification of Amplicon, Primer Specificity, and Gene-Specific PCR Amplification Efficiency

Table 1 List of primer sequences and related information for each candidate reference gene

Results

Primer sequence (5′→3′) (forward)

Primer sequence (5′→3′) (reverse)

To select suitable reference genes, gene expression stability was calculated using online tools for four different statistical approaches: Delta Ct method [39], geNorm (ver. 3.5) [40], BestKeeper (ver. 1.0) [41], and NormFinder (ver. 0.953) [42]. An additional tool, RefFinder, was used to calculate the rank of the 14 candidate reference genes [37]. The qRT-PCR data was obtained from the CFX manager (Bio-Rad) and exported into an Excel datasheet (Microsoft Excel 2007). The Ct values were converted according to the requirements of each software program. Next, each software program was used to generate a measurement of the stability of the reference genes. This measurement was then used to rank the order of the candidate reference genes. Standard curves were generated in order to calculate genespecific PCR efficiency. These were performed for each primer pair using a 10-fold serial dilution of the control leaf cDNA template. The correlation coefficients (R2) and slope values were obtained from the standard curve. Finally, the corresponding PCR amplification efficiencies (E) were calculated according to the following equation: E=(10(−1/slop) −1)×100 [43].

TCTGGCGGACTACAATATCCA CGAACCCAGCCTTCACCATAC AGAGACCAGAAGACCCAGGTACAG GCCGAGATCAGATGGTTCA TTATTCTCGGCATACACAAG TCCTCTGTCATTCCTGTA CAGGTGGATGAAGAATAGA CAATTACCAGACACTAACG CAATCTTGCCACCATTCTGA CTTATGCTTGTGCTTGAT GGAGAGCAGGACCTTAGA CGTTCCTCCTTCGTTCAT AGAGACGACATTCTACGA GCATTCCTTGTTGAAGAC

Amplicon length (bp)

Statistical Analyses

CAGCGAGGGCTCAATAATTCCA GCGAGCTTCCCTGTAGGTA TTCGTGGTGGCCAGTAAG GCTCTACGACATCTGCTTCC TGTTCAAGACCCAGTAGAG GTGGTTATGGTAAGATGGT TATATGACGGAGGCTACT CTACCACATCCAAGGAAG CCTGTCTAGCCACTCCTATT GACTACAACATCCAGAAG ATCAACAGCAATGGAAGTATATG CTCAGACCACCTCTCAGA CACGAGTAATAATCTAATCTTCAG AACGAGCATATAGCCTTG

sample. The final threshold cycle (Ct) values were calculated as the means of the nine values.

FL955474.1 FL724919.1

Amplification efficiency (%)

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in transcript levels between the 14 reference genes, we assessed the median Ct range and calculated the coefficient of variance for each gene across all samples. As expected, the median Ct values varied between the different genes and ranged from 11 to 32 cycles (Fig. 2). Half of the candidate reference genes displayed median Ct values ranging from 22 to 27, which is considered a moderate to high level of expression. The 18S rRNA1 gene showed a relatively high level of expression and had a median Ct value of 11. ACT2, GAPDH, TUB, CYP2, RNA polymerase I subunit (RNAp1), and CBP20 all showed relatively low gene expression with median Ct values ranging from 25 to 32. Standard deviation of Ct values across biological replicates, tissue types, and stress treatments can reveal the expression stability of candidate reference genes. In our study, polyubiquitin (UBQ2) displayed the most narrow variance and was therefore considered to be the most stably expressed reference transcript. Alternatively, the TUB gene displayed the largest variation in transcript levels.

Stability Ranking of Candidate Reference Genes The methods used in this study for identifying stable switchgrass reference genes are based on different models and assumptions. We found that different results were produced from the same data set (Fig. 3 and Table S1). In order to identify the best reference genes for qRT-PCR data normalization in all of the biological samples, the Ct values for each candidate reference gene were used for stability comparison in the NormFinder, geNorm, BestKeeper, and Delta Ct programs. We also employed RefFinder to calculate the recommended comprehensive ranking. The results of the analyses for the 14 genes are given in Fig. 3. Our results indicate that the best reference genes for qRT-PCR are dependent upon the different stress conditions. Under drought stress, six genes (ACT2, CBP 20, TUB, ubiquitin-conjugating enzyme 2 (UCE2), UBQ3, and CYP2) were identified as potential reference genes using the different programs. The three best were ACT2, CBP20, and TUB based on the Recommended Comprehensive Ranking method (Fig. 3d; Table S1d). Six genes (18S rRNA1, ACT2, TUB, NAC domain protein (NAC), UBQ3, and UBC) were identified as potential reference genes for analyzing differential gene expression under salt treatment. 18S rRNA1, ACT2, and TUB were the three most stable reference genes (Fig. 3e; Table S1e). U n d e r c o l d s t r e s s , U B C / C B P2 0 , T U B / U B Q 3 , ACT2/UBC, and TUB/UBC were the top two reference genes basing on the Delta Ct method, BestKeeper, NormFinder, and geNorm programs, respectively. Overall, when studying cold stresses, the three best reference genes to use were found to be UBC, TUB, and CYP2 (Fig. 3f; Table S1f).

Similarly, for studying heat stress, four genes (18S rRNA1, UBC, CBP20, and ACT2) were identified as potential reference genes using the different programs. Of these, the 18S rRNA1, UBC, and TUB genes were found to be the most suitable (Fig. 3g; Table S1g). With regard to gene expression analysis under waterlogging treatment, six genes (ACT2, 18S rRNA1, TUB, UCE2, CBP20, and UBQ3) were identified as potential reference genes using the different programs. ACT2, 18S rRNA1, and UBQ3 were found to be the three most stable reference genes in the different switchgrass tissues (Fig. 3h; Table S1h). We also found that reference gene selection in switchgrass is tissue/organ-specific. In leaf samples (Fig. 3b; Table S1b), the top three reference genes were ACT2, GAPDH, and 18S rRNA1. Considering their responses to all stress conditions, these were identified as the preferred reference genes using the four computational programs. In root samples, the top three reference genes that were the most stably expressed under all stress conditions were 18S rRNA2, TUB, and UBC (Fig. 3c; Table S1c). The data obtained from all of the samples (leaves and roots under different abiotic stress) were included in the analysis. The results of this analysis identified five genes (ACT2, 18S rRNA2, TUB, UCE2, and CBP20) that were suitable to be used as reference genes. Among these, ACT2, TUB, and UCE2 were identified as the best reference candidates, while RNAp1, UBQ1, and GAPDH were not suitable for use in gene expression studies (Fig. 3a; Table S1c).

Validation of the Usefulness of the Reference Genes Identified from This Study In order to validate the performance of the reference genes identified in this study on known abiotic stress-inducible genes, we quantified the expression of the dehydrationresponsive element-binding (DREB) gene, which has been shown to be upregulated during drought stress [44–46]. After qRT-PCR, the expression of DREB was normalized using a representative least stable reference gene (GAPDH) and a representative most stable reference gene (ACT2). As shown in Fig. 4, using GAPDH for normalization suggests that DREB is induced twofold after 4, 6, and 8 h. However, there were no significant changes in gene expression after 4 h. In contrast, using ACT2 as the reference gene revealed greater overall fold changes in expression of DREB. Therefore, if GAPDH was used as the reference gene, we would have consistently failed to detect drought-induced gene expression changes in switchgrass leaves for DREB after 4 to 8 h (Fig. 4). The upregulation of the DREB gene, however, was reliably detected when the expression levels were normalized with ACT2.

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ƒFig.

1 Gene specificity and amplicon size. a Agarose gel (2.0 %) electrophoresis showing amplification of a specific PCR product of the expected size for each gene. b Melting curves of 14 reference genes showing single peaks. M represents a 100-bp DNA marker

Discussion qRT-PCR is an effective way to detect differences in gene expression due to the high sensitivity and specificity of this method [24–26]. However, there are several factors that could significantly affect the accuracy of qRT-PCR analysis, such as RNA quality, the quantity of cDNA, the efficiency of reverse transcription, and the selection of reference genes [41]. Reference gene stability is the main factor affecting the accuracy of qRT-PCR because the Ct values from the stably expressed reference gene are the basis for the quantification of the relative expression levels of other genes of interest [47]. Therefore, the selected reference genes for gene expression studies should display stable expression levels under all treatment conditions and in all tissues types. In this study, the expression stabilities of 14 switchgrass candidate reference genes were evaluated in different tissues and under five abiotic stresses. No single gene was identified that was stably expressed under every treatment condition. This may be because all candidate reference genes responded to abiotic stresses differently. Therefore, it is necessary to screen and validate potential reference genes using studies similar to ours

Fig. 2 Median cycle threshold (Ct) values for 14 candidate reference genes for all samples. The filled diamond symbol indicates median Ct values. The bars indicate standard deviation

instead of randomly selecting reference genes. The results of our study also emphasize the importance of researches such as ours, especially in the case of the DREB gene and for other genes that are affected by abiotic and biotic stresses. In this study, expression stabilities of 14 candidate reference genes were analyzed based on four computational programs: Delta Ct, BestKeeper, NormFinder, and geNorm. The results from these four programs were not the same for the top-ranked reference gene. Additionally, not a single reference gene was calculated as the best reference gene by all four programs. From the qRT-PCR data for all samples, Delta Ct and NormFinder identified that ACT2 was the most stable reference gene. Alternatively, BestKeeper and NormFinder identified that TUB was the most stable. geNorm identified that UCE2 and 18S rRNA2 were the two most stable reference genes. Similar results in ranking discrepancies among the programs were previously reported, possibly because these programs rank and calculate parameters in different ways [48]. Therefore, RefFinder was used to compare and rank the tested candidate reference genes based on the results obtained from geNorm, NormFinder, BestKeeper, and Delta Ct [37]. The recommended comprehensive ranking, based on the RefFinder output, was recognized as the final ranking in this study. Housekeeping function (HK) genes maintain basic cellular functions are expressed in metabolically active cells and/or are essential for successful completion of the cell cycle [49]. Due to their functional nature, HK genes are good candidate reference genes for gene expression studies [50–52]. However,

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ƒFig. 3

Stability ranking of 14 candidate reference genes. a all samples, b leaf tissue, c root tissue, d drought stress, e salt stress, f cold stress, g heat stress, and h waterlogging stress

several studies have demonstrated that such HK genes are frequently unstable or unsuitable for use as references. In this study, we identified the ACT2 gene as the most stable reference gene under drought and waterlogging stresses in switchgrass (Fig. 3). Other studies have identified ACT2 as strongly expressed in almost all the vegetative tissues in seedlings, juvenile plants, and mature plants [53], and ACT2 was regarded as an appropriate reference gene [54]. However, one experiment indicated that ACT2 was reported to be the least stably expressed gene among 27 other genes tested in Arabidopsis [48]. The TUB gene plays a crucial role in cell structure maintenance. In our study, TUB ranked among the three best genes under all stresses, except waterlogging and was ranked the second most stable for all samples (Fig. 3). In peach and litchi (Litchi chinensis), this gene was also identified as the most stable gene using NormFinder analysis [55, 56]. However, TUB was regarded as a poor reference gene in grape, potato, and soybean [31, 57, 58] and for nitrogendeficient stress in tomato [34]. Moreover, we found that one widely used reference gene, GAPDH, was unstable in switchgrass [59–61]. In this study, GAPDH ranked the lowest under drought, cold, heat, and waterlogging stresses for all samples (Fig. 3). GAPDH is a key enzyme involved in glycolysis; however, unstable expression of this gene under abiotic stresses suggests that it may also be involved in other biological processes or responses. Numerous researches have indicated that utilizing two or more reference genes, especially when evaluating target gene expression, is more profound and reliable [62, 63, 57]. Fig. 4 Relative quantification of DREB expression in switchgrass leaves using different reference genes (ACT2 and GAPDH) under drought stress

Therefore, in this study, we recommended the three most stable reference genes under each abiotic stress. This information will aid in the selection of appropriate reference genes for future gene expression studies in switchgrass. The expression level of reference genes should be consistent with target genes. A recent study, aimed at discerning suitable reference genes for normalizing mRNA levels in human pulmonary tuberculosis, has found that a standard deviation of less than twofold (low expression) from the mean expression level of the gene is a requirement for suitable reference gene. This was because some of the target genes were low-copy number cytokines [64]. Therefore, knowing the expression level of target genes will help researchers choose suitable reference genes. In this study, the expression levels of the 14 candidate reference genes are listed in Fig. 2. Researchers can select suitable reference genes based on their target gene’s expression.

Conclusion To our knowledge, a similar report has selected and validated reference genes for gene expression analysis in switchgrass under different stresses and tissues [38]; however, the previous study only uses two distinct algorithms and does not consider the stability of candidate reference genes under each different stress condition. Additionally, the candidate reference genes used in the present study are largely different than those that were previously reported. In this study, ACT2, CBP20, and TUB were found to be the three best reference genes to use under drought stresses. The 18S rRNA1, UBC, and TUB genes were the most stably expressed genes under salt stress. UBC and TUB were also found to be the most

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suitable reference genes to study differential gene expression under cold treatment. Under heat stress, the 18S rRNA1, UBC, and TUB genes showed the best results. ACT2, 18S rRNA1, and UBQ3 were found to be the top three reference genes under waterlogging treatment. In leaf samples, ACT2, GAPDH, and 18S rRNA1 were identified as the preferred reference genes. The top three reference genes for use in root samples were 18S rRNA2, TUB, and UBC. Our study provides these genes as the reference genes which are suitable for gene expression studies under different abiotic stress conditions in switchgrass. These results are also suggestive for such studies in other closely related grasses. Acknowledgements This work was supported by the National HighTechnology Research and Development Program (863 Program) of China (No. 2012AA101801-02), the National Natural Science Foundation of China (NSFC) (No. 31201845), the spring plan of Ministry of Education, and the Sichuan Agricultural University Students Innovation Plan (No. 121062603). Conflict of Interest The authors declare that they have no competing interests.

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