Document Detail

Selection of reference genes for quantitative gene expression studies in Platycladus orientalis (Cupressaceae) Using real-time PCR.
Jump to Full Text
MedLine Citation:
PMID:  22479379     Owner:  NLM     Status:  MEDLINE    
Platycladus orientalis is a tree species that is highly resistant, widely adaptable, and long-lived, with lifespans of even thousands of years. To explore the mechanisms underlying these characteristics, gene expressions have been investigated at the transcriptome level by RNA-seq combined with a digital gene expression (DGE) technique. So, it is crucial to have a reliable set of reference genes to normalize the expressions of genes in P. orientalis under various conditions using the most accurate and sensitive method of quantitative real-time PCR (qRT-PCR). In this study, we selected 10 reference gene candidates from transcriptome data of P. orientalis, and examined their expression profiles by qRT-PCR using 29 different samples of P. orientalis, which were collected from plants of different ages, different tissues, and plants subjected to different treatments including cold, heat, salinity, polyethylene glycol (PEG), and abscisic acid (ABA). Three analytical software packages (geNorm, Bestkeeper, and NormFinder) were used to assess the stability of gene expression. The results showed that ubiquitin-conjugating enzyme E2 (UBC) and alpha-tubulin (aTUB) were the optimum pair of reference genes at all developmental stages and under all stress conditions. ACT7 was the most stable gene across different tissues and cold-treated samples, while UBQ was the most stably expressed reference gene for NaCl- and ABA-treated samples. In parallel, aTUB and UBC were used singly or in combination as reference genes to examine the expression levels of NAC (a homolog of AtNAC2) in plants subjected to various treatments with qRT-PCR. The results further proved the reliability of the two selected reference genes. Our study will benefit future research on the expression of genes in response to stress/senescence in P. orientalis and other members of the Cupressaceae.
Ermei Chang; Shengqing Shi; Jianfeng Liu; Tielong Cheng; Liang Xue; Xiuyan Yang; Wenjuan Yang; Qian Lan; Zeping Jiang
Related Documents :
22336329 - Polycomb: a paradigm for genome organization from one to three dimensions.
22879379 - Inducible and reversible regulation of endogenous gene in mouse.
22430369 - Comparability of imazapyr-resistant arabidopsis created by transgenesis and mutagenesis.
22467519 - The ly6 neurotoxin-like molecule target of wit (twit) regulates spontaneous neurotransm...
22297689 - Differences of z chromosome and genomic expression between early- and late-feathering c...
22665139 - Identification of germline genomic copy number variation in familial pancreatic cancer.
23597269 - Enhanced transfection efficiency and reduced cytotoxicity of novel lipid-polymer hybrid...
21269459 - Proportionality between variances in gene expression induced by noise and mutation: con...
25048709 - A magnetic nanoparticle-based multiple-gene delivery system for transfection of porcine...
Publication Detail:
Type:  Journal Article; Research Support, Non-U.S. Gov't     Date:  2012-03-30
Journal Detail:
Title:  PloS one     Volume:  7     ISSN:  1932-6203     ISO Abbreviation:  PLoS ONE     Publication Date:  2012  
Date Detail:
Created Date:  2012-04-05     Completed Date:  2012-08-28     Revised Date:  2013-06-26    
Medline Journal Info:
Nlm Unique ID:  101285081     Medline TA:  PLoS One     Country:  United States    
Other Details:
Languages:  eng     Pagination:  e33278     Citation Subset:  IM    
State Key Laboratory of Tree Genetics and Breeding, Research Institute of Forestry, Chinese Academy of Forestry, Beijing, People's Republic of China.
Export Citation:
APA/MLA Format     Download EndNote     Download BibTex
MeSH Terms
Abscisic Acid / pharmacology
Base Sequence
Cold Temperature
Cupressaceae / genetics*,  growth & development
Gene Expression Profiling / methods*,  standards
Gene Expression Regulation, Developmental / drug effects
Gene Expression Regulation, Plant / drug effects
Genes, Plant / genetics*
Hot Temperature
Molecular Sequence Data
Plant Growth Regulators / pharmacology
Polyethylene Glycols / pharmacology
Reference Standards
Reverse Transcriptase Polymerase Chain Reaction / methods*
Sodium Chloride / pharmacology
Time Factors
Reg. No./Substance:
0/Plant Growth Regulators; 0/Polyethylene Glycols; 21293-29-8/Abscisic Acid; 7647-14-5/Sodium Chloride

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine

Full Text
Journal Information
Journal ID (nlm-ta): PLoS One
Journal ID (iso-abbrev): PLoS ONE
Journal ID (publisher-id): plos
Journal ID (pmc): plosone
ISSN: 1932-6203
Publisher: Public Library of Science, San Francisco, USA
Article Information
Download PDF
Chang 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.
Received Day: 11 Month: 11 Year: 2011
Accepted Day: 6 Month: 2 Year: 2012
collection publication date: Year: 2012
Electronic publication date: Day: 30 Month: 3 Year: 2012
Volume: 7 Issue: 3
E-location ID: e33278
ID: 3316566
PubMed Id: 22479379
Publisher Id: PONE-D-11-22567
DOI: 10.1371/journal.pone.0033278

Selection of Reference Genes for Quantitative Gene Expression Studies in Platycladus orientalis (Cupressaceae) Using Real-Time PCR Alternate Title:Reference Genes for Platycladus orientalis
Ermei Chang1
Shengqing Shi1
Jianfeng Liu1
Tielong Cheng2
Liang Xue1
Xiuyan Yang1
Wenjuan Yang1
Qian Lan1
Zeping Jiang1*
Ji Hoon Ahnedit1 Role: Editor
1State Key Laboratory of Tree Genetics and Breeding, Research Institute of Forestry, Chinese Academy of Forestry, Beijing, People’s Republic of China
2Sci-tech Management Division, Chinese Academy of Forestry, Beijing, People’s Republic of China
Korea University, Republic of Korea
Correspondence: * E-mail:
Contributed by footnote: Conceived and designed the experiments: ZJ EC SS. Performed the experiments: EC SS. Analyzed the data: EC SS XY. Contributed reagents/materials/analysis tools: JL TC LX XY QL WY. Wrote the paper: EC SS XY.


Quantitative real-time PCR (qRT-PCR) allows sensitive, specific, and reproducible quantification of nucleic acids [1], and it has been widely used to analyze mRNA in different organisms, transgenic and gene mutation experiments, and to identify parasitic organisms [2][5]. However, there are substantial variations in RNA stability, quantity, purity, and the efficiency of reverse transcription (RT) and polymerase chain reactions (PCR) [6]. Therefore, it is important to select a suitable reference gene and to use a set of standardized experimental conditions to accurately quantify gene expression by qRT-PCR; otherwise, it may give inaccurate results [7][14].

Reference genes are those that are constitutively expressed and are required for cellular survival. They include genes that encode products with functions in maintaining cell wall structure and primary metabolism. Some examples of reference genes include 18S ribosomal RNA (18S rRNA), actin (ACT), tubulin (TUB), glyceraldehyde-3-phosphate dehydrogenase (GAPDH), polyubiquitin (UBQ), and elongation factor 1-a (EF1a) [15], [16], [17]. Some of them, such as EF1a, ACT2, and TUA, do not satisfy certain basic requirements for use as an internal control in Arabidopsis thaliana[16] and tomato [17]. Recently, some novel reference genes that show highly stable expression were identified from analyses of microarray data sets from A. thaliana. These new reference genes include SAND and TIP41, which encode a SAND family protein and a TIP41-like family protein, respectively [16]. Both of these reference genes outperform the classical ones; for example, CAC and TIP41 were also among the most stably expressed genes in tomato. A recent study demonstrated that even some stress-related genes can serve as reference genes in some experiments, such as those encoding SKP1/Ask-interacting protein 16 (SKIP16), metalloprotease (MTP), RNA polymerase subunit (RPII), and F-box protein (F-box) [18][22]. Therefore, it is important to select a suitable reference gene with a constant level of expression under certain experimental conditions and among various species [16].

To date, many stable reference genes have been screened in both model and crop species, such as A. thaliana[23], rice [24], [25], Brachypodium distachyon[26], wheat [27], barley [28], soybean [29], [30], tomato [31], potato [32], sugarcane [33] and poplar [34]. However, no suitable internal controls for gene expression studies have been defined for Platycladus orientalis, which limits further studies on this species at the transcriptome level.

P. orientalis, as a member of the Cupressaceae, is used extensively as a medicinal ingredient and as an ornamental landscape plant that can tolerate a wide range of environmental extremes [35], [36]. Many individuals of this species have lifespans of one to several thousands of years in China [37]. However, why do some trees, such as P. orientalis, live for so long? This question has received much attention in recent years [38], [39]. The long lifespan and hardiness of P. orientalis make it an ideal material in which to study expression patterns of genes related to stress responses and longevity of tree species. In our previous research, the transcriptome and digital expression profiles were compared between young and old trees of P. orientalis by RNA-seq combined with the digital gene expression (DGE) technique (data not shown). To further elucidate the excellent genetic traits of P. orientalis, further research is required to analyze expression of particular genes at various developmental stages and under certain stress conditions. However, several studies have shown that the transcription levels of genes vary considerably under various experimental conditions [40], [41], [42]. Therefore, it is urgent to identify a set of stable reference genes for further analyses of gene expression profiles in P. orientalis.

In this study, we used qRT-PCR to examine variations in the expressions of 10 candidate reference genes, including 8 traditional housekeeping genes and 2 novel reference genes selected from transcriptome data of P. orientalis. Then, we compared their stabilities across a large set of P. orientalis samples representing different developmental stages, organs, and stress treatments using statistical and graphical methods. The results demonstrated that the expressions of the selected genes showed different degrees of variations among samples. Furthermore, we examined the expression of a NAC transcription factor, a homolog of the stress- and senescence-responsive gene AtNAC2[43][47], in P. orientalis subjected to various stress treatments, using UBC and aTUB in combination as internal control genes. This work will benefit future studies on gene expression in P. orientalis and other members of the Cupressaceae.

Expression Profiles of Reference Genes

We selected 10 candidate reference genes, including 8 traditional genes (GAPDH, ACT7, aTUB, bTUB, UBC, UBQ, EF1a and DNAJ) and 2 novel candidate reference genes (SAND and CAC) (Table 1, Text S1). The stability of gene expression was determined by quantifying the mRNA level by qRT-PCR. For each gene, we calculated the cycle threshold (Ct) value, which represents the cycle at which a significant increase of the PCR product occurs. In general, this is marked by the middle of the exponential phase of amplification [48], [49]. The expression levels of these 10 reference genes varied widely with Ct values ranging from 19 to 39 cycles (Fig. 1), and most of the Ct values were between 23 and 27 cycles. UBQ was the most abundantly transcribed with Ct values of less than 24 cycles; aTUB, UBC and GAPDH were moderately expressed mRNAs with most of the Ct values between 25 and 27 cycles; ACT7, SAND, CAC and DNAJ showed Ct values between 26 and 30 or slightly higher. However, EF1a showed the lowest level of expression in all samples with Ct values as high as 39 cycles. The calculated coefficient of variance (CV) of the Ct values gives an indication of the expression stability of a particular gene. A narrow range of CV values indicates that a given gene is expressed stably in different samples. Among the 10 candidate reference genes in this study, EF1a showed much greater variations in its expression levels than the other genes, whose CV value was more than 5 cycles, whereas UBC and aTUB showed narrow mean Ct value ranges in their respective expressions with minimal CV. Thus, it is essential to select a set of reliable reference genes to normalize gene expression under certain conditions to obtain accurate gene expression data.

GeNorm Analysis

We analyzed gene expression stabilities of the 10 reference genes in all of the designated conditions as described by Vandesompele et al.[13] using geNorm software. GeNorm automatically calculates the average expression stability value (M) as the average pairwise variation (V) of a particular gene with all other control genes and determines the V values with all other control genes as the standard deviation of the logarithmically transformed expression ratios [26]. The gene with the lowest M value is that with the most stable expression, while the gene with the highest M value has the least stable expression. As shown in Figure 2, we analyzed data from seven sets of treatments. When all the results from all 29 samples of P. orientalis were combined, aTUB and UBC showed the lowest M value (0.64) and CAC showed the highest M value (1.31). Therefore, aTUB and UBC had the most stable expressions, and CAC the least stable expression (Fig. 2A). Among the tissues of different ages, the most stably expressed genes were UBC and aTUB, while CAC was the least stably expressed, consistent with the pattern observed across all samples (Fig. 2B). For the different organs, the DNAJ and ACT7 genes showed the greatest stability of expression, and CAC showed the least stable expressions (Fig. 2C). Similarly, the stabilities of reference genes varied among samples under different stress treatments. As shown in Figure 2D, SAND and ACT7 were the most stably expressed under cold stress, and EF1a the least stably expressed. In heat-treated samples, UBC and aTUB were expressed more stably than the other eight reference genes, while EF1a was the least stably expressed (Fig. 2E). Under NaCl stress, DANJ and UBQ were the most stably expressed genes, while EF1a and CAC were the least stably expressed genes (Fig. 2F). In contrast, under PEG stress, aTUB and EF1a were the most stably expressed genes and ACT7 was the most variable (Fig. 2G). The exogenous application of ABA had the least effect on expressions of UBC and UBQ, and the greatest effect on expression of EF1a (Fig. 2H). The geNorm analysis indicated that EF1a and CAC were the least stably expressed reference gene, although EF1a was stably expressed in PEG-treated samples, which is consistent with its roles in stress responses and development. Overall, all of the tested reference genes showed relatively high stability with low M values of less than 1.35, which is below the default limit of M≤1.5. Evaluation of all expression data revealed that UBC and aTUB were the most stably expressed genes; therefore, these may be suitable reference genes for analyses of gene expression in a wide variety of tissue types, developmental stages, and stress treatments in P. orientalis (Fig. 2).

To obtain reliable results from RT-PCR studies, it is recommended that two or more reference genes be used. Therefore, Vandesompele et al. [13] proposed 0.15 as the cut-off value for V, below which the inclusion of an additional control gene is not required; that is, if Vn/n+1 < 0.15, it is not necessary to use ≥ n+1 reference genes as internal controls. The paired variable coefficients (V2/V3) shown in Fig. 3B, C, D, E, F, G and H indicate that the inclusion of the third reference gene did not contribute significantly to the variation of the normalization factor (V2/3 < 0.15). That is, the two most stable reference genes for each subset would be sufficient for accurate normalization. When all the samples were pooled for analysis, the pairwise variation of V2/3 and V3/4 was greater than 0.15 (0.247 and 0.182, respectively), while that of V4/5 was 0.149 (Fig. 3A), which indicated that four reference genes, UBC, aTUB, GAPDH and UBQ, were necessary to normalize gene expression for all the treatments of P. orientalis in this study.

BestKeeper Analyses

For analyses using BestKeeper, an Excel-based tool, the average Ct value of each duplicate reaction is used (without conversion to quantity) to analyze the stabilities of candidate reference genes [50]. BestKeeper evaluates the stabilities of candidate reference genes based on the coefficient of correlation to the BestKeeper index, which is the geometric mean of the Ct values of all candidate reference genes [50], [51]. BestKeeper also calculates the coefficient of variance (CV) and the standard deviation (SD) of the Ct values using the whole data set and all the Ct values are analyzed as a total data set [51]. Reference genes are identified as the most stable genes, as they exhibit the lowest coefficient of variance and standard deviation (CV±SD). Genes that show a SD greater than 1 are considered unacceptable [52]. In this study, UBC and SAND had CV±SD values of 1.58±0.38 and 2.02±0.55, respectively, and showed remarkably stable expression in all the samples. However, GAPDH and bTUB had CV±SD values of 3.18±0.78 and 3.09±0.82, respectively, and showed the least stable expression (Table 2). These results differed to those obtained using geNorm (Fig. 2). For the different ages of P. orientalis, the most stable reference genes (lowest CV±SD) were aTUB and UBC, while UBQ had the highest CV±SD of all the selected genes. Bestkeeper analyses indicated that UBC and DNAJ were the most stably expressed, and GAPDH and UBQ were the least stably expressed among different tissue types and PEG-treated samples. UBQ and aTUB were the most stably expressed genes under cold stress; and UBC and aTUB had the lowest coefficient of variance under heat treatment, which was consistent with the results obtained using geNorm and NormFinder. In ABA-treated samples, BestKeeper analysis indicated that the most stable genes were SAND and ACT7 and the least stable were GAPDH and EF1a. The results obtained from BestKeeper showed slight differences from those obtained from geNorm (Fig. 2).

NormFinder Analysis

Similar to geNorm, the NormFinder program is a Visual Basic application tool for Microsoft Excel used to determine expression stabilities of reference genes [53]. As in the geNorm method, the gene with the lowest M value is that with the most stable expression, and the gene with the highest M value has the least stable expression. The results of the NormFinder analysis were showed in Table 3. The NormFinder outputs with and without different sample subgroups showed some common features. The NormFinder analysis ranked UBC and aTUB in the top positions for all the samples, different age samples, and heat-treated samples, while EF1a and CAC were ranked as less stable, consistent with the result obtained from geNorm. Among the different tissues, DNAJ and ACT7 were the most stably expressed with values of 0.093 and 0.101, respectively, while expressions of CAC and bTUB were the least stable. Under cold, ABA, and NaCl treatments, UBC was calculated to be the most stably expressed gene, and EF1a was the least stable. Under PEG treatment, bTUB and aTUB were predicted as the best internal controls, while ACT7 and SAND were the least stably expressed genes. The results obtained from NormFinder were highly consistent with those obtained from geNorm (Fig. 2).

Evaluation of Reference Genes for Determining Age- and Stress-responsive NAC Expression

To further evaluate the reliability of the top two reference genes aTUB and UBC, NAC transcription factor AtNAC2 (At5g39610; also named ANAC092), which plays a crucial role in protecting plants against abiotic stresses, as well as in leaf senescence [45], was selected to qRT-PCR analysis with aTUB and UBC singly or in combination as reference genes. In our previous study, we found that the NAC domain gene from P. orientalis, a homolog of AtNAC2 in A. thaliana, was differentially expressed between young and old trees by using RNA-seq combined with a DGE technique (data not shown). Therefore, this gene was selected to further evaluate the reliability of the reference genes by qRT-PCR. We selected aTUB and UBC in combination as the reference genes and determined their average expression patterns (Fig. 4). The highest level of NAC expression was in leaves of 5-year-old P. orientalis. The expression of NAC in leaves increased significantly from 20- to 2000-year-old individuals of P. orientalis (P < 0.05) (Fig. 4A), which matches the result obtained from the DGE analysis. NAC showed evidently different expression levels in the tissues, such as seeds, fruits, roots, stems, and leaves (P < 0.05) (Fig. 4B). Cold and heat stress significantly increased the expression of NAC (Fig. 4C) by 8.0 and 5.7 folds, respectively, at 48 h compared with that at 0 h (P < 0.05). The expression profiles of NAC showed similar trends under NaCl, PEG, and ABA treatments, and its abundance increased to maximum levels at 24 h, increasing to 12.9, 5.6 and 12.2 folds compared with that at 0 h, respectively (P < 0.05) (Fig. 4C). In addition, the expression patterns of NAC showed similar trends to those of aTUB and UBC as internal controls either singly or in combination (Fig. 4; Fig. S1). There were no significant differences in the expression patterns of NAC using either aTUB or UBC singly as the internal control (P > 0.05), although it seems that there was a bit of difference between aTUB and UBC at the 48-hour treatments (P  =  0.059) (Fig. S1), which further indicated that aTUB and UBC were suitable as reference genes.


In plant molecular biological research, studies on gene expression patterns help us to understand biological processes. The qRT-PCR technique is one of the most common methods to quantify gene expression levels, which is a crucial step in identifying gene function [26]. However, to accurately analyze expression of a particular gene, it is essential to have a reliable method to normalize its expression. Thus, an appropriate internal reference gene is required for reliable quantification of gene transcripts.

To screen for appropriate reference genes suitable for studies on age- and stress-responsive gene expression in P. orientalis, we examined the expressions of 10 reference genes from its transcriptome in a set of different conditions. We then analyzed their expressions using three different software packages: geNorm, NormFinder and Bestkeeper. Analysis with geNorm is an easy method to determine the optimal number of stable housekeeping genes for accurate normalization [29], whereas NormFinder and Bestkeeper were used to assess the quality of the ranking obtained by geNorm [54]. Our data showed that the top two positions of reference genes for all samples, for samples of different ages and different tissue types, and for heat-treated samples, were almost the same when determined by geNorm, Bestkeeper, and NormFinder. The top two positions of reference genes in the cold-, PEG- and ABA-treated samples determined by geNorm were highly similar to those determined by NormFinder but not Bestkeeper. The top two positions of reference genes in the NaCl-treated samples predicted by NormFinder were similar to those determined by Bestkeeper but not geNorm (Table S1). The three software packages use different calculation algorithms [55] and therefore can give different results. However, all three software packages showed that aTUB and UBC were the most stable reference genes. In addition, different types of samples had their own best reference genes among the 10 selected candidate reference genes (Fig. 2; Table 2, 3). For example, DNAJ was one of the best reference genes for different tissues types and NaCl-treated samples, whereas UBQ performed better than ACT7 as a reference gene for NaCl- and ABA-treated samples. In general, UBC and aTUB were the optimum pair of reference genes for all the samples, all developmental stages, and in heat-treated samples. Therefore, it is necessary to validate the expression stability of the control gene under specific experimental conditions prior to its use for normalization [26].

Our data demonstrate that UBC, aTUB, DNAJ and ACT7 were ranked in top positions in all the samples of P. orientalis based on the results from the three software packages. Here, UBC was found to be one of the most stably expressed genes in all samples of P. orientalis, which was consistent with the result in B. distachyon[26], whereas it showed less stable expression in A. thaliana under heavy metal (Cu and Cd) stress [23], therefore expression levels of reference genes vary among different species [20]. In this study, aTUB expression did not vary, or varied little, among tissues of different ages and under PEG stress (Fig. 2; Table 2), aTUB was also identified as being stably expressed across various developmental stages of soybean [29] and different tissues of poplar [34]. DNAJ showed remarkably stable expression in the different tissues and NaCl-treated samples of P. orientalis, which was consistent with an earlier study on DNAJ expression in tomato [56]. As noted previously, expression of ACT7 is relatively weak in soybean, rice, potato, and sugarcane [23][25], and rather variable in A. thaliana[16]. In our study, geNorm analysis indicated that expression of ACT7 was most stable across different tissues and cold-treated samples of P. orientalis.

All three software packages indicated that UBQ, bTUB, and GAPDH ranked in middle positions in all samples of P. orientalis. In the present study, UBQ was expressed stably in NaCl- and ABA-treated samples. UBQ also showed very stable expression in A. thaliana and B. distachyon[26], but was unsatisfactory as a reference gene in soybean [29] and grape [57]. GAPDH is one of the most commonly used reference genes to normalize gene expression data in qRT-PCR assays [58]. Here, we found that GAPDH was the most stably expressed reference gene in different ages, but was expressed less stably in different tissues of P. orientalis. bTUB was reported to perform poorly as a reference gene in grape and potato [32], [58], while in this study, NormFinder analysis indicated that bTUB showed highly stable expression in PEG-treated samples of P. orientalis.

Previously, EF1a was reported to be stably expressed during biotic and abiotic stress in both potato and rice [29], [32]. In this study, however, analyses using all three software packages ranked EF1a in the bottom positions. Similarly, the novel reference gene CAC was the lowest ranked gene in analyses from geNorm and NormFinder in all samples. These results indicated that EF1a and CAC were both unsuitable reference genes in all P. orientalis samples. Additionally, the novel reference gene SAND was not the best choice to analyze P. orientalis gene expression over a wider range of conditions, although it was stably expressed under cold stress in P. orientalis. However, SAND and CAC were the recommended reference genes for studies on development in berry [58] and tomato [31], respectively. Therefore, to investigate the transcript stability of the commonly used reference genes and to identify novel and superior reference genes, it is necessary to collect as many data as possible about gene expression in different organisms, organs, and experimental conditions.

To further validate the applicability of the screened reference genes, we analyzed the expression of the NAC domain gene, a homolog AtNAC2 of A. thaliana, in P. orientalis. AtNAC2 has recently been discovered as a central regulator of age-dependent and salt-promoted senescence in A. thaliana[45]. In this study, we examined the expression of NAC using a combination of UBC and aTUB as reference genes. The result showed that NAC exhibited a leaf age-dependent expression pattern with low to moderate expression in 20-year-old tree leaves, and significantly high expression in leaves of 1000- and 2000-year-old individuals of P. orientalis (P < 0.05). This pattern of expression was highly similar to that obtained from the DGE profile (data not shown), and the changes in the expression patterns of NAC under all the designated treatments (Fig. 4) showed similar trends to those of aTUB and UBC under the same conditions (Fig. S1). However, because subtle changes in expression are of critical importance in some experiments, it may not be suitable to use a single reference gene [13]. Therefore, to understand the molecular mechanisms underlying the stress tolerance and longevity of P. orientalis, it would be helpful to use two or more reliable reference genes for normalization of expression of genes of interest.

Above all, we identified 10 reference genes that were suitable for normalization of qRT-PCR data obtained from P. orientalis samples of different ages, from different tissues, and from plants subjected to various exogenous treatments. Evaluations using geNorm, NormFinder and BestKeeper indicated that the two most suitable reference genes in P. orientalis were aTUB and UBC, while the two least suitable reference genes were EF1a and CAC. To obtain the most reliable results from gene expression studies of P. orientalis, it is recommended that two or more reference genes are used as internal controls for relative gene quantification.

Materials and Methods
Plant Materials and Treatments

The trees sampled in this study were 20-, 100-, 1000-, and 2000-year-old individuals of P. orientalis growing in similar conditions in Zhongshan Park, Beijing. Nine-month-old and 5-year-old seedlings of P. orientalis were cultivated in pots with soil in the greenhouse at the Chinese Academy of Forestry, Beijing. For salt-, osmotic-, and ABA-treatments, 9-month-old seedlings were carefully pulled out from pots, washed cleanly with tap water, and placed in the solutions of NaCl (200 mM), PEG6000 (10%), and ABA (150 µM), respectively, for 0, 12, 24, and 48 h in the greenhouse. For the cold- and heat-treatments, the seedlings in pots were placed at 4°C or 40°C, respectively, in chambers with a 14-h light/10-h dark photoperiod for 0, 12, 24, and 48 h.

For the collection of tissues of trees in 20-, 100-, 1000-, and 2000-year-old, fresh leaves were collected from the branches in different directions in June and August 2011. Different tissues including leaves, roots, stems, fruits, and seeds were collected from 5-year-old seedlings. Leaves from 9-month-old seedlings were collected from the whole seedlings subjected to various treatments, immediately frozen in liquid nitrogen and stored at -80°C. Samples above were collected from 3 trees to give 3 replicas.

Total RNA Extraction and cDNA Synthesis

Total RNA was extracted from treated samples using a Column Plant RNAout kit (TIANDZ, CHINA), and then genomic DNA and polysaccharides were eliminated using RNase-free DNase I (TIANDZ, CHINA) and Polysaccharide Erasol (TIANDZ, CHINA) kits, respectively. The purity of the total RNA extracted was determined using a NanoDrop DU8000 spectrophotometer. RNA samples with an absorbance ratio at OD260/280 between 1.9 and 2.2 and OD260/230 ≈ 2.0 were used for further analyses. RNA integrity was verified by 2% agarose gel electrophoresis and ethidium bromide staining. Samples with 28S/18S ribosomal RNA between 1.5 and 2.0 and without smears on the agarose gel were used for the following experiment. First-strand cDNA was synthesized from 600 ng total RNA in a volume of 20 µl using the PrimeScript® RT reagent kit (TaKaRa, Japan) according to the manufacturer’s protocol. cDNA was diluted 7.5 folds before quantification and determinations of quantity and quality.

Quantitative Real-time RT-PCR

The primers for the 10 reference genes from the transcriptome of P. orientalis were designed using Primer 3 software ( All primer pairs were initially tested via standard RT-PCR using the Premix Ex Taq (TaKaRa, Japan) and a single amplification product of the expected size for each gene was verified by electrophoresis on a 3% agarose gel and staining with ethidium bromide. qRT-PCR reactions were carried out in 96-well blocks with an Applied Biosystems 7500 Real-Time PCR system using SYBR® Premix Ex TaqTM (TaKaRa, Japan) in a 20 µl reaction volume (containing 2 µl cDNA reaction mixture, 10 µl 2×SYBR Premix Ex TaqTM, 0.4 µl ROX Reference Dye II, and 0.4 µl each primer). The reaction conditions were those recommended by the manufacturer (30 s at 95°C, 40 cycles of 95°C for 5 s, and 60°C for 34 s). The dissociation curve was obtained by heating the amplicon from 60 to 95°C. All qRT-PCR reactions were carried out in technical and biological triplicate. The final threshold cycle (Ct) values were the mean of nine values (biological triplicate, each in technical triplicate).

Statistical Analyses

To select a suitable reference gene, the stability of mRNA expression of each reference gene was statistically analyzed with three different types of Microsoft Excel-based software: geNorm [59], NormFinder [60], and BestKeeper [61]. All three software packages were used according to the manufacturer’s instructions. For geNorm and NormFinder, the raw Ct values were transformed into the required data input format. The maximum expression level (the lowest Ct value) of each gene was used as a control and was set to a value of 1. Relative expression levels were then calculated from Ct values using the formula: 2-△Ct, in which △Ct = each corresponding Ct value–minimum Ct value. The obtained data were further analyzed with geNorm and NormFinder. BestKeeper analyses were based on untransformed Ct values.

Standard curves were generated using Excel software by plotting cycles at threshold fluorescence (Ct) against the logarithmic values of standard RNA amounts. Quantities of standard RNA were prepared by diluting 200 ng cDNA (1, 1/5, 1/25, 1/125, 1/625, 1/3125; each gene in triplicate). Only Ct values of less than 40 were used to calculate correlation coefficients (r2 values) and amplification efficiencies (E) from the given slope generated in Microsoft Excel 2003 according to the equation E =  [5-(1/ slope) – 1]×100%. All PCR assays showed efficiency values between 95 and 105%.

Data were compared and analysed with analysis of variance (ANOVA) and multiple comparisons using the statistical analysis software of SPSS. Differences were scored as statistical significance at the P < 0.05 or P < 0.01 level.

Supporting Information Text S1

A list of sequences of candidate housekeeping genes and NAC domain protein gene.


Click here for additional data file (pone.0033278.s001.doc)

Figure S1

The expression profile of NAC responsive to aging and stresses in Platycladus orientalis (studied by qRT-PCR with UBC and aTUB as reference genes, respectively).


Click here for additional data file (pone.0033278.s002.doc)

Table S1

The ranking of 10 reference genes and the assembly of the comparisons in different samples of Platycladus orientalis as calculated by geNorm, Bestkeeper, and NormFinder.


Click here for additional data file (pone.0033278.s003.doc)


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

Funding: This work was supported by a Grant for National Non-profit Research Institutions of CAF (RIF2010-10) and National Natural Science Foundation of China (31100490). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

We are very grateful to Prof. Dr. Mengzu Lu for good suggestion and careful proofreading of the revised manuscript, and Prof. Dr. Deyou Qiu for good suggestions. We are also grateful to Zhongshan Park for providing the experimental materials. We also thank Edanz Editing for editing the manuscript.

1. Yoo WG,Kim TI,Li S,Kwon OS,Cho PY,et al. Year: 2009Reference genes for quantitative analysis on Clonorchis sinensis gene expression by real-time PCR.Parasitol Res104232132818815810
2. Ohdan T,Francisco PB,Sawada T,Hirose T,Terao T,et al. Year: 2005Expression profiling of genes involved in starch synthesis in sink and source organs of rice.J Exp Bot5633293244
3. Ishimaru T,Hirose T,Matsuda T,Goto A,Takahashi K,et al. Year: 2005Expression patterns of genes encoding carbohydrate-metabolizing enzymes and their relationship to grain filling in rice (Oryza sativa L.): comparsion of caryopses located at different positions in a panicle.Plant Cell Physiol4662062815701658
4. Kanegae H,Miyoshi K,Hirose T,Tsuchimoto S,Mori M,et al. Year: 2005Expressions of rice sucrose non- fermenting-1 related protein kinase 1 genes are differently regulated during the caryopsis development.Plant Physiol Biochem4366967916087344
5. Narayanan NN,Vasconcelos MW,Grusak MA. Year: 2007Expression profiling of Oryza sativa metal homeostasis genes in different rice cultivars using a cDNA macroarray.Plant Physiol Biochem4527728617468002
6. Mahoney DJ,Carey K,Fu MH,Snow R,Cameron-Smith D,et al. Year: 2004Real-time RT-PCR analysis of housekeeping genes in human skeletal muscle following acute exercise.Physiol Genomics1822623115161965
7. Demidenko NV,Logacheva MD,Penin AA. Year: 2011Selection and validation of reference genes for quantitative real-time PCR in Buckwheat (Fagopyrum esculentum) based on transcriptome sequence data.PLoS ONE65e1943421589908
8. Tong ZG,Gao ZH,Wang F,Zhou J,Zhang Z. Year: 2009Selection of reliable reference genes for gene expression studies in peach using real-time PCR.BMC Mol Biol107119619301
9. Kwon MJ,Oh E,Lee S,Roh MR,Kim SE,et al. Year: 2009Identification of novel reference genes using multiplatform expression data and their validation for quantitative gene expression analysis.PLoS ONE47e616219584937
10. Løvdal T,Lillo C. Year: 2009Reference gene selection for quantitative real-time PCR normalization in tomato subjected to nitrogen, cold, and light stress.Anal Biochem387223824219454243
11. Hoenemann C,Hohe A. Year: 2011Selection of reference genes for normalization of quantitative real-time PCR in cell cultures of Cyclamen persicum.Electron J Biotechnol1411213
12. Everaert BR,Boulet GA,Timmermans JP,Vrints CJ. Year: 2011Importance of suitable reference gene selection for quantitative real-time PCR: special reference to mouse myocardial infarction studies.PLoS ONE68e2379321858224
13. Vandesompele J,De Preter K,Pattyn F,Poppe B,Van Roy N,et al. Year: 2002Accurate normalization of real-time quantitative RT-PCR data by geometric averaging of multiple internal control genes.Genome Biol37research0034research0034.1112184808
14. Tricarico C,Pinzani P,Bianchi S,Paglierani M,Distante V,et al. Year: 2002Quantitative real-time reverse transcription polymerase chain reaction: normalization to rRNA or single housekeeping genes is inappropriate for human tissue biopsies.Anal Biochem309229330012413463
15. Radonic A,Thulke S,Mackay IM,Landt O,Siegert W,et al. Year: 2004Guideline to reference gene selection for quantitative real-time PCR.Biochem Biophys Res Commun313485686214706621
16. Czechowski T,Stitt M,Altmann T,Udvardi MK,Scheible WR. Year: 2005Genome-wide identification and testing of superior reference genes for transcript normalization in Arabidopsis.Plant Physiol13951716166256
17. Yang LT,Pan AH,Jia JW,Ding JY,Chen JX,et al. Year: 2005Validation of a tomato-specific gene, LAT52, used as an endogenous reference gene in qualitative and real-time quantitative PCR detection of transgenic tomatoes.J Agric Food Chem53218319015656646
18. Chen X,Truksa M,Shah S,Weselake RJ. Year: 2010A survey of quantitative real-time polymerase chain reaction internal reference genes for expression studies in Brassica napus.Anal Biochem405113814020522329
19. Artico S,Nardeli SM,Brilhante O,Grossi-de-Sa MF,Alves-Ferreira M. Year: 2010Identification and evaluation of new reference genes in Gossypium hirsutum for accurate normalization of real-time quantitative RT-PCR data.BMC Plant Biol104920302670
20. Barsalobres-Cavallari CF,Severino FE,Maluf MP,Maia IG. Year: 2009Identification of suitable internal control genes for expression studies in Coffea arabica under different experimental conditions.BMC Mol Biol10119126214
21. Die JV,Román B,Nadal S,González-Verdejo CI. Year: 2010Evaluation of candidate reference genes for expression studies in Pisumsativum under different experimental conditions.Planta232114515320379832
22. Luo HL,Chen SM,Wan HJ,Chen FD,Gu CS,et al. Year: 2010Candidate reference genes for gene expression studies in water lily.Anal Biochem404110010220452325
23. Remans T,Smeets K,Opdenakker K,Mathijsen D,Vangronsveld J,et al. Year: 2008Normalisation of real-time RT-PCR gene expression measurements in Arabidopsis thaliana exposed to increased metal concentrations.Planta22761343134918273637
24. Jain M,Nijhawan A,Tyagi AK,Khurana JP. Year: 2006Validation of housekeeping genes as internal control for studying gene expression in rice by quantitative real-time PCR.Biochem Biophys Res Commun345264665116690022
25. Kim BR,Nam HY,Kim SU,Kim SI,Chang YJ. Year: 2003Normalization of reverse transcription quantitative-PCR with housekeeping genes in rice.Biotechnol Lett25211869187214677714
26. Hong SY,Seo PJ,Yang MS,Xiang F,Park CM. Year: 2008Exploring valid reference genes for gene expression studies in Brachypodium distachyon by real-time PCR.BMC Plant Biol811218992143
27. Paolacci AR,Tanzarella OA,Porceddu E,Ciaffi M. Year: 2009Identification and validation of reference genes for quantitative RT-PCR normalization in wheat.BMC Mol Biol101119232096
28. Faccioli P,Ciceri GP,Provero P,Stanca AM,Morcia C,et al. Year: 2007A combined strategy of “in silico” transcriptome analysis and web search engine optimization allows an agile identification of reference genes suitable for normalization in gene expression studies.Plant Mol Biol63567968817143578
29. Jian B,Liu B,Bi YR,Hou WS,Wu CX,et al. Year: 2008Validation of internal control for gene expression study in soybean by quantitative real-time PCR.BMC Mol Biol95918573215
30. Libault M,Thibivilliers S,Bilgin D,Radwan O,Benitez M,et al. Year: 2008Identification of four soybean reference genes for gene expression normalization.Plant Genome114454
31. Expósito-Rodríguez M,Borges AA,Borges-Pérez A,Pérez JA. Year: 2008Selection of internal control genes for quantitative real-time RT-PCR studies during tomato development process.BMC Plant Biol813119102748
32. Nicot N,Hausman JF,Hoffmann L,Evers D. Year: 2005Housekeeping gene selection for real-time RT-PCR normalization in potato during biotic and abiotic stress.J Exp Bot564212907291416188960
33. Iskandar HM,Simpson RS,Casu RE,Bonnett GD,Maclean DJ. Year: 2004Comparison of reference genes for quantitative real-time polymerase chain reaction analysis of gene expression in sugarcane.Plant Mol Biol224325337
34. Brunner AM,Yakovlev IA,Strauss SH. Year: 2004Validating internal controls for quantitative plant gene expression studies.BMC Plant Biol41415317655
35. Li XP,He YP,Wu XJ,Ren QF. Year: 2011Water stress experiments of Platycladus orientalis and Pinus tablaeformis young trees.Forest Res 24(1)91–96 (in Chinese with English abstract)
36. Jiang P,Shi J,Niu PX,Lu Y. Year: 2009Effect on activities of defensive enzymes and MDA content in leaves of Platycladus orientalis under naturally decreasing temperature.J Shihezi Univ (Natural Science) 2730–33 (in Chinese with English abstract)
37. Zhang YJ,Cong RC,Zhao Q,Zhang GH,Li YH,et al. Year: 2010Physiological indexes applied to characterize aging old trees.Sci Silv Sin 46(3)134–138 (in Chinese with English abstract)
38. Lanner RM. Year: 2002Why do trees live so long?Ageing Res Rev1465367112362893
39. Peñuelas J. Year: 2005A big issue for trees.Nature43796596616222288
40. Thellin O,Zorzi W,Lakaye B,De Borman B,Coumans B,et al. Year: 1999Housekeeping genes as internal standards: use and limits.J Biotechnol752-329129510617337
41. Suzuki T,Higgins PJ,Crawford DR. Year: 2000Control selection for RNA quantitation.Biotechniques29233233710948434
42. Glare EM,Divjak M,Bailey MJ,Walters EH. Year: 2002β-Actin and GAPDH housekeeping gene expression in asthmatic airways is variable and not suitable for normalising mRNA levels.Thorax5776577012200519
43. He XJ,Mu RL,Cao WH,Zhang ZG,Zhang JS,et al. Year: 2005AtNAC2, a transcription factor downstream of ethylene and auxin signaling pathways, is involved in salt stress response and lateral root development.Plant J44690391616359384
44. Kim JH,Woo HR,Kim J,Lim PO,Lee IC,et al. Year: 2009Trifurcate feed-forward regulation of age-dependent cell death involving miR164 in Arabidopsis.Science32359171053105719229035
45. Balazadeh S,Siddiqui H,Allu AD,Matallana-Ramirez LP,Caldana C,et al. Year: 2010A gene regulatory network controlled by NAC transcription factor ANAC092/AtNAC2/ORE1 during salt-promoted senescence.Plant J62225026420113437
46. Breeze E,Harrison E,McHattie S,Hughes L,Hickman R,et al. Year: 2011High-resolution temporal profiling of transcripts during Arabidopsis leaf senescence reveals a distinct chronology of processes and regulation.Plant Cell23387389421447789
47. McCurley AT,Callard GV. Year: 2008Characterization of housekeeping genes in zebrafish: male-female differences and effects of tissue type, developmental stage and chemical treatment.BMC Mol Biol910219014500
48. Bustin SA. Year: 2000Absolute quantification of mRNA using real-time reverse transcription polymerase chain reaction assays.J Mol Endocrinol25216919311013345
49. Scharlaken B,de Graaf DC,Goossens K,Brunain M,Peelman LJ,et al. Year: 2008Reference gene selection for insect expression studies using quantitative real-time PCR: The head of the honeybee, Apis mellifera, after a bacterial challenge.J Insect Sci833110
50. Zhao WJ,Li Y,Gao PF,Sun ZH,Sun TS,et al. Year: 2011Validation of reference genes for real-time quantitative PCR studies in gene expression levels of Lactobacillus casei Zhang.J Ind Microbiol Biotechnol3891279128621104423
51. Pfaffl MW,Tichopad A,Prgomet C,Neuvians TP. Year: 2004Determination of stable housekeeping genes, differentially regulated target genes and sample integrity: BestKeeper-Excel-based tool using pair-wise correlations.Biotechnol Lett26650951515127793
52. Migocka M,Papierniak A. Year: 2010Identification of suitable reference genes for studying gene expression in cucumber plants subjected to abiotic stress and growth regulators.Mol Breeding283343357
53. Xu M,Zhang B,Su XH,Zhang SG,Huang MR. Year: 2011Reference gene selection for quantitative real-time polymerase chain reaction in Populus.Anal Biochem408233733920816740
54. Marten M,Stefanie S,Stefan L. Year: 2010Selection of reliable reference genes during THP-1 monocyte differentiation into macrophages.BMC Mol Biol119021122122
55. Coker JS,Davis E. Year: 2003Selection of candidate housekeeping controls in tomato plants using EST data.Biotechniques35474074814579739
56. Reid KE,Olsson N,Schlosser J,Peng F,Lund ST. Year: 2006An optimized grapevine RNA isolation procedure and statistical determination of reference genes for real-time RT-PCR during berry development.BMC Plant Biol62717105665
57. Yan HZ,Liou RF. Year: 2006Selection of internal control genes for real-time quantitative RT-PCR assays in the oomycete plant pathogen Phytophthora parasitica.Fungal Genet Biol43643043816531084
58. geNorm software website.9 Available: [Http://]. Accessed 2011 Sept.
59. NormFinder software website.9 Available: []. Accessed 2011 Sept.
60. BestKeeper software website.9 Available: [Http://]. Accessed 2011 Sept.


[Figure ID: pone-0033278-g001]
doi: 10.1371/journal.pone.0033278.g001.
Figure 1 

Expression levels of candidate reference genes in different plant samples.

[Figure ID: pone-0033278-g002]
doi: 10.1371/journal.pone.0033278.g002.
Figure 2  Gene expression stability and ranking of 10 reference genes as calculated by geNorm.

[Figure ID: pone-0033278-g003]
doi: 10.1371/journal.pone.0033278.g003.
Figure 3  Determination of the optimal number of reference genes for normalization by pairwise variation (V) using geNorm.

[Figure ID: pone-0033278-g004]
doi: 10.1371/journal.pone.0033278.g004.
Figure 4  Expression profiles of NAC in different-aged tissues and in response to stresses in Platycladus orientalis (as determined by qRT-PCR with UBC and aTUB in combination as reference genes).

[TableWrap ID: pone-0033278-t001] doi: 10.1371/journal.pone.0033278.t001.
Table 1  Descriptions of candidate genes from Platycladus orientalis for qRT-PCR.
Gene symbol Gene name Arabidopsis homolog locus Primer sequence (5'–3') Size (bp) PCR efficiency
GAPDH Glyceraldehyde-3-Phosphate dehydrogenase AT1G79530 [gene: GAGATTCCATGGGGTGATTTTG] 150 99.2%
ACT7 Actin 7 AT5G09810 [gene: GGAGGTTCCACCATGTTTCC] 152 101.1%
aTUB Alpha-tubulin AT5G19770 [gene: CCACATCTCTTAGGTTTGATGGAG] 205 103.6%
bTUB Beta tubulin AT1G20010 [gene: TCCCATCGCCTAAGGTATCG] 197 101.6%
UBC Ubiquitin-conjugating enzyme E2 AT3G57870 [gene: TCTTGCTGAAGAGCGGAAGG] 108 99.2%
UBQ Ubiquitin 10 AT5G20620 [gene: AGGGGAGGCATGCAGATTTT] 120 101.9%
EF1a Elongation factor 1-alpha AT1G07940 [gene: TCTGCCCCTTCAGGATGTTT] 144 98%
DNAJ DanJ-like protein AT3G44110 [gene: TTCGTGAAGGCACACAGCAT] 133 98.9%
SAND Sand family protein AT2G28390 [gene: TGGTGGTCTGCATGTGGAAG] 134 95.5%
CAC Clathrin adaptor complexes AT1G56590 [gene: ACTGGGGAAGTAATGCTTGAGA] 100 104.2%
NAC NAC domain protein AT5G39610 [gene: AGAGGAGAAGGAAGCGAAGG] 169 104.3%

Note: All reference gene sequences from transcriptome data of Platycladus orientalis were searched with BLAST using sequences of Arabidopsis thaliana in GenBank. Sequences of candidate housekeeping genes and NAC domain protein gene are provided in the Supporting Information.

[TableWrap ID: pone-0033278-t002] doi: 10.1371/journal.pone.0033278.t002.
Table 2  Ranking of candidate reference genes in order of their expression stability as calculated by BestKeeper.
Rank All(A) Age(B) Tissue(C) Cold(D) Heat(E) NaCl(F) PEG(G) ABA(H)
CV±SD 1.58±0.38 1.50±0.35 1.50±0.36 0.45±0.10 0.89±0.22 0.43±0.11 0.93±0.26 0.53±0.14
CV±SD 2.02±0.55 2.10±0.51 1.59±0.43 1.45±0.35 1.02±0.21 0.43±0.29 1.17±0.51 0.79±0.10
CV±SD 2.22±0.62 2.15±0.59 1.60±0.41 1.64±0.58 1.21±0.26 0.44±0.11 1.19±0.32 0.82±0.20
CV±SD 2.42±0.57 2.43±0.69 1.73±0.40 1.97±0.56 1.45±0.39 1.01±0.28 1.43±0.37 1.23±0.29
CV±SD 2.56±0.68 2.65±0.70 1.75±0.47 1.97±0.49 1.59±0.40 1.28±0.35 1.54±0.56 1.25±0.35
CV±SD 2.57±0.94 2.76±0.73 1.87±0.69 2.13±0.57 1.86±0.50 1.81±0.49 1.82±0.42 1.28±0.34
CV±SD 2.65±0.74 2.97±0.73 1.90±0.51 2.2±0.62 2.58±0.95 1.99±0.44 2.20±0.60 1.76±0.38
CV±SD 2.97±0.63 3.22±1.16 2.22±0.58 2.52±0.64 3.47±0.96 2.21±0.80 2.27±0.61 1.86±0.51
CV±SD 3.09±0.82 3.40±0.95 2.65±0.55 2.85±0.79 3.55±1.00 2.48±0.66 3.3±0.69 1.99±0.49
CV±SD 3.18±0.78 3.97±0.85 2.92±0.70 3.21±0.89 3.78±1.02 3.73±1.04 3.48±0.83 2.74±1.01

Note: Expression stability and ranking of 10 reference genes as calculated by Bestkeeper in all samples (A), different ages (B), different tissue types (C), cold-treated (D), heat-treated (E), NaCl-treated (F), PEG-treated (G), ABA-treated (H). Descriptive statistics of 10 candidate genes based on their coefficient of variance (CV) and standard deviation (SD) of Ct values were determined using the whole data set, and all Ct values were analyzed as a total data set. Reference genes are identified as the most stable genes (those with the lowest coefficient of variance and standard deviation; CV±SD).

[TableWrap ID: pone-0033278-t003] doi: 10.1371/journal.pone.0033278.t003.
Table 3  Ranking of candidate reference genes in order of their expression stability as calculated by NormFinder.
Rank All(A) Age(B) Tissues(C) Cold(D) Heat(E) NaCl(F) PEG(G) ABA(H)
M value 0.263 0.099 0.093 0.139 0.058 0.067 0.094 0.082
M value 0.362 0.270 0.101 0.227 0.089 0.067 0.121 0.232
M value 0.533 0.351 0.273 0.292 0.180 0.251 0.155 0.245
M value 0.553 0.494 0.279 0.388 0.226 0.259 0.228 0.262
M value 0.591 0.594 0.312 0.402 0.495 0.352 0.237 0.264
M value 0.603 0.663 0.395 0.415 0.509 0.409 0.361 0.274
M value 0.608 0.678 0.450 0.467 0.696 0.522 0.479 0.366
M value 0.715 0.680 0.552 0.487 0.781 0.635 0.523 0.391
M value 0.852 0.810 0.572 0.665 0.795 0.757 0.547 0.519
M value 1.126 0.937 0.673 1.199 0.923 0.871 0.662 0.689

Note: Expression stability and ranking of 10 reference genes as calculated by NormFinder in all samples (A), different ages (B), different tissue types (C), cold-treated (D), heat-treated (E), NaCl-treated (F), PEG-treated (G), ABA-treated (H). Lower average expression stability (M value) indicates more stable expression.

Article Categories:
  • Research Article
Article Categories:
  • Biology
    • Computational Biology
      • Genomics
        • Genome Analysis Tools
      • Molecular Genetics
    • Genetics
    • Genomics
      • Genome Analysis Tools
    • Molecular Cell Biology
    • Plant Science
      • Plants

Previous Document:  Evaluation of genetic mutations associated with Mycobacterium tuberculosis resistance to amikacin, k...
Next Document:  Genetic and non-genetic influences during pregnancy on infant global and site specific DNA methylati...