Identification and validation of Aeluropus littoralis reference genes for Quantitative Real-Time PCR Normalization
- Seyyed Hamidreza Hashemi1,
- Ghorbanali Nematzadeh†1,
- Gholamreza Ahmadian†2,
- Ahad Yamchi3 and
- Markus Kuhlmann4Email author
© The Author(s) 2016
Received: 19 November 2015
Accepted: 28 June 2016
Published: 19 July 2016
The use of stably expressed genes as normalizers has crucial role in accurate and reliable expression analysis estimated by quantitative real-time polymerase chain reaction (qPCR). Recent studies have shown that, the expression levels of common housekeeping genes are varying in different tissues and experimental conditions. The genomic DNA contamination in RNA samples is another important factor that also influence the interpretation of the data obtained from qPCR. It is estimated that the gDNA contamination in gene expression analysis lead to an overestimation of the RNA transcript level. The aim of this study was to validate the most stably expressed reference genes in two different tissues of Aeluropus littoralis—halophyte grass at salt stress and recovery condition. Also, a qPCR-based approach for monitoring contamination with gDNA was conducted.
Ten candidate reference genes participating in different biological processes were analyzed in four groups of samples including root and leaf tissues, salt stress and recovery condition. To determine the most stably expressed reference genes, three statistical methods (geNorm, NormFinder and BestKeeper) were applied. According to results obtained, ten candidate reference genes were ranked based on the stability of their expression. Here, our results show that a set of four housekeeping genes (HKGs) e.g. RPS3, EF1A, GTF and RPS12 could be used as general reference genes for the all selected conditions and tissues. Also, four set of reference genes were proposed for each tissue and condition including: RPS3, EF1A and UBQ for salt stress and root samples; RPS3, EF1A, UBQ as well as GAPDH for recovery condition; U2SURP and GTF for leaf samples. Additionally, for assessing DNA contamination in RNA samples, a set of unique primers were designed based on the conserved region of ribosomal DNA (rDNA). The universality, specificity and sensitivity of these primer pairs were also evaluated in Poaceae.
Overall, the sets of reference genes proposed in this study are ideal normalizers for qPCR analysis in A. littoralis transcriptome. The novel reference gene e.g. RPS3 that applied this study had higher expression stability than commonly used housekeeping genes. The application of rDNA-based primers in qPCR analysis was addressed.
KeywordsReference genes qPCR Aeluropus littoralis Salt stress Recovery condition Halophyte DNA contamination rDNA rRNA
The use of wild plant species or their halophytic relatives has been considered in plant breeding programs for developing salt and drought tolerant crops . In this view, several researchers focused on the Aeluropus littoralis as a halophyte model for identification and isolation of the novel adaptation genes. Aeluropus littoralis is a perennial monocot grass with the small haploid genome of 349 Mb (2n = 2X = 14) using the C4 mechanism for carbon fixation  that grows in dry salty areas or marshes . Aeluropus littoralis can survive where the water salinity is periodically high  and tolerate up to 1100 mM sodium chloride . Therefore, A. littoralis can serve as valuable genetic resource for understanding the molecular mechanisms of stress-responses in monocots, and can potentially be used for improving tolerance to abiotic stresses in economically important crops . Several morphological, anatomical, ecological, and physiological traits of A. littoralis have been investigated so far [5, 7, 8]. Meanwhile, a number of ESTs (expressed sequence tag), genes and promoters induced by the salt and drought stresses were isolated, sequenced and annotated at a molecular level [1, 3].
With the availability of genome and transcriptome sequence information in most plant species, the identification and characterization of stress-induced genes is more feasible now. Network analysis of transcriptome data might lead to better understanding of complex traits such as survival, growth and differentiation [9, 10]. Quantitative real-time polymerase chain reaction (qPCR) analysis is one of the most currently used approaches for measuring gene expression level . The sensitivity, specificity and simplicity of this technique is incomparable with other methods such as Northern and in situ hybridization, RNase protection assays and semi-quantitative reverse transcription-polymerase chain reaction (RT-PCR) . For accurate and reliable quantification of the gene expression, some important issues need to be considered when a qPCR approach is used. This includes variations in the amount of starting material, RNA quality and quantity, efficiency of cDNA synthesis, and PCR efficiency among different cells and tissues [13, 14]. Among various procedures that have been applied to minimize variability and maximize reproducibility, the internal control genes, often referred to as reference genes or housekeeping genes (HKGs) are most frequently used for normalization . The expression level of an ideal reference gene should be constant across all respected cells, tissues and experimental conditions beside of its least expression variance between the group of samples analyzed . Recent findings show that there is no universal reference gene (with high expression stability) for all biological questions addressed . Based on the above-mentioned facts, valid reference genes for every organism should be verified for each tissue, condition and developmental stage. Fortunately, in most studies, only a limited number of tissues and treatments are examined, so probably one or more genes are stably expressed under that condition .
Until recently, the HKGs 18S rRNA, UBIQUITIN (UBQ), ACTIN (ACT), B-TUBULIN (TUB), and glyceraldehyde-3-phosphate dehydrogenase (GAPDH), all involved in the basic cellular processes, were used as internal controls without proper validation of their presumed stability of expression . However, the stability of common HKGs (e.g. ACT, GAPDH and 18S rRNA) have been reported to vary considerably in given tissues or experimental conditions and therefore they are not generally suitable for all gene expression studies . Generally, selecting an appropriate reference gene is done in two steps: (1) identification of the candidate reference genes, and (2) determination of their expression stability on representative samples. Meanwhile, different strategies such as NormFinder, geNorm and BestKeeper were developed to select the best HKGs based on model-based variance estimation approach , geometric averaging of multiple internal control genes  and pair-wise correlations , respectively.
In the most RNA preparation methods, a low level of genomic DNA (gDNA) usually remain in RNA samples that cause to non-specific amplification in qPCR  and consequently cause an overestimation of the transcription level. Caldana et al.  proposed the use of the intergenic regions and intron of the genes to monitoring of the gDNA contamination. Because of different accessibility of chromosomal sites to DNase I, it is emphasized to use different primer pairs that are spread on several chromosomes. In this study, the universal primers based on the ribosomal DNA (rDNA) were designed for the assessment of DNA contamination. In order to identify the most stably expressed reference genes, the expression of ten candidate reference genes participating in different biological processes was analyzed in two different tissues of A. littoralis at salt stress and recovery condition.
DNA contamination assay
Primer sequences, amplicon characteristics and expected amplicon size in DNA contamination assay
Amplicon (primer pair) name
Expected amplicon size (bp)
Amplify partial 17–18S rRNA, complete ITS1 and partial 5.8 S rRNA
Amplify partial 5.8 S rRNA complete ITS2 and partial 25–28S rRNA,
Contains internal transcribed spacer 1
The two regions of ITS1 and ITS2 are not part of the ribosome genes and are spliced and removed in mature rRNA. In our hypothesis, no amplification must be observed in DNase-treated RNA samples when two regions of SSU-5.8S and 5.8S-LSU are amplified. To monitor residual gDNA contamination in RNA sample, the total RNA samples were examined by these primers in qPCR. In this view, observation of any band on the agarose gel or melting curve peak in qPCR analysis could be considered as gDNA contamination. In this study, all RNA samples were tested by this procedure for DNA contamination assay.
Selection of candidate reference genes
Candidate reference genes were used for the assessment of the expression stability in qPCR analysis
Structural constituent of cytoskeleton
1 × 10−66
U2snRNP-associated SURP motif-containing protein-like
RNA binding, required for spliceosome assembly to participate in splicing
3 × 10−55
Elongation factor-1 alpha
Translation elongation factor activity
1 × 10−04
Biologically significant role in protein delivery to proteasomes and recruitment of proteasomes to transcription sites.
9 × 10−19
Microtubule-based process and structural constituent of cytoskeleton
5 × 10−20
Eukaryotic translation initiation factor 3 subunit B-like
Translation initiation factor activity
1 × 10−90
General transcription factor 3C polypeptide
Involved in RNA polymerase III-mediated transcription
8 × 10−39
40S ribosomal protein S12-like
Structural constituent of ribosome involved in RNA methylation, photorespiration, translation
1 × 10−91
Dehydrogenase, Oxidoreductase in glycolysis and gluconeogenesis
40S ribosomal protein S3-like
Structural constituent of ribosome involved in RNA methylation, photorespiration, translation
2 × 10−59
Primer and reaction validation
Cq values analysis
In this study, the Cq variation of candidate reference genes in the leaf and recovered plants were low as expected. It should be noted that during recovery treatment the plant returns to normal condition (partially or completely) and enable damage repair upon stress relief. GTF (0.39) and U2SURP (0.48) had lowest SD value of expression level in the leaf while UBQ (0.51) and eIF3 (0.53) had lowest SD value in recovered plants. As represented in Fig. 3, the root and salt stressed samples had a high Cq variation and SD value. The expression level of UBQ, RPS3 and EF1A in the roots and the expression level of ACT11, UBQ, TUB and RPS3 in the salt stressed plants had a SD value lower than one. The high Cq variation in the root samples may be related to direct contact of root cells with salt stress. As the roots are the first line of defense when the cells encounter salinity stress, the diversity in their gene expression is expected.
Expression stability analysis
In order to select the best candidate reference gene, the qPCR results were analyzed by BestKeeper, geNorm and NormFinder programs. In analysis of expression stability of HKGs by BestKeeper program, the descriptive statistics of each HKG were computed. The descriptive statistics of the derived quantification cycle (Cq) of each HKG are given in Additional file 6: Table S2. BestKeeper determined the optimal HKG by applying the pair-wise correlation analysis of all pairs of candidate genes, and select the best ones based on variables of SD, percent covariance and power of the reference gene [17, 18]. The integrity of samples (uniformity in quantity and quality of starting mRNA and cDNA preparations) as well as their expression stability was checked by an intrinsic variance (InVar) factor (Additional file 7: Table S3). Based on InVar factor, the most of samples had low Cq variation with the few fold changes in expression level. Two of the root investigated samples had higher variation in Cq value and were excluded from BestKeeper analysis (removal often recommended over the threefold changes). Our analysis showed that the most Cq SD values in all remained-samples (N = 14) varied from 0.47 to 0.81 cycle. Analysis of the pair HKG relations of all possible combination across all remained-samples (N = 14) showed a strong correlation (0.324 ≤ r ≤ 0.906). Although the high Cq variation (SD value above one) were observed in expression level of U2SURP, GAPDH, eIF3 and GTF, but after removing two root samples, the Cq variation of all candidate reference genes has been become lower than one. The rankings of candidate reference genes derived from BestKeeper expression stability analysis are given in Additional file 9: Table S5.
The RPS3, UBQ, RPS12, eIF3 and EF1A had high degree of expression stability in root samples while in leaf samples ranking of top five HKGs were as follows: GTF > U2SURP > UBQ > eIF3 > EF1A. When all samples were analyzed together in BestKeeper, the top five HKGs ranking were as follow: UBQ > GTF > eIF3 > RPS3 > RPS12/EF1A. The five candidate genes of ACT11 > UBQ > TUB > RPS3 > EF1A had highest expression stability across salt-stressed plants, while the order of top most stably expressed gene in recovery condition was as follows: UBQ > eIF3 > EF1A > GAPDH > GTF.
In contrast to geNorm program, the NormFinder, by calculating expression stability of each single gene independently, shows less sensitivity to co-regulation of the reference genes [13, 17]. In NormFinder, those genes with the lowest stability value were supposed to have the highest expression stability. The results of NormFinder based on different tissues and conditions are given in Additional file 8: Table S4B. EF1A and eIF3 with the stability value of 0.048 and 0.023 were selected as best reference genes in root and leaf, respectively. When all samples (N = 16) were considered together, EF1A with stability value of 0.038 were selected as the most stably expressed gene. Stability value for the best combination of two genes was 0.028 that belong to the GTF and EF1A. The top candidate genes in salt-stressed plants were observed as follows: RPS3 > RPS12 > GTF > GAPDH > EF1A. The order of most stably expressed gene in recovered plants was as follows: EF1A > GTF > GAPDH > RPS3 > ACT11 (Additional file 9: Table S5).
Appropriate reference genes
A set of optimal reference genes is recommended for each sample group
Optimal reference genes
RPS3, EF1A and UBQ
RPS3, EF1A,UBQ and GAPDH
RPS3, EF1A and UBQ
U2SURP and GTF
All selected conditions and tissues
RPS3, EF1A, GTF and RPS12
The qPCR approach is one of the most widely used techniques for gene expression analysis. The advantage of this method is the sensitivity, specificity, accuracy and reproducibility. However, to get reliable results especially when different tissues and treatments are examined, the accurate normalization of gene expression against a reference genes is a critical prerequisite . On the other hand, the high specificity of primers is required in qPCR to prevent any non-specific amplification that arises from primer dimer or gDNA. Quality control (QC) of RNA preparation particularly in terms of gDNA contamination is important for the correct interpretation of the data obtained from qPCR. It is expected that contamination of RNA samples with gDNA will lead to an overestimation of the RNA transcript level . No reverse-transcriptase (NRT) control typically is included in qPCR experiments for evaluation of the amount of gDNA contamination that present in RNA samples. In this study, we proposed a qPCR-based approach for monitoring gDNA contamination that eliminates the need for NRT in each reaction. The two unique universal primer pairs based on rDNA genes including SSU-5.8S and 5.8-LSU were proposed for tracking of DNA contamination in Poaceae. The eukaryotic genomes contain up to thousands of rDNA genes that can be arranged in tandem repeating units (placed side by side) . The main role of these genes is the generation of ribosomal subunits required for protein synthesis. Transcription of these units is initialized by RNA polymerase I (RNA pol I) in a polycistronic manner. The rDNA genes are initially transcribed to a pre-rRNA, which is then processed by removing of the two internal transcribed spacer regions (including ITS1 and ITS2) to produce the three mature rRNAs including 17–18S rRNA, 5.8S and 25–28S rRNA . Owing to the nature of multigene family, highly conserved sequences, high copy numbers in the genome and potential presence at more than one chromosomal location , the rDNA genes compared to intronic sequences of protein coding genes represent good DNA regions for reliable tracking of DNA contamination in the most flowering plants. However, the use of ribosomal RNA as proper reference genes in relative gene expression analysis is problematic due to several disadvantages, such as different biogenesis, imbalance between rRNA and mRNA fractions, use of hexamer primers and overestimation in mRNA copy numbers (up to 19-fold) [19, 27]. But some aforesaid constrains could be considered an important advantage for DNA contamination monitoring. For instance, high frequency of these multigene rDNAs significantly increased the capability of DNA contamination detection and then improved the sensitivity. These unique universal primer pairs probably amplify the same regions on the different chromosomes and thus could monitor the presence of the residual gDNA contamination based on various chromosomal regions. It should be noted that the universality and specificity of these primer pairs in different plant species were also addressed in this study (Additional files 1: Figure S1 and S2). Because of the high rate of mutation in these ITSs, they are widely used for studying phylogenetic relationship at the inter- and intra-specific levels in plants . In gene expression analysis, the species-specific primers based on these regions not only can be used for gDNA contamination assay, but also could be especially applied in absolute quantification analysis at plant–microbe interaction studies.
It is likely that the transcriptome of each cell type is very different from that of other cell types, tissues and organs  or experimental conditions. Results of many reports suggest that the HKGs are regulated differently in different plant species . Meanwhile, the best reference genes in one organism cannot be generalized to another organism at a given experiment [16, 19, 24]. It should be noted that the use of single HKGs, typically ACTB or GAPDH is a common strategy for normalization in most the qPCR relative analysis. However, it has been estimated that the magnitude of change of these genes varies up to 10-fold across different samples . Because of the expression variability of HKGs in different tissues and conditions, it is proposed that more than one reference gene should be used for qPCR normalization [10, 13, 17]. To identify the most stably expressed reference genes in A. littoralis we initially selected ten candidate references genes, some of them (such as EF1A, GAPDH and UBQ, eIF and ACTB) are commonly used as HKGs in plants. The expression stability of these genes was analyzed across the root and leaf tissue and salt-stress and recovered plants. Among these most common HKGs reported in many studies, EF1A, GAPDH and UBQ displayed the highest expression stability in this study. In contrast, the expression of eIF3 and ACT11 genes among candidate reference genes showed high expression variation in some examined tissues and conditions. Because of the wide HKGs expression variation across samples, we recommend using multiple reference genes for each condition and tissue.
By all applied methods (BestKeeper, geNorm and NormFinder), a set of three reference genes including RPS3, EF1A and UBQ had highest rank in comparisons, and therefore their Cq geometric mean could be used as normalizer at salt-stressed, root and recovered samples in qPCR expression analysis. Here we propose novel reference gene e.g. RPS3 with a much lower level of variance in expression across tissues and experimental conditions than commonly used housekeeping genes. RPS3 encodes ribosomal proteins involved in protein biosynthesis. Also, the U2SURP and GTF had highest expression stability in leaf. U2SURP as a functional spliceosome-associated protein and GTF as tightly associated component of the DNA-binding TFIIIC2 subcomplex involve in RNA processing, and RNA polymerase III-mediated transcription, respectively.
In conclusion, the use of two unique rDNA-based primer pairs (e.g. SSU-5.8S and 5.8S-LSU) was addressed for tracking residual DNA in RNA samples. These primers act as intron-flanking primers, and by their multiple targeting site on the different chromosomes are more sensitive in comparison to other common intron-based primers. Also by applying these primers, the need for NRT control could be eliminated in each qPCR experiment. Since the genomic annotation of the most wild plants is not available by now, the qPCR primer design based on exon–exon expanding is not possible yet. But, according to specificity, universality and versatility of these proposed primer pairs, the gDNA contamination detection of the Poaceae and the most flowering plant species is achievable. To our knowledge, this is the first report that explains application of these unique universal primer pairs in gene expression and qPCR analysis. Also, this is the first study to validate a set of candidate reference genes for normalization of expression levels in A. littoralis as a representative of halophytic Poaceae. We provided five set of reference genes as well as their optimal number by using the three programs BestKeeper, geNorm and NormFinder for each tissue and condition, i.e., root, leaf, salt-stressed and recovered plants. Comparing the results of three algorithms showed that the two genes of EF1A and RPS3 had high rank in all given experiments except leaf, and were proposed as common reference gene in our study. Here we showed novel candidate reference genes e.g. RPS3 with much higher expression stability among different tissues and experimental conditions than commonly used housekeeping genes. Our identified candidate reference genes can be used in future qPCR experiments on Aeluropus littoralis studies.
Aeluropus littoralis seeds were collected from Isfahan province (Roddasht region) in Iran and the sterilized seeds plated on full strength MS medium with vitamins, 3 % sucrose and 0.7 % agar (pH 5.8). Two weeks after germination, the seedlings were transferred to hydroponic culture containing Hoagland’s solution. The 30 day-old seedlings were stressed in 600 mM of sodium chloride at six passages (received 100 mM sodium chloride per 48 h up to 600 mM). Leaves and roots were sampled in parallel. At the end of the sixth passage, samples were collected at 6, 24, 48 h and 1 week time-course. In order to plant recovery, the remained plants were transferred to a sodium chloride-free Hoagland’s solution, and then were collected after 6, 24 h and 1 week. All samples as well as control were immediately frozen in liquid nitrogen and stored at −70 °C for RNA extraction.
Total RNA and DNA extraction
Total RNA was extracted using TRIzol reagent (Invitrogen Life Technologies, Karlsruhe, Germany) according to the manufacturer’s instructions. For the test of universality of rDNA-based primers, DNA of several plant species including wheat (Triticum aestivum), barley (Hordeum vulgare), rice (Oryza sativa), alfalfa (Medicago sativa), Aeluropus littoralis, cucumber (Cucumis sativus), tobacco (Nicotiana tabacum), berseem clover (Trifolium alexandrinum), faba bean (Vicia faba) and Arabidopsis thaliana as well as the endophytic fungus Piriformospora indica was extracted according to Dellaporta procedures . The quality and quantity of the extracted nucleic acid were checked by measuring absorbance at 260/280 nm using a NanoDrop spectrophotometer (Biochrom WPA Biowave II, UK). Further, the purity and integrity of RNA and DNA were tested by running on 1.2 and 0.7 % agarose gel electrophoresis, respectively.
DNA contamination assay
Residual gDNA contaminating RNA extracts was removed by DNase treatment (DNase I RNase-free, Thermo Scientific, USA). The qPCR with three rDNA-based primers was applied for DNA contamination assay by using RNA as template. The forward and reverse primer on the 5.8 S rRNA target were designed based on a conserved motif  in the 5.8S ribosomal RNA gene in flowering plants. The 17–18S forward primer and 25–28S reverse primer were designed based on conserved regions in Poaceae. The ITS primer sequences were designed based on conserved regions in ITS1 of Aeluropus (NCBI taxid number: 110873). BLAST searches were carried out using public database at NCBI. The conserved regions from various species were aligned by ClustalW . rDNA-based primers were designed after taxa specific/cross-species analysis with AlleleID v7.0 software (Premier Biosoft International, Palo Alto, CA). Primers were selected according their specificity and universality by BLASTN search. Motif logo of 5.8S primer was generated by WebLogo (http://weblogo.berkeley.edu). The primer sequences are presented in Table 1.
Bioinformatic analysis, reference gene primer design and validation
cDNA synthesis and qPCR analysis
The cDNA was synthesized using the QuantiTect reverse transcription kit (Qiagen, Germany) according to the manufacturer’s instructions. The final cDNA reactions were diluted 1:10, and stored at −20 °C. Targets were amplified by the Maxima SYBR Green/ROX qPCR Master Mix (Thermo Scientific, USA) with two-step cycling in CFX96 real-time PCR instrument (Bio-Rad, USA) according to the company’s suggestions. After amplification, all PCR reactions were subjected to a thermal melt with continuous fluorescence measurement from 55 to 95 °C for dissociation curve analysis. Curves were analyzed by CFX Manager (Bio-Rad) with single threshold cycle and subtracted curve fit method. At least one non-template control (NTC) was used for each primer pair master mix. The amplification efficiency for each reaction was calculated by LinRegPCR . For assessment of the expression stability of the candidate reference genes across samples of the root and leaf, salt stress and recovery the quantification cycle (Cq) values were analyzed using geNorm , NormFinder  and BestKeeper .
quantitative real-time polymerase chain reaction
- HKGs :
internal transcribed spacer
- SSU rRNA:
small subunit rRNA
large subunit rRNA
no reverse-transcriptase control
the National Centre for Biotechnology Information
SHH conducted field experiments, lab work and performed data analyses. AH helped the data analysis. GN and GA conceived the study and designed the project. SHH and MK wrote the manuscript. All authors read and approved the final manuscript.
This research was supported by the Genetics and Agricultural Biotechnology Institute of Tabarestan (GABIT). Independent Research Group Abiotic Stress Genomics is funded by the Interdisciplinary Center for Crop Plant Research (IZN). The publication of this article was funded by the Open Access fund of the Leibniz Association.
The authors declare that they have no competing interests.
Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
- Ben-Saad R, Ben-Ramdhan W, Zouari N, Azaza J, Mieulet D, Guiderdoni E, et al. Marker-free transgenic durum wheat cv. Karim expressing the AlSAP gene exhibits a high level of tolerance to salinity and dehydration stresses. Mol Breeding. 2012;30:521–33.View ArticleGoogle Scholar
- Wang RZ. Plant functional types and their ecological responses to salinization in saline grasslands, Northeastern China. Photosynthetica. 2004;42:511–9.View ArticleGoogle Scholar
- Saad RB, Romdhan WB, Zouari N, Azaza J, Mieulet D, Verdeil J-L, et al. Promoter of the AlSAP gene from the halophyte grass Aeluropus littoralis directs developmental-regulated, stress-inducible, and organ-specific gene expression in transgenic tobacco. Transgenic Res. 2011;20:1003–18.View ArticlePubMedGoogle Scholar
- Mesléard F, Ham LT, Boy V, van Wijck C, Grillas P. Competition between an introduced and an indigenous species: the case of Paspalum paspalodes (Michx) Schribner and Aeluropus littoralis (Gouan) in the Camargue (southern France). Oecologia. 1993;94:204–9.View ArticleGoogle Scholar
- Barhoumi Z, Djebali W, Chaïbi W, Abdelly C, Smaoui A. Salt impact on photosynthesis and leaf ultrastructure of Aeluropus littoralis. J Plant Res. 2007;120:529–37.View ArticlePubMedGoogle Scholar
- Ben Saad R, Zouari N, Ramdhan WB, Azaza J, Meynard D, Guiderdoni E, et al. Improved drought and salt stress tolerance in transgenic tobacco overexpressing a novel A20/AN1 zinc-finger “AlSAP” gene isolated from the halophyte grass Aeluropus littoralis. Plant Mol Biol. 2010;72:171–90.View ArticlePubMedGoogle Scholar
- Rezvani M, Zaefarian F, Miransari M, Nematzadeh GA. Uptake and translocation of cadmium and nutrients by Aeluropus littoralis. Arch Agronomy Soil Sci. 2012;58:1413–25.View ArticleGoogle Scholar
- Barhoumi Z, Djebali W, Abdelly C, Chaïbi W, Smaoui A. Ultrastructure of Aeluropus littoralis leaf salt glands under NaCl stress. Protoplasma. 2008;233:195–202.View ArticlePubMedGoogle Scholar
- Ståhlberg A, Zoric N, Åman P, Kubista M. Quantitative real-time PCR for cancer detection: the lymphoma case. Expert Rev Mol Diagn. 2005;5:221–30.View ArticlePubMedGoogle Scholar
- Vandesompele J, De Preter K, Pattyn F, Poppe B, Van Roy N, De Paepe A, et al. Accurate normalization of real-time quantitative RT-PCR data by geometric averaging of multiple internal control genes. Genome Biol. 2002;3:1.View ArticleGoogle Scholar
- Gutierrez L, Mauriat M, Pelloux J, Bellini C, Van Wuytswinkel O. Towards a systematic validation of references in real-time RT-PCR. Plant Cell. 2008;20:1734–5.View ArticlePubMedPubMed CentralGoogle Scholar
- Bustin SA. Absolute quantification of mRNA using real-time reverse transcription polymerase chain reaction assays. J Mol Endocrinol. 2000;25:169–93.View ArticlePubMedGoogle Scholar
- Andersen CL, Jensen JL, Ørntoft TF. Normalization of real-time quantitative reverse transcription-PCR data: a model-based variance estimation approach to identify genes suited for normalization, applied to bladder and colon cancer data sets. Cancer Res. 2004;64:5245–50.View ArticlePubMedGoogle Scholar
- Bustin SA, Benes V, Garson JA, Hellemans J, Huggett J, Kubista M, et al. The MIQE guidelines: minimum information for publication of quantitative real-time PCR experiments. Clin Chem. 2009;55:611–22.View ArticlePubMedGoogle Scholar
- Nolan T, Hands RE, Bustin SA. Quantification of mRNA using real-time RT-PCR. Nat Protoc. 2006;1:1559–82.View ArticlePubMedGoogle Scholar
- Jain M, Nijhawan A, Tyagi AK, Khurana JP. Validation of housekeeping genes as internal control for studying gene expression in rice by quantitative real-time PCR. Biochem Biophys Res Commun. 2006;345:646–51.View ArticlePubMedGoogle Scholar
- Galiveti CR, Rozhdestvensky TS, Brosius J, Lehrach H, Konthur Z. Application of housekeeping npcRNAs for quantitative expression analysis of human transcriptome by real-time PCR. RNA. 2010;16:450–61.View ArticlePubMedPubMed CentralGoogle Scholar
- Pfaffl MW, Tichopad A, Prgomet C, Neuvians TP. Determination of stable housekeeping genes, differentially regulated target genes and sample integrity: bestKeeper–excel-based tool using pair-wise correlations. Biotechnol Lett. 2004;26:509–15.View ArticlePubMedGoogle Scholar
- Caldana C, Scheible W-R, Mueller-Roeber B, Ruzicic S. A quantitative RT-PCR platform for high-throughput expression profiling of 2500 rice transcription factors. Plant Methods. 2007;3:7.View ArticlePubMedPubMed CentralGoogle Scholar
- Boisvert F-M, van Koningsbruggen S, Navascués J, Lamond AI. The multifunctional nucleolus. Nat Rev Mol Cell Biol. 2007;8:574–85.View ArticlePubMedGoogle Scholar
- Ramakers C, Ruijter JM, Deprez RHL, Moorman AF. Assumption-free analysis of quantitative real-time polymerase chain reaction (PCR) data. Neurosci Lett. 2003;339:62–6.View ArticlePubMedGoogle Scholar
- Ruijter J, Ramakers C, Hoogaars W, Karlen Y, Bakker O, Van den Hoff M, et al. Amplification efficiency: linking baseline and bias in the analysis of quantitative PCR data. Nucleic Acids Res. 2009;37:e45.View ArticlePubMedPubMed CentralGoogle Scholar
- Wei L, Miao H, Zhao R, Han X, Zhang T, Zhang H. Identification and testing of reference genes for Sesame gene expression analysis by quantitative real-time PCR. Planta. 2013;237:873–89.View ArticlePubMedGoogle Scholar
- Xu Y, Zhu X, Gong Y, Xu L, Wang Y, Liu L. Evaluation of reference genes for gene expression studies in radish (Raphanus sativus L.) using quantitative real-time PCR. Biochemical Biophys Res Commun. 2012;424:398–403.View ArticleGoogle Scholar
- Lawrence RJ, Pikaard CS. Chromatin turn ons and turn offs of ribosomal RNA genes. Cell Cycle. 2004;3:880–3.View ArticlePubMedGoogle Scholar
- Álvarez I, Wendel JF. Ribosomal ITS sequences and plant phylogenetic inference. Molecular Phylogenet Evol. 2003;29:417–34.View ArticleGoogle Scholar
- Dheda K, Huggett JF, Bustin SA, Johnson MA, Rook G, Zumla A. Validation of housekeeping genes for normalizing RNA expression in real-time PCR. Biotechniques. 2004;37:112–4.PubMedGoogle Scholar
- Czechowski T, Stitt M, Altmann T, Udvardi MK, Scheible W-R. Genome-wide identification and testing of superior reference genes for transcript normalization in Arabidopsis. Plant Physiol. 2005;139:5–17.View ArticlePubMedPubMed CentralGoogle Scholar
- Huis R, Hawkins S, Neutelings G. Selection of reference genes for quantitative gene expression normalization in flax (Linum usitatissimum L.). BMC Plant Biol. 2010;10:71.View ArticlePubMedPubMed CentralGoogle Scholar
- Dellaporta SL, Wood J, Hicks JB. A plant DNA minipreparation: version II. Plant Mol Biol Reporter. 1983;1:19–21.View ArticleGoogle Scholar
- Jobes DV, Thien LB. A conserved motif in the 5.8 S ribosomal RNA (rRNA) gene is a useful diagnostic marker for plant internal transcribed spacer (ITS) sequences. Plant Mol Biol Reporter. 1997;15:326–34.View ArticleGoogle Scholar
- Zhang Z, Schwartz S, Wagner L, Miller W. A greedy algorithm for aligning DNA sequences. J Comput Biol. 2000;7:203–14.View ArticlePubMedGoogle Scholar