Reference genes for gene expression studies in wheat flag leaves grown under different farming conditions
© Maquet et al; licensee BioMed Central Ltd. 2010
Received: 13 April 2011
Accepted: 27 September 2011
Published: 27 September 2011
Internal control genes with highly uniform expression throughout the experimental conditions are required for accurate gene expression analysis as no universal reference genes exists. In this study, the expression stability of 24 candidate genes from Triticum aestivum cv. Cubus flag leaves grown under organic and conventional farming systems was evaluated in two locations in order to select suitable genes that can be used for normalization of real-time quantitative reverse-transcription PCR (RT-qPCR) reactions. The genes were selected among the most common used reference genes as well as genes encoding proteins involved in several metabolic pathways.
Individual genes displayed different expression rates across all samples assayed. Applying geNorm, a set of three potential reference genes were suitable for normalization of RT-qPCR reactions in winter wheat flag leaves cv. Cubus: TaFNRII (ferredoxin-NADP(H) oxidoreductase; AJ457980.1), ACT2 (actin 2; TC234027), and rrn26 (a putative homologue to RNA 26S gene; AL827977.1). In addition of these three genes that were also top-ranked by NormFinder, two extra genes: CYP18-2 (Cyclophilin A, AY456122.1) and TaWIN1 (14-3-3 like protein, AB042193) were most consistently stably expressed.
Furthermore, we showed that TaFNRII, ACT2, and CYP18-2 are suitable for gene expression normalization in other two winter wheat varieties (Tommi and Centenaire) grown under three treatments (organic, conventional and no nitrogen) and a different environment than the one tested with cv. Cubus.
This study provides a new set of reference genes which should improve the accuracy of gene expression analyses when using wheat flag leaves as those related to the improvement of nitrogen use efficiency for cereal production.
In the post-genomic era, the development of transcriptomic, proteomic and metabolomic approaches has been proposed to investigate the regulation of plant growth, development and physiology of plant cells and, more recently, as a new tool for food authentication [1, 2]. Real-time quantitative reverse-transcription PCR (RT-qPCR) represents one of the most widely used technologies for quantification of mRNA transcription levels, due to its sensitivity, specificity, dynamic range and high throughput capacity [3, 4]. RT-qPCR requires normalization of variability using one or several reference gene(s) (known as housekeeping gene), which is one of the most important steps for the correct evaluation of gene expression [5, 6].
Usually, the reference genes used in gene expression analysis are chosen for their known or suspected housekeeping roles, but numerous studies revealed that these genes (actins, beta-globulin, 18S rRNA) as such are insufficiently stable during the treatments [5, 7, 8]. Until now, no gene can be considered as a universal, reliable reference gene which is expressed at the same level in all types of plant tissue assayed and is not treatment dependent. For instance, the 18S rRNA gene, one of the most widely used housekeeping genes for normalization, is far from being ideal . It requires the use of total RNA and random primers for the RT-qPCR reaction and is expressed at very high levels. In addition, there can be imbalances in rRNA and mRNA fractions between different samples and 18S is not always constantly expressed in all conditions. Also, the 18S gene expression levels appear to be affected to a lesser extent by partial RNA degradation than are mRNA expression levels [5, 9]. The evidence shows that the transcription level of these genes can vary considerably in response to changes in experimental conditions, and/or tissue type, variety used, etc so none of them can be considered as a universal reference. However, every experimental design, which uses gene expression analysis, should first search for a stable expressed reference genes, which are appropriate for the specific set of experimental conditions and types of tissue used .
Wheat (Triticum aestivum L.) is one of the three most important cereal crops worldwide. There is little doubt that wheat will retain its dominant position in European agriculture due to its adaptability and consumer acceptance. However, it may also need to adapt to face changing requirements notably reducing inputs . A number of projects worldwide are therefore focusing on understanding the processes that determine the efficiency of nitrogen uptake, assimilation, and utilization of nitrogen in order to improve the efficiency of nitrogen recovery in the grain [15, 16]. Whilst the physical processes of nitrogen and sulphur remobilization have been studied in detail, the genetic control of these processes and their contribution to agronomic productivity are less well understood . The particular complex structure of the flag leaf allows for an efficient translocation of assimilates until the very late stages of leaf senescence , and the relative contribution of the flag leaves to the final grain nitrogen level is essential . In this context, organic farming relies on a number of objectives and principles, as well as common practices designed to minimise the human impact on the environment, while ensuring that the agricultural system operates as naturally as possible. Typical organic farming practices prescribe strict limits on chemical/synthetic pesticide and synthetic fertiliser use [20, 21]. In addition, winter wheat is the most demanding cereal in organic food market for bread consumption . On the other hand in conventional agricultural practices there is an increasing interest in fertilisers using nitrogen in solution. Such fertilisers increase the distribution efficiency of nitrogen and permit marginal direct leaf uptake of the dissolved forms of nitrogen.
To our knowledge, transcriptional profiling methods, such as RT-qPCR, in selection and identification of potential discriminative genes in wheat cultivated under organic and conventional environmental conditions have not yet been fully developed. A previous study was carried out regarding the identification of gene markers in wheat grown with organic and inorganic fertilisers . It has been suggested that several genes are differentially expressed in grain endosperm when nitrogen is supplied in both organic and inorganic conditions and these genes are related to nitrogen metabolism and storage protein synthesis.
With regard to the selection of a potential reference gene in wheat, a new in silico method based on data publicly available in databases such as UniGene http://www.ncbi.nlm.nih.gov/UniGene and TIGR Gene Indice http://compbio.dfci.harvard.edu/tgi/plant.html has been recently proposed . Expression stability was tested among 32 genes including housekeeping genes and new candidates genes in one wheat variety, and three new reference genes, Ta.54227 (AAA-superfamily of ATP-ases), Ta.2291 (ADP-ribosylation factor), and Ta.2776 (RNase L inhibitor-like protein) were selected as stable reference genes for normalization of gene expression in different tissues and developmental stages of wheat. Even more recently, using genome-wide identification of novel reference genes in the wheat Chinese Spring variety under controlled environmental conditions, Long et al. showed that 15 new candidate genes are better internal controls than traditional reference genes . Nonetheless, none of them correspond to the three reference genes selected by Paolacci et al . These results emphasise the importance to select appropriate reference genes for specific experimental design.
Contrary to the previous studies, we used RT-qPCR gene expression analysis to search for genes which are uniformly expressed in more than one winter wheat variety grown in open field conditions under conventional vs. organic farming systems. The expression stability of 24 candidate genes was evaluated by applying two of the available software tools (i.e. geNorm and NormFinder), which have been developed to select the most stably expressed genes to use under given experimental conditions [25, 26]. We have selected a set of potential reference genes for normalization of gene expression in winter wheat flag leaves. Further, the expression stability of the selected reference genes in cv. Cubus was evaluated in two additional winter wheat varieties, Tommi and Centenaire, grown under three field conditions: no nitrogen, organic and conventional farming systems.
Plant material and environmental design
Flag leaves (the last leaf before the ear) of winter wheat, Triticum aestivum, cv. Cubus were collected in 2006 at the milk stage of ear development in two environments located in the Walloon region of Belgium (i.e. Marloie and Barvaux-en-Condroz). In each environment, certified organic and conventional fields were separated by short distances (i.e. < 5 km) though otherwise located in similar microclimatic conditions with comparable soil properties . A set of 15 flag leaves were collected from each field representing a total of 60 samples (i.e. 2 environments × 2 fields/environment × 15 samples/field). Leaves were sliced longitudinally, submersed in RNAlater™ buffer (Applied Biosystems/Ambion, Belgium) and stored on ice upon arrival in the lab. The tissue was grinded to powder in liquid nitrogen with a mixer mill MM301 (Retsch, Germany) and the powder stored at -80°C until processing.
Two other varieties, Tommi and Centenaire, grown under three treatments (i.e. no nitrogen fertilisation, organic and conventional agricultural systems) were collected in 2008 at the same developmental stage in one additional environment (i.e. Ciney) of the Walloon Region (Belgium). A similar experimental design was applied except for the number of samples per field of which 30 samples were used (i.e. 5 flag leaves/field × 3 fields/variety × 2 varieties).
All data related to the crop husbandry applied by the farmers during the full study were recorded and available on request.
Total RNA isolation and cDNA synthesis
Total RNA was isolated from wheat flag leaves (100 mg powder) using a Qiagen RNA isolation kit (Qiagen, Carlsberg, CA, USA) following the manufacturer's instructions. Quality and integrity of RNA was assayed using an Agilent RNA 6000 Nano kit following the manufacturer's protocol (Agilent 2100 Bioanalyzer, cat # 5067-1511). The RNA quality varied between samples and occasionally partial degradation of RNA was observed. For further analysis the RNA integrity number (RIN) was determined using Agilent RNA 6000 Nano kit and the samples with a RIN value higher than 5.5 were used. The RNA concentration was determined using the RediPlate 96 RiboGreen RNA quantification kit (Quant-IT RiboGreen RNA reagent and kit; Invitrogen; cat # R11419). The purity of RNA was estimated not only from the ratio between absorbance readings at 260 nm and 280 nm (suitable range 1.8-2.1) but also from the ratio A260/A230, which values where higher than 2 indicating neither protein contamination nor other contaminations with reagents used in the extraction. Further, the contaminating genomic DNA was removed by incubation of total RNA (2 μg) with 2 U/μL Turbo DNAseI enzyme (Applied Biosystems/Ambion kit, cat # AM1907) for 30 min at 37°C and the reaction was stopped using a DNAseI inhibitor reagent.
First strand complementary DNA was synthesized using the High-capacity cDNA reverse transcription kit (M-MLV: Moloney Murine Leukemia Virus Reverse Transcriptase, cat # 4374966) according to the manufacture instruction's (Applied Biosystems). Total RNA (2.5 μg) was reverse-transcribed in cDNA using with the same amount of RNA in all samples. The method is based on using the random primers for initiating the cDNA synthesis. The reaction was carried out in a total volume of 20 μL. The advantage of using M-MLV for reverse-transcription is due to the fact that the random hexamers will introduce the least bias in the resulting cDNA. All cDNA samples were diluted 1:5 before used in RT-qPCR analysis.
Selection of gene sequences: primers and probe design
Description of the traditional reference candidate genes tested.
Best hit (characterised)
Arabidopsis thaliana translation initiation factor
Eukaryotic translation initiation factor 4B 
18S subunit ribosomal protein
Zea mays 18S ribosomal RNA
18S subunit ribosomal protein
Populus tremuloides 26S ribosomal RNA
Ribosomal RNA 
Oryza sativa (CYP2)
Cyclophilin A 
Elongation factor 1 beta
O. sativa (japonica cultivar-group) mRNA for elongation factor 1 beta'
Elongation factor 1 beta 
Actin II (ACT2)
Gossypium hirsutum actin (ACT2)
Actin 11 (ACT11)
Ricinus communis actin (ACT)
Saccharum officinarum actin
G. hirsutum actin (ACT2)
Hordeum.vulgare mRNA for alpha-tubulin 2 (TUB2)
Beta-tubulin 2 (Tubb2)
Eleusine indica beta-tubulin 2 (TUB2)
Beta-tubulin 2 
Beta-tubulin 4 (Tubb4)
Z. mays beta-7 tubulin (TUBB7)
Beta-tubulin 4 
Description of a new set of candidate genes tested.
Best hit (characterised)
O. sativa 14-3-3 protein homolog
14-3-3 like protein family 
ADP glucose pyrophosphorylase small subunit
H. vulgare mRNA for ADP-glucose pyrophosphorylase small subunit
ADP-glucose pyrophosphorylase small subunit 
Triticum aestivum ribulose-1,5-bisphosphate carboxylase activase B (RcaB)
H. vulgare rubisco activase (Rca B)
Rubisco activase 
Z. mays L-FNRII mRNA for ferredoxin
Ferredoxin-NADP(H) oxidoreductase 
Rubisco small subunit (rbcS)
H. vulgare ribulose-1,5-bisphosphate carboxylase small subunit mRNA
Photosynthesis related genes 
T. aestivum mitochondrion rrn26 gene for rRNA large subunit 26S (Z11889.1)
T. aestivum mitochondrion rrn26 gene for rRNA large subunit (26S)
Similar to mitochondrion rRNA 26S 
O. sativa nitrate transporter (NRT1)
Low affinity nitrate transporter NTR 1.1 
ADP-glucose pyrophosphorylase small subunit
H. vulgare subsp. vulgare ADP-glucose pyrophosphorylase small subunit
ADP-glucose pyrophosphorylase small subunit 
CLP gene for proteolytic subunit of ATP-dependent protease
Wheat chloroplast clpP gene for proteolytic subunit of ATP-dependent protease
RuBisCO large subunit
T. aestivum ribulose-1,5-bisphosphate carboxylase/oxygenase large subunit
ribulose-1,5-bisphosphate carboxylase/oxygenase large subunit 
ADP-glucose pyrophosophorylase (WL:AGA.1)
H. vulgare ADP-glucose pyrophosphorylase large subunit (blpl)
ADP-glucose pyrophosophorylase 
Quantitative real-time PCR
The total PCR reaction volume was 25 μL comprising: TaqMan® Universal PCR Master Mix (2 ×) 12.5 μL; primers/probe premix (25 ×) 1 μL; diluted cDNA 5 μL (10 ×; approximately 100 ng) and milliQ water. The primers and probes were premixed (100 μM and 20 μM, respectively) to obtain a 25 × stock with final concentrations of 900 nM and 200 nM, respectively. PCR amplification and analysis were achieved using an ABI Prism cycler 7900 (Applied Biosystems). A three steps PCR procedure was used: 50°C for 2 minutes; denaturation by a pre-incubation for 10 minutes at 95°C and amplification of template 60 s at 60°C and 15 s at 95°C for 40 cycles. The reactions were carried out in duplicates in a 384 well plate (pipetting was performed with an automated liquid handling system, Freedom EVOware, Tecan, Mechelen, Belgium). The maximum difference accepted between Cq values of technical replicates was 0.5. All reaction components without template were used as a negative control (NTC). The Cq value (raw quantification cycle, also known as Ct, threshold cycle) according to RDML guideline (Real-time PCR Data Markup Language)  was determined automatically using the SDS 2.2.2 software (Applied Biosystems, Halle, Belgium).
Determination of PCR efficiency
The PCR amplification efficiency was determined for each primer combination by the slope of the standard curve obtained by plotting the fluorescence versus given concentration of a mixture of all sample cDNAs (ranging from 1:1 to 1:5000 dilution of the cDNA mixture sample) using the equation: E = 10(-1/slope) -1 . The PCR efficiencies ranged from 1.83 to 2.00. The R2 values are included in Additional files 1 and 2. No signals were detected in no-template and no-RT controls (no reverse-transcription).
Assessment of reference genes stability
For analysis of gene expression stability we used two widely known Microsoft Excel applets, geNorm  and NormFinder . Before running geNorm, relative quantities of all tested candidate reference genes were determined by the number of amplification cycles needed to reach a specific threshold level of detection. Cq values were converted into relative quantities via the modified delta-delta Cq method implemented in qbasePLUS. The relative quantities were imported in geNorm software (version 3.5) to assess gene stability . The program geNorm computes the expression stability M of a candidate gene assayed on the average pair-wise variation between all genes included in the experiment. The M-value measure relies on the principle that the expression ratio of two hypothetical ideal control genes is identical in all samples, regardless of the experimental conditions or treatments. The lowest M values are produced by genes with the most stable expression. By stepwise exclusion of the least stable gene and recalculation of the M values, the most stable genes are identified. Finally, a normalization factor (NF) is calculated automatically based on the geometric mean of the expression levels of the best performing reference genes. NF varies between 0.31 and 3.24. The software determines also the optimal number of reference genes necessary for gene expression analysis based on pair-wise variation Vn/Vn+1 between sequential normalization factors containing the reference candidate genes. Based on a cut-off value of Vn/Vn+1 < 0.15, the recommended number of references genes is given by n.
NormFinder algorithm use the ANOVA-based model to estimate inter- and intra-group variation and combines these estimations to provide a direct measure of the variation of expression for each gene . In current study, two groups corresponding to the agricultural production systems (organic vs. conventional) were defined. Genes with the least inter-group variation combined to a low intra-group variations were considered to be stable.
Systat 13 (Erkrath, Germany) was used in order to perform descriptive statistics.
Selection of a reference gene for gene expression analysis in winter wheat flag leaves
In this study, the stability of gene expression of several candidate reference genes was performed in order to select the most stable expressed gene which can be further used for normalization of PCR reaction for future gene expression studies in winter wheat flag leaves.
Candidate genes ranked according to their expression stability as determined by geNorm and NormFinder
Stability value (M)
NormFinder algorithm, which is based on a variance estimation approach  and ranks the genes according to their stability under a given experimental conditions, was used to asses the gene stability. Table 3 presents the ranking of the 24 genes based on their expression stability. The most stable gene TaWIN1 (AB042193) is encoding for a 14-3-3 like protein which had also a stability value (M) estimated by geNorm lower than 1.
Although the order of the top-ranked candidate genes estimated by NormFinder was not the same as the one calculated by geNorm, the genes with accession numbers AJ457980.1, AB042193, TC234027, AY456122.1, and AL827977.1 were found as the best consensus considering their gene rankings in both methods.
Evaluation of stability of selected reference genes in different winter wheat varieties
The use of gene expression profile in organic farming comes mainly from the large interest of researchers to identify potential diagnostic genes that can improve the nitrogen use efficiency of wheat [17, 31, 32]. Moreover if specific gene expression is diagnostic for use of organic sources of nitrogen fertiliser they may therefore have useful applications in defining the differences between organically and conventionally grown wheat. Indeed, protection of the rights of consumers, genuine food processors, and prevention of fraudulent or deceptive practices and the adulteration of food is an important and challenge facing the food industry . Recently attempts were made to apply RT-qPCR aiming to solve food authentication problems [23, 34]. This approach is however emerging and requires further experimental evaluation . Our study contributes to these challenges by identifying reliable reference genes for normalising gene expression in winter wheat flag leaves.
In wheat flag leaves, previously transcriptome analysis has shown that several genes are up-regulated such as transcription factors (WRKY) or down-regulated such as genes from hormone pathways or carbohydrate metabolism . In general the gene expression profile is heterogeneous and complex and depends on the tissues analysed, developmental stages of the plant and environmental conditions. In this study, the expression of several traditional used reference genes and new set of genes was measured in wheat flag leaves grown under organic and conventional agricultural production systems. The reference genes will be further use in a large scale gene expression analysis for identification of changes in transcripts accumulation associated with a certain agricultural production system. Transcription is a very complex and versatile process that responds to biotic and abiotic factors, with some genes being highly inducible while others are constitutively expressed. The environmental conditions might influence the gene expression, but in this experiment we were interested to search and select such genes, which were stable expressed without monitoring these factors as usual performed in controlled environmental condition. For that reason, a first set of winter wheat flag leaves were collected in 2006 from two Belgian locations and in each of them two paired open fields (certified organic vs. conventional) were sampled while a second set of winter wheat flag leaves were collected in 2008 from a third Belgian location.
Because a universal reference gene does not exist and the genes are regulated differently depending on specific conditions it is necessary to search for suitable genes in every experimental design situation . However, to achieve reliable results, it is essential to determine the expression stability of several candidate reference genes to normalize the variation given by RNA sample quality, reverse-transcription efficiency, etc. In previous gene expression studies several genes involved in cellular processes such as ubiquitin and cyclophilin or in cell structures such as actins and tubulins were the most used genes for normalization of PCR reaction, but several studies have shown that the expression of such genes is not as independent of experimental condition as expected [37–40]. Utilization of a single reference gene, reported before in many gene expression studies [41, 42], may lead to an inaccurate biological conclusion . Therefore, utilization of additional reference genes can give more accurate and reliable results [25, 44–46].
Several approaches have been proposed to identify reference genes in wheat such as all genome-wide transcriptome or microarray analysis [7, 47], but generally the experimental design differs starting with the type of biological material used, experimental conditions, developmental stages of plants and finally with the selection of software tools (e.g. geNorm, NormFinder or BestKeeper) used to examine the stability of gene expression [4, 25, 48]. All methods aim at defining the "most stable" gene from a set of genes, wherein "expression stability" is referred to as the least variation of constitutive expression levels in the group of samples analysed . There is no clear answer which strategy should be used to address the issue of reference gene expression stability for a specific experimental setup. Nonetheless, studies comparing several algorithms obtained similar rankings of the reference genes [4, 50]. Recently, using microarray approach on Triticeae species, Gimenez et al. identified six candidate genes which showed stable expression in their experimental design. Based on their analysis, the ranking of the reference genes in terms of stability is not identical in all the cases assays and the best choice of the reference genes would differ upon the species being compared .
In the present study, the selection of genes was based on the hypothesis that differential level of gene expression could be expected between organically and conventionally grown wheat due to different crop husbandries (e.g. phytosanitary or fertiliser treatments; data available on request) and a valid internal control to normalise the gene expression is needed. The gene expression stability was evaluated on several commonly used reference genes encoding for actins, tubulins, elongation factor 1 beta or cyclophilin, and other genes such as 18S ribosomal protein or genes chosen from different metabolic functions such as, the defence/resistance genes, RuBisCO small and large subunits, low affinity nitrate transporter or ADP-glucose pyrophosphorylase.
Tubulins (Tubb2 and alpha-tubulin), and elongation factor 1 beta were not stably expressed in wheat flag leaves under our experimental conditions. Several studies conducted in grapes, sugar cane and potato showed that the TUB gene performed poorly as a reference gene . On the other hand, ubiquitin UBQ5 and elongation factor 1 alpha were the most stable genes expressed in rice . However, several potential reference genes seem to be regulated differentially in different plant species or under different environmental conditions.
In previous gene expression analyses, 18S was the most often used gene for normalization of PCR reactions , but some other studies showed that the expression of this gene was regulated [26, 53] and transcript abundance varied from sample to sample. In this study, we have included a 18S subunit ribosomal protein (AF475127.1) which displayed a median Cq of 18.7 with CV of 11.1% across all samples assayed (Figure 1) and had low rank stability values estimated by both geNorm and NormFinder algorithms (Table 3).
Beta-actin was also used as a reference gene in previous studies, but increasingly it has become apparent that its expression level is not as stable as had been expected . For example, the poor performance of ACT2 as reference gene has been already showed in potato, rice and soybean [54–56], while the ACT2/7 was seen to be rather variable in Arabidopsis. However other studies reported that actins are stably expressed such as ACT1 in rice seeds or ACT11 in soybean tissues at various developmental stages, varied photoperiodic treatments, and a range of soybean cultivars, which makes them suitable for PCR normalization [11, 58]. Among the five actin-like genes tested in our study for their stability, the ACT2 gene (TC234027) was expressed most stably in all wheat flag leaves samples assayed. The other actins were variably expressed in wheat flag leaves and this variation could be related to the developmental stage of the wheat leaf. This is in concordance with several studies in Arabidopsis showing that several actins genes are divergent and differentially expressed in different tissues .
Using the geNorm approach the optimal number of reference genes was determined by pair-wise variation analysis of normalization factors with a cut-off of 0.15 . This value is not absolute, and, in some cases, may not be achieved . In the current study, V3/4 = 0.149 suggesting that three reference genes should be sufficient for normalization of RT-qPCR data from winter wheat cv. Cubus flag leaves: TaFNRII encoding for the ferredoxin-NADP(H) oxidoreductase (AJ457980.1), an important enzyme which belongs to a large family of flavoproteins founded in higher plants  and ACT2 were the most stable reference genes in both organic and conventional wheat flag leaves, followed by rrn26, a wheat mitochondrial gene that encodes the mitochondrial large-subunit rRNA 26S (AL827977.1) but its function in wheat is still unknown. In addition of these three genes that were also top-ranked by NormFinder, two extra genes with accession numbers AY456122.1 and AB042193 were most consistently stably expressed (Table 3).
Cyclophilins, as the cyclophilin A gene CYP18-2 (AY456122.1), are enzymes that catalyse the rate-limiting step of peptidyl-prolyl cis-trans isomerisation. They are known to be up-regulated during wheat endosperm development and suggested to be important for folding of the storage proteins, thus influencing wheat quality . However this gene was stably expressed in wheat flag leaves sampled in organic and conventional fields considering that this organ allows for an efficient translocation of assimilates and contribute to the final grain nitrogen level.
TaWIN1 (AB042193) encodes a 14-3-3 protein that binds to a wheat protein kinase (WPK4) which most likely is responsible for controlling the nitrogen metabolic pathway; nitrate reductase is inactivated by phosphorylation followed by 14-3-3 protein binding .
Two additional winter wheat varieties (i.e. cv Tommi and Centenaire), included here as a validation group, were grown in 2008 under organic, conventional and without nitrogen fertilisation conditions in order to test the stability of the selected three reference genes. Results showed that three genes, ACT2, TaFNRII, and CYP18-2 but not rrn26, remained the most stable genes in flag leaves in all varieties tested. In a microarray study performed in wheat grains grown under organic and inorganic treatments, it has been suggested that the gene rrn26 (AL827977.1) can be a diagnostic gene for organically grown grains . The gene displayed a higher gene expression when grains were grown on farmyard cattle manure. In our study, we observed that the expression of this gene in flag leaves could differ from one cultivar to another while the level of expression seemed to be not influenced by the environments. This indicates the complexity of the transcriptional event in plant developmental stages and that some genes which can be used as a reference gene in certain organ or experimental conditions may not be suitable to normalize gene expression under other condition . Therefore, it is necessary to determine the most stable genes under different experimental conditions , which in our case means various wheat varieties and environmental conditions including existing variability in the agricultural production systems.
Moreover these results confirm the recent recommendations to select not only stably expressed genes in a pilot experiment with representative samples but also to assess their expression stability in the final experiment .
In conclusion, we have identify the most three stable and uniformly expressed genes, suitable for normalization of RT-qPCR results in three winter wheat cultivars grown in open fields under different well recorded crop husbandries at three locations and collected in different years. These data provide a new set of reference genes that can be useful for further gene expression studies in winter wheat flag leaves as for instance those related to the improvement of nitrogen use efficiency for cereal production.
The research was mainly supported by the TRACE research project (N°006942) funded by the European Commission. For the wheat cv. Cubus, the Union Nationale des Agrobiologistes Belges - UNAB (Gembloux, Belgium) identified the certified organic farmers as well as the farmers growing the variety under a conventional agriculture system. For wheat cv. Tommi and Centenaire, the Centre Wallon de Recherches Agronomiques - CRA-W (Gembloux, Belgium) provided the certified organic wheat and plant materials grown under no nitrogen treatment while the École Technique Provinciale de Ciney (Belgium) and the Société Coopérative Agricole de la Meuse - SCAM (Ciney, Belgium) identified the conventional farmers. We thank all the farmers who provided us the samples as well as our colleagues O. De Rudder for the sampling; as well as F. Ulberth and A.-M. Jensen for their valuable remarks to the manuscript.
- Sun D-W, ed: Modern Techniques for Food Authentication. 2008, Burlington, MA, USA: Academic Press (Elsevier)Google Scholar
- Weckwerth W: Integration of metabolomics and proteomics in molecular plant physiology - coping with the complexity by data-dimensionality reduction. Physiol Plantarum. 2008, 132 (2): 176-189. 10.1111/j.1399-3054.2007.01011.x.View ArticleGoogle Scholar
- Bustin SA, ed: A-Z of quantitative PCR. 2004, La Jolla, CA, USA: International University LineGoogle Scholar
- Vandesompele J, Kubista M, Pfaffl MW: Reference gene validation software for improved normalization. Real-time PCR: Current Technology and Applications. Edited by: Logan J, Edwards K. 2009, Saunders N: Caister Academic PressGoogle Scholar
- Bustin SA: Quantification of mRNA using real-time reverse transcription PCR (RT-PCR): trends and problems. J Mol Endocrinol. 2002, 29: 23-39. 10.1677/jme.0.0290023.PubMedView ArticleGoogle Scholar
- Huggett J, Dheda K, Bustin SA, Zumla A: Real-time RT-PCR normalisation; strategies and considerations. Gene Immunity. 2005, 6: 279-284. 10.1038/sj.gene.6364190.View ArticleGoogle Scholar
- Paolacci A, Tanzarella O, Porceddu E, Ciaffi M: Identification and validation of reference genes for quantitative RT-PCR normalization in wheat. BMC Mol Biol. 2009, 10 (1): 11-10.1186/1471-2199-10-11.PubMedPubMed CentralView ArticleGoogle Scholar
- Thellin O, Zorzi W, Lakaye B, De Borman B, Coumans B, Hennen G, Grisar T, Igout A, Heinen E: Housekeeping genes as internal standards: use and limits. J Biotechnol. 1999, 75 (2-3): 291-295. 10.1016/S0168-1656(99)00163-7.PubMedView ArticleGoogle Scholar
- Brunner AM, Yakovlev IA, Strauss SH: Validating internal controls for quantitative plant gene expression studies. BMC Plant Biol. 2004, 4 (14): 7.Google Scholar
- Bustin SA, Benes V, Garson JA, Hellemans J, Huggett J, Kubista M, Mueller R, Nolan T, Pfaffl MW, Shipley GL, et al: The MIQE Guidelines: Minimum Information for Publication of Quantitative Real-Time PCR Experiments. Clin Chem. 2009, 55 (4): 611-622. 10.1373/clinchem.2008.112797.PubMedView ArticleGoogle Scholar
- Hu R, Fan C, Li H, Zhang Q, Fu Y-F: Evaluation of putative reference genes for gene expression normalization in soybean by quantitative real-time RT-PCR. BMC Mol Biol. 2009, 10 (1): 93-10.1186/1471-2199-10-93.PubMedPubMed CentralView ArticleGoogle Scholar
- Lallemant B, Evrard A, Combescure C, Chapuis H, Chambon G, Raynal C, Reynaud C, Sabra O, Joubert D, Hollande F, et al: Reference gene selection for head and neck squamous cell carcinoma gene expression studies. BMC Mol Biol. 2009, 10 (1): 78-10.1186/1471-2199-10-78.PubMedPubMed CentralView ArticleGoogle Scholar
- Teste M-A, Duquenne M, Francois J, Parrou J-L: Validation of reference genes for quantitative expression analysis by real-time RT-PCR in Saccharomyces cerevisiae. BMC Mol Biol. 2009, 10 (1): 99-10.1186/1471-2199-10-99.PubMedPubMed CentralView ArticleGoogle Scholar
- Tong Z, Gao Z, Wang F, Zhou J, Zhang Z: Selection of reliable reference genes for gene expression studies in peach using real-time PCR. BMC Mol Biol. 2009, 10 (1): 71-10.1186/1471-2199-10-71.PubMedPubMed CentralView ArticleGoogle Scholar
- Shewry PR: Wheat. J Exp Bot. 2009, 60 (6): 1537-1553. 10.1093/jxb/erp058.PubMedView ArticleGoogle Scholar
- Foulkes MJ, Hawkesford MJ, Barraclough PB, Holdsworth MJ, Kerr S, Kightley S, Shewry PR: Identifying traits to improve the nitrogen economy of wheat: Recent advances and future prospects. Field Crop Res. 2009, 114 (3): 329-342. 10.1016/j.fcr.2009.09.005.View ArticleGoogle Scholar
- Howarth JR, Parmar S, Jones J, Shepherd CE, Corol D-I, Galster AM, Hawkins ND, Miller SJ, Baker JM, Verrier PJ, et al: Co-ordinated expression of amino acid metabolism in response to N and S deficiency during wheat grain filling. J Exp Bot. 2008, 59 (13): 3675-3689. 10.1093/jxb/ern218.PubMedPubMed CentralView ArticleGoogle Scholar
- Kichey T, Le Gouis J, Sangwan B, Hirel B, Dubois F: Changes in the cellular and subcellular localization of glutamine synthetase and glutamate dehydrogenase during flag leaf senescence in wheat (Triticum aestivum L.). Plant Cell Physiol. 2005, 46 (6): 964-974. 10.1093/pcp/pci105.PubMedView ArticleGoogle Scholar
- Lopes M, Cortadellas N, Kichey T, Dubois F, Habash D, Araus J: Wheat nitrogen metabolism during grain filling: comparative role of glumes and the flag leaf. Planta. 2006, 225 (1): 165-181. 10.1007/s00425-006-0338-5.PubMedView ArticleGoogle Scholar
- European Commission: Council Regulation (EC) N°834/2007 of 28 June 2007 on organic production and labelling of organic products and repealing Regulation (EEC) N°2092/91 - In force consolidated legislation. Official Journal. 2008, 2007R0834 (001.001): 35.Google Scholar
- IFOAM: Principles of organic agriculture - Preamble. 2009, Bonn, Germany: International Federation of Organic Agriculture Movements (IFOAM), 4.Google Scholar
- VETAB: Guide des pratiques de l'Agriculture Biologique en grandes cultures. Projet "Valoriser l'Experience Transfrontalière en Agriculture Biologique", Programme INTERREG III, EC. 2005, Phalempin, France: GABNOR (Groupement des Agriculteurs Biologiques du Nord Pas-de-Calais), CARAH (Centre pour l'Agronomie et l'Agro-industrie de la province du Hainaut), CEB (Centre d'Essais Bio), Chambre Régionale d'Agriculture du Nord - Pas de Calais, CRA-W (Centre Wallon de Recherches Agronomiques), FREDON Nord-Pas de Calais (Fédération Régionale de Défense contre les Organismes Nuisibles), PCBT (Interprovinciaal Proefcentrum voor de Biologische Teelt), SRPV (Service Régional de Protection des Végétaux), 45.Google Scholar
- Lu C, Hawkesford MJ, Barraclough PB, Poulton PR, Wilson ID, Barker GL, Edwards KJ: Markedly different gene expression in wheat grown with organic or inorganic fertilizer. P Roy Soc Lond B Bio. 2005, 272 (1575): 1901-1908. 10.1098/rspb.2005.3161.View ArticleGoogle Scholar
- Long X-Y, Wang J-R, Ouellet T, Rocheleau H, Wei Y-M, Pu Z-E, Jiang Q-T, Lan X-J, Zheng Y-L: Genome-wide identification and evaluation of novel internal control genes for Q-PCR based transcript normalization in wheat. Plant Mol Biol. 2010, 74 (3): 307-311. 10.1007/s11103-010-9666-8.PubMedView ArticleGoogle 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 (15): 5245-5250. 10.1158/0008-5472.CAN-04-0496.PubMedView ArticleGoogle Scholar
- Vandesompele J, De Preter K, Pattyn F, Poppe B, Van Roy N, De Paepe A, Speleman F: Accurate normalization of real-time quantitative RT-PCR data by geometric averaging of multiple internal control genes. Genome Biol. 2002, 3 (7): Research0034.0031-0034.0011.View ArticleGoogle Scholar
- Lindahl P, Maquet A, Hult M, Gasparro J, Marissens G, González de Orduña R: Natural radioactivity in winter wheat from organic and conventional agricultural systems. J Environ Radioactiv. 2011, 102 (2): 163-169. 10.1016/j.jenvrad.2010.11.007.View ArticleGoogle Scholar
- Anonymous: geNorm Manual. 2008, Ghent, Belgium: Ghent University, 16.Google Scholar
- Hellemans J, Mortier G, De Paepe A, Speleman F, Vandesompele J: qBase relative quantification framework and software for management and automated analysis of real-time quantitative PCR data. Genome Biol. 2007, 8: R19-10.1186/gb-2007-8-2-r19.PubMedPubMed CentralView ArticleGoogle Scholar
- Gabrielsson BG, Olofsson LE, Sjogren A, Jernas M, Elander A, Lonn M, Rudemo M, Carlsson LMS: Evaluation of Reference Genes for Studies of Gene Expression in Human Adipose Tissue. Obesity. 2005, 13 (4): 649-652. 10.1038/oby.2005.72.View ArticleGoogle Scholar
- Hirel B, Le Gouis J, Ney B, Gallais A: The challenge of improving nitrogen use efficiency in crop plants: towards a more central role for genetic variability and quantitative genetics within integrated approaches. J Exp Bot. 2007, 58 (9): 2369-2387. 10.1093/jxb/erm097.PubMedView ArticleGoogle Scholar
- Sylvester-Bradley R, Kindred DR: Analysing nitrogen responses of cereals to prioritize routes to the improvement of nitrogen use efficiency. J Exp Bot. 2009, 60 (7): 1939-1951. 10.1093/jxb/erp116.PubMedView ArticleGoogle Scholar
- Sellam M, Ait-Aissa M, Bozec A, de Carne O, Carotte G, Cotillon C, Dauguet S, Descamps P, Ferrari G, Gallois P, et al: Traçabilité - Guide pratique pour l'agriculture & l'industrie alimentaire. 2007, Paris, France: Association de coordination technique agricole (ACTA) & Association de coordination technique pour l'industrie agr-alimentaire (ACTIA)Google Scholar
- Prache S: Diet authentication in sheep from the composition of animal tissues and products. Rev Bras Zootecn. 2009, 38:Google Scholar
- Wiseman G: Real-time PCR: Application to food authenticity and legislation. Real-time PCR: Current Technology and Applications. Edited by: Logan J, Edwards K, Saunders N. 2009, Norfolk, UK: Caister Academic Press, 253-267.Google Scholar
- Gregersen PL, Holm PB: Transcriptome analysis of senescence in the flag leaf of wheat (Triticum aestivum L.). Plant Biotechnol J. 2007, 5 (1): 192-206. 10.1111/j.1467-7652.2006.00232.x.PubMedView ArticleGoogle Scholar
- Radonic A, Thulke S, Mackay IM, Landt O, Siegert W, Nitsche A: Guideline to reference gene selection for quantitative real-time PCR. Biochem Bioph Res Co. 2004, 313 (4): 856-862. 10.1016/j.bbrc.2003.11.177.View ArticleGoogle Scholar
- Ruan W, Lai M: Actin, a reliable marker of internal control?. Clin Chim Acta. 2007, 385 (1-2): 1-5. 10.1016/j.cca.2007.07.003.PubMedView ArticleGoogle Scholar
- Selvey S, Thompson EW, Matthaei K, Lea RA, Irving MG, Griffiths LR: b-Actin, an unsuitable internal control for RT-PCR. Mol Cell Probe. 2001, 15: 307-311. 10.1006/mcpr.2001.0376.View ArticleGoogle Scholar
- Thorrez L, Van Deun K, Tranchevent L-C, Van Lommel L, Engelen K, Marchal K, Moreau Y, Van Mechelen I, Schuit F: Using ribosomal protein genes as reference: A tale of caution. PLoS ONE. 2008, 3 (3): e1854-10.1371/journal.pone.0001854.PubMedPubMed CentralView ArticleGoogle Scholar
- Kong L, Anderson JM, Ohm HW: Induction of wheat defense and stress-related genes in response to Fusarium graminearum. Genome. 2005, 48 (1): 29-40. 10.1139/g04-097.PubMedView ArticleGoogle Scholar
- Vágújfalvi A, Aprile A, Miller A, Dubcovsky J, Delugu G, Galiba G, Cattivelli L: The expression of several Cbf genes at the Fr-A2 locus is linked to frost resistance in wheat. Mol Genet Genomics. 2005, 274 (5): 506-514. 10.1007/s00438-005-0047-y.PubMedView 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 (1): 112-119.PubMedGoogle Scholar
- Kessler Y, Helfer-Hungerbuehler AK, Cattori V, Meli M, Zellweger B, Ossent P, Riond B, Reusch C, Lutz H, Hofmann-Lehmann R: Quantitative TaqMan(R) real-time PCR assays for gene expression normalisation in feline tissues. BMC Mol Biol. 2009, 10 (1): 106-10.1186/1471-2199-10-106.PubMedPubMed CentralView ArticleGoogle Scholar
- Ndong C, Danyluk J, Wilson KE, Pocock T, Huner NPA, Sarhan F: Cold-regulated cereal chloroplast late embryogenesis abundant-like proteins. Molecular characterization and functional analyses. Plant Physiol. 2002, 129 (3): 1368-1381. 10.1104/pp.001925.PubMedPubMed CentralView ArticleGoogle Scholar
- Derveaux S, Vandesompele J, Hellemans J: How to do successful gene expression analysis using real-time PCR. Methods. 2010, 50 (4): 227-230. 10.1016/j.ymeth.2009.11.001.PubMedView ArticleGoogle Scholar
- Wan Y, Poole R, Huttly A, Toscano-Underwood C, Feeney K, Welham S, Gooding M, Mills C, Edwards K, Shewry P, et al: Transcriptome analysis of grain development in hexaploid wheat. BMC Genomics. 2008, 9 (1): 121-10.1186/1471-2164-9-121.PubMedPubMed CentralView ArticleGoogle 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 (6): 509-515.PubMedView ArticleGoogle 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 (2): 450-461. 10.1261/rna.1755810.PubMedPubMed CentralView ArticleGoogle Scholar
- Jain M: Genome-wide identification of novel internal control genes for normalization of gene expression during various stages of development in rice. Plant Sci. 2009, 176 (5): 702-706. 10.1016/j.plantsci.2009.02.001.View ArticleGoogle Scholar
- Giménez M, Pistón F, Atienza S: Identification of suitable reference genes for normalization of qPCR data in comparative transcriptomics analyses in the Triticeae. Planta. 2011, 233 (1): 163-173. 10.1007/s00425-010-1290-y.PubMedView ArticleGoogle Scholar
- Burleigh SH: Relative quantitative RT-PCR to study the expression of plant nutrient transporters in arbuscular mycorrhizas. Plant Sci. 2001, 160 (5): 899-904. 10.1016/S0168-9452(00)00460-X.PubMedView ArticleGoogle Scholar
- Vera MI, Norambuena L, Álvarez M, Figueroa J, Molina A, León G, Krauskopf M: Reprogramming of nucleolar gene expression during the acclimatization of the carp. Cell Mol Biol Res. 1993, 39 (7): 665-674.PubMedGoogle Scholar
- Kim B-R, Nam H-Y, Kim S-U, Kim S-I, Chang Y-J: Normalization of reverse transcription quantitative-PCR with housekeeping genes in rice. Biotechnol Lett. 2003, 25 (21): 1869-1872.PubMedView ArticleGoogle Scholar
- Libault M, Thibivilliers S, Bilgin DD, Radwan O, Benitez M, Clough SJ, Stacey G: Identification of four soybean reference genes for gene expression normalization. Plant Genome. 2008, 1 (1): 44-54. 10.3835/plantgenome2008.02.0091.View ArticleGoogle Scholar
- Nicot N, Hausman J-F, Hoffmann L, Evers D: Housekeeping gene selection for real-time RT-PCR normalization in potato during biotic and abiotic stress. J Exp Bot. 2005, 56 (421): 2907-2914. 10.1093/jxb/eri285.PubMedView ArticleGoogle Scholar
- McDowell JM, Huang S, McKinney EC, An YQ, Meagher RB: Structure and evolution of the actin gene family in Arabidopsis thaliana. Genetics. 1996, 142 (2): 587-602.PubMedPubMed CentralGoogle Scholar
- Li Q-F, Sun S, Yuan D-Y, Yu H-X, Gu M-H, Liu Q-Q: Validation of candidate reference genes for the accurate normalization of real-time quantitative RT-PCR data in rice during seed development. Plant Mol Biol Rep. 2010, 28 (1): 49-57. 10.1007/s11105-009-0124-1.View ArticleGoogle Scholar
- Maroufi A, Van Bockstaele E, De Loose M: Validation of reference genes for gene expression analysis in chicory (Cichorium intybus) using quantitative real-time PCR. BMC Mol Biol. 2010, 11 (1): 15-10.1186/1471-2199-11-15.PubMedPubMed CentralView ArticleGoogle Scholar
- Gummadova JO, Fletcher GJ, Moolna A, Hanke GT, Hase T, Bowsher CG: Expression of multiple forms of ferredoxin NADP+ oxidoreductase in wheat leaves. J Exp Bot. 2007, 58 (14): 3971-3985. 10.1093/jxb/erm252.PubMedView ArticleGoogle Scholar
- Johnson JC, Bhave M: Characterisation and physical mapping of cyclophilin A genes and identification of new classes of cyclophilins in wheat. J Cereal Sci. 2004, 40: 137-150. 10.1016/j.jcs.2004.05.002.View ArticleGoogle Scholar
- Ikeda Y, Koizumi N, Kusano T, Sano H: Specific binding of a 14-3-3 protein to autophosphorylated WPK4, an SNF1-related wheat protein kinase, and to WPK4-phosphorylated nitrate reductase. J Biol Chem. 2000, 275 (41): 31695-31700.PubMedView ArticleGoogle Scholar
- Dheda K, Huggett JF, Chang JS, Kim LU, Bustin SA, Johnson MA, Rook GAW, Zumla A: The implications of using an inappropriate reference gene for real-time reverse transcription PCR data normalization. Anal Biochem. 2005, 344 (1): 141-143. 10.1016/j.ab.2005.05.022.PubMedView ArticleGoogle Scholar
- Metz AM, Wong KCH, Malmström SA, Browning KS: Eukaryotic initiation factor 4B from wheat and Arabidopsis thaliana is a member of a multigene family. Biochem Bioph Res Co. 1999, 266 (2): 314-321. 10.1006/bbrc.1999.1814.View ArticleGoogle Scholar
- Kuzoff RK, Sweere JA, Soltis DE, Soltis PS, Zimmer EA: The phylogenetic potential of entire 26S rDNA sequences in plants. Mol Biol Evol. 1998, 15 (3): 251-263.PubMedView ArticleGoogle Scholar
- Johnson JC, Clarke BC, Bhave M: Isolation and characterisation of cDNAs encoding protein disulphide isomerases and cyclophilins in wheat. J Cereal Sci. 2001, 34 (2): 159-171. 10.1006/jcrs.2001.0382.View ArticleGoogle Scholar
- Oizumi N, Matsumoto S, Taira H, Ejiri S-i: Nucleotide sequence of cDNA encoding wheat elongation factor 1b'. Nucl Acids Res. 1992, 20 (19): 5225-10.1093/nar/20.19.5225.PubMedPubMed CentralView ArticleGoogle Scholar
- Li X-B, Fan X-P, Wang X-L, Cai L, Yang W-C: The Cotton ACTIN1 gene is functionally expressed in fibers and participates in fiber elongation. Plant Cell. 2005, 17 (3): 859-875. 10.1105/tpc.104.029629.PubMedPubMed CentralView ArticleGoogle Scholar
- Farajalla MR, Gulick PJ: The α-tubulin gene family in wheat (Triticum aestivum L.) and differential gene expression during cold acclimation. Genome. 2007, 50 (5): 502-510. 10.1139/G07-027.View ArticleGoogle Scholar
- Rogers HJ, Greenland AJ, Hussey PJ: Four members of the maize b-tubulin gene family are expressed in the male gametophyte. Plant J. 1993, 4 (5): 875-882. 10.1046/j.1365-313X.1993.04050875.x.PubMedView ArticleGoogle Scholar
- Smidansky ED, Clancy M, Meyer FD, Lanning SP, Blake NK, Talbert LE, Giroux MJ: Enhanced ADP-glucose pyrophosphorylase activity in wheat endosperm increases seed yield. P Natl Acad Sci USA. 2002, 99 (3): 1724-1729. 10.1073/pnas.022635299.View ArticleGoogle Scholar
- Rundle SJ, Zielinski RE: Alterations in barley ribulose-1,5-bisphosphate carboxylase/oxygenase activase gene expression during development and in response to illumination. J Biol Chem. 1991, 266 (22): 14802-14807.PubMedGoogle Scholar
- Onda Y, Matsumura T, Kimata-Ariga Y, Sakakibara H, Sugiyama T, Hase T: Differential interaction of maize root ferredoxin:NADP+ oxidoreductase with photosynthetic and non-photosynthetic ferredoxin isoproteins. Plant Physiol. 2000, 123 (3): 1037-1046. 10.1104/pp.123.3.1037.PubMedPubMed CentralView ArticleGoogle Scholar
- Lin C-M, Koh S, Stacey G, Yu S-M, Lin T-Y, Tsay Y-F: Cloning and functional characterization of a constitutively expressed nitrate transporter gene, OsNRT1, from rice. Plant Physiol. 2000, 122 (2): 379-388. 10.1104/pp.122.2.379.PubMedPubMed CentralView ArticleGoogle Scholar
- Johnson PE, Patron NJ, Bottrill AR, Dinges JR, Fahy BF, Parker ML, Waite DN, Denyer K: A low-starch barley mutant, Risø 16, lacking the cytosolic small subunit of ADP-glucose pyrophosphorylase, reveals the importance of the cytosolic isoform and the identity of the plastidial small subunit. Plant Physiol. 2003, 131 (2): 684-696. 10.1104/pp.013094.PubMedPubMed CentralView ArticleGoogle Scholar
- Wostrikoff K, Stern D: Rubisco large-subunit translation is autoregulated in response to its assembly state in tobacco chloroplasts. P Natl Acad Sci USA. 2007, 104 (15): 6466-6471. 10.1073/pnas.0610586104.View ArticleGoogle Scholar
- Villand P, Aalen R, Olsen O-A, Lüthi E, Lönneborg A, Kleczkowski LA: PCR amplification and sequences of cDNA clones for the small and large subunits of ADP-glucose pyrophosphorylase from barley tissues. Plant Mol Biol. 1992, 19 (3): 381-389. 10.1007/BF00023385.PubMedView ArticleGoogle Scholar
This article is published under license to BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.