http://orcid.org/0000-0003-4165-6754
Figures
Abstract
Arabis alpina is a perennial arctic-alpine plant and an upcoming model organism for genetics and molecular biology for the Brassicaceae family. One essential method for most molecular approaches is the analysis of gene expression by reverse-transcription quantitative Real-Time PCR (RT-qPCR). For the normalisation of expression data in RT-qPCR experiments, it is essential to use reliable reference genes that are not affected under a wide range of conditions. In this study we establish a set of 15 A. alpina reference genes that were tested under different conditions including cold, drought, heat, salt and gibberellic acid treatments. Data analyses with geNORM, BestKeeper and NormFinder revealed the most stable reference genes for the tested conditions: RAN3, HCF and PSB33 are most suitable for cold treatments; UBQ10 and TUA5 for drought; RAN3, PSB33 and EIF4a for heat; CAC, TUA5, ACTIN 2 and PSB33 for salt and PSB33 and TUA5 for gibberellic acid treatments. CAC and ACTIN 2 showed the least variation over all tested samples. In addition, we show that two reference genes are sufficient to normalize RT-qPCR data under our treatment conditions. In future studies, these reference genes can be used for an adequate normalisation and thus help to generate high quality RT-qPCR data in A. alpina.
Citation: Stephan L, Tilmes V, Hülskamp M (2019) Selection and validation of reference genes for quantitative Real-Time PCR in Arabis alpina. PLoS ONE 14(3): e0211172. https://doi.org/10.1371/journal.pone.0211172
Editor: Miguel A. Blázquez, Instituto de Biologia Molecular y Celular de Plantas, SPAIN
Received: January 8, 2019; Accepted: February 19, 2019; Published: March 4, 2019
Copyright: © 2019 Stephan et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: All relevant data are within the manuscript and its Supporting Information files.
Funding: This work was funded by the Deutsche Forschungsgemeinschaft (https://www.dfg.de/) to MH and by the International Max Planck Research School to LS. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors have declared that no competing interests exist.
Introduction
Recently, A. alpina has been established as a new model system in the Brassicaceae family [1,2]. It is native to mountains and arctic-alpine habitats [3,4] and combines several features enabling genetic and molecular studies: it is diploid, self-fertile, has a small and sequenced genome and can be transformed with Agrobacterium tumefaciens [1]. A. alpina has an evolutionary distance to A. thaliana of about 26 to 40 million years [4,5]. This facilitates functional comparisons of biological processes, as orthologous genes can be identified by sequence similarity and synteny [6].
Most molecular studies require quantitative analyses of the expression of genes of interest by reverse-transcription quantitative Real-Time PCR (RT-qPCR). For proper comparisons of expression levels, the expression data of the genes under study are normalized using genes as a reference that show no or very little variation under different conditions. In 2009, the Minimum Information for Publication of Quantitative Real-Time PCR Experiments (MIQE) guidelines were published, with the aim to provide a consensus on correct performance and interpretation of RT-qPCR experiments [7]. These guidelines should ensure that the normalisation enables the comparison of transcripts in different samples by correcting variations in yields of extraction and reverse transcription and the efficiency of amplification. A pre-requisite for any RT-qPCR analysis are suitable primer sets for reference genes that are thoroughly tested. These need to fulfil various requirements: primers should create a specific amplicon of 80 to 200 bp, without creating primer dimers. The amplification should be carried out with close to 100% efficiency and show a linear standard curve with a correlation of more than 0.99. In general, there should be minimal variation between replicates, indicating consistent performance of the primers.
In this study we established primer pairs for 15 reference genes that can be used for future RT-qPCR studies in A. alpina: ADENOSINE TRIPHOSPHATASE (ATPase), THIOREDOXIN, HIGH CHLOROPHYLL FLUORESCENCE 164 (HCF), EUKARYOTIC TRANSLATION INITIATION FACTOR 4A1 (EIF4a), RAN GTPASE 3 (RAN3), UBIQUITIN 10 (UBQ10), ACTIN 2, PHOTOSYSTEM B PROTEIN 33 (PSB33), HISTONE H3, NAD(P)H PLASTOQUINONE DEHYDROGENASE COMPLEX SUBUNIT O (NdhO), TUBULIN ALPHA 5 (TUA5), 18s RIBOSOMAL RNA (18srRNA), CLATHRIN ADAPTOR COMPLEX MEDIUM SUBUNIT (CAC), SAND family protein (SAND) and HEAT SHOCK PROTEIN 81.2/90 (HSP81.2/90). The primers were thoroughly tested, and reference genes were evaluated for variations in their expression under different conditions including cold, drought, heat and salt in whole seedlings and gibberellic acid (GA) treatments in leaves. Using genes specifically responding to the different treatment, we demonstrate the impact of normalization with our reference genes.
Results
Selection and validation of reference genes
To compile a set of suitable reference genes, we pursued three approaches: first, we selected A. thaliana genes that are known to show little variation under different conditions. Corresponding orthologs in A. alpina were then identified by sequence similarity and synteny. Second, we identified genes which show stable expression in A. alpina over an extended period of time. Third, we included a well-established reference gene from A. alpina from former studies. Thus, we created a set of 15 reference genes (Table 1), including nine orthologs to A. thaliana reference genes, five novel reference genes and RAN3, a known reference gene for RT-qPCR in A. alpina [1]. We first amplified the gene fragments and verified the amplicon by sequencing. Additionally, we analysed the melting curves to exclude unspecific products and/or primer dimers (S1 Fig).
Subsequently, we determined the primer efficiencies and correlation coefficients to demonstrate the quality of the primer pairs (Table 2, S2 Fig). All reference gene primers displayed an efficiency between 96.42 and 107.01% and a correlation higher than 0.99.
Expression stability of reference genes after treatments
The expression levels of the reference genes were detected as cycle quantification (Cq) values. The mean Cq values over all treatments ranged between 15.10 (18srRNA) and 26.68 (SAND). The 18srRNA, UBQ10 and ACTIN 2 genes showed the highest expression/lowest Cq (Fig 1). Overall, Cq values for a single reference gene varied 4.30 on average, with a minimum variation of 2.82 (UBQ10) and a maximum variation of 8.12 (HSP81.2/90).
The box includes all data points between the 25% quantile and the 75% quantile. The whiskers span all values below and above, excluding outliers which are displayed as dots. The median, which marks the 50% quantile, is displayed as a line.
Next, we analysed the expression of the selected reference genes under different conditions, including cold, drought, heat, salt and gibberellic acid treatments (Table 3, S1 Table). The stability was calculated using three common statistical algorithms: NormFinder [8], BestKeeper [9] and geNorm [10]. These algorithms can be used to rank reference genes of a given set by their stability and determine the most stable genes for the tested conditions. In contrast to BestKeeper and NormFinder, geNorm additionally provides a cut off value (1.5), below which primer pairs are considered adequate for normalisation. All genes in all treatments, except for HSP81.2/90 under heat stress conditions, met this criterion. Thus, all reference genes reported here can be considered suitable for a wide range of conditions. The most stable genes for cold treatments were RAN3, HCF and PSB33. According to the BestKeeper algorithm, EIF4a could be considered as a reference for cold treatments as well. However, geNorm advises against this gene, ranking it at position eight. After drought treatment, UBQ10 and TUA5 showed the least overall variation in the three methods. Here in particular, the results strongly differed between the algorithms. While HISTONE H3 ranked first for BestKeeper, it was found to be least stable in the other two methods. On the contrary, EIF4a and THIOREDOXIN showed high stability in NormFinder and geNorm, but low values in BestKeeper. Under heat conditions, RAN3, PSB33 and EIF4a were most stable over all algorithms and under salt conditions CAC, TUA5, ACTIN 2 and PSB33 performed best. Finally, under GA treatment, PSB33 and TUA5 had the least expression variation, while THIOREDOXIN showed stable expression only for NormFinder and geNorm. When taking all stress treatments into account, THIOREDOXIN and CAC showed the least variation. When considering all treatments, CAC and ACTIN 2 showed the most stable expression (S2 Table).
Genes were ranked using the three commonly used statistical algorithms NormFinder, BestKeeper and geNorm. The stability value describes the variance (NormFinder and geNorm) or standard deviation (BestKeeper).
Optimal number of reference genes
For the optimal normalisation, it is necessary to use two or more reference genes in each experiment. The optimal number of reference genes can be determined with the geNorm algorithm, which calculates the pairwise variation Vn/n+1 based on the normalisation factors NFn and NFn+1, with n≥2. If Vn/n+1 is below 0.15, n is the optimal number of reference genes. For all tested treatments, individually or combined, two reference genes are sufficient to normalise RT-qPCR measurements (Fig 2). The use of a third reference would not improve the results significantly.
The geNorm algorithm was used to determine the pairwise variation (V) between the reference genes for treatments with cold, drought, heat, salt and gibberellic acid. The threshold for adequate normalisation is V≤0.15, indicated by the green dashed line.
Impact of reference genes on the normalisation of samples in abiotic stress and hormone treatments
The efficiencies of treatments with cold, drought, heat, salt and gibberellic acid were determined by the expression analysis of specific stress response genes by RT-qPCR using primers for RD29A (RESPONSIVE TO DESICCATION 29A, cold and drought responsive), HSP81.2/90 (heat responsive), TSPO (OUTER MEMBRANE TRYPTOPHAN-RICH SENSORY PROTEIN-RELATED, salt responsive) and GA3ox1 (GIBBERELLIN 3-OXIDASE 1, GA responsive). All primers showed efficiencies of 80.28 to 104.22% and correlations of more than 0.99 (Table 4).
Normalisation of the results was carried out with one or two of the most stable reference genes determined in this study (Fig 3). For cold, normalisation was carried out with RAN3 and/or HCF. Drought samples were normalised to UBQ10 and/or TUA5. GA and salt responsive genes were normalised with PSB33 and/or TUA5. Finally, the heat stress samples were normalised with RAN3 and/or PSB33. The results clearly show that all treatments were successful, leading to an increased (cold, drought, heat, salt) or decreased (GA) expression of the responsive genes. Individual normalisation with each reference gene led to differences in the calculated fold changes of 28.9% (cold), 7.5% (drought), 3.6% (GA), 9.3% (heat) and 10.4% (salt) between reference gene 1 and 2, respectively. These results clearly show the necessity to use two reference genes simultaneously.
Normalisation of the stress response genes RD29A (RESPONSIVE TO DESICCATION 29A, cold and drought responsive), HSP81.2/90 (heat responsive), TSPO (OUTER MEMBRANE TRYPTOPHAN-RICH SENSORY PROTEIN-RELATED, salt responsive) and GA3ox1 (GIBBERELLIN 3-OXIDASE 1, GA responsive) was carried out with one or two reference genes (RG1 and RG2): cold–RAN3 and HCF, drought–UBQ10 and TUA5, GA and salt–PSB33 and TUA5, heat–RAN3 and PSB33.
Material and methods
Plant growth conditions
For the abiotic stress treatments, seeds were surface sterilised with increasing concentrations of ethanol and grown on MS [11] plates without additional sucrose. The seeds were stratified for five days. Subsequently, plants were grown under long day conditions (16h light/8h darkness) at 21±1°C and 100±20 μmol/m2s light intensity.
The heat and cold treatments were carried out by transferring 5–7 day old seedlings to 38°C and 4°C, respectively. Samples were taken after 2 h. Drought was induced on MS plates which were exposed to liquid MS containing 20% PEG 8000 for 24 h prior to the experiment. Samples were taken after 24 h. For salt treatments, two to three 5–7 day old seedlings were transferred from plates to liquid ½ MS (control) or liquid ½ MS containing 125 mM NaCl for 4 h under constant shaking.
For the GA treatment, plants were grown on soil under long day conditions (16h light/8h darkness) at 21±1°C and 200±20 μmol/m2s light intensity. The treatment was started right after germination and continued twice per week. Plants were sprayed with 20 μM GA4 (Sigma Aldrich, stock solution: 100 mM GA4 in EtOH, 0.1% Silwet L-77 Loveland industries) or mock (0.1% EtOH, 0.1% Silwet). Leaves were harvested 14 days after germination at Zeitgeber time (ZT) 8.
All experiments were performed in three independent biological replicates. All samples were immediately frozen in liquid nitrogen and stored at -80°C.
RNA extraction and cDNA synthesis
The frozen seedling samples were ruptured using a TissueLyser (Qiagen) and total RNA extraction was carried out with Tri-Reagent (Ambion by Life Technologies). All samples were treated with DNAseI (Thermo Fisher Scientific). The frozen leaf samples were ruptured using a TissueLyser (Qiagen), extracted with the RNAeasy Plant Mini Kit (Qiagen) and treated with DNAse (Ambion by Life Technologies). RNA was analysed on a bleach gel [12], resulting in distinct bands for 28s and 18s rRNA, respectively. RNA concentration and purity was measured using a photometer (Eppendorf). cDNA was synthesised from 500 ng total RNA with oligodT primers, using the RevertAid First Strand cDNA Synthesis Kit (Thermo Fisher Scientific) according to the provided protocol. The cDNA was tested for DNA contamination and integrity by PCR and subsequent gel electrophoresis. The cDNA was diluted (1:10, 1:20, 1:40, 1:80, 1:160, and 1:320) to analyse primer efficiency and determine the correlation coefficient.
Choice of candidate genes and primer design
The eight reference gene candidates EIF4a, ACTIN 2, CAC, TUA5, HISTONE H3, HSP81.2/90, 18srRNA and SAND were chosen due to their stable expression in other species [13–17]. PSB33, ATPase, THIOREDOXIN, HCF and NdhO were chosen because of their robust expression levels in Arabis alpina in a time-course RNAseq experiment (data provided by Eva Willing, MPIPZ, Cologne) and/or due to their essential function for the plant. The RAN3 primers were taken from[1]. UBQ10 primers were kindly provided by Pan Pan Jiang (Botanical Institute, University of Cologne).
Sequences were taken from TAIR [18], NCBI (National Centre for Biotechnology Information, www.ncbi.nlm.nih.gov) and the Genomic resources for Arabis alpina website (www.arabis-alpina.org) [2]. In silico sequence analysis was carried out with CLC DNA Workbench version 5.6.1. The primers were designed using GenScript Real-time PCR Primer Design (www.genscript.com) at an optimum Tm of 60±2°C. Amplicons showed a single band of the expected size in gel electrophoresis and a single peak in the melting curve (S1 Fig). The PCR products were sequenced by GATC/ Eurofins Genomics to confirm specific amplification, using published sequences as a reference (www.arabis-alpina.org) [2].
Quantitative real-time PCR (qPCR)
qPCRs were carried out in a QuantStudio 5 System (ABI/Life Technologies) equipped with the QuantStudio Design and Analysis Software version 1.4.1. The qPCRs were performed using plates (96 well, 0.2 ml) and cover foil (Opti-Seal Optical Disposable Adhesive) from BIOplastics. Reaction mixtures of 10 μl were composed from 5 μl SYBR Green (Thermo Fisher Scientific), 0.2 μl of each primer (10 μM), 1 μl diluted cDNA and 3.6 μl ddH2O. Amplification was carried out with the standard settings of the QuantStudio 5 System (50°C for 2 min, 95°C for 10 sec, 40 cycles at 95°C for 15 sec and 60°C for 1 min, followed by 95°C for 15 sec and a final dissociation curve from 60°C to 95°C). For each reference gene sample, three biological and three technical replicates were analysed. The impact of normalisation on stress/hormone responsive genes was analysed for 1:40 diluted cDNA in three technical replicates of one biological replicate.
Analysis of RT-qPCR data
Efficiency calculations were carried out manually using Excel 2007. Outliers were removed using a two-sided Grubbs test at a significance level of 0.05. Efficiency of primers was calculated using the cDNA dilution series. The average Cq values (y) and log10 values of the dilutions (x) were used to calculate the slope of a regression line with the formula . The slope was then used to calculate the efficiency of the primers
. The correlation R2 of the values was calculated using the formula
. Primers for reference genes were accepted with an efficiency of 90–110% and a correlation between -1 and -0.99. Primers for genes of interest were accepted with an efficiency of 80–120% and a correlation between -1 and -0.99.
The stability of the reference genes within a given set of different treatments was calculated from the efficiency corrected data using the algorithms NormFinder [8], BestKeeper [9] and geNorm [10]. The geNorm algorithm was also used to define the number of reference genes necessary for normalisation.
Normalisation against one reference gene was carried out using the normalisation factor, normalisation against two reference genes was carried out using the geometric mean of the normalisation factors, according to the geNorm manual [10]. In accordance with this manual, standard deviations between biological replicates were calculated over the means of the single replicates, rather than the raw data.
Discussion
Gene expression analysis by RT-qPCR is a high-throughput method, which is considered to be very sensitive and reproducible. However, the accuracy of the results strongly depends on the experimental design, adequate normalisation and exact analysis of the produced data [7]. Moreover, the RT-qPCR primers must be specific, efficient, and—in the case of reference genes—stable in the tested conditions [7]. In this study, we analysed primers for one established reference gene (RAN3) [1] and 14 novel reference genes for Arabis alpina in several abiotic stress and hormone treatments. EIF4a, ACTIN 2, CAC, TUA5, HISTONE H3, HSP81.2/90 and SAND were chosen because they were already established as reference genes in other species [13–17,19,20]. In addition, we considered PSB33, ATPase, THIOREDOXIN, HCF and NdhO. The reference genes selected here are involved in various basic cellular functions including translation, proton transport, photosynthesis, protein degradation and cytoskeletal organisation. Our data suggest that all genes reported here are appropriate reference genes for the tissues used under non-stress conditions. As PSB33 and HCF are functionally related to photosynthesis, they may be more appropriate for photosynthetic tissues.
The reference gene primers presented here have efficiency values between 96.42 and 107.01%. Although these are very good efficiencies, it is essential to correct the RT-qPCR results with these values because of the non-linearity of the PCR amplification steps [21]. Consequently, we used efficiency-corrected data for identifying the most stable reference genes and for the analysis of the impact on normalisation. There is currently no consensus in the community, which of the three statistical algorithms–geNorm, NormFinder or BestKeeper–is most appropriate. One advantage of the geNorm algorithm is that it can be used for small sample sizes [22]. However, the geNorm method is biased towards genes that are co-regulated [23]. The algorithm takes into consideration, whether genes show a similar expression pattern [24], since it is assumed that similar changes in the expression of two independent genes reflect technical differences, such as the cDNA concentration, rather than changes caused by a treatment. By contrast, NormFinder considers variations across subgroups [8]. The algorithm assumes that there is no systematic variation of the average of the tested samples, which can lead to a preference for reference genes with similar systematic variation [23]. BestKeeper takes the standard deviation of each individual reference gene into account, which is an advantage over the other methods. The disadvantage of this algorithm is the use of a parametric method (Pearson correlation), which requires normally distributed data with a homogenous variance [9], which is not always the case. Additionally, BestKeeper uses the raw Cq values, while geNorm and Normfinder require normalised quantities. Therefore, the results obtained with BestKeeper are often different from those of the other two methods.
It is generally recommended to use more than one reference gene to guarantee optimal normalisation [7]. Our data support this view. The normalised fold change values varied up to 28.9% between two references. Moreover, we recommend that at least one of the reference gene amplicons contains introns at the genomic level to recognize potential contaminations with genomic DNA.
For cold treatments, we recommend RAN3, as the amplicon contains an intron, combined with HCF or PSB33. As HCF and PSB33 are photosynthesis-associated proteins located in the thylakoid membrane, there is no obvious reason to prefer one over the other. For drought treatments, UBQ10 and TUA5 were the most stable transcripts, with TUA5 also containing an intron. In heat, RAN3 can be recommended in combination with PSB33 or EIF4a. As setups for heat treatments often go along with specific light settings, it might be advisable to use EIF4a as a second reference here. For the salt treatment, we found that CAC, TUA5 and ACTIN 2 are suitable, intron containing reference genes, which can be combined with each other or PSB33. The combination TUA5 and PSB33 might be the most efficient choice, as it is also the best option for GA treatments.
We found that there is no single reference gene, which is the best choice for all treatments. However, all genes tested in this study, except for HSP81.2/90 under heat conditions, meet the requirements necessary for adequate normalisation. Thus, this study provides data for the selection of suitable reference gene combinations for Arabis alpina under cold, drought, heat, salt and GA treatments. With this, we enable adequate normalisation in RT-qPCR experiments under these conditions and provide novel reference genes for future experiments addressing other stresses or stimuli.
Supporting information
S1 Table. Raw Cq values of treatments with cold, drought, heat, salt and gibberellic acid.
https://doi.org/10.1371/journal.pone.0211172.s001
(DOCX)
S1 Fig. Amplicon analysis of candidate reference genes and stress/hormone responsive genes.
https://doi.org/10.1371/journal.pone.0211172.s002
(DOCX)
S2 Fig. Standard curves of candidate reference genes and stress/hormone responsive genes.
https://doi.org/10.1371/journal.pone.0211172.s003
(DOCX)
S2 Table. Ranking of gene expression stability under abiotic stress conditions and the combination of all treatments.
https://doi.org/10.1371/journal.pone.0211172.s004
(DOCX)
Acknowledgments
We thank Pan Pan Jiang and Eva Willing for providing us with primer sequences and expression data, respectively. We also thank Jessica Pietsch and Tamara Gigolashvili for critically reading the manuscript.
References
- 1. Wang R, Farrona S, Vincent C, Joecker A, Schoof H, Turck F, et al. PEP1 regulates perennial flowering in Arabis alpina. Nature [Internet]. 2009 May 15 [cited 2018 May 7];459(7245):423–7. Available from: http://www.ncbi.nlm.nih.gov/pubmed/19369938 pmid:19369938
- 2. Willing E-M, Rawat V, Mandáková T, Maumus F, James GV, Nordström KJV, et al. Genome expansion of Arabis alpina linked with retrotransposition and reduced symmetric DNA methylation. Nat Plants [Internet]. 2015 Feb 2 [cited 2018 Dec 3];1(2):14023. Available from: http://www.nature.com/articles/nplants201423
- 3.
Meusel H, JÄGER EJ, WEINERT E. Vergleichende Chorologie der Zentraleuropaeischen Flora. Jena, Germany: Fischer Verlag; 1965.
- 4. Koch MA, KIEFER C, EHRICH D, VOGEL J, BROCHMANN C, MUMMENHOFF K. Three times out of Asia Minor: the phylogeography of Arabis alpina L. (Brassicaceae). Mol Ecol [Internet]. 2006 Feb 23 [cited 2018 Jul 30];15(3):825–39. Available from: http://doi.wiley.com/10.1111/j.1365-294X.2005.02848.x pmid:16499705
- 5. Beilstein MA, Nagalingum NS, Clements MD, Manchester SR, Mathews S. Dated molecular phylogenies indicate a Miocene origin for Arabidopsis thaliana. Proc Natl Acad Sci U S A [Internet]. 2010 Oct 26 [cited 2017 Apr 30];107(43):18724–8. Available from: http://www.pnas.org/cgi/doi/10.1073/pnas.0909766107 pmid:20921408
- 6. Chopra D, Wolff H, Span J, Schellmann S, Coupland G, Albani MC, et al. Analysis of TTG1 function in Arabis alpina. BMC Plant Biol [Internet]. 2014 [cited 2016 Aug 3];14(1):16. Available from: http://bmcplantbiol.biomedcentral.com/articles/10.1186/1471-2229-14-16
- 7. 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 [Internet]. 2009 [cited 2017 Aug 29];55(4). Available from: http://clinchem.aaccjnls.org/content/55/4/611/tab-figures-data
- 8. 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 [Internet]. 2004 Aug 1 [cited 2018 May 6];64(15):5245–50. Available from: http://cancerres.aacrjournals.org/lookup/doi/10.1158/0008-5472.CAN-04-0496 pmid:15289330
- 9. 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 [Internet]. 2004 Mar [cited 2018 May 6];26(6):509–15. Available from: http://link.springer.com/10.1023/B:BILE.0000019559.84305.47 pmid:15127793
- 10. 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 [Internet]. 2002 Jun 18 [cited 2018 May 6];3(7):research0034.1. Available from: http://genomebiology.biomedcentral.com/articles/10.1186/gb-2002-3-7-research0034
- 11. Murashige T, Skoog F. A Revised Medium for Rapid Growth and Bio Assays with Tobacco Tissue Cultures. Physiol Plant [Internet]. 1962 Jul 1 [cited 2018 May 6];15(3):473–97. Available from: http://doi.wiley.com/10.1111/j.1399-3054.1962.tb08052.x
- 12. Aranda PS, LaJoie DM, Jorcyk CL. Bleach gel: a simple agarose gel for analyzing RNA quality. Electrophoresis [Internet]. 2012 Jan [cited 2018 Aug 2];33(2):366–9. Available from: http://www.ncbi.nlm.nih.gov/pubmed/22222980 pmid:22222980
- 13. Tian C, Jiang Q, Wang F, Wang G-L, Xu Z-S, Xiong A-S. Selection of Suitable Reference Genes for qPCR Normalization under Abiotic Stresses and Hormone Stimuli in Carrot Leaves. Jain M, editor. PLoS One [Internet]. 2015 Feb 6 [cited 2017 Mar 2];10(2):e0117569. Available from: http://dx.plos.org/10.1371/journal.pone.0117569 pmid:25658122
- 14. Wang H, Wang J, Jiang J, Chen S, Guan Z, Liao Y, et al. Reference genes for normalizing transcription in diploid and tetraploid Arabidopsis. Sci Rep [Internet]. 2015 May 27 [cited 2018 Aug 2];4(1):6781. Available from: http://www.nature.com/articles/srep06781
- 15. Demidenko N V., Logacheva MD, Penin AA. Selection and Validation of Reference Genes for Quantitative Real-Time PCR in Buckwheat (Fagopyrum esculentum) Based on Transcriptome Sequence Data. Mariño-Ramírez L, editor. PLoS One [Internet]. 2011 May 12 [cited 2018 Aug 2];6(5):e19434. Available from: http://dx.plos.org/10.1371/journal.pone.0019434 pmid:21589908
- 16. 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 [Internet]. 2005 Sep [cited 2017 Jun 9];139(1):5–17. Available from: http://www.ncbi.nlm.nih.gov/pubmed/16166256 pmid:16166256
- 17. Wan Q, Chen S, Shan Z, Yang Z, Chen L, Zhang C, et al. Stability evaluation of reference genes for gene expression analysis by RT-qPCR in soybean under different conditions. Li X, editor. PLoS One [Internet]. 2017 Dec 13 [cited 2018 Nov 28];12(12):e0189405. Available from: http://www.ncbi.nlm.nih.gov/pubmed/29236756 pmid:29236756
- 18. Berardini TZ, Reiser L, Li D, Mezheritsky Y, Muller R, Strait E, et al. The arabidopsis information resource: Making and mining the “gold standard” annotated reference plant genome. genesis [Internet]. 2015 Aug 1 [cited 2018 Jan 25];53(8):474–85. Available from: http://doi.wiley.com/10.1002/dvg.22877 pmid:26201819
- 19. Wu J, Zhang H, Liu L, Li W, Wei Y, Shi S. Validation of Reference Genes for RT-qPCR Studies of Gene Expression in Preharvest and Postharvest Longan Fruits under Different Experimental Conditions. Front Plant Sci [Internet]. 2016 Jun 3 [cited 2018 Nov 28];7:780. Available from: http://www.ncbi.nlm.nih.gov/pubmed/27375640 pmid:27375640
- 20. Najafpanah MJ, Sadeghi M, Bakhtiarizadeh MR. Reference Genes Selection for Quantitative Real-Time PCR Using RankAggreg Method in Different Tissues of Capra hircus. Niemann H, editor. PLoS One [Internet]. 2013 Dec 16 [cited 2018 Nov 28];8(12):e83041. Available from: http://www.ncbi.nlm.nih.gov/pubmed/24358246 pmid:24358246
- 21. Ruijter JM, Ramakers C, Hoogaars WMH, Karlen Y, Bakker O, van den Hoff MJB, et al. Amplification efficiency: linking baseline and bias in the analysis of quantitative PCR data. Nucleic Acids Res [Internet]. 2009 Apr 1 [cited 2018 Dec 13];37(6):e45–e45. Available from: http://www.ncbi.nlm.nih.gov/pubmed/19237396 pmid:19237396
- 22. Serrano M, Moreno-Sánchez N, González C, Marcos-Carcavilla A, Van Poucke M, Calvo JH, et al. Use of Maximum Likelihood-Mixed Models to select stable reference genes: a case of heat stress response in sheep. BMC Mol Biol [Internet]. 2011 Aug 17 [cited 2018 Dec 9];12(1):36. Available from: http://bmcmolbiol.biomedcentral.com/articles/10.1186/1471-2199-12-36
- 23. Mehdi Khanlou K, Van Bockstaele E. A critique of widely used normalization software tools and an alternative method to identify reliable reference genes in red clover (Trifolium pratense L.). Planta [Internet]. 2012 Nov 21 [cited 2018 Dec 9];236(5):1381–93. Available from: http://www.ncbi.nlm.nih.gov/pubmed/22718310 pmid:22718310
- 24. 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 [Internet]. 2002 Jun 18 [cited 2018 Apr 19];3(7):RESEARCH0034. Available from: http://www.ncbi.nlm.nih.gov/pubmed/12184808