- Methodology article
- Open Access
Selection of reference genes for gene expression studies in pig tissues using SYBR green qPCR
© Nygaard et al; licensee BioMed Central Ltd. 2007
- Received: 04 December 2006
- Accepted: 15 August 2007
- Published: 15 August 2007
Real-time quantitative PCR (qPCR) is a method for rapid and reliable quantification of mRNA transcription. Internal standards such as reference genes are used to normalise mRNA levels between different samples for an exact comparison of mRNA transcription level. Selection of high quality reference genes is of crucial importance for the interpretation of data generated by real-time qPCR.
In this study nine commonly used reference genes were investigated in 17 different pig tissues using real-time qPCR with SYBR green. The genes included beta-actin (ACTB), beta-2-microglobulin (B2M), glyceraldehyde-3-phosphate dehydrogenase (GAPDH), hydroxymethylbilane synthase (HMBS), hypoxanthine phosphoribosyltransferase 1 (HPRT1), ribosomal protein L4 (RPL4), succinate dehydrogenase complex subunit A (SDHA), TATA box binding protein (TPB)and tyrosine 3-monooxygenase/tryptophan 5-monooxygenase activation protein zeta polypeptide (YWHAZ). The stability of these reference genes in different pig tissues was investigated using the geNorm application. The range of expression stability in the genes analysed was (from the most stable to the least stable): ACTB/RPL4, TBP, HPRT, HMBS, YWHAZ, SDHA, B2M and GAPDH.
Expression stability varies greatly between genes. ACTB, RPL4, TPB and HPRT1 were found to have the highest stability across tissues. Based on both expression stability and expression level, our data suggest that ACTB and RPL4 are good reference genes for high abundant transcripts while TPB and HPRT1 are good reference genes for low abundant transcripts in expression studies across different pig tissues.
- Reference Gene
- Expression Stability
- Candidate Reference Gene
- Stable Reference Gene
- Suitable Reference Gene
Real-time quantitative PCR (qPCR) is an efficient method for quantification of mRNA transcription levels due to its high sensitivity, reproducibility and large dynamic range; in addition, real-time qPCR is fast, easy to use and provides simultaneous measurement of gene expression in many different samples for a limited number of genes. One of the critical steps in comparing transcription profiles is accurate normalisation, therefore, a number of variables should be controlled such as amount of starting material, enzymatic efficiencies, differences in transcriptional activity and presence of inhibitors in different sample materials. One way to standardize samples is to quantify the starting material i.e. number of cells or quantity of RNA. However, this does not take the RNA quality and enzymatic efficiencies into account. To control these variables, which are not a result of the experimental design, normalisation using reference genes are routinely used e.g. [1–3]. The accurate quantification of reference genes allows normalisation of differences in amount of the amplified cDNA in individual samples. The normalisation adjusts for differences in amount and quality of starting material and differences in RNA preparation and cDNA synthesis, since the reference gene is exposed to the same preparation steps as the gene of interest. Recently a set of reference genes (ACTB, TBP and topoisomerase (DNA) II beta (TOP2B)) have proven suitable for normalisation of qPCR data of backfat and longissimus dorsi muscle in the pig .
In the present study the expression stability and expression level of nine potential reference genes have been compared in 17 different pig tissues. This has enabled us to assess the suitability of the genes for normalisation of mRNA across several tissues leading to the identification of the best reference genes for studies of high and low abundance transcripts, respectively. The suitable reference genes from this study can be used for normalisation of qPCR data in several tissues. Thus, the study gives useful information of importance for a broad range of different functional studies.
Candidate reference genes
Selected candidate reference genes
involved in cell motility, structure and integrity
cytoskeletal protein involved in cell locomotion
Hypoxanthine phosphoribosyltransferase 1
purine ribonulceoside salvage
Ribosomal protein L4
Structural constituent of ribosome
Succinate dehydrogenase complex, subunit A
Tricarboxylic acid cycle
TATA box binding protein
transcription initiation from RNA polymerase II promotor
Tyrosine 3-monooxygenase/tryptophan 5-monooxygenase activation protein, zeta polypeptide
Protein domain specific binding
QPCR efficiency and variability
Standard curves were generated using relative concentration vs. the threshold cycle (Ct). The linear correlation coefficient (R2) of all the nine genes ranged from 0.998 to 1.000. Based on the slopes of the standard curves, the amplification efficiencies of the standards ranged from 93%~103%, (derived from the formula E = 10 1/-slope -1). This calculation method results in efficiencies higher than 100% which is an overestimate of the "real efficiency" . The not normalised Ct values of all the nine genes in all the samples were within 13.5 to 30.5 cycles, covered by the range of the standard curves.
Expression level and stability of putative reference genes in various tissues
RNA transcription levels (normalised Ct values) of the candidate reference genes. Values in parentheses shows the ranking of genes in the specific tissue calculated by GeNorm. Values in bold have an M-value above 1.5.
For an exact comparison of mRNA transcription in different samples or tissues it is crucial to choose the appropriate reference gene. The optimal reference gene should be constantly transcribed in all types of cells at any time in cell cycle and differentiation. Moreover the transcription of such a gene should not be regulated by internal or external influences, at least not more than the general variation in RNA synthesis. The reference gene used for normalisation of gene expression in real-time qPCR studies should also pass through the same steps of analysis as the gene to be quantified. However, such a perfect reference gene does probably not exist. The stability in expression of often used reference genes such as GAPDH and ACTB has been shown to vary considerably and are consequently unsuitable as reference genes for normalisation of gene expression analysis in some cases [5, 10–12]. Also the low expressed reference gene TBP is highly regulated when comparing normal and tumour tissues .
Numerous studies have been carried out in order to evaluate reference genes in specific tissues in several species. The majority of these studies are directed towards specific tissues [1, 3, 7, 14]. They clearly demonstrate that it is very difficult to find a 'universal' reference gene having stable expression in all cell types and tissues, and in particular to find reference genes that remain stable between samples taken at different time points under different experimental conditions. The first priority, however, is to identify genes with stable expression preferably across cell types since many real-time qPCR studies are performed on cDNA isolated from tissues with a mixed cell population.
There have been limited experiments of reference gene selection for use in other livestock and production animal species. Bogaert et al.  proposed ubiquitin (UBB), ACTB and B2M as reference genes for normalisation of qPCR data for normal equine skin. In cattle studies, De Ketelaere et al.  selected SDHA, YWHAZ, and 18S rRNA as being the most stable genes for accurate normalisation of qPCR of bovine polymorphonuclear leukocytes and Goossens et al.  found YWHAZ, GAPDH and SDHA to be the most stable reference genes across different preimplantation embryonic stages. Common for the bovine studies are the target of the studies, which are single cell populations. Garcia-Crespo et al.  compared expression of six potential reference genes in sheep tissues and found GAPDH with the lowest variation among the panel of six tissues, whereas ACTB and YWHAZ showed the worst score in variability. This is not in agreement with our findings but can be a result of the tissue composition. At present three studies have examined reference genes in pig i.e. Foss et al.  describes the use of GAPDH, ACTB and HPRT in immune cells and tissues using northern blot and PCR. In this study GAPDH was shown to be more stable than ACTB. Erkens et al  validated mRNA expression stability of 10 reference genes in porcine backfat and longissimus dorsi muscle. The most stable reference genes suitable for normalisation of qPCR data of backfat and longissimus dorsi muscle in the pig was ACTB, TBP and TOP2B. Kuijk et al.  investigated transcription levels of seven frequently used reference genes (inclusive B2M, ACTB, GAPDH) at different stages of early porcine embryonic development. Our study is the first detailed study of the stability and level of pig reference genes across a large number of tissues. Our findings confirm studies demonstrating tissue specific regulation of some of the commonly used housekeeping genes. Furthermore, it provides recommendations for choice of reference genes in studies where high and low abundant transcripts are under investigation. In agreement with the findings of Erkens et al.  both TBP and ACTB are suitable reference genes. However, our data show that ACTB is most relevant for high abundant transcripts, while TPB is most relevant for low abundant transcripts. It is clear from both Erkens et al.  and our study that GAPDH is quite unstable and not suitable as a reference gene.
Our study demonstrates that expression stability varies greatly between genes. Seven of the nine genes investigated (i.e. ACTB, RPL4, TBP, HPRT1, HMBS, YWHAZ, SDHA) were found to have a high stability across tissues. Two of the genes investigated were shown to have tissue-specific regulation, i.e. HMBS is clearly up-regulated in bone marrow and GAPDH is clearly up-regulated in muscle. Based on both expression stability and expression level, our data suggest that ACTB and RPL4 are good reference genes for high abundant transcripts while TPB and HPRT1 are good reference genes for low abundant transcripts in expression studies across different pig tissues.
Candidate reference genes and primer design
Nine housekeeping genes were selected from commonly used reference genes [5–7]. The porcine sequences of the genes were obtained by fasta search with the human cDNA sequence for each gene against a porcine EST database. The consensus sequences were used for comparison with genomic porcine sequences, if available, to reveal the exon-intron structure. When no genomic porcine sequence was available the gene structure was obtained by comparison of the pig sequence with human and mouse genomic sequence assuming that the exon-exon boundaries are conserved between human, mouse and pig.
Primers, PCR conditions and qPCR efficiency
Oligo sequence (5'→3')
Seventeen different porcine tissues were collected from three young female siblings. Total RNA was extracted using different protocols depending of the tissue: TRIreagent® (Molecular Research Centre, inc.) for liver, kidney, thymus, RNeasy lipid kit (Qiagen) for adipose (subcutaneous), cortex cerebri, cerebellum, hippocampus, lymph nodules (jejunal), RNeasy Fibrous Kit (Qiagen) for muscle (longissimus dorsi), heart (muscle), skin (dermis and epidermis) and RNeasy kit (Qiagen) for pancreas, bone marrow, bladder, lung, stomach (mucosal membranes), small intestine (mucosal membranes) according to each manufacturer protocol. Contaminating DNA was degraded by treating each sample with RQ1 RNase-free DNase (Promega) according to the instructions manual, followed by a spin-column purification (Qiagen RNeasy). The total RNA was quantified by optical density and the quality was evaluated by gel electrophoresis. Intact rRNA subunit of 28S and 18S were observed on the gel indicating minimal degradation of the RNA.
One μg of total RNA was reverse transcribed at 42°C using Improm-II™ reverse trancriptase (Promega) and Oligo(dT) according to the manufacturers recommendations. Prior to use in qPCR cDNA was diluted 1:8 with H2O.
Quantitative PCR with SYBR green
For each transcript a standard curve was constructed using the purified PCR product generated for each specific primer pair. Single reactions were prepared for each cDNA along with each serial of dilution using the Brilliant® SYBR® Green Master Mix (Stratagene). Each PCR reaction also included a reverse transcription negative control (without reverse transcriptase) to confirm the absence of genomic DNA, a non template negative control to check for primer-dimer and a porcine genomic DNA control to verify no specific amplification with the primers. Each reaction consisted of 20 μl containing 2 μl of cDNA and 5 pmol of each primer. The real time qPCR was run on MX3000p (Stratagene). The cycling conditions were 1 cycle of denaturation at 95°C/10 min, followed by 40 three-segment cycles of amplification (95°C/30 sec, 58°C–63°C (gene depending, see table 2)/1 min, 72°C/30 sec) where the fluorescence was automatically measured during PCR and one three-segment cycle of product melting (95°C/1 min, 55°C/30 sec, 95°C/30 sec). The baseline adjustment method of the Mx3000 (Stratagene) software was used to determine the Ct in each reaction. A melting curve was constructed for each primer pair to verify the presence of one gene-specific peak and the absence of primer dimmer. All samples were amplified in duplicates and the mean was used for further analysis.
Analysis of expression stability
The gene expression levels were measured by real-time qPCR, and the expression stabilities were evaluated by the M value of geNorm . The M value for each reference gene is the average pairwise variation for that gene with all the other tested control genes. Stepwise exclusion of the gene with the highest M value allows ranking of the tested genes according to their expression stability.
The authors would like to thank Tina Mahler and Majken Madvig Jansen for excellent technical assistance. This work was funded by grants from the Danish Research Council, and a Ph.D. stipend from The Royal Veterinary and Agricultural University, Denmark to Ann-Britt Nygaard.
- de Kok JB, Roelofs RW, Giesendorf BA, Pennings JL, Waas ET, Feuth T, Swinkels DW, Span PN: Normalization of gene expression measurements in tumor tissues: comparison of 13 endogenous control genes. Lab Invest 2005, 85: 154-159.View ArticlePubMedGoogle Scholar
- Goossens K, Van Poucke M, Van Soom A, Vandesompele J, Van Zeveren A, Peelman LJ: Selection of reference genes for quantitative real-time PCR in bovine preimplantation embryos. BMC Dev Biol 2005, 5: 27. 10.1186/1471-213X-5-27PubMed CentralView ArticlePubMedGoogle Scholar
- Ohl F, Jung M, Xu C, Stephan C, Rabien A, Burkhardt M, Nitsche A, Kristiansen G, Loening SA, Radonic A, Jung K: Gene expression studies in prostate cancer tissue: which reference gene should be selected for normalization? J Mol Med 2005, 83: 1014-1024. 10.1007/s00109-005-0703-zView ArticlePubMedGoogle Scholar
- Erkens T, Van Poucke M, Vandesompele J, Goossens K, Van Zeveren A, Peelman LJ: Development of a new set of reference genes for normalization of real-time RT-PCR data of porcine backfat and longissimus dorsi muscle, and evaluation with PPARGC1A. BMC Biotechnol 2006, 6: 41. 10.1186/1472-6750-6-41PubMed CentralView ArticlePubMedGoogle Scholar
- Radonic A, Thulke S, Mackay IM, Landt O, Siegert W, Nitsche A: Guideline to reference gene selection for quantitative real-time PCR. Biochem Biophys Res Commun 2004, 313: 856-862. 10.1016/j.bbrc.2003.11.177View ArticlePubMedGoogle 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: 34. 10.1186/gb-2002-3-7-research0034View ArticleGoogle Scholar
- Zhang X, Ding L, Sandford AJ: Selection of reference genes for gene expression studies in human neutrophils by real-time PCR. BMC Mol Biol 2005, 6: 4. 10.1186/1471-2199-6-4PubMed CentralView ArticlePubMedGoogle Scholar
- Gorodkin J, Cirera S, Hedegaard J, Gilchrist MJ, Panitz F, Jorgensen C, Scheibye-Knudsen K, Arvin T, Lumholdt S, Sawera M, Green T, Nielsen BJ, Havgaard JH, Rosenkilde C, Wang J, Li H, Li R, Liu B, Hu S, Dong W, Li W, Yu J, Wang J, Staefeldt HH, Wernersson R, Madsen LB, Thomsen B, Hornshoj H, Bujie Z, Wang X, Wang X, Bolund L, Brunak S, Yang H, Bendixen C, Fredholm M: Porcine transcriptome analysis based on 97 non-normalized cDNA libraries and assembly of 1,021,891 expressed sequence tags. Genome Biol 2007, 8: R45. 10.1186/gb-2007-8-4-r45PubMed CentralView ArticlePubMedGoogle Scholar
- Bustin SA: A-Z of quantitative PCR. 1st edition. Edited by: Bustin SA. La Jolla, International University Line, CA, USA; 2004.Google Scholar
- Selvey S, Thompson EW, Matthaei K, Lea RA, Irving MG, Griffiths LR: Beta-actin--an unsuitable internal control for RT-PCR. Mol Cell Probes 2001, 15: 307-311. 10.1006/mcpr.2001.0376View ArticlePubMedGoogle Scholar
- Glare EM, Divjak M, Bailey MJ, Walters EH: beta-Actin and GAPDH housekeeping gene expression in asthmatic airways is variable and not suitable for normalising mRNA levels. Thorax 2002, 57: 765-770. 10.1136/thorax.57.9.765PubMed CentralView ArticlePubMedGoogle Scholar
- Deindl E, Boengler K, Van Royen N, Schaper W: Differential expression of GAPDH and beta3-actin in growing collateral arteries. Mol Cell Biochem 2002, 236: 139-146. 10.1023/A:1016166127465View ArticlePubMedGoogle Scholar
- Tricarico C, Salvadori B, Villari D, Nicita G, Della MA, Pinzani P, Ziche M, Pazzagli M: Quantitative RT-PCR assay for VEGF mRNA in human tumors of the kidney. Int J Biol Markers 1999, 14: 247-250.PubMedGoogle Scholar
- Silver N, Best S, Jiang J, Thein SL: Selection of housekeeping genes for gene expression studies in human reticulocytes using real-time PCR. BMC Mol Biol 2006, 7: 33. 10.1186/1471-2199-7-33PubMed CentralView ArticlePubMedGoogle Scholar
- Bogaert L, Van PM, De BC, Peelman L, Gasthuys F, Martens A: Selection of a set of reliable reference genes for quantitative real-time PCR in normal equine skin and in equine sarcoids. BMC Biotechnol 2006, 6: 24. 10.1186/1472-6750-6-24PubMed CentralView ArticlePubMedGoogle Scholar
- De Ketelaere A, Goossens K, Peelman L, Burvenich C: Technical note: validation of internal control genes for gene expression analysis in bovine polymorphonuclear leukocytes. J Dairy Sci 2006, 89: 4066-4069.View ArticlePubMedGoogle Scholar
- Garcia-Crespo D, Juste RA, Hurtado A: Selection of ovine housekeeping genes for normalisation by real-time RT-PCR; analysis of PrP gene expression and genetic susceptibility to scrapie. BMC Vet Res 2005, 1: 3. 10.1186/1746-6148-1-3PubMed CentralView ArticlePubMedGoogle Scholar
- Foss DL, Baarsch MJ, Murtaugh MP: Regulation of hypoxanthine phosphoribosyltransferase, glyceraldehyde-3-phosphate dehydrogenase and beta-actin mRNA expression in porcine immune cells and tissues. Anim Biotechnol 1998, 9: 67-78.View ArticlePubMedGoogle Scholar
- Kuijk EW, du PL, van Tol HT, Haagsman HP, Colenbrander B, Roelen BA: Validation of reference genes for quantitative RT-PCR studies in porcine oocytes and preimplantation embryos. BMC Dev Biol 2007, 7: 58. 10.1186/1471-213X-7-58PubMed CentralView ArticlePubMedGoogle Scholar
- Rozen S, Skaletsky H: Primer3 on the WWW for general users and for biologist programmers. Methods Mol Biol 2000, 132: 365-386.PubMedGoogle Scholar
- Zuker M: Mfold web server for nucleic acid folding and hybridization prediction. Nucleic Acids Res 2003, 31: 3406-3415. 10.1093/nar/gkg595PubMed CentralView ArticlePubMedGoogle 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.