Evaluation of suitable reference genes for gene expression studies in porcine alveolar macrophages in response to LPS and LTA
© Cinar et al; licensee BioMed Central Ltd. 2012
Received: 17 November 2011
Accepted: 18 February 2012
Published: 18 February 2012
To obtain reliable quantitative real-time PCR data, normalization relative to stable housekeeping genes (HKGs) is required. However, in practice, expression levels of 'typical' housekeeping genes have been found to vary between tissues and under different experimental conditions. To date, validation studies of reference genes in pigs are relatively rare and have never been performed in porcine alveolar macrophages (AMs). In this study, expression stability of putative housekeeping genes were identified in the porcine AMs in response to the stimulation with two pathogen-associated molecular patterns (PAMPs) lipopolysaccharide (LPS) and lipoteichoic acid (LTA). Three different algorithms (geNorm, Normfinder and BestKeeper) were applied to assess the stability of HKGs.
The mRNA expression stability of nine commonly used reference genes (B2M, BLM, GAPDH, HPRT1, PPIA, RPL4, SDHA, TBP and YWHAZ) was determined by qRT-PCR in AMs that were stimulated by LPS and LTA in vitro. mRNA expression levels of all genes were found to be affected by the type of stimulation and duration of the stimulation (P < 0.0001). geNorm software revealed that SDHA, B2M and RPL4 showed a high expression stability in the irrespective to the stimulation group, while SDHA, YWHAZ and RPL4 showed high stability in non-stimulated control group. In all cases, GAPDH showed the least stability in geNorm. NormFinder revealed that SDHA was the most stable gene in all the groups. Moreover, geNorm software suggested that the geometric mean of the three most stable genes would be the suitable combination for accurate normalization of gene expression study.
There was discrepancy in the ranking order of reference genes obtained by different analysing algorithms. In conclusion, the geometric mean of the SDHA, YWHAZ and RPL4 seemed to be the most appropriate combination of HKGs for accurate normalization of gene expression data in porcine AMs without knowing the type of bacterial pathogenic status of the animals.
KeywordsCandidate reference genes Alveolar macrophage LPS LTA Pigs
Alveolar macrophages (AMs) are thought to be critical in the pathogenesis of several lung diseases . Swine respiratory diseases, which has been described world-wide, affects swine of all ages and has a serious impact on economy, ecology and animal welfare in the pig rearing industry . Both Gram-positive and Gram-negative bacteria are causing respiratory disease in pigs . As an in vitro model for the development of lung inflammation, AMs stimulation with PAMPs in culture is being frequently used for immunogenetic research in pigs [4–7]. Lipopolysaccharide (LPS) and lipoteichoic acid (LTA) are the PAMPs of the Gram-negative and the Gram-positive bacterial cell wall that cause activation of an acute inflammatory response in vitro as well as in vivo. Gene expression assay is a common way to investigate the defensive role of AMs in the bacterial infections as well as to dissect the pathogenesis of bacterial lung diseases. With this purposes, several studies focusing on gene expressions have been conducted in AMs in vitro [4–7]. The gene expression are required to normalize for housekeeping genes (HKGs) which have tremendous effect on the results of expression study . Therefore, it is crucial to know whether the expression stability of HKGs in AMs is affected by various PAMPs from infectious agents but these data are currently unavailable for pigs.
Quantitative real-time PCR (qRT-PCR) is a powerful technique for gene expression studies, which have become increasingly important in a large number of clinical and scientific fields [8, 9]. Besides being a powerful technique, inappropriate data normalization is the most important problem in qRT-PCR . For an exact comparison of mRNA transcription in different samples or tissues, it is crucial to choose the appropriate reference gene . The most accepted approach to mRNA quantification is normalization of the expression level of a gene of interest (target gene) to the expression level of a stably expressed internal reference gene [8, 9]. Normalizing to a reference gene is a widely used method because it is simple in theory. Normalizing to a single reference gene is often used but Vandesompele et al.  suggested that geometric mean of multiple carefully selected HKGs is recommendable and suitable for accurate normalization. The normalization adjusts for differences in the quality or quantity of template RNA or 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. This allows the direct comparison of normalized transcript expression levels between samples. Reference genes should ideally be constitutively expressed by all cell types and should not be affected by disease and experimental procedure. To date, a universal reference gene has not been identified. HKGs are most commonly used reference genes . Although HKGs are expressed by any cell, their expression varies among different cell types/organs, age, sex and treatment or experimental conditions [10–17]. Use of HKGs as reference genes for a particular sample type should be, therefore, validated.
Ideally, the conditions of the experiment should not influence the expression of the reference genes . However, the mRNA expression of reference genes from different cells and tissues [18–21] such as from AMs [1, 10] may fluctuate due to infectious agents in vitro. Alveolar macrophages are being used as an important model to dissect the pathogenesis and genetics behind the infection through gene expression studies [5, 6, 22, 23]. To date, no reference genes have been validated for expression studies of AMs in pigs. The aim of this study was therefore to identify a set of stably expressed reference genes in porcine AMs cells irrespective of stimulation as well as in the case of stimulation by bacterial LTA and LPS in vitro.
Animals and preparation of alveolar macrophage cells
Fourty-day-old three German Landrace piglets were euthanized for sampling. All animals were healthy and exhibited no signs of hypoxia or asphyxia or infections. Animals were kept and euthanized in the research station of Frankenforst at University of Bonn, following German pig breeding guidelines . AMs were obtained from bronchoalveolar lavage (BAL) of animals. In brief, lungs were lavaged with 200 ml ice-cold sterile calcium-magnesium free Dulbecco's phosphate-buffered saline (PBS) (pH 7.4) that was instilled gently in 25 ml aliquots into the each of two adjacent lung subsegments and withdrawn immediately. BAL fluid from each animal was collected in separate tubes and filtered through sterile gauze. Cells were centrifuged at 4°C for 10 min at 400 × g. Pellets of bronchoalveolar cells were washed twice with sterile D-PBS at 250 × g for 10 min and resuspended in 2 mM L-glutamine-containing complete RPMI-1640 media (Sigma) supplemented with 10% fetal calf serum (Invitrogen) and containing antibiotics and antimycotics (penicillin, streptomycin and amphotericin, Invitrogen). The average purity of AM cells was 91% and other cells were mostly polymorphonuclear cells (8%) and remaining was lymphocytes. The cell viability was determined by Trypan blue dye exclusion method (> 98% in all cases).
Stimulation of alveolar macrophage cells with LPS and LTA
The cells were counted using Haemocytometer (AbCam) and concentration was adjusted. The AMs were plated in ultra-low attachment polystyrene 24-wells plate (CellStar) at 2 × 106 cells in 1 ml medium in each well. Plates were incubated at 37°C with 5% CO2 (Heraeus Instrument) for 48 h. After 1 h incubation, cells were stimulated with LPS of Escherichia coli 055:B5 (Sigma) (10 μg per ml per well), LTA of Staphylococcus aureus (Sigma) (10 μg per ml per well) and with both of LPS and LTA (10 μg per ml per well). Cells were then collected at 1, 4, 8, 12, 24 and 48 h after stimulation for RNA extraction and stored at -80°C. For every time point non-stimulated control group was also included.
RNA extraction and cDNA synthesis
Harvested AM cells were washed in RPMI-1640 medium and the total RNA was extracted using Pico-Pure RNA isolation kit following the manufacturer's protocol (Arcturus, Applied Biosystems). In order to remove possible contaminating genomic DNA, the extracted RNA was treated with 5 μl RQ1 DNase buffer, 5 units DNase and 40 units of RNase inhibitor in a 40 μl reaction volume. The mixture was incubated at 37°C for 1 h followed by purification with the RNeasy Mini Kit (Qiagen). Concentration of clean-up RNA was determined spectrophotometrically by using the NanoDrop ND-8000 (Thermo Scientific) instrument; the purity of RNA was estimated by the ratio A260/A280 with respect to contaminants that absorb in the UV. Additional examination of integrity was done by denaturing agarose gel electrophoresis and ethidium bromide staining. Finally, the purified RNA was stored at -80°C for further analysis. Approximately 1.5 μg of total RNA for each sample was transcribed into cDNA. cDNA was synthesised with SuperScript-II RT kit (Invitrogen). All samples were reverse transcribed under the same conditions. The synthesized cDNA was stored at -20°C and used in qRT-PCR reactions as a template.
Selection of reference genes and primer design
Selected candidate reference genes, primers, and PCR reactions efficiencies
GenBank accession number
Amplicon length (bp)
Amplification efficiency (%)
Average Ct of cDNA
Quantitative real-time PCR (qRT-PCR)
Determination of reference gene expression stability
The raw qRT-PCR amplification data was exported from the StepOne® software (Applied Biosystem) to Microsoft® Excel. The averages of the Ct-values for each duplicate were used for stability comparison of candidate reference genes in the NormFinder, geNorm and BestKeeper software. For easy understanding, the samples were grouped into 5 different categories such as LPS stimulated, LTA stimulated, LPS + LTA (combined), control and irrespective to stimulation group (when all the stimulated and non-stimulated control were considered together). The effect of stimulation and time on the expression of housekeeping genes was tested using GLM procedure of the SAS software (ver.9.2; SAS, SAS Institute Inc., Cary, NC, USA). Differences in gene expression levels between time and stimulation were determined using t-test in SAS. P < 0.05 was considered statistically significant.
Ct-values of all samples were exported to Excel, ordered for use in geNormPlus software (15 days free trial version qBasePlus; http://www.biogazelle.com) and geNorm transformed to relative quantities using the gene-specific PCR amplification efficiency . These relative quantities were then exported to geNormPlus to analyze gene expression stability . The approach of reference gene selection implemented in geNorm relies on the principle that the expression ratio of two ideal reference genes should be identical in all samples, independent of the treatment, condition, or tissue type. Increasing variations in the expression ratio between two genes correspond to lower expression stability across samples. geNorm calculates the stability using a pairwise comparison model . geNorm determines the level of pairwise variation for each reference gene with all other reference genes as the standard deviation of the logarithmically transformed expression ratios. In this way, the reference gene expression stability measure (M value) was calculated as the average pairwise variation of a particular gene with all other control genes included in the analysis [8, 15]. Lower M values represent higher expression stabilities. Sequential elimination of the least stable gene (highest M value) generates a ranking of genes according to their M values and results in the identification of the genes with the most stable expression in the samples under analysis. geNorm was also used to estimate the normalization factor (NF n ) using n multiple reference genes, by calculating the geometric mean of the expression levels of the n best reference genes . The optimisation of the number of reference genes starts with the inclusion of the two genes with the lowest M value, and continues by sequentially adding genes with increasing values of M. Thus, geNorm calculates the pairwise variation V n /Vn+1between two sequential normalization factors NF n and NFn+1containing an increasing number of reference genes . A large variation means that the added gene has a significant effect on the normalization and should preferably be included for calculation of a reliable normalization factor. Ideally, extra reference genes are included until the variation V n /Vn+1drops below a given threshold. According to geNorm, if Vn/n+1 < 0.15 the inclusion of an additional reference gene is not required and the recommended number of reference genes is given by n .
NormFinder uses an ANOVA-based model . The software calculates a stability value for all candidate reference genes tested. The stability value is based on the combined estimate of intra- and inter-group expression variations of the genes studied . For each gene, the average Ct value of each duplicate reaction was converted to relative quantity data as described for geNorm, to calculate the stability value with NormFinder program . The NormFinder reference tool was applied to rank the candidate reference gene expression stability for all samples with no subgroup determination (irrespective to stimulation) as well as with stimulation (LPS, LTA, and both LPS and LTA) as subgroup. A low stability value, indicating a low combined intra- and inter-group variation, indicates high expression stability .
The average Ct-value of each duplicate reaction was used (without conversion to relative quantity) in BestKeeper to analyze the stability value of studied genes . BestKeeper creates a pairwise correlation coefficient between each gene and the BestKeeper index (BI). This index is the geometric mean of the Ct-values of all candidate reference genes grouped together. BestKeeper also calculates standard deviation (SD) of the Ct-values between the whole data set. The gene with the highest coefficient of correlation with the BI indicates the highest stability .
Purity, quantity of extracted RNA and verification of amplicons
The optical density (OD) ratio A260/A280 nm measured with a Nanodrop spectrophotometer was 1.94 ± 0.17 (OD A260/A280 ratio ± SD). The average RNA concentration after extraction using Pico Pure was 10.33 μg/μl ± 1.1 (μg/μl ± SD). The results of the averaged amplification efficiencies are shown in Table 1. The amplification efficiencies for the nine candidate reference genes ranged between 89.45% and 99.43%. The agarose gel electrophoresis (Figure 1a) and melting curve analysis (Figure 1b-j and Table 1) revealed that all primer pairs amplified a single PCR product with expected size. Furthermore, sequence analysis of cloned amplicons revealed that all sequenced amplified fragments were identical to sequences used for primer design from GenBank (data not shown).
Expression levels of candidate reference genes
LPS and LTA affect expression level of reference genes
Identification of optimal reference genes
NormFinder software ranked all HKGs according to their stability value (Figure 4f-j) . The expression stability was not always consistent between the used softwares. By using NormFinder, genes SDHA, YWHAZ and HPRT1 were ranked as the most stable HKGs in irrespective to stimulation group (Figure 4f). In the non-stimulated control group and LPS stimulated group, SDHA, YWHAZ and RPL4 remained the most stable genes (Figure 4g-h). In the LTA stimulated group, SDHA, YWHAZ and PPIA were ranked as the most stable HKGs (Figure 4i). In the combined LPS and LTA stimulation group, SDHA, HPRT1 and TBP were found to be most stable HKGs (Figure 4j). PPIA remained the least stable HKGs followed by GAPDH and BLM according to the NormFinder algorithm.
Expression stability of nine candidate reference genes evaluated by BestKeeper software
Irrespective to stimulation
SD [± Ct]
CV [% Ct]
SD [± Ct]
CV [% Ct]
SD [± Ct]
CV [% Ct]
SD [± Ct]
CV [% Ct]
LTA + LPS
SD [± Ct]
CV [% Ct]
Determination of the optimal number of reference genes for normalization
Using reference genes that have a stable expression between the compared groups is crucial in gene expression studies. Several studies have shown that the use of different reference genes can change the outcome and conclusions of a study [13, 19, 38]. Ideally, the internal control gene for quantitative gene expression studies should not be influenced by the conditions of the experiment. However, our study showed that expression of the HKGs was affected by stimulation type as well as stimulation duration (Additional file 1: Table S1). Therefore it is generally recommended that the stability of HKGs is being validated prior to expression studies. There are some reports of the expression levels of HKGs in various cells and tissues and also of the methods used to analyse the stability of these genes. Recent research has demonstrated that the expression of HKGs may be altered due to state of the organ [21, 39], age [17, 21, 26] and experimental conditions [18, 20, 40]. In the characterization of the course of an inflammatory reaction, quantitative real-time PCR has become a powerful tool for detection of inflammatory parameters, including cytokines and Toll-like receptors (TLRs). This tool is particularly useful in pigs since commercial species-specific antibodies directed against pig cytokines and TLRs are not commonly available. To best of our knowledge, there has not yet been a detailed evaluation of HKGs in swine AMs. Moreover, there has not been a detailed study under different types of stimulation such as LPS, LTA and combined LPS and LTA that might be indicated Gram-negative, Gram-positive bacterial infection or co-infection of both types of bacteria in vivo. Although no in depth studies are apparent in the AMs cells, there have been numerous research papers which have used single HKGs for normalisation of gene expression in AMs. These have included the use of HPRT1 , GAPDH  and 18S rRNA  for normalisation of gene expression. As a consequence, in this study, we evaluated the gene expression stability of nine commonly used HKGs in porcine AMs, and furthermore, assessed their stability in states of different inflammatory models such as in response to LPS and LTA.
In recent years, there have been a number of research papers and reviews evaluating the selection and effect of controls on normalised gene expression data in various pig tissues. Gu et al.  involved in the validation of 20 common endogenous control genes in 56 fat- and muscle-type tissues. Nygard et al.  investigated a vast number of tissues for 10 HKGs. Studies focusing on more specific tissues, including the backfat, longissimus dorsi muscle , liver , adipose , stomach  and mesenchymal stem cells  are being reported in pigs. Taken together, 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 under different experimental or infectious conditions. According to the NCBI-PubMed statistics , GAPDH and ACTB are the two mostly used porcine HKGs. But they have been shown to vary considerably and are consequently unsuitable as reference genes for normalization of gene expression analysis in many cases [43–45]. We applied three software programs to our data as complementary analyses to obtain the most suitable genes for our experiments. Both algorithms resulted in an overall comparable order of genes. Two of the three best genes were always presented by geNorm and NormFinder. Although BestKeeper  is found on the same principle as geNorm, not in every case both algorithms displayed overlapping suitable HKGs.
In the present study, geNorm and NormFinder showed that SDHA, YWHAZ and RPL4 are the most stable three HKGs in the control (without any stimulation) group as well as in stimulation groups (Figure 4). Our results are in good agreement with Piórkowska et al.  who identified GAPDH and TBP as the least stable HKGs for the porcine adipose tissue. Beside, TBP was always found to be as a moderately stably expressed gene in this study. Nygard et al.  reported that RPL4, TBP and YWHAZ have the highest stability across tissues collected from healthy pigs which are somewhat consistent with the present study. Pierzchala et al.  recently reported that HPRT1 and TBP are the most stable HKGs in porcine liver and in different skeletal muscle tissues but it could be found that HPRT1 and TBP is moderately stable through different experiments conditions in this study (Figure 4). Moreover, Svobodová et al.  estimated HPRT1 has the highest stability while GAPDH was unstable across different porcine tissues which are in agreement with our result for the GAPDH but not for HPRT1. Because HPRT1 was found to be moderately stable in our experiment, except in combined LPS and LTA group.
To our knowledge, there are only two studies evaluating the stability of reference genes in AMs. One being in human AMs  and the other being in the horse ; no data is available on the stability of reference genes in AMs of other mammalian species. Ishii et al.  reported that HPRT1 is the most stable HKG, whereas TBP is the least stable HKG in both the LPS stimulated and non-stimulated AMs in human which is in good agreement with our result using geNorm. (Figure 4b-c). Beekman et al.  used geNorm to validate the candidate HKGs and found that GAPDH, SDHA, HPRT and RPL32 were the most stably expressed genes in bronchoalveolar lavage cells of horses with inflammatory airway disease with corticosteroids treatment. In this study, SDHA was identified as suitable reference gene by using NormFinder through the experiments which is agreement with the report in horse .
According to the BestKeeper analysis software, in the irrespective to stimulation group YWHAZ was detected in accordance with the NormFinder and partially with the geNorm results (Table 2; Figure 4). SDHA was identified as the most stable gene in both geNorm and NormFinder (Figure 4); however, BestKeeper identified this gene as unsuitable according to its algorithm criteria. In the control group, although PPIA was identified as a stably expressed HKG by BestKeeper (Table 2), this gene was identified moderately stable in geNorm and NormFinder (Figure 4). By using the three software algorithms similar results were obtained in LPS stimulated group, where SDHA was identified as the most stable HKG. In the LTA stimulated group, although BLM was identified as the most stable HKG by BestKeeper, but showed very low expression stability in geNorm and NormFinder. In case of the combined LPS and LTA stimulated group, RPL4 was found to be the most stable gene by BestKeeper (Table 2); however, this gene ranked as the fourth most stable HKG by geNorm (Figure 4). Several studies previously reported similar discrepancies for the findings of BestKeeper [15, 31, 37] and importantly, few studies followed the BestKeeper analysis method compared to geNorm and NormFinder. It is important to note that very similar discrepancies between the different algorithms have been observed in previous studies comparing statistical analysis methods [10, 15, 31, 37, 47].
However, we found that the first three most stable reference genes in most cases were consistent between the software geNorm and NormFinder, even if they were not in the exact same ranking order. Similar findings are reported by previous studies in horse, human and plants [10, 13, 15, 47]. Such discrepancy could be explained by genes' co-regulation. Indeed, co-regulated genes may become highly ranked independently of their expression stabilities with geNorm software . Moreover, NormFinder takes into account variation across subgroups, thus avoiding artificial selection of co-regulated genes by analyzing the expression stability of candidate genes independently from each other . However, no studies dealing with porcine reference genes stability used other analysis methods except geNorm [11, 12, 16, 26, 31, 42].
As described above, geNorm also provides a measure for the best number of reference genes that should be used for optimal normalization. In agreement with several previous studies, we postulate that the use of more than one reference gene allows for a more accurate normalization than the use of only one reference gene [8, 12, 35]. Based on a cut-off point for the V value, as described by Vandesompele et al. , a combination of the several most stable reference genes was calculated as being optimal for gene expression studies in control and PAMPs stimulated porcine AMs (Figure 5). However, as we described above and other studies [8, 12] recommended, the combination of the most three stable genes are appropriate for accurate normalization.
In conclusion, this investigation found evidence that there can be variation in the expression of commonly used HKGs due to different PAMPs. Due to the new influx of data suggesting alterations in mRNA expression according to bacteria type, we feel that beside therapy uses or experimental condition, there needs to be special consideration given to the selection of HKGs based upon the bacterial pathogen identification. This indicates that the choice of reference genes cannot be transposed from on study to the other without validation for the specifics of each experimental protocol. Since different bacterial pathogens are cooperating in the respiratory tract as co-infection, our results will shed light on pathogenic or disease status of experiments. In general, we recommend using the geometric mean of SDHA, B2M and RPL4 to guarantee suitable normalization in across the AMs with unknown respiratory pathogenic condition in pigs. Since in the most cases, Gram-negative and Gram-positive bacteria are observed together in respiratory diseases, HPRT1, YWHAZ and SDHA might be an appropriate set of reference genes for the gene expression normalization in AM studies. SDHA, YWHAZ and RPL4 could be suggested in case of AMs without any stimulation. This study offers an appropriate set of HKGs that might be used in the normalization of gene expression data in vitro cultured porcine AMs.
quantitative real-time reverse transcriptase polymerase chain reaction
- B2M :
- BLM :
Bloom syndrome: RecQ helicase-like
- GAPDH :
Glyceraldehyde 3-phosphate dehydrogenase
- HPRT1 :
Hypoxanthine phosphoribosyltransferase 1
- PPIA :
Peptidylprolyl isomerase A (cyclophilin A)
- RPL4 :
Ribosomal protein L4
- SDHA :
Succinate dehydrogenase complex subunit A flavoprotein
- TBP :
TATA box binding protein
- YWHAZ :
Tyrosine 3/tryptophan 5-monooxygenase activation protein zeta polypeptide
This project was supported by the Gene Dialog project, FUGATO Plus, BMBF, grant no: 0315130C, Germany. The authors are indebted to Prof. Dr. Florian M. W. Grundler, Molecular Phytomedicine, University of Bonn, Germany for providing with StepOnePlus Real-time PCR system (Applied Biosystem) during experiment. Authors are also thankful to Ms. Nadine Leyer for her technical assistance during experiments.
- Ishii T, Wallace AM, Zhang X, Gosselink J, Abboud RT, English JC, Pare PD, Sandford AJ: Stability of housekeeping genes in alveolar macrophages from COPD patients. Eur Respir J. 2006, 27 (2): 300-306. 10.1183/09031936.06.00090405.PubMedView ArticleGoogle Scholar
- Rose N, Madec F: Occurrence of respiratory disease outbreaks in fattening pigs: relation with the features of a densely and a sparsely populated pig area in France. Vet Res. 2002, 33 (2): 179-190. 10.1051/vetres:2002100.PubMedView ArticleGoogle Scholar
- Sørensen V, Jorsal SE, Mousing J: Diseases of the respiratory system. Diseases of Swine. Edited by: Straw BE, Zimmerman JJ, Allaire SD', Taylor DJ. 2006, Blackwell Publishing, Ames, IA, 149-177. 9Google Scholar
- Chung WB, Backstrom L, Mcdonald J, Collins MT: Actinobacillus-pleuropneumoniae culture supernatants interfere with killing of pasteurella-multocida by swine pulmonary alveolar macrophages. Can J Vet Res. 1993, 57 (3): 190-197.PubMedPubMed CentralGoogle Scholar
- Giuffra E, Genini S, Delputte PL, Malinverni R, Cecere M, Stella A, Nauwynck HJ: Genome-wide transcriptional response of primary alveolar macrophages following infection with porcine reproductive and respiratory syndrome virus. J Gen Virol. 2008, 89: 2550-2564. 10.1099/vir.0.2008/003244-0.PubMedPubMed CentralView ArticleGoogle Scholar
- Lin GF, Pearson AE, Scamurra RW, Zhou YL, Baarsch MJ, Weiss DJ, Murtaugh MP: Regulation of interleukin-8 expression in porcine alveolar macrophages by bacterial lipopolysaccharide. J Biol Chem. 1994, 269 (1): 77-85.PubMedGoogle Scholar
- Miller LC, Lager KM, Kehrli ME: Role of Toll-like receptors in activation of porcine alveolar macrophages by porcine reproductive and respiratory syndrome virus. Clin Vaccine Immunol. 2009, 16 (3): 360-365. 10.1128/CVI.00269-08.PubMedPubMed CentralView 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: research0034.1-research0034.11. 10.1186/gb-2002-3-7-research0034.View 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 Biophys Res Commun. 2004, 313 (4): 856-862. 10.1016/j.bbrc.2003.11.177.PubMedView ArticleGoogle Scholar
- Beekman L, Tohver T, Dardari R, Leguillette R: Evaluation of suitable reference genes for gene expression studies in bronchoalveolar lavage cells from horses with inflammatory airway disease. BMC Mol Biol. 2011, 12: 5-10.1186/1471-2199-12-5.PubMedPubMed CentralView ArticleGoogle 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: 41-10.1186/1472-6750-6-41.PubMedPubMed CentralView ArticleGoogle Scholar
- Gu Y, Li M, Zhang K, Chen L, Jiang A, Wang J, Li X: Evaluation of endogenous control genes for gene expression studies across multiple tissues and in the specific sets of fat- and muscle-type samples of the pig. J Anim Breed Genet. 2011, 128: doi:10.1111/j.1439-0388.2011.00920.xGoogle Scholar
- Kriegova E, Arakelyan A, Fillerova R, Zatloukal J, Mrazek F, Navratilova Z, Kolek V, du Bois RM, Petrek M: PSMB2 and RPL32 are suitable denominators to normalize gene expression profiles in bronchoalveolar cells. BMC Mol Biol. 2008, 9: 69-10.1186/1471-2199-9-69.PubMedPubMed CentralView ArticleGoogle Scholar
- Lovdal T, Lillo C: Reference gene selection for quantitative real-time PCR normalization in tomato subjected to nitrogen, cold, and light stress. Anal Biochem. 2009, 387 (2): 238-242. 10.1016/j.ab.2009.01.024.PubMedView 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: 15-10.1186/1471-2199-11-15.PubMedPubMed CentralView ArticleGoogle Scholar
- Nygard AB, Jorgensen CB, Cirera S, Fredholm M: Selection of reference genes for gene expression studies in pig tissues using SYBR green qPCR. BMC Mol Biol. 2007, 8: 67-10.1186/1471-2199-8-67.PubMedPubMed CentralView ArticleGoogle Scholar
- Touchberry CD, Wacker MJ, Richmond SR, Whitman SA, Godard MP: Age-related changes in relative expression of real-time PCR housekeeping genes in human skeletal muscle. J Biomol Tech. 2006, 17 (2): 157-162.PubMedPubMed CentralGoogle Scholar
- Schmittgen TD, Zakrajsek BA: Effect of experimental treatment on housekeeping gene expression: validation by real-time, quantitative RT-PCR. J Biochem Biophys Methods. 2000, 46 (1-2): 69-81. 10.1016/S0165-022X(00)00129-9.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): 112-114. 116, 118-119PubMedGoogle Scholar
- Manzano R, Osta R, Toivonen JM, Calvo AC, Munoz MJ, Zaragoza P: Housekeeping gene expression in myogenic cell cultures from neurodegeneration and denervation animal models. Biochem Biophys Res Commun. 2011, 407 (4): 758-763. 10.1016/j.bbrc.2011.03.096.PubMedView ArticleGoogle Scholar
- Uddin MJ, Cinar MU, Tesfaye D, Looft C, Tholen E, Schellander K: Age-related changes in relative expression stability of commonly used housekeeping genes in selected porcine tissues. BMC Res Notes. 2011, 4 (1): 441-10.1186/1756-0500-4-441.PubMedPubMed CentralView ArticleGoogle Scholar
- Iglesias G, Pijoan C, Molitor T: Interactions of pseudorabies virus with swine alveolar macrophages--effects of virus-infection on cell functions. J Leukoc Biol. 1989, 45 (5): 410-415.PubMedGoogle Scholar
- Kim HM, Lee YW, Lee KJ, Kim HS, Cho SW, van Rooijen N, Guan Y, Seo SH: Alveolar macrophages are indispensable for controlling influenza viruses in lungs of pigs. J Virol. 2008, 82 (9): 4265-4274. 10.1128/JVI.02602-07.PubMedPubMed CentralView ArticleGoogle Scholar
- ZDS: Richtlinie fuer die Stationspruefung auf Mastleistung, Schlachtkoerperwert und Fleischbe-schaffenheit beim Schwein. 2003, Zentral Verband der Deutschen Schweineproduktion e.V, Bonn, Germany, 10.12.2003 2003Google Scholar
- Pierzchala M, Lisowski P, Urbanski P, Pareek CS, Cooper RG, Jolanta K: Evaluation based selection of housekeeping genes for studies of gene expression in the porcine muscle and liver tissues. J Anim Vet Adv. 2011, 10 (4): 401-405.View ArticleGoogle Scholar
- Piorkowska K, Oczkowicz M, Rozycki M, Ropka-Molik K, Piestrzynska-Kajtoch A: Novel porcine housekeeping genes for real-time RT-PCR experiments normalization in adipose tissue: assessment of leptin mRNA quantity in different pig breeds. Meat Sci. 2011, 87 (3): 191-195. 10.1016/j.meatsci.2010.10.008.PubMedView ArticleGoogle Scholar
- Kaewmala K, Uddin MJ, Cinar MU, Grosse-Brinkhaus C, Jonas E, Tesfaye D, Phatsara C, Tholen E, Looft C, Schellander K: Association study and expression analysis of CD9 as candidate gene for boar sperm quality and fertility traits. Anim Reprod Sci. 2011, 125 (1-4): 170-179. 10.1016/j.anireprosci.2011.02.017.PubMedView ArticleGoogle Scholar
- Kayan A, Cinar MU, Uddin MJ, Phatsara C, Wimmers K, Ponsuksili S, Tesfaye D, Looft C, Juengst H, Tholen E, et al: Polymorphism and expression of the porcine Tenascin C gene associated with meat and carcass quality. Meat Sci. 2011, 89 (1): 76-83. 10.1016/j.meatsci.2011.04.001.PubMedView ArticleGoogle Scholar
- Kayan A, Uddin MJ, Cinar MU, Grosse-Brinkhaus C, Phatsara C, Wimmers K, Ponsuksili S, Tesfaye D, Looft C, Juengst H, et al: Investigation on interferon alpha-inducible protein 6 (IFI6) gene as a candidate for meat and carcass quality in pig. Meat Sci. 2011, 88 (4): 755-760. 10.1016/j.meatsci.2011.03.009.PubMedView ArticleGoogle Scholar
- Laenoi W, Uddin MJ, Cinar MU, Phatsara C, Tesfaye D, Scholz AM, Tholen E, Looft C, Mielenz M, Sauerwein H, et al: Molecular characterization and methylation study of matrix gla protein in articular cartilage from pig with osteochondrosis. Gene. 2010, 459 (1-2): 24-31. 10.1016/j.gene.2010.03.009.PubMedView ArticleGoogle Scholar
- Oczkowicz M, Różycki M, Piórkowska K, Piestrzyńska-Kajtoch A, Rejduch B: A new set of endogenous reference genes for gene expression studies of porcine stomach. J Anim Feed Sci. 2010, 19: 570-576.Google Scholar
- Cinar MU, Kayan A, Uddin MJ, Jonas E, Tesfaye D, Phatsara C, Ponsuksili S, Wimmers K, Tholen E, Looft C, et al: Association and expression quantitative trait loci (eQTL) analysis of porcine AMBP, GC and PPP1R3B genes with meat quality traits. Mol Biol Rep. 2011, doi: 10.1007/s11033-011-1274-4Google 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
- 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 (2): R19-10.1186/gb-2007-8-2-r19.PubMedPubMed CentralView ArticleGoogle Scholar
- Andersen CL, Jensen JL, Orntoft 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
- 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
- Stern-Straeter J, Bonaterra GA, Hormann K, Kinscherf R, Goessler UR: Identification of valid reference genes during the differentiation of human myoblasts. BMC Mol Biol. 2009, 10: 66-10.1186/1471-2199-10-66.PubMedPubMed CentralView ArticleGoogle 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.57.9.765.PubMedPubMed CentralView ArticleGoogle Scholar
- Silver N, Cotroneo E, Proctor G, Osailan S, Paterson KL, Carpenter GH: Selection of housekeeping genes for gene expression studies in the adult rat submandibular gland under normal, inflamed, atrophic and regenerative states. Bmc Molecular Biology. 2008, 9: 64-10.1186/1471-2199-9-64.PubMedPubMed CentralView ArticleGoogle Scholar
- De Boever S, Vangestel C, De Backer P, Croubels S, Sys SU: Identification and validation of housekeeping genes as internal control for gene expression in an intravenous LPS inflammation model in chickens. Vet Immunol Immunopathol. 2008, 122 (3-4): 312-317. 10.1016/j.vetimm.2007.12.002.PubMedView ArticleGoogle Scholar
- de Greeff A, Benga L, Wichgers Schreur PJ, Valentin-Weigand P, Rebel JM, Smith HE: Involvement of NF-kappaB and MAP-kinases in the transcriptional response of alveolar macrophages to Streptococcus suis. Vet Microbiol. 2010, 141 (1-2): 59-67. 10.1016/j.vetmic.2009.07.031.PubMedView ArticleGoogle Scholar
- Monaco E, Bionaz M, de Lima AS, Hurley WL, Loor JJ, Wheeler MB: Selection and reliability of internal reference genes for quantitative PCR verification of transcriptomics during the differentiation process of porcine adult mesenchymal stem cells. Stem Cell Res Ther. 2010, 1 (1): 7-10.1186/scrt7.PubMedPubMed CentralView ArticleGoogle Scholar
- Barber RD, Harmer DW, Coleman RA, Clark BJ: GAPDH as a housekeeping gene: analysis of GAPDH mRNA expression in a panel of 72 human tissues. Physiol Genomics. 2005, 21 (3): 389-395. 10.1152/physiolgenomics.00025.2005.PubMedView ArticleGoogle Scholar
- Jung M, Ramankulov A, Roigas J, Johannsen M, Ringsdorf M, Kristiansen G, Jung K: In search of suitable reference genes for gene expression studies of human renal cell carcinoma by real-time PCR. BMC Mol Biol. 2007, 8: 47-10.1186/1471-2199-8-47.PubMedPubMed CentralView ArticleGoogle 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 (5): 307-311. 10.1006/mcpr.2001.0376.PubMedView ArticleGoogle Scholar
- Svobodova K, Bilek K, Knoll A: Verification of reference genes for relative quantification of gene expression by real-time reverse transcription PCR in the pig. J Appl Genet. 2008, 49 (3): 263-265. 10.1007/BF03195623.PubMedView ArticleGoogle Scholar
- Cappelli K, Felicetti M, Capomaccio S, Spinsanti G, Silvestrelli M, Supplizi AV: Exercise induced stress in horses: selection of the most stable reference genes for quantitative RT-PCR normalization. BMC Mol Biol. 2008, 9: 49-10.1186/1471-2199-9-49.PubMedPubMed CentralView 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.