Evaluation of suitable reference genes for gene expression studies in porcine alveolar macrophages in response to LPS and LTA

BACKGROUND: 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. RESULTS: 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. CONCLUSIONS: 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.