1. Selection and validation of reference house-keeping genes in the J774A1 macrophage cell line for quantitative real-time PCR.
- Author
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Ferraz FB and Fernandez JH
- Subjects
- Animals, Cell Line, Data Accuracy, Mice, RNA, Messenger, Real-Time Polymerase Chain Reaction methods, Reference Standards, Gene Expression, Genes, Essential genetics, Macrophages metabolism, Real-Time Polymerase Chain Reaction standards
- Abstract
Macrophages are essential components of the innate and adaptive immune responses, playing a decisive role in atherosclerosis, asthma, obesity, and cancer. The differential gene expression resulting from adhesion of macrophages to the extra-cellular matrix (ECM) has been studied in the J774A1 murine macrophage cell line using quantitative polymerase chain reaction (qPCR). The goal of this study was to identify housekeeping genes (HKGs) that remain stable and unaltered under normal culture conditions and in the presence of laminin after a time lapse of 6 and 24 h. The expression stabilities of eight commonly used reference genes were analyzed by determining the comparative threshold cycle ((ΔΔ)Ct) values, and using the BestKeeper, NormFinder, and geNorm algorithms. BestKeeper analysis revealed that the glyceraldehyde-3-phosphate dehydrogenase (GAPDH), peptidylprolyl isomerase A (PPIA), and ribosomal protein L13a (RPL13A) genes were highly stable, confirming the results of the (ΔΔ)Ct analysis. On the other hand, NormFinder proposed RPL13A and beta-glucuronidase (GUSB) to be the most suitable combination, and geNorm adjudged RPL13A, PPIA, and GUSB to be the most stable across all culture conditions. All programs discarded the use of actin beta and beta-2-microglobulin for normalization. The collected data indicated that RPL13A, PPIA, GAPDH, and GUSB as highly suitable as reference genes for qPCR analysis of murine macrophages under normal and ECM-simulated culture conditions. This study also emphasizes the importance of evaluating HKGs used for normalization to ensure the accuracy of qPCR data.
- Published
- 2016
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