Identification of internal control genes for quantitative polymerase chain reaction in mammary tissue of lactating cows receiving lipid supplements.

Dietary lipid supplements affect mammary lipid metabolism partly through changes in lipogenic gene expression. Quantitative PCR (qPCR) is a sensitive, reliable, and accurate technique for gene expression analysis. However, variation introduced in qPCR data by analytical or technical errors needs to be accounted for via normalization using appropriate internal control genes (ICG). Objectives were to mine individual bovine mammary microarray data on >13,000 genes across 66 cows from 2 independent studies to identify the most suitable ICG for qPCR normalization. In addition to unsupplemented control diets, cows were fed saturated or unsaturated lipids for 21 d or were infused with supplements (butterfat, conjugated linoleic acid mixture, long-chain fatty acids) into the abomasum to modify milk fat synthesis and fatty acid profiles. We identified 49 genes that did not vary in expression across the 66 samples. Subsequent gene network analysis revealed that 22 of those genes were not co-regulated. Among those COPS7A, CORO1B, DNAJC19, EIF3K, EMD, GOLGA5, MTG1, UXT, MRPL39, GPR175, and MARVELD1 (sample/reference expression ratio = 1 +/- 0.1) were selected for PCR analysis upon verification of goodness of BLAT/BLAST sequence and primer design. Relative expression of B2M, GAPDH, and ACTB, previously used as ICG in bovine mammary tissue, was highly variable (0.9 +/- 0.6) across studies. Gene stability analysis via geNorm software uncovered MRPL39, GPR175, UXT, and EIF3K as having the most stable expression ratio and, thus, suitable as ICG. Analysis also indicated that use of 3 ICG was most appropriate for calculating a normalization factor. Overall, the geometric average of MRPL39, UXT, and EIF3K is ideal for normalization of mammary qPCR data in studies involving lipid supplementation of dairy cows. These novel ICG could be used for normalization in similar studies as alternatives to the less-reliable ACTB, GAPDH, or B2M.