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

Abstract

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.

Conjugated linoleic acid-induced milk fat reduction associated with depressed expression of lipogenic genes in lactating Holstein mammary glands

“…Abomasal infusion of trans-10, cis-12 CLA was shown to decrease the mRNA abundance of LPL, ACACA, and SCD on Northern blot analysis , whereas infusion of CLA reduced the mRNA expression of ACACA, FASN, LPL, and SCD on real-time PCR with one reference gene (Gervais et al, 2009). We observed numeric reductions in the mRNA levels of lipogenic genes (LPL, ACACA, FASN, and SCD), and our data were more accurate and more reliable owing to analysis with real-time PCR using 3 appropriate reference genes for data normalization (Kadegowda et al, 2009). Taken together, these results demonstrated that the mechanism of milk fat depression with CLA supplementation involves the inhibition of de novo synthesis and uptake from circulation within the mammary tissue.…”

“…The primer sequences and reaction efficiencies of the standard curve for the target genes are shown in Table S1. The relative messenger RNA (mRNA) expression levels of lipogenic genes were normalized by 3 housekeeping genes -eukaryotic translation initiation factor 3, subunit K; ubiquitously expressed transcript, and mitochondrial ribosomal protein L39 -in real-time RT-PCR assays (Kadegowda et al, 2009). To calculate the relative expression ratio of the target gene, we used the formula described by Pfaffl (2001) and Vandesompele et al (2002), which included correction for PCR efficiency (E) and 3 reference genes ( ).…”

Identification of reference genes for RT-qPCR in ovine mammary tissue during late pregnancy and lactation and in response to maternal nutritional programming

“…The ability of RT-qPCR to accurately detect changes in gene expression relies upon the selection of stably expressed reference genes. Studies in other species have shown that the expression of commonly used reference genes may vary between physiological and nutritional states and experimental treatments (2,4,19,37). Variation in expression of reference genes may limit the ability to detect and verify changes in expression of target genes, thus reducing the percentage of genes that validate.…”

“…Therefore, in the present study, candidate reference genes were selected from RNA-seq expression data (PRP3, CUL1, SF1, SENP2, and IPO9) and from studies conducted in other species [MRPL39 (4,19,37), EIF6 (4), and ATP1A1 (9,24)]. These genes were evaluated across pooled and individual RNA samples.…”

Expression Analysis of Solute Carrier (SLC2A) Genes in Milk Derived Mammary Epithelial Cells during Different Stages of Lactation in Sahiwal (Bos indicus) Cows

“…These four genes were selected as most appropriate from a set of 10 HKGs evaluated using excel-based visual basic macros tools viz., geNorm, Normfinder and Bestkeeper (unpublished data). Several studies have recommended more than one HKG as an effective means for normalization of qPCR data to account for the experimental variations [20][21][22]. The C T (cycle threshold) values of SLC2A and HK2 mRNA were normalized with the geometric mean of C T values of the 4 HKGs (RPL4, EEF1A1, GAPDH, and ACTB) to calculate ΔC T .…”