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© 2008 American Society for Nutrition J. Nutr. 138:1158-1164, June 2008

Methodology and Mathematical Modeling

Gene Expression Ratio Stability Evaluation in Prepubertal Bovine Mammary Tissue from Calves Fed Different Milk Replacers Reveals Novel Internal Controls for Quantitative Polymerase Chain Reaction1,2

Paola Piantoni3,4, Massimo Bionaz3,4, Daniel E. Graugnard3,4, Kristy M. Daniels5, R. Michael Akers5 and Juan J. Loor3,4,*

3 Mammalian NutriPhysioGenomics, Department of Animal Sciences and 4 Division of Nutritional Sciences, University of Illinois, Urbana, IL 61801 and 5 Dairy Science Department, Virginia Tech, Blacksburg, VA 24061

* To whom correspondence should be addressed. E-mail: jloor{at}uiuc.edu.


   ABSTRACT
TOP
 ABSTRACT
Introduction
 Materials and Methods
 Results and Discussion
 LITERATURE CITED
 
Prepubertal mammary development can be affected by nutrition partly through alterations in gene network expression. Quantitative PCR (qPCR) remains the most accurate method to measure mRNA expression but is subject to analytical errors that introduce variation. Thus, qPCR data normalization through the use of internal control genes (ICG) is required. The objective of this study was to mine microarray data (>10,000 genes) from prepubertal mammary parenchyma and stroma to identify the most suitable ICG for normalization of qPCR. Tissue for RNA extraction was obtained from calves (~63 d old; n = 5/diet) fed a control (200 g/kg crude protein, 210 g/kg crude fat, fed at 441 g/d dry matter) or a high-protein milk replacer (280 g/kg crude protein, 200 g/kg crude fat, fed at 951 g/d dry matter). ICG were selected based on both absence of expression variation across treatment and of coregulation (gene network analysis). Genes evaluated were ubiquitously expressed transcript, protein phosphatase 1 regulatory (inhibitor) subunit 11 (PPP1R11), matrix metallopeptidase 14 (MMP14), ClpB caseinolytic peptidase B, SAPS domain family member 1 (SAPS1), mitochondrial GTPase 1 (MTG1), mitochondrial ribosomal protein L39, ribosomal protein S15a (RPS15A), and actin β (ACTB). Network analysis demonstrated that MMP14 and ACTB are coregulated by v-myc myelocytomatosis viral oncogene, tumor protein p53, and potentially insulin-like growth factor 1. Pairwise comparison of expression ratios showed that ACTB, MMP14, and SAPS1 had the lowest stability and were unsuitable as ICG. PPP1R11, RPS15A, and MTG1 were the most stable among ICG tested. We conclude that the geometric mean of PPP1R11, RPS15A, and MTG1 is ideal for normalization of qPCR data in prepubertal bovine mammary tissue. This study provides a list of candidate ICG that could be used by researchers working in bovine mammary development and allied fields.


   Introduction
TOP
 ABSTRACT
 Introduction
Materials and Methods
 Results and Discussion
 LITERATURE CITED
 
The period from birth to 2–3 mo age (i.e. preweaning period) is the first developmental stage in which nutrition alters neonatal mammary parenchymal and stromal weight (1,2) as well as epithelial cell proliferation (3). Therefore, understanding molecular mechanisms controlling mammary development in both compartments as affected by nutrition early in life should serve practical goals such as maximizing milk production without compromising animal health, reproduction, and longevity.

The lack of commercially available antibodies for livestock species has hampered proteomics research of tissue development and function and a growing number of scientists are focusing on gene expression regulation. Livestock-specific microarrays are available through companies (e.g. Affymetrix, Operon) or USDA-funded consortiums (e.g. Swine Protein-Annotated Oligonucleotide Microarray). However, this technology still is not widely used in the livestock scientific community due to expense as well as the need for infrastructure and reliable protocols.

Bovine-specific microarrays developed at the University of Illinois have allowed evaluation of gene expression in key bovine tissues on a comprehensive scale (4,5). Despite advantages of microarray technology, quantitative PCR (qPCR)6 is still the most accurate method of mRNA expression of selected genes and verification of microarray results. qPCR requires data normalization to obtain high precision because of procedural errors that introduce variation (6). The most reliable method for qPCR data normalization is the use of internal control genes (ICG), often referred to as housekeeping genes. This method takes into account quantity of input RNA, sample loss during handling, and variation in the kinetics of the reverse-transcription reaction. Central to the concept of ICG for normalization (6) is the notion that their expression should not be affected in response to experimental treatments or physiological state under investigation and level of expression should not differ between tissues/cell types. Identification of relationships between ICG and other molecules that could result in similar behavior (i.e. coregulation) also is important when evaluating suitable ICG. Farre et al. (7), using a bioinformatics approach, provided evidence that transcription regulators such as v-myc myelocytomatosis viral oncogene (MYC), tumor protein p53 (TP53), and SP1 possess predicted binding motifs that are overrepresented in the promoters of genes with ubiquitous or nearly ubiquitous expression, i.e. housekeeping genes, across >50 tissues (7). Others showed similar effects of transcription regulators on ribosomal RNA (rRNA) (8). MYC and TP53 are key transcriptional regulators involved in mammary development, at least in nonruminants. Unlike TP53, expression of MYC in bovine prepubertal (9,10) and lactating mammary tissue (11) has been measured previously.

Proper evaluation of ICG should be performed prior to qPCR normalization to avoid additional variation and errors in the final data (12,13). Despite extensive work on bovine prepubertal mammary development, evaluation of ICG at this physiological state has not been conducted. Previous investigations have relied on 18S rRNA (14,15) or genes such as glyceraldehyde 3-phosphate dehydrogenase (GAPDH) (9,16). The use of 18S rRNA as ICG, in particular, poses serious limitations (6). A similar case can be made against GAPDH, which was an unreliable ICG in experiments dealing with bovine mammary gland from lactating animals (11) or liver (17). The objective of the current study was to evaluate several genes that could serve as suitable ICG to normalize qPCR data in both mammary parenchyma and stroma from heifer calves fed conventional or high-protein milk replacers (i.e. an intensified-growth nutritional protocol). Microarray data from both tissues (18) were mined to obtain a set of stable genes. Analysis also included previously used internal controls such as GAPDH, actin β (ACTB), ubiquitously expressed transcript (UXT), ribosomal protein S15a (RPS15A), and mitochondrial GTPase 1 (MTG1).


   Materials and Methods
TOP
 ABSTRACT
 Introduction
 Materials and Methods
Results and Discussion
 LITERATURE CITED
 
    Animals and sampling. All procedures were conducted under protocols approved by the Virginia Tech Animal Care Committee. Holstein heifer calves (1–3 d old) were fed a control (200 g/kg crude protein, 210 g/kg crude fat, fed at 441 g dry matter/d; n = 5) or high-protein milk replacer (280 g/kg crude protein, 200 g/kg crude fat, fed at 951 g dry matter/d; n = 5). Calves consumed a "starter" diet composed of corn grain (444 g/kg diet), soybean meal (444 g/kg diet), cottonseed hull (111 g/kg diet), dried molasses (10 g/kg diet) (200 g/kg crude protein, 13 g/kg crude fat), and water ad libitum. Both groups were a subset of a larger experiment that included an additional 2 dietary treatments (18). Heifers were killed at ~63 d of age by phenobarbitol injection followed by exsanguination. Mammary glands were removed. The whole gland was weighed and bisected along the median suspensory ligament. The right hemigland was reweighed, wrapped in foil, and submerged in liquid nitrogen and stored at –80°C. Later, mammary hemiglands were removed from the freezer and dissected. Stromal tissue was harvested from the fat pad adjacent to the body wall and parenchymal tissue was harvested from the macroscopic epithelial portion of the gland adjacent to the teat. Subsamples of parenchyma and stroma were snap-frozen in liquid N2, shipped overnight to the University of Illinois, then stored in liquid N2 until use.

    RNA extraction, RNA quality assessment, PCR, primer design, and primer testing. Specific details of these procedures are presented in the Supplemental Materials and Methods.

    Relative mRNA abundance calculation. To examine potential dilution of ICG expression due to large increases in mRNA of tissue-specific genes [e.g. fatty acid binding protein-4 (FABP4) and adiponectin (ADIPOQ) in stroma], we calculated the relative percentage mRNA abundance as 1/E(Ct) [where E = efficiency (10[–1/slope]) and Ct = cycle determined by the threshold applied to the maximum amplification of the standard curve], i.e. the percentage mRNA was obtained using Ct values corrected by the efficiency of amplification of the standard curve. In general, the Ct allows for quick evaluation of differences in mRNA abundance between genes in the same sample (larger Ct corresponding to lower-expressed gene). However, for a proper comparison, the efficiency of amplification should be considered.

    Selection of genes and ICG stability evaluation. Microarray data from parenchyma and stroma (18) were used to select potential ICG. Development, testing, and application of this bovine microarray platform have been reported previously (19). Calves from the high-protein milk replacer diet were chosen specifically for the present study, because this treatment resulted in the greatest changes in gene expression profiles (18). GeneSpring GX software (Agilent Technologies) was used initially to evaluate gene expression stability among tissues and treatments (Supplemental Fig. 1). Figure 1 depicts the selection criteria of potential ICG from microarray data. Gene expression stability was evaluated using the geNorm software (20) following the procedures of Vandesompele et al. (6). Briefly, stability (M = gene-stability measure) refers to the constancy of the expression ratio between 2 noncoregulated genes among all samples tested. The more stable the expression ratio among 2 genes, the more likely that the genes are appropriate internal controls, i.e. 2 ideal control genes should have an identical expression ratio in all samples regardless of experimental conditions, cell, and/or tissue type. The lower the M value, the higher the stability. geNorm also performs an analysis to determine the utility of including >2 genes for normalization by calculating the pairwise variation (V) between the normalization factor (NF) obtained using n genes (best references) (NFn) and the NF obtained using n + 1 genes (addition of an extra less stable reference gene) (NFn+1). A large decrease in the pairwise variation indicates that addition of the subsequent more stable gene (i.e. with lowest M value) has a significant effect and should be included for calculation of the NF (6). Once the most stable internal reference genes are selected, the NF is calculated using the geometric mean between them to normalize qPCR data.


Figure 1
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  		FIGURE 1  Flow diagram of selection criteria used to identify suitable ICG from prepubertal bovine mammary stroma and parenchyma microarray data for qPCR normalization.

 
    Statistical analysis. Nonnormalized gene expression data (Table 1) were analyzed using a MIXED model in SAS (SAS Institute) to assess effects of tissue, diet, and their interaction on mRNA expression. Correlation analysis was conducted using the PROC CORR procedure (SAS Institute). Data reported in Table 1 and Supplemental Figure 2 are means ± SEM. When the interaction was significant, means were compared using Tukey's post-hoc test. Differences were considered significant when P < 0.05.


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  		TABLE 1 Relative nonnormalized gene expression patterns of potential ICG in bovine mammary parenchyma and stroma of prepubertal Holstein heifer calves fed a control or high-protein milk replacer1

 

   Results and Discussion
TOP
 ABSTRACT
 Introduction
 Materials and Methods
 Results and Discussion
LITERATURE CITED
 
    General considerations for selecting ICG. Criteria for selecting ICG (Fig. 1) were based on absence of expression variation across treatments and tissues but more importantly on absence of coregulation (6). Upon applying the first criterion, we found that protein phosphatase 1, regulatory (inhibitor) subunit 11 (PPP1R11), SAPS domain family, member 1 (SAPS1), matrix metallopeptidase 14 (MMP14), and ClpB caseinolytic peptidase B (CLPB) had the most stable expression ratio (1.0 ± 0.4 tissue/reference standard) among >10,000 genes evaluated (Supplemental Fig. 1). To perform a more thorough analysis of ICG (6), we included several genes previously tested as ICG (e.g. 12,17,21), such as the widely used ACTB and GAPDH, RPS15A, MTG1, mitochondrial ribosomal protein L39 (MRPL39), and UXT. Supplemental Figure 2 depicts the pattern of microarray expression across diet and tissue of genes chosen for further analysis. Visual inspection clearly shows that ACTB and GAPDH had the most unstable expression, as previously shown by other qPCR experiments (6,17).

Absence of coregulation is the 2nd criterion that should be considered for the evaluation of reliable ICG. Vandesompele et al. (6) developed geNorm as a tool to evaluate potential ICG based on the principle that 2 "real" ICG should equally trace the errors occurring at every passage from RNA extraction through qPCR. Furthermore, their mRNA expression should not be affected by treatments or physiological state. As a requirement of the method, genes tested as ICG should not share upstream effectors of their mRNA abundance (e.g. transcription regulators, proteins that bind DNA or RNA, hormones, or cytokines); otherwise, the pairwise comparison will be biased by the common regulation (i.e. coregulation).

    Coregulation analysis of potential ICG. Ingenuity Pathway Analysis (Ingenuity Systems) was used to assess coregulation among potential ICG (Fig. 2). This software allows the examination of pathways and networks within microarray gene expression datasets. Resulting networks provide evidence of potential connections between genes of interest as well as hormones [e.g. growth hormone and insulin] or growth factors [e.g. insulin-like growth factor-1 (IGF-I) and tumor necrosis factor {alpha}]. Networks are generated based on published relationships across several organisms, including human, mouse, and rat. Ingenuity pathway analysis revealed that, among all genes tested, few had currently known coregulation events (Fig. 2). Expression of ACTB, GAPDH, RPS9, and RPS23 is directly regulated by MYC (8), whereas TP53 regulates expression of ACTB, GAPDH, and MYC (22–24). MYC binds the promoter region of UXT and, although not demonstrated in vivo, could regulate its expression (25). UXT was found to be an appropriate ICG for longitudinal studies of bovine mammary gene expression (11).


Figure 2
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  		FIGURE 2  Interactions and cellular location of genes tested as ICG in bovine mammary stroma and parenchyma. Networks were developed using ingenuity pathway analysis. Genes selected as potential ICG included PPP1R11, RPS15A, MRPL39, UXT, MMP14, SAPS1, CLPB, and MTG1.

 
Hormones and/or growth factors also can affect expression and function of MYC and TP53, as well as widely accepted ICG. For example, IGF-I can induce expression of MYC in bovine cells (26) and GAPDH in human mammary cells (27). Whether these effects also occur in vivo in mammary tissue has not yet been established. MMP14 expression is indirectly regulated by tumor necrosis factor {alpha} and IGF-I (28,29). The latter regulates gene expression of GAPDH and MYC. Even though direct coregulation between MMP14 and other genes evaluated as ICG is not apparent, the presence of an indirect regulation can potentially introduce bias when genes are tested using geNorm. Clearly, coregulation analysis should help avoid the selection of inappropriate genes as ICG. In fact, it prevented us from using ACTB and GAPDH to calculate the NF. This information was crucial, because ACTB and GAPDH had been previously used as ICG in studies dealing with bovine mammary tissue or cells (e.g. 15,16,30).

A recent study (7) demonstrated that a number of transcription factors (e.g. ATF, E2F, HIF1A, and SP1), including MYC, bind to promoter motifs that are specifically overrepresented in "housekeeping genes", i.e. those genes with ubiquitous or nearly ubiquitous expression (n = ~1000) across >50 tissues studied including mammary gland and adipose. Molecular functions overrepresented in the majority of housekeeping genes (>500) found include nucleotide binding and RNA binding (7). Over 700 housekeeping genes are components of the cytoplasm (7). Both MYC and TP53 are key transcription factors associated with mammary development in nonruminants. Expression of MYC and TP53, transcription regulators known to be upstream of commonly used ICG (e.g. ACTB, GAPDH, and 18S rRNA) (Fig. 2; 9), was quantified by qPCR and data were normalized using the geometrical mean of the 3 most stable ICG (RPS15A, PPP1R11, and MTG1). Results (nonnormalized and normalized) revealed higher expression of MYC and TP53 in parenchyma vs. stroma (Supplemental Fig. 1). In parenchyma, MYC and ACTB (r = 0.88; P = 0.001), TP53 (r = 0.72; P = 0.02), or UXT (r = 0.74; P = 0.02) were all significantly correlated. Correlation between TP53 and ACTB was positive (r = 0.59) but not significant (P = 0.09). Taken together, these data suggest a positive effect of MYC and, potentially, TP53 on expression of ACTB, which might partly explain the higher expression of this gene in parenchyma vs. stroma. Overall, results showed that ACTB is not a reliable ICG for studies dealing with prepubertal bovine mammary tissue, because it might result in biased data. This is supported by previous similar analyses with bovine tissues consistently showing that ACTB is a poor internal reference gene for normalization (11,17).


Figure 3
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  		FIGURE 3  Median Ct of potential ICG and tissue-specific genes (A). Threshold of detection was set at a median Ct of 30. Relative mRNA expression was considered high at median Ct ≤ 20. Percentage of each mRNA of tissue-specific, highly expressed genes, relative to the sum of all 5 genes shown (B).

 
PPP1R11, MMP14, CLPB, SAPS1, MTG1, MRLP39, and RPS15A have no currently known coregulation. Thus, they represented a novel pool of candidate ICG for normalization of qPCR data. In addition, these genes do not have currently known relationships with MYC and TP53 (Fig. 2). Despite the potential regulation of UXT by MYC, this gene was added to the above pool because of the absence of coregulation with other tested ICG (Fig. 2). We chose to examine ACTB using geNorm along with other candidate ICG, despite potential coregulation with UXT, namely because it is the most widely used ICG in studies of bovine mammary gland (Supplemental Fig. 3).

    Pairwise comparison of expression ratios among potential ICG. Results using geNorm indicated that the most stable genes in our study were PPP1R11, RPS15A, and MTG1 (Fig. 4A,B). In a previous study, RPS15A also was among 3 ideal ICG for longitudinal bovine mammary gene expression analysis for tissue from lactating cows (11). Although it would have been ideal to use 5 ICG for normalization (i.e. higher stability value) of qPCR results in the present study (Fig. 4B), only the 3 most stable genes were used for normalization, namely on grounds of practicality and in light of the higher stability using a more stringent cut-off (0.10 vs. 0.15) than suggested by Vandesompele et al. (6). Among ICG tested, ACTB, MMP14, and SAPS1 had the lowest stability values (Supplemental Fig. 3), rendering them unsuitable as ICG in the present and similar studies.


Figure 4
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  		FIGURE 4  Stability (M) of gene expression ratios of potential ICG using geNorm software (A). Values are reported as stepwise exclusion of the least stable control gene. geNorm analysis of optimal number of internal reference genes for qPCR normalization (B). The optimal number of ICG were determined by assessment of the pairwise variation V (Vn/n+1) between the normalization factors NFn and NFn+1.

 
    Additional considerations for selecting potential ICG. Statistical analysis of nonnormalized qPCR data (Table 1) showed that ICG had greater expression in parenchyma, with the exception of MRLP39. The greater expression of ICG in parenchyma vs. stroma might be seen as a "limitation" in their use as ICG. However, there are other factors that should be taken into consideration regarding this point. Parenchyma had greater RNA concentration (µg/g tissue) than stroma regardless of diet (Supplemental Fig. 2). Stroma is composed primarily of adipocytes (e.g. 3) containing large lipid droplets that increase cell volume and thus reduce the amount of RNA per weight of tissue. Parenchymal cells have smaller volume and will result in a greater amount of RNA per weight of tissue. Therefore, the amount of RNA per weight of tissue is not an appropriate measure of the concentration of RNA per cell. A more appropriate approach would be to use the ratio of tissue RNA:DNA. Recent studies have observed RNA:DNA ratios ~1.5 in parenchyma from 8-wk-old calves (1), but similar relationships for stroma have not been reported. In our experiment, the RNA:DNA ratio did not differ (tissue, P = 0.46) and was 0.33, 0.48, 0.41, and 0.47 (SEM = 0.13) for control stroma, control parenchyma, high-protein milk replacer stroma, and high-protein milk replacer parenchyma, respectively. Control calves weighed 66 ± 8 kg and those in the high-protein milk replacer group weighed 86 ± 8 kg at slaughter, values similar to those reported by Brown et al. (1). However, RNA concentrations obtained in our study were lower than those reported by Brown et al. (1), which could partly explain discrepancies in RNA:DNA. Differences in techniques for nucleic acid quantification also might account for discrepancies in RNA. Bionaz and Loor (11) observed previously a dilution effect in bovine mammary tissue, where the gradual increase in RNA as cows progressed from late pregnancy through peak lactation was due to an increase in mRNA abundance of genes associated with milk component synthesis (i.e. caseins and lactalbumin). Because the starting amount of RNA used for qPCR is the same across all samples, the much higher expression of genes such as {alpha}-lactalbumin unavoidably made genes with stable expression appear to be less expressed.

Analysis of selected genes with very large amounts of mRNA in stroma provides evidence of an artificial dilution of ICG expression. Microarray analysis (18) revealed several genes that are highly expressed in stroma vs. parenchyma and vice versa. For example, highly expressed genes in stroma included FABP4 and ADIPOQ and highly expressed genes in parenchyma included secreted phosphoprotein 1 and lactotransferrin. To verify the above results, we evaluated their relative mRNA abundance using qPCR (Fig. 3A,B). ACTB was most abundant among the 5 genes tested in both parenchyma and stroma, but it was only 1.3-fold higher in parenchyma vs. stroma. In contrast, stroma had substantially greater mRNA abundance of the lipid metabolism-related genes FABP4 and ADIPOQ. The presence of several genes with large mRNA abundance might potentially increase tissue RNA/DNA, resulting in an apparent "dilution" of stably expressed genes. Bionaz and Loor (11) provided compelling evidence that a simple statistical analysis of raw data to assess time effects was not a reliable method to select appropriate ICG due to a dilution effect.

Microarray data provided a wealth of information (18) for discovery of potential ICG in prepubertal mammary parenchyma and stroma. Network analysis highlighted complex levels of coregulation encompassing commonly used ICG (e.g. GAPDH and ACTB), including effects of growth factors and hormones and transcriptional regulation (via MYC and TP53). Pairwise comparison of expression ratios was valuable for selection of suitable ICG. Of the 9 genes tested as potential internal controls, PPP1R11, RPS15A, and MTG1 were most stable and, thus, best for normalization of qPCR data. With the exception of RPS15A, those genes had not been previously used as ICG for mammary gene expression analysis. All genes deemed suitable as ICG had an apparent tissue effect, which might have been a consequence of increased mRNA from highly expressed and tissue-specific genes (e.g. FABP4 and ADIPOQ in stroma vs. parenchyma).

This study provides novel ICG that could be used by researchers working in this or allied fields. Our approach highlights that: 1) several genes should be evaluated as potential internal controls for qPCR studies; 2) use of actively regulated genes should be avoided; 3) potential dilution effects also should be considered when choosing the most appropriate ICG; 4) pairwise comparison of expression ratios for multiple ICG performed by geNorm, instead of using a single ICG, circumvents the "requirement" for stable expression across tissues and/or treatments and accounts for potential dilution effects; and 5) caution should be taken when interpreting qPCR data from studies lacking proper evaluation of ICG. We conclude that the geometric mean of PPP1R11, RPS15A, and MTG1 can be used for normalization in future studies of prepubertal mammary development or to verify important relationships that have arisen from bovine microarray studies (e.g. 18).


   ACKNOWLEDGMENTS
 
We thank Dr. Robin E. Everts for his continued support with annotation of the bovine microarray platform.


   FOOTNOTES
 
1 Author disclosures: P. Piantoni, M. Bionaz, D. E. Graugnard, K. M. Daniels, R. M. Akers, and J. J. Loor, no conflicts of interest. Back

2 Supplemental Materials and Methods, Supplemental Tables 1 and 2, and Supplemental Figures 1–3 are available with the online posting of this paper at jn.nutrition.org. Back

6 Abbreviations used: ACTB, actin β; ADIPOQ, adiponectin; CLPB, ClpB caseinolytic peptidase B; Ct, cycle determined by the threshold applied to the maximum amplification of the standard curve during quantitative PCR; E, efficiency of quantitative PCR reaction; FABP4, fatty acid binding protein-4; GAPDH, glyceraldehyde 3-phosphate dehydrogenase; ICG, internal control gene; IGF-I, insulin-like growth factor 1; MMP14, matrix metallopeptidase 14; MRLP39, mitochondrial ribosomal protein L39; MTG1, mitochondrial GTPase 1; MYC, v-myc myelocytomatosis viral oncogene; NF, normalization factor; PPP1R11, protein phosphatase 1, regulatory (inhibitor) subunit 11; qPCR, quantitative PCR; RPS15A, ribosomal protein S15a; SAPS1, SAPS domain family, member 1; TP53, tumor protein p53; UXT, ubiquitously expressed transcript. Back

Manuscript received 9 January 2008. Initial review completed 8 February 2008. Revision accepted 26 March 2008.


   LITERATURE CITED
TOP
 ABSTRACT
 Introduction
 Materials and Methods
 Results and Discussion
 LITERATURE CITED
 

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