Reference genes for normalization of gene expression studies in human osteoarthritic articular cartilage

Background  

Assessment of gene expression is an important component of osteoarthritis (OA) research, greatly improved by the development of quantitative real-time PCR (qPCR). This technique requires normalization for precise results, yet no suitable reference genes have been identified in human articular cartilage. We have examined ten well-known reference genes to determine the most adequate for this application.     

Evaluation of candidate reference genes in Clostridium difficile for gene expression normalization

Quantitative real-time polymerase chain reaction (qPCR) is a sensitive, efficient and reproducible technique for studying gene expression. Identification of stably expressed reference genes is required to avoid bias in these studies yet mostly unvalidated reference genes are used in studying gene expression in Clostridium difficile. Here, we sought to identify a set of stable reference genes used to normalize C. difficile expression data comparing exponential versus stationary phases of growth. Eight candidate reference genes (rpoA, rrs, gyrA, gluD, adk, rpsJ, tpi, and rho) were assessed in 3 C. difficile genotypes (ribotypes 027, 078, and 001). The primers were analyzed for efficiency and the 8 genes were ranked according to their stability. Overall, the genes rrs, adk, and rpsJ ranked among the most stable. Identification of the most stable genes was, however, strain dependent and suggests that selection of reference genes in a heterogeneous species, such as C. difficile, requires multiple genes to be assessed to confirm their stability within the strains being studied.     

Reference genes for measuring mRNA expression

The aim of this review is to find answers to some of the questions surrounding reference genes and their reliability for quantitative experiments. Reference genes are assumed to be at a constant expression level, over a range of conditions such as temperature. These genes, such as GADPH and beta-actin, are used extensively for gene expression studies using techniques like quantitative PCR. There have been several studies carried out on identifying reference genes. However, a lot of evidence indicates issues to the general suitability of these genes. Recent studies had shown that different factors, including the environment and methods, play an important role in changing the expression levels of the reference genes. Thus, we conclude that there is no reference gene that can deemed suitable for all the experimental conditions. In addition, we believe that every experiment will require the scientific evaluation and selection of the best candidate gene for use as a reference gene to obtain reliable scientific results.     

Histological evidence: housekeeping genes beta-actin and GAPDH are of limited value for normalization of gene expression

Housekeeping genes are widely used as internal controls for gene expression normalization for western blotting, northern blotting, RT-PCR, etc. They are generally thought to be expressed in all cells of the organism at similar levels because it is assumed that these genes are required for the maintenance of basic cellular function as constitutive genes. However, real-time RT-PCR experiments revealed that their expression may vary depending on the developmental stage, type of tissue examined, experimental condition, and so on. To date, no histological data on their expression are available for embryonic development. In the present study, we compared the histological expression profile of two commonly used housekeeping genes, GAPDH and beta-actin, in the developing chicken embryo by using section and whole mount in situ hybridization supplemented by RT-PCR. Our results show that neither GAPDH mRNA nor beta-actin mRNA is expressed in all cell types or tissues at high levels. Strikingly, expression levels are very low in some organs. Moreover, the two genes show partially complementary expression patterns in the liver, the vascular system and the digestive tract. For example, GAPDH is more strongly expressed in the liver than beta-actin, but at lower levels in the arteries. Vice versa, beta-actin is more strongly expressed in the gizzard than GAPDH, but it is almost absent from cardiac muscle cells. Researchers should consider these histological results when using GAPGD and beta-actin for gene expression normalization in their experiments.     

Reference gene selection for quantitative real-time PCR normalization: Application in the caprine mammary gland

In dairy animals, gene expression analysis has become increasing key to understand the biological processes occurring in mammary gland development that shape future milk potential. Selecting high-stability reference genes is crucial to interpret real-time qPCR data. This study investigated the expression stability of five top-ranked candidate reference genes in the goat mammary gland through three assays comparing different experimental conditions (physiological states, sample types and experimental treatments). The expression stability of genes including β-actin, glyceraldehyde-3-phosphate dehydrogenase, 18S rRNA, cyclophilin A and ribosomal protein large P0 was analyzed. Normalization for each experimental condition expression data revealed a different reference gene. Nevertheless, in our various assays, genes encoding for ribosomal proteins, 18S rRNA and RPLP0 presented the best expression stability. This result has been confirmed using a combined analysis of stability on the three assays. All genes showed the same distribution within and among the three assays and a different distribution between Ct variability and GeNorm normalization. In addition, the application on Catenin B1 expression using an inappropriate reference gene confirmed erroneous variations in interpretation. To conclude, there is no single ideal reference gene for caprine mammary gland studies and we recommend using a panel of top-ranked reference genes, including RPLP0, at the beginning of each experiment to validate the most stable(s) gene(s).     

Centralization: a new method for the normalization of gene expression data

Microarrays measure values that are approximately proportional to the numbers of copies of different mRNA molecules in samples. Due to technical difficulties, the constant of proportionality between the measured intensities and the numbers of mRNA copies per cell is unknown and may vary for different arrays. Usually, the data are normalized (i.e., array-wise multiplied by appropriate factors) in order to compensate for this effect and to enable informative comparisons between different experiments. Centralization is a new two-step method for the computation of such normalization factors that is both biologically better motivated and more robust than standard approaches. First, for each pair of arrays the quotient of the constants of proportionality is estimated. Second, from the resulting matrix of pairwise quotients an optimally consistent scaling of the samples is computed.     

Identification of reference genes for normalization of gene expression in thoroughbred and Jeju native horse(Jeju pony) tissues

Quantitative analysis of horse gene expression profiles under diverse experimental conditions is limited by the lack of reliable reference genes for normalization of mRNA levels. Therefore, in this study, the expression of potential reference genes was compared between thoroughbred and Jeju native horse (Jeju pony). We compared the expression of nine genes by quantitative real-time RT-PCR in fourteen tissues between the two horse breeds and analyzed their stability using the geNorm and NormFinder programs. The data obtained in this study suggest that the UBB gene could serve as a reference gene in gene expression analysis of thoroughbred and Jeju native horses.     

Bayesian normalization and identification for differential gene expression data.

Commonly accepted intensity-dependent normalization in spotted microarray studies takes account of measurement errors in the differential expression ratio but ignores measurement errors in the total intensity, although the definitions imply the same measurement error components are involved in both statistics. Furthermore, identification of differentially expressed genes is usually considered separately following normalization, which is statistically problematic. By incorporating the measurement errors in both total intensities and differential expression ratios, we propose a measurement-error model for intensity-dependent normalization and identification of differentially expressed genes. This model is also flexible enough to incorporate intra-array and inter-array effects. A Bayesian framework is proposed for the analysis of the proposed measurement-error model to avoid the potential risk of using the common two-step procedure. We also propose a Bayesian identification of differentially expressed genes to control the false discovery rate instead of the ad hoc thresholding of the posterior odds ratio. The simulation study and an application to real microarray data demonstrate promising results.