Evaluation and validation of internal control genes for studying gene expression in the dinoflagellate Prorocentrum minimum using real-time PCR

Housekeeping genes (HKGs) are required for the normalization of expression levels in real-time PCR, and their selection is critical for gene expression studies. However, stable expressions of candidate HKGs vary among organisms and tissues and even according to environmental conditions. Here, we evaluated the gene expressions of 10 frequently used HKGs, including 18S rRNA, P2, ACT, TUA, TUB, CYC, eIF4E, MDH, UBQ, and GAPDH, with a total of 17 RNA samples extracted from the dinoflagellate Prorocentrum minimum. All the RNAs were prepared from various cells cultured under different conditions, including thermal shocks, toxic chemical exposures, and different life stages. Via C(T) analyses of the 10 HKGs using the geNorm software, TUA was selected as the most stable HKG for gene expression studies of the dinoflagellate, followed by MDH. Pair-wise variation (V) analysis showed that at least 2 genes were required for accurate normalization of gene expression studies depending on the experimental conditions. These HKGs are useful internal controls for the normalization of gene expression in P. minimum.     

Predicting housekeeping genes based on Fourier analysis

Housekeeping genes (HKGs) generally have fundamental functions in basic biochemical processes in organisms, and usually have relatively steady expression levels across various tissues. They play an important role in the normalization of microarray technology. Using Fourier analysis we transformed gene expression time-series from a Hela cell cycle gene expression dataset into Fourier spectra, and designed an effective computational method for discriminating between HKGs and non-HKGs using the support vector machine (SVM) supervised learning algorithm which can extract significant features of the spectra, providing a basis for identifying specific gene expression patterns. Using our method we identified 510 human HKGs, and then validated them by comparison with two independent sets of tissue expression profiles. Results showed that our predicted HKG set is more reliable than three previously identified sets of HKGs.     

Microarray platform affords improved product analysis in mammalian cell growth studies

High throughput (HT) platforms serve as a cost‐efficient and rapid screening method for evaluating the effect of cell‐culture conditions and screening of chemicals. We report the development of a HT cell‐based microarray platform to assess the effect of culture conditions on Chinese hamster ovary (CHO) cells. Specifically, growth, transgene expression and metabolism of a GS/methionine sulphoximine (MSX) CHO cell line, which produces a therapeutic monoclonal antibody, was examined using a microarray system in conjunction with a conventional shake flask platform in a non‐proprietary medium. The microarray system consists of 60‐nL spots of cells encapsulated in alginate and separated in groups via an 8‐well chamber system attached to the chip. Results show the non‐proprietary medium developed allows cell growth, production, and normal glycosylation of recombinant antibody and metabolism of the recombinant CHO cells in both the microarray and shake flask platforms. In addition, 10.3 mM glutamate addition to the defined base medium results in lactate metabolism shift in the recombinant GS/MSX CHO cells in the shake flask platform. Ultimately, the results demonstrate that the HT microarray platform has the potential to be utilized for evaluating the impact of media additives on cellular processes, such as cell growth, metabolism, and productivity.     

Identification of Novel Reference Genes Using Multiplatform Expression Data and Their Validation for Quantitative Gene Expression Analysis

Normalization of mRNA levels using endogenous reference genes (ERGs) is critical for an accurate comparison of gene expression between different samples. Despite the popularity of traditional ERGs (tERGs) such as GAPDH and ACTB, their expression variability in different tissues or disease status has been reported. Here, we first selected candidate housekeeping genes (HKGs) using human gene expression data from different platforms including EST, SAGE, and microarray, and 13 novel ERGs (nERGs) (ARL8B, CTBP1, CUL1, DIMT1L, FBXW2, GPBP1, LUC7L2, OAZ1, PAPOLA, SPG21, TRIM27, UBQLN1, ZNF207) were further identified from these HKGs. The mean coefficient variation (CV) values of nERGs were significantly lower than those of tERGs and the expression level of most nERGs was relatively lower than high expressing tERGs in all dataset. The higher expression stability and lower expression levels of most nERGs were validated in 108 human samples including formalin-fixed paraffin-embedded (FFPE) tissues, frozen tissues and cell lines, through quantitative real-time RT-PCR (qRT-PCR). Furthermore, the optimal number of nERGs required for accurate normalization was as few as two, while four genes were required when using tERGs in FFPE tissues. Most nERGs identified in this study should be better reference genes than tERGs, based on their higher expression stability and fewer numbers needed for normalization when multiple ERGs are required.     

Validation of Reference Housekeeping Genes for Gene Expression Studies in Western Corn Rootworm (Diabrotica virgifera virgifera)

Quantitative Real-time PCR (qRT-PCR) is a powerful technique to investigate comparative gene expression. In general, normalization of results using a highly stable housekeeping gene (HKG) as an internal control is recommended and necessary. However, there are several reports suggesting that regulation of some HKGs is affected by different conditions. The western corn rootworm (WCR), Diabrotica virgifera virgifera LeConte (Coleoptera: Chrysomelidae), is a serious pest of corn in the United States and Europe. The expression profile of target genes related to insecticide exposure, resistance, and RNA interference has become an important experimental technique for study of western corn rootworms; however, lack of information on reliable HKGs under different conditions makes the interpretation of qRT-PCR results difficult. In this study, four distinct algorithms (Genorm, NormFinder, BestKeeper and delta-CT) and five candidate HKGs to genes of reference (β-actin; GAPDH, glyceraldehyde-3-phosphate dehydrogenase; β-tubulin; RPS9, ribosomal protein S9; EF1a, elongation factor-1α) were evaluated to determine the most reliable HKG under different experimental conditions including exposure to dsRNA and Bt toxins and among different tissues and developmental stages. Although all the HKGs tested exhibited relatively stable expression among the different treatments, some differences were noted. Among the five candidate reference genes evaluated, β-actin exhibited highly stable expression among different life stages. RPS9 exhibited the most similar pattern of expression among dsRNA treatments, and both experiments indicated that EF1a was the second most stable gene. EF1a was also the most stable for Bt exposure and among different tissues. These results will enable researchers to use more accurate and reliable normalization of qRT-PCR data in WCR experiments.     

Development and characterization of a Chinese hamster ovary cell-specific oligonucleotide microarray

The Chinese hamster ovary (CHO) cell line is one of the most widely used mammalian cell lines for biopharmaceutical production. We have developed and characterized a gene expression microarray (WyeHamster2a) specific for CHO cells that has enabled the study of ~3,500 sequences. Analysis of multiple sets of replicate scans showed that data derived from the WyeHamster2a array is highly reproducible confirming it as a robust tool for profiling. Twelve gene sequences were selected for follow-up RT-qPCR to confirm the accuracy and precision of the microarray results. In all but the most subtle gene expression differences, the microarray proved to be a reliable measure of differential gene expression. Finally, we were able to quantify the difference between using a bona fide CHO-specific microarray for profiling CHO cells versus an alternate, commercially available, rodent microarray such as a mouse or rat-specific format.     

Validation of rat reference genes for improved quantitative gene expression analysis using low density arrays

Real-time PCR has become increasingly important in gene expression profiling research, and it is widely agreed that normalized data are required for accurate estimates of messenger RNA (mRNA) expression. With increased gene expression profiling in preclinical research and toxicogenomics, a need for reference genes in the rat has emerged, and the studies in this area have not yet been thoroughly evaluated. The purpose of our study was to evaluate a panel of rat reference genes for variation of gene expression in different tissue types. We selected 48 known target genes based on their putative invariability. The gene expression of all targets was examined in 11 types of rat tissues using TaqMan low density array (LDA) technology. The variability of each gene was assessed using a two-step statistical model. The analysis of mean expression using multiple reference genes was shown to provide accurate and reliable normalized expression data. The least five variable genes from each specific tissue were recommended for future tissue-specific studies. Finally, a subset of investigated rat reference genes showing the least variation is recommended for further evaluation using the LDA platform. Our work should considerably enhance a researcher's ability to simply and efficiently identify appropriate reference genes for given experiments.     

PlantRGS: a web server for the identification of most suitable candidate reference genes for quantitative gene expression studies in plants

Normalization of quantitative gene expression data with a suitable reference gene is essential for accurate and reliable results. However, the availability and choice of most suitable reference gene(s) showing uniform expression across all the experimental conditions remain a drawback. We have developed a web server, PlantRGS (http://www.nipgr.res.in/PlantRGS), for the identification of most suitable candidate reference gene(s) at the whole-genome level using microarray data for quantitative gene expression studies in plants. Microarray data from more than 11 000 tissue samples for nine plant species have been included in the PlantRGS for meta-analysis. The web server provides a user-friendly graphical user interface-based analysis tool for the identification of most suitable reference genes in the selected plant species under user-defined experimental conditions. Various parameter options and output formats will help users to investigate desired number of most suitable reference genes with wide range of expression levels. Validation of results revealed that novel reference genes identified by the PlantRGS outperforms the traditionally used reference genes in terms of expression stability. We anticipate that the PlantRGS will provide a platform for the identification of most suitable reference gene(s) under given experimental conditions and facilitate quantitative gene expression studies in plants.     

Evaluation of DNA microarray results with quantitative gene expression platforms

We have evaluated the performance characteristics of three quantitative gene expression technologies and correlated their expression measurements to those of five commercial microarray platforms, based on the MicroArray Quality Control (MAQC) data set. The limit of detection, assay range, precision, accuracy and fold-change correlations were assessed for 997 TaqMan Gene Expression Assays, 205 Standardized RT (Sta)RT-PCR assays and 244 QuantiGene assays. TaqMan is a registered trademark of Roche Molecular Systems, Inc. We observed high correlation between quantitative gene expression values and microarray platform results and found few discordant measurements among all platforms. The main cause of variability was differences in probe sequence and thus target location. A second source of variability was the limited and variable sensitivity of the different microarray platforms for detecting weakly expressed genes, which affected interplatform and intersite reproducibility of differentially expressed genes. From this analysis, we conclude that the MAQC microarray data set has been validated by alternative quantitative gene expression platforms thus supporting the use of microarray platforms for the quantitative characterization of gene expression.     

Transcriptional signatures of normal human mammary epithelial cells in response to benzo[a]pyrene exposure: a comparison of three microarray platforms

Microarrays are used to study gene expression in a variety of biological systems. A number of different platforms have been developed, but few studies exist that have directly compared the performance of one platform with another. The goal of this study was to determine array variation by analyzing the same RNA samples with three different array platforms. Using gene expression responses to benzo[a]pyrene exposure in normal human mammary epithelial cells (NHMECs), we compared the results of gene expression profiling using three microarray platforms: photolithographic oligonucleotide arrays (Affymetrix), spotted oligonucleotide arrays (Amersham), and spotted cDNA arrays (NCI). While most previous reports comparing microarrays have analyzed pre-existing data from different platforms, this comparison study used the same sample assayed on all three platforms, allowing for analysis of variation from each array platform. In general, poor correlation was found with corresponding measurements from each platform. Each platform yielded different gene expression profiles, suggesting that while microarray analysis is a useful discovery tool, further validation is needed to extrapolate results for broad use of the data. Also, microarray variability needs to be taken into consideration, not only in the data analysis but also in specific probe selection for each array type.