Validation of reference genes as internal control for studying viral infections in cereals by quantitative real-time RT-PCR

Background  

Reference genes are commonly used as the endogenous normalisation measure for the relative quantification of target genes. The appropriate application of quantitative real-time PCR (RT-qPCR), however, requires the use of reference genes whose level of expression is not affected by the test, by general physiological conditions or by inter-individual variability. For this purpose, seven reference genes were investigated in tissues of the most important cereals (wheat, barley and oats). Titre of Barley yellow dwarf virus (BYDV) was determined in oats using relative quantification with different reference genes and absolute quantification, and the results were compared.     

Validation of Zebrafish （Danio redo） Reference Genes for Quantitative Real-time RT-PCR Normalization

The normalization of quantitative real time RT-PCR （qRT-PCR） is important to obtain accurate gene expression data. The most common method for qRT-PCR normalization is to use reference, or housekeeping genes. However, there is emerging evidence that even reference genes can be regulated under different conditions, qRT-PCR has only recently been used in terms of zebrafish gene expression studies and there is no validated set of reference genes. This study characterizes the expression of nine possible reference genes during zebrafish embryonic development and in a zebrafish tissue panel. All nine reference genes exhibited variable expression. The fl-actin, EFlot and Rpll3ot genes comprise a validated reference gene panel for zebrafish developmental time course studies, and the EF1 or, Rpll3α and 18S rRNA genes are more suitable as a reference gene panel for zebrafish tissue analysis. Importantly, the zebrafish GAPDH gene appears unsuitable as reference gene for both types of studies.     

Selection of optimal reference genes for normalization in quantitative RT-PCR

Background  

Normalization in real-time qRT-PCR is necessary to compensate for experimental variation. A popular normalization strategy employs reference gene(s), which may introduce additional variability into normalized expression levels due to innate variation (between tissues, individuals, etc). To minimize this innate variability, multiple reference genes are used. Current methods of selecting reference genes make an assumption of independence in their innate variation. This assumption is not always justified, which may lead to selecting a suboptimal set of reference genes.     

Q-Gene: processing quantitative real-time RT-PCR data

SUMMARY: Q-Gene is an application for the processing of quantitative real-time RT-PCR data. It offers the user the possibility to freely choose between two principally different procedures to calculate normalized gene expressions as either means of Normalized Expressions or Mean Normalized Expressions. In this contribution it will be shown that the calculation of Mean Normalized Expressions has to be used for processing simplex PCR data, while multiplex PCR data should preferably be processed by calculating Normalized Expressions. The two procedures, which are currently in widespread use and regarded as more or less equivalent alternatives, should therefore specifically be applied according to the quantification procedure used. AVAILABILITY: Web access to this program is provided at http://www.biotechniques.com/softlib/qgene.html     

Processing of gene expression data generated by quantitative real-time RT-PCR

Quantitative real-time PCR represents a highly sensitive and powerful technique for the quantitation of nucleic acids. It has a tremendous potential for the high-throughput analysis of gene expression in research and routine diagnostics. However, the major hurdle is not the practical performance of the experiments themselves but rather the efficient evaluation and the mathematical and statistical analysis of the enormous amount of data gained by this technology, as these functions are not included in the software provided by the manufacturers of the detection systems. In this work, we focus on the mathematical evaluation and analysis of the data generated by quantitative real-time PCR, the calculation of the final results, the propagation of experimental variation of the measured values to the final results, and the statistical analysis. We developed a Microsoft Excel-based software application coded in Visual Basic for Applications, called Q-Gene, which addresses these points. Q-Gene manages and expedites the planning, performance, and evaluation of quantitative real-time PCR experiments, as well as the mathematical and statistical analysis, storage, and graphical presentation of the data. The Q-Gene software application is a tool to cope with complex quantitative real-time PCR experiments at a high-throughput scale and considerably expedites and rationalizes the experimental setup, data analysis, and data management while ensuring highest reproducibility.     

Selection of control genes for quantitative RT-PCR based on microarray data

Use of internal reference gene(s) is necessary for adequate quantification of target gene expression by RT-PCR. Herein, we elaborated a strategy of control gene selection based on microarray data and illustrated it by analyzing endomyocardial biopsies with acute cardiac rejection and infection. Using order statistics and binomial distribution we evaluated the probability of finding low-varying genes by chance. For analysis, the microarray data were divided into two sample subsets. Among the first 10% of genes with the lowest standard deviations, we found 14 genes common to both subsets. After normalization using two selected genes, high correlation was observed between expression of target genes evaluated by microarray and RT-PCR, and in independent dataset by RT-PCR (r = 0.9, p < 0.001). In conclusion, we showed a simple and reliable strategy of selection and validation of control genes for RT-PCR from microarray data that can be easily applied for different experimental designs and tissues.     

Identification and validation of reference genes for quantitative RT-PCR normalization in wheat

Background  

Usually the reference genes used in gene expression analysis have been chosen for their known or suspected housekeeping roles, however the variation observed in most of them hinders their effective use. The assessed lack of validated reference genes emphasizes the importance of a systematic study for their identification. For selecting candidate reference genes we have developed a simple in silico method based on the data publicly available in the wheat databases Unigene and TIGR.     

Evaluation of coffee reference genes for relative expression studies by quantitative real-time RT-PCR

Accuracy in quantitative real-time polymerase chain reaction (qPCR) requires the use of stable endogenous controls. Normalization with multiple reference genes is the gold standard, but their identification is a laborious task, especially in species with limited sequence information. Coffee (Coffea ssp.) is an important agricultural commodity and, due to its economic relevance, is the subject of increasing research in genetics and biotechnology, in which gene expression analysis is one of the most important fields. Notwithstanding, relatively few works have focused on the analysis of gene expression in coffee. Moreover, most of these works have used less accurate techniques such as northern blot assays instead of more accurate techniques (e.g., qPCR) that have already been extensively used in other plant species. Aiming to boost the use of qPCR in studies of gene expression in coffee, we uncovered reference genes to be used in a number of different experimental conditions. Using two distinct algorithms implemented by geNorm and Norm Finder, we evaluated a total of eight candidate reference genes (psaB, PP2A, AP47, S24, GAPDH, rpl39, UBQ10, and UBI9) in four different experimental sets (control versus drought-stressed leaves, control versus drought-stressed roots, leaves of three different coffee cultivars, and four different coffee organs). The most suitable combination of reference genes was indicated in each experimental set for use as internal control for reliable qPCR data normalization. This study also provides useful guidelines for reference gene selection for researchers working with coffee plant samples under conditions other than those tested here. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

Stable internal reference genes for normalization of real-time RT-PCR in tobacco (Nicotiana tabacum) during development and abiotic stress

Real-time RT-PCR is a powerful technique for the measurement of gene expression, but its accuracy depends on the stability of the internal reference gene(s) used for data normalization. Tobacco (Nicotiana tabacum) is an important model in studies of plant gene expression, but stable reference genes have not been well-studied in the tobacco system. We address this problem by analysing the expression stability of eight potential tobacco reference genes. Primers targeting each gene (18S rRNA, EF-1α, Ntubc2, α- and β-tubulin, PP2A, L25 and actin) were developed and optimized. The expression of each gene was then measured by real-time PCR in a diverse set of 22 tobacco cDNA samples derived from developmentally distinct tissues and from plants exposed to several abiotic stresses. L25 and EF-1α demonstrated the highest expression stability, followed by Ntubc2. Measurement of L25 and EF-1α was sufficient for accurate normalization in either the developmental or stress-treated samples, but Ntubc2 was also required when considering the entire sample set. Analysis of a tobacco circadian gene (NTCP-23) verified these reference genes in an additional context, and all techniques were optimized to enable a high-throughput approach. These results provide a foundation for the more accurate and widespread use of real-time RT-PCR in tobacco.     

Validation of housekeeping genes as internal control for studying gene expression in rice by quantitative real-time PCR

For accurate and reliable gene expression results, normalization of real-time PCR data is required against a control gene, which displays highly uniform expression in living organisms during various phases of development and under different environmental conditions. We assessed the gene expression of 10 frequently used housekeeping genes, including 18S rRNA, 25S rRNA, UBC, UBQ5, UBQ10, ACT11, GAPDH, eEF-1alpha, eIF-4a, and beta-TUB, in a diverse set of 25 rice samples. Their expression varied considerably in different tissue samples analyzed. The expression of UBQ5 and eEF-1alpha was most stable across all the tissue samples examined. However, 18S and 25S rRNA exhibited most stable expression in plants grown under various environmental conditions. Also, a set of two genes was found to be better as control for normalization of the data. The expression of these genes (with more uniform expression) can be used for normalization of real-time PCR results for gene expression studies in a wide variety of samples in rice.