Using Ribosomal Protein Genes as Reference: A Tale of Caution

Background: Housekeeping genes are needed in every tissue as their expression is required for survival, integrity or duplication of every cell. Housekeeping genes commonly have been used as reference genes to normalize gene expression data, the underlying assumption being that they are expressed in every cell type at approximately the same level. Often, the terms "reference genes'' and "housekeeping genes'' are used interchangeably. In this paper, we would like to distinguish between these terms. Consensus is growing that housekeeping genes which have traditionally been used to normalize gene expression data are not good reference genes. Recently, ribosomal protein genes have been suggested as reference genes based on a meta-analysis of publicly available microarray data. Methodology/Principal Findings: We have applied several statistical tools on a dataset of 70 microarrays representing 22 different tissues, to assess and visualize expression stability of ribosomal protein genes. We confirmed the housekeeping status of these genes, but further estimated expression stability across tissues in order to assess their potential as reference genes. One- and two-way ANOVA revealed that all ribosomal protein genes have significant expression variation across tissues and exhibit tissue-dependent expression behavior as a group. Via multidimensional unfolding analysis, we visualized this tissue-dependency. In addition, we explored mechanisms that may cause tissue dependent effects of individual ribosomal protein genes. Conclusions/Significance: Here we provide statistical and biological evidence that ribosomal protein genes exhibit important tissue-dependent variation in mRNA expression. Though these genes are most stably expressed of all investigated genes in a meta-analysis they cannot be considered true reference genes

A comprehensive functional analysis of tissue specificity of human gene expression

<p>Abstract</p> <p>Background</p> <p>In recent years, the maturation of microarray technology has allowed the genome-wide analysis of gene expression patterns to identify tissue-specific and ubiquitously expressed ('housekeeping') genes. We have performed a functional and topological analysis of housekeeping and tissue-specific networks to identify universally necessary biological processes, and those unique to or characteristic of particular tissues.</p> <p>Results</p> <p>We measured whole genome expression in 31 human tissues, identifying 2374 housekeeping genes expressed in all tissues, and genes uniquely expressed in each tissue. Comprehensive functional analysis showed that the housekeeping set is substantially larger than previously thought, and is enriched with vital processes such as oxidative phosphorylation, ubiquitin-dependent proteolysis, translation and energy metabolism. Network topology of the housekeeping network was characterized by higher connectivity and shorter paths between the proteins than the global network. Ontology enrichment scoring and network topology of tissue-specific genes were consistent with each tissue's function and expression patterns clustered together in accordance with tissue origin. Tissue-specific genes were twice as likely as housekeeping genes to be drug targets, allowing the identification of tissue 'signature networks' that will facilitate the discovery of new therapeutic targets and biomarkers of tissue-targeted diseases.</p> <p>Conclusion</p> <p>A comprehensive functional analysis of housekeeping and tissue-specific genes showed that the biological function of housekeeping and tissue-specific genes was consistent with tissue origin. Network analysis revealed that tissue-specific networks have distinct network properties related to each tissue's function. Tissue 'signature networks' promise to be a rich source of targets and biomarkers for disease treatment and diagnosis.</p

Reference Gene Selection for Quantitative Real-time PCR Normalization in Quercus suber

The use of reverse transcription quantitative PCR technology to assess gene expression levels requires an accurate normalization of data in order to avoid misinterpretation of experimental results and erroneous analyses. Despite being the focus of several transcriptomics projects, oaks, and particularly cork oak (Quercus suber), have not been investigated regarding the identification of reference genes suitable for the normalization of real-time quantitative PCR data. In this study, ten candidate reference genes (Act, CACs, EF-1α, GAPDH, His3, PsaH, Sand, PP2A, ß-Tub and Ubq) were evaluated to determine the most stable internal reference for quantitative PCR normalization in cork oak. The transcript abundance of these genes was analysed in several tissues of cork oak, including leaves, reproduction cork, and periderm from branches at different developmental stages (1-, 2-, and 3-year old) or collected in different dates (active growth period versus dormancy). The three statistical methods (geNorm, NormFinder, and CV method) used in the evaluation of the most suitable combination of reference genes identified Act and CACs as the most stable candidates when all the samples were analysed together, while ß-Tub and PsaH showed the lowest expression stability. However, when different tissues, developmental stages, and collection dates were analysed separately, the reference genes exhibited some variation in their expression levels. In this study, and for the first time, we have identified and validated reference genes in cork oak that can be used for quantification of target gene expression in different tissues and experimental conditions and will be useful as a starting point for gene expression studies in other oaks