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Cross-platform microarray meta-analysis for the mouse jejunum selects novel reference genes with highly uniform levels of expression.

ABSTRACT: Reference genes (RGs) with uniform expression are used for normalization of reverse transcription quantitative PCR (RT-qPCR) data. Their optimization for a specific biological context, e.g. a specific tissue, has been increasingly considered. In this article, we compare RGs identified by expression data meta-analysis restricted to the context tissue, the jejunum of Mus musculus domesticus, i) to traditional RGs, ii) to expressed interspersed repeated DNA elements, and iii) to RGs identified by meta-analysis of expression data from diverse tissues and conditions. To select the set of candidate RGs, we developed a novel protocol for the cross-platform meta-analysis of microarray data. The expression stability of twenty-four putative RGs was analysed by RT-qPCR in at least 14 jejunum samples of the mouse strains C57Bl/6N, CD1, and OF1. Across strains, the levels of expression of the novel RGs Plekha7, Zfx, and Ube2v1 as well as of Oaz1 varied less than two-fold irrespective of genotype, sex or their combination. The gene set consisting of Plekha7 and Oaz1 showed superior expression stability analysed with the tool RefFinder. The novel RGs are functionally diverse. This facilitates expression studies over a wide range of conditions. The highly uniform expression of the optimized RGs in the jejunum points towards their involvement in tightly regulated pathways in this tissue. We also applied our novel protocol of cross-microarray platform meta-analysis to the identification of RGs in the duodenum, the ileum and the entire small intestine. The selection of RGs with improved expression stability in a specific biological context can reduce the number of RGs for the normalization step of RT-qPCR expression analysis, thus reducing the number of samples and experimental costs.

SUBMITTER: Meyer FR

PROVIDER: S-EPMC3650031 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature

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Cross-platform microarray meta-analysis for the mouse jejunum selects novel reference genes with highly uniform levels of expression.

Meyer Florian R L FR Grausgruber Heinrich H Binter Claudia C Mair Georg E GE Guelly Christian C Vogl Claus C Steinborn Ralf R

PloS one 20130509 5

Reference genes (RGs) with uniform expression are used for normalization of reverse transcription quantitative PCR (RT-qPCR) data. Their optimization for a specific biological context, e.g. a specific tissue, has been increasingly considered. In this article, we compare RGs identified by expression data meta-analysis restricted to the context tissue, the jejunum of Mus musculus domesticus, i) to traditional RGs, ii) to expressed interspersed repeated DNA elements, and iii) to RGs identified by m ...[more]

PMID: 23671661

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Project description:UnlabelledMicroarray expression profiling has become a valuable tool in the evaluation of the genetic consequences of metabolic disease. Although 3'-biased gene expression microarray platforms were the first generation to have widespread availability, newer platforms are gradually emerging that have more up-to-date content and/or higher cost efficiency. Deciphering the relative strengths and weaknesses of these various platforms for metabolic pathway-level analyses can be daunting. We sought to determine the practical strengths and weaknesses of four leading commercially available expression array platforms relative to biologic investigations, as well as assess the feasibility of cross-platform data integration for purposes of biochemical pathway analyses.MethodsLiver RNA from B6.Alb/cre,Pdss2(loxP/loxP) mice having primary coenzyme Q deficiency was extracted either at baseline or following treatment with an antioxidant/antihyperlipidemic agent, probucol. Target RNA samples were prepared and hybridized to Affymetrix 430 2.0, Affymetrix Gene 1.0 ST, Affymetrix Exon 1.0 ST, and Illumina Mouse WG-6 expression arrays. Probes on all platforms were re-mapped to coding sequences in the current version of the mouse genome. Data processing and statistical analysis were performed by R/Bioconductor functions, and pathway analyses were carried out by KEGG Atlas and GSEA.ResultsExpression measurements were generally consistent across platforms. However, intensive probe-level comparison suggested that differences in probe locations were a major source of inter-platform variance. In addition, genes expressed at low or intermediate levels had lower inter-platform reproducibility than highly expressed genes. All platforms showed similar patterns of differential expression between sample groups, with 'steroid biosynthesis' consistently identified as the most down-regulated metabolic pathway by probucol treatment.ConclusionsThis work offers a timely guide for metabolic disease investigators to enable informed end-user decisions regarding choice of expression microarray platform best-suited to specific research project goals. Successful cross-platform integration of biochemical pathway expression data is also demonstrated, especially for well-annotated and highly expressed genes. However, integration of gene-level expression data is limited by individual platform probe design and the expression level of target genes. Cross-platform analyses of biochemical pathway data will require additional data processing and novel computational bioinformatics tools to address unique statistical challenges.

Project description:Microarray expression profiling has become a valuable tool in the evaluation of the genetic consequences of metabolic disease. Although 3’-biased gene expression microarray platforms were the first generation to have widespread availability, newer platforms are gradually emerging that have more up-to-date content and/or higher cost efficiency. Deciphering the relative strengths and weaknesses of these various platforms for metabolic pathway level analyses can be daunting. We sought to determine the practical strengths and weaknesses of four leading commercially-available expression array platforms relative to biologic investigations, as well as assess the feasibility of cross-platform data integration for purposes of biochemical pathway analyses. METHODS: Liver RNA from B6.Alb/cre,Pdss2loxP/loxP mice having primary Coenzyme Q deficiency was extracted either at baseline or following treatment with an antioxidant/antihyperlipidemic agent, probucol. Target RNA samples were prepared and hybridized to Affymetrix 430 2.0, Affymetrix Gene 1.0 ST, Affymetrix Exon 1.0 ST, and Illumina Mouse WG-6 expression arrays. Probes on all platforms were re-mapped to coding sequences in the current version of the mouse genome. Data processing and statistical analysis were performed by R/Bioconductor functions, and pathway analyses were carried out by KEGG Atlas and GSEA. RESULTS: Expression measurements were generally consistent across platforms. However, intensive probe-level comparison suggested that differences in probe locations were a major source of inter-platform variance. In addition, genes expressed at low or intermediate levels had lower inter-platform reproducibility than highly expressed genes. All platforms showed similar patterns of differential expression between sample groups, with ‘steroid biosynthesis’ consistently identified as the most down-regulated metabolic pathway by probucol treatment. CONCLUSIONS: This work offers a timely guide for metabolic disease investigators to enable informed end-user decisions regarding choice of expression microarray platform best-suited to specific research project goals. Successful cross-platform integration of biochemical pathway expression data is also demonstrated, especially for well-annotated and highly-expressed genes. However, integration of gene-level expression data is limited by individual platform probe design and the expression level of target genes. Cross-platform analyses of biochemical pathway data will require additional data processing and novel computational bioinformatics tools to address unique statistical challenges.

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Cross-platform microarray meta-analysis for the mouse jejunum selects novel reference genes with highly uniform levels of expression.

Publications

Cross-platform microarray meta-analysis for the mouse jejunum selects novel reference genes with highly uniform levels of expression.

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