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Long Oligonucleotide Microarrays for African Green Monkey Gene Expression Profile Analysis

ABSTRACT: Nonhuman primates including African Green Monkey (AGM) are important models for biomedical research. The information on monkey genomes is still limited and no versatile gene expression screening tool is available. We tested human whole genome microarrays for cross-species reactivity with AGM transcripts using both long oligonucleotide arrays (60-mer probes, Applied Biosystems-ABI) and short oligonucleotide arrays (25-mer, Affymetrix). Using the long oligonucleotide arrays we detected four times more AGM transcripts than with the short oligonucleotide technology. The number of detected transcripts was comparable to that detected using human RNA, with 87% of the detected genes being shared between both species. The specificity of the signals obtained with the long oligonucleotide arrays was determined by analyzing the transcriptome of concanavalin A activated CD4+ T cells versus non-activated T cells of two monkey species AGM and macaque. For both species, the genes showing the most significant changes in expression, such as IL-2R, were those known to be also regulated in human CD4+ T cell activation. Finally, tissue-specificity of the signals was established by comparing the transcription profiles of AGM brain and tonsil cells. In conclusion, the ABI human microarray platform provides a highly valuable tool for the assessment of AGM gene expression profiles. Keywords: Defining a microarray platform to study the AGM transcriptome by cross-species and cross-platform comparison

OTHER RELATED OMICS DATASETS IN: PRJNA99293

ORGANISM(S): Homo sapiens Chlorocebus sabaeus Macaca mulatta

PROVIDER: GSE6982 | GEO | 2007/10/01

SECONDARY ACCESSION(S): PRJNA99293

REPOSITORIES: GEO

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Project description:Microarrays have been widely used for the analysis of gene expression and several commercial platforms are available. The combined use of multiple platforms can overcome the inherent biases of each approach, and may represent an alternative that is complementary to RT-PCR for identification of the more robust changes in gene expression profiles. In this paper, we combined statistical and functional analysis for the cross platform validation of two oligonucleotide-based technologies, Affymetrix (AFFX) and Applied Biosystems (ABI), and for the identification of differentially expressed genes. In this study, we analysed differentially expressed genes after treatment of an ovarian carcinoma cell line with a cell cycle inhibitor. Treated versus control RNA was analysed for expression of 16425 genes represented on both platforms. We assessed reproducibility between replicates for each platform using CAT plots, and we found it high for both, with better scores for AFFX. We then applied integrative correlation analysis to assess reproducibility of gene expression patterns across studies, bypassing the need for normalizing expression measurements across platforms. We identified 930 genes as differentially expressed on AFFX and 908 on ABI, with ~80% common to both platforms. Despite the different absolute values, the range of intensities of the differentially expressed genes detected by each platform was similar. ABI showed a slightly higher dynamic range in FC values, which might be associated with its detection system. 62/66 genes identified as differentially expressed by Microarray were confirmed by RT-PCR. In this study we present a cross-platform validation of two oligonucleotide-based technologies, AFFX and ABI. We found good reproducibility between replicates, and showed that both platforms can be used to select differentially expressed genes with substantial agreement. Pathway analysis of the affected functions identified themes well in agreement with those expected for a cell cycle inhibitor, suggesting that this procedure is appropriate to facilitate the identification of biologically relevant signatures associated with compound treatment. The high rate of confirmation found for both common and platform-specific genes suggests that the combination of platforms may overcome biases related to probe design and technical features, thereby accelerating the identification of trustworthy differentially expressed genes. accepted for publication on BMC Bioinformatics Keywords: platform comparison

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Long Oligonucleotide Microarrays for African Green Monkey Gene Expression Profile Analysis

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