Probabilistic Analysis of Probe Reliability in Differential Gene Expression Studies with Short Oligonucleotide Arrays

Background Multiple sclerosis-associated genetic variants indicate that the adaptive immune system plays an important role in the risk of developing multiple sclerosis. It is currently not well understood how these multiple sclerosis-associated genetic variants contribute to multiple sclerosis risk. CD4+ T cells are suggested to be involved in multiple sclerosis disease processes. Objective We aim to identify CD4+ T cell differential gene expression between multiple sclerosis patients and healthy controls in order to understand better the role of these cells in multiple sclerosis. Methods We applied RNA sequencing on CD4+ T cells from multiple sclerosis patients and healthy controls. Results We did not identify significantly differentially expressed genes in CD4+ T cells from multiple sclerosis patients. Furthermore, pathway analyses did not identify enrichment for specific pathways in multiple sclerosis. When we investigated genes near multiple sclerosis-associated genetic variants, we did not observe significant enrichment of differentially expressed genes. Conclusion We conclude that CD4+ T cells from multiple sclerosis patients do not show significant differential gene expression. Therefore, gene expression studies of all circulating CD4+ T cells may not result in viable biomarkers. Gene expression studies of more specific subsets of CD4+ T cells remain justified to understand better which CD4+ T cell subsets contribute to multiple sclerosis pathology.

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Analysis of differential gene expression in human melanocytic tumour lesions by custom made oligonucleotide arrays

British Journal of Cancer ◽

10.1038/sj.bjc.6602612 ◽

2005 ◽

Vol 92 (12) ◽

pp. 2249-2261 ◽

Cited By ~ 58

Author(s):

N J W de Wit ◽

J Rijntjes ◽

J H S Diepstra ◽

T H van Kuppevelt ◽

U H Weidle ◽

...

Keyword(s):

Gene Expression ◽

Differential Gene Expression ◽

Oligonucleotide Arrays ◽

Custom Made ◽

Differential Gene

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Differential gene expression during hypersensitive response inPhylloxera-resistant rootstock ‘Börner’ using custom oligonucleotide arrays

Journal of Plant Interactions ◽

10.1080/17429140903254697 ◽

2009 ◽

Vol 4 (4) ◽

pp. 261-269 ◽

Cited By ~ 11

Author(s):

Livia Blank ◽

Tatjana Wolf ◽

Klaus Eimert ◽

Max-Bernhard Schröder

Keyword(s):

Gene Expression ◽

Differential Gene Expression ◽

Hypersensitive Response ◽

Oligonucleotide Arrays ◽

Differential Gene ◽

Resistant Rootstock

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Dissecting tBHQ induced ARE-driven gene expression through long and short oligonucleotide arrays

Physiological Genomics ◽

10.1152/physiolgenomics.00214.2004 ◽

2005 ◽

Vol 21 (1) ◽

pp. 43-58 ◽

Cited By ~ 11

Author(s):

Jiang Li ◽

Maria L. Spletter ◽

Jeffrey A. Johnson

Keyword(s):

Gene Expression ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Housekeeping Genes ◽

Data Sets ◽

Oligonucleotide Arrays ◽

Short Oligonucleotide ◽

Differential Gene ◽

Alternative Approaches ◽

Responsive Element

This paper compares the gene expression profiles identified by short (Affymetrix U95AV2) or long (Agilent Hu1A) oligonucleotide arrays on a model for upregulation of a cluster of antioxidant responsive element-driven genes by treatment with tert-butylhydroquinone. MAS 5.0, dCHIP, and RMA were applied to normalize the Affymetrix data, and Lowess regression was considered for Agilent data. SAM was used to identify the differential gene expression. A set of biological markers and housekeeping genes were chosen to evaluate the performance of multiple normalization approaches. Both arrays illustrated a definite set of overlapping genes between the data sets regardless of data mining tools used. However, unique gene expression profiles based on the platform used were also revealed and confirmed by quantitative RT-PCR. Further analysis of the data revealed by alternative approaches suggested that alternative splicing, multiple vs. single probe(s) measurement, and use or nonuse of mismatch probes may account for the discrepant data. Therefore, these two microarray technologies offer relatively reliable data. Integration of the gene expression profiles from different array platforms may not only help for cross-validation but also provide a more complete view of the transcriptional scenario.

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