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This article is part of the supplement: UT-ORNL-KBRIN Bioinformatics Summit 2011

Open Access Meeting abstract

Statistical analysis of microarray gene expression data from a mouse model of toxoplasmosis

Shrikant Pawar12*, Cheryl D Davis12 and Claire A Rinehart12

Author Affiliations

1 Department of Biology, Western Kentucky University, Bowling Green, KY 42101, USA

2 Bioinformatics and Information Science Center, Western Kentucky University, Bowling Green, KY 42101, USA

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BMC Bioinformatics 2011, 12(Suppl 7):A19  doi:10.1186/1471-2105-12-S7-A19

The electronic version of this article is the complete one and can be found online at: http://www.biomedcentral.com/1471-2105/12/S7/A19


Published:5 August 2011

© 2011 Pawar et al; licensee BioMed Central Ltd.

This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Background

Toxoplasmosis, caused by the protozoan parasite Toxoplasma gondii is a major cause of morbidity and mortality in patients with AIDS and an important cause of miscarriage, stillbirth and congenital disease in newborns. Previous studies have provided evidence that dietary supplementation with vitamin E and selenium is harmful during experimental toxoplasmosis in mice, whereas a diet deficient in vitamin E and selenium results in decreased numbers of tissue cysts in the brain and dramatically reduced brain pathology. The overall goal of the present study was to determine the impact of dietary supplementation with antioxidants on gene expression in the brains of non-infected mice and in mice infected with T. gondii using microarray analysis. RNA was isolated from the brains of C57BL/6 mice, and an Agilent Oligo Whole Mouse Genome Microarray (Agilent Technologies, Inc.) was performed. A total of 48 chips were normalized by Z ratios and the Data Driven Harr Fisch Normalization methods. Differentially expressed genes were identified by applying thresholds to identify significant values and the results were compared between the normalization methods. These differentially expressed genes and their respective fold change ratios were used in Ingenuity Pathway Analysis (IPA) software to analyze the pathways involved with these genes.

Acknowledgements

Support from the National Center for Research Resources NIH Grant Number 2 P20 RR-16481 and from the WKU Bioinformatics and Information Science Center is gratefully acknowledged.