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20 (page number not for citation purposes) BMC Genomics Open Access Research article Meta-coexpression conservation analysis of microarray data: a subset approach provides insight into brain-derived neurotrophic factor regulation Tamara Aid-Pavlidis*?1, Pavlos Pavlidis?2 and T?nis Timmusk1 Address: 1Department of Gene Technology, Tallinn University of Technology, Akadeemia tee 15,

19086 Tallinn, Estonia and 2Department of Biology, Section of Evolutionary Biology, University of Munich, Grosshaderner Strasse 2,

82152 Planegg-Martinsried, Germany Email: Tamara Aid-Pavlidis* - tamara.

aid@gmail.com;

Pavlos Pavlidis - pavlidis@zi.biologie.uni-muenchen.de;

T?nis Timmusk - tonis.timmusk@ttu.ee * Corresponding author ?Equal contributors Abstract Background: Alterations in brain-derived neurotrophic factor (BDNF) gene expression contribute to serious pathologies such as depression, epilepsy, cancer, Alzheimer'

s, Huntington and Parkinson'

s disease. Therefore, exploring the mechanisms of BDNF regulation represents a great clinical importance. Studying BDNF expression remains difficult due to its multiple neural activity- dependent and tissue-specific promoters. Thus, microarray data could provide insight into the regulation of this complex gene. Conventional microarray co-expression analysis is usually carried out by merging the datasets or by confirming the re-occurrence of significant correlations across datasets. However, co-expression patterns can be different under various conditions that are represented by subsets in a dataset. Therefore, assessing co-expression by measuring correlation coefficient across merged samples of a dataset or by merging datasets might not capture all correlation patterns. Results: In our study, we performed meta-coexpression analysis of publicly available microarray data using BDNF as a guide-gene introducing a subset approach. The key steps of the analysis included: dividing datasets into subsets with biologically meaningful sample content (e.g. tissue, gender or disease state subsets);

analyzing co-expression with the BDNF gene in each subset separately;

and confirming co- expression links across subsets. Finally, we analyzed conservation in co-expression with BDNF between human, mouse and rat, and sought for conserved over- represented TFBSs in BDNF and BDNF-correlated genes. Correlated genes discovered in this study regulate nervous system development, and are associated with various types of cancer and neurological disorders. Also, several transcription factor identified here have been reported to regulate BDNF expression in vitro and in vivo. Conclusion: The study demonstrates the potential of the subset approach in co-expression conservation analysis for studying the regulation of single genes and proposes novel regulators of BDNF gene expression. Published:

8 September

2009 BMC Genomics 2009, 10:420 doi:10.1186/1471-2164-10-420 Received:

29 December

2008 Accepted:

8 September

2009 This article is available from: http://www.biomedcentral.com/1471-2164/10/420 ?

2009 Aid-Pavlidis 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. BMC Genomics 2009, 10:420 http://www.biomedcentral.com/1471-2164/10/420 Page

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20 (page number not for citation purposes) Background The accumulation of genome-wide gene expression data has enabled biologists to investigate gene regulatory mechanisms using system biology approaches. Recent developments in microarray technologies and bioinfor- matics have driven the progress of this field [1]. Moreover, publicly available microarray data provide information on human genome-wide gene expression under various experimental conditions, which for most researchers would be difficult to access otherwise. BDNF (brain-derived neurotrophic factor) plays an important role in the development of the vertebrates'