Meta-coexpression conservation analysis of microarray data: a "subset" approach provides insight into brain-derived neurotrophic factor regulation

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.