Transcriptional override: a regulatory network model of indirect responses to modulations in microRNA expression (miRNA)

MicroRNAs are small non-coding molecules that have been shown to repress the translation of thousands of genes. Changes in microRNA expression in a variety of diseases, including cancer, are leading to the development of microRNAs as early indicators of disease, and to their potential use as therapeutic agents. A significant hurdle to the use of microRNAs as therapeutics is our inability to predict the molecular and cellular consequences of perturbations in the levels of specific microRNAs on targeted cells. While the direct gene (mRNA) targets of individual microRNAs can be computationally predicted and are often experimentally validated, assessing the indirect effects of microRNA variation remains a major challenge in molecular systems biology. We present experimental evidence for a computational model that quantifies the extent to which down-regulated transcriptional repressors contribute to the unanticipated upregulation of putative microRNA targets. An appreciation of the effects of these repressors may provide a more complete understanding of the indirect effects of microRNA dysregulation in diseases such as cancer, and to their successful clinical application. This is a re-analysis of GSE23383, as described below in “overall design”. mRNA were collected from the surface epithelial cells of 3 normal ovaries and from laser capture microdisection of 3 ovarian tumors. microRNA expression was captured on Affymetrix Ambion mirCHIP 14K chips. All signals were then log2 normalized. This is a re-analysis of GSE23383 but using both probe sets, labelled A and B, for each of the six original samples (3 normal + 3 cancer) to determine the signals of human (hsa-*) microRNAs. The associated samples are GSM573599, GSM573600, GSM573601, GSM573602, GSM573603, GSM573604. Data processing: For each probe, an estimated background value is subtracted that is derived from the median signal of a set of G-C-matched anti-genomic controls. Arrays within a specific analysis experiment were normalized together according to the variance stabilization method described by Huber et al. (Huber et al., 2002). Detection calls were based on a Wilcoxon rank-sum test of the miRNA probe signal compared to the distribution of signals from GC-content matched anti-genomic probes. A two-sample t-Test, with assumption of equal variance, was applied for statistical hypothesis testing. This test defined which probes are considered to be significantly differentially expressed based on a p-value of 0.01 and log2 difference ≥ 1. Processed data uploaded as CHP files.