Abstract
BackgroundDocumented changes in levels of microRNAs (miRNA) in a variety of diseases including cancer are leading to their development as early indicators of disease, and as a potential new class of therapeutic agents. A significant hurdle to the rational application of miRNAs as therapeutics is our current inability to reliably predict the range of molecular and cellular consequences of perturbations in the levels of specific miRNAs on targeted cells. While the direct gene (mRNA) targets of individual miRNAs can be computationally predicted with reasonable degrees of accuracy, reliable predictions of the indirect molecular effects of perturbations in miRNA levels remain a major challenge in molecular systems biology.ResultsChanges in gene (mRNA) and miRNA expression levels between normal precursor and ovarian cancer cells isolated from patient tissue samples were measured by microarray. Expression of 31 miRNAs was significantly elevated in the cancer samples. Consistent with previous reports, the expected decrease in expression of the mRNA targets of upregulated miRNAs was observed in only 20-30% of the cancer samples. We present and provide experimental support for a network model (The Transcriptional Override Model; TOM) to account for the unexpected regulatory consequences of modulations in the expression of miRNAs on expression levels of their target mRNAs in ovarian cancer.ConclusionsThe direct and indirect regulatory effects of changes in miRNA expression levels in vivo are interactive and complex but amenable to systems level modeling. Although TOM has been developed and validated within the context of ovarian cancer, it may be applicable in other biological contexts as well, including of potential future use in the rational design of miRNA-based strategies for the treatment of cancers and other diseases.
Highlights
Documented changes in levels of microRNAs in a variety of diseases including cancer are leading to their development as early indicators of disease, and as a potential new class of therapeutic agents
Employing three commonly used miRNA target prediction algorithms, we identified putative miRNAs targeting individual human genes (mRNAs) targets of these 31 miRNAs to determine if differences in their levels of expression between the ovarian surface epithelial cells (OSE) and Cancer epithelial cells (CEPI) samples were inversely correlated (IC), positively correlated (PC) or unchanged (NC)
While we have evaluated transcriptional override model (TOM) within the context of its ability to account for global patterns of changes in gene expression, the model provides a framework for predicting interactions between specific miRNAs and target genes (e.g., Figure 1)
Summary
Documented changes in levels of microRNAs (miRNA) in a variety of diseases including cancer are leading to their development as early indicators of disease, and as a potential new class of therapeutic agents. MRNA expression changes are expected to be inversely correlated (IC) with changes in levels of their targeting miRNAs. As a consequence, mRNA expression changes are expected to be inversely correlated (IC) with changes in levels of their targeting miRNAs This expectation has been validated in studies of individual miRNAs and specific mRNA targets, the expected inverse relationship is often not observed in global transcriptome level studies [2,3,4]. While these unexpected findings may, in some instances, be attributed to inaccuracies in miRNA target prediction algorithms [5], recent evidence suggests that many of the unexpected regulatory effects may be the result of feed-back or feed-forward loops and/or other system level complexities [3,6]
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