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Open Access Research article

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

Christopher G Hill1, Lilya V Matyunina1, DeEtte Walker1, Benedict B Benigno2 and John F McDonald12*

Author Affiliations

1 Integrated Cancer Research Center, School of Biology, and Parker H. Petit Institute of Bioengineering and Biosciences, Georgia Institute of Technology, 315 Ferst Dr, Atlanta, GA 30332, USA

2 Ovarian Cancer Institute, 960 Johnson Ferry Road Suite 130, Atlanta, GA 30342, USA

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BMC Systems Biology 2014, 8:36  doi:10.1186/1752-0509-8-36

Published: 25 March 2014

Abstract

Background

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. 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.

Results

Changes 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.

Conclusions

The 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.

Keywords:
Cancer systems biology; Feed-forward loops; Gene regulation; miRNAs; Ovarian cancer