MODELING HUMAN GENOME-WIDE COMBINATORIAL REGULATORY NETWORKS INITIATED BY TRANSCRIPTION FACTORS AND MICRORNAS USING FORWARD AND REVERSE ENGINEERING

MicroRNAs are short endogenous non-coding transcripts which regulate their target mRNAs by translational inhibition or mRNA degradation. Recent microRNA transfection experiments show strong evidence that microRNAs influence not only their target but also non-target genes, but how the regulatory signals are transduced from microRNAs to the downstream genes remains to been elucidated. We suspect that primary and secondary regulatory mechanisms, initially triggered by microRNAs, form refined local networks in the cell. In light of this hypothesis, a comprehensive strategy was developed to reconstruct combinatory networks of primary and secondary microRNA regulatory cascades, using microRNA's target and non-target gene expression profiles and information of microRNA-regulated transcription factors (TF) and TF regulated genes. This strategy was then applied to 53 microRNA transfection expression datasets and led to discovery of combinatorial regulatory networks triggered by 20 microRNAs. Many of these networks were enriched with genes whose functional roles were consistent with known regulatory roles of microRNAs. More importantly, a tumor-related regulatory network and related pathways were discovered, in which novel discoveries were integrated with existing knowledge on the regulatory mechanisms of four microRNAs. In the network, by activating mir-34 family, the tumor suppressor gene p53 can inhibit five target oncogenes, four of which have never been reported. Our approach was carried out on a sizeable number of public micro RNA transfection experiment datasets, enabling a global view of combinatory regulatory networks triggered by microRNAs. Through reconstructing micro RNA-triggered combinatory regulatory networks, the work help identify the true degradation targets of mammal microRNAs, and more importantly, aid in fundamental understanding of microRNA related biological functional processes.