Systems Biology Reveals MicroRNA-Mediated Gene Regulation

Abstract

MicroRNAs (miRNAs) are members of the small non-coding RNAs, which are principally known for their functions as post-transcriptional regulators of target genes. Regulation by miRNAs is triggered by the translational repression or degradation of their complementary target messenger RNAs (mRNAs). The growing number of reported miRNAs and the estimate that hundreds or thousands of genes are regulated by them suggest a magnificent gene regulatory network in which these molecules are embedded. Indeed, recent reports have suggested critical roles for miRNAs in various biological functions, such as cell differentiation, development, oncogenesis, and the immune responses, which are mediated by systems-wide changes in gene expression profiles. Therefore, it is essential to analyze this complex regulatory network at the transcriptome and proteome levels, which should be possible with approaches that include both high-throughput experiments and computational methodologies. Here, we introduce several systems-level approaches that have been applied to miRNA research, and discuss their potential to reveal miRNA-guided gene regulatory systems and their impacts on biological functions.

Keywords: gene regulatory network; immunoprecipitation; microRNA; proteome; systems biology; transcriptome.

Figure 1

Systems biology approaches to identifying miRNA targets. Flow chart describing the combined high-throughput experimental approach and computational approach for the systems-level analysis of miRNA targets. Pre-analysis using published data can be performed computationally, followed by high-throughput experimental analyses, in which the samples are prepared by overexpressing or inhibiting miRNAs in a partial population, and using the untreated population as the control. The data obtained are normalized and analyzed statistically to produce a preliminary list of genes with significantly up- or down-regulated expression. Further validation analysis is conducted to extract the biological information hidden behind the mass of data. The raw and analyzed data are distributed within databases or web services, allowing other researchers to make use of this information.

Figure 2

Schematic representation of SILAC labeling and proteome analysis. Cells are split and cultured in heavy or light medium containing different amino acid isotopes. The miRNAs are then overexpressed or inhibited within these cells, and the cells are incubated for several more hours. The cells are collected and their proteins are purified for further mass spectrometric analysis. The protein levels in the two samples are compared by quantifying the heavy and light peptides, because isotopic labeling will affect their migration times.

Figure 3

Flow chart of the photoactivatable-ribonucleoside- enhanced cross-linking and immunoprecipitation (PAR-CLIP) methodology. PAR-CLIP analysis of miRISC component-binding RNAs. The cells are first cultured with photoreactive 4-thiouridine (^4SU), which causes uridine to be incorporated during culture, and UV cross-linked to miRNP (U^XL). The cross-linked miRNP–RNA complexes are immunoprecipitated using an antibody directed against miRNP, and then size fractionized by SDS-PAGE. The miRNP–RNA complexes are extracted from the gel and digested with protease. The recovered RNA molecules are converted into cDNA, where the incorporated 4-thiouridine causes T → C transitions. This transition plays a key role in the accurate mapping of the miRNP-binding sites. The cDNA library is analyzed with the deep sequencing method to determine the RNA sequences capable of interacting with miRNP.