Inferring MicroRNA Activities by Combining Gene Expression with MicroRNA Target Prediction

Chao Cheng,Lei M Li,Sui Huang

doi:10.1371/journal.pone.0001989

Abstract

BackgroundMicroRNAs (miRNAs) play crucial roles in a variety of biological processes via regulating expression of their target genes at the mRNA level. A number of computational approaches regarding miRNAs have been proposed, but most of them focus on miRNA gene finding or target predictions. Little computational work has been done to investigate the effective regulation of miRNAs.Methodology/Principal FindingsWe propose a method to infer the effective regulatory activities of miRNAs by integrating microarray expression data with miRNA target predictions. The method is based on the idea that regulatory activity changes of miRNAs could be reflected by the expression changes of their target transcripts measured by microarray. To validate this method, we apply it to the microarray data sets that measure gene expression changes in cell lines after transfection or inhibition of several specific miRNAs. The results indicate that our method can detect activity enhancement of the transfected miRNAs as well as activity reduction of the inhibited miRNAs with high sensitivity and specificity. Furthermore, we show that our inference is robust with respect to false positives of target prediction.Conclusions/SignificanceA huge amount of gene expression data sets are available in the literature, but miRNA regulation underlying these data sets is largely unknown. The method is easy to be implemented and can be used to investigate the miRNA effective regulation underlying the expression change profiles obtained from microarray experiments.

Highlights

MicroRNAs are small non-coding RNAs of 19–24 nucleotides in length that down-regulate gene expression during a variety of crucial cell processes, including cell proliferation [1], apoptosis [2], development [3], differentiation [4], and metabolism [5]
A natural question to us is: can we infer the modification of miRNA effective regulation from the expressions of their target genes? In this paper, we propose a method that combines microarray expression data with miRNA target predictions to infer the relative activities of miRNAs underlying the gene expression changes
We apply our method to the microarray expression data from miRNA transfection experiments performed by Lim et al [23]. In this data, using non-transfected Hela cell as reference the relative expression levels of genes are measured in the HeLa cells at the 12 (12 h) and 24 (24 h) hour after respective transfection with two wild-type miRNAs, two mutant miRNAs (124mut5-6 and 124mut9-10) and two chimeric miRNA, which results in a total of 12 expression change profiles

Summary

Introduction

MicroRNAs (miRNAs) are small non-coding RNAs of 19–24 nucleotides in length that down-regulate gene expression during a variety of crucial cell processes, including cell proliferation [1], apoptosis [2], development [3], differentiation [4], and metabolism [5]. MiRNA genes can be located in introns or exons of protein-coding genes, or within the intergenic regions between protein-coding genes. They can either exist individually or form polycistronic clusters [8,9,10]. MicroRNAs (miRNAs) play crucial roles in a variety of biological processes via regulating expression of their target genes at the mRNA level. Little computational work has been done to investigate the effective regulation of miRNAs

Methods

Results

Conclusion