Inferring microRNA activities by combining gene expression with microRNA target prediction

Abstract

Background: MicroRNAs (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 findings: We 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/significance: A 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.

Figure 1. Distribution of AC scores for 211 known human miRNAs in (A) miR-1 (B) miR-124 (C) 124mut5-6 (D) 124mut9-10 (E) chimiR1/124 and (F) chimiR124/1 transfection data.

The AC scores for miR-1 and miR-124 are marked as red circles and blue rectangles, respectively.

Figure 2. Examples of pre-score calculation for miR-124 and four randomly selected miRNAs in the miR-124 transfection profile at 24 h.

The g(i) function is shown as the black curve in (A); the f(i) functions in (A) and the g(i)–f(i) function in (B) for miR-124 and the randomly selected miRNAs are shown as the red lines and green lines, respectively. The dashed lines show the positions at which maximum deviations (pre-scores) are achieved for these miRNAs.

Figure 3. Average AC scores and p-values for miR-1 in the miR-1 transfection profile at 12 h (A and B) and 24 h (C and D) based on perturbed miR-1 target prediction scores.

The x-axis shows the perturbing percentage from 0% to 50%. At each perturbing percentage, 100 perturbed binding score profiles of miR-1 are produced by exchanging the binding scores of target and non-target genes of miR-1. The standard deviations of AC scores and p-values are also shown.

Figure 4. Box-plots of AC scores for the 211 miRNAs in the miR-124 transfection profiles at 12 h (A) and 24 h (B) based on the continuous and discretized binding score data.

C corresponds to the continuous binding score data; D1, D2, D3, and D4 correspond to the discretized binding score data with cut-off value of 12, 60, 80 and 100, respectively. The AC score for miR-124 is marked as blue rectangle.

Figure 5. The inferred AC scores of miR-124 in the microarray time course of miR-124 transfection.

Figure 6. Distribution of the pre-scores for miR-124 in 10,000 permutated data sets.