miRNA target identification and prediction as a function of time in gene expression data

Abstract

The understanding of miRNA target interactions is still limited due to conflicting data and the fact that high-quality validation of targets is a time-consuming process. Faster methods like high-throughput screens and bioinformatics predictions are employed but suffer from several problems. One of these, namely the potential occurrence of downstream (i.e. secondary) effects in high-throughput screens has been only little discussed so far. However, such effects limit usage for both the identification of interactions and for the training of bioinformatics tools. In order to analyse this problem more closely, we performed time-dependent microarray screening experiments overexpressing human miR-517a-3p, and, together with published time-dependent datasets of human miR-17-5p, miR-135b and miR-124 overexpression, we analysed the dynamics of deregulated genes. We show that the number of deregulated targets increases over time, whereas seed sequence content and performance of several miRNA target prediction algorithms actually decrease over time. Bioinformatics recognition success of validated miR-17 targets was comparable to that of data gained only 12 h post-transfection. We therefore argue that the timing of microarray experiments is of critical importance for detecting direct targets with high confidence and for the usability of these data for the training of bioinformatics prediction tools.

Keywords: bioinformatics; miR-124; miR-135b; miR-17; miR-517a; miRNA; miRNA target identification; miRNA target predictions.

Figure 1.

Downregulation of transcripts over multiple time points upon miRNA overexpression. Total number of the downregulated transcripts after the overexpression of miR-17 (a), miR-517a (b) and miR-315b (c) was considered as 100% and their occurrence at the individual time points is shown as the respective percentages. Dark blue colour indicates the transcripts whose expression is reduced at 12 h, 24 h and 36/48 h, light blue colour – at 24 h and 36/48 h, orange colour – at 12 h and 24 h and yellow colour – at 12 h and 36/48 h. The fractions of downregulated transcripts at single time points are indicated below the respective time point.

Figure 2.

Time-dependent occurrence of the seed sequences following the overexpression of miR-17-5p (a), miR-517a-3p (b) and miR-135b-5p (c). Seed sequence presence and localization within the transcript were determined as described in Materials and Methods. Data are provided as a percentage of all the experimentally determined downregulated genes, transcripts of which possess respective seed sequence complements for miR-17-5p (‘GCACUUU’), miR-517a-3p (‘UGCACGA’) and miR-135b-5p (‘AAGCCAU’).

Figure 3.

Bioinformatics target detection by different algorithms with respect to the presence of seed sequences. Downregulated transcripts at 12 h post-transfection of miR-17-5p (a), miR-517a-3p (b) and miR-135b-5p (c) were analysed. Black colour represents a fraction of targets possessing at least a single seed sequence and detected as true positives by a respective algorithm, white colour corresponds to not recognized mRNAs with seed sequences. Red colour shows transcripts without seed sequences but predicted as targets and blue colour – not recognized targets without seed sequence.

Figure 4.

Fraction of experimental miR-17 (a), miR-517a (b), miR-135b (c) and low-throughput-validated miR-17 (d) targets recognized by varying number of target prediction algorithms.