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.

由于数据相互矛盾，而且高质量的靶标验证是一个耗时的过程，因此对 miRNA 靶标相互作用的理解仍然有限。采用了高通量筛选和生物信息学预测等更快的方法，但存在一些问题。其中之一，即高通量筛选中可能发生的下游（即次级）效应，迄今为止很少被讨论。然而，这种效应限制了相互作用识别和生物信息学工具训练的使用。为了更仔细地分析这个问题，我们进行了过表达人 miR-517a-3p 的时间依赖性微阵列筛选实验，并结合已发表的人 miR-17-5p、miR-135b 和 miR-124 过表达的时间依赖性数据集，我们分析了失调基因的动态。我们发现，解除管制的靶标数量随着时间的推移而增加，而几种 miRNA 靶标预测算法的种子序列内容和性能实际上随着时间的推移而下降。经验证的 miR-17 靶标的生物信息学识别成功率与转染后仅 12 小时获得的数据相当。因此，我们认为，微阵列实验的时机对于高置信度地检测直接目标以及这些数据用于训练生物信息学预测工具的可用性至关重要。