MS2-TRAP (MS2-tagged RNA affinity purification): tagging RNA to identify associated miRNAs

Cellular transcripts of all types, including coding messenger (m)RNAs and noncoding (nc)RNAs, are subject to extensive post-transcriptional regulation. Among the factors that elicit post-transcriptional control, microRNAs (miRNAs) have emerged as a major class of small regulatory RNAs. Since RNA-RNA interactions can be modeled computationally, several excellent programs have been developed to predict the interaction of miRNAs with target transcripts. However, many such predictions are not realized for different reasons, including absent or low-abundance expression of the miRNA in the cell, the existence of competing factors or conformational changes masking the microRNA site, and the possibility that target transcripts are not present in the prediction databases, as is the case for long ncRNAs. Here, we provide a systematic approach termed MS2-TRAP (tagged RNA affinity purification) for identifying miRNAs associated with a target transcript in the cellular context. We illustrate the use of this methodology by identifying microRNAs that associate with a long intergenic (li)ncRNA, based on the expression of the lincRNA tagged with MS2 RNA hairpins (lincRNA-p21-MS2) and the concomitant expression of a fusion protein recognizing the MS2 RNA hairpins, MS2-GST. After affinity pulldown of the ribonucleoprotein (RNP) complex comprising [MS2-GST/lincRNA-p21-MS2], the RNA in the pulldown material was isolated and reverse transcribed (RT). Subsequent assessment of the microRNAs present in the pulldown complex by using real-time quantitative (q)PCR analysis led to the identification of bona fide miRNAs that interact with and control the abundance of lincRNA-p21. We describe alternative designs and applications of this approach, and discuss its implications in deciphering post-transcriptional gene regulatory schemes.