Statistical Use of Argonaute Expression and RISC Assembly in microRNA Target Identification

MicroRNAs (miRNAs) posttranscriptionally regulate targeted messenger RNAs (mRNAs) by inducing cleavage or otherwise repressing their translation. We address the problem of detecting m/miRNA targeting relationships in homo sapiens from microarray data by developing statistical models that are motivated by the biological mechanisms used by miRNAs. The focus of our modeling is the construction, activity, and mediation of RNA-induced silencing complexes (RISCs) competent for targeted mRNA cleavage. We demonstrate that regression models accommodating RISC abundance and controlling for other mediating factors fit the expression profiles of known target pairs substantially better than models based on m/miRNA expressions alone, and lead to verifications of computational target pair predictions that are more sensitive than those based on marginal expression levels. Because our models are fully independent of exogenous results from sequence-based computational methods, they are appropriate for use as either a primary or secondary source of information regarding m/miRNA target pair relationships, especially in conjunction with high-throughput expression studies.Author Summary: MicroRNAs are a family of small RNAs that play important roles in the development, physiological function and stress responses of a wide variety of organisms, and if abnormally expressed are associated with multiple types of cancer in humans. Rather than being translated into proteins, members of the family of microRNAs operate by preventing the translation of messenger RNAs to which they have some degree of sequence complementarity. Although sequence-based bioinformatics techniques have yielded large numbers of predicted messenger- and microRNA targeting relationships, verifying these as bona fide has proven practically difficult. We have developed a novel statistical approach based on the system biology of microRNAs in humans to detect such targeting relationships using high-throughput RNA expression data. Because our approach is not based on information from external target pair predictions, it can play a fully independent role in verifying such predictions as well as be used to obtain de novo target pair predictions. Using two separate data studies, we show that our approach is capable of both reproducing previously observed target pairs and verifying putative target pairs predicted from sequence data, at rates substantially better than marginal comparisons of messenger- and microRNA expression levels.