Victor Ambros

MicroRNAs comprise a few percent of animal genes and have been recognized as important regulators of a diverse range of biological processes. Understanding the biological functions of miRNAs requires effective means to identify their targets. Combined efforts from computational prediction, miRNA over-expression or depletion, and biochemical purification have identified thousands of potential miRNA-target pairs in cells and organisms. Complementarity to the miRNA seed sequence appears to be a common principle in target recognition. Other features, including miRNA-target duplex stability, binding site accessibility, and local UTR structure might affect target recognition. Yet computational approaches using such contextual features have yielded largely nonoverlapping results and experimental assessment of their impact has been limited. Here, we compare two large sets of miRNA targets: targets identified using an improved Ago1 immunopurification method and targets identified among transcripts up-regulated after Ago1 depletion. We found surprisingly limited overlap between these sets. The two sets showed enrichment for target sites with different molecular, structural and functional properties. Intriguingly, we found a strong correlation between UTR length and other contextual features that distinguish the two groups. This finding was extended to all predicted microRNA targets. Distinct repression mechanisms could have evolved to regulate targets with different contextual features. This study reveals a complex relationship among different features in miRNA-target recognition and poses a new challenge for computational prediction.

A new prediction algorithm for microRNA targets, mirWIP, is presented. The algorithm weights target site features based on their enrichment in an experimentally defined immunoprecipitation dataset and identifies verified miRNA-mRNA interactions in Caenorhabditis elegans with improved specificity compared to current methods. Target prediction for animal microRNAs (miRNAs) has been hindered by the small number of verified targets available to evaluate the accuracy of predicted miRNA-target interactions. Recently, a dataset of 3,404 miRNA-associated mRNA transcripts was identified by immunoprecipitation of the RNA-induced silencing complex components AIN-1 and AIN-2. Our analysis of this AIN-IP dataset revealed enrichment for defining characteristics of functional miRNA-target interactions, including structural accessibility of target sequences, total free energy of miRNA-target hybridization and topology of base-pairing to the 5′ seed region of the miRNA. We used these enriched characteristics as the basis for a quantitative miRNA target prediction method, miRNA targets by weighting immunoprecipitation-enriched parameters (mirWIP), which optimizes sensitivity to verified miRNA-target interactions and specificity to the AIN-IP dataset. MirWIP can be used to capture all known conserved miRNA-mRNA target relationships in Caenorhabditis elegans at a lower false-positive rate than can the current standard methods.

The Drosophila let-7-Complex (let-7-C) is a polycistronic locus encoding three ancient microRNAs: let-7, miR-100, and fly lin-4 (miR-125). We find that the let-7-C locus is principally expressed in the pupal and adult neuromusculature. let-7-C knockout flies appear normal externally but display defects in adult behaviors (e.g., flight, motility, and fertility) as well as clear juvenile features in their neuromusculature. We find that the function of let-7-C to ensure the appropriate remodeling of the abdominal neuromusculature during the larval-to-adult transition is carried out predominantly by let-7 alone. This heterochronic role of let-7 is likely just one of the ways in which let-7-C promotes adult behavior.