BMC Genomics 2005, 6:88 http://www.biomedcentral.com/1471-2164/6/88 FmiigRuNrAe-2dependent regulation of CrebA, Eip74 and ttk miRNA-dependent regulation of CrebA, Eip74 and ttk. Each panel shows at top the distribution of the miRNA target sites in the CrebA (A), Eip74 (B) and ttk (C) 3' UTRs. For panels A and C, the vertical bars above the line indicate sites for miR-92b, while vertical bars below the line indicate sites for miR-312. Luciferase expression data are presented as in Fig. 1. sensory organ cells, and which has been shown to be translationally repressed by the RNA-binding protein Musashi [40]. ttk is not a predicted miRNA target using the strict biolog- ical constraints described above. However, by relaxing these constraints to require a maximum free energy of miRNA/target hybridization of -12 kcal/mole at room temperature and a minimum of 6 out of 8 consecutive base pairs in the miRNA 5' end, MovingTargets predicts that mir-9c, mir-92a, mir-92b, and mir-312 (the latter three are closely related miRNAs) target ttk. We tested mir-92b and mir-312 in the S2 cell assay, and both miRNAs repress expression of the luc/ttk reporter (Figure 2C). Discussion The nature of the interaction between a miRNA and its tar- get – incomplete and interrupted base pairing – creates a substantial challenge for the prediction of candidate miRNA targets. Furthermore, very few mRNAs have been shown to be under miRNA regulation, limiting the number of examples from which the basic rules governing miRNA/target interactions can be determined. The latter problem is particularly acute, since even in mRNAs known to be regulated, the actual target sites are usually only inferred from their partial complementarity to the miRNA. Thus a precise description of these rules is a mov- ing target, and will undoubtedly be refined as additional targets are identified by methods not biased by current prediction strategies. Our approach to predicting miRNA targets addresses both of these difficulties. The combination of a conserved 3' UTR database and the MovingTargets algorithm allowed us to predict 83 miRNA targets that meet stringent biolog- ical constraints based on features of the probable or proven interactions between individual miRNAs and the mRNAs under their control. Each of three target predic- tions chosen for testing was verified in the S2 cell transfec- tion assay. Thus the algorithm succeeds in predicting miRNA targets. At present we have restricted the database of potential target sequences to those from 3' UTRs, but this could be expanded to include entire mRNAs given evidence that miRNAs bind to other regions of animal mRNAs. The biological relevance of a predicted miRNA/ target interaction depends on whether the miRNA and tar- get are expressed at appropriate concentrations in a partic- ular cell type. Thus, this and all other prediction methods represent only the first step, albeit an important one, in identifying bona fide miRNA targets. Page 8 of 11 (page number not for citation purposes)