Identification of protein targets for microRNAs (miRNAs)is a significant challenge due to the complexity of miRNAmediated regulation. We have previously demonstrated that miR-193b targets estrogen receptor-␣ (ER␣) and inhibits estrogen-induced growth of breast cancer cells.Here, we applied a high-throughput strategy using quantitative iTRAQ (isobaric tag for relative and absolute quantitation) reagents to identify other target proteins regulated by miR-193b in breast cancer cells.iTRAQ analysis of pre-miR-193b transfected MCF-7 cells resulted in identification of 743 unique proteins, of which 39 were down-regulated and 44 up-regulated as compared with negative control transfected cells. Computationally predicted targets of miR-193b were highly enriched (sevenfold) among the proteins whose level of expression decreased after miR-193b transfection. Only a minority of these (13%) showed similar effect at the mRNA level illustrating the importance of post-transcriptional regulation. The most significantly repressed proteins were selected for validation experiments. These data confirmed 14 -3-3 (YWHAZ), serine hydroxyl transferase (SHMT2), and aldo-keto reductase family 1, member C2 (AKR1C2) as direct, previously uncharacterized, targets of miR-193b. Functional RNAi assays demonstrated that specific combinations of knockdowns of these target genes by siRNAs inhibited growth of MCF-7 cells, mimicking the effects of the miR-193b overexpression. Interestingly, the data imply that besides targeting ER␣, the miR-193b effects include suppression of the local production of estrogens and other steroid hormones mediated by the AKR1C2 gene, thus provoking two separate molecular mechanisms inhibiting steroid-dependent growth of breast cancer cells.In conclusion, we present here a proteomic screen to identify targets of miR-193b, and a systems biological approach to mimic its effects at the level of cellular phe-
MicroRNAs (miRNAs)1 regulate gene expression post-transcriptionally by binding primarily to the 3Јuntranslated region (3ЈUTR) of their target mRNAs, resulting in mRNA destabilization or translational repression (1, 2). Genes encoding 1048 human miRNAs have so far been identified (miRBase v.16.0) (3), and miRNAs are predicted to regulate the expression of up to 60% of all human protein-encoding genes (4). A large number of bioinformatic methods have been developed for miRNA target prediction (1), but these are still highly unspecific and inaccurate because miRNAs typically contain an imperfect match to their target sequences. In addition, a single miRNA can target hundreds of proteins and a single protein can be influenced by multiple miRNAs (1, 5, 6). Hence, comprehensive understanding of the phenotypic effects of miRNAs at the level of the entire cell is currently difficult. mRNA profiling by microarrays has been widely used for miRNA target identification, but microarrays only detect the effects of miRNAs at the transcriptional level, and will miss targets repressed solely at the translational level. However, compare...