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Combinatorial microRNA target predictions

Nature Genetics volume 37, pages 495–500 (2005)Cite this article

Abstract

MicroRNAs are small noncoding RNAs that recognize and bind to partially complementary sites in the 3′ untranslated regions of target genes in animals and, by unknown mechanisms, regulate protein production of the target transcript1,2,3. Different combinations of microRNAs are expressed in different cell types and may coordinately regulate cell-specific target genes. Here, we present PicTar, a computational method for identifying common targets of microRNAs. Statistical tests using genome-wide alignments of eight vertebrate genomes, PicTar's ability to specifically recover published microRNA targets, and experimental validation of seven predicted targets suggest that PicTar has an excellent success rate in predicting targets for single microRNAs and for combinations of microRNAs. We find that vertebrate microRNAs target, on average, roughly 200 transcripts each. Furthermore, our results suggest widespread coordinate control executed by microRNAs. In particular, we experimentally validate common regulation of Mtpn by miR-375, miR-124 and let-7b and thus provide evidence for coordinate microRNA control in mammals.

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Figure 1: The PicTar algorithm.
Figure 2: Signal-to-noise ratio for vertebrate microRNA target site predictions.
Figure 3: Estimate of the number of coordinately regulated targets for sets of three microRNAs.
Figure 4: Regulation of Mtpn by miR-375, miR-124 and let-7b.

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Acknowledgements

We thank V. Miljkovic and S. Pueblas for preparing figures for the manuscript. N. Rajewsky thanks T. Tuschl, P. Macino and F. Piano for discussions. This project was funded in part by a grant from the US National Institutes of Health (to M.S.). D.G. acknowledges a scholarship by the German Academic Exchange Service. K.C.G. and P.M. were supported by grants from the US National Institutes of Health (to F. Píaro) and the US National Science Foundation (to K.C.G.). This research was supported in part by the Howard Hughes Medical Institute grant through the Undergraduate Biological Sciences Education Program to New York University.

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Author notes

  1. Azra Krek, Dominic Grün and Matthew N Poy: These authors contributed equally to this work.

Authors and Affiliations

  1. Department of Biology, Center for Comparative Functional Genomics, New York University, 100 Washington Square East, New York, 10003, New York, USA

    Azra Krek, Dominic Grün, Rachel Wolf, Lauren Rosenberg, Philip MacMenamin, Isabelle da Piedade, Kristin C Gunsalus & Nikolaus Rajewsky

  2. Department of Physics, New York University, New York, 10003, New York, USA

    Azra Krek

  3. Laboratory of Metabolic Diseases, The Rockefeller University, New York, 10021, New York, USA

    Matthew N Poy, Eric J Epstein & Markus Stoffel

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  1. Azra Krek
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Corresponding author

Correspondence to Nikolaus Rajewsky.

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The authors declare no competing financial interests.

Supplementary information

Supplementary Table 1

Set of 58 unique microRNAs conserved in human/chimp/mouse/rat/dog/chicken. (XLS 12 kb)

Supplementary Table 2

Predictions for single microRNA targets based on conservation in human/chimp/mouse/rat/dog/chicken. (XLS 2795 kb)

Supplementary Table 3

Predictions for single microRNA targets based on conservation in human/chimp/mouse/rat/dog. (XLS 8613 kb)

Supplementary Table 4

Predictions for combinatorially targeted transcripts in four different tissues for sets of co-expressed microRNAs. (XLS 393 kb)

Supplementary Note

Detailed description of the PicTar probabilistic scoring method. (PDF 69 kb)

Supplementary Methods

The nematode sequence datasets, evolutionary conservation check and testing the quality of vertebrate alignments. (PDF 19 kb)

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Krek, A., Grün, D., Poy, M. et al. Combinatorial microRNA target predictions. Nat Genet 37, 495–500 (2005). https://doi.org/10.1038/ng1536

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