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. 2004 Nov;2(11):e363.
doi: 10.1371/journal.pbio.0020363. Epub 2004 Oct 5.

Human MicroRNA targets

Bino John  1 , Anton J Enright, Alexei Aravin, Thomas Tuschl, Chris Sander, Debora S Marks
Affiliations

Human MicroRNA targets

Bino John et al. PLoS Biol. 2004 Nov.

Erratum in

  • PLoS Biol. 2005 Jul;3(7):e264

Abstract

MicroRNAs (miRNAs) interact with target mRNAs at specific sites to induce cleavage of the message or inhibit translation. The specific function of most mammalian miRNAs is unknown. We have predicted target sites on the 3' untranslated regions of human gene transcripts for all currently known 218 mammalian miRNAs to facilitate focused experiments. We report about 2,000 human genes with miRNA target sites conserved in mammals and about 250 human genes conserved as targets between mammals and fish. The prediction algorithm optimizes sequence complementarity using position-specific rules and relies on strict requirements of interspecies conservation. Experimental support for the validity of the method comes from known targets and from strong enrichment of predicted targets in mRNAs associated with the fragile X mental retardation protein in mammals. This is consistent with the hypothesis that miRNAs act as sequence-specific adaptors in the interaction of ribonuclear particles with translationally regulated messages. Overrepresented groups of targets include mRNAs coding for transcription factors, components of the miRNA machinery, and other proteins involved in translational regulation, as well as components of the ubiquitin machinery, representing novel feedback loops in gene regulation. Detailed information about target genes, target processes, and open-source software for target prediction (miRanda) is available at http://www.microrna.org. Our analysis suggests that miRNA genes, which are about 1% of all human genes, regulate protein production for 10% or more of all human genes.

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Conflict of interest statement

The authors have declared that no conflicts of interest exist.

Figures

Figure 1
Figure 1. Target Prediction Pipeline for miRNA Targets in Vertebrates
The mammalian (human, mouse, and rat) and fish (zebra and fugu) 3′ UTRs were first scanned for miRNA target sites using position-specific rules of sequence complementarity. Next, aligned UTRs of orthologous genes were used to check for conservation of miRNA–target relationships (“target conservation”) between mammalian genomes and, separately, between fish genomes. The main results (bottom) are the conserved mammalian and conserved fish targets, for each miRNA, as well as a smaller set of super-conserved vertebrate targets.
Figure 2
Figure 2. Distribution of Transcripts with Cooperativity of Target Sites and Estimated Number of False Positives
Each bar reflects the number of human transcripts with a given number of target sites on their UTR. Estimated rate of false positives (e.g., 39% for ≥2 targets) is given by the number of target sites predicted using shuffled miRNAs processed in a way identical to real miRNAs, including the use of interspecies conservation filter.
Figure 3
Figure 3. Multiplicity and Cooperativity in miRNA–Target Interactions
One miRNA can target more than one gene (multiplicity) (A), and one gene can be controlled by more than one miRNA (cooperativity) (B). The distributions are based on ordered (ranked) lists and decay approximately exponentially (approximate straight line in log-linear plot). (A) Some miRNAs appear to be very promiscuous (top left), with hundreds of predicted targets, but most miRNAs control only a few genes (bottom right). (B) Some target genes appear to be subject to highly cooperative control (top left), but most genes do not have more than four targets sites (bottom right). Although specific values are likely to change with refinement of target prediction rules, the overall character of the distribution may well be a biologically relevant feature reflecting system properties of regulation by miRNAs.
Figure 4
Figure 4. Potential miRNA Target Sites in the 3′ UTRs of Selected Genes
Nucleotide sequence conservation between the 3′ UTRs of human and the closest mouse or rat orthologous genes is averaged for each block of 40 base pairs (long rectangles; white indicates 0% identical nucleotides, black indicates 100% identical nucleotides, and grey indicates intermediate values). The positions of target sites for specific miRNAs (triangles above rectangles, with numbers indicating miR miRNAs, e.g. “130” is “mir-130”) are, in general, distributed nonuniformly. Sequence motifs other than target sites (triangles below rectangles) are mRNA stability elements (APP), a G-quartet (DLG4), and an AU-rich element (ELAVL1), representing possible protein-binding sites. Detailed alignments between the miRNA and the predicted target sites (arbitrary selection) illustrate, in general, stronger match density at the 5′ end of miRNAs than at the 3′ end, as required by the algorithm and as observed in experimentally validated targets. The nonconserved nucleotides in the target sites are highlighted in red. Gene names map to the following Ensembl identifiers (142192 is ENSG00000142192, etc.): APP, 142192; CPEB2, 137449; DLG4, 132535; EFNB1, 090776; EIF2c1, 092847; ELAVL1, 066044; EPHB1, 154928; EPHB3, 182580; FMR1, 102081; FMR2, 155966; FXR1, 114416; FXR2, 129245; and PTEN, 171862.
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