Human microRNAs target a functionally distinct population of genes with AT-rich 3′ UTRs

While investigating microRNA targets, we have found that human genes divide into two roughly equal populations, based on the fraction of A plus T bases in their 3′ UTRs. Using the Gene Ontology database, we find significant functional differences between the two gene populations, with AT-rich genes implicated in transcription and translation processes, and GC-rich genes implicated in signal transduction and posttranslational protein modification. Better understanding of the background distribution of nucleotides in 3′ UTRs may allow improved prediction of microRNA-targeted genes in humans. We predict at least 1,200 KnownGene transcripts to be regulated by microRNAs. The large majority of these microRNA targets are in the AT-rich 3′ UTR population. However, notwithstanding this preference for AT-rich targets, microRNA targets are found preferentially to be regulatory genes themselves, including both transcription factors and posttranslational modifiers. These results suggest that some processes involving mRNA, of which microRNA regulation may be just one, require AT-richness of 3′ UTRs for functionality. A relationship, not simply one-to-one, between these 3′ UTR populations and large-scale genomic isochores is described.

[1]  C. Burge,et al.  Conserved Seed Pairing, Often Flanked by Adenosines, Indicates that Thousands of Human Genes are MicroRNA Targets , 2005, Cell.

[2]  Gregory J. Hannon,et al.  MicroRNA-dependent localization of targeted mRNAs to mammalian P-bodies , 2005, Nature Cell Biology.

[3]  K. Gunsalus,et al.  Combinatorial microRNA target predictions , 2005, Nature Genetics.

[4]  John G Doench,et al.  Specificity of microRNA target selection in translational repression. , 2004, Genes & development.

[5]  B Marshall,et al.  Gene Ontology Consortium: The Gene Ontology (GO) database and informatics resource , 2004, Nucleic Acids Res..

[6]  D. Bartel.  MicroRNAs Genomics, Biogenesis, Mechanism, and Function , 2004, Cell.

[7]  C. Burge,et al.  Prediction of Mammalian MicroRNA Targets , 2003, Cell.

[8]  Terrence S. Furey,et al.  The UCSC Genome Browser Database , 2003, Nucleic Acids Res..

[9]  Y. Li,et al.  Incorporating structure to predict microRNA targets. , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[10]  R. Lockshin,et al.  Programmed cell death. , 1974, Life sciences.

[11]  J. Castle,et al.  Microarray analysis shows that some microRNAs downregulate large numbers of target mRNAs , 2005, Nature.

[12]  Dan Graur,et al.  GC composition of the human genome: in search of isochores. , 2005, Molecular biology and evolution.

[13]  G Bernardi,et al.  Isochores and the evolutionary genomics of vertebrates. , 2000, Gene.

[14]  T. Moon.  The expectation-maximization algorithm , 1996, IEEE Signal Process. Mag..

[15]  Geoffrey J. McLachlan,et al.  Finite Mixture Models , 2019, Annual Review of Statistics and Its Application.

[16]  C. Glover,et al.  Gene expression profiling for hematopoietic cell culture , 2006 .

[17]  H. Blau,et al.  Argonaute 2/RISC resides in sites of mammalian mRNA decay known as cytoplasmic bodies , 2005, Nature Cell Biology.

[18]  G Bernardi,et al.  The mosaic genome of warm-blooded vertebrates. , 1985, Science.

[19]  V. Ambros.  The functions of animal microRNAs , 2004, Nature.

[20]  Alexander E Vinogradov,et al.  Isochores and tissue-specificity. , 2003, Nucleic acids research.

[21]  Mark Pagel,et al.  Predicting functional gene-links from phylogenetic-statistical analyses of whole genomes , 2005, CSB Workshops.

[22]  Laurence D. Hurst,et al.  The evolution of isochores , 2001, Nature Reviews Genetics.

[23]  W. Hammerschmidt,et al.  Epstein-Barr Virus Provides a New Paradigm: A Requirement for the Immediate Inhibition of Apoptosis , 2005, PLoS biology.