title: A study of the evolution of human microRNAs by their apparent repression effectiveness on target genes.
abstract: BACKGROUND
  Even though the genomes of many model species have already been sequenced, our knowledge of gene regulation in evolution is still very limited. One big obstacle is that it is hard to predict the target genes of transcriptional factors accurately from sequences. In this respect, microRNAs (miRNAs) are different from transcriptional factors, as target genes of miRNAs can be readily predicted from sequences. This feature of miRNAs offers an unprecedented vantage point for evolutionary analysis of gene regulation.
  
  
  RESULTS
  In this study, we analyzed a particular aspect of miRNA evolution, the differences in the "apparent repression effectiveness (ARE)" between human miRNAs of different conservational levels. ARE is a measure we designed to evaluate the repression effect of miRNAs on target genes based on publicly available gene expression data in normal tissues and miRNA targeting and expression data. We found that ARE values of more conserved miRNAs are significantly higher than those of less conserved miRNAs in general. We also found the gain in expression abundance and broadness of miRNAs in evolution contributed to the gain in ARE.
  
  
  CONCLUSIONS
  The ARE measure quantifies the repressive effects of miRNAs and enables us to study the influences of many factors on miRNA-mediated repression, such as conservational levels and expression levels of miRNAs. The gain in ARE can be explained by the existence of a trend of miRNAs in evolution to effectively control more target genes, which is beneficial to the miRNAs but not necessarily to the organism at all times. Our results from miRNAs gave us an insight of the complex interplay between regulators and target genes in evolution.
authors.affiliation: Department of Genetics, Development, and Cell Biology, Center for Bioinformatics and Biological Statistics, Iowa State University, Ames, Iowa, United States of America.
authors.forename: Yong
authors.initials: Y
authors.lastname: Huang
authors.forename: Xun
authors.initials: X
authors.lastname: Gu
mesh_terms: D000704:Analysis of Variance; D000818:Animals; D019295:Computational Biology / Q000379:methods*; D004285:Dogs; D019143:Evolution, Molecular; D020868:Gene Silencing; D006801:Humans; D051379:Mice; D035683:MicroRNAs / Q000235:genetics* / Q000378:metabolism; D009928:Organ Specificity; D012333:RNA, Messenger / Q000235:genetics / Q000378:metabolism; D051381:Rats; D013045:Species Specificity
chemical_list: D035683:MicroRNAs; D012333:RNA, Messenger