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Regulatory circuit of human microRNA biogenesis

Lee, J and Li, Z and Brower-Sinning, R and John, B (2007) Regulatory circuit of human microRNA biogenesis. PLoS Computational Biology, 3 (4). 721 - 732. ISSN 1553-734X

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miRNAs (microRNAs) are a class of endogenous small RNAs that are thought to negatively regulate protein production. Aberrant expression of many miRNAs is linked to cancer and other diseases. Little is known about the factors that regulate the expression of miRNAs. We have identified numerous regulatory elements upstream of miRNA genes that are likely to be essential to the transcriptional and posttranscriptional regulation of miRNAs. Newly identified regulatory motifs occur frequently and in multiple copies upstream of miRNAs. The motifs are highly enriched in G and C nucleotides, in comparison with the nucleotide composition of miRNA upstream sequences. Although the motifs were predicted using sequences that are upstream of miRNAs, we find that 99% of the top-predicted motifs preferentially occur within the first 500 nucleotides upstream of the transcription start sites of protein-coding genes; the observed preference in location underscores the validity and importance of the motifs identified in this study. Our study also raises the possibility that a considerable number of well-characterized, disease-associated transcription factors (TFs) of protein-coding genes contribute to the abnormal miRNA expression in diseases such as cancer. Further analysis of predicted miRNA-protein interactions lead us to hypothesize that TFs that include c-Myb, NF-Y, Sp-1, MTF-1, and AP-2α are master-regulators of miRNA expression. Our predictions are a solid starting point for the systematic elucidation of the causative basis for aberrant expression patterns of disease-related (e.g., cancer) miRNAs. Thus, we point out that focused studies of the TFs that regulate miRNAs will be paramount in developing cures for miRNA-related diseases. The identification of the miRNA regulatory motifs was facilitated by a new computational method, K-Factor. K-Factor predicts regulatory motifs in a set of functionally related sequences, without relying on evolutionary conservation. © 2007 Lee et al.


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Item Type: Article
Status: Published
CreatorsEmailPitt UsernameORCID
Lee, J
Li, Z
Brower-Sinning, R
John, B
ContributionContributors NameEmailPitt UsernameORCID
Centers: Other Centers, Institutes, Offices, or Units > Pittsburgh Cancer Institute
Date: 1 April 2007
Date Type: Publication
Journal or Publication Title: PLoS Computational Biology
Volume: 3
Number: 4
Page Range: 721 - 732
DOI or Unique Handle: 10.1371/journal.pcbi.0030067
Schools and Programs: School of Medicine > Computational Biology
Refereed: Yes
ISSN: 1553-734X
PubMed ID: 17447837
Date Deposited: 11 Jul 2012 17:50
Last Modified: 02 Feb 2019 21:55


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