microRNAs (miRNAs) are a recently discovered class of small non-coding RNAs. Their mature form is 20-25 nucleotides (nt) long, and is derived from a longer hairpin precursor. Almost 700 miRNAs are currently known in human. miRNAs regulate their target genes by blocking their translation into protein, or by compromising the stability of a messenger RNA (mRNA), leading to its degradation. To understand the biological function of microRNAs it is crucial to identify their target genes. miRNAs and mRNAs interact through partial Watson-Crick complementarity, and it is known that binding sites of conserved miRNAs are often conserved themselves. It is believed that several miRNAs can regulate the same gene simultaneously. Taking into account the characteristics of miRNA::mRNA interaction, we designed a probabilistic sequence model that enables us to estimate the likelihood for each gene in the data set to be a target of a given set of miRNAs. By requiring the presence of some number of binding sites conserved in related species, we are applying a filter that keeps the false positive predictions number at an acceptable level. We used the method to predict targets of all human, worm and fruit fly miRNAs, and many predictions were later confirmed by experiments. Perhaps surprisingly, our data predicted that a single miRNA regulated the expression of hundreds of mRNAs and that altogether thousands of human genes are regulated by miRNAs. This prediction has by now been widely accepted. Among other predictions, we found that a mouse gene Myothrophin is predicted to be coordinately targeted by three miRNAs, and this was experimentally confirmed in cell lines. In order to discover the sequence motifs that are mediating the effect of miRNAs on their targets, we analyzed the genome-wide expression data obtained by miRNAs. Using an iterative linear regression model, we could select the motifs that best explain the genome-wide changes in expression imposed by the activity of miRNAs. In the end, we apply both methods to analyze experiments performed on B cells (immune system), and were able to in part uncover the role of miRNAs in B cell development.
|Commitee:||Brujic, Jasna, Grosberg, Alexander, Hohenberg, Pierre C., Percus, Jerome K., Rajewsky, Nikolaus|
|School:||New York University|
|School Location:||United States -- New York|
|Source:||DAI-B 70/07, Dissertation Abstracts International|
|Keywords:||Gene regulation, Genome-wide expression profiles, Hidden Markov models, microRNAs|
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