The specificity and evolution of gene regulatory elements
Author(s)Friedman, Robin Carl
Massachusetts Institute of Technology. Computational and Systems Biology Program.
Christopher B. Burge and David P. Bartel.
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The regulation of gene expression underlies the morphological, physiological, and functional differences between human cell types, developmental stages, and healthy and disease states. Gene regulation in eukaryotes is controlled by a complex milieu including transcription factors, microRNAs (miRNAs), cis-regulatory DNA and RNA. It is the quantitative and combinatorial interactions of these regulatory elements that defines gene expression, but these interactions are incompletely understood. In this thesis, I present two new methods for determining the quantitative specificity of gene regulatory factors. First, I present a comparative genomics approach that utilizes signatures of natural selection to detect the conserved biological relevance of miRNAs and their targets. Using this method, I quantify the abundance of different conserved miRNA target types, including different seed matches and 30-compensatory targets. I show that over 60% of mammalian mRNAs are conserved targets of miRNAs and that a surprising amount of conserved miRNA targeting is mediated by seed matches with relatively low efficacy. Extending this method from mammals to other organisms, I find that miRNA targeting rules are mostly conserved, although I show evidence for new types of miRNA targets in nematodes. Taking advantage of variations in 30 UTR lengths between species, I describe general properties of miRNA targeting that are affected by 30 UTR length. Finally, I introduce a new, high-throughput assay for the quantification of transcription factor in vitro binding affinity to millions of sequences. I apply this method to GCN4, a yeast transcription factor, and reconstruct all known properties of its binding preferences. Additionally, I discover some new subtleties in its specificity and estimate dissociation constants for hundreds of thousands of sequences. I verify the utility of the binding affinities by comparing to in vivo binding data and to the regulatory response following GCN4 induction.
Thesis (Ph. D.)--Massachusetts Institute of Technology, Computational and Systems Biology Program, 2010.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Cataloged from student-submitted PDF version of thesis.Includes bibliographical references.
DepartmentMassachusetts Institute of Technology. Computational and Systems Biology Program.
Massachusetts Institute of Technology
Computational and Systems Biology Program.