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Computational prediction of regulatory element combinations and transcription factor cooperativity Fulton, Debra Louise

Abstract

Cellular identity and function is determined, in part, by the subset of genes transcribed. Gene transcription regulation is directed by a subgroup of proteins called transcription factors (TF), which can interact directly or indirectly with DNA to promote transcription initiation. In multi-cellular eukaryotes, gene expression often derives from synergistic and/or antagonistic interplay of multiple TFs with coordinate activity in response to physiological, developmental, and environmental stimuli. Sequence-specific interactions of TFs with DNA occur at TF binding sites (TFBS). Such TFBS can be predicted based on previously observed target DNA sequence specificity of a TF. Experimental studies have confirmed that proximally situated TFBS are often associated with synergistic interactions of multiple proteins that lead to cooperative regulation. The identification of clustered TFBS combinations (often called cis-regulatory modules) in a set of co-expressed genes can implicate regulatory roles for homologous groups of TFs that may contribute to co-regulation of a gene cohort. The identification of the specific TFs that interact with TFBS motifs is an important step in deciphering mechanisms of co-regulation. My thesis research addressed these challenges, firstly, through the design and development of a Combination Site Analysis (CSA) algorithm to identify over-representation of combinations of TFBS in co-expressed genes and, secondly, the assembly of a comprehensive wiki-based catalog of human-mouse TFs (TFCat) using literature curation and homolog prediction approaches. These applications were incorporated within a new promoter sequence analyses procedure for the identification of TFs that may be acting cooperatively to co-regulate expression of myelin-associated genes during myelin production in the CNS. Dysregulation of gene expression is frequently implicated in human pathologies and development of approaches that identify the molecular components of transcriptional regulatory systems is an important step towards the elucidation of molecular mechanisms for the design of therapeutic interventions.

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