High-Throughput Identification of Tissue/Cell-Type-Specific Cis Regulatory Module

Brigham And Women'S Hospital, Boston MA

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Abstract

In metazoans, gene expression is regulated in a tissue/cell-type specific manner predominantly via stretches of noncoding sequence referred to as cis regulatory modules (CRMs) that regulate the expression of (typically) the adjacent gene(s). CRMs usually contain 1 or more DNA binding sites for 1 or more sequence-specific, regulatory transcription factors (TFs) that function to activate or repress the target gene(s);CRMs that activate gene expression are frequently referred to as "transcriptional enhancers", and have been the focus of many computational and experimental studies. Identification of tissue/cell-type-specific enhancers in metazoans remains a significant challenge. Moreover, despite recent technological advances, a major, rate-limiting bottleneck that is impeding rapid progress in the field is the still quite low-throughput experimental testing of candidate enhancers. The overarching goals of this project are to develop and apply novel 'wet-lab'technology for high-throughput experimental identification of tissue/cell-type-specific transcriptional enhancers. In this project we will focus on the developing embryonic mesoderm in Drosophila as a model system. We will identify cis regulatory modules and analyze their constituent cis regulatory codes that operate in somatic mesoderm (SM) founder cells (FCs) and fusion competent myoblasts (FCMs). Specifically, we will: develop and apply novel 'wet-lab'technology for high-throughput experimental identification of tissue/cell- type-specific transcriptional enhancers;determine the DNA binding specificities of ~75 known and predicted TFs expressed in the Drosophila embryonic mesoderm;predict CRMs and infer cis regulatory codes considering highly combinatorial input from large, high-resolution TF-DNA binding specificity dictionaries;and experimentally validate newly discovered enhancers. Importantly, we anticipate that the technologies, approaches, tools, and data resulting from this project will be generally applicable to other systems and organisms.