Decoding the Regulatory Genome: Quantitative Analysis of Transcriptional Regulation in Escherichia coli

Abstract

Over the past decades DNA sequencing has become significantly cheaper and faster, which has enabled the accumulation of a huge amount of genomic data. However, much of this genomic data is illegible to us. For noncoding regions of the genome in particular, it is difficult to determine what role is played by specific DNA sequences. Here we focus on regions of DNA that play a role in transcriptional regulation. We develop models and techniques that allow us to discover new regulatory sequences and better understand how DNA sequence determines regulatory output. We start by considering how quantitative models serve as a powerful tool for testing our understanding of biological systems. We apply a statistical mechanical framework that incorporates the Monod-Wyman-Changeux model to analyze the effects of allostery in simple repression, using the lac operon as a test case. By fitting our model to experimental data, we are able to determine the values of the unknown parameter values in our model. We then show that we can use the model to accurately predict the induction responses of an array of simple repression constructs with a variety of repressor copy numbers and repressor binding energies. Next, we consider how the DNA sequence of a promoter region can provide details about how the promoter is regulated. We begin by describing an approach for discovering regulatory architectures for promoters whose regulation has not previously been studied. We focus on six promoters from E. coli including three well-studied promoters (rel, mar, and lac) to serve as test cases. We use the massively parallel reporter assay Sort-Seq to identify transcription factor binding sites with base-pair resolution, determine the regulatory role of each binding site, and infer energy matrices for each binding site. Then, we use DNA affinity chromatography and mass spectrometry to identify each transcription factor. We conclude with an in vivo approach for analyzing the sequence-dependence of transcription factor binding energies. Again using Sort-Seq, we show that we can represent transcription factor binding sites using energy matrices in absolute energy units. We then show that these energy matrices can be used to accurately predict the binding energies of mutated binding sites. We provide several examples of how understanding the relationship between DNA sequence and transcription factor binding provides us with a foundation for addressing additional scientific topics, such as the co-evolution of transcription factors and their binding sites.