Bacterial genomes typically encode hundreds of transcription factors (TFs). Decades of work on TFs in Escherichia coli has led to a deep mechanistic understanding of TF function. However, relatively few bacterial TFs have been studied on a genomic scale. Our data and those of other groups indicate that only a small fraction of TF binding sites have been identified, even for well-studied TFs. Consequently, the numerous investigators utilizing the extensive E. coli regulatory network data in public databases are relying on a highly incomplete, and potentially misleading, dataset. Our long-term goal is to develop a fully predictive model for transcription regulation by TFs in E. coli. The goa of this proposal is to inform such a model by mapping the regulons of all E. coli TFs and to use these data as the basis to investigate fundamental aspects of TF function. Global mapping data for bacterial TFs indicate that well-established rules of TF function apply to only a subset of binding sites. In particular, DNA sequence is often insufficient to predict TF binding location, suggesting that factors other than DNA binding site sequence contribute to TF-DNA interactions in vivo. Given the fundamental importance of gene regulation, it is vital that we better understand the relationship between DNA sequence and TF binding in vivo. We propose to experimentally generate a high-resolution regulatory network for E. coli that includes regulon information for all known and predicted TFs. This will serve as a valuable resource for the scientific community. We will use these data as a framework for accurate modeling of the regulatory network, and to inform our targeted studies of the relationship between DNA sequence and TF binding in vivo. We expect to generate a high-resolution regulatory network for E. coli. This will serve as a valuable resource for the scientific community. The equivalent resource for the model eukaryote, Saccharomyces cerevisiae, was generated over 10 years ago and has contributed greatly to our understanding of eukaryotic transcription regulation. The most complete resource for a bacterium is currently that for Mycobacterium tuberculosis, which lacks tractability as an experimental organism. Generating an equivalent resource for E. coli will greatly facilitate studies of bacterial gene regulation. We further expect to use our regulatory network model as a basis to understand the relationship between DNA sequence and TF binding in vivo. We expect to reveal complex interplay between pairs of TFs and between TFs and global regulatory proteins. Knowledge of these interactions is critical for a detailed understanding of TF function. Together, the work described in this proposal will bring our understanding of bacterial transcription regulation into the post-genomic era.

Public Health Relevance

Bacterial genes are regulated at the level of transcription (RNA synthesis) by proteins called transcription factors. We will identify all regulatory targets fr all known and predicted transcription factors in the model bacterium, Escherichia coli. These data will serve as a valuable resource for the scientific community and will allow us to build a computational model of transcription factor function, and investigate the relationship between DNA sequence and the interaction between transcription factors and DNA in living cells.

National Institute of Health (NIH)
National Institute of General Medical Sciences (NIGMS)
Research Project (R01)
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Genomics, Computational Biology and Technology Study Section (GCAT)
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Sledjeski, Darren D
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Boston University
Engineering (All Types)
Biomed Engr/Col Engr/Engr Sta
United States
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Wade, Joseph T; Grainger, David C (2018) Waking the neighbours: disruption of H-NS repression by overlapping transcription. Mol Microbiol 108:221-225
Fitzgerald, Devon M; Smith, Carol; Lapierre, Pascal et al. (2018) The evolutionary impact of intragenic FliA promoters in proteobacteria. Mol Microbiol 108:361-378
Aquino, Patricia; Honda, Brent; Jaini, Suma et al. (2017) Coordinated regulation of acid resistance in Escherichia coli. BMC Syst Biol 11:1
Sharp, Jared D; Singh, Atul K; Park, Sang Tae et al. (2016) Comprehensive Definition of the SigH Regulon of Mycobacterium tuberculosis Reveals Transcriptional Control of Diverse Stress Responses. PLoS One 11:e0152145