Decoding the genome-wide regulatory landscape of Escherichia coli in response to environmental stress: iron availability | AIChE

Decoding the genome-wide regulatory landscape of Escherichia coli in response to environmental stress: iron availability

Authors 

Kim, D., University of California, San Diego
O'Brien, E. J., University of California, San Diego
Szubin, R., University of California, San Diego

Understanding the genome-wide regulatory network in response to environmental stress is prerequisite to rationally design microbes surviving against stresses. Iron availability is an important environmental factor for cell survival due to its involvement in many fundamental cellular processes such as N2 fixation, DNA synthesis, TCA cycle, and respiration. The ferric uptake regulator (Fur) plays a critical role in the transcriptional regulation of iron metabolism. However, the full regulatory potential of Fur remains undefined. Here, we comprehensively reconstructed the Fur transcriptional regulatory network in Escherichia coli K-12 MG1655 in response to iron availability using genome-wide measurements (ChIP-exo and RNA-seq). Integrative data analysis revealed that a total of 81 genes in 42 transcription units are directly regulated by three different modes of Fur regulation, including apo- and holo-Fur activation and holo-Fur repression. We showed that Fur connects iron transport and utilization enzymes with negative-feedback loop pairs for iron homeostasis. In addition, direct involvement of Fur in the regulation of DNA synthesis, energy metabolism, and biofilm development was found. These results show how Fur exhibits a comprehensive regulatory role affecting many fundamental cellular processes linked to iron metabolism in order to coordinate the overall response of E. coli to iron availability. We believe that the incorporation of these types of comprehensive operon structures that account for cellular regulation along with regulatory network into current computational model would make it possible to mechanistically model and predict the complex regulatory interactions and thus allow us to more accurately compute complex phenotypes and design genomes for synthetic cells in the future.