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Abstract A transcription factor (TF), OmpR, plays a critical role in transcriptional regulation of the osmotic stress response in bacteria. Here, we reveal a genome-scale OmpR regulon in Escherichia coli K-12 MG1655. Integrative data analysis reveals that a total of 37 genes in 24 transcription units (TUs) belong to OmpR regulon. Among them, 26 genes show more than two-fold changes in expression level in an OmpR knock-out strain. Specifically, we find that: 1) OmpR regulates mostly membrane-located gene products involved in diverse fundamental biological processes, such as narU (encoding nitrate/nitrite transporter), ompX (encoding outer membrane protein X), and nuoN (encoding NADH:ubiquinone oxidoreductase); 2) by investigating co-regulation of entire sets of genes regulated by other stress-response TFs, stresses are surprisingly independently regulated among each other; and, 3) a detailed investigation of the physiological roles of the newly discovered OmpR regulon genes reveals that activation of narU represents a novel strategy to significantly improve osmotic stress tolerance of E. coli. Thus, the genome-scale approach to elucidating regulons comprehensively identifies regulated genes and leads to fundamental discoveries related to stress responses.

Balancing the amounts of enzymes is one of the important factors to achieve optimum performance of a designed metabolic pathway. However, the random mutagenesis approach is impractical since it requires searching an unnecessarily large number of variants and often results in searching a narrow range of expression levels which are out of optimal level. Here, we developed a predictive combinatorial design method, called UTR Library Designer, which systematically searches a large combinatorial space of expression levels. It accomplishes this by designing synthetic translation initiation region of mRNAs in a predictive way based on a thermodynamic model and genetic algorithm. Using this approach, we successfully enhanced lysine and hydrogen production in Escherichia coli. Our method significantly reduced the number of variants to be explored for covering large combinatorial space and efficiently enhanced pathway efficiency, thereby facilitating future efforts in metabolic engineering and synthetic biology. [ABSTRACT FROM AUTHOR]