Despite decades of intense genetic, biochemical, and evolutionary characterizations of bacterial promoters, we still lack the basic ability to identify or predict transcriptional activities of promoters using primary sequence. Even in simple, well-characterized organisms such as E. coli there is little agreement on the number, location, and strength of promoters. Here, we use a genomically-encoded massively parallel reporter assay to perform the first full characterization of autonomous promoter activity across the E. coli genome. We measure promoter activity of >300,000 sequences spanning the entire genome and precisely map 2,228 promoters active in rich media. We show that antisense promoters have a profound effect on global transcription and how codon usage has adapted to encode intragenic promoters. Furthermore, we perform a scanning mutagenesis of 2,057 promoters to uncover regulatory sequences responsible for regulating promoter activity. Finally, we show that despite these large datasets and modern machine learning algorithms, the task of predicting promoter activity from primary sequence sequence is still challenging.

A crucial step towards engineering biological systems is the ability to precisely tune the genetic response to environmental stimuli. In the case of Escherichia coli inducible promoters, our incomplete understanding of the relationship between sequence composition and gene expression hinders our ability to predictably control transcriptional responses. Here, we profile the expression dynamics of 8269 rationally designed, IPTG-inducible promoters that collectively explore the individual and combinatorial effects of RNA polymerase and LacI repressor binding site strengths. We then fit a statistical mechanics model to measured expression that accurately models gene expression and reveals properties of theoretically optimal inducible promoters. Furthermore, we characterize three alternative promoter architectures and show that repositioning binding sites within promoters influences the types of combinatorial effects observed between promoter elements. In total, this approach enables us to deconstruct relationships between inducible promoter elements and discover practical insights for engineering inducible promoters with desirable characteristics.

Mycobacteriophages Deby, LaterM, LilPharaoh, Paola, SgtBeansprout, and Sulley were isolated from soil using Mycobacterium smegmatis mc2155. Genomic analysis indicated that they belong to subclusters K1 and K5.

A crucial step towards engineering biological systems is the ability to precisely tune the genetic response to environmental stimuli. In the case of Escherichia coli inducible promoters, our incomplete understanding of the relationship between sequence composition and gene expression hinders our ability to predictably control transcriptional responses. Here, we profile the expression dynamics of 8,269 rationally designed IPTG-inducible promoters that collectively explore the individual and combinatorial effects of RNA polymerase and LacI repressor binding site strengths. Using these data, we fit a statistical mechanics model that accurately models gene expression and reveals properties of theoretically optimal inducible promoters. Furthermore, we characterize three novel promoter architectures and show that repositioning binding sites within promoters influences the types of combinatorial effects observed between promoter elements. In total, this approach enables us to deconstruct relationships between inducible promoter elements and discover practical insights for engineering inducible promoters with desirable characteristics.