The inability to predict heterologous gene expression levels precisely hinders our ability to engineer biological systems. Using well-characterized regulatory elements offers a potential solution only if such elements behave predictably when combined. We synthesized 12,563 combinations of common promoters and ribosome binding sites and simultaneously measured DNA, RNA, and protein levels from the entire library. Using a simple model, we found that RNA and protein expression were within twofold of expected levels 80% and 64% of the time, respectively. The large dataset allowed quantitation of global effects, such as translation rate on mRNA stability and mRNA secondary structure on translation rate. However, the worst 5% of constructs deviated from prediction by 13-fold on average, which could hinder large-scale genetic engineering projects. The ease and scale this of approach indicates that rather than relying on prediction or standardization, we can screen synthetic libraries for desired behavior.

The widespread natural ability of RNA to sense small molecules and regulate genes has become an important tool for synthetic biology in applications as diverse as environmental sensing and metabolic engineering. Previous work in RNA synthetic biology has engineered RNA mechanisms that independently regulate multiple targets and integrate regulatory signals. However, intracellular regulatory networks built with these systems have required proteins to propagate regulatory signals. In this work, we remove this requirement and expand the RNA synthetic biology toolkit by engineering three unique features of the plasmid pT181 antisense-RNA-mediated transcription attenuation mechanism. First, because the antisense RNA mechanism relies on RNA-RNA interactions, we show how the specificity of the natural system can be engineered to create variants that independently regulate multiple targets in the same cell. Second, because the pT181 mechanism controls transcription, we show how independently acting variants can be configured in tandem to integrate regulatory signals and perform genetic logic. Finally, because both the input and output of the attenuator is RNA, we show how these variants can be configured to directly propagate RNA regulatory signals by constructing an RNA-meditated transcriptional cascade. The combination of these three features within a single RNA-based regulatory mechanism has the potential to simplify the design and construction of genetic networks by directly propagating signals as RNA molecules.

Bacteriophages (phages) are critical players in the dynamics and function of microbial communities and drive processes as diverse as global biogeochemical cycles and human health. Phages tend to be predators finely tuned to attack specific hosts, even down to the strain level, which in turn defend themselves using an array of mechanisms. However, to date, efforts to rapidly and comprehensively identify bacterial host factors important in phage infection and resistance have yet to be fully realized. Here, we globally map the host genetic determinants involved in resistance to 14 phylogenetically diverse double-stranded DNA phages using two model Escherichia coli strains (K-12 and BL21) with known sequence divergence to demonstrate strain-specific differences. Using genome-wide loss-of-function and gain-of-function genetic technologies, we are able to confirm previously described phage receptors as well as uncover a number of previously unknown host factors that confer resistance to one or more of these phages. We uncover differences in resistance factors that strongly align with the susceptibility of K-12 and BL21 to specific phage. We also identify both phage-specific mechanisms, such as the unexpected role of cyclic-di-GMP in host sensitivity to phage N4, and more generic defenses, such as the overproduction of colanic acid capsular polysaccharide that defends against a wide array of phages. Our results indicate that host responses to phages can occur via diverse cellular mechanisms. Our systematic and high-throughput genetic workflow to characterize phage-host interaction determinants can be extended to diverse bacteria to generate datasets that allow predictive models of how phage-mediated selection will shape bacterial phenotype and evolution. The results of this study and future efforts to map the phage resistance landscape will lead to new insights into the coevolution of hosts and their phage, which can ultimately be used to design better phage therapeutic treatments and tools for precision microbiome engineering.