Synthetic gene circuits enable systems-level biosensor discovery at the host-microbe interface
The composition and function of the gut microbiota are strongly associated with human health, and dysbiosis is linked to an array of diseases, ranging from obesity and diabetes to infection and inflammation. Engineering synthetic circuits into gut bacteria to sense, record and respond to in vivo signals is a promising new approach for the diagnosis, treatment and prevention of disease. Here, we repurpose a synthetic bacterial memory circuit to rapidly screen for and discover new in vivo-responsive biosensors in commensal gut Escherichia coli. We develop a pipeline for rapid systems-level library construction and screening, using next-generation sequencing and computational analysis, which demonstrates remarkably robust identification of responsive biosensors from pooled libraries. By testing both genome-wide and curated libraries of potential biosensor triggers--each consisting of a promoter and ribosome binding site (RBS)--and using RBS variation to augment the range of trigger sensitivity, we identify and validate triggers that selectively activate our synthetic memory circuit during transit through the gut. We further identify biosensors with increased response in the inflamed gut through comparative screening of our libraries in healthy mice and those with intestinal inflammation. Our results demonstrate the power of systems-level screening for the identification of novel biosensors in the gut and provide a platform for disease-specific screening using synthetic circuits, capable of contributing to both the understanding and clinical management of intestinal illness. IMPORTANCEThe gut is a largely obscure and inaccessible environment. The use of live, engineered probiotics to detect and respond to disease signals in vivo represents a new frontier in the management of gut diseases. Engineered probiotics have also shown promise as a novel mechanism for drug delivery. However, the design and construction of effective strains that respond to the in vivo environment is hindered by our limited understanding of bacterial behavior in the gut. Our work expands the pool of potential biosensors for the healthy and diseased gut, providing insight into host-microbe interactions and enabling future development of increasingly complex synthetic circuits. This method also provides a framework for rapid prototyping of engineered systems and for application across bacterial strains and disease models.