(716c) An Algorithmic Approach to Program Probiotic Bacterial Strains for In Vivo Diagnostics | AIChE

(716c) An Algorithmic Approach to Program Probiotic Bacterial Strains for In Vivo Diagnostics


Lebovich, M. - Presenter, University of Massachusetts Amherst
Andrews, L. B., University of Massachusetts Amherst
Through the implementation of designable genetic circuits, probiotic strains of bacteria can be used as non-invasive diagnostic tools for the gastrointestinal tract. For these programmed cells to report biomarkers after exiting the gut, the genetic circuits need to be able to detect and record the relevant signals within the gut environment using genetically encoded memory. Complex circuits with memory would allow for multiplexed detection and reporting of biomarkers in vivo. We previously developed a computational approach for the scalable design of genetic circuits that contain memory, known as sequential logic circuits. However, the ways that performance of the genetic circuits and components varies for probiotic bacterial strains and growth conditions remained poorly understood. Here, we demonstrate that the theory-based approach to design sequential logic circuits from simple logic gate responses can be implemented in gut bacteria, and these circuits can be integrated with gut metabolite biosensors. For this work, we used repressors of the TetR family to construct logic NOT/NOR gates that can be composed into complex genetic circuits and those containing memory. The genetic logic gates were characterized in the probiotic strain E. coli Nissle 1917. Using this data, we designed and predicted the behavior of larger circuit designs. We present a set of genetic circuits that encode sequential logic and show that the circuit outputs are in close agreement with our quantitative predictions from the design algorithm. In addition, we have generated and characterized a set of genetically encoded biosensors in E. coli Nissle 1917 for biochemicals that may be found in the human gut, including propionate and choline. Using the genetic circuit design algorithm, these sensors were integrated into genetic circuits to sense and record specified concentration ranges of these molecules as a test case for the use of this platform for the design of diagnostic gut bacteria.