Sensor selector strategy for directed evolution of biosynthetic pathways

Metabolites biosynthetically produced in nature hold enormous potential for the sustainable production of chemicals, materials and therapeutics. This potential remains largely unfulfilled because technological limitations severely restrict the types of metabolites that can be readily detected and the number of biosynthetic pathway prototypes that can be evaluated. Thus, despite advances in genetic diversification methods, phenotype evaluation remains the major rate-limiting step. Here we present a broadly extensible synthetic selection system, known as sensor-selector, that simultaneously expands our phenotype evaluation capabilities in two ways: 1) a wide range of metabolites are detectable by drawing on the diversity of natural protein and RNA sensors and 2) directed evolution is harnessed to select the highest-producing biosynthetic pathways from libraries orders of magnitude larger than current screening methods permit.   To overcome the problem of unproductive escape mutants that has previously plagued selection-based pathway optimizations, we employ a toggled selection scheme with dual selective markers to robustly eliminate cells capable of circumventing selection. We characterize sensor-selectors for 15 metabolites belonging to diverse classes – polyketide antibiotics, flavonoids, polymer-precursor diacids, vitamins, long-chain alkanes and sugars. We have developed a general suite of genetic interventions capable of expanding library sizes by four orders of magnitude and programming sensor-selectors to function across user-specified metabolite concentration ranges.  Evaluating more than a billion cells per day, we deploy sensor-selector to optimize biosynthesis of the industrially useful metabolite, naringenin, yielding nearly 50 fold improvement, through four iterations of directed evolution.