Directed evolution of diacetylchitobiose deacetylase via high-throughput droplet sorting with a novel, bacteria-based biosensor

Abstract

Numerous biological disciplines rely on high-throughput cell sorting. Flow cytometry, the current gold standard, is capable of ultrahigh–throughput cell sorting, but measurements are primarily limited to cell size and surface marker. Droplet sorting technology is gaining increasing attention with the ability to provide an individual environment for the analysis of single-cell secretion. Although various droplet detecting methods, such as fluorescence, absorbance, mass spectrum, imaging analysis, have been developed for droplet sorting, it remains challenging to establish high-throughput sorting methods for numerous analytes. We aim to develop a high-throughput sorting system based on the glucosamine (GlcN) measurement for the directed evolution of diacetylchitobiose deacetylase (Dac), the key enzyme for GlcN production. To overcome the limitation that no high-throughput sorting system existed for GlcN, we designed a novel bacteria-based biosensor capable of converting GlcN to a positively correlated fluorescence signal. Through characterization and optimization, it was possible to detect GlcN in droplets for high-throughput droplet sorting. We recovered the best Dac mutant S60I/R157T/F168S after sorting ∼0.2 million Dac mutants; its activity was 48.6 ± 1.5 U/mL, which was 1.8-times that of our previously discovered Dac mutant R157T (27.2 ± 1.8 U/mL). This result successfully demonstrated the combination of high-throughput droplet sorting technology and a bacteria-based biosensor, which could facilitate the industrial production of GlcN and serve as a model for similar droplet sorting applications.