Explore or exploit? A model-based screening strategy for PETase
secretion by Corynebacterium glutamicum
Abstract
Extracellular production of target proteins simplifies downstream
processing due to obsolete cell disruption. However, optimal
combinations of a heterologous protein, suitable signal peptide and
secretion host can currently not be predicted, resulting in large strain
libraries that need to be tested. On the experimental side, this
challenge can be tackled by miniaturization, parallelization and
automation, which provide high-throughput screening data. These data
need to be condensed into a candidate ranking for decision making to
focus bioprocess development on the most promising candidates. We
screened for Bacillus subtilis signal peptides mediating Sec
secretion of two polyethylene terephthalate degrading enzymes (PETases),
leaf-branch compost cutinase (LCC) and polyester hydrolase (PE-H)
mutants, by Corynebacterium glutamicum. We developed a fully
automated screening process and constructed an accompanying Bayesian
statistical modeling framework, which we applied in screenings for
highest activity in 4-nitrophenyl palmitate degradation. In contrast to
classical evaluation methods, batch effects and biological errors are
taken into account and their uncertainty is quantified. Within only two
rounds of screening, the most suitable signal peptide was identified for
each PETase. Results from LCC secretion in microliter-scale cultivation
were shown to be scalable to laboratory-scale bioreactors. This work
demonstrates an experiment-modeling loop that can accelerate early-stage
screening in a way that experimental capacities are focused to the most
promising strain candidates. Combined with high-throughput cloning, this
paves the way for using large strain libraries of several hundreds of
strains in a Design-Build-Test-Learn approach.