Abstract
SummaryDirected evolution is a powerful method to optimize proteins and metabolic reactions towards user‐defined goals. It usually involves subjecting genes or pathways to iterative rounds of mutagenesis, selection and amplification. While powerful, systematic searches through large sequence‐spaces is a labour‐intensive task, and can be further limited by a priori knowledge about the optimal initial search space, and/or limits in terms of screening throughput. Here, we demonstrate an integrated directed evolution workflow for metabolic pathway enzymes that continuously generate enzyme variants using the recently developed orthogonal replication system, OrthoRep and screens for optimal performance in high‐throughput using a transcription factor‐based biosensor. We demonstrate the strengths of this workflow by evolving a rate‐limiting enzymatic reaction of the biosynthetic pathway for cis,cis‐muconic acid (CCM), a precursor used for bioplastic and coatings, in Saccharomyces cerevisiae. After two weeks of simply iterating between passaging of cells to generate variant enzymes via OrthoRep and high‐throughput sorting of best‐performing variants using a transcription factor‐based biosensor for CCM, we ultimately identified variant enzymes improving CCM titers > 13‐fold compared with reference enzymes. Taken together, the combination of synthetic biology tools as adopted in this study is an efficient approach to debottleneck repetitive workflows associated with directed evolution of metabolic enzymes.
Highlights
Industrial biotechnology has offered commercialization of environmentally friendly transportation fuels, amino acids and value-added chemicals by the use of fermentation feedstocks and microbial cell factories (Choi et al, 2019)
We demonstrate an integrated directed evolution workflow for metabolic pathway enzymes that continuously generates enzyme variants using the recently developed orthogonal replication system, OrthoRep, and screens for optimal performance in high-throughput using a transcription factor-based biosensor
We demonstrate the strengths of this workflow by evolving a ratelimiting enzymatic reaction of the biosynthetic pathway for cis,cis-muconic acid (CCM), a precursor used for bioplastic and coatings, in Saccharomyces cerevisiae
Summary
Industrial biotechnology has offered commercialization of environmentally friendly transportation fuels, amino acids and value-added chemicals by the use of fermentation feedstocks and microbial cell factories (Choi et al, 2019). In the design of cell factories for fermentation-based manufacturing of value-added chemicals and therapeutics, biosynthetic pathways are often composed of enzymes from several different sources, and with enzyme activities and expression levels requiring careful balancing in order to achieve optimal pathway flux (Galanie et al, 2015; Zhang et al, 2020) While such multi-dimensional optimization can be streamlined using design-of-experiment approaches and machine learning algorithms (Jeschek et al, 2016; Xu et al, 2017; Carbonell et al, 2018), the regulatory and cellular complexity of living cells and the constraints in speed, scale, depth, and costs of even rational trial-and-error engineering approaches challenge the development of microbial cell factories. With orthogonal in vivo evolution machineries at hand, any trait that can be coupled to growth (e.g. antibiotic resistance, tolerance to cultivation conditions, and/or complementation of auxotrophies) enables facile identification of improved target genes without need for direct screening (Esvelt et al, 2011; Ravikumar et al, 2014; García-García et al, 2020; Rix et al, 2020)
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