Toward a Generalized and High-throughput Enzyme Screening System Based on Artificial Genetic Circuits

Abstract

Large-scale screening of enzyme libraries is essential for the development of cost-effective biological processes, which will be indispensable for the production of sustainable biobased chemicals. Here, we introduce a genetic circuit termed the Genetic Enzyme Screening System that is highly useful for high-throughput enzyme screening from diverse microbial metagenomes. The circuit consists of two AND logics. The first AND logic, the two inputs of which are the target enzyme and its substrate, is responsible for the accumulation of a phenol compound in cell. Then, the phenol compound and its inducible transcription factor, whose activation turns on the expression of a reporter gene, interact in the other logic gate. We confirmed that an individual cell harboring this genetic circuit can present approximately a 100-fold higher cellular fluorescence than the negative control and can be easily quantified by flow cytometry depending on the amounts of phenolic derivatives. The high sensitivity of the genetic circuit enables the rapid discovery of novel enzymes from metagenomic libraries, even for genes that show marginal activities in a host system. The crucial feature of this approach is that this single system can be used to screen a variety of enzymes that produce a phenol compound from respective synthetic phenyl-substrates, including cellulase, lipase, alkaline phosphatase, tyrosine phenol-lyase, and methyl parathion hydrolase. Consequently, the highly sensitive and quantitative nature of this genetic circuit along with flow cytometry techniques could provide a widely applicable toolkit for discovering and engineering novel enzymes at a single cell level.

Acclimation of bacterial cell state for high-throughput enzyme engineering using a DmpR-dependent transcriptional activation system

“…The plasmid pGESS constructed in our previous study was used as the genetic circuit system; it comprised a dmpR transcriptional activator and Po promoter from Pseudomonas putida KCTC 1452 as well as GFP as the reporter 23 . The pGESS was transformed into five E. coli strains [BL21, BL21(DE3), DH5α, EPI300 and JM109(DE3)] obtained from Novagen, Invitrogen, Epicentre and Promega using the calcium chloride transformation method to compare plasmid expression in different strains 41 .…”

“…We previously reported a genetic enzyme screening system (GESS) wherein the σ 54 -dependent TF DmpR has been used to sense phenolic compounds in Escherichia coli 23,24 . Other than DmpR, GESS consists of the Po promoter of the dmp operon from Pseudomonas sp.…”

“…Other than DmpR, GESS consists of the Po promoter of the dmp operon from Pseudomonas sp. and reporter proteins to facilitate analysis of enzyme-derived catalytic activity 23,24 . Thus, DmpR-GESS is a TF-based biosensor that senses phenolic compounds as products of enzymatic reactions.…”

Developments in the Tools and Methodologies of Synthetic Biology

“…As previously noted, PoPs and RIPS cannot be measured directly; instead they are calculated using fluorescence data from a reporter protein (e.g., GFP), growth data (OD), and largely assumed values for other parameters including protein or mRNA concentrations. These data are generally measured in vivo within a plate reader setup, though flow cytometry-based characterization efforts are increasingly being adopted and are set to progress metrology at the single cell level (Díaz et al, 2010; Tracy et al, 2010; Choi et al, 2013; Zuleta et al, 2014). In either case, if experimental setups are sufficiently standardized, it is possible to convert measurements between several widely adopted standards: RPU, PoPs/RIPS, and absolute measurements such as GFP cell −1  s −1 (Kelly et al, 2009).…”

“…To ensure consistency at such scale, high-throughput workflows typically couple liquid-handling robots with plate readers (Keren et al, 2013), flow cytometry (Piyasena and Graves, 2014; Zuleta et al, 2014), or microfluidics (Lin and Levchenko, 2012; Benedetto et al, 2014) in order to automate the majority of the experimental workflow. Several high-throughput platforms have been described, the majority of which were used to characterize DNA regulatory elements (Keren et al, 2013; Mutalik et al, 2013a,b), however, this is expanding to include the characterization of enzymes (Choi et al, 2013), multi-gene operons (Chizzolini et al, 2013), and RNA aptamers (Cho et al, 2013; Szeto et al, 2014). When coupled with automated data analysis and modeling, these technologies and workflows could become rapid prototyping platforms, enabling a truly biological design cycle approach (Kitney and Freemont, 2012).…”