Novel High-Throughput DNA Part Characterization Technique for Synthetic Biology.

This study presents a novel DNA part characterization technique that increases throughput by combinatorial DNA part assembly, solid plate-based quantitative fluorescence assay for phenotyping, and barcode tagging-based long-read sequencing for genotyping. We confirmed that the fluorescence intensities of colonies on plates were comparable to fluorescence at the single-cell level from a high-end, flow-cytometry device and developed a high-throughput image analysis pipeline. The barcode tagging-based long-read sequencing technique enabled rapid identification of all DNA parts and their combinations with a single sequencing experiment. Using our techniques, forty-four DNA parts (21 promoters and 23 RBSs) were successfully characterized in 72 h without any automated equipment. We anticipate that this high-throughput and easy-to-use part characterization technique will contribute to increasing part diversity and be useful for building genetic circuits and metabolic pathways in synthetic biology.

Machine learning linked evolutionary biosensor array for highly sensitive and specific molecular identification.

Bacteria initiate complicated signaling cascades from the detection of intracellular metabolites or exogenous substances by hundreds of transcription factors, which have been widely investigated as genetically-encoded biosensors for molecular recognition. However, the limited number of transcription factors and their broad substrate specificity result in ambiguity in small molecule identification. This study presents a new small molecule fingerprinting technique using evolutionary biosensor arrays with a machine learning technique that can capture highly specific substrate signals. Employing multiple mutant transcription factors derived from a single transcription factor has effectively circumvented the limited availability of transcription factors induced by a small molecule of our interest. This method achieved up to 95.3% true positive rate for identifying small molecules, and the high-resolution protein engineering technique improved the limit of detection 75-fold. The signal trade-offs with background noises caused by the complex cellular biochemistry of mutant transcription factors enable the biosensor arrays to be more informative in terms of statistical variance. The machine learning technology, coupled with the single transcription factor-driven evolutionary biosensor array, will open new avenues for molecular fingerprinting technologies.

Adaptive laboratory evolution of Escherichia coli lacking cellular byproduct formation for enhanced acetate utilization through compensatory ATP consumption.

Acetate has attracted great attention as a carbon source to develop economically feasible bioprocesses for sustainable bioproducts. Acetate is a less-preferred carbon source and a well-known growth inhibitor of Escherichia coli. In this study, we carried out adaptive laboratory evolution of an E. coli strain lacking four genes (adhE, pta, ldhA, and frdA) involved in acetyl-CoA consumption, allowing the efficient utilization of acetate as its sole carbon and energy source. Four genomic mutations were found in the evolved strain through whole-genome sequencing, and two major mutations (in cspC and patZ) mainly contributed to efficient utilization of acetate and tolerance to acetate. Transcriptomic reprogramming was examined by analyzing the genome-wide transcriptome with different carbon sources. The evolved strain showed high levels of intracellular ATP by upregulation of genes involved in NADH and ATP biosynthesis, which facilitated the production of enhanced green fluorescent protein, mevalonate, and n-butanol using acetate alone. This new strain, given its high acetate tolerance and high ATP levels, has potential as a starting host for cell factories targeting the production of acetyl-CoA-derived products from acetate or of products requiring high ATP levels.

CRISPR interference-guided multiplex repression of endogenous competing pathway genes for redirecting metabolic flux in Escherichia coli.

BACKGROUND: Multiplex control of metabolic pathway genes is essential for maximizing product titers and conversion yields of fuels, chemicals, and pharmaceuticals in metabolic engineering. To achieve this goal, artificial transcriptional regulators, such as clustered regularly interspaced short palindromic repeats (CRISPR) interference (CRISPRi), have been developed to specifically repress genes of interest. RESULTS: In this study, we deployed a tunable CRISPRi system for multiplex repression of competing pathway genes and, thus, directed carbon flux toward production of molecules of interest in Escherichia coli. The tunable CRISPRi system with an array of sgRNAs successfully repressed four endogenous genes (pta, frdA, ldhA, and adhE) individually and in double, triple, or quadruple combination that are involved in the formation of byproducts (acetate, succinate, lactate, and ethanol) and the consumption of NADH in E. coli. Single-target CRISPRi effectively reduced the amount of each byproduct and, interestingly, pta repression also decreased ethanol production (41%), whereas ldhA repression increased ethanol production (197%). CRISPRi-mediated multiplex repression of competing pathway genes also resulted in simultaneous reductions of acetate, succinate, lactate, and ethanol production in E. coli. Among 15 conditions repressing byproduct-formation genes, we chose the quadruple-target CRISPRi condition to produce n-butanol in E. coli as a case study. When heterologous n-butanol-pathway enzymes were introduced into E. coli simultaneously repressing the expression of the pta, frdA, ldhA, and adhE genes via CRISPRi, n-butanol yield and productivity increased up to 5.4- and 3.2-fold, respectively. CONCLUSIONS: We demonstrated the tunable CRISPRi system to be a robust platform for multiplex modulation of endogenous gene expression that can be used to enhance biosynthetic pathway productivity, with n-butanol as the test case. CRISPRi applications potentially enable the development of microbial "smart cell" factories capable of producing other industrially valuable products.

Leucine zipper-mediated targeting of multi-enzyme cascade reactions to inclusion bodies in Escherichia coli for enhanced production of 1-butanol.

Metabolons in nature have evolved to facilitate more efficient catalysis of multistep reactions through the co-localization of functionally related enzymes to cellular organelles or membrane structures. To mimic the natural metabolon architecture, we present a novel artificial metabolon that was created by targeting multi-enzyme cascade reactions onto inclusion body (IB) in Escherichia coli. The utility of this system was examined by co-localizing four heterologous enzymes of the 1-butanol pathway onto an IB that was formed in E. coli through overexpression of the cellulose binding domain (CBD) of Cellulomonas fimi exoglucanase. To target the 1-butanol pathway enzymes to the CBD IB, we utilized a peptide-peptide interaction between leucine zipper (LZ) peptides. We genetically fused the LZ peptide to the N-termini of four heterologous genes involved in the synthetic 1-butanol pathway, whereas an antiparallel LZ peptide was fused to the CBD gene. The in vivo activity of the CBD IB-based metabolon was examined through the determination of 1-butanol synthesis using E. coli transformed with two plasmids containing the LZ-fused CBD and LZ-fused 1-butanol pathway genes, respectively. In vivo synthesis of 1-butanol using the engineered E. coli yielded 1.98g/L of 1-butanol from glucose, representing a 1.5-fold increase over that obtained from E. coli expressing the LZ-fused 1-butanol pathway genes alone. In an attempt to examine the in vitro 1-butanol productivity, we reconstituted CBD IB-based metabolon using CBD IB and individual enzymes of 1-butanol pathway. The 1-butanol productivity of in vitro reconstituted CBD IB-based metabolon using acetoacetyl-CoA as the starting material was 2.29mg/L/h, 7.9-fold higher than that obtained from metabolon-free enzymes of 1-butanol pathway. Therefore, this novel CBD-based artificial metabolon may prove useful in metabolic engineering both in vivo and in vitro for the efficient production of desired products.

Multi-enzyme Screening Using a High-throughput Genetic Enzyme Screening System.

The recent development of a high-throughput single-cell assay technique enables the screening of novel enzymes based on functional activities from a large-scale metagenomic library(1). We previously proposed a genetic enzyme screening system (GESS) that uses dimethylphenol regulator activated by phenol or p-nitrophenol. Since a vast amount of natural enzymatic reactions produce these phenolic compounds from phenol deriving substrates, this single genetic screening system can be theoretically applied to screen over 200 different enzymes in the BRENDA database. Despite the general applicability of GESS, applying the screening process requires a specific procedure to reach the maximum flow cytometry signals. Here, we detail the developed screening process, which includes metagenome preprocessing with GESS and the operation of a flow cytometry sorter. Three different phenolic substrates (p-nitrophenyl acetate, p-nitrophenyl-ß-D-cellobioside, and phenyl phosphate) with GESS were used to screen and to identify three different enzymes (lipase, cellulase, and alkaline phosphatase), respectively. The selected metagenomic enzyme activities were confirmed only with the flow cytometry but DNA sequencing and diverse in vitro analysis can be used for further gene identification.

CRISPR interference-guided balancing of a biosynthetic mevalonate pathway increases terpenoid production.

Methods for simple and efficient regulation of metabolic pathway genes are essential for maximizing product titers and conversion yields, and for minimizing the metabolic burden caused by heterologous expression of multiple genes often in the operon context. Clustered regularly interspaced short palindromic repeats (CRISPR) interference (CRISPRi) is emerging as a promising tool for transcriptional modulation. In this study, we developed a regulatable CRISPRi system for fine-tuning biosynthetic pathways and thus directing carbon flux toward target product synthesis. By exploiting engineered Escherichia coli harboring a biosynthetic mevalonate (MVA) pathway and plant-derived terpenoid synthases, the CRISPRi system successfully modulated the expression of all the MVA pathway genes in the context of operon and blocked the transcription of the acetoacetyl-CoA thiolase enzyme that catalyzes the first step in the MVA pathway. This CRISPRi-guided balancing of expression of MVA pathway genes led to enhanced production of (-)-α-bisabolol (C15) and lycopene (C40) and alleviation of cell growth inhibition that may be caused by expression of multiple enzymes or production of toxic intermediate metabolites in the MVA pathway. Coupling CRISPRi to cell growth by regulating an endogenous essential gene (ispA) increased isoprene (C5) production. The regulatable CRISPRi system proved to be a robust platform for systematic modulation of biosynthetic and endogenous gene expression, and can be used to tune biosynthetic metabolic pathways. Its application can enable the development of microbial 'smart cell' factories that can produce other industrially valuable products in the future.

A Cell-Cell Communication-Based Screening System for Novel Microbes with Target Enzyme Activities.

The development of synthetic biological devices has increased rapidly in recent years and the practical benefits of such biological devices are becoming increasingly clear. Here, we further improved the design of a previously reported high-throughput genetic enzyme screening system by investigating device-compatible biological components and phenol-mediated cell-cell communication, both of which increased the efficiency and practicality of the screening device without requiring the use of flow cytometry analysis. A sensor cell was designed to detect novel microbes with target enzyme activities on solid media by forming clear, circular colonies with fluorescence around the unknown microbes producing target enzymes. This mechanism of detection was enabled by the combination of pre-effector phenolic substrate treatment in the presence of target enzyme-producing microbes and control of the growth and fluorescence of remote sensor cells via phenol-mediated cell-cell communication. The sensor cells were applied to screen soil bacteria with phosphatase activity using phenyl phosphate as phenolic substrates. The sensor cells facilitated successful visualization of phosphatase activity in unknown microbes, which were identified by 16S rRNA analysis. Enzyme activity assays confirmed that the proposed screening technique was able to find 23 positive clones out of 33 selected colonies. Since many natural enzymatic reactions produce phenolic compounds from phenol-derived substrates, we anticipate that the proposed technique may have broad applications in the assessment and screening of novel microbes with target enzymes of interest. This method also can provide insights into the identification of novel enzymes for which screening assays are not yet available.