Biosynthesis of natural and halogenated plant monoterpene indole alkaloids in yeast.

Monoterpenoid indole alkaloids (MIAs) represent a large class of plant natural products with marketed pharmaceutical activities against a wide range of indications, including cancer, malaria and hypertension. Halogenated MIAs have shown improved pharmaceutical properties; however, synthesis of new-to-nature halogenated MIAs remains a challenge. Here we demonstrate a platform for de novo biosynthesis of two MIAs, serpentine and alstonine, in baker's yeast Saccharomyces cerevisiae and deploy it to systematically explore the biocatalytic potential of refactored MIA pathways for the production of halogenated MIAs. From this, we demonstrate conversion of individual haloindole derivatives to a total of 19 different new-to-nature haloserpentine and haloalstonine analogs. Furthermore, by process optimization and heterologous expression of a modified halogenase in the microbial MIA platform, we document de novo halogenation and biosynthesis of chloroalstonine. Together, this study highlights a microbial platform for enzymatic exploration and production of complex natural and new-to-nature MIAs with therapeutic potential.

CRISPR/Cas9 recombineering-mediated deep mutational scanning of essential genes in Escherichia coli.

Deep mutational scanning can provide significant insights into the function of essential genes in bacteria. Here, we developed a high-throughput method for mutating essential genes of Escherichia coli in their native genetic context. We used Cas9-mediated recombineering to introduce a library of mutations, created by error-prone PCR, within a gene fragment on the genome using a single gRNA pre-validated for high efficiency. Tracking mutation frequency through deep sequencing revealed biases in the position and the number of the introduced mutations. We overcame these biases by increasing the homology arm length and blocking mismatch repair to achieve a mutation efficiency of 85% for non-essential genes and 55% for essential genes. These experiments also improved our understanding of poorly characterized recombineering process using dsDNA donors with single nucleotide changes. Finally, we applied our technology to target rpoB, the beta subunit of RNA polymerase, to study resistance against rifampicin. In a single experiment, we validate multiple biochemical and clinical observations made in the previous decades and provide insights into resistance compensation with the study of double mutants.

A versatile platform strain for high-fidelity multiplex genome editing.

Precision genome editing accelerates the discovery of the genetic determinants of phenotype and the engineering of novel behaviors in organisms. Advances in DNA synthesis and recombineering have enabled high-throughput engineering of genetic circuits and biosynthetic pathways via directed mutagenesis of bacterial chromosomes. However, the highest recombination efficiencies have to date been reported in persistent mutator strains, which suffer from reduced genomic fidelity. The absence of inducible transcriptional regulators in these strains also prevents concurrent control of genome engineering tools and engineered functions. Here, we introduce a new recombineering platform strain, BioDesignER, which incorporates (i) a refactored λ-Red recombination system that reduces toxicity and accelerates multi-cycle recombination, (ii) genetic modifications that boost recombination efficiency, and (iii) four independent inducible regulators to control engineered functions. These modifications resulted in single-cycle recombineering efficiencies of up to 25% with a 7-fold increase in recombineering fidelity compared to the widely used recombineering strain EcNR2. To facilitate genome engineering in BioDesignER, we have curated eight context--neutral genomic loci, termed Safe Sites, for stable gene expression and consistent recombination efficiency. BioDesignER is a platform to develop and optimize engineered cellular functions and can serve as a model to implement comparable recombination and regulatory systems in other bacteria.

Genome engineering of E. coli for improved styrene production.

Microbial production of exogenous organic compounds is challenging as biosynthetic pathways are often complex and produce metabolites that are toxic to the hosts. Biogenic styrene is an example of this problem, which if addressed could result in a more sustainable supply of this important component of the plastics industry. In this study, we engineered Escherichia coli for the production of styrene. We systematically optimized the production capability by first screening different pathway expression levels in E. coli strains. We then further designed and constructed a transcription regulator library targeting 54 genes with 85,420 mutations, and tested this library for increased styrene resistance and production. A series of tolerant mutants not only exhibited improved styrene tolerance but also produced higher styrene concentrations compared to the parent strain. The best producing mutant, ST05 LexA_E45I, produced a 3.45-fold increase in styrene compared to the parent strain. The produced styrene was extracted via gas stripping into dodecane and used in a direct free radical synthesis of polystyrene.

Multiplex navigation of global regulatory networks (MINR) in yeast for improved ethanol tolerance and production.

Multiplex navigation of global regulatory networks (MINR) is an approach for combinatorially reprogramming gene expression to manipulate complex phenotypes. We designed, constructed, and mapped MINR libraries containing 43,020 specific mutations in 25 regulatory genes expected to perturb the yeast regulatory network. We selected growth competition experiments for library mutants conferring increased ethanol and/or glucose tolerance. We identified specific mutants that not only possessed improved ethanol and/or glucose tolerance but also produced ethanol at concentrations up to 2-fold higher than those produced by the wild-type strain. We further determined that mutations increasing ethanol tolerance were transferable to a diploid industrial yeast strain. The facile construction and mapping of 43,020 designer regulatory mutations provide a roadmap for how to access and engineer complex phenotypes in future synthetic biology and broader efforts.

Deep scanning lysine metabolism in Escherichia coli.

Our limited ability to predict genotype-phenotype relationships has called for strategies that allow testing of thousands of hypotheses in parallel. Deep scanning mutagenesis has been successfully implemented to map genotype-phenotype relationships at a single-protein scale, allowing scientists to elucidate properties that are difficult to predict. However, most phenotypes are dictated by several proteins that are interconnected through complex and robust regulatory and metabolic networks. These sophisticated networks hinder our understanding of the phenotype of interest and limit our capabilities to rewire cellular functions. Here, we leveraged CRISPR-EnAbled Trackable genome Engineering to attempt a parallel and high-resolution interrogation of complex networks, deep scanning multiple proteins associated with lysine metabolism in Escherichia coli We designed over 16,000 mutations to perturb this pathway and mapped their contribution toward resistance to an amino acid analog. By doing so, we identified different routes that can alter pathway function and flux, uncovering mechanisms that would be difficult to rationally design. This approach sets a framework for forward investigation of complex multigenic phenotypes.

Directed combinatorial mutagenesis of Escherichia coli for complex phenotype engineering.

Strain engineering for industrial production requires a targeted improvement of multiple complex traits, which range from pathway flux to tolerance to mixed sugar utilization. Here, we report the use of an iterative CRISPR EnAbled Trackable genome Engineering (iCREATE) method to engineer rapid glucose and xylose co-consumption and tolerance to hydrolysate inhibitors in E. coli. Deep mutagenesis libraries were rationally designed, constructed, and screened to target ~40,000 mutations across 30 genes. These libraries included global and high-level regulators that regulate global gene expression, transcription factors that play important roles in genome-level transcription, enzymes that function in the sugar transport system, NAD(P)H metabolism, and the aldehyde reduction system. Specific mutants that conferred increased growth in mixed sugars and hydrolysate tolerance conditions were isolated, confirmed, and evaluated for changes in genome-wide expression levels. We tested the strain with positive combinatorial mutations for 3-hydroxypropionic acid (3HP) production under high furfural and high acetate hydrolysate fermentation, which demonstrated a 7- and 8-fold increase in 3HP productivity relative to the parent strain, respectively.

Iterative genome editing of Escherichia coli for 3-hydroxypropionic acid production.

Synthetic biology requires strategies for the targeted, efficient, and combinatorial engineering of biological sub-systems at the molecular level. Here, we report the use of the iterative CRISPR EnAbled Trackable genome Engineering (iCREATE) method for the rapid construction of combinatorially modified genomes. We coupled this genome engineering strategy with high-throughput phenotypic screening and selections to recursively engineer multiple traits in Escherichia coli for improved production of the platform chemical 3-hydroxypropionic acid (3HP). Specifically, we engineered i) central carbon metabolism, ii) 3HP synthesis, and (iii) 3HP tolerance through design, construction and testing of ~ 162,000 mutations across 115 genes spanning global regulators, transcription factors, and enzymes involved in 3HP synthesis and tolerance. The iCREATE process required ~ 1 month to perform 13 rounds of combinatorial genome modifications with targeted gene knockouts, expression modification by ribosomal binding site (RBS) engineering, and genome-level site-saturation mutagenesis. Specific mutants conferring increased 3HP titer, yield, and productivity were identified and then combined to produce 3HP at a yield and concentration ~ 60-fold higher than the wild-type strain.

Combinatorial pathway engineering using type I-E CRISPR interference.

Optimization of metabolic flux is a difficult and time-consuming process that often involves changing the expression levels of multiple genes simultaneously. While some pathways have a known rate limiting step, more complex metabolic networks can require a trial-and-error approach of tuning the expression of multiple genes to achieve a desired distribution of metabolic resources. Here we present an efficient method for generating expression diversity on a combinatorial scale using CRISPR interference. We use a modified native Escherichia coli Type I-E CRISPR-Cas system and an iterative cloning strategy for construction of guide RNA arrays. This approach allowed us to build a combinatorial gene expression library three orders of magnitude larger than previous studies. In less than 1 month, we generated ∼12,000 combinatorial gene expression variants that target six different genes and screened these variants for increased malonyl-CoA flux and 3-hydroxypropionate (3HP) production. We were able to identify a set of variants that exhibited a significant increase in malonyl-CoA flux and up to a 98% increase in 3HP production. This approach provides a fast and easy-to-implement strategy for engineering metabolic pathway flux for development of industrially relevant strains, as well as investigation of fundamental biological questions.

CRISPR EnAbled Trackable genome Engineering for isopropanol production in Escherichia coli.

Isopropanol is an important target molecule for sustainable production of fuels and chemicals. Increases in DNA synthesis and synthetic biology capabilities have resulted in the development of a range of new strategies for the more rapid design, construction, and testing of production strains. Here, we report on the use of such capabilities to construct and test 903 different variants of the isopropanol production pathway in Escherichia coli. We first constructed variants to explore the effect of codon optimization, copy number, and translation initiation rates on isopropanol production. The best strain (PA06) produced isopropanol at titers of 17.5g/L, with a yield of 0.62 (mol/mol), and maximum productivity of 0.40g/L/h. We next integrated the isopropanol synthetic pathway into the genome and then used the CRISPR EnAbled Trackable genome Engineering (CREATE) strategy to generate an additional 640 individual RBS library variants for further evaluation. After testing each of these variants, we constructed a combinatorial library containing 256 total variants from four different RBS levels for each gene. The best producing variant, PA14, produced isopropanol at titers of 7.1g/L at 24h, with a yield of 0.75 (mol/mol), and maximum productivity of 0.62g/L/h (which was 0.22g/L/h above the parent strain PA07). We demonstrate the ability to rapidly construct and test close to ~1000 designer strains and identify superior performers.

Multiplex growth rate phenotyping of synthetic mutants in selection to engineer glucose and xylose co-utilization in Escherichia coli.

Engineering the simultaneous consumption of glucose and xylose sugars is critical to enable the sustainable production of biofuels from lignocellulosic biomass. In most major industrial microorganisms glucose completely inhibits the uptake of xylose, limiting efficient sugar mixture conversion. In E. coli removal of the major glucose transporter PTS allows for glucose and xylose co-consumption but only after prolonged adaptation, which is an effective process but hard to control and prone to co-evolving undesired traits. Here we synthetically engineer mutants to target sugar co-consumption properties; we subject a PTS- mutant to a short adaptive step and subsequently either delete or overexpress key genes previously suggested to affect sugar consumption. Screening the co-consumption properties of these mutants individually is very laborious. We show we can evaluate sugar co-consumption properties in parallel by culturing the mutants in selection and applying a novel approach that computes mutant growth rates in selection using chromosomal barcode counts obtained from Next-Generation Sequencing. We validate this multiplex growth rate phenotyping approach with individual mutant pure cultures, identify new instances of mutants cross-feeding on metabolic byproducts, and, importantly, find that the rates of glucose and xylose co-consumption can be tuned by altering glucokinase expression in our PTS- background. Biotechnol. Bioeng. 2017;114: 885-893. © 2016 Wiley Periodicals, Inc.

ROS mediated selection for increased NADPH availability in Escherichia coli.

The economical production of chemicals and fuels by microbial processes remains an intense area of interest in biotechnology. A key limitation in such efforts concerns the availability of key co-factors, in this case NADPH, required for target pathways. Many of the strategies pursued for increasing NADPH availability in Escherichia coli involve manipulations to the central metabolism, which can create redox imbalances and overall growth defects. In this study we used a reactive oxygen species based selection to search for novel methods of increasing NADPH availability. We report a loss of function mutation in the gene hdfR appears to increase NADPH availability in E. coli. Additionally, we show this excess NADPH can be used to improve the production of 3HP in E. coli.

Genome scale engineering techniques for metabolic engineering.

Metabolic engineering has expanded from a focus on designs requiring a small number of genetic modifications to increasingly complex designs driven by advances in genome-scale engineering technologies. Metabolic engineering has been generally defined by the use of iterative cycles of rational genome modifications, strain analysis and characterization, and a synthesis step that fuels additional hypothesis generation. This cycle mirrors the Design-Build-Test-Learn cycle followed throughout various engineering fields that has recently become a defining aspect of synthetic biology. This review will attempt to summarize recent genome-scale design, build, test, and learn technologies and relate their use to a range of metabolic engineering applications.

Comparison of genome-wide selection strategies to identify furfural tolerance genes in Escherichia coli.

Engineering both feedstock and product tolerance is important for transitioning towards next-generation biofuels derived from renewable sources. Tolerance to chemical inhibitors typically results in complex phenotypes, for which multiple genetic changes must often be made to confer tolerance. Here, we performed a genome-wide search for furfural-tolerant alleles using the TRackable Multiplex Recombineering (TRMR) method (Warner et al. (2010), Nature Biotechnology), which uses chromosomally integrated mutations directed towards increased or decreased expression of virtually every gene in Escherichia coli. We employed various growth selection strategies to assess the role of selection design towards growth enrichments. We also compared genes with increased fitness from our TRMR selection to those from a previously reported genome-wide identification study of furfural tolerance genes using a plasmid-based genomic library approach (Glebes et al. (2014) PLOS ONE). In several cases, growth improvements were observed for the chromosomally integrated promoter/RBS mutations but not for the plasmid-based overexpression constructs. Through this assessment, four novel tolerance genes, ahpC, yhjH, rna, and dicA, were identified and confirmed for their effect on improving growth in the presence of furfural.

Strategy for directing combinatorial genome engineering in Escherichia coli.

We describe a directed genome-engineering approach that combines genome-wide methods for mapping genes to traits [Warner JR, Reeder PJ, Karimpour-Fard A, Woodruff LBA, Gill RT (2010) Nat Biotechnol 28:856-862] with strategies for rapidly creating combinatorial ribosomal binding site (RBS) mutation libraries containing billions of targeted modifications [Wang HH, et al. (2009) Nature 460:894-898]. This approach should prove broadly applicable to various efforts focused on improving production of fuels, chemicals, and pharmaceuticals, among other products. We used barcoded promoter mutation libraries to map the effect of increased or decreased expression of nearly every gene in Escherichia coli onto growth in several model environments (cellulosic hydrolysate, low pH, and high acetate). Based on these data, we created and evaluated RBS mutant libraries (containing greater than 100,000,000 targeted mutations), targeting the genes identified to most affect growth. On laboratory timescales, we successfully identified a broad range of mutations (>25 growth-enhancing mutations confirmed), which improved growth rate 10-200% for several different conditions. Although successful, our efforts to identify superior combinations of growth-enhancing genes emphasized the importance of epistatic interactions among the targeted genes (synergistic, antagonistic) for taking full advantage of this approach to directed genome engineering.

Engineering improved ethanol production in Escherichia coli with a genome-wide approach.

A key challenge to the commercial production of commodity chemical and fuels is the toxicity of such molecules to the microbial host. While a number of studies have attempted to engineer improved tolerance for such compounds, the majority of these studies have been performed in wild-type strains and culturing conditions that differ considerably from production conditions. Here we applied the multiscalar analysis of library enrichments (SCALEs) method and performed a growth selection in an ethanol production system to quantitatively map in parallel all genes in the genome onto ethanol tolerance and production. In order to perform the selection in an ethanol-producing system, we used a previously engineered Escherichia coli ethanol production strain (LW06; ATCC BAA-2466) (Woodruff et al., in press), as the host strain for the multiscalar genomic library analysis (>10(6) clones for each library of 1, 2, or 4kb overlapping genomic fragments). By testing individually selected clones, we confirmed that growth selections enriched for clones with both improved ethanol tolerance and production phenotypes. We performed combinatorial testing of the top genes identified (uspC, otsA, otsB) to investigate their ability to confer improved ethanol tolerance or ethanol production. We determined that overexpression of otsA was required for improved tolerance and productivity phenotypes, with the best performing strains showing up to 75% improvement relative to the parent production strain.

Genome-scale identification and characterization of ethanol tolerance genes in Escherichia coli.

The identification of relevant gene targets for engineering a desired trait is a key step in combinatorial strain engineering. Here, we applied the multi-Scalar Analysis of Library Enrichments (SCALEs) approach to map ethanol tolerance onto 1,000,000 genomic-library clones in Escherichia coli. We assigned fitness scores to each of the ∼4,300 genes in E. coli, and through follow-up confirmatory studies identified 9 novel genetic targets (12 genes total) that increase E. coli ethanol tolerance (up to 6-fold improved growth). These genetic targets are involved in the processes related to cell membrane composition, translation, serine biosynthesis, and transcription regulation. Transcriptional profiling of the ethanol stress response in 5 of these ethanol-tolerant clones revealed a total of 700 genes with significantly altered expression (mapped to 615 significantly enriched gene ontology terms) across all five clones, with similar overall changes in global gene expression between two clone clusters. All ethanol-tolerant clones analyzed shared 6% of the overexpressed genes and showed enrichment for transcription regulation-related GO terms. iTRAQ-based proteomic analysis of ethanol-tolerant strains identified upregulation of proteins related to ROS mitigation, fatty acid biosynthesis, and vitamin biosynthesis as compared to the parent strain's ethanol response. The approach we outline here will be useful for engineering a variety of other traits and further improvements in alcohol tolerance.

Towards a metabolic engineering strain "commons": an Escherichia coli platform strain for ethanol production.

In the genome-engineering era, it is increasingly important that researchers have access to a common set of platform strains that can serve as debugged production chassis and the basis for applying new metabolic engineering strategies for modeling and characterizing flux, engineering complex traits, and optimizing overall performance. Here, we describe such a platform strain of E. coli engineered for ethanol production. Starting with a fully characterized host strain (BW25113), we site-specifically integrated the genes required for homoethanol production under the control of a strong inducible promoter into the genome and deleted the genes encoding four enzymes from competing pathways. This strain is capable of producing >30 g/L of ethanol in minimal media with <2 g/L produced of any fermentative byproduct. Using this platform strain, we tested previously identified ethanol tolerance genes and found that while tolerance was improved under certain conditions, any effect on ethanol production or tolerance was lost when grown under production conditions. Thus, our findings reinforce the need for a metabolic engineering "commons" that could provide a set of platform strains for use in more sophisticated genome-engineering strategies. Towards this end, we have made this production strain available to the scientific community.

Inverse metabolic engineering to improve Escherichia coli as an N-glycosylation host.

An inverse metabolic engineering strategy was used to select for Escherichia coli cells with an increased capability to N-glycosylate a specific target protein. We developed a screen for E. coli cells containing extra-chromosomal DNA fragments for improved ability to add precise sugar groups onto the AcrA protein using the glycosylation system from Campylobacter jejuni. Four different sized (1, 2, 4, and 8 kb) genomic DNA libraries were screened, and the sequences that conferred a yield advantage were determined. These advantageous genomic fragments were mapped onto the E. coli W3110 chromosome. Five candidate genes (identified across two or more libraries) were subsequently selected for forward engineering verification in E. coli CLM24 cells, utilizing a combination of internal standards for absolute quantitation and pseudo-selective reaction monitoring (pSRM) and Western blotting validation. An increase in glycosylated protein was quantified in cells overexpressing 4-α-glucantransferase and a phosphoenolpyruvate-dependent sugar phosphotransferase system, amounting to a 3.8-fold (engineered cells total = 5.3 mg L(-1) ) and 6.7-fold (engineered cells total = 9.4 mg L(-1) ) improvement compared to control cells, respectively. Furthermore, increased glycosylation efficiency was observed in cells overexpressing enzymes involved with glycosylation precursor synthesis, enzymes 1-deoxyxylulose-5-phosphate synthase (1.3-fold) and UDP-N-acetylglucosamine pyrophosphorylase (1.6-fold). To evaluate the wider implications of the engineering, we tested a modified Fc fragment of an IgG antibody as the target glycoprotein with two of our engineered cells, and achieved a ca. 75% improved glycosylation efficiency.

Strategies for the multiplex mapping of genes to traits.

Rewiring and optimization of metabolic networks to enable the production of commercially valuable chemicals is a central goal of metabolic engineering. This prospect is challenged by the complexity of metabolic networks, lack of complete knowledge of gene function(s), and the vast combinatorial genotype space that is available for exploration and optimization. Various approaches have thus been developed to aid in the efficient identification of genes that contribute to a variety of different phenotypes, allowing more rapid design and engineering of traits desired for industrial applications. This review will highlight recent technologies that have enhanced capabilities to map genotype-phenotype relationships on a genome wide scale and emphasize how such approaches enable more efficient design and engineering of complex phenotypes.