Registry in a tube: multiplexed pools of retrievable parts for genetic design space exploration.

Genetic designs can consist of dozens of genes and hundreds of genetic parts. After evaluating a design, it is desirable to implement changes without the cost and burden of starting the construction process from scratch. Here, we report a two-step process where a large design space is divided into deep pools of composite parts, from which individuals are retrieved and assembled to build a final construct. The pools are built via multiplexed assembly and sequenced using next-generation sequencing. Each pool consists of ∼20 Mb of up to 5000 unique and sequence-verified composite parts that are barcoded for retrieval by PCR. This approach is applied to a 16-gene nitrogen fixation pathway, which is broken into pools containing a total of 55 848 composite parts (71.0 Mb). The pools encompass an enormous design space (1043 possible 23 kb constructs), from which an algorithm-guided 192-member 4.5 Mb library is built. Next, all 1030 possible genetic circuits based on 10 repressors (NOR/NOT gates) are encoded in pools where each repressor is fused to all permutations of input promoters. These demonstrate that multiplexing can be applied to encompass entire design spaces from which individuals can be accessed and evaluated.

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.

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.

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.

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.

Engineering genomes in multiplex.

Efficiently engineering robust complex traits is a key challenge facing metabolic engineering efforts to synthesize valuable products in vivo. Recent advances in genome engineering confront this barrier and significantly enhance the ability to map functional changes targeted throughout the genome and combinatorially optimize complex (multigenic) traits using multiplex recombineering. We describe a framework for efficiently searching genome-wide combinatorial space to optimize complex traits and highlight recent advances in genome engineering that enable this approach.

Rapid profiling of a microbial genome using mixtures of barcoded oligonucleotides.

A fundamental goal in biotechnology and biology is the development of approaches to better understand the genetic basis of traits. Here we report a versatile method, trackable multiplex recombineering (TRMR), whereby thousands of specific genetic modifications are created and evaluated simultaneously. To demonstrate TRMR, in a single day we modified the expression of >95% of the genes in Escherichia coli by inserting synthetic DNA cassettes and molecular barcodes upstream of each gene. Barcode sequences and microarrays were then used to quantify population dynamics. Within a week we mapped thousands of genes that affect E. coli growth in various media (rich, minimal and cellulosic hydrolysate) and in the presence of several growth inhibitors (beta-glucoside, D-fucose, valine and methylglyoxal). This approach can be applied to a broad range of traits to identify targets for future genome-engineering endeavors.