Multiplexed tracking of combinatorial genomic mutations in engineered cell populations.

Multiplexed genome engineering approaches can be used to generate targeted genetic diversity in cell populations on laboratory timescales, but methods to track mutations and link them to phenotypes have been lacking. We present an approach for tracking combinatorial engineered libraries (TRACE) through the simultaneous mapping of millions of combinatorially engineered genomes at single-cell resolution. Distal genomic sites are assembled into individual DNA constructs that are compatible with next-generation sequencing strategies. We used TRACE to map growth selection dynamics for Escherichia coli combinatorial libraries created by recursive multiplex recombineering at a depth 10(4)-fold greater than before. TRACE was used to identify genotype-to-phenotype correlations and to map the evolutionary trajectory of two individual combinatorial mutants in E. coli. Combinatorial mutations in the human ES2 ovarian carcinoma cell line were also assessed with TRACE. TRACE completes the combinatorial engineering cycle and enables more sophisticated approaches to genome engineering in both bacteria and eukaryotic cells than are currently possible.

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.

Genome-wide mapping of furfural tolerance genes in Escherichia coli.

Advances in genomics have improved the ability to map complex genotype-to-phenotype relationships, like those required for engineering chemical tolerance. Here, we have applied the multiSCale Analysis of Library Enrichments (SCALEs; Lynch et al. (2007) Nat. Method.) approach to map, in parallel, the effect of increased dosage for >10(5) different fragments of the Escherichia coli genome onto furfural tolerance (furfural is a key toxin of lignocellulosic hydrolysate). Only 268 of >4,000 E. coli genes (∼ 6%) were enriched after growth selections in the presence of furfural. Several of the enriched genes were cloned and tested individually for their effect on furfural tolerance. Overexpression of thyA, lpcA, or groESL individually increased growth in the presence of furfural. Overexpression of lpcA, but not groESL or thyA, resulted in increased furfural reduction rate, a previously identified mechanism underlying furfural tolerance. We additionally show that plasmid-based expression of functional LpcA or GroESL is required to confer furfural tolerance. This study identifies new furfural tolerant genes, which can be applied in future strain design efforts focused on the production of fuels and chemicals from lignocellulosic hydrolysate.

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.