Genomic engineering of Escherichia coli for improved ethanol tolerance and ethanol production

With tremendous advancements in the genomics era, engineered microbes are being used as biological factories to produce an array of products, including pharmaceuticals, bioplastics, commodity chemicals, and biofuels. The microbial machinery can be tailored through genetic modifications that result in traits which enable these industrial processes, such as new products, nonnative feedstocks, more efficient production, or more robust cellular biocatalysts. Nevertheless, identifying these trait-conferring genetic modifications is often laborious, and the toxicity of the product is a key challenge for commercial production of fuels and chemicals. In this thesis research, we have used the E. coli model system to study genome-wide approaches to identify trait-conferring genetic modifications for improved tolerance to and production of ethanol, an industrially-relevant biofuel. We mapped the genome for ethanol tolerance in a wild-type strain using the high resolution multiscalar analysis of library enrichments (SCALEs) approach. By testing the highest fitness genes identified, we confirmed nine novel genetic targets that confer improved ethanol tolerance. Transcriptomic and proteomic analysis of the ethanol stress response in these engineered ethanol-tolerant clones identified expression changes relating to a subset of biological processes shared among these clones. We constructed an E. coli ethanol production platform strain containing fully characterized genetic modifications that produces ethanol comparable to the best previously reported, and this strain is openly available from the ATCC. Using the LW06 production platform, we tested ethanol tolerance-conferring genes we previously identified, and the tolerance-conferring quality of these genes was largely dependent on the system, such as the host genotype, metabolism, and media. Based on this finding, we designed a genome-wide selection for ethanol production using SCALEs and mapped the fitness of the entire genome under ethanol production conditions. We selected for clones with both improved ethanol tolerance and production and through combinatorial testing, identified genes which confer improved ethanol tolerance and increased rates of ethanol production. From the quantitative genome-wide mapping, we propose future work to further engineer complex, multigenic traits for improved E. coli ethanol production. Collectively, the work presented here has identified strategies applicable to engineering improved production of inhibitory products