Abstract
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 >105 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.
Highlights
Genome engineering strategies are limited by the massive combinatorial search space created when multiple genetic units must be optimized in tandem [1,2]
Libraries cultured on minimal medium plates with no furfural served as the control in order to account for growth on minimal medium alone
The selection was performed on plates to provide a microenvironment where clones were spatially isolated, in an effort to remove population effects that might interfere with assessing individual clone fitness [31]
Summary
Genome engineering strategies are limited by the massive combinatorial search space created when multiple genetic units must be optimized in tandem [1,2]. While early efforts focusing on engineering a small number of genetic parts have resulted in several impressive results [3,4,5], efforts focused on the engineering of complex phenotypes have remained a key challenge for the field. Multiplex genome-modification strategies can be used to develop combinatorial mutants of multiple alleles identified during genome mapping [19,20,21,22] Together, these strategies represent an approach for rationally searching genetic space during genome engineering efforts [1]
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