2-33: Engineering Escherichia coli KO11 for improved ethanol yield from gluconate

Tuesday, April 30, 2013
Exhibit Hall
Amanda Hildebrand1, Rebeccah Warmack2, Theresa Tutor1, Zhiliang (Julia) Fan1 and Takao Kasuga3, (1)Biological and Agricultural Engineering, University of California, Davis, Davis, CA, (2)Plant Sciences, University of California, Davis, Davis, CA, (3)Plant Pathology, University of California, Davis, Davis, CA
The conventional biochemical platform for cellulosic ethanol involves five distinct steps: pretreatment, cellulase production, enzymatic hydrolysis, fermentation, and product recovery. Sugars are produced as the reactive intermediate for the subsequent fermentation.  Our research efforts aim to develop a novel biochemical route for fuels and chemical production which replaces the three most expensive steps in the conventional platform (pretreatment, cellulase production, enzymatic hydrolysis) with a single biological step.  Cellulolytic microorganism(s) that can hydrolyze cellulose and hemicellulose in spite of the presence of lignin can be modified to convert most of the carbohydrate contained in the cellulosic biomass to sugar aldonates.  By over-expressing cellobiose dehydrogenase (CDH) and knocking down beta-glucosidase, we can enhance cellobionate production.  In a second step, sugar aldonates are utilized as the carbon source to produce ethanol and other products.  Experiments in our lab have demonstrated that the ethanologen Escherichia coli KO11 can metabolize gluconate via the Entner-Doudoroff pathway and produce ethanol at high yield and a rate even faster than that of glucose.  However, a significant portion of the carbon flow is directed to lactic and acetic acid production.  By knocking out genes for competing pathways (ldh, pfl, pdh), the carbon flow can be directed to ethanol production with minimal byproduct formation.  In this study, we report the effects of knocking out the ldh, pfl, and pdh genes on ethanol yield and culture density, compare those results to the in silico predictions based on metabolic flux analysis, and elucidate the roles of these genes in gluconate utilization.