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Dissecting a Complex Chemical Stress: Chemogenomic Profiling of Plant Hydrolysates

Abstract

The efficient production of biofuels from cellulosic feedstocks will require the efficient fermentation of the sugars in hydrolyzed plant material. Unfortunately, plant hydrolysates also contain many compounds that inhibit microbial growth and fermentation. We used DNA-barcoded mutant libraries to identify genes that are important for hydrolysate tolerance in both Zymomonas mobilis (44 genes) and Saccharomyces cerevisiae (99 genes). Overexpression of a Z. mobilis tolerance gene of unknown function (ZMO1875) improved its specific ethanol productivity 2.4-fold in the presence of miscanthus hydrolysate. However, a mixture of 37 hydrolysate-derived inhibitors was not sufficient to explain the fitness profile of plant hydrolysate. To deconstruct the fitness profile of hydrolysate, we profiled the 37 inhibitors against a library of Z. mobilis mutants and we modeled fitness in hydrolysate as a mixture of fitness in its components. By examining outliers in this model, we identified methylglyoxal as a previously unknown component of hydrolysate. Our work provides a general strategy to dissect how microbes respond to a complex chemical stress and should enable further engineering of hydrolysate tolerance.

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References
1.
Sassetti C, Boyd D, Rubin E . Comprehensive identification of conditionally essential genes in mycobacteria. Proc Natl Acad Sci U S A. 2001; 98(22):12712-7. PMC: 60119. DOI: 10.1073/pnas.231275498. View

2.
Nakata T, Miyafuji H, Saka S . Bioethanol from cellulose with supercritical water treatment followed by enzymatic hydrolysis. Appl Biochem Biotechnol. 2006; 129-132:476-85. DOI: 10.1385/abab:130:1:476. View

3.
Pereira F, Guimaraes P, Gomes D, Mira N, Teixeira M, Sa-Correia I . Identification of candidate genes for yeast engineering to improve bioethanol production in very high gravity and lignocellulosic biomass industrial fermentations. Biotechnol Biofuels. 2011; 4(1):57. PMC: 3287136. DOI: 10.1186/1754-6834-4-57. View

4.
Mergaert P, Uchiumi T, Alunni B, Evanno G, Cheron A, Catrice O . Eukaryotic control on bacterial cell cycle and differentiation in the Rhizobium-legume symbiosis. Proc Natl Acad Sci U S A. 2006; 103(13):5230-5. PMC: 1458823. DOI: 10.1073/pnas.0600912103. View

5.
Miller E, Jarboe L, Yomano L, York S, Shanmugam K, Ingram L . Silencing of NADPH-dependent oxidoreductase genes (yqhD and dkgA) in furfural-resistant ethanologenic Escherichia coli. Appl Environ Microbiol. 2009; 75(13):4315-23. PMC: 2704836. DOI: 10.1128/AEM.00567-09. View