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United States Department of Agriculture

Agricultural Research Service

Research Project: GENOMICS AND ENGINEERING OF STRESS TOLERANT MICROBES FOR LOWER COST PRODUCTION OF ETHANOL FROM LIGNOCELLULOSE

Location: Bioenergy Research Unit

Title: Genomics of yeast tolerance and in situ detoxification

Author
item Liu, Zonglin

Submitted to: Book Chapter
Publication Type: Book / Chapter
Publication Acceptance Date: April 15, 2011
Publication Date: September 19, 2011
Citation: Liu, Z. 2011. Genomics of yeast tolerance and in situ detoxification. In: Liu, Z.L., editor. Microbial Stress Tolerance for Biofuels. Microbiology Monographs No. 22. Berlin Heidelberg: Springer-Verlag. p. 1-28.

Technical Abstract: Inhibitory compounds generated by pretreatment of lignocellulose biomass interfere with microbial growth and subsequent fermentation. Remediation of the inhibitors by current physical, chemical, and biological abatement means is economically impractical. Overcoming the inhibitory effects of lignocellulose hydrolysate poses a significant technical challenge for economical cellulosic biofuels production. Development of tolerant ethanologenic yeast has demonstrated a potential of in situ detoxification for numerous aldehyde inhibitors derived from the biomass pretreatment and conversion. In the last decade, significant progress has been made in understanding mechanisms of yeast tolerance for tolerant strain development. At least, important candidate genes have been indentified. Enriched genetic backgrounds, enhanced expression, interplay, and global integration of many key genes enable yeast tolerance. Reprogrammed pathways support yeast functions to withstand the inhibitor stress, detoxify the toxic compounds, maintain energy and redox balance, and complete active metabolism for ethanol fermentation. Complex gene interactions and regulatory networks as well as co-regulation are recognized as being involved in yeast adaptation and tolerance. This chapter outlines our current understanding of the yeast tolerance using genome-based approaches.

Last Modified: 7/30/2014
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