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Comprehensive metabolic profile of Saccharomyces cerevisiae during fermentation of lignocellulosic sugars for second generation ethanol production

Grant number: 16/10949-6
Support type:Program for Research on Bioenergy (BIOEN) - Regular Program Grants
Duration: October 01, 2016 - September 30, 2018
Field of knowledge:Biological Sciences - Genetics - Molecular Genetics and Genetics of Microorganisms
Principal Investigator:Osmar Vaz de Carvalho Netto
Grantee:Osmar Vaz de Carvalho Netto
Home Institution: Instituto de Biologia (IB). Universidade Estadual de Campinas (UNICAMP). Campinas , SP, Brazil
Assoc. researchers:Fabio Cesar Gozzo ; Gonçalo Amarante Guimarães Pereira ; Marcelo Falsarella Carazzolle

Abstract

The cellulosic ethanol technology (2G) consists in converting the plant cell wall polymers into ethanol. The pre-treatment step disrupts the lignocellulosic structure allowing access of the enzymes during the hydrolysis, and ensuing monomerization of the polymers, releasing sugars to be metabolized by yeasts. By the fact that naturally does not consume pentose, Saccharomyces cerevisiae is genetically engineered using two pathways: reductive / oxidative and isomerization. In the first, the consumption of xylose generates a redox imbalance; the second does not have a very efficient enzyme, slowing down the consumption. Another bottleneck is the catabolite repression, which inhibits the xylose consumption in the presence of glucose. The recent commencement of operations of the first industrial 2G units in Brazil have allowed the evaluation of fermentation performance of yeasts developed and tested so far only in the laboratory scale. Therefore, opens up a window of opportunity for the identification of characteristics that can be observed only at industrial scale. In this context, this project proposes a comprehensive study of the metabolism of a commercial yeast genetically modified using transcriptome techniques in combination with proteomic analysis during 2G fermentation on a laboratory and industrial scales. This approach will enable the analysis of the effects at the molecular level of given stimuli on the yeast according to the growth medium and the scale of the assay, allowing the identification of potential bottlenecks in their metabolism. Furthermore, from the data obtained it will be possible generate a data bank of proteins and genes differentially expressed in distinct points of the fermentation. The data will be used to validate the assumptions identified and it will be used in breeding programs to optimize the performance of industrial strains, consequently reducing the production costs. (AU)