Fermentation performance and intracellular metabolite patterns in laboratory and industrial xylose-fermenting Saccharomyces cerevisiae

Abstract

Heterologous genes for xylose utilization were introduced into an industrial Saccharomyces cerevisiae, strain A, with the aim of producing fuel ethanol from lignocellulosic feedstocks. Two transformants, A4 and A6, were evaluated by comparing the performance in 4-l anaerobic batch cultivations to both the parent strain and a laboratory xylose-utilizing strain: S. cerevisiae TMB 3001. During growth in a minimal medium containing a mixture of glucose and xylose (50 g/l each), glucose was preferentially consumed. During the first growth phase on glucose, the specific growth rates were 0.26, 0.32, 0.27 and 0.30 h(-1) for strains TMB 3001, A (parental strain), A4, and A6, respectively. The specific ethanol productivities were 0.04, 0.13, 0.04 and 0.03 g/g.per hour, for TMB 3001, A, A4 and A6, respectively. The specific xylose consumption rates were 0.06, 0.21 and 0.14 g/g.per hour, respectively for strains TMB 3001, A4 and A6. Xylose consumption resulted mainly in the formation of xylitol, with biomass and ethanol being minor products. The metabolite profile of intermediates in the pentose phosphate pathway and key glycolytic intermediates were determined during growth on glucose and xylose, respectively. The metabolite pattern differed depending on whether glucose or xylose was utilized. The levels of intracellular metabolites were higher in the industrial strains than in the laboratory strain during growth on xylose.

Integrated analysis, transcriptome-lipidome, reveals the effects of INO- level (INO2 and INO4) on lipid metabolism in yeast

“…Biomass production was evaluated by measuring of optical density (OD 600 ) and dry cell weight. Glucose, glycerol, ethanol, and acetate concentrations were determined by HPLC analysis using an Aminex HPX-87H column (Biorad, Hercules, CA) [43]. …”

Systematic Applications of Metabolomics in Metabolic Engineering

“…Metabolic engineering is the (usually genetic) control of the metabolic activities of a living organism to establish and optimize the production of desirable metabolites – the class of small molecules that comprise the primary resources and intermediates of all cellular activity. With widespread and growing interest in environmentally sustainable industrial technologies, metabolic engineering is poised to provide an effective and efficient means for producing various small molecule chemicals from clean and renewable sources, such as biofuels derived from lignocellulosic feedstock [2,3,4,5,6,7,8,9,10,11,12]. …”

“…Frequently, metabolic engineering studies use targeted analysis of a few carefully selected intracellular or secreted extracellular compounds to drive or assess the progress of their efforts [2,9,13,14,15,16,17,18,19,20,21,22,23,24]. High-Performance Liquid Chromatography (HPLC) and enzymatic assays have typically been the methods of choice to generate this data, used in engineering S. cerevisiae [2,13,21,23], E. coli [16,19], Clostridium acetobutylicum [14,18], and other organisms.…”

“…High-Performance Liquid Chromatography (HPLC) and enzymatic assays have typically been the methods of choice to generate this data, used in engineering S. cerevisiae [2,13,21,23], E. coli [16,19], Clostridium acetobutylicum [14,18], and other organisms. These measurements may be direct readouts of the performance of an engineered strain [2], or they may be interpreted as performance and response characteristics (for example, trehalose as a marker for stress response in yeast [21,23]). These analyses are typically focused on effects at the level of individual pathways [2,19,21,25].…”

“…These measurements may be direct readouts of the performance of an engineered strain [2], or they may be interpreted as performance and response characteristics (for example, trehalose as a marker for stress response in yeast [21,23]). These analyses are typically focused on effects at the level of individual pathways [2,19,21,25]. …”