Improved galactose fermentation of Saccharomyces cerevisiae through inverse metabolic engineering

School of Biotechnology and Bioengineering, Sungkyunkwan University, Suwon, Korea.
Biotechnology and Bioengineering (Impact Factor: 4.13). 03/2011; 108(3):621-31. DOI: 10.1002/bit.22988
Source: PubMed


Although Saccharomyces cerevisiae is capable of fermenting galactose into ethanol, ethanol yield and productivity from galactose are significantly lower than those from glucose. An inverse metabolic engineering approach was undertaken to improve ethanol yield and productivity from galactose in S. cerevisiae. A genome-wide perturbation library was introduced into S. cerevisiae, and then fast galactose-fermenting transformants were screened using three different enrichment methods. The characterization of genetic perturbations in the isolated transformants revealed three target genes whose overexpression elicited enhanced galactose utilization. One confirmatory (SEC53 coding for phosphomannomutase) and two novel targets (SNR84 coding for a small nuclear RNA and a truncated form of TUP1 coding for a general repressor of transcription) were identified as overexpression targets that potentially improve galactose fermentation. Beneficial effects of overexpression of SEC53 may be similar to the mechanisms exerted by overexpression of PGM2 coding for phosphoglucomutase. While the mechanism is largely unknown, overexpression of SNR84, improved both growth and ethanol production from galactose. The most remarkable improvement of galactose fermentation was achieved by overexpression of the truncated TUP1 (tTUP1) gene, resulting in unrivalled galactose fermentation capability, that is 250% higher in both galactose consumption rate and ethanol productivity compared to the control strain. Moreover, the overexpression of tTUP1 significantly shortened lag periods that occurs when substrate is changed from glucose to galactose. Based on these results we proposed a hypothesis that the mutant Tup1 without C-terminal repression domain might bring in earlier and higher expression of GAL genes through partial alleviation of glucose repression. mRNA levels of GAL genes (GAL1, GAL4, and GAL80) indeed increased upon overexpression of tTUP. The results presented in this study illustrate that alteration of global regulatory networks through overexpression of the identified targets (SNR84 and tTUP1) is as effective as overexpression of a rate limiting metabolic gene (PGM2) in the galactose assimilation pathway for efficient galactose fermentation in S. cerevisiae. In addition, these results will be industrially useful in the biofuels area as galactose is one of the abundant sugars in marine plant biomass such as red seaweed as well as cheese whey and molasses.

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    • "The data (Fig. 3) revealed that hexoses are key carbon sources for the tested bacteria, and their utilizations have a sequence, such as glucose or/and galactose firstly, followed by the mannose as the alternative sugar . These results coincide with previous research, reporting that among the fermentable sugars in hydrolysates, glucose is considered as the desirable fermentation substrate for oleaginous microorganisms, while other hemicellulose-derived monosaccharides, such as mannose and galactose, can also be selectively utilized depending on the microbial species (Jin et al. 2015; Lee et al. 2011). Considering that xylose is the second most abundant sugar in hardwood, obtaining bacteria with xylose-degrading ability is of importance for better utilization of its hydrolysate. "
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    ABSTRACT: Metabolic synthesis of single cell oils (SCOs) for biodiesel application by heterotrophic oleaginous microorganisms is being hampered by the high cost of culture media. This study investigated the possibility of using loblolly pine and sweetgum autohydrolysates as economic feedstocks for microbial lipid production by oleaginous Rhodococcus opacus (R. opacus) PD630 and DSM 1069. Results revealed that when the substrates were detoxified by the removal of inhibitors (such as HMF-hydroxymethyl-furfural), the two strains exhibited viable growth patterns after a short adaptation/lag phase. R. opacus PD630 accumulated as much as 28.6 % of its cell dry weight (CDW) in lipids while growing on detoxified sweetgum autohydrolysate (DSAH) that translates to 0.25 g/l lipid yield. The accumulation of SCOs reached the level of oleagenicity in DSM 1069 cells (28.3 % of CDW) as well, while being cultured on detoxified pine autohydrolysate (DPAH), with the maximum lipid yield of 0.31 g/l. The composition of the obtained microbial oils varied depending on the substrates provided. These results indicate that lignocellulosic autohydrolysates can be used as low-cost fermentation substrates for microbial lipid production by wild-type R. opacus species. Consequently, the variety of applications for aqueous liquors from lignocellulosic pretreatment has been expanded, allowing for the further optimization of the integrated biorefinery.
    Applied Microbiology and Biotechnology 07/2015; 99(17). DOI:10.1007/s00253-015-6752-5 · 3.34 Impact Factor
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    • "Genes, gene fragments or fragments of entire operons that favorably affect a desired property can be isolated from vector libraries co-expressing genomic fragments. Genomic libraries have been screened in order to identify genes that enhance alcohol tolerance/production and galactose fermentation in S. cerevisiae [38–40]; acetate and butanol tolerance [41, 42], lycopene [43] and membrane protein production [44] in E. coli; butyrate tolerance in Clostridium acetobutylicum [45], and in other cases. In addition, individual enhancer genes can be identified using the ASKA library, a library of all the E. coli open reading frames (ORFs) transcribed from the strong and inducible T5lac promoter [46] or the FLEXgene collection, an analogous library encoding yeast ORFs from S. cerevisiae [47], both of which are publicly available. "
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    ABSTRACT: Traditional metabolic engineering analyzes biosynthetic and physiological pathways, identifies bottlenecks, and makes targeted genetic modifications with the ultimate goal of increasing the production of high-value products in living cells. Such efforts have led to the development of a variety of organisms with industrially relevant properties. However, there are a number of cellular phenotypes important for research and the industry for which the rational selection of cellular targets for modification is not easy or possible. In these cases, strain engineering can be alternatively carried out using "inverse metabolic engineering", an approach that first generates genetic diversity by subjecting a population of cells to a particular mutagenic process, and then utilizes genetic screens or selections to identify the clones exhibiting the desired phenotype. Given the availability of an appropriate screen for a particular property, the success of inverse metabolic engineering efforts usually depends on the level and quality of genetic diversity which can be generated. Here, we review classic and recently developed combinatorial approaches for creating such genetic diversity and discuss the use of these methodologies in inverse metabolic engineering applications.
    Computational and Structural Biotechnology Journal 10/2012; 3(4):e201210021. DOI:10.5936/csbj.201210021
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    • "Aside from rational design stochastic methods based on inverse metabolic engineering have been developed for S. cerevisiae to identify key target reactions and associated gene sequences enabling the desired new cellular property (Bailey et al. 2002; Bengtsson et al. 2008; Bro et al. 2005; Hong et al. 2010; Jin et al. 2005; Lee et al. 2010). Differently, methods targeting on the induction of a cellular property, such as growth, increase of substrate conversion rate or enhancing resistance to environmental stress, that is hardly to capture by in silico design because of its highly intricate metabolic relations that have to be satisfied, rely on the cellular adaptability to a certain environmental stress by evolution (Cakar et al. 2009; Cakar et al. 2005; Garcia Sanchez et al. 2010; Kuyper et al. 2005; Sonderegger and Sauer 2003; Wisselink et al. 2009). "
    Metabolomics, 02/2012; , ISBN: 978-953-51-0046-1
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