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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    • "For example, although S. cerevisiae can ferment galactose into ethanol, its yield is significantly lower than that from glucose. As galactose is one of the most abundant sugars in marine plant biomass, efficiently using it for growth and ethanol production is advantageous in the biofuels industry (Lee et al., 2011). "
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    ABSTRACT: The global proteomic alterations in the budding yeast Saccharomyces cerevisiae due to differences in carbon sources can be comprehensively examined using mass spectrometry-based multiplexing strategies. Here we investigate changes in the S. cerevisiae proteome resulting from cultures grown in minimal media using galactose, glucose, or raffinose as the carbon source. We used a TMT9-plex strategy to determine alterations in relative protein abundance due to a particular carbon source, in triplicate, thereby permitting subsequent statistical analyses. We quantified over 4700 proteins across all 9 samples, of which 1003 demonstrated statistically significant differences in abundance in at least one condition. The majority of altered proteins were classified as functioning in metabolic processes and as having cellular origins of plasma membrane and mitochondria. In contrast, proteins remaining relatively unchanged in abundance included those having nucleic acid-related processes, such as transcription and RNA processing. In addition, the comprehensiveness of the dataset enabled the analysis of subsets of functionally-related proteins, such as phosphatases, kinases, and transcription factors. As a resource, these data can be mined further in efforts to understand better the roles of carbon source fermentation in yeast metabolic pathways and the alterations observed therein, potentially for industrial applications, such as biofuel feedstock production.
    Full-text · Article · Sep 2015 · Molecular biology of the cell
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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.
    Full-text · Article · Oct 2012 · Computational and Structural Biotechnology Journal
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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). "

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