Global Metabolic Engineering of Glycolytic Pathway via Multicopy

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Global metabolic engineering of glycolytic pathway via multi-copy integration in Saccharomyces cerevisiae Ryosuke Yamada, Kazuki Wakita, and Hiroyasu Ogino ACS Synth. Biol., Just Accepted Manuscript • DOI: 10.1021/acssynbio.6b00281 • Publication Date (Web): 12 Jan 2017 Downloaded from http://pubs.acs.org on January 13, 2017

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ACS Synthetic Biology

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Global metabolic engineering of glycolytic pathway via multi-copy integration in

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Saccharomyces cerevisiae

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Ryosuke Yamada†, Kazuki Wakita†, Hiroyasu Ogino*

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Department of Chemical Engineering, Osaka Prefecture University, 1-1 Gakuen-cho, Naka-ku,

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Sakai, Osaka 599-8531, Japan

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†

These authors contributed equally to this work.

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* Corresponding author: Hiroyasu Ogino

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TEL/FAX: +81-72-254-9296

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E-mail address: [email protected]

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ABSTRACT The use of renewable feedstocks for producing biofuels and bio-based chemicals by

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engineering metabolic pathways of yeast Saccharomyces cerevisiae has recently become an

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attractive option. Many researchers attempted to increase glucose consumption rate by

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overexpressing some glycolytic enzymes because most target bio-based chemicals are derived

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through glycolysis. However these attempts have met with little success. In this study, to

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create a S. cerevisiae strain with high glucose consumption rate, we used multi-copy

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integration to develop a global metabolic engineering strategy. Among approximately 350

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metabolically engineered strains, YPH499/dPdA3-34 exhibited the highest glucose

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consumption rate. This strain showed 1.3-fold higher cell growth rate and glucose

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consumption rate than the control strain. Real-time PCR analysis revealed that transcription

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levels of glycolysis-related genes such as HXK2, PFK1, PFK2, PYK2, PGI1, and PGK1 in

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YPH499/dPdA3-34 were increased. Our strategy is thus a promising approach to optimize

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global metabolic pathways in S. cerevisiae.

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Keywords

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Saccharomyces cerevisiae, glycolysis, metabolic engineering, multi-copy integration


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INTRODUCTION Utilization of renewable feedstocks for the production of biofuels and bio-based

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chemicals by engineering metabolic pathways of various kinds of microorganisms has

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recently become an attractive option because of the environmental problems associated with

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the combustion of fossil fuels.1 Among host microorganisms used for metabolic engineering,

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yeast Saccharomyces cerevisiae is particularly attractive for safety and convenience. S.

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cerevisiae is nonpathogenic, classified as “generally regarded as safe” (GRAS), and has long

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been used to produce consumables like ethanol.2 Thus, the fermentation and processing

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technology for large-scale production is well established in S. cerevisiae.

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Two renewable feedstocks are promising for use with yeast: cellulose and

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hemicellulose, as well as their constituent sugars glucose and xylose.3 Yeast convert most of

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the glucose they uptake into pyruvate thorough the glycolytic pathway, which is then further

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metabolized to other compounds (Fig. 1). Thus, target bio-based chemicals obtained through

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the metabolic engineering of S. cerevisiae are primarily derived from pyruvate.1 In the target

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bio-based chemicals production, it generates a considerable amount of by-products that limits

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the efficiency and yield of bio-based chemicals production. Thus, it is necessary to improve

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the glucose consumption rate and reduce the amount of by-products.

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Many researchers have attempted to increase glucose consumption rate by

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overexpressing glycolytic enzymes in organisms such as S. cerevisiae,4,5 Escherichia coli,6 3 ACS Paragon Plus Environment


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Lactococcus lactis,7,8 and Zymomonas mobilis,9 but these attempts have met with little success.

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In most cases, although increases to glycolytic enzyme activity were observed, glucose

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consumption rate remained unaffected. For example, phosphofructokinase and pyruvate

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kinase are two enzymes implicated in the rate-limiting steps of yeast glycolysis, respectively

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converting fructose-6-phosphate to fructose-1,6-phosphate and phosphoenolpyruvate into

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pyruvate.10 However, co-overexpression of these two enzymes failed to increase glucose

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consumption rate in S. cerevisiae, suggesting that the insertion and overexpression of more

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genes coding for glycolytic enzymes are necessary before an effect can be observed.4,5 The most commonly used tool to overexpress foreign genes in S. cerevisiae has been

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the yeast episomal plasmid (YEp).11 However, YEp vectors are mitotically unstable under

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non-selective conditions, such as long-term industrial operations in poorly defined media.12,13

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As an alternative, the yeast integrative plasmids (YIp) allow stable foreign gene expression,

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but it remains unsuitable as an overexpression vector because only a few gene copies can be

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integrated into the genome.13 Therefore, multi-copy integration methods such as δ-integration

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and rDNA-integration have been developed that allow stable overexpression of foreign genes.

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14,15

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cellulolytic enzymes for effective cellulose degradation in yeast.16,17

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Of these methods, δ-integration has been applied successfully to optimize expression of 3

In addition to suitable methods for gene overexpression, the selection of appropriate promoters is also critical. Da Silva et al. (2012) reported that the strength of promoters 4 ACS Paragon Plus Environment

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drastically affected target protein expression in S. cerevisiae.18 Moreover, in a study of 26

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promoter sequences combined with 3 target proteins in the yeast Pichia pastoris, Stadlmayr et

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al. (2010) reported that promoter strength was largely dependent on the compatibility between

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promoter and target protein genes.19 Based on this understanding, we have recently developed

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a strategy to select appropriate promoters from a combinatorial expression cassette library for

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gene overexpression in P. pastoris.20 This strategy should also prove useful in S. cerevisiae.

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Although S. cerevisiae is a promising host strain for bio-based chemicals production,

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the synthesis of ethanol as a by-product is a major drawback. Even under fully aerobic

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conditions, S. cerevisiae undergoes the Crabtree effect, producing ethanol when excess sugars

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are present.21 As a result, many researchers have attempted to reduce ethanol production

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deleting related genes such as ADH1 (alcohol dehydrogenase) and PDC1 (pyruvate

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decarboxylase).22-24

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In this study, we aimed to develop a host S. cerevisiae strain with low ethanol

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production and high sugar consumption for the production of bio-based chemicals. To that

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end, we designed and employed a global metabolic engineering strategy via multi-copy

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integration. First, PDC1 and ADH1 were deleted to reduce ethanol production. Then, glucose

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consumption of the PDC1/ADH1-deleted strain was increased via global metabolic

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engineering. Finally, we investigated the mechanism behind the increased glucose

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consumption. 5 ACS Paragon Plus Environment


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RESULTS AND DISCUSSION

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Cell growth and metabolites of the PDC1/ADH1-deleted strain YPH499/dPdAW

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The synthesis of ethanol as a by-product in S. cerevisiae is a major drawback. Thus

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ethanol production related genes ADH1 and PDC1 (Fig. 1) deleted strain YPH499/dPdAW

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was constructed. Then cell growth and metabolites of the YPH499/dPdAW and the control

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strain YPH499/W were compared.

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As shown in Figs. 2 A and B, cell growth and glucose consumption of

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YPH499/dPdAW were lower than the control strain YPH499/W. After a 74-h cultivation, both

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OD600 and glucose consumption were lowered by 1.3-fold in YPH499/dPdAW versus

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YPH499/W. Additionally, YPH499/dPdAW exhibited a 1.9-fold decrease in ethanol

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production compared with YPH499/W (Fig. 2 C), whereas glycerol production did not differ

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between the two strains (Fig. 2 D). Exact values for cell growth, glucose consumption rate,

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ethanol yield, and glycerol yield are presented in Table 1.

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In accordance with previous research,22 we chose to reduce ethanol production

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through a double deletion of PCD1/ADH1. Both enzymes encoded by these genes are critical

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in ethanol production, with pyruvate decarboxylase catalyzing the first step in

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pyruvate-to-ethanol conversion and alcohol dehydrogenase catalyzing

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acetaldehyde-to-ethanol conversion. Based on their function, it is reasonable to assume that 6 ACS Paragon Plus Environment

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eliminating pyruvate decarboxylase activity will sufficient in reducing ethanol productivity.

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However, this strategy was not successful in S. cerevisiae, as single-gene deletions of

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pyruvate decarboxylase isozymes PDC1, PDC5, or PDC6 failed to sufficiently reduce ethanol

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productivity.25 Additionally, double-gene deletion of PDC1 and PDC526,27 or triple-gene

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deletion of PDC1, PDC5, and PDC628,29 strongly impaired cell growth from glucose, in part

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due to the low production of cytosolic acetyl-CoA from acetaldehyde in these strains.29

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Eliminating alcohol dehydrogenase activity to reduce ethanol productivity also caused

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problems. Specifically, deleting ADH1, the primary enzyme responsible for

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acetaldehyde-to-ethanol conversion in S. cerevisiae,30 resulted in poor cell growth due to the

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toxic accumulation of acetaldehyde.31 Thus, a double deletion of PDC1/ADH1 might be the

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only method that could reduce ethanol production without seriously hampering cell growth

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and glucose consumption rates. We believe that future studies can further reduce ethanol

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by-product in bio-based chemicals production by optimizing the combinations of deleted

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pyruvate decarboxylase and alcohol dehydrogenase genes.

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Global metabolic engineered yeast strain with high glucose consumption rate

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To develop a S. cerevisiae strain with low ethanol production and high sugar

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consumption, we designed and applied a global metabolic engineering strategy via multi-copy

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integration for PDC1/ADH1-deleted strain YPH499/dPdAW. In the strategy, the DNA 7 ACS Paragon Plus Environment


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fragments encoding 13 glycolysis-related enzymes with various promoters were randomly

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integrated into the yeast genome, and the transformant with the highest glucose consumption

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rate was selected.

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As shown in Fig. 3, approximately 63% of S. cerevisiae transformants showed higher

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glucose consumption rates than the control strain YPH499/dPdAW. The transformant with the

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highest glucose consumption rate was named YPH499/dPdA3-34.

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Cell growth, metabolites, and copy numbers and transcription levels of

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glycolysis-related genes of the engineered yeast strain

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To further investigate the cultural characteristics of yeast strain YPH499/dPdA3-34,

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engineered and control strain were cultivated and cell growth, metabolites, and copy numbers

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and transcription levels of glycolysis-related genes were evaluated.

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As shown in Figs. 4 A and B, YPH499/dPdA3-34 exhibited increased cell growth and

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glucose consumption compared with the control strain YPH499/dPdAW. After a 74-h

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cultivation, OD600 and glucose consumption increased by 1.3-fold in YPH499/dPdA3-34

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versus YPH499/dPdAW. Moreover, the ethanol production of YPH499/dPdA3-34 exhibited a

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1.3-fold decrease in ethanol production compared with control (Fig. 4C), and a 2.9-fold

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increase in glycerol production compared with control (Fig. 4D). Again, exact values for cell

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growth, glucose consumption rate, ethanol yield, and glycerol yield are presented in Table 1. 8 ACS Paragon Plus Environment

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As shown in Table 2, 2 copies of PGI1 and 1 copy of PFK1, PFK2, and PGK1 were

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integrated into the genome of YPH499/dPdA3-34. As shown in Fig. 5, genes encoding

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glycolytic enzymes (e.g., HXK2, PFK1, PFK2, and PYK2) exhibited higher transcription

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levels in YPH499/dPdA3-34 than in YPH499/dPdAW. The up-regulation occurred for genes

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encoding both enzymes that catalysed irreversible reaction steps (e.g., HXK2, PFK1, PFK2,

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and PYK2) and reversible ones (e.g., PGI1 and PGK1). In contrast, the transcription levels of

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other genes (e.g., HXT7, FBA1, TPI1, TDH3, GPM1, ENO2, and PYC2) did not differ

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between YPH499/dPdA3-34 and YPH499/dPdAW.

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Our global metabolic engineering strategy increased glucose consumption rate (Fig.

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4; Table 1). Specifically, we were able to overexpress genes encoding 6 major enzymes that

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catalyze both irreversible and reversible reaction steps in glycolysis, in contrast to previous

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studies that only overexpressed two.4,5 The method we have developed provides a significant

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advantage over previous attempts to up-regulate specific glycolytic enzymes, because it has

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the potential to optimize the global metabolic pathway in yeast without exact knowledge of

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rate-limiting steps. Attempting to select appropriate genes for overexpression based on

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rate-limiting steps has proven to be difficult, with uncertain or conflicting results. For

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example, glucose phosphate isomerase PGI1 catalyzes the conversion of glucose-6-phosphate

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to fructose-6-phosphate, a process that competes with the conversion of glucose-6-phosphate

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to trehalose-6-phosphate.32 Thus, PGI1 overexpression should reduce the conversion of 9 ACS Paragon Plus Environment


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glucose-6-phosphate to trehalose-6-phosphate. As the latter inhibits glycolytic enzyme HXK2,

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PGI1 overexpression should then indirectly improve HXK2 activity and therefore, glucose

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consumption rate.32 Yamamoto et al. (2012) also reported that glucose consumption rate was a

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little improved by over expression of glucose phosphate isomerase gene in gram-positive

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bacteria Corynebacterium glutamicum.33 Our method also addresses the issue of

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overexpression effects being dependent on S. cerevisiae strain, culture condition, and target

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products, as reported in a study on HXT1 or HXT7 (hexose transporter) overexpression

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improving glucose consumption rate.34 The metabolic pathway optimization described in the

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present study can be carried out in any S. cerevisiae strain and under any culture condition,

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without altering the outcome.

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Integrated gene copy number of glycolysis-related genes had a well correlation with

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transcription level (Fig. 5; Table 2). Although gene integration of HXK2 and PYK2, whose

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transcription levels were up-regulated, were not observed, those transcription levels would be

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up-regulated by indirect effect of metabolic change via other genes integration and

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concomitant altering glucose consumption rate. Indeed, most of glycolysis-related gene

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transcription levels were affected by surrounding glucose concentration.35

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Of note, we found that glycerol by-product increased in conjunction with the increase

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in glucose consumption rate of YPH499/dPdA3-34 (Fig. 4). This pattern is due to yeast cells

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compensating for NADH excess and NAD+ depletion caused by PDC1/ADH1 deletion. 10 ACS Paragon Plus Environment

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However, YPH499/dPdAW was also a PDC1/ADH1-deleted strain, and yet produced less

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glycerol compared with YPH499/dPdA3-34. These results suggest that attempts to improve

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glucose consumption may have unexpected effects on glycerol production. This uncertain

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influence is potentially concerning because intracellular co-factor imbalance is a major issue

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in S. cerevisiae metabolic engineering, and the production/consumption of glycerol is often

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used for balancing NADH and NAD+.36 However, we consider that intracellular co-factor

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imbalance is unlikely to be a concern in cases where the target compound does not disrupt

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co-factor balance, such as lactic acid production from glucose. Furthermore, applying our

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global metabolic engineering strategy to pathways that can alter co-factor balance should

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greatly enhance its usefulness and practicality.

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Additionally, two PDC1/ADH1-deleted strains YPH499/dPdAW and

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YPH499/dPdA3-34 could not consume glucose completely within 120 h fermentation

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duration (Fig. 4). Although, PDC1/ADH1 deletion did not cause seriously hampering cell

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growth, this incomplete glucose consumption should be resolved for practical application

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because incomplete glucose consumption will lead to lower yield of target compounds. The

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incomplete glucose consumption did not due to the low culture pH derived from acidic

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metabolites such as acetic acid, because little difference existed between time course of

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culture pH of complete glucose consuming strain YPH499/W and that of incomplete glucose

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consuming strains YPH499/dPdAW and YPH499/dPdA3-34 (data not shown). Interestingly, 11 ACS Paragon Plus Environment


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the incomplete glucose consumption could be solved by using richer selective or

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non-selective medium (data not shown). The medium composition for cultivation of global

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metabolic engineered strain is not restricted, because our global metabolic engineering

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strategy is based on δ-integration which realize stable expression even in non-selective

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medium.37 Thus, for efficient target compounds production by global metabolic engineered

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strain, selection of medium should be important.

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CONCLUSIONS

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In this study, ethanol productivity was successfully reduced by PDC1/ADH1 deletion.

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Furthermore, glucose consumption rate was successfully increased through a global metabolic

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engineering strategy, involving multi-copy gene integration of 13 glycolysis-related genes and

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the selection of strains with high glucose consumption rates. Although a previous study had

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optimized the expression of 3 cellulolytic enzymes in yeast using δ-integration,16 ours is the

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first study to simultaneously optimize the expression of more than 10 metabolic enzymes. The

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method detailed here could be a promising approach to optimize S. cerevisiae metabolic

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pathways, thereby improving bio-based chemicals production using this organism.

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METHODS

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Strains and media 12 ACS Paragon Plus Environment

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Table 3 summarizes the relevant features of E. coli and S. cerevisiae strains used in

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this study. The E. coli strain HST08 (TaKaRa Bio, Otsu, Japan) was used as a host for

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recombinant DNA manipulations. Recombinant E. coli cells were cultivated on Luria–Bertani

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(LB) medium (10 g/L tryptone [Nacalai Tesque, Kyoto, Japan], 5 g/L yeast extract [Nacalai

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Tesque], and 5 g/L NaCl), supplemented with 100 µg/mL ampicillin sodium salt.

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The yeast strain S. cerevisiae YPH499 (NBRC 10505) was used as a host for

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metabolic engineering. Recombinant S. cerevisiae cells were cultivated using synthetic

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dextrose (SD) medium (6.7 g/L yeast nitrogen base without amino acids [Formedium, Norfolk,

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UK] and 20 g/L glucose [Nacalai Tesque]), supplemented with appropriate amino acids and

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nucleic acids. Flask cultivation was performed in 150 mL of medium using a 500-mL flask

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with baffles and a rotary shaker set to 30°C and 150 rpm. Microplate cultivation was

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performed in 0.9 mL of medium using a 2-mL, 96-well deep well plate, equipped with a gas

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permeable seal, as well as a rotary plate shaker set to 30°C and 2000 rpm.

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Gene deletion in yeast Following previously described methods,38 PDC1 and ADH1 of YPH499 were

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deleted. All primers used in this study are summarized in supplementary Table 1. First, we

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amplified the DNA fragment containing a URA3 marker gene for PDC1 deletion, using

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pRS406 (Stratagene, CA, USA) as the template and primers PDC1d_URA3F1 and 13 ACS Paragon Plus Environment


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PDC1_URA3R. A second PCR was performed using the DNA fragment from the first PCR as

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the template and primers PDC1d_URA3F2 and PDC1_URA3R to extend the homology

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region for gene recombination. Next, the product of the second PCR was transformed into

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YPH499, and PDC1 was deleted by replacing URA3. Finally, marker recycling was

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performed using the toxic uracil analog 5-fluoroorotic acid (Tokyo Chemical Industry, Tokyo,

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Japan), and the resultant PDC1-deleted strain was named YPH499/dP. We used the same

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procedure for deleting ADH1 from YPH499/dP, and the final PDC1/ADH1-deleted strain was

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named YPH499/dPdA. Gene deletion was confirmed via yeast colony-directed PCR,39 using

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the primers PDC1d_CheckF and PDC1d_CheckR for PDC1 deletion, as well as

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ADH1d_CheckF and ADH1d_CheckR for ADH1 deletion.

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Plasmid construction and yeast transformation We constructed 13 δ-integrative plasmid libraries for expressing glycolysis-related

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enzymes with various promoters (Fig. 6), as follows. The DNA fragments, encoding 15

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promoters from S. cerevisiae, P. pastoris, and H. polymorpha, were PCR-amplified using the

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plasmid library pPPE_LibBTLAnc20 as the template, as well as ProF and ProR for the primers.

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Next, the DNA fragments, encoding 13 glycolysis-related enzymes (HXT7, HXK2, PGI1,

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PFK1 PFK2, FBA1, TPI1, TDH3, PGK1, GPM1, ENO2, PYK2, and PYC2) and their

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terminator sequences, were PCR-amplified a second time using S. cerevisiae genomic DNA as 14 ACS Paragon Plus Environment

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the template and the corresponding primers (supplementary Table 1). The PCR products,

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encoding 15 promoters and HXT7, were inserted into the NotI site of the δ-integrative vacant

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plasmid pδW40 using an In-Fusion HD Cloning Kit (TaKaRa Bio). The final δ-integrative

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plasmid library was named pδW_LibHXT7. Other δ-integrative plasmid libraries were

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similarly generated and named: pδW_LibHXK2, pδW_LibPGI1, pδW_LibPFK1,

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pδW_LibPFK2, pδW_LibFBA1, pδW_LibTPI1, pδW_LibTDH3, pδW_LibPGK1,

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pδW_LibGPM1, pδW_LibENO2, pδW_LibPYK2, and pδW_LibPYC2.

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The δ-integrative vacant plasmid pδW was linearized with AscI and transformed into

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S. cerevisiae YPH499 and YPH499/dPdA, following the previously described lithium acetate

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method.41 The resultant strains were named YPH499/W and YPH499/dPdAW, respectively.

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Construction of yeast strains with high glucose consumption rate using global metabolic

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engineering

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The global metabolic engineering scheme to construct yeast strains with high glucose

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consumption rate is shown in Fig. 7. After the linearized plasmid libraries were transformed

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into the YPH499/dPdA strains, approximately 350 transformants were subjected to

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high-throughput screening for those with high glucose consumption rate. Transformants were

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first cultivated in 0.9 mL of SD media for 48 h at 30°C, with rotary shaking at 2000 rpm. Next,

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50 µL of each culture was inoculated into 0.9 mL of fresh SD media and cultivated for 50 h at 15 ACS Paragon Plus Environment


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30°C, again with rotary shaking at 2000 rpm. Finally, cells were removed via centrifugation

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(3000 × g for 5 min at 4°C), and glucose concentrations of the supernatants were measured

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using the Glucose CII Test kit (Wako Pure Chemical, Osaka, Japan) and the Multiskan GO

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microplate reader (Thermo Scientific, Rochester, NY, USA). The transformant with the

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highest glucose consumption rate was YPH499/dPdA3-34.

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Analysis of growth and metabolites We cultivated YPH499/W, YPH499/dPdAW, and YPH499/dPdA3-34 in 150 mL of

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SD medium at 30°C, with rotary shaking at 150 rpm. The OD600 of each culture broth was

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measured using the UVmini-1240 spectrophotometer (Shimadzu, Kyoto, Japan). To determine

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glucose, ethanol, and glycerol concentrations, culture broths were also centrifuged at 14 000 ×

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g for 10 min, and then added to Shim-pack SCR-101C columns (Shimadzu) for measurements

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using high-performance liquid chromatography (HPLC; Shimadzu). The columns were

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maintained at 80°C and eluted at a flow rate of 0.6 mL/min, with water as the mobile phase.

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The eluate was monitored using a refractive index detector (Shimadzu, RID-10A).

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The ethanol and glycerol yields were calculated using equations (1) and (2),

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respectively.

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Ethanol yield − = glucose consumed g

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Glycerol yield − = glucose consumed g

ethanol produced g

glycerol produced g

(1) (2) 16

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297 298 299

Real-time PCR analysis Template total DNA was isolated from yeast cells cultivated in SD medium for 50 h

300

as described by Hoffman et al (1987).42 Total RNA was isolated from yeast cells cultivated in

301

SD medium for 50 h at 30°C, using the RiboPure Yeast Kit (Ambion, TX, USA) Next, cDNA

302

was synthesized from extracted total RNA using the ReverTra Ace qPCR RT Kit (Toyobo,

303

Osaka, Japan).

304

The copy numbers and transcription levels of glycolysis-related genes were

305

quantified with real-time PCR using total DNA as the template and with reverse-transcription

306

(RT) real-time PCR using cDNA as the template, respectively. The primers used in real-time

307

PCR analysis were shown in supplementary Table 1. We performed the PCR using a

308

Rotor-Gene 6000 Real-Time QPCR System (Corbett Research, Cambridge, UK) with a

309

Thunderbird SYBR qPCR Mix (Toyobo). The gene copy number and transcription level were

310

calculated using the 2−∆∆CT method,43 and normalized to those of PDA1 (pyruvate

311

dehydrogenase) as the housekeeping gene.44

312 313

ASSOCIATED CONTENT

314

Supporting Information. Supplementary Table 1: Primers used in this study.

315



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316 317

ACKNOWLEDGEMENTS This work was supported in part by the New Chemical Technology Research

318

Encouragement Award from the Japan Association for Chemical Innovation, and the

319

Grant-in-Aid for Young Scientists (B) (No. 26870504 to R.Y.) from Japan Society for the

320

Promotion of Science.


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Trehalose-6-phosphate, a new regulator of yeast glycolysis that inhibits hexokinases.

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Glucose triggers different global responses in yeast, depending on the strength of the

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36. Dugar, D., and Stephanopoulos, G. (2011) Relative potential of biosynthetic pathways for

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PCR-mediated seamless gene deletion and marker recycling in Saccharomyces cerevisiae.

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Yeast. 23, 399–405.

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39. Akada, R., Murakane, T., and Nishizawa, Y. (2000) DNA extraction method for screening yeast clones by PCR. BioTechniques 28, 668–674.

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from raw starch. Appl. Microbiol. Biotechnol. 85, 1491–1498.

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41. Chen, D.-C., Yang, B.-C., and Kuo, T.-T. (1992) One-step transformation of yeast in stationary phase. Curr. Genet. 21, 83–84. 42. Hoffman, C.-S., and Winston, F. (1987) A ten-minute DNA preparation from yeast 24 ACS Paragon Plus Environment

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435

efficiently releases autonomous plasmids for transformation of Escherichia coli. Gene 57,

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267–272.

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43. Livak, K.-J., and Schmittgen, T.-D. (2001) Analysis of Relative Gene Expression Data

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Using Real-Time Quantitative PCR and the 2−∆∆CT Method. Methods. 25, 402–408.

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44. Wenzel, T.-J., Teunissen, A.-W., and de Steensma, H.-Y. (1995) PDA1 mRNA: a

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standard for quantitation of mRNA in Saccharomyces cerevisiae superior to ACT1

441

mRNA. Nucleic Acids Res. 23, 883–884.



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442

FIGURE LEGENDS

443

Fig. 1 Glycolysis in yeast Saccharomyces cerevisiae

444

Directional arrows and bidirectional arrows represent irreversible reactions and reversible

445

reactions, respectively. The names of genes encoding glycolysis-related enzymes are as

446

follows: HXT7, hexose transporter; HXK2, hexokinase; PGI1, phosphoglucoisomerase; PFK1

447

and PFK2, phosphofructokinase; FBA1, fructose 1,6-bisphosphate aldolase; TPI1,

448

triose-phosphate isomerase; TDH3, triose-phosphate dehydrogenase; PGK1,

449

3-phosphoglycerate kinase; GPM1, glycerate phosphomutase; ENO2, enolase; PYK2,

450

pyruvate kinase; PYC2, pyruvate carboxylase; PDC1, pyruvate decarboxylase; ADH1, alcohol

451

dehydrogenase. The genes of enzymes that catalyze irreversible reactions are in white boxes.

452

Genes in black boxes were deleted in this study.

453 454

Fig. 2 Time course of OD600 and metabolite concentrations of the PDC1/ADH1-deleted

455

strain, YPH499/dPdAW

456

(A) OD600, (B) glucose concentrations, (C) ethanol concentrations, and (D) glycerol

457

concentrations are shown. The triangles and circles represent the PDC1/ADH1-deleted strain

458

YPH499/dPdAW and the control strain YPH499/W, respectively. Data are presented as the

459

average of 3 independent experiments. Error bars represent means ± standard deviation.

460


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461

Fig. 3 Glucose consumption rate of transformants with engineered glycolytic pathways

462

Relative glucose consumption of approximately 350 transformants compared with

463

PDC1/ADH1-deleted strain YPH499/dPdAW. Transformants are presented in descending

464

order starting from left to right.

465 466

Fig. 4 Time course of OD600 and metabolite concentrations of the global metabolic

467

engineered strain YPH499/dPdA3-34

468

(A) OD600, (B) glucose concentrations, (C) ethanol concentrations, and (D) glycerol

469

concentrations are shown. The squares and triangles represent the global metabolic

470

engineered strain YPH499/dPdA3-34 and the PDC1/ADH1-deleted strain YPH499/dPdAW,

471

respectively. Data are presented as the average of 3 independent experiments. Error bars

472

represent means ± standard deviation.

473 474

Fig. 5 Relative transcription levels of glycolysis-related enzymes in the global metabolic

475

engineered strain YPH499/dPdA3-34 compared with the control strain YPH499/ dPdAW

476

Data are presented as the average of 3 independent experiments. Error bars represent means ±

477

standard deviation.

478 479

Fig. 6 δ-Integrative plasmid libraries for expressing glycolysis-related enzymes 27 ACS Paragon Plus Environment


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480

“Promoters” indicate regions 1000 bp upstream of the start codons for TDH3, TEF1, HXT10,

481

PGK1, and SED1 from Saccharomyces cerevisiae; GAP, TEF1, HXT7, PGK, and ENO1 from

482

Pichia pastoris; GAP, TEF2, HXT, TPS1, and SED1 from Hansenula polymorpha.

483

“Glycolysis-related genes with terminator regions” indicate: HXT7, HXK2, PGI1, PFK1,

484

PFK2, FBA1, TPI1, TDH3, PGK1, GPM1, ENO2, PYK2, or PYC2 from S. cerevisiae and the

485

regions 200 bp downstream from their stop codons.

486 487

Fig. 7 Construction scheme of yeast strains with high glucose consumption rate using

488

global metabolic engineering


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489

490


Table 1. Properties of the YPH499/W, YPH499/dPdAW, and YPH499/dPdA3-34 strains

a

Strain

OD600 at 74 h [-]

YPH499/Wa

3.18 ± 0.008

YPH499/dPdAWb

Glucose

Ethanol yield at 74 h [-]

Glycerol yield at 74 h [-]

0.200 ± 0.009

0.310 ± 0.010

0.058 ± 0.011

2.39 ± 0.007

0.155 ± 0.005

0.208 ± 0.009

0.080 ± 0.003

YPH499/dPdA3-34c 3.06 ± 0.010

0.204 ± 0.004

0.210 ± 0.002

0.188 ± 0.008

b

consumption rate in 74 h [g/L/h]

c

Control strain; PDC1/ADH1-deleted strain; Global metabolic engineered strain.

491

29



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48

492

Table 2 Gene copy numbers of glycolysis-related enzymes in the global metabolic engineered strain YPH499/dPdA3-34

Gene

Expected gene copy number [-]

HXT7

1

HXK2

1

PGI1

3

PFK1

2

PFK2

2

FBA1

1

TPI1

1

TDH3

1

PGK1

2

GPM1

1

ENO2

1

PYK2

1

PYC2

1

493

30


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494


Table 3. Escherichia coli and Saccharomyces cerevisiae strains used in this study Strains and plasmids

Relevant features

Escherichia coli strain Escherichia coli HST08

F–, endA1, supE44, thi-1, recA1, relA1, gyrA96, phoA,Φ80dlacZ∆M15, ∆(lacZYA-argF)U169, ∆(mrr-hsdRMS-mcrBC), ∆mcrA, λ–

Saccharomyces cerevisiae strain YPH499

MATa ura3-52 lys2-801_amber ade2-101_ochre trp1-∆63 his3-∆200 leu2-∆1

YPH499/W

YPH499 transformed with pδW

YPH499/dP

YPH499 ∆PDC1

YPH499/dPdA

YPH499 ∆PDC1/∆ADH1

YPH499/dPdAW

YPH499 ∆PDC1/∆ADH1 transformed with pδW

YPH499/dPdA3-34

YPH499 ∆PDC1/∆ADH1 global metabolic engineered yeast strain with high glucose consumption rate

Plasmid and plasmid library pδW

δ-Integrative vacant plasmid with TRP1 as selection marker a

Plasmid library for expression of HXT7

a

Plasmid library for expression of HXK2

pδW_LibHXT7

pδW_LibHXK2 a

pδW_LibPGI1

Plasmid library for expression of PGI1

pδW_LibPFK1a

Plasmid library for expression of PFK1 31



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48

pδW_LibPFK2a

Plasmid library for expression of PFK2

a

Plasmid library for expression of FBA1

pδW_LibFBA1 a

pδW_LibTPI1

Plasmid library for expression of TPI1 a

Plasmid library for expression of TDH3

pδW_LibPGK1a

Plasmid library for expression of PGK1

a

pδW_LibGPM1

Plasmid library for expression of GPM1

a

Plasmid library for expression of ENO2

a

pδW_LibPYK2

Plasmid library for expression of PYK2

pδW_LibPYC2a

Plasmid library for expression of PYC2

pδW_LibTDH3

pδW_LibENO2

495

a

Fifteen promoters were used in all plasmid libraries.

496

32


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497


FIGURES

33



498

(A)

(B)

4

25

20

Glucose [g/L]

OD600 [-]

3

2

1

15

10

5

0

0

0

20

40

60 80 Time [h]

100

120

140

0

(C)

20

40

60 80 Time [h]

100 120 140

20

40

60 80 Time [h]

100 120 140

(D)

6

3.5

5

3

4

Glycerol [g/L]

Ethanol [g/L]

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48

Page 34 of 40

3 2

2.5 2 1.5 1

1 0.5

0 0

20

40

60 80 Time [h]

100

120

140

0 0

34


Fig. 2

Page 35 of 40

499

2

Relative glucose consumption rate compared to YPH499/dPdAW [-]

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48


1.5

1

0.5

0 0

50

100

150 200 250 Transformants

300

350

Fig. 3 35



500

(A)

(B)

4

25

20 Glucose [g/L]

OD600 [-]

3

2

1

15

10

5

0

0 0

20

40

60 80 Time [h]

100

120

140

0

(C)

20

40

60 80 100 120 140 Time [h]

20

40

60 80 100 120 140 Time [h]

(D)

6

3.5

5

3 2.5

Glycerol [g/L]

4

Ethanol [g/L]

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48

Page 36 of 40

3 2 1

2 1.5 1 0.5

0

0 0

20

40

60 80 100 120 140 Time [h]

0

36


Fig. 4

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501

HXT7 HXK2 PGI1 PFK1 PFK2 FBA1 TPI1 TDH3 PGK1 GPM1 ENO2 PYK2 PYC2 0

1

2

3

4

5

6

7

Relative transcription level compared to YPH499/dPdAW [-]

Fig. 5 37



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502

38


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503

39



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504

40


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Global Metabolic Engineering of Glycolytic Pathway via Multicopy

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