54NISR Research GRANT
2008 Young Investigator Research Grant

Fundamental researches on increasing applicability ofSaccharomyces cerevisiae as the host cells for production ofuseful metabolites
Takashi HIRASAWA
Department of Bioinformatic Engineering, Graduate School of Information Science and Technology, Osaka University

Research aims
As for the glucose metabolism in yeast, it is well known
that yeast shows crabtree effect exhibiting high ethanol formation under high glucose concentration due to the repression of the expression of the genes responsible for respiration. By using this phenomenon, the yeast Saccharomyces
cerevisiae has been mainly used for production of alcoholic
beverages and bioethanol. However, it has been sometimes
difficult to use S. cerevisiae for producing useful metabolites other than ethanol under high glucose concentration
due to the decreased metabolic flow to the metabolic reactions leading to such metabolites. Thus, the yeast strainsthat exhibit lower ethanol production are highly desirable
for the production of useful metabolites other than ethanol.
In this study, the metabolic pathway(s) whose disruption
leads to the decreased ethanol production was identified by
the use of two different methods, namely, screening of asingle gene deletion strain library for mutants exhibiting
low ethanol production and in silico simulation using agenome-scale metabolic model.
Methods(1) Identification of the single gene deletion strains thatexhibited low ethanol production
Each strain of a single gene deletion strains set (YeastMATalpha collection; Open Biosystems)1) was cultivated in
YP10D medium (2% bacto yeast extract, 1% bacto peptoneand 10% glucose) using 96-well microtiter plates coveredwith plastic films at 30ºC for two days with shaking to prevent cell sedimentation. After cultivation, culture supernatant was obtained by centrifugation and was used for themeasurement of ethanol concentration. For measuring
ethanol concentration in the culture supernatant, E-kitethanol (Scil Diagnostics GmbH) and microplate reader(Perkin Elmer) were used. The deletion strains that showedsignificantly low ethanol production were identified.
(2) Identification of metabolic pathway whose disruption
exhibited decreased ethanol production by simulation using
a genome-scale model
In this study, a modified genome-scale model of S. cerevisiae iND7502) including 1,268 reactions and 646 metabolites was used. In this model, cellular compartment such as

cytoplasm, mitochondria, peroxisome, nucleus, golgi body,vacuole and endoplasmic reticulam, and the transport reaction between inside and outside of cells were taken into account. The effect of disruption of each metabolic reaction
on flux distribution was simulated by calculating maximum
cellular growth (i.e. flux for biomass formation) using linear programming. In the simulation, upper and lower limitsof the flux for each metabolic reaction were set to –1,000
and 1,000 (mmol/g dry cell/h), respectively. For simulation,
programming software Matlab 2006b (The Mathworks Inc.)
and LINDO API (LINDO Systems Inc.) were used.
Results(1) Identification of the single gene deletion strains thatexhibited low ethanol production
To identify the deletion strains of S. cerevisiae that exhibited low ethanol production, 4,826 single gene deletionstrains were cultured in YP10D medium and amounts ofethanol produced by these strains were measured. Cultivation was performed twice. The mean of ethanol productions
in all the deletion strains was 33 g/l, and the deletion strains
whose ethanol productions were less than 10 g/l were identified as the strains whose ethanol productions were decreased by single-gene deletion. As a result, 75 deletionstrains were determined as the strain whose ethanol production was significantly low. However, among these strains,seven strains exhibited a significant growth defect. Thus,
these seven strains were omitted from further analysis. It isnotable that the deletion strain of ADH1, a gene encodingthe major alcohol dehydrogenase in S. cerevisiae, wasfound to be included in the seven strains.
Table 1 shows functional categories of the genes whosedeletion showed decreased ethanol production in S. cerevisiae. Many functional categories were found, but statistical overrepresentation of some of these functional categories was not detected (hypergeometric test, p0.01; data
not shown).
(2) Identification of metabolic pathway whose disruption
exhibited decreased ethanol production by simulation using
a genome-scale model
Next, the metabolic pathways whose disruption led to thedecreased ethanol production by S. cerevisiae were determined by simulating the effect of disruption of each meta-55
Table 1

Functional categories of the genes whose deletion showed decreased ethanol production by S. cerevisiae.

GO term
Transport
Response to chemical stimulus
RNA metabolic process
Membrane organization
Response to stress
Transcription
Protein modification process
Cellular homeostasis
Protein folding
Generation of precursor metabolites and energy
Cell cycle
Protein complex biogenesis
Vesicle-mediated transport
Cytoskeleton organization
Vacuole organization
Chromosome organization
Cellular carbohydrate metabolic process
Mitochondrion organization
Cellular component morphogenesis
Transposition
Signal transduction
Heterocycle metabolic process
Conjugation
Translation
Cell wall organization
Peroxisome organization
Cellular protein catabolic process
Cytokinesis
Cellular respiration
Chromosome segregation
Meiosis
Vesicle organization
Sporulation resulting in formation of a cellular spore
Cellular amino acid and derivative metabolic process
Nucleus organization
Pseudohyphal growth
Ribosome biogenesis
Cellular lipid metabolic process
Cell budding
Cellular aromatic compound metabolic process
Biological process unknownGene
PHO88, NUP84, ATG9, PEX7, CUP5, ECM10, FTR1, ATP7, MRS4, PEP3, VRP1, NPL6,YDJ1, TOM70, SLG1, VTC3
PRX1, GRX7, BRE1, DHH1, MNI1, HSP104, PEP3, STE11, ASC1, YDJ1
BRE1, DHH1, DEG1, TAD1, CTK2, DLS1, MRS4, DCS1, IKI3, NPL6
NUP84, ATG9, CUP5, PEP3, VRP1, TOM70, SLG1, VTC3
PRX1, GRX7, NUP84, ATG9, HSP104, STE11, NPL6, SLG1
BRE1, CTK2, DLS1, IKI3, NPL6
BRE1, RUB1, UBP6, CTK2, STE11
PRX1, FYV5, CUP5, FTR1, VMA13CNE1, ECM10, HSP104, YDJ1
CYC7, YVH1, ATP7, GLG1
SAP4, YVH1, BFA1, VRP1
ATG9, PEX7, ATP7, PKR1
CUP5, PEP3, VRP1, SLG1
VRP1, SLG1, CTF19
CUP5, PEP3, VTC3
HHT1, BRE1, NPL6
YVH1, GLG1, HSP104
ECM10, YDJ1, TOM70
DHH1, VRP1, SLG1
HHT1, BRE1, ASC1
STE11, ASC1, SLG1
GUD1, HIS1, ATP7
DHH1, STE11
CTK2, ASC1
UTR2, SLG1
PEX32, PEX7
CNE1, YDJ1
VRP1CYC7CTF19
YVH1
ATG9YVH1HIS1NUP84
STE11
NUP84
LRO1
VRP1GUD1
YBL083C, YBR013C, YDL071C, YDL094C, YDR010C, YDR015C, OMS1, AIM8, YEL028W,MTC7, YEL068C, EMC4, YIL024C, YKR012C, YKR075C, YMR082C, JLP2, YMR279C,
YNL043C, YOR059C

Gene ontology (GO) slim terms (process) were used as functional categories. Analysis was performed by using Saccharomyces genome database (SGD)
gene ontology slim mapper (http://www.yeastgenome.org/cgi-bin/GO/goSlimMapper.pl).

bolic reaction on flux distribution using a genome-scale
metabolic model of S. cerevisiae. Good agreement of theflux distribution previously reported by Paula et al.3) withthat predicted by our genome-scale model was confirmed(data not shown). In this simulation, the specific glucose
and oxygen uptake rates were set to 15 and 1 mmol/g drycell/h, respectively. As a result of simulation, disruption of
the reaction catalyzed by triosephosphate isomerase in glycolysis as well as those responsible for ethanol formation
was found to decrease ethanol production. However, increased glycerol production was also predicted to reoxidize
NADH to NAD. In addition, other metabolic pathways
whose disruption led to decreased ethanol production could
not be found. These results of our simulation analysis suggest that the ethanol production by S. cerevisiae might berobust against perturbations to maintain redox balance.

Conclusion
In this study, the metabolic pathway(s) whose disruption
leads to the decreased ethanol production was identified byscreening of a single gene deletion strain library for mutants exhibiting low ethanol production and by in silico
simulation using a genome-scale metabolic model. The results suggest that some gene disruptions might be effective
in decreasing ethanol production by S. cerevisiae. Investigation of effectiveness of such gene disruptions on the production of useful metabolites other than ethanol is necessary. However, in both analyses, i.e. cultivation of genedeletion strains and in silico simulation, the number of candidates of gene disruption or metabolic pathway disruptionthat exhibit decreased ethanol production is low. These suggest that the ethanol production by S. cerevisiae might berobust against perturbations. For effective production of

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useful metabolites other than ethanol using yeast cells, genetic modification and optimization of culture conditions
that give an extraordinary impact on this robustness (i.e.
ethanol production) would be necessary.
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