Oxygen Response of the Wine Yeast Saccharomyces cerevisiae EC1118 Grown under Carbon-Sufficient, Nitrogen-Limited Enological Condition
ABSTRACT Discrete additions of oxygen play a critical role in alcoholic fermentation. However, few studies have quantitated the fate of dissolved oxygen and its impact on wine yeast cell physiology under enological conditions. We simulated the range of dissolved oxygen concentrations that occur after a pump-over during the winemaking process by sparging nitrogen-limited continuous cultures with oxygen-nitrogen gaseous mixtures. When the dissolved oxygen concentration increased from 1.2 to 2.7 μM, yeast cells changed from a fully fermentative to a mixed respirofermentative metabolism. This transition is characterized by a switch in the operation of the tricarboxylic acid cycle (TCA) and an activation of NADH shuttling from the cytosol to mitochondria. Nevertheless, fermentative ethanol production remained the major cytosolic NADH sink under all oxygen conditions, suggesting that the limitation of mitochondrial NADH reoxidation is the major cause of the Crabtree effect. This is reinforced by the induction of several key respiratory genes by oxygen, despite the high sugar concentration, indicating that oxygen overrides glucose repression. Genes associated with other processes, such as proline uptake, cell wall remodeling, and oxidative stress, were also significantly affected by oxygen. The results of this study indicate that respiration is responsible for a substantial part of the oxygen response in yeast cells during alcoholic fermentation. This information will facilitate the development of temporal oxygen addition strategies to optimize yeast performance in industrial fermentations
- SourceAvailable from: Manuel Quirós[Show abstract] [Hide abstract]
ABSTRACT: Saccharomyces cerevisiae is the most relevant yeast species conducting the alcoholic fermentation that takes place during winemaking. Although the physiology of this model organism has been extensively studied, systematic quantitative physiology studies of this yeast under winemaking conditions are still scarce, thus limiting the understanding of fermentative metabolism of wine yeast strains and the systematic description, modelling and prediction of fermentation processes. In this study, we implemented and validated the use of chemostat cultures as a tool to simulate different stages of a standard wine fermentation, thereby allowing to implement metabolic flux analyses describing the sequence of metabolic states of S. cerevisae along the wine fermentation.Microbial Cell Factories 06/2014; 13(1):85. · 4.25 Impact Factor
- [Show abstract] [Hide abstract]
ABSTRACT: In this study, the Saccharomyces cerevisiae wild type strain and engineered strain with an overexpressed heterologous ATP-citrate lyase (acl) were cultured in medium with different carbon and nitrogen concentrations, and their fatty acid production levels were investigated. The results showed that when the S. cerevisiae engineered strain was cultivated under nitrogen limited culture condition, the yield of mono-unsaturated fatty acids showed higher than that under non-nitrogen limited condition; with the carbon concentration increased, the accumulation become more apparent, whereas in the wild type strain, no such correlation was found. Besides, the citrate level in the S. cerevisiae under nitrogen limited condition was found to be much higher than that under non-nitrogen limited condition, which indicated a relationship between the diminution of nitrogen and accumulation of citrate in the S. cerevisiae. The accumulated citrate could be further cleaved by acl to provide substrate for fatty acid synthesis.Bioresource Technology 03/2014; 162C:200-206. · 5.04 Impact Factor
- [Show abstract] [Hide abstract]
ABSTRACT: Dynamic flux balance analysis (dFBA) has been widely employed in metabolic engineering to predict the effect of genetic modifications and environmental conditions in the cell's metabolism during dynamic cultures. However, the importance of the model parameters used in these methodologies has not been properly addressed. Here, we present a novel and simple procedure to identify dFBA parameters that are relevant for model calibration. The procedure uses metaheuristic optimization and pre/post regression diagnostics, fixing iteratively the model parameters that do not have a significant role. We evaluated this protocol in a Saccharomyces cerevisiae dFBA framework calibrated for aerobic fed-batch and anaerobic batch cultivations. The model structures achieved have only significant, sensible and uncorrelated parameters and are able to calibrate different experimental data. We show that consumption, suboptimal growth and production rates are more useful for calibrating dynamic S. cerevisiae metabolic models than Boolean gene expression rules, biomass requirements and ATP maintenance.Metabolic Engineering 09/2014; 25:159 - 173. · 6.86 Impact Factor
Oxygen Response of the Wine Yeast Saccharomyces cerevisiae EC1118
Grown under Carbon-Sufficient, Nitrogen-Limited Enological
Felipe F. Aceituno,aMarcelo Orellana,aJorge Torres,aSebastián Mendoza,aAlex W. Slater,b,cFrancisco Melo,b,cand Eduardo Agosina,d
Department of Chemical and Bioprocess Engineering, School of Engineering, Pontificia Universidad Católica de Chile, Santiago, Chilea; Molecular Bioinformatics
Laboratory, Millennium Institute on Immunology and Immunotherapy, Santiago, Chileb; Department of Molecular Genetics and Microbiology, Faculty of Biological
Sciences, Pontificia Universidad Católica de Chile, Santiago, Chilec; and ASIS-UC Interdisciplinary Research Program on Tasty, Safe and Healthy Foods, Pontificia
Universidad Católica de Chile, Santiago, Chiled
through pump-overs, where dissolved oxygen concentrations
could transiently reach up to 100 ?M afterwards. The oxygen is
consumed completely in approximately 1 h but depends on the
wine variety and the oxygen addition method employed (M. I.
Moenne, P. Saa, J. R. Perez-Correa, F. Laurie, and E. Agosin, un-
published data). The wide range of resulting dissolved oxygen
concentrations has a deep impact on the physiology of wine yeast
(55). These effects are partially explained because oxygen is re-
quired for sterol biosynthesis, proline uptake, and unsaturated
a global approach is required for a quantitative understanding of
the impact of dissolved oxygen on the physiology of yeast cells
grown under enological conditions, i.e., sugar-sufficient, nitro-
gen-limited, acidic cultures.
The presence of oxygen in the culture medium can shift the
50, 60). However, Saccharomyces cerevisiae shows a fully respira-
tory metabolism only at low growth rates with low sugar concen-
trations (20). Above a critical specific growth rate or sugar con-
centration, yeast cells synthesize ethanol, regardless of the oxygen
level (60). The latter is known as the Crabtree effect (66). Several
hypotheses have been suggested to explain this phenomenon,
such as the catabolic repression of respiratory enzymes (21), a
bottleneck of the carbon flux toward the tricarboxylic acid (TCA)
cycle (47), and a limitation of yeast respiratory enzymes to reoxi-
xygen is discretely added during winemaking to avoid slug-
gish and stuck fermentations (55). This is generally achieved
channeled toward ethanol biosynthesis in the cytosol, instead of
the TCA cycle, causing “overflow metabolism,” the foundation of
nitrogen-limited and carbon-sufficient culture conditions, e.g.,
the operation conditions of winemaking (60). However, to the
best of our knowledge, it is not clear how the specific oxygen
uptake rate (OUR) influences overflow metabolism under nitro-
gen-limited conditions and whether it is compatible or not with
An understanding of overflow metabolism requires an under-
standing of the redox biochemistry of alcoholic fermentation in
yeast. Under anaerobic conditions, the NADH spent in the glyco-
lytic pathway must be reoxidized. Ethanol production is a way to
caused by the release of fully oxidized carbon as CO2. Redox bal-
ance is restored by diverting carbon toward glycerol production,
which also reoxidizes NADH (64). This is the only way to achieve
redox balance under anaerobic conditions. On the contrary, un-
Received 24 July 2012 Accepted 14 September 2012
Published ahead of print 21 September 2012
Address correspondence to Eduardo Agosin, email@example.com.
F.F.A. and M.O. contributed equally to this work.
Supplemental material for this article may be found at http://aem.asm.org/.
Copyright © 2012, American Society for Microbiology. All Rights Reserved.
aem.asm.orgApplied and Environmental Microbiologyp. 8340–8352December 2012 Volume 78 Number 23
to establish this balance. For this, two requisites are needed under
enological conditions: respiration has to be active under high-
sugar, nitrogen-limited conditions, and there must be functional
shuttles of NADH from the cytosol to the mitochondria.
mitochondria: the glycerol-3-phosphate (Gut2p) shuttle, the ex-
drial alcohol dehydrogenase (Adh3p) shuttle (5, 49). The Adh3p
reaction consumes the ethanol that diffuses from the cytoplasm
and produces acetaldehyde and NADH in the mitochondrial ma-
trix. Acetaldehyde can diffuse back into the cytoplasm, while
NADH is reoxidized by oxygen in the electron transport chain.
its ?G value is low (33.5 kJ/mol at pH 5 at 25°C) (3). The Nde1p
reaction, on the contrary, directly couples cytosolic NADH oxi-
dization to the respiratory chain. The latter is similar to Gut2p,
the mitochondrial quinone pool. Adh3p has been regarded as the
main shuttle (12, 38). However, there has been only one study on
the function of these shuttles under nitrogen-limited conditions
(41), emphasizing that this issue deserves further investigation.
To tackle such issues, enological conditions need to be simu-
tures, allow a tightly controlled environment while keeping yeast
cells at a defined specific growth rate. Thus, we can simulate cul-
ture conditions prevailing at the end of the exponential phase of
nitrogen limited) and the effect of the addition of oxygen is max-
imal (55). Chemostats, as they operate in the steady state, are
The simulation of different oxygen conditions in a winemak-
ing setting requires the characterization of the relationship be-
cells increases linearly with oxygen availability up to a critical dis-
solved oxygen concentration, at which the OUR is maximal, i.e.,
the “critical” OUR (16). When an oxygen impulse is carried out
during fermentation, yeast cells are subjected to oxygen contents
within the oxygen-limiting regime; the whole oxygen concentra-
tion range achieved during an oxygen impulse under enological
conditions could be simulated by increasing the dissolved oxygen
concentrations in nitrogen-limited continuous cultures.
at the steady state are valuable inputs for metabolic flux analysis
(MFA). MFA allows the determination of intracellular metabolic
fluxes, difficult to determine in vivo, from a set of extracellular
uptake and production rates, which are easily measurable. MFA
has been successfully used to understand yeast metabolism under
anaerobic conditions in both carbon- and nitrogen-limited set-
tings (40, 63). The incorporation of oxygen consumption path-
ways into these networks is a task that could help to explain the
In this research, we combined the use of carbon-sufficient,
nitrogen-limited continuous cultures; MFA models; and DNA
microarrays to simulate and examine the response of S. cerevisiae
to increasing dissolved oxygen concentrations under enological
conditions. MFA analysis indicated that yeast cells underwent re-
spirofermentative metabolism under wine fermentation condi-
tions above a certain dissolved oxygen threshold. Transcriptome
genes were induced by oxygen. Other enologically relevant pro-
cesses, such as proline uptake and cell wall remodeling, were also
affected by oxygen. Altogether, this research provides an under-
standing of the mechanisms through which oxygen can influence
yeast performance in industrial fermentations.
MATERIALS AND METHODS
Yeast strain and culture conditions. Saccharomyces cerevisiae EC1118
(Lalvin, Switzerland), an industrial strain used worldwide by the wine
industry, was employed throughout this study. Initial seed cultures were
grown in 50 ml yeast extract-peptone-dextrose (YPD) broth at 28°C un-
der aerobic conditions to a mid-logarithmic growth phase. For continu-
ous cultivation, a 2.0-liter Biostat B bioreactor (Sartorius Biotech, Ger-
the microbial broth to obtain an initial cell density of 106cells ml?1. The
defined artificial must that limited growth by nitrogen, with a constant
0.1 h?1. Agitation, temperature, and pH were maintained at 200 rpm,
20°C, and 3.5, respectively, to simulate white wine fermentation. All ex-
periments were performed in triplicate.
Gases were provided by means of a GFC mass flow controller (Aal-
borg), at a rate of 0.25 liter min?1in all experiments, except under con-
ditions with the highest dissolved oxygen concentration (21 ?M) (de-
tailed below), where the gas flow was provided at a rate of 1 liter min?1.
Polyurethane tubing and butyl rubber septa were used to minimize oxy-
ide and oxygen concentrations were measured online with Gascard NG
(Edinburgh Instruments, United Kingdom) and Parox 1000 (Messtech-
nick Engineering, Switzerland) gas analyzers, respectively. Afterwards, a
were acquired on a Simatic PCS7 distributed control system with a mon-
itoring station (Siemens, Germany).
Dissolved oxygen levels. To achieve different levels of dissolved oxy-
Chile) was passed through an HPIOT3-2 oxygen trap (Agilent) to reduce
by the sparging of 1%, 5%, and 21% oxygen-nitrogen mixtures (Indura,
Chile) directly into the culture, reaching concentrations of 1.2 ?M, 2.7
?M, and 5.0 ?M dissolved oxygen at the steady state, respectively (Table
1). For the last oxygen concentration, the gaseous flow with the 21%
oxygen mixture was increased from 0.25 to 1 liter min?1. At this point,
?M dissolved oxygen at the steady state. Experiments at all dissolved
state was reached within 5 residence times (60 h) and kept for at least 1.5
more residence times. The dissolved oxygen concentration was measured
online with an InPro model 6950 probe (Mettler, Toledo, OH). This
probe has a detection limit of 0.03 ?M.
juice was used in the bioreactor fermentations. The residual glucose con-
sustain glucose repression at the same level in all experiments (45, 60).
Briefly, the feed glucose concentrations were 80 g liter?1for the cells
grown anaerobically and with 1.2 ?M dissolved oxygen and 85 g liter?1
respectively. Fructose was not included in the formulation. The compo-
Impact of Oxygen on Wine Yeast Physiology
December 2012 Volume 78 Number 23aem.asm.org 8341
fermentations, and its use in chemostats resulted in 100 mg liter?1of
residual nitrogen (data not shown). The modified medium, called
cMS300 medium, was different from MS300 medium in its phosphate,
sulfate, and biotin contents. cMS300 medium contained 3 g of KH2PO4,
according to the mineral medium described previously by Pizarro et al.
medium. The yeast assimilable nitrogen (YAN) concentration corre-
sponded to 380 mg N liter?1under all culture conditions. However, un-
der anaerobic conditions, the YAN content corresponded to 300 mg N
liter?1, since proline is not assimilable by yeast under anaerobic growth
moniacal nitrogen (NH4Cl), 16.9% (wt/wt) L-proline, 1.25% (wt/wt) L-
glutamine, 6% (wt/wt) L-arginine, 4.9% (wt/wt) L-tryptophan, 4% (wt/
wt) L-alanine, 2.6% (wt/wt) L-glutamic acid, 2.6% (wt/wt) L-serine, 1.6%
(wt/wt) L-threonine,1.5%(wt/wt) L-leucine,1.5%(wt/wt) L-asparticacid,
1.3% (wt/wt) L-valine,1.1% (wt/wt) L-phenylalanine, 1.1% (wt/wt) L-iso-
leucine, 1.1% (wt/wt) L-histidine, 0.6% (wt/wt) L-methionine, 0.6% (wt/
wt) L-tyrosine, 0.6% (wt/wt) L-glycine, and 0.4% (wt/wt) L-lysine and
All culture media were supplemented with 10 mg of ergosterol and 30
anaerobic factors were lower than those in the original MS300 formula-
tion (15 mg liter?1ergosterol and 90 mg liter?1oleic acid) for all experi-
ments performed, to avoid the masking effect of oxygen on the synthesis
and oleic acid in S. cerevisiae strain EC1118 in chemostats, as described
previously (37). The ergosterol and oleic acid concentrations in the final
culture medium were set 3-fold above their biomass yields to ensure an
excess but were still lower than the concentrations in the original anaero-
detecting these anaerobic factors in the supernatants of all cultures at the
steady state; i.e., continuous cultures were limited only in nitrogen.
Metabolite sampling and analysis. For each of the three biological
replicates corresponding to the dissolved oxygen levels evaluated, steady-
state culture samples were taken after at least six residence times by using
an ice-cold sterile 50-ml plastic syringe plugged into the sampling device
of the bioreactor. The samples were rapidly transferred into an ice-cold
50-ml sterile plastic tube, where either the cell dry weight (DW) of the
culture was determined (see below) or the cultures were transferred into
Eppendorf tubes. The latter cultures were centrifuged at 10,000 ? g for 3
min, and 1-ml supernatant aliquots were stored at ?80°C until further
analysis. Biomass was determined on a dry weight basis by filtering 10-ml
(Whatman). The filter was washed twice with 10 ml milliQ water and
dried in an infrared drier-equipped balance (Precisa, Switzerland) to a
constant weight at 65°C.
Extracellular metabolites were determined by high-performance liq-
ples was injected into a LaChrom L-7000 HPLC system (Hitachi, Japan).
Organic acids, alcohols, and sugars were separated by using an Aminex
HPX-87H anion-exchange column (Bio-Rad), with 5 mM H2SO4as the
mobile phase. Organic acids were detected by using a LaChrom L-7450A
Each compound was quantified by using a calibration curve made with
known concentration standards.
?-phthaldehyde–N-acetyl-L-cysteine spectrophotometric assay (NOPA)
procedure, as described previously (63).
Proline was detected by HPLC, as described previously (36). Briefly,
amino acids in the supernatant samples were derivatized with 6-amino-
separated in a AccQ Tag column (Waters). Once separated, the deriva-
tized amino acids were detected by fluorescence and quantified by using
one of the three biological replicates under the five oxygen conditions
were extracted by rapidly placing 20 ml of culture into a Falcon tube
filled with 35 to 40 ml of crushed ice (45). From this tube, 1 ml of
sample was transferred into a 1.5-ml Eppendorf tube; after centrifu-
gation at 10,000 ? g for 3 min, the supernatant was removed, and the
pellet was directly frozen in liquid nitrogen and stored at ?80°C until
Before RNA isolation, all water-based reagents were treated with di-
ethyl pyrocarbonate (DEPC). RNA was extracted from frozen cell pellets
by using the AxyPrep Multisource RNA kit (Axygen Biosciences), modi-
fied for use with glass beads for cell lysis. Briefly, we resuspended the cell
tubes for three cycles of 45 s in a Mini Bead Beater (Biospec), with
the tubes standing for an equivalent time in ice between cycles. Later, the
lysate was centrifuged at 10,000 ? g for 1 min, and the supernatant was
transferred into a tube with 250 ?l of isopropanol. After this point, we
followed the instructions provided by the kit manufacturer. RNA was
checked for integrity in a 1.3% agarose gel, prepared in a buffer with 20
mM morpholinepropanesulfonic acid (MOPS), 5 mM sodium acetate,
and 1 mM EDTA. After heating, we added 2% (vol/vol) formaldehyde.
This was also used as the electrophoresis running buffer. RNA was quan-
absorbance ratios at 260 nm/280 nm and 260 nm/230 nm of ?1.8 were
used for further processing.
Microarray analysis. We used the Yeast Genome 2.0 chip from Af-
fymetrix. Triplicate arrays for each one of the five oxygen conditions
were performed. RNA for hybridization was prepared according to the
manufacturer’s instructions (2). Hybridization, staining, and scan-
ning onto the microarrays were performed according to the manufac-
turer’s instructions (1). Gene expression data were imported from the
TABLE 1 Steady-state metabolite concentrations and metabolite consumption and production rates in S. cerevisiae EC1118 grown under different
dissolved oxygen culture conditions
Metabolite concna(g liter?1)
Specific consumption and production ratea
(C-mmol g DW?1h?1)
?0.1 ? 0.0
?0.7 ? 0.3
?2.3 ? 0.3
?3.9 ? 0.1
13.8 ? 1.8
12.0 ? 1.2
11.5 ? 0.2
13.3 ? 0.1
11.0 ? 0.2
0.0 ? 0.00
1.2 ? 0.06
2.7 ? 0.28
5.0 ? 0.02
21.0 ? 1.1
3.1 ? 0.2
3.7 ? 0.05
4.2 ? 0.1
5.6 ? 0.3
5.8 ? 0.2
42.1 ? 1.8
40.5 ? 5.4
40.7 ? 3.0
37.5 ? 3.0
41.7 ? 1.4
20.3 ? 2.4
21.1 ? 0.5
23.5 ? 2.8
24.2 ? 2.8
16.2 ? 0.8
?43.6 ? 5.5
?43.9 ? 2.6
?37.5 ? 0.0
?37.4 ? 1.0
?21.6 ? 0.2
27.7 ? 4.0
25.2 ? 0.6
25.4 ? 1.2
18.0 ? 2.3
12.3 ? 0.2
1.5 ? 0.6
1.4 ? 0.2
1.4 ? 0.0
1.0 ? 0.3
0.4 ? 0.0
0.06 ? 0.02
0.02 ? 0.04
0.27 ? 0.05
0.22 ? 0.00
0.15 ? 0.01
aData are averages ? standard deviations for three independent chemostat steady states.
bOUR, specific oxygen uptake rate.
cRQ, respiratory quotient; NA, not applicable.
Aceituno et al.
aem.asm.org Applied and Environmental Microbiology
CEL files into R, and quality was assessed by using the tools in the
simpleaffy package from Bioconductor (22). After the quality check
step, data were normalized in R by using robust multiarray analysis
(RMA) from the affy package from Bioconductor (22). Differential
expression analyses between data under the different conditions were
discovery rate of 0.1. We also performed hierarchical clustering on the
data, using group average and Euclidian distance as a distance mea-
sure. All calculations were performed by using R statistical software
(48a). The resulting lists of genes from the clusters or differential
expression analyses were then submitted to AmiGO term enrichment
analysis (11), to find out which Gene Ontology (GO) annotations and
functions were overrepresented among the genes regulated by oxygen
levels. The full gene annotation was then extracted from the Saccha-
romyces Genome Database (SGD) (13). Alternatively, the GO enrich-
ment analysis was performed by using Blast2GO software (14). This
software has the Gossip package integrated for statistical assessment,
which employs Fisher’s exact test and corrects for multiple testing to
find enriched GO terms between two sets of sequences (6). The com-
plete set of annotated genes for Saccharomyces cerevisiae from the SGD
was used as the reference control group, with a false-positive discovery
rate value of 0.05.
was performed by using Euclidean distance to measure differences be-
correlation coefficient (CCC) (CCC ? 0.61) compared to single linkage
(CCC ? 0.41), complete linkage (CCC ? 0.58), and Ward’s method
(CCC ? 0.55). The CCC values were calculated as previously described
(58). A distance cutoff value of 2.26 was selected to make the partition of
the clustered data into 56 groups with different gene expression patterns
under the tested conditions. The specific clustering algorithm and dis-
tance cutoff value were defined after a partition-based cluster structure
distance cutoff values to split the clustered data into k different groups
were tested, and Silhouette values for each partition were calculated. Ac-
ette measure was obtained when using the group average clustering algo-
rithm and a distance cutoff value of 2.26, thus producing 56 independent
gene groups. The resulting partition was then visually inspected for con-
genes in each group. The dendrogram with the heat maps for each gene
was built by using the iTOL Web server (http://itol.embl.de/) (33). A
color-coded strip illustrating the independent gene clusters was added to
Metabolic flux analysis. We built a stoichiometric matrix based on a
model developed previously by Varela et al. (63), which includes glycoly-
sis, TCA cycle, fermentative, and anaplerotic reactions as well as essential
anabolic pathways. We added the following oxygen-related reactions to
this matrix: proline consumption, NADH- and reduced flavin adenine
dinucleotide (FADH2)-dependent respiration pathways (each one
thesis. These reactions were extracted from the YeastCyc database (http:
by Nissen et al. (40) for the biomass equation, leaving every biomass
component (protein, DNA, RNA, carbohydrate, and lipids) as individual
products. Moreover, we separated the lipids into sterols and unsaturated
and saturated fatty acids, in order to have a better description of the
oxygen-dependent pathways. The resulting matrix had 47 reactions and
number of the matrix was 44, indicating that the model is numerically
Flux estimation. Since the model has three degrees of freedom, the
measured. We evaluated inputs, including all of the possible combina-
tally measured extracellular rates of substrate uptake or metabolic prod-
uct production: glucose, ethanol, glycerol, succinate, acetate, CO2,
oxygen, proline, biomass components (carbohydrates, DNA, RNA, and
tal material. Since the resulting matrix is not square, we carried out a
“pseudoinverse” operation (59) to solve the mass balance equation and
estimate the flux vector, including the unselected rates. An evaluation of
more accurate the estimations of the model. This prompted us to use 13
This was validated by sensitivity analyses (see below). The redistribution
of intracellular fluxes under different oxygen conditions was assessed by
determining the flux vector for the three individual replicates under each
condition and comparing the resulting fluxes to each other by using a t
Consistency and sensitivity analysis. Data consistency was checked
by using a method described previously by Wang and Stephanopoulos
(67). After confirming the absence of gross measurement errors, we per-
formed a custom sensitivity analysis, based on the normalized error dis-
tribution, which was calculated as the differences between the estimated
fluxes and the fluxes calculated by using modified rates (specific rates ?
experimental errors). We then calculated the square sum of these differ-
ences and took their square root to obtain the error value. Finally, we
normalized the errors by the value of the corresponding flux calculated
with the unmodified rates. The specific rates were modified by the exper-
imental error one at a time, and the sum of the errors on all the fluxes of
the specific rates of ethanol and CO2fluxes to validate the model. These
estimations had less than an 11% error (see Table S2 in the supplemental
scripts in the R computer language.
Microarray data accession number. The raw microarray data were
submitted to the Gene Expression Omnibus (GEO) (http://www.ncbi
.nlm.nih.gov/geo/), where they are available under accession number
Simulation of dissolved oxygen concentrations and oxygen up-
take rates under enological conditions. To characterize wine
yeast physiology at the different dissolved oxygen concentrations
found after oxygen impulses in winemaking, we built a specific
a constant increase with concentrations of dissolved oxygen up to
21 ?M. From this critical value, the OUR was constant at 3.9
mmol g DW?1h?1. This empirical critical value was close to the
value reported previously for the OUR in fully aerobic, nitrogen-
limited, continuous cultures (3.5 mmol g DW?1h?1) (32). Thus,
five different levels of dissolved oxygen spanning were selected to
cover the complete range from anaerobic conditions to the max-
imum OUR (Table 1). These concentrations covered the entire
oxygen-limited range for the yeast cells under enological condi-
Carbon balances and specific rates of substrate uptake and
ment for quantitative assessments of metabolic physiology. We
measured sugar and oxygen uptake rates as well as the accumula-
tion of several organic compounds. Carbon balances, calculated
through the yields in glucose (in C mol per C mol of glucose
Impact of Oxygen on Wine Yeast Physiology
December 2012 Volume 78 Number 23aem.asm.org 8343
mental material), indicating that no major product or substrate
had been left out.
The biomass significantly increased with the availability of
dissolved oxygen, almost doubling its concentration at 5 ?M
oxygen, compared to that under anaerobic conditions (Table
1). This also correlated with an increase in proline consump-
tion, in line with the limitation of all other nitrogen sources
(29). The proline consumption rate and biomass increase
2.7 ?M, according to the biomass yield in nitrogen for this
strain under anaerobic conditions (45; this work) (see Table S4
in the supplemental material). This correspondence did not
occur with higher dissolved oxygen concentrations, likely be-
cause the biomass yield in nitrogen under aerobic conditions is
different from that calculated under anaerobic conditions.
els (from 141 to 197 C-mmol glucose liter?1h?1for 0 to 5 ?M
dissolved oxygen, respectively). Nevertheless, a further in-
crease in the dissolved oxygen level resulted in only a modest
increase in biomass and a decrease of the glucose consumption
volumetric rate (to 125 C-mmol glucose liter?1h?1). Surpris-
ingly, the biomass yield in glucose (Yx/Glc) increased, while eth-
anol and CO2yields in glucose decreased (see Table S1 in the
The impact of oxygen on cell physiology was more evident
when specific rates were analyzed (Table 1). A negative corre-
lation with dissolved oxygen was observed for glucose con-
sumption and ethanol- and glycerol-specific production rates.
However, in all cases, significant ethanol production was
found, consistent with the presence of the Crabtree effect. Ac-
under all oxygenated conditions (Table 1). Organic acid pro-
duction was also affected by oxygen availability. For instance,
acetic acid was produced only under conditions of strict anaer-
obiosis (0.3 C-mmol g DW?1h?1). On the other hand, a strik-
ing and significant (P ? 0.01) increase in the level of succinic
acid production occurred between the 1.2 and 2.7 ?M dis-
solved oxygen conditions (from 0.02 to 0.27 C-mmol g DW?1
h?1). This increase was unexpected, as there is no known
mechanism of succinic acid export in yeast, although succinic
acid production has been consistently reported in previous
studies (44, 45).
Metabolic flux analysis. The redistribution of intracellular
carbon fluxes in the central metabolic pathways of Saccharomyces
cerevisiae occurring in response to the increasing availability of
dissolved oxygen under enological conditions was determined by
using the stoichiometric model. The carbon flux toward fermen-
flux through the TCA cycle, as the dissolved oxygen content in-
creased (Fig. 2). We confirmed that the TCA cycle was not func-
tional and operated in two branches under conditions of anaero-
biosis, as suggested previously for anaerobic, carbon-limited
conditions (10, 40); a similar situation was observed for the 1.2
trations of 2.7 ?M and higher, the TCA cycle followed its canon-
ical direction, with a large increase in carbon flux circulation
increase of the flux toward carbohydrate synthesis under condi-
tions with 21 ?M dissolved oxygen, increasing more than 60%
compared with the flux under the other conditions (Fig. 2). This
finding is supported by experimental data (Table 2).
Sources and sinks of nucleotide cofactors. MFA showed that
mitochondria played an increasingly important role in NADH
oxygen. Fluxes through respiration and cytosol to mitochondrial
tions increased (Fig. 2). Mitochondrial NADH production and
utilization significantly increased with increasing oxygen concen-
trations (P ? 0.05), rising from 25% to 60% of the total NADH
production rate in the cell when the dissolved oxygen level was
augmented from 2.7 to 21 ?M, respectively. Analyses of NADH
by glycolysis under all the conditions tested (Table 3). Below a con-
centration of 2.7 ?M dissolved oxygen, glycerol is the only other
electron sink, albeit a minor one (?4% of NADH reoxidization).
test); moreover, glycerol synthesis showed almost no contribution
Oxygen also extended the mitochondrial contribution to
ATP production. Under anaerobic conditions, ATP was pro-
duced in glycolysis by substrate-level phosphorylation. On the
other hand, the ATP produced by oxidative phosphorylation
had a noticeable contribution, increasing from 5.7% to 21% of
the total ATP when the dissolved oxygen content increased
from 2.7 ?M to 21 ?M, respectively. The glycolytic pathway
any means was significantly reduced, decreasing by approximately
Saccharomyces cerevisiae strain EC1118 and the dissolved oxygen concentra-
tion. Gray symbols correspond to steady-state, nitrogen-limited continuous
cultures with increasing dissolved oxygen concentrations. The corresponding
OURs were determined for the same cultures. Black squares represent the five
dissolved oxygen conditions evaluated in this research.
Aceituno et al.
aem.asm.orgApplied and Environmental Microbiology
reaction. The level of NADPH production by this reaction de-
creased from 4% to 1% when the available oxygen concentration
increased from 0 ?M to 1.2 ?M dissolved oxygen. The rest of the
flux that slightly increased when comparing the anaerobic condi-
tions to the rest of the conditions. However, the flux through this
pathway represents only 2% of the carbon under all conditions
(see Table S5 in the supplemental material), confirming results
reported previously by Varela et al. (63).
Gene expression analysis. To complement the metabolic flux
oxygen conditions. Numbers indicate specific reactions related with oxygen consumption: 1, respiration; 2, unsaturated lipid synthesis; 3, ergosterol synthesis;
4, proline uptake. The fluxes are expressed as percentages of the total carbon uptake. Negative numbers indicate flux in the reverse direction. The flux for
phoglycerate; GLYC, glycerol; SER, serine; PEP, phosphoenolpyruvate; AC, acetate; ACCoA, acetyl coenzyme A; LIP, lipids; OAA, oxaloacetate; PYR, pyruvate;
ADE, acetaldehyde; ETOH, ethanol; ASP, aspartate; ISOCIT, isocitrate; GLN, glutamine; FUM, fumarate; AKG, ?-ketoglutarate; GLU, glutamic acid; PROT,
ERG, ergosterol; UN_LIP, unsaturated lipids.
TABLE 2 Specific production rates of major biomass components at different dissolved oxygen concentrations
Rate (C-mmol g DW?1h?1) at dissolved oxygen concna(?M) of:
0 1.22.75 21
0.915 ? 0.039
0.006 ? 0.000
0.864 ? 0.056
0.062 ? 0.006
2.13E?09 ? 0.00
1.15E?07 ? 1.1E?08
2.20E?08 ? 1.9E?09
0 ? 0.000
0.951 ? 0.041
0.007 ? 0.000
0.874 ? 0.060
0.059 ? 0.005
0.009 ? 0.000
1.02E?07 ? 1.3E?08
2.30E?08 ? 2.1E?09
?0.108 ? 0.003
1.070 ? 0.048
0.006 ? 0.000
0.836 ? 0.054
0.044 ? 0.004
0.022 ? 0.001
1.56E?07 ? 1.4E?08
2.60E?08 ? 2.2E?09
?0.244 ? 0.005
1.020 ? 0.045
0.0063 ? 0.000
0.877 ? 0.061
0.054 ? 0.005
0.018 ? 0.001
1.73E?07 ? 9E?09
2.90E?08 ? 2.7E?09
?0.242 ? 0.005
2.588 ? 0.129
0.006 ? 0.000
0.865 ? 0.051
0.061 ? 0.006
0.004 ? 0.000
1.71E?07 ? 1.0E?08
2.87E?08 ? 2.2E?09
?0.322 ? 0.007
aData are averages ? standard deviations for three independent chemostat steady states.
bSpecific rate of proline consumption.
Impact of Oxygen on Wine Yeast Physiology
December 2012 Volume 78 Number 23aem.asm.org 8345
data, global gene expression under the five dissolved oxygen con-
ditions was determined. As a whole, the effect of oxygen on the
genes) were affected by oxygen availability. The latter was calcu-
lated by taking into account the differentially expressed genes in
all possible comparisons of the different oxygen conditions. The
with the highest concentration of dissolved oxygen (21 ?M)
tent with data from previous reports (60), where 371 genes were
in nitrogen-limited, continuous cultures.
Analysis of oxygen level transitions. To simulate the oxygen
winemaking aeration operations (M. I. Moenne and E. Agosin,
unpublished data), we simulated a pseudodynamic setting by
comparing the data for one condition with those for conditions
The largest effect (200 differentially expressed genes) occurred
upon the onset of the addition of oxygen, i.e., between the 0 and
1.2 ?M dissolved oxygen concentrations. The transition between
the 5 and 21 ?M concentrations had an equivalent impact, caus-
ing differential changes in 195 genes. Remarkably, this was the
transition between oxygen-limited and oxygen-saturated condi-
tions (Fig. 1). The effect was much smaller when the intermediate
transitions were compared (1.2 with 2.7 ?M and 2.7 with 5 ?M
dissolved oxygen), affecting the expression of 25 and 19 genes,
solved oxygen conditions. The genes differentially expressed be-
tween 0 and 1.2 ?M dissolved oxygen conditions can be classified
into two groups. The first group consisted of six genes, annotated
TABLE 3 Contribution to NADH and ATP turnover of different metabolic pathways in S. cerevisiae EC1118 grown under different dissolved oxygen
Total NADH synthesis (mmol g DW?1h?1)
Dissolved oxygen concna(?M) of:
01.2 2.75 21
13.7 ? 1.7
0.1 ? 0.2
13.8 ? 0.8
0.2 ? 0.1
11.6 ? 0.1
3.4 ? 0.0
11.1 ? 0.5
7.1 ? 0.2
6.7 ? 0.5
9.5 ? 0.2
Cytosolic NADH consumed (%) for:
96.0 ? 1.7
4.0 ? 0.2
96.0 ? 0.8
4.0 ? 0.1
89.6 ? 0.0
3.4 ? 0.0
7.0 ? 0.0
85.0 ? 0.5
3.0 ? 0.1
12.0 ? 0.0
73.5 ? 0.5
1.2 ? 0.1
25.3 ? 0.1
Total ATP produced (mmol g DW?1h?1)
27.2 ? 3.4
27.5 ? 1.6
0.047 ? 0.05
23.1 ? 0.0
0.61 ? 0.06
1.45 ? 0.34
22.0 ? 0.9
1.32 ? 0.06
2.85 ? 0.07
13.2 ? 0.9
1.70 ? 0.17
3.20 ? 0.40
aData are averages ? standard deviations for three independent experiments.
TABLE 4 Characterization of differentially expressed genes across different oxygen levels
Transition of oxygen
concn (?M) (gene
No. of differentially
(FDR ? 0.1)a
Enriched GO biological process term(s),
P ? 0.01b(GO term frequency)c
0–1.2 (induced) 39Siderophore transport (10.3/0.2), amino acid
Mitochondrial intermembrane space (27.3/0.8),
inorganic anion transmembrane transporter
Cell wall (18.9/1.8), sterol transport (13.5/0.9)
FRE3 (ferric reductase), ENB2 (enterobactin), PUT4 (proline
NDE1 (external NADH dehydrogenase), CYC1 (cytochrome c),
SUL1 (high-affinity sulfate transporter), PHO84 (phosphate
HPF1 (haze-protective mannoprotein), TIR2 (cell wall
mannoprotein), UPC2 (sterol regulatory element binding
protein), AUS1 (ABC-type sterol transporter)
CYC1 (cytochrome c, isoform 1), COX7 (subunit VII of
cytochrome c oxidase)
FRE3 (ferric reductase), ENB2 (enterobactin), PHO84
(phosphate transporter), SUL1 (high affinity-sulfate
Respiratory chain (7/0.5)
5.0–21.0 (repressed)95 Iron ion homeostasis (23.2/0.9), transport
aFDR, false discovery rate, as determined by the rank products method.
bAs determined by the GO term enrichment tool at AmiGO (http://amigo.geneontology.org/).
cData in parentheses represent the frequency of the term among the differentially expressed genes/frequency of the term among the whole transcriptome of S. cerevisiae.
dNF, no significant GO term was found.
Aceituno et al.
aem.asm.orgApplied and Environmental Microbiology
as “siderophore transport” genes, that participate in iron uptake.
The putative increase in iron uptake could be related to the over-
expression of hemoprotein-related genes, such as cytochromes.
transport” genes including the proline transporter PUT4. This
highlights both the induction of respiratory genes and the impact
of oxygen on the regulation of the nutrient uptake of yeast.
Genes differentially expressed between the 1.2 and 2.7 ?M
dissolved oxygen conditions. Between the 1.2 and 2.7 ?M dis-
equivalents directly into the respiratory chain. GUT2, another
gene responsible for a shuttle mechanism (49), was also induced
repressed genes, two members of the TIR genes and the HPF1
on wine quality, modulating astringency and other organoleptic
properties (24). The AUS1 gene, involved in fatty acid and sterol
uptake, was also repressed.
gen concentrations of 5 and 21 ?M consolidated the trend in the
induction of respiratory genes. Induced genes included more re-
supporting the idea of active respiration, even under these high-
glucose conditions. Other induced genes were stress response
genes, such as CTT1 (catalase), HSP12 (membrane heat shock
protein), and GRX4 (glutaredoxin), hinting at a potential oxida-
tive stress occurring under this condition. The repressed genes at
Other gene expression changes. The genes annotated as be-
longing to the “TCA cycle” GO term showed no differential ex-
pression in the transitions analyzed. However, when anaerobic
conditions and 21 ?M dissolved oxygen conditions were com-
pared, the TCA cycle term was statistically enriched among in-
(citrate synthase) genes. On the other hand, the FRD1 gene was
with 5 ?M dissolved oxygen conditions. FRD1 encodes a soluble
fumarate reductase that is responsible for the operation of the
reductive branch of the TCA cycle. This observation was in line
with the redistribution of metabolic carbon fluxes observed by
MFA with increasing dissolved oxygen concentrations: an in-
crease of TCA fluxes and a disappearance of the anaerobic, two-
branch operation of the TCA cycle.
Clustering analysis. Genes that responded to oxygen under at
least one condition were classified into 56 clusters (Fig. 3), by
ing to their major tendencies with regard to the dissolved oxygen
concentration. We analyzed the 12 clusters that showed at least
one enriched GO term according to the GO term enrichment
the clusters according to their general trends in relation to the
dissolved oxygen concentration. We found four broad categories:
clusters with genes downregulated with 21 ?M dissolved oxygen,
and genes negatively and positively correlated with dissolved ox-
ygen. We describe the genes in these clusters below.
Genes downregulated with 21 ?M dissolved oxygen. Several
gene clusters were downregulated with 21 ?M dissolved oxygen.
2, 26, and 52 were induced with 1.2 ?M dissolved oxygen, con-
firming the induction of iron uptake at low oxygen levels, a trend
that is reversed at high oxygen concentrations. Besides the iron
metabolism genes, several genes involved in ergosterol metabo-
sible product repression when enough oxygen is present to syn-
thesize these compounds (15).
clusters showed a common negative response to low levels of ox-
ygen. Gene expression levels in clusters 25 and 29 dropped signif-
icantly with 1.2 ?M dissolved oxygen, although they increased
concomitantly with increasing oxygen concentrations for the
higher oxygen levels (Fig. 3). Several transcription factors belong
to these clusters, most notably two positive regulators of nitrogen
catabolism, DAL81 and GZF3 (23). Other genes, such as those
from cluster 37, showed very low expression levels. This is char-
acteristic of silenced transposable elements, the expression levels
of which were further decreased with increasing oxygen concen-
trations. Cluster 43 also showed this pattern. The latter cluster
includes FRD1, the fumarate reductase gene, confirming data
from the two-branch TCA transition analysis, as well as SLC1, a
key enzyme in phospholipid metabolism (4).
ily of TIR genes appears both in cluster 53 and with the 1.2 to 2.7
ygen (Fig. 3). These results suggest that cell wall remodeling is
taking place as the oxygen level increases, which could also be
linked to increasing levels of ergosterol production (Table 2).
Consistently, the UPC2 transcription factor, which regulates er-
oxygen level increased (cluster 13).
Genes positively correlated with dissolved oxygen. Support-
ing the idea of a respiratory metabolism under these conditions,
several genes related to complex IV of the respiratory chain were
induced together with dissolved oxygen, and they are grouped
into cluster 32 (Fig. 3).
Despite these findings, some metabolic pathways showed phe-
notypic changes that were not reflected in gene expression levels,
such as acetate production. For instance, we observed that ALD
production (7), were not affected by oxygen levels despite the fact
that acetate is detected only in anaerobic cultures.
This research addresses the impact of different levels of dissolved
oxygen on the physiology of an industrial strain of S. cerevisiae
tions. We experimentally captured a subset of dissolved oxygen
concentrations, aiming to represent the oxygen-limiting range of
dissolved oxygen concentrations found in discrete enological aer-
effects, reflected by changes in the levels of production of several
extracellular compounds and the metabolic flux redistribution
within the cell. For instance, ethanol- and glycerol-specific pro-
Impact of Oxygen on Wine Yeast Physiology
December 2012 Volume 78 Number 23aem.asm.org 8347
duction rates decreased when the oxygen level was increased,
along with the respiratory quotient. This is an indication of a
transition from fermentative to mixed respirofermentative me-
tabolism. Nevertheless, the respiratory quotient values indicate
that fully respiratory metabolism was never achieved. Although
this finding was reported previously for laboratory strains under
nitrogen-limited conditions (60), the evidence of active respira-
tory metabolism under enological conditions is striking, since
there is a general belief that respiration is under catabolic repres-
sion under these conditions (21, 51, 66).
Respiratory quotients showed a large decrease between con-
centrations of 1.2 and 2.7 ?M dissolved oxygen. Consistently,
metabolic flux analysis predicts two very different metabolic con-
figurations depending on the concentration of dissolved oxygen:
fully fermentative metabolism (including 0 and 1.2 ?M dissolved
oxygen) and mixed respirofermentative metabolism (dissolved
FIG 3 Hierarchical clustering of the S. cerevisiae transcriptome data obtained under the five dissolved oxygen conditions. From the center to the outside, the
and green represent low and high gene expression levels, respectively. Increasing dissolved oxygen concentrations, from 0 ?M (most internal) to 21 ?M (most
external), are shown. The scale of the calculated distances in the dendrogram is also illustrated.
Aceituno et al.
aem.asm.orgApplied and Environmental Microbiology
oxygen concentration of 2.7 ?M and higher). Fully fermentative
conditions featured low carbon fluxes and a two-branch opera-
ation of the two-branch TCA cycle under anaerobic conditions
was initially suggested by MFA (40) and later proven by a13C-
based metabolomic analysis (10) under carbon-limited condi-
tions. To the best of our knowledge, this is the first report of the
operation of a two-branch TCA cycle under carbon-sufficient,
Cultures with more than 2.7 ?M dissolved oxygen showed a
mixed respirofermentative metabolism despite the high external
sugar concentration (40 g/liter). It is worth noting that the model
assumes that respiration is working under these conditions. Un-
der this assumption, the estimations of ethanol and CO2produc-
tion were reliable (see Table S2 in the supplemental material),
providing further support for a functional and active respiratory
pathway under these culture conditions. The main features of the
significant respiratory activity, TCA cycle operation in its canon-
ical direction, increased levels of succinic acid production, and a
mitochondrial redox shuttle working as a significant cytosolic
NADH sink. This effect of oxygen on yeast metabolism is mir-
genes (respiration), the repression of the fumarate reductase gene
(reductive branch of the TCA cycle), and the induction of the
NDE1 and GUT2 genes (mitochondrial electron shuttles). More-
over, TCA genes such as the ACO1 (aconitase) and CIT1 (citrate
synthase) genes are induced at the highest level of oxygen tested.
Altogether, the data confirm the occurrence of a respiratory me-
tabolism. Nevertheless, ATP and NADH were still produced
mainly by glycolysis, confirming that this is the major pathway
regarding carbon flow operating under these conditions.
One of the hallmarks of mixed respirofermentative metabo-
lism was the large increase (approximately 10-fold) in the level of
succinic acid production between 1.2 and 2.7 ?M dissolved oxy-
gen. Most likely, this resulted from the much larger flux toward
Nevertheless, how succinic acid is exported remains unclear. Sev-
eral succinic acid transporters of the mitochondrial membrane
However, no transporter for succinic acid at the plasma mem-
brane has yet been identified. Therefore, succinic acid could be
exported by diffusion and/or active transport, although the diffu-
sion mechanism implicates an actual production rate 6-fold
higher than the one observed (see the supplemental material).
Preliminary experiments argued in favor of an active transport
mechanism, since batch cultures of S. cerevisiae EC1118 with ap-
acid despite being supplemented with exogenous succinic acid.
The export of acetic acid also showed an interesting trend,
being present only under strict anaerobic conditions. With a low
dissolved oxygen level (1.2 ?M), acetic acid production disap-
peared. This correlated with a decrease of the flux through the
aldehyde dehydrogenase reaction and a slight increase of the flux
acetate production in anaerobiosis is necessary to provide
NADPH to the cell, a function that is taken over by the pentose
phosphate pathway when oxygen is available. In fact, both path-
of NADPH in glucose-containing media (26). While the mech-
anism for the coordination of these two pathways is unclear,
the lack of a change in the expression level of the ALD6 gene
gests a nontranscriptional mechanism. The involvement of
Ald6p in a calcium/calmodulin-dependent signaling pathway
supports this hypothesis (9).
Another feature of the mixed respirofermentative metabolism
under the culture conditions of this study was the increase in the
shuttling of redox equivalents from the cytoplasm to the mito-
tle is necessary to explain the ethanol reduction under 21 ?M
dissolved oxygen conditions. However, the function of neither
out, since the replacement of Adh3p with Nde1p does not change
model estimations. Moreover, the inclusion of the Gut2p shuttle
in the MFA model yielded good estimations under all aerobic
conditions (data not shown). Experimental evidence for Nde1p
and Gut2p suggested that both mechanisms are active, as the en-
zymatic activities of both mechanisms were detected under aero-
bic, nitrogen-limited conditions (41). Moreover, at the oxygen
metabolic threshold (between 1.2 and 2.7 ?M dissolved oxygen),
we found an induction of the Nde1p gene at the transcriptional
by oxygen, contradicting previous studies where this gene was
reported to be catabolically repressed (49). Therefore, the in-
could be attributed to the presence of these shuttles, as well as of
the Adh3p shuttle, which showed constant, significant gene ex-
pression regardless of the oxygen level.
Despite the increase in the mitochondrial shuttle activity, mi-
tochondria showed a limited reoxidization capacity, as reflected
by the large contribution of the ethanol production pathway to
NADH reoxidization, even when the yeast was at its fastest OUR
inability of Saccharomyces cerevisiae to develop a fully respiratory
metabolism under conditions of nitrogen limitation. Whether
this limitation occurs at the shuttle level or at the level of the
activity of the respiratory enzymes is unclear from our experi-
ments. Nevertheless, the shuttle hypothesis is in line with recent
reports that proposed that mitochondrial membrane surface
availability is crucial in regulating the respirofermentative transi-
This could be useful, for example, to lower the level of ethanol
in metabolic engineering (for example, see reference 18).
The hypothesis of limited mitochondrial reoxidization as
the major cause of the Crabtree effect was reinforced by data
from gene expression analyses. We found little evidence of glu-
cose catabolic repression of respiratory enzymes, as the COX
and cytochrome b and c (CYB2 and CYC1) genes are responsive
to oxygen despite high external sugar concentrations. This was
also observed previously by Tai et al. (60). Even under anaero-
bic conditions, the expression levels of these genes were higher
than the average (Fig. 3), discarding any repressive effect from
the high external glucose concentration. The data also suggest
that oxygen was able to override glucose catabolic repression,
Impact of Oxygen on Wine Yeast Physiology
December 2012 Volume 78 Number 23aem.asm.org 8349
as the expressions of many responsive genes (such as COX)
were positively correlated with dissolved oxygen concentra-
tions (Fig. 3). Therefore, heme-dependent oxygen induction,
through the HAP transcription factors (46), could be able to
bypass catabolic repression. However, it was not possible to
detect significant changes in transcription factor activities by
using network component analysis (35), suggesting that the
mechanisms involved in this phenomenon are too elaborate to
be inferred only from gene expression profiles. This regulatory
landscape appears to encompass all conditions in the presence
of oxygen. However, at the highest dissolved oxygen level (21
?M), a puzzling gene expression scheme occurred. Respiratory
genes were induced, but those encoding ion transporters, such
as iron and copper, were repressed. This is the opposite of what
occurs upon oxygen exposure, where both iron transport and
respiratory genes are induced coordinately, since iron is a re-
quirement for the building of the essential hemoproteins of the
respiratory chain. The situation with 21 ?M dissolved oxygen
could be a major physiological reconfiguration when the max-
imal OUR capacity of yeast cells is reached (Fig. 1). One com-
ponent of this reconfiguration could be oxidative stress, which
would also explain the iron uptake restriction, as an excess of
iron generates more free radicals in the cell (19). Furthermore,
some oxidative stress marker genes are induced, such as GRX4.
transcriptional factor regulating iron metabolism. The latter
provides a mechanism to explain iron transporter repression.
Aft1p could also influence nitrogen metabolism (57), which is
indeed the case under high-oxygen conditions. For instance,
DAL81 (regulator of allantoin utilization), a transcription fac-
tor that positively regulates the utilization of alternative nitro-
gen sources, shows the highest expression levels under anaero-
bic conditions and with 21 ?M dissolved oxygen as well, both
conditions under which nitrogen is effectively unavailable.
Aft1p could influence the metabolisms of other nutrients (57),
for which we also found transporter repression (Table 4).
The repression of the nutrient transporters could explain the
modest biomass increase when 21 ?M dissolved oxygen condi-
tions were compared with 5 ?M dissolved oxygen conditions.
Also, limited nutrient availability could play a role in establishing
limitation can impair the cell’s capacity to build more respiratory
the critical OUR is much higher in carbon-limited cultures (32),
indicating that the catalytic capacity of the respiratory chain can
sustain a higher OUR. Therefore, the low critical OUR under ni-
respiratory complexes available.
and nutritional stress under oxygen-saturated conditions (21
?M). Consistently, we observed that with dissolved oxygen con-
centrations higher than 21 ?M (Fig. 1), the biomass can drop as
low as 4 g liter?1(data not shown), suggesting that at 21 ?M
dissolved oxygen, yeast cells are at the edge of their biomass-pro-
ducing capacities. Additionally, under this condition, there is a
strong increase in carbohydrate synthesis (Table 2), which is a
landmark of physiological stress responses in microorganisms
(34). Altogether, these data suggest that yeast cells are possibly
under multifactorial stress under nitrogen-limited conditions
with the OUR saturation regime. Increased carbohydrate synthe-
sis may also explain the simultaneous increase of the biomass/
glucose yield and decrease of ethanol and CO2/glucose yields, an-
other puzzling feature of the 21 ?M dissolved oxygen conditions
(see Table S1 in the supplemental material).
The changes in dissolved oxygen concentrations also impact
the cell wall. For instance, SLC1, encoding a key enzyme in phos-
pholipid metabolism, is repressed with 1.2 ?M dissolved oxygen.
At the highest oxygen concentrations, these changes can be an-
other source of stress. For example, ergosterol and unsaturated
lipid biosynthetic gene expression levels were significantly de-
creased with 21 ?M dissolved oxygen. In fact, the ergosterol con-
tent and synthesis rate decreased by 77% when the 5 and 21 ?M
dissolved oxygen conditions were compared (Table 2). This re-
duction in the level of ergosterol could also contribute to the es-
as a protective compound against oxidative stress (31). On the
other hand, oxygen represses several mannoprotein-encoding
genes (TIR) in a dose-dependent fashion This could be caused by
Upc2p, an inducer of ergosterol biosynthesis and of TIR genes
(15). The Upc2p level decreases at high dissolved oxygen levels
mechanism. Moreover, oxygen represses another cell wall-related
gene, MUC1 (also called FLO11), which is critical for yeast floc-
From a winemaking perspective, metabolic flux analysis and
gene expression data suggest that elevated dissolved oxygen con-
centrations could affect yeast performance during and after fer-
?M would reduce the ethanol yield (see Table S1 in the supple-
mental material), and reaching levels of 21 ?M or higher would
induce a severe stress on the yeast cell, further decreasing its fer-
mentative capacity. These oxygen levels are easily achieved in in-
dustrial winemaking practice (Moenne et al., unpublished).
Therefore, these results indicate that it is advisable not to keep
wine yeast cells at these oxygen levels for an extended period of
time. Furthermore, the repression of mannoprotein genes by ox-
linked to many beneficial effects, such as increased mouthfeel,
aroma retention, and astringency reduction (24). Furthermore,
wine clarification by protein haze removal (17). Another re-
pressed gene product, Flo1p, is crucial for flocculation, a process
proteins could be a novel mechanism of how oxygen can affect
wine quality, besides its known oxidative effect on phenolic com-
which is beneficial since acetic acid is a common “off-flavor” in
wine. The viability and stress resistance of the wine yeast might
also increase, as the specific ergosterol content, a protective com-
pound against stresses in wine fermentation (31), increases to its
maximum (M. Orellana, F. F. Aceituno, and E. Agosin, unpub-
lished data). Moreover, nitrogen-deficient musts can be more ef-
ficiently utilized in winemaking, since the proline carrier (PUT4)
?M oxygen (Table 2), in turn increasing biomass synthesis. Fur-
ther research will be aimed at finding a tradeoff between oxygen
addition and limitation under winemaking conditions.
Aceituno et al.
aem.asm.orgApplied and Environmental Microbiology
In conclusion, we found that in a nitrogen-limited setting, ox-
ygen exerted a large metabolic effect on yeast mitochondria, and
there is a threshold that separates fermentative and respirofer-
mentative metabolisms. This is related to the expressions of some
key genes, such as COX, NDE1, GUT2, and FRD1. Changes in the
expression levels of these genes could explain most of the flux
changes estimated in relation to respiration, cytosolic NADH
shuttling to the mitochondria, and two-branch cycle operation.
Furthermore, gene induction casts doubts on the operation of
glucose catabolic repression under nitrogen-limited conditions,
since it can be overridden by oxygen. Other genes affected by
oxygen were mannoprotein-encoding genes, which were re-
pressed as part of the global remodeling of the cell wall. This re-
pression could have negative consequences in winemaking, high-
lighting the dual role of oxygen in “making or breaking wines.”
CONICYT, Chile, to E.A.; doctoral thesis support grant AT-24100170
from CONICYT, Chile, to F.F.A.; and an ICM (Iniciativa Científica
Milenio, Chile) grant (no. P09-016-F) to F.M. We are grateful to Lalle-
mand, Inc. (Canada), for financial support and Indura S.A. (Chile) for
providing gas mixtures. F.F.A., M.O., and A.W.S. were supported by
CONICYT and VRI-UC doctoral fellowships.
nical support; Leonardo I. Almonacid (Molecular Bioinformatics Labo-
ratory, Millennium Institute on Immunology and Immunotherapy) for
ogy, Faculty of Biological Sciences, Pontificia Universidad Católica de
Chile) for the use of microarray facilities and technical support.
1. Affymetrix. 2009. Gene Chip expression analysis technical manual. Af-
fymetrix, Santa Clara, CA.
3. Alberty RA. 2006. Biochemical thermodynamics: applications of math-
ematica. Methods Biochem. Anal. 48:1–458.
4. Athenstaedt K, Daum G. 1997. Biosynthesis of phosphatidic acid in lipid
particles and endoplasmic reticulum of Saccharomyces cerevisiae. J. Bac-
5. Bakker BM, et al. 2001. Stoichiometry and compartmentation of NADH
6. Bluthgen N, et al. 2005. Biological profiling of gene groups utilizing Gene
Ontology. Genome Inform. 16:106–115.
7. Boubekeur S, Camougrand N, Bunoust O, Rigoulet M, Guerin B. 2001.
Participation of acetaldehyde dehydrogenases in ethanol and pyruvate
metabolism of the yeast Saccharomyces cerevisiae. Eur. J. Biochem. 268:
8. Breitling R, Armengaud P, Amtmann A, Herzyk P. 2004. Rank products:
in replicated microarray experiments. FEBS Lett. 573:83–92.
9. Butcher RA, Schreiber SL. 2004. Identification of Ald6p as the target of a
class of small-molecule suppressors of FK506 and their use in network
dissection. Proc. Natl. Acad. Sci. U. S. A. 101:7868–7873.
10. Camarasa C, Grivet JP, Dequin S. 2003. Investigation by 13C-NMR and
tricarboxylic acid (TCA) deletion mutant analysis of pathways for succi-
nate formation in Saccharomyces cerevisiae during anaerobic fermenta-
tion. Microbiology 149:2669–2678.
11. Carbon S, et al. 2009. AmiGO: online access to ontology and annotation
data. Bioinformatics 25:288–289.
12. Celton M, Goelzer A, Camarasa C, Fromion V, Dequin S. 2012. A
constraint-based model analysis of the metabolic consequences of in-
creased NADPH oxidation in Saccharomyces cerevisiae. Metab. Eng. 14:
13. Christie KR, et al. 2004. Saccharomyces Genome Database (SGD) pro-
vides tools to identify and analyze sequences from Saccharomyces cerevi-
siae and related sequences from other organisms. Nucleic Acids Res. 32:
14. Conesa A, et al. 2005. Blast2GO: a universal tool for annotation, visual-
ization and analysis in functional genomics research. Bioinformatics 21:
15. Davies BSJ, Rine J. 2006. A role for sterol levels in oxygen sensing in
Saccharomyces cerevisiae. Genetics 174:191–201.
16. Doran PM. 1995. Bioprocess engineering principles. Academic Press,
London, United Kingdom.
17. Dupin IVS, et al. 2000. Saccharomyces cerevisiae mannoproteins that
protect wine from protein haze: evaluation of extraction methods and
immunolocalization. J. Agric. Food Chem. 48:1086–1095.
18. Ehsani M, Fernandez MR, Biosca JA, Julien A, Dequin S. 2009. Engi-
neering of 2,3-butanediol dehydrogenase to reduce acetoin formation by
viron. Microbiol. 75:3196–3205.
19. Eide DJ. 1998. The molecular biology of metal ion transport in Saccharo-
myces cerevisiae. Annu. Rev. Nutr. 18:441–469.
20. Frick O, Wittmann C. 2005. Characterization of the metabolic shift
between oxidative and fermentative growth in Saccharomyces cerevisiae
by comparative C-13 flux analysis. Microb. Cell Fact. 4:30. doi:10.1186/
21. Gancedo JM. 1998. Yeast carbon catabolite repression. Microbiol. Mol.
Biol. Rev. 62:334–361.
22. Gentleman RC, et al. 2004. Bioconductor: open software development
for computational biology and bioinformatics. Genome Biol. 5:R80. doi:
23. Georis I, Feller A, Vierendeels F, Dubois E. 2009. The yeast GATA factor
Gat1 occupies a central position in nitrogen catabolite repression-
sensitive gene activation. Mol. Cell. Biol. 29:3803–3815.
24. Gonzalez-Ramos D, Cebollero E, Gonzalez R. 2008. A recombinant
Saccharomyces cerevisiae strain overproducing mannoproteins stabilizes
wine against protein haze. Appl. Environ. Microbiol. 74:5533–5540.
25. Govender P, Kroppenstedt S, Bauer FF. 2011. Novel wine-mediated
FLO11 flocculation phenotype of commercial Saccharomyces cerevisiae
wine yeast strains with modified FLO gene expression. FEMS Microbiol.
26. Grabowska D, Chelstowska A. 2003. The ALD6 gene product is indis-
dehydrogenase activity. J. Biol. Chem. 278:13984–13988.
27. Hazelwood LA, Daran JM, van Maris AJ, Pronk JT, Dickinson JR. 2008.
Saccharomyces cerevisiae metabolism. Appl. Environ. Microbiol. 74:
28. Hoskisson PA, Hobbs G. 2005. Continuous culture—making a come-
back? Microbiology 151:3153–3159.
29. Ingledew WM, Magnus CA, Sosulski FW. 1987. Influence of oxygen on
proline utilization during the wine fermentation. Am. J. Enol. Vitic. 38:
generation of Saccharomyces cerevisiae CEN.PK113-1A. BMC Syst. Biol.
31. Landolfo S, et al. 2010. Oleic acid and ergosterol supplementation miti-
gates oxidative stress in wine strains of Saccharomyces cerevisiae. Int. J.
Food Microbiol. 141:229–235.
32. Larsson C, Vonstockar U, Marison I, Gustafsson L. 1993. Growth and
bon-limiting, nitrogen-limiting, or carbon-limiting and nitrogen-
limiting conditions. J. Bacteriol. 175:4809–4816.
phylogenetic tree display and annotation. Bioinformatics 23:127–128.
cerevisiae in response to various stresses. Biochem. Biophys. Res. Com-
35. Liao JC, et al. 2003. Network component analysis: reconstruction of
regulatory signals in biological systems. Proc. Natl. Acad. Sci. U. S. A.
36. Liu H, Sanuda-Pena MC, Harvey-White JD, Kalra S, Cohen SA. 1998.
Determination of submicromolar concentrations of neurotransmitter
amino acids by fluorescence detection using a modification of the 6-ami-
noquinolyl-N-hydroxysuccinimidyl carbamate method for amino acid
analysis. J. Chromatogr. A 828:383–395.
Impact of Oxygen on Wine Yeast Physiology
December 2012 Volume 78 Number 23aem.asm.org 8351
37. Mateles RI, Battat E. 1974. Continuous culture used for media optimi-
zation. Appl. Microbiol. 28:901–905.
38. Murray DB, Haynes K, Tomita M. 2011. Redox regulation in respiring
Saccharomyces cerevisiae. Biochim. Biophys. Acta 1810:945–958.
39. Nielsen J, Villadsen J, Liden G. 2003. Bioreaction engineering principles,
2nd ed. Kluwer Academic/Plenum Publisher, New York, NY.
40. Nissen TL, Schulze U, Nielsen J, Villadsen J. 1997. Flux distributions in
anaerobic, glucose-limited continuous cultures of Saccharomyces cerevi-
siae. Microbiology 143(Pt 1):203–218.
41. Pahlman IL, Gustafsson L, Rigoulet M, Larsson C. 2001. Cytosolic redox
metabolism in aerobic chemostat cultures of Saccharomyces cerevisiae.
42. Palmieri L, Runswick MJ, Fiermonte G, Walker JE, Palmieri F. 2000.
tion and metabolic significance. J. Bioenerg. Biomembr. 32:67–77.
43. Pearson RK, Zylkin T, Schwaber JS, Gonye GE. 2004. Quantitative
evaluation of clustering results using computational negative controls, p
188–199. In Berry MW, Umeshwar D, Kamath C, Skilliconr D (ed), Pro-
ceedings of the Fourth SIAM International Conference on Data Mining.
Society for Industrial and Applied Mathematics, Lake Buena Vista, FL.
44. Pizarro F, et al. 2007. Coupling kinetic expressions and metabolic net-
works for predicting wine fermentations. Biotechnol. Bioeng. 98:986–
45. Pizarro FJ, Jewett MC, Nielsen J, Agosin E. 2008. Growth temperature
exerts differential physiological and transcriptional responses in labora-
tory and wine strains of Saccharomyces cerevisiae. Appl. Environ. Micro-
microorganisms. Microbiology (Russia) 78:535–546.
47. Pronk JT, Steensma HY, vanDijken JP. 1996. Pyruvate metabolism in
Saccharomyces cerevisiae. Yeast 12:1607–1633.
48. Pujol-Carrion N, Belli G, Herrero E, Nogues A, de la Torre-Ruiz MA.
2006. Glutaredoxins Grx3 and Grx4 regulate nuclear localisation of Aft1
and the oxidative stress response in Saccharomyces cerevisiae. J. Cell Sci.
48a.R Development Core Team. 2012. R: a language and environment for
statistical computing. The R Foundation for Statistical Computing,
49. Rigoulet M, et al. 2004. Organization and regulation of the cytosolic
NADH metabolism in the yeast Saccharomyces cerevisiae. Mol. Cell.
metabolism. OMICS 15:461–476.
51. Rosenfeld E, Beauvoit B. 2003. Role of the non-respiratory pathways in
the utilization of molecular oxygen by Saccharomyces cerevisiae. Yeast
52. Rosenfeld E, Beauvoit B, Blondin B, Salmon JM. 2003. Oxygen con-
tions: effect on fermentation kinetics. Appl. Environ. Microbiol. 69:113–
53. Rosenfeld E, Beauvoit B, Rigoulet M, Salmon JM. 2002. Non-respiratory
oxygen consumption pathways in anaerobically-grown Saccharomyces
cerevisiae: evidence and partial characterization. Yeast 19:1299–1321.
54. Saa PA, Moenne MI, Perez-Correa JR, Agosin E. 2012. Modeling oxygen
dissolution and biological uptake during pulse oxygen additions in oeno-
logical fermentations. Bioprocess Biosyst. Eng. 35:1167–1178.
55. Sablayrolles JM, Julien A, Roustan JL, Dulau L. 2000. Comparison of
nitrogen and oxygen demands of enological yeasts: technological conse-
quences. Am. J. Enol. Vitic. 51:215–222.
56. Salmon JM, Barre P. 1998. Improvement of nitrogen assimilation and
fermentation kinetics under enological conditions by derepression of al-
ternative nitrogen-assimilatory pathways in an industrial Saccharomyces
cerevisiae strain. Appl. Environ. Microbiol. 64:3831–3837.
57. Shakoury-Elizeh M, et al. 2004. Transcriptional remodeling in response
to iron deprivation in Saccharomyces cerevisiae. Mol. Biol. Cell 15:1233–
58. Sokal RR, Rohlf FJ. 1962. The comparison of dendrograms by objective
methods. Taxon 11:33–40.
59. Stephanopoulos G, Aristidou A, Nielsen J. 1998. Metabolic engineering:
principles and methodologies. Academic Press, San Diego, CA.
cultures. Combinatorial effects of oxygen availability and macronutrient
limitation in Saccharomyces cerevisiae. J. Biol. Chem. 280:437–447.
61. Reference deleted.
62. Valero E, Millan C, Ortega JM. 2001. Influence of oxygen addition
during growth phase on the biosynthesis of lipids in Saccharomyces
cerevisiae (M330-9) in enological fermentations. J. Biosci. Bioeng. 92:
63. Varela C, Pizarro F, Agosin E. 2004. Biomass content governs fermen-
tation rate in nitrogen-deficient wine musts. Appl. Environ. Microbiol.
64. Vargas FA, Aceituno FF, Agosin E. 2010. Biochemistry and molecular
Comprehensive food fermentation and biotechnology, vol 2. Asiatech
Publishers, New Delhi, India.
65. Vemuri GN, Eiteman MA, McEwen JE, Olsson L, Nielsen J. 2007.
Increasing NADH oxidation reduces overflow metabolism in Saccharo-
myces cerevisiae. Proc. Natl. Acad. Sci. U. S. A. 104:2402–2407.
66. Walker GM. 1998. Yeast physiology and biotechnology. John Wiley &
Sons, West Sussex, United Kingdom.
to the identification of gross measurement errors. Biotechnol. Bioeng.
68. Waterhouse AL, Laurie VF. 2006. Oxidation of wine phenolics: a critical
evaluation and hypotheses. Am. J. Enol. Vitic. 57:306–313.
69. Zhuang K, Vemuri GN, Mahadevan R. 2011. Economics of membrane
occupancy and respiro-fermentation. Mol. Syst. Biol. 7:500. doi:10.1038/
Aceituno et al.
aem.asm.orgApplied and Environmental Microbiology