Transcription profiling of sparkling wine second fermentation

Vanessa Penachoa, Eva Valerob and Ramon Gonzaleza*

a Instituto de Ciencias de la Vid y del Vino (CSIC-UR-CAR), 26006 Logroño, La

Rioja, Spain .

b Universidad Pablo de Olavide, Departamento de Biología Molecular e

Ingeniería Bioquímica, 41013 Sevilla, Spain.

*Corresponding autor:

Instituto de Ciencias de la Vid y del Vino (CSIC-UR-CAR)

C. Madre de Dios, 51

26006 Logroño, La Rioja

Spain

Phone: +34 941 299684

Fax: +34 941 299608

e-mail: rgonzalez@icvv.es

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Abstract

There is a specific set of stress factors that yeast cells must overcome under

second fermentation conditions, during the production of sparkling wines by the

traditional (Champegnoise) method. Some of them are the same as those of the

primary fermentation of still wines, although perhaps with a different intensity

(high ethanol concentration, low pH, nitrogen starvation) while others are more

specific to second fermentation (low temperature, CO2 overpressure). The

transcription profile of Saccharomyces cerevisiae during primary wine

fermentation has been studied by several research groups, but this is the first

report on yeast transcriptome under second fermentation conditions. Our results

indicate that the main pathways affected by these particular conditions are

related to aerobic respiration, but genes related to vacuolar and peroxisomal

functions were also highlighted in this study. A parallelism between the

transcription profile of wine yeast during primary and second fermentation is

appreciated, with ethanol appearing as the main factor driving gene

transcription during second fermentation. Low temperature seems to also

influence yeast transcription profile under these particular winemaking

conditions.

Keywords: Saccharomyces cerevisiae; sparkling wine; transcriptome; ethanol; low-

temperature; stress response

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1. Introduction

The production of sparkling wines by the traditional method involves two major

and differentiated fermentative steps. The primary fermentation converts grape

juice into a base wine, similar to most high quality white wines. The second

fermentation is induced by the addition of yeast, sucrose, and a small amount of

bentonite. It is followed by a prolonged period of aging in contact with yeast

lees. Second fermentation and aging take place in closed bottles at low

temperatures (12-16 ºC). The whole process is known by the French term "prise

de mousse". At the end of the aging period yeast lees are removed from the

bottles by disgorging. Dosage is added in order to compensate for wine loss

during disgorging as well as to give each wine its own distinctive finish

(Carrascosa et al., 2011).

Yeast contributes to the properties of sparkling wines in two ways during the

"prise de mousse" process. Initially, second fermentation results in increased

ethanol content and carbon dioxide overpressure. Afterwards, during wine

aging, yeast cells release mannoproteins and other molecules, including

precursors of aroma compounds, through autolysis and other mechanisms.

Most of these compounds contribute positively to the aroma, taste and foaming

properties of sparkling wines (Charpentier and Feuillat, 1993; Martinez-

Rodriguez et al., 2001; Troton et al., 1989).

Under second fermentation conditions yeast growth and metabolism are

affected by several stress factors, either common to all winemaking styles or

specific to second fermentation. This includes high ethanol content of the base

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wine, nitrogen starvation, low pH, or low growth temperature. In spite of the

procedures implemented by winemakers to adapt yeast to the high ethanol

content of the base wine, there is always a lag phase after inoculation, which is

followed by a short cell proliferation step. The second fermentation step usually

lasts for 15-20 days. Afterwards cell viability decays quickly, and almost no

viable cells are detected after 60 days.

Adaptation of yeast to alcoholic fermentation of wine must has been studied by

several approaches, including transcription profiles during the fermentation of

synthetic or grape must by wine yeast strains (Beltran et al., 2004; Marks et al.,

2008; Rossignol et al., 2003; Varela et al., 2005; Zuzuarregui et al., 2006); the

effect of winemaking related stress factors on gene expression for either

laboratory strains (Alexandre et al., 2001; Tai et al., 2007) or wine yeast strains

(Rossignol et al., 2006); as well as the phenotypic characterization of genome

scale yeast knock-out deletion collections concerning survival to ethanol stress

(Fujita et al., 2006; Teixeira et al., 2009). Among other findings, these studies

have revealed or confirmed the impact of increasing ethanol content and

nitrogen starvation on yeast survival and physiology, or the requirement for

mitochondrial function and oxidative stress response, as well as vacuolar and

peroxisomal functions, to cope with industrial fermentation conditions. A

fermentation stress response (FSR), characterized by the induction of a set of

223 genes under fermentation conditions, was recently defined (Marks et al.,

2008).

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In order to better understand the main factors that influence yeast physiology

during second fermentation we have performed a genome-wide expression

analysis of an industrial wine yeast strain under real second fermentation

conditions, and tried to identify the main factors affecting gene transcription

under these conditions, as well as similarities and differences with

transcriptional responses under standard winemaking conditions.

2. Materials and Methods

2.1. Strains

The Saccharomyces cerevisiae industrial wine strain employed in this study

was EC1118, provided by Lallemand Inc. (Montreal, Canada).

2.2. Media

A standardized commercial “cava” base wine was used for the second

fermentation experiments (Cavas Freixenet, Sant Sadurní D’Anoia, Spain). It is

a balanced blend of Macabeo, Parellada and Xarel·lo varietal wines. A chemical

analysis report on this base wine is shown in supplementary table S1.

Adaptation medium contained 0.08 g/L diammonium phosphate, 2.4 g/L tartaric

acid, 20 g/L sucrose, and 482 mL/L base wine. Pre-cultures were grown on

YPD: 1% yeast extract, 2% peptone, and 2% glucose; 2% agar was added for

YPD plates.

2.3. Second fermentation experiments

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Second fermentation assays were performed as described by Cebollero and

Gonzalez (2006). Pre-cultures were grown on YPD at 28 ºC and 150 rpm for 48

h. Yeasts were then inoculated into adaptation medium at 106 cells/mL and

incubated for 4 to 5 days at 28 ºC and 150 rpm. Adapted cells were inoculated

at 106 cells/mL into base wine containing 20g/L sucrose, and bottled in

commercial sparkling wine bottles. Bottles (about 60 per batch) were incubated

at 16 ºC for about 40 days. Three different batches were prepared at different

times of the year in order to obtain three independent biological replicates.

Every 2-4 days three bottles were opened for ethanol quantification. Cells were

washed once with water and frozen at -80 ºC. Cells from three time points

(SP1-3) were selected for transcription studies, as described below. A sample

corresponding to exponential growth phase (OD600 about 0.2) under unstressed

conditions (YPD at 28 ºC) was prepared as external reference (SP0).

2.4. RNA isolation

Total RNA was extracted from 1x109 yeast cells using Trizol® (Gibco BRL,

Invitrogen, Life Technologies, Carlsbad, CA, USA) as described by

Chomczynski and Sacchi (1987). Total RNA was subsequently purified using

the RNeasy Mini Kit-RNA Cleanup from Qiagen (Hilden, Germany) according to

the manufacturer's instructions. Concentration of total RNA was measured at

260 nm in a ND-1000 Spectrophotometer (Nanodrop, Thermo Fisher Scientific

Inc, Wilmington, DE, USA) and sample quality was checked using RNA Nano

Labchips in a Bioanalyzer 6000 (Agilent Technologies, Santa Clara, CA, USA).

{Cebollero, 2006 #48}

2.5. Microarray analysis of differential gene expression

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Three independent RNA preparations were obtained; one from each biological

replicate, for each of the four sample points (SP0-3). Double stranded cDNA

was synthesized from 5 μg of total RNA using “Onecycle cDNA Synthesis Kit”

(Affymetrix, Santa Clara, CA, USA). After cDNA purification using the

“GeneChip Sample Cleanup Module” (Affymetrix), this DNA was used as

template for the in vitro transcription using the “3' IVT Express Labeling Kit”

(Affymetrix) to obtain the biotin labelled cRNA. Its size profile was evaluated

with Bioanalyzer 6000 (Agilent Technologies). Fifteen µg of cRNA were

fragmented and hybridized to the Affymetrix GeneChip® Yeast Genome 2.0.

Array in “GeneChip® Hybridization Oven 640” for 16 h at 45 °C. Hybridized

microarrays were washed and stained with a streptavidin-phycoerythrin

conjugate (SAPE) in a “GeneChip® Fluidics Station 450”. All these procedures

were carried out as suggested by the manufacturer. Hybridized cRNA was

finally identified by the fluorescence signal in a “GeneChip® 3000” scanner. The

.CEL files generated from the scanning were converted to gene expression

signals using the RMA (Robust Microarray Analysis) software (Bolstad et al.,

2003; Irizarry et al., 2003a, 2003b ). Normalized results from this algorithm gave

rise to absolute expression values in logarithmic scale (Log2). Subsequently,

differentially expressed genes were identified trough SAM algorithm

(Significance Analysis of Microarrays) between microarrays from the 4-sample

point studied: SP0-3. All p-values were calculated using Student's t-test and

adjusted by FDR (False Discovery Rate) (q-values or FDR adjusted p-values)

(Benjamini et al., 1995).

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Pairwise comparisons, involving the three sample points taken during the

second fermentation process (SP1, SP2 and SP3) as well as the reference

sample point (SP0) resulted in a total of six comparisons. The signal log2 ratio

for each gene was obtained as the mean of the signal log2 ratio in the

replicates. Genes with a FDR adjusted p-value < 0.1, and fold-change > 2 or <

0.5 in each comparison, were considered as statistically significant and

differentially expressed genes. An ontological analysis of these genes was

performed using GENECODIS software (Gene Annotation Co-ocurrence

Discovery; http://genecodis.dacya.ucm.es) (Carmona-Saez et al., 2007;

Nogales-Cadenas et al., 2009) in order to integrate biological process according

to GENE ONTOLOGY terms (GOGO; http://www.geneontology.org/)

(Ashburner et al., 2000) and pathway maps according to KEGG (Kyoto

Encyclopedia of Genes and Genomes; http://www.genome.jp/kegg/) (Kanehisa

y Goto, 2000; Kanehisa et al., 2006). An hypergeometric distribution was

employed to determinate p-value of each annotation; FDR method was used to

adjust p-values (statistically significant clasification: FDR adjusted p-value <

0.05). Clustering and visualization of the data, and PCA (Principal Component

Analysis) were performed using MeV software (TIGR) (Saeed et al., 2003).

Overlapping probability between gene sets from transcriptomic comparisons

were calculated using Fisher's exact test, package integrated in R software

(http://www.r-project.org) (Ihaka y Gentleman, 1996). The data discussed in

this publication have been deposited in NCBI's Gene Expression Omnibus

(Edgar et al., 2002) and are accessible through GEO Series accession number

GSE29273 (http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE29273).

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2.6. qRT-PCR assays

First-strand cDNAs were synthesized from 1 µg of total RNA in 20 µl final

volume, using the High Capacity cDNA Archive kit (Applied Biosystems, Foster

City, CA, USA) following the recommendations of the manufacturer. As control

for genomic contamination, the same reactions were performed in the absence

of reverse transcriptase. Real-time qPCR assays were carried out in an ABI

7900HT Fast Real-Time PCR instrument (Applied Biosystems) using standard

PCR conditions. Probes and primers were designed at the ‘Universal

ProbeLibrary Assay Design Center’ (Roche Applied Sciences) using gene

sequences obtained from SGD (http://www.yeastgenome.org/). Quantification of

ATG8, COX4, MCR1, SDH4, NUP85, RPL18B and RDN18-1 (18S rRNA)

transcripts was performed using respectively probes #18, #159, #11, #86, #57,

#21, #29, and #40, from the Universal Probe Library and the ‘TaqMan Universal

PCR Master Mix No AmpErase UNG’ kit (Applied Biosystems). Primer

sequences will be provided upon request. All quantifications were performed in

triplicate. The expression of each gene was estimated by the "Comparative Ct

Method" with respect to SP0 and with 18S ribosomal RNA as reference.

3. Results and Discussion

Second fermentation assays were carried out as described in Materials and

Methods. The evolution of ethanol content and cell numbers is shown in Figure

1. Samples from days 7, 15 and 19 (sample points SP1, SP2 and SP3),

representing respectively the end of the lag phase, the maximum ethanol

production rate and the first obvious inflexion point after maximal fermentation

rate, were selected for transcription analysis. Comparisons involving SP1, SP2

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and SP3 as well as reference sample point (SP0), were performed as described

in Materials and Methods, in order to identify differential steady state mRNA

levels that would be indicative of the transcriptional response of yeast cells to

industrial second fermentation conditions. For each comparison, only genes

showing a differential expression with p-value < 0.05, FDR adjusted-p-value <

0.1, and fold expression changes above 2.0 or below 0.5 were considered for

further data analysis. During the time course of second fermentation,

expression of 348 genes was simultaneously up-regulated in sample points

SP1-3, while 125 genes were down-regulated in all three sampling points.

There was high similarity in gene expression profiles in the three sample points

during second fermentation, confirmed by pairwise comparison of gene

expression between them, as well as clustering and PCA analyses (data not

shown).

We have analyzed the biological annotations that are significantly associated

with genes in the lists of genes that have been up-regulated or down-regulated

in all sample points during second fermentation by using the GENECODIS tool.

Transcripts encoding products involved in aerobic respiration, autophagy,

peroxisomal and vacuolar function were significantly overrepresented

throughout second fermentation (Table 1), while transcripts encoding products

involved in protein synthesis and cell growth were underrepresented (Table 2).

This is discussed in more detail in the following sections.

3.1. Effect of ethanol

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Several categories in Table 1 are clearly related to aerobic respiration in S.

cerevisiae, including GO biological processes such as "mitochondrial electron

transport", "oxidation-reduction process", "aerobic respiration", and "cellular

response to oxidative stress"; or the KEGG pathways "oxidative

phosphorylation" and "TCA cycle". At first sight this was surprising, since cells in

the SP0 sample were growing in the presence of oxygen, while those in

samples SP1-3 came from closed bottles, almost full of wine; and depending on

the time point, saturated with CO2. Some representative genes were chosen in

order to verify, by quantitative real-time PCR, the induction during second

fermentation of genes related to aerobic respiration. These were COX4, coding

for a subunit of cytochrome c oxidase, part of the mitochondrial inner membrane

electron transport chain; MCR1, coding for mitochondrial NADH-cytochrome b5

reductase, involved in ergosterol biosynthesis; and SDH4, coding for a subunit

of succinate dehydrogenase, involved in the TCA cycle and the mitochondrial

respiratory chain. All of them showed induction patterns similar to those seen by

microarray analysis (Figure 2). Also Rossignol et al. (2003) found indications of

response to oxidative stress in a time-course transcriptomic analysis of the

fermentation of a synthetic must, based on the expression of AAD genes,

encoding putative aryl-alcohol dehydrogenases (Delneri et al.,1999; Dickinson

et al., 2003). Several AAD genes showed increased expression troughout

second fermentation (Supplementary tables S2-S4). Also, among other stress

factors, Zuzuarregui and del Olmo (2004) found a positive correlation between

resistance to oxidative stress in several wine yeast strains and their

fermentation performance. High expression levels of genes coding for

mitochondrial proteins and respiration functions seem to be characteristic of S.

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cerevisiae in other industrial fermentation processes, like brewing (James et al.,

2003).

The ethanol content of the base wine is probably the main environmental factor

responsible for the transcription pattern observed under second fermentation

conditions. There is growing evidence for the involvement of genes related to

mitochondrial function and oxidative metabolism in ethanol stress tolerance.

Tolerance to ethanol had already been related to the properties of the

mitochondrial genome by Jimenez and Benitez (1988). Accordingly, Teixeira et

al. (2009) found 30 genes encoding for mitochondrial proteins as determinants

of resistance to high concentrations of ethanol. Those genes were mainly

involved in mitochondrial protein synthesis, respiration and maintenance of the

mitochondrial genome. In a recent review, Stanley et al. (2010a) analyzed

results from several genome-wide studies on yeast adaptation to ethanol stress.

They also identified mitochondrial function as a common motif involved in

ethanol stress response and tolerance by S. cerevisiae, with aerobic respiration

among the most relevant GO terms. The same researchers found evidence

relating differences in ethanol tolerance by different yeast strains with different

mitochondrial and NADH oxidation activities.

The KEGG pathway “peroxisome” is also enriched among genes up-regulated

during second fermentation. Similar to above, the main environmental factor

that would explain this expression pattern seems to be the ethanol content of

the base wine. The MIPS functional category “peroxisomal transport” was

identified in the study by Yoshikawa et al. (2009) as being relevant for ethanol

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tolerance; and Teixeira et al. (2009), with a similar approach, reached the same

conclusion concerning the peroxisome protein import machinery.

Overexpression of genes involved in peroxisomal function has also been

observed for wine yeast strains under winemaking conditions (see below).

The related categories “CVT pathway” and "autophagic vacuole assembly" are

also overrepresented in all three sample points. This would be in agreement

with our previous results, showing that autophagy takes place during second

fermentation of sparkling wines, even before sugar was exhausted (Cebollero et

al., 2005; Cebollero and Gonzalez, 2006). The expression pattern of ATG8 was

analyzed by quantitative real-time PCR, and this confirmed the expression

pattern found for ATG genes by microarray analysis (Figure 2). Once again, this

result would pinpoint ethanol as the main condition affecting transcriptional

adaptation to second fermentation conditions, since vacuolar function is a

recurrent motif arising from several studies of the adaptation of S. cerevisiae to

high ethanol media (see for example Teixeira et al., 2009; Yoshikawa et al.,

2009).

We also explored the relevance of the ethanol response for the transcription

profile of EC1118 under second fermentation conditions by comparing our

results with genes induced in S. cerevisiae in response to ethanol challenges

(Alexandre et al., 2001; Stanley et al. 2010b), or with genes relevant to survive

ethanol stress identified by genome-wide screenings of yeast deletion mutants

(Teixeira et al., 2009; Yoshikawa et al., 2009) . In all cases we found statistically

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significant coincidences (Fisher's exact test; p-value < 0.05) with two or all three

sample points during second fermentation (data not shown).

Enhanced expression of genes involved in biosynthesis of tryptophan has also

been related to ethanol stress tolerance (Hirasawa et al., 2007), and

"metabolism of tryptophan" appeared as the main category in a systematic

analysis of deletion strains for ethanol sensitivity (Yoshikawa et al., 2009).

However, we did not find a significant overexpression of TRP genes under

second fermentation conditions.

3.2. Effect of other environmental factors

Apart from the stress induced by ethanol, other environmental factors

characteristic of second fermentation might also be responsible for the

observed transcription profile. One prominent environmental constraint

characteristic of second fermentation is low temperature. Tai et al. (2007)

identified genes consistently up-regulated (91 genes) or down-regulated (48

genes) in data sets from three different studies of temperature shift from 30 ºC

to 4 ºC or 10 ºC, by Murata et al. (2006), Sahara et al. (2002) and Schade et al.

(2004). By comparing our data to these two gene sets we found a good

correspondence with the expression profile in SP1 and SP2 (Figure 3) with

respectively 32 and 37 of the induced genes included among the 91 genes

induced by cold according to Tai et al. (2007), and 8 and 6 of the repressed

genes among the 48 repressed by cold. The correspondence was not as good,

according to the Fisher's exact test (p-value < 0.05), for SP3 (Figure 3).

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Similar to Tai et al. (2007), we found little overlap between our data and those

by Beltran et al. (2006), who compared gene expression during grape must

fermentation at 25 ºC or 13 ºC, either focusing on the “cold sensitivity” cluster

identified by them, or considering comparison between sample points at both

temperatures, or the time course of gene expression at 13 ºC. Tai et al. (2007)

suggested that these differences would be due to must composition and

physicochemical properties, including pH, sugar composition and concentration,

or drift from aerobic to anaerobic conditions. Even though some properties of

the growth medium (pH or sugar composition) in second fermentation (base

wine) are close to those by Beltran et al. (2006), our experimental set up is

more closely related to those by the other authors, and would explain the

proximity of our results to theirs.

Most of the genes down-regulated during second fermentation are related to

cell growth, including general metabolic functions required for the biosynthesis

of nucleic acids and proteins, and gene expression (Table 2). The expression

pattern of NUP85 and RPL18B related to cell cycle and ribosomal biogenesis,

respectively, was analyzed by quantitative real-time PCR. Both genes were

repressed in the three sample points (Figure 2). The most obvious explanation

for down regulation of genes in these categories is growth rate, since it is

notably different between the control condition and any time point along the

second fermentation process. Reduced expression of genes involved in energy

demanding growth related processes also appeared as a common trait of

studies on ethanol tolerance (reviewed by Stanley et al., 2010a).

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Since nitrogen availability is poor in the base wine, nitrogen starvation would

also contribute to explain the expression pattern during second fermentation.

Backhus et al. (2001) showed increased expression of genes involved in

oxidative carbon metabolism for a wine yeast strain under low nitrogen

conditions. According to that, in addition to the effect of ethanol discussed

above, nitrogen starvation would somehow contribute to the observed induction

of genes related to aerobic respiration. Indeed, Mendes-Ferreira et al. (2010)

found increased ROS, Sod2p and Cat1p activities, following nitrogen limitation

under winemaking conditions. Up-regulation of autophagy, and specifically of

ATG8 (which is induced in all three sample points during second fermentation),

could also be related to nitrogen limitation, since nitrogen starvation was one of

the first conditions identified to induce autophagy (Takeshige et al., 1992); and

ATG8 overexpression was related to nitrogen limited alcoholic fermentation by

Mendes-Ferreira et al. (2010).

Finally, Dirmeier et al. (2002) found transient oxidative stress to be induced by a

shift of yeast cells to anoxia, as indicated by protein carbonylation, DNA

modification, and the expression level of SOD1. And Landolfo et al. (2008)

showed oxidative damage to different cell structures during the fermentation of

media containing 24% sugars under hypoxic conditions. However, this oxidative

stress response was apparently also related to ethanol accumulation, and

induced a response involving SOD activity, trehalose accumulation and protein

turnover. Overexpression of genes related to aerobic respiration and TCA cycle

was also found for S. cerevisiae cells growing under low oxygen levels (0.5% to

2.8%) as compared to fully aerobic or fully anaerobic conditions (Rintala et al.,

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2009). However, it is doubtful that equivalent oxygen levels were still present in

the sparkling wine bottles, especially for late sample points.

3.3. Comparison with transcription profiles during primary wine fermentation.

Our study comes after the publication of several others on transcriptome

dynamics during the fermentation of grape or synthetic musts, including time-

course experiments and/or comparison between different strains or

fermentation conditions. Rossignol et al. (2003) suggested a specific response

to ethanol as an important component of the transcriptional adaptation of wine

yeast to the fermentation of a synthetic must, which is also in agreement with

the increased expression of genes involved in aerobic respiration, peroxisomal

functions or autophagy found during second fermentation, as discussed above.

We have found a significant overlapping between genes expressed in all

sample points during second fermentation and those overexpressed in each

stage of the primary fermentation as shown by Rossignol et al. (2003) (Figure

4A). The similarity is not so striking in the case of repressed genes, but it is still

significant for stages 3 to 6 (Figure 4B). One indication of the relevance of this

observation, in addition to statistic analysis, is that the extent of overlapping is

similar to the one we found by comparing the FSR genes described for primary

wine fermentation and genes in the different stages of the Rossignol et al.

(2003) study (Figure 4C). On the other hand, we have compared genes induced

during second fermentation of sparkling wine with the 223 FSR genes from this

later study. About 38 % (84 genes) of the FSR genes were induced in at least

one sample point during second fermentation experiments, and 31 of them were

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induced in all sample points (Supplementary table S5). Induction of FSR genes

was statistically significant for all sample points. Based on their transcriptomic

results Marks et al. (2008) also pointed to ethanol, rather than nutrient

depletion, as the main factor leading to the entry of yeast cells into stationary

phase. The transcriptional response of EC1118 under second fermentation

conditions is thus similar to the one previously described for primary wine

fermentation (Rossignol et al. 2003) and consistent with the activation of a

fermentation stress response resembling that proposed by Marks et al. (2008).

3.4. Conclusions.

The transcription profile during the second fermentation of sparkling wines

described in this work shares many traits with the response to primary

fermentation conditions previously described (Marks et al., 2008; Rossignol et

al., 2003). Common themes arising from the comparison of time course

transcriptome analyses of must fermentation (Marks et al., 2008; Rossignol et

al., 2003) and the transcriptome profiling of second fermentation, include

expression of genes involved in respiratory metabolism, oxidative stress

response, autophagy, or peroxisomal function. This expression pattern is

consistent with ethanol being the main environmental factor influencing

transcriptional responses to winemaking conditions. Other traits of the

transcription pattern described in this work would be better explained in terms of

low fermentation temperature and reduced growth rate, and to a lesser extent

by low nitrogen availability. This transcription profile did not point out other

putative stress factors, like low pH or CO2 overpressure, as relevant constraints

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for the adaptation of wine yeast cells to sparkling wine production. Probably

specific responses to most other stress factors would have been masked by the

strong transcriptional response elicited by ethanol.

In addition to improving our understanding of yeast adaptation to second

fermentation conditions, this study would be a useful source of information for

the development of biotechnological tools specific to second fermentation wine

yeast strains (for example by guiding the choice of promoters for controlled

expression of transgenic constructs).

Acknowledgements

This work was supported by a fellowship from INIA for V. P. (grant 18-BOE208

31-08-2006) and the project RTA2005-00169-00-00 from the Spanish

Government. The authors are grateful to José M. Barcenilla (CIAL-CSIC) for

help on sparkling wine production; Jesús García-Cantalejo (Unidad de

Genómica-Parque Científico de Madrid) for microarray experiments, qRT-PCR

assays, and data analysis; Jérôme Grimplet (ICVV) for advice on bioinformatics.

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Figure legends

Figure 1. Evolution of ethanol content (open circles) and cell population (closed

circles) during the second fermentation of a base wine with EC1118.

Figure 2. Quantitative RT-PCR analysis of the expression of some

representative genes in the strain EC1118 during second fermentation

experiments. A) overexpressed genes. B) underexpressed genes. Expression

data are relative to 18S rRNA in SP0.

Figure 3. Overlapping between genes up- or down-regulated in EC1118 during

second fermentation experiments and genes involved in yeast transcriptional

adaptation to low temperature according to Tai et al. (2007).

Figure 4. A) Comparison between genes overexpressed throughout second

fermentation (SP1-3) and genes overexpressed in each stage of primary wine

fermentation. B) Comparison between genes underexpressed throughout

second fermentation (SP1-3) and genes underexpressed in each stage of

primary wine fermentation. C) Comparison between FSR-Fermentation Stress

Response genes as described by Marks et al. (2008) and genes overexpressed

in each stage of primary wine fermentation. Complete bars indicate the number

of genes induced or repressed in each stage of primary wine fermentation

according to Rossignol et al. (2003). Black bars indicate the number of

overlapping genes. Significant overlap is indicated by an asterisk (FDR adjusted

p-values < 0.05).

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