- Open Access
Identification of new Saccharomyces cerevisiae variants of the MET2 and SKP2 genes controlling the sulfur assimilation pathway and the production of undesirable sulfur compounds during alcoholic fermentation
© Noble et al.; licensee BioMed Central. 2015
- Received: 22 October 2014
- Accepted: 13 April 2015
- Published: 8 May 2015
Wine yeasts can produce undesirable sulfur compounds during alcoholic fermentation, such as SO2 and H2S, in variable amounts depending mostly on the yeast strain but also on the conditions. However, although sulfur metabolism has been widely studied, some of the genetic determinants of differences in sulfite and/or sulfide production between wine yeast strains remain to be identified. In this study, we used an integrated approach to decipher the genetic determinants of variation in the production of undesirable sulfur compounds.
We examined the kinetics of SO2 production by two parental strains, one high and one low sulfite producer. These strains displayed similar production profiles but only the high-sulfite producer strain continued to produce SO2 in the stationary phase. Transcriptomic analysis revealed that the low-sulfite producer strain overexpressed genes of the sulfur assimilation pathway, which is the mark of a lower flux through the pathway consistent with a lower intracellular concentration in cysteine. A QTL mapping strategy then enabled us to identify MET2 and SKP2 as the genes responsible for these phenotypic differences between strains and we identified new variants of these genes in the low-sulfite producer strain. MET2 influences the availability of a metabolic intermediate, O-acetylhomoserine, whereas SKP2 affects the activity of a key enzyme of the sulfur assimilation branch of the pathway, the APS kinase, encoded by MET14. Furthermore, these genes also affected the production of propanol and acetaldehyde. These pleiotropic effects are probably linked to the influence of these genes on interconnected pathways and to the chemical reactivity of sulfite with other metabolites.
This study provides new insight into the regulation of sulfur metabolism in wine yeasts and identifies variants of MET2 and SKP2 genes, that control the activity of both branches of the sulfur amino acid synthesis pathway and modulate sulfite/sulfide production and other related phenotypes. These results provide novel targets for the improvement of wine yeast strains.
- Wine yeast
The control of metabolite production by yeast during alcoholic fermentation is a key issue for various fermented beverages, especially for wines. Among the metabolites released by yeast, those derived from sulfur metabolism are particularly important because they strongly influence the organoleptic quality of fermented beverages. Sulfites (SO2) and sulfide (H2S) are important metabolites in yeast metabolism and enology. They are key intermediates of the sulfur assimilation pathway and are also excreted by yeast into media. The excessive production of H2S can lead to off-flavors [1,2] and a high concentration of sulfites can delay the onset of malolactic fermentation by inhibiting lactic acid bacteria [3,4] and is also a source of health concerns. Indeed, given their toxicity, the final concentration of sulfites in wine is regulated by law. Therefore, the production of these compounds by yeast has to be tightly controlled at all steps of the fermentation process. The production of sulfites and sulfide by wine yeasts are highly strain-dependent, and despite strong selective processes some commercial yeast still produce high amounts of these sulfur compounds in some circumstances. The genetic basis of this variation between strains is unclear, although it has been proposed that sulfite reductase plays an important role in the production of H2S [5,6]. The identification of genes involved in such variations between strains will enable the optimization of the fermentation process and the construction of strains that produce low amounts of negative sulfur metabolites through breeding strategies.
Sulfur metabolism in wine yeasts has been widely studied and the pathways involved in sulfate assimilation and in the synthesis of sulfur-containing amino acids are well known . The entire pathway is highly regulated and coordinated by several control mechanisms in response to the intracellular concentration of cysteine. These mechanisms notably involve the transcription of genes of the sulfur assimilation pathway, which are regulated by the binding of the transcription factor MET4 to their promoter and its association with auxiliary factors, Met28p, Cbf1p, Met31p and Met32p [8-11]. MET4 is controlled through an inhibitory mechanism mediated by MET30 , which encodes an F-box protein that is part of an ubiquitin-proteasome complex [13,14]. This complex targets Met4p for degradation by the proteasome depending on the intracellular concentration of cysteine . Furthermore, Natarjan et al.  showed that several genes of sulfur metabolism are also controlled by GCN4, which regulates the transcriptional activity of genes involved in amino acid synthesis either directly (MET16 and MET25) or indirectly (SUL1, SUL2, MET3, MET14, MET10, MET1, MET25, MET6, MET2, MET28 and MET4). In addition, Yoshida et al.  identified a new mechanism involving the F-box protein skp2p, which forms part of a complex, SCF SKP2 , which controls the stability of Met14p, and regulates the transcription of the STR1, 2 and 4 genes.
The production of sulfites and sulfide depends on environmental factors including the concentration of nutrients in the media, and in particular that of nitrogen-containing compounds (ammonium, amino acids and especially sulfur-containing amino acids). Nitrogen concentration affects differently the production of SO2 and H2S: SO2 production is favored in the presence of high nitrogen concentrations , whereas H2S production is favored in nitrogen-deficient musts [19-21]. Supplementation with amino acids and/or ammonium can significantly affect SO2 and H2S production depending on the amount of added compound and the time of addition [19,20,22]. SO2 and H2S production is also affected by the concentration of sulfates and vitamins, such as pantothenate, and by pH and probably several other factors [23-26]. However, the largest source of variation in the production of sulfur compounds is the yeast strain itself. Wine yeasts produce sulfites at concentrations ranging from less than 10 mg/L to more than 100 mg/L . Similarly, sulfide production is undetectable for some strains whereas other strains produce high amounts of sulfide [27,28]. Several genes involved in sulfur metabolism have been implicated in the ability of strains to produce sulfite and/or sulfide, suggesting that this phenotypic property is controlled by multiple genetic loci. Several studies have examined the effect of the deletion or the overexpression of genes of the sulfur assimilation pathway [29-32]. Some studies have also focused on variants of genes of the sulfur assimilation pathway that affect hydrogen sulfide formation, and in particular on variants of sulfite reductase, to identify mutants showing defects in the conversion of sulfite into sulfide [5,33,34]. However, the molecular basis responsible for differences in the production of sulfur compounds, and in particular that of sulfite, between yeast strains is still not fully understood. In this study, we used a QTL mapping strategy to search for genes responsible for phenotypic variation in SO2 and H2S production between yeast strains. This genetic approach is now widely used to study continuous phenotypes and has been successfully applied to several wine yeast traits, including complex traits governed by several loci [35-38]. We focused on two wine yeast strains; a high sulfite-producing strain and a low sulfite-producing strain. We built and characterized a population of recombined meiotic segregants to perform linkage analysis. This analysis revealed a double QTL on chromosome XIV containing two genes involved in sulfur metabolism, MET2 and SKP2, which displayed allelic variations between the two strains. We show that these alleles modulate the production of sulfite, sulfide and acetaldehyde and we provide a new comprehensive view of the mechanisms responsible for variation in the production of sulfur compounds by wine yeasts.
Characterization of sulfite production during alcoholic fermentation
Thus, sulfite production was tightly associated with growth phase for the low sulfite-producing strain. However, the high producing strain, JN10, continued to produce sulfite during the beginning of the stationary phase (about 40 mg/L SO2 was produced during this phase). This observation, in addition to the high release of sulfites by this strain, is probably explained by an overflow of sulfites during growth and a lack of adjustment of the sulfur pathway in response to growth arrest, as occurs in the JN17 strain.
Production of SO 2 , H 2 S and acetaldehyde of by the parental strains
SO 2 (mg/l)
H 2 S (color of the H 2 S detection strip)
Comparative transcriptomic analysis of high and low sulfite-producing strains
We analyzed the transcriptome of the two yeast strains during the sulfite production phase, just after entry into the stationary phase, at the same stage of fermentation (36 g of CO2 released for both strains). This time point is the most representative of differences in sulfite production between strains, because it corresponds to the point at which sulfite production stopped in the JN17 strain but carried on in the JN10 strain. Moreover, a time point during the stationary phase was preferable to one during the transition between phases, because substantial transcriptomic alterations take place upon entry into the stationary phase . RNA was extracted from both cell populations, and was labeled and hybridized to microarrays as described in Material and Methods.
Genetic study of phenotypic variation in SO2, H2S, and acetaldehyde production
We used a QTL mapping strategy to identify the molecular basis of phenotypic differences in SO2, H2S and acetaldehyde production. Stable haploid derivatives from the JN10 and JN17 strains (JN10 ho:: KanMX mat a and JN17 ho:: KanMx mat α) were crossed to obtain a hybrid, H53-A5. We then obtained 60 spores from 26 asci with an average viability of 2.3 spores per ascus (only one complete tetrad was obtained) and we analyzed the phenotypic and genotypic characteristics of this population of meiotic segregants.
We then used comparative genome hybridization on high-density oligonucleotide microarrays (Affymetrix SG98) to identify molecular markers to distinguish the parental strains and we genotyped 28 randomly selected meiotic segregants from the population. These segregants originated from 16 different asci, containing one to four viable spores. From the genotyping data, we constructed a genetic map constituted of 1512 molecular markers, which were fairly uniformly distributed along the genome, although some regions were less covered than others, such as a region located in the middle of chromosome XVI (see Additional file 1 for a physical map of the molecular markers). The mean density of the markers was one marker every 8 kb. The genotype of the meiotic segregants was equally distributed between the two parental strains, with 49% of the markers from JN10 and 51% from JN17. We used the distribution of the markers among the 28 segregants to construct a recombination map (see Additional file 1), and we estimated that the mean number of recombination events was 212 per meiosis. This number is higher than that reported in the literature (approximately 80 events per meiosis) [43,44], possibly because the recombination capacity of our wine yeast strains is higher than that of other strains, and in particular, laboratory strains.
Location and characteristics of the QTL identified on the XIV chromosome
LOD score maximum
Significativity level of LOD score
Start position (pb)
End position (pb)
Sequencing and functional validation of the candidate genes
We sequenced both candidate genes and their promoter regions (from 370 and 384 pb upstream from the SKP2 and MET2 genes, respectively) in the two parental strains. Two SNPs were found in the SKP2 coding sequence: one at position 1,048 pb and another at 1,070 pb. Both SNPs are non-synonymous and alter the amino acid sequence of Skp2. The SNP at 1,048 pb is a C > T transversion leading to the replacement of isoleucine with valine at position 350 (I350V) in the JN17 strain, and that at position 1,070 pb is a G > A transversion, leading to the replacement of threonine with isoleucine (T357I) in the JN17 strain. The I357 residue seems to be specific to the JN17 strain because the amino acid at position 350 is threonine in sequenced genomes of S. cerevisiae available in databases and in other species of the Saccharomyces genus. However, the V350 residue, which is present in JN17, seems to be more common than the I350 and is thus probably the ancestral allele. We also found a SNP in the MET2 coding sequence at position 560 pb. This SNP leads to a C > G transversion resulting in the replacement of arginine with glycine at position 301 (R301G) in the JN17 strain. The G 301 residue corresponds to that of the S288C reference strain sequence, whereas the R 301 residue is present in several other S. cerevisiae strains and in other species of the Saccharomyces genus, and thus appears to be the ancestral allele.
We report here a physiological and genetic study of two wine yeast strains that differ substantially in their ability to produce sulfite. This analysis identifies new variants of the MET2 and SKP2 genes that influence the production of sulfite, sulfide, acetaldehyde and propanol under conditions of alcoholic fermentation.
The physiological analysis of these strains revealed large differences in their ability to produce SO2. Indeed, the parental JN10 strain produced at least five times more sulfite than the parental JN17 strain, and we also identified large differences between strains in the production of metabolites directly related to sulfites, including acetaldehyde and sulfide. Kinetic analysis of the fermentation process revealed that the high production of sulfite was related to a prolonged production phase, which persisted after the end of the growth phase whereas for the JN17 strain showed a tight relation between growth phase and sulfite production. Indeed, about 80% of total sulfite produced by the JN10 strain occurred during the stationary phase. The coordination between growth phase and sulfite production was confirmed under other conditions of temperature or nitrogen content (data not shown) and is consistent with previous findings . This coupling may be due to the activation of the sulfur pathway following the rapid depletion of sulfur-containing amino-acids as described by Rossignol et al. . A transcriptomic analysis just after the entry in stationary phase then revealed that components of the sulfur metabolism pathway were more strongly expressed in the low sulfite-producing strain than in the high sulfite-producing strain. This reflects the low availability of sulfur-containing amino acids in the low sulfite-producing strain, because the expression of this pathway is controlled by feedback. Nevertheless, this strain did not seem to be affected by a deprivation in sulfur-containing amino acids, because its fermentation capacity and cell growth were closed to that of the high sulfite-producing strain. We then used a QTL mapping strategy to identify genetic variants associated with phenotypic differences between strains. Analysis of the distribution of phenotypes in a population of meiotic segregants provided insight into the genetic determinism of sulfite production. This analysis suggested that a major locus confers a low sulfite-producing phenotype and is probably also involved in the control of related phenotypes, such as acetaldehyde, sulfide and propanol production. Linkage analysis of the meiotic segregants identified a double QTL located on chromosome XIV containing two relevant genes related to sulfur metabolism, SKP2 and MET2.
The SKP2 gene was previously identified by Yoshida et al., as involved in the control of the sulfur assimilation pathway . Indeed, Met14p, the adenylylsulfate kinase, responsible for the conversion of 5′-adenylylsulfate (APS) to 3′-5′-adenylylsulfate (PAPS), is more stable in an skp2-null mutant than in a wild type background. This finding may be explained by the implication of SKP2 in an SCF (Skp1 Cdc53 F-box protein) ubiquitin protease complex. The ubiquitin proteasome system regulates the abundance of many proteins involved in a wide variety of pathways. In this system, proteins are targeted for degradation by the binding of ubiquitin (Ub). Ub is first activated by a Ub-activating enzyme E1 and is then transferred to a Ub-conjugating enzyme, E2. Finally, the association of E2 with a ubiquitin ligase, E3, guides the transfer of Ub to the substrate. SCF complexes are a class of E3 ubiquitin ligases. The substrate specificity of SCF complexes is determined by the interchangeable F-box protein. SCFMET30 is a well-known SCF complex involved in the regulation of the sulfur assimilation pathway. A target of SCFMET30 is the transcription factor MET4, which regulates the expression of methionine biosynthetic genes [12,14]. Skp2p possesses an F-box domain, a degenerated motif of about 40 amino acids that enables its interaction with Skp1p . Skp2p also interacts with Met14p  and thus Met14p is probably one of the targets of the SCFSKP2 complex. The amino acid sequence of Skp2 differs at two positions between the two parental strains. These substitutions were not located in the F-box domain and therefore should not alter the interaction with Skp1p. However, they may potentially affect the efficiency substrate recognition. In the case of the JN17 strain, we hypothesize that Skp2 recognizes Met14p with high efficiency, thus promoting its degradation. The low stability of Met14p potentially limits the flux through the assimilatory part of the pathway, thus impairing the conversion of sulfate to sulfites. This probably leads to a low rate of synthesis of SO2 and H2S. Consistent with this hypothesis, the overexpression of MET14 promotes SO2 production and its conversion to H2S . The coding sequence of the MET2 gene differed at one position between the two parental strains. This substitution may affect the efficiency of homoserine trans-acetylase and probably leads to the synthesis of higher amounts of O-acetylhomoserine and a greater incorporation of H2S into carbon skeleton in the JN17 strain than in the JN10 strain.
The high production and release of sulfite into media by the JN10 strain may thus be explained by the following factors: (1) the high stability of Met14p leading to high flux through the reductive part of the sulfite assimilation pathway; and (2) the synthesis of inadequate amounts of O-acetylhomoserine, which is the precursor of the incorporation of H2S into homocysteine, resulting in a disequilibrium between the synthesis and incorporation of SO2/H2S.
SKP2 appeared to influence strongly the flux through the sulfate assimilation pathway and the production of SO2 and acetaldehyde. Allelic replacement of the MET2 gene and complementary experiments confirmed the strength of this influence. First, SO2 and acetaldehyde production were similar between a JN17 strain carrying the MET2 JN10 allele and the parental JN17 strain. We then analyzed SO2 production by 30 meiotic segregants in a must supplemented with 1 g/L of threonine (see Additional file 2). Threonine concentration is involved in a feedback mechanism that controls the activity of aspartate kinase (which is encoded by HOM3) . Aspartate kinase is responsible for the first step of the conversion of aspartate to homoserine, which is subsequently converted to O-acetylhomoserine. Thus, high threonine concentrations impair the activity of the branch of the pathway that produces carbon precursors needed for the incorporation of H2S. Segregants producing low amounts of SO2 were not affected by the addition of threonine; however, segregants producing moderate to high amounts of SO2 strongly responded to threonine and produced between 30 and 70% more SO2 than in conditions without added threonine. Genetic analysis of the segregants identified no relationship between the identity of the MET2 allele and response to added threonine. Thus, the SKP2 JN17 allele controls sulfite production regardless of the identity of the MET2 allele, and limits sulfite production even under highly favorable conditions. We also examined SO2 production by parental strains lacking a functional MET2 allele in media supplemented with methionine to restore their growth. Sulfite production was twice as high in the JN17ΔMET2 strain than in the parental JN17 strain (40 +/− 3 and 20 +/− 2 mg/L, respectively) and was substantially higher in the JN10ΔMET2 strain than in the parental JN10 strain (129 +/− 4 and 40 +/− 2 mg/L, respectively) (data not shown). This demonstrates that sulfite production, and indirectly that of the acetaldehyde, are predominantly controlled by SKP2 and not MET2. This finding can also be applied to the branch of the sulfur assimilation pathway leading to the synthesis of O-acetylhomoserine.
Nonetheless, an efficient homoserine trans-acetylase is probably required for the complete control of H2S production. Indeed, SKP2 tightly regulates the rate of conversion of sulfate into sulfite, but it is probably the rate of incorporation of H2S into carbon precursors also affects its release into the medium. Complementary experiments involving allelic replacement of both genes should provide a clear demonstration of this assumed additive effect.
The impact of the identified genes on other metabolites of importance in enology could also be of great interest. In our study, we could observe that there was no influence on the glycerol production, whereas a link through the redox balance could have been awaited. We noticed that the difference in glycerol production between the parental strains is weak (5.46 g/l +/− 0.01 and 5.30 g/l +/− 0.04 for the strains JN10 and JN17 respectively). A sample of 15 segregants revealed that the glycerol production varied from 4.87 mg/l +/− 0.01 to 6.31 mg/l +/− 0.09 but there was no relation between the glycerol and the SO2 production (Pearson coefficient of 0.2781). The variation range in the sulfur metabolites seemed to be too low to impact the glycerol production. On another hand, sulfur compounds derivative from H2S such as ethanethiol, methanethiol or methyl sulfide and disulfide could also be analyzed as we expect a decrease of those compounds responsible for off-flavors proportional to that of H2S. Such a diminution has already been observed on natural must when comparing the JN17 strain with other commercial wine strains for the methanethiol for instance (data not shown).
Moreover, other loci with minor effects may be involved in the modulation of the high production of sulfite. Additional minor QTLs could maybe be identified with an increased number of genotyped segregants and/or a higher number of molecular markers thus increasing the density of markers and filling the gaps in their distribution along the genetic map.
The molecular basis of many of the enological properties of wine yeasts remains unknown. Although many studies have investigated sulfur metabolism in wine yeast, some of the genetic variants responsible for differences in sulfite and sulfide production between strains remain to be characterized. Emphasis in previous studies has often been placed on sulfide production, which is responsible for off-flavors, and sulfite reductase mutants that cannot convert sulfite into sulfide have been developed. However, these strains release large amounts of sulfites into media [5,6], which negatively affects the organoleptic properties of wine, delays malolactic fermentation, and has implications for human health. Therefore, much interest has been placed in methods to control sulfite production. Current trends in winemaking tend towards a diminution or even a total abolition of sulfite use and winemakers need to be provided with low sulfite-producing strains.
In this study, we used a QTL mapping strategy coupled with physiologic and transcriptomic studies to identify mechanisms underlying the control of sulfite production as well as phenotypes related to this process. We show that the SKP2 and MET2 genes influence SO2, H2S and acetaldehyde productions and we identified new variants of these genes in a low sulfite-producing strain. These variants control two aspects of sulfur metabolism: sulfate assimilation and the synthesis of carbon precursors. The rarity of these alleles, in particular the SKP2 JN17 allele, suggests that alternative mechanisms and genes/alleles combination can also restrict sulfite and sulfide production in other low producers yeast strains. Nevertheless the robust low sulfite-producing phenotype associated with the combination of these alleles suggests that their transfer to any high producer strain of wine yeast should be sufficient to control sulfite/sulfide and acetaldehyde production in most cases. The transfer of these alleles via a non-GMO route may be possible through backcrossing approaches that have been previously used to improve wine yeasts . Furthermore, these genes are genetically linked, and would therefore be easy to transfer simultaneously during backcrossing cycles. Our study thus provides new perspectives for the improvement of wine yeast. The transfer of these alleles to commercial strains can be considered an alternative to common strategies currently used to control H2S production such as those involving the restriction of sulfite reductase activity which result in the uncontrolled release of SO2 .
List of Saccharomyces cerevisiae strains used in this study
Homozygous diploid obtained from a high sulfite producer wine yeast
Homozygous diploid HO/HO
Homozygous diploid obtained from a low sulfite producer wine yeast
Homozygous diploid HO/HO
JN10 mat a
Haploid spore of JN10
Haploid ho:: KanMX mat a
JN17 mat α
Haploid spore of JN17
Haploid ho:: KanMX mat α
Hybrid of JN10 mat a and JN17 mat α
JN10 MET2 JN17
Allelic replacement for the MET2 gene in a JN10 background
Haploid MET2 JN17
JN17 MET2 JN10
Allelic replacement for the MET2 gene in a JN17 background
Haploid MET2 JN10
Hemizygote between JN10 and JN17 skp2:: HPH
Diploid SKP2 JN10/skp2 JN17:: HPH
Hemizygote between JN10 skp2:: HPH and JN17
Diploid skp2 JN10:: HPH/SKP2 JN17
Growth and fermentation media
Yeast were grown in YEPD medium (2% glucose, 1% yeast extract, 2% bactopeptone, and 2% agar if necessary) at 28°C. Geneticin (G418, 200 μg/mL) was added to solid YEPD medium to select for transformed yeast strains. Sporulation was induced by transferring yeast cells onto sporulation medium (1% potassium acetate, 0.1% yeast extract, 0.05% glucose, 0.002% adenine and 2% agar) after 48 h of growth at 28°C on presporulation medium (10% glucose, 1% yeast extract, 0.5% bactopeptone and 2% agar). Plates of sporulation medium were incubated at 28°C for at least four days. Microdissection of asci was performed with a micromanipulator (Singer Instruments) on a micromanipulation medium (0.2% yeast extract, 0.2% glucose, 2% ultrapure agar).
Fermentation was carried out in synthetic musts mimicking natural must, as described by Bely et al.  with some minor modifications. Sugar was provided by an equimolar mix of glucose and fructose at a combined total of 200 g/L and the content of anaerobic factors was 75% lower than that described by Bely et al. Assimilable nitrogen content varied from 100 mg/L to 425 mg/L. Temperature was maintained either at 16°C or at 28°C depending on the phenotype measured. Fermentation media was inoculated at 106 cells/mL after two sequential pre-cultures. A first pre-culture was performed in liquid YEPD for one day, and was transferred to synthetic must in agitated flasks that were left to grow for another day. Fermentation units of 1.2 L and 0.3 L were used. Both kinds of bioreactors were equipped with airlocks to maintain anaerobiosis and were under permanent stirring.
Determination of phenotypic variables
CO2 release was monitored by weight loss, which was assessed either automatically for the 1.2 L unit (one acquisition every 20 minutes) or manually for the 0.3 L unit. The rate of CO2 production was calculated with a method of polynomial smoothing from the weight loss data of the 1.2 L fermentation units. Cell number was determined with an electronic particle counter (Coulter, Beckman). Sulfite production was determined in small fermentation units at 16°C on a nitrogen rich media after 90% of the fermentation process was complete. The media contained 425 mg/L of assimilable nitrogen because these conditions were determined to be optimal for the assessment of differences in sulfite production between strains (data not shown). SO2 concentration was measured with an enzymatic UV assay (r-Biopharm) according to the manufacturer’s instructions. This method measures the total amount of sulfite (free and carbonyl-bound sulfite) with a detection limit of 0.3 mg/l.
H2S detection strips (Fluka) were used to determine H2S production and were placed in the CO2 release flow in the fermenter bells of 1.2 L fermentation units. H2S production was assessed at 28°C in nitrogen-poor media (100 mg/l assimilable nitrogen), because these conditions have been previously described to favor H2S production. The amount of H2S was estimated visually according to the blackening of the strips and provides a binary response, production or lack of production of H2S, with a detection limit about 0.5 to 1 μg/l [52,53]. Acetaldehyde production was determined with an enzymatic UV method in the same conditions described for SO2 production. Fifty microliters of supernatant were mixed with 1500 μL of a premix solution (1 mg/mL NAD in a buffered solution of pyrophosphate acid, pH9). The formation of NADH after the oxidation of acetaldehyde by aldehyde dehydrogenase (10 μL of an enzymatic suspension at 45 U/mL, Sigma) was determined from optical density at 340 nm.
Propanol concentrations were measured by head-space gas chromatography (GC Agilent 6890) in the same conditions used to determine SO2 production.
Analysis of gene expression
Gene expression was analyzed with microarrays spotted with the 6308 oligonucleotides (70 mer) of the S. cerevisiae Oligoset (Operon) in duplicate on UltraGap chips (Biochip Platform, Toulouse, France). RNA was extracted with Trizol reagent with a method adapted from Chomczynski and Sacchi . Reverse transcription and labeling were performed with a ChipShot direct labeling and clean-up system kit (Promega) according to the manufacturer’s instructions. Microarray hybridization was carried out with a Pronto Universal Microarray kit (Corning) according to the manufacturer’s instructions. Two biological replicates were included according to a dye swap design. Microarrays were scanned with a GenePix pro 3 scanner (Axon Instruments). Data were processed with the R software (R2.9.2) and the Limma package [55-59]. Intra-array normalization was carried out with the print-tip loess method and inter-array normalization with the quantile method. Differentially expressed genes were identified through a linear model approach and a Benjamini-Hochberg method was used to adjust the p-values . A gene was considered as differentially expressed at a significance level of 5% if its adjusted p-value was less than 0.05. The complete data set is available at Gene Expression Omnibus with the accession number GSE55083. Statistical analysis to determine functional groups of genes that were over-represented in the data set was performed with the web-based tool FunSpec  (available online at http://funspec.med.utoronto.ca/, p-value <0.05, and Bonferroni correction) and genes were classified with the GO database.
Genotyping with high-density oligonucleotide microarrays
Genomic DNA was extracted with the Genomic Tip 100G kit (Qiagen) according to the manufacturer’s instructions. Three independent extractions were performed for each parental strain and one for each meiotic segregant. Genomic DNA was fragmented, labeled and hybridized onto Yeast Genome S98 arrays (Affymetrix) by the genomic platform ProfilExpert (IFR Neuroscience, Lyon). Microarrays were scanned by an Affymetrix scanner. Raw data were submitted to multiple filters and statistical analyses with the R software as previously described  to identify informative markers. Molecular markers were positioned on a physical map and on a genetic map, using a conversion factor of 3000 bp for 1 cM.
Linkage analysis between phenotypic and genotypic datasets was performed by an interval mapping method  implemented in the R/qtl package . A non-parametric model was applied to all the studied phenotypes, except for H2S production, which was analyzed with a binary model because of its semi-quantitative character . The values 1 and 2 of the arbitrary color scale were combined into one single value. The significance level was determined through permutation tests (1000 permutations). The confidence interval for the location of each QTL was defined as 1-LOD support interval: the region in which the LOD score is within 1 unit of the linkage peak.
The SKP2 and the MET2 genes were amplified with the following primers: pSKP2F4, 5′-TCATCATGTTACCGTGGAACA-3′ and pSKP2R, 5′-AGTCCACTACAAAAAGTCAT-3′ for SKP2; pMET2F, 5′-TTGTTAGTGGCTCCCCAC-3′ and pMET2R, 5′-ATGTTATGCCTGAGGTAT-3′ for MET2. Both genes were sequenced by Eurofins MWG (Ebersberg, Germany) with the above-listed amplification primers and the following primers for fragments between 500 and 800 pb: pMET2seqW1, 5′-TAACGACTTAGCATTCGA-3′; pMET2seqW2, 5′-CACCGCATCTTCTTCGGA-3′; pMET2seqC1, 5′-TGTGGATTTGTAGGGAGT-3′; pMET2seqC2, 5′-TCACCAGCTTCATTCAGT-3′ and pSKP2seq1, 5′-CTACAATTTGATTACGAATG-3′, pSKP2seq2, 5′-CAGTAAATTCGACTTATTGT-3′, pSKP2seq3, 5′-TGGGAACATCTAGCAAGAAC-3′.
Construction of strains for functional analysis
Allelic replacement was performed to confirm functionally the implication of MET2 in phenotypic variation. The MET2 gene was deleted in one haploid parental strain and replaced with a cassette conferring resistance to ClonNat (plasmid pAG25, NAT1 gene) by the transformation protocol described previously in the “Yeast strains” section. The following primers were used for the deletion: pMET2delF 5′-GACATCAGCAAGACATTCTGCCTGGTGCATATCGTGGTCTTGCCTCGTCCCCGCCGGGTC-3′ and pMET2delR 5′-CAGCCAAAAATTCTTGTTCTGAATATGTGAACAGTCCATCCAGTATAGCGACCAGCATTC-3′, and the replacement was verified with pClonNatF 5′-CTCACATCACATCCGAACAT-3′ and pMET2R primers. Strains lacking a functional MET2 gene were auxotroph for methionine. Transformants were then transformed again with a plasmid encoding the MET2 gene of the other parental strain that was amplified previously. The MET2 gene was amplified with the primers pMET2F4 5′-AAGAATATGGTTGCTCTGGC-3′ and pMET2R2 5′-TGCGACTTCGGTATGTGCT-3′. Transformants that were prototroph for methionine were selected and the following strains were obtained: JN10 ho:: KanMX4 mat a MET2 JN17 and JN17 ho:: KanMX4 mat α MET2 JN10.
A counter-selection approach could not be used to test the implication of SKP2 in phenotypic variation because the inactivation of this gene does not lead to a discriminant phenotype. Therefore, we performed a reciprocal hemizygosity analysis and deleted the SKP2 gene in one haploid parental strain through replacement with a cassette conferring resistance to ClonNat (plasmid pAG25, NAT1 gene). The following primers were used for the deletion: pSKP2delF 5′-AAGTTGAACCGCATTTTCAAACGTTCAAACCAACCGAATCTGCCTCGTCCCCGCCGGGTC-3′ and pSKP2delR 5′-TGCATAAATATGCTATATAAAGTCCACTACAAAAAGTCATCAGTATAGCGACCAGCATTC-3′ and the replacement was verified with pClonNatF and pSKP2R primers. The strain deleted for SKP2 was then crossed with the other parental strain resulting in a diploid strain possessing only one active copy of the SKP2 gene, thus giving rise to the strains JN10/JN17 Δskp2 and JN17/JN10 Δskp2.
This work has been the subject of a Ph.D. thesis and all research was conducted at the UMR Sciences pour l’Oenologie. We thank Daniel Granes from the Institut Cooperatif du Vin and Anne Ortiz-Julien from Lallemand SAS for their support of this project.
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