Evaluation of these and other candidate strains (yeast or bacteria) have used a staged approach, commencing with ‘micro-fermentations’ conducted in 96-well plates. Apart from their great convenience, these micro-fermentations showed good agreement [4] with results obtained on the more traditional ‘laboratory’ scale (i.e. ~100 mL) trials. Subsequent trials occur in ever-increasing volumes (100 mL, 10 L, etc) with a reducing number of candidates according to the analytical needs (e.g. sensory or descriptive analysis of wines) and the attribute in question.

*New bacteria through directed evolution*

Given the inherently inhibitory nature of wine to the growth and activity of lactic acid bacteria, a directed evolution approach seems ideal for the improvement of these fastidious organisms. In another example of the early application of this approach to wine microbes, Betteridge [5] cultured the commercial *Oenococcus oeni* strain SB3 (Laffort) continuously in MRSAJ (MRS broth + 20% [v/v] apple juice) containing modest ethanol content (~5% [v/v]), which was progressively increased to ~15% (v/v). Regular evaluation of the evolving culture in comparison to the parent eventually revealed an improved phenotype (Fig. 3). Ongoing characterisation of isolate 90 includes physiological and genomic analyses. Several single nucleotide polymorphisms have been identified in the evolved strains compared to the parent. The biological significance of these is under investigation. In an assessment of the ethanol tolerance of strain 90, excellent resistance to 17% (v/v) ethanol in MRSJA at 22 °C was demonstrated through complete catabolism of 3 g/L of malic acid in ~25 days compared to trace malic acid catabolism in the same time by SB3 (data not shown). Further studies of the strain in real wines are underway.

<figure><img src="image_97.png" /><figcaption>**Fig. 3** - Evaluation of malic acid metabolism by parent strain SB3 and four clonal isolates from the directed evolution culture described in the text. Strains were grown in 10 mL of MRSAJ at 22 °C supplemented with 15% (v/v) ethanol. Values are the averages of three biological replicates.</figcaption></figure>

*Guidance for strain improvement from genetic resources*

One advantage of research on *Saccharomyces cerevisiae* is the multitude of genetic resources and data available for this model organism. Libraries of deletants (each containing a single unique gene deletion) are commercially available for interrogation of the response of deletants to specific growth conditions or physicochemical challenges [6]. In such studies, the failure of a deletant to perform well implies the importance of the corresponding deleted gene in coping with the challenge applied. With this knowledge it is possible to quickly identify the gene and/or cellular process that underpins the sought-after attribute. Such deletion libraries have been widely exploited to determine the identity of genes involved in tolerance of challenges relevant to winemaking, e.g. ethanol, low/high temperature, acetic acid, etc. Whilst useful, such studies may have some limitations. The background for the deletion library is not a wine strain. Previous versions of these libraries have auxotrophic markers mandating the addition of amino acids and thereby complicating the study of nitrogen-related attributes in these strains (important in oenology since nitrogen is often limiting). Also, unlike a wine fermentation where many challenges are faced sequentially and in parallel over an extended time-frame of many days, such studies tend to focus on a single challenge over a short time-frame. Our recent work sought to address these limitations and identify the genes essential for fermentation under wine-like conditions [7]. These so-called fermentation essential genes, i.e. genes without which fermentation was not completed in a designated time, number around 90 and constitute the “fermentome”. As validation of the approach used, one third of the genes identified have not previously been linked to fermentation, whilst the remainder have been highlighted in one or more single
stress screens of relevance to winemaking. This work has highlighted the importance of some core biological processes such as ion homeostasis and vacuolar acidification to efficient performance during wine fermentation, particularly high-sugar fermentations. Such information is helping to tailor future strain optimisation strategies.

Other genetic resources include a sequenced set of 96 recombinant F2 progenies dissected from the F1 generation of a cross between two winemaking strains [8]. This collection is being used to identify many Quantitative Trait Loci (QTLs), including those linked to the retention of pigmented compounds in the culture supernatant vs adhering to the biomass in model wine fermentations containing a polyphenol extract from grapes. The work has so far highlighted several candidate regions for further investigation. These contain genes responsible for the synthesis of cell-wall components, which may conceivably be involved in different degrees of binding of polyphenol pigments from the fermentation medium to the yeast cell wall (thereby removing them from the culture supernatant).

## Conclusion

This work has demonstrated the utility of several methods for the identification of yeast and bacterial strains with enhanced properties. In being non-recombinant in their nature, such strains can be readily trialed in industry if not quickly taken up for commercial production. Characterisation of these strains is also providing insight into the basis for their enhancement, thereby yielding fundamental information and guidance to future optimisation efforts. Characterisation of deletion mutant libraries as well as QTL analysis of hybrid collections is also revealing possible genetic targets for exploitation in the development of strains tailored for fermentation robustness of specific compositional contributions to wine.

## Acknowledgements

Major financial support for this work has been and is being received from: the University of Adelaide, the Australian Grape and Wine Authority and the Australian Research Council. VJ is the recipient of an Endeavour Fellowship from the Australian government.

## References

1. McBryde C, Gardner JM, de Barros Lopes M et al (2006) Generation of novel wine yeast strains by adaptive evolution. *Am J Enol Vitic* **57**: 423-430.

2. Henderson CM, Lozada-Contreras M, Jiranek V et al (2013) Ethanol production and maximum cell growth are highly correlated with membrane lipid composition during fermentation as determined by lipidomic analysis of 22 *Saccharomyces cerevisiae* strains. *Appl Environ Microbiol* **79**: 91-104.

3. Liccioli T (2010) Improving fructose utilization in wine yeast using adaptive evolution. *PhD thesis*, University of Adelaide.

4. Liccioli T, Tran T, Cozzolino D et al (2010) Microvinification – How small can we go?” *Appl Microbiol Biotechnol* **89**: 1621-1628.

5. Betteridge A (2015) Enhanced wine-making efficiency through fool-proof malolactic fermentation: evolution of superior lactic acid bacteria. *PhD thesis*, University of Adelaide.

6. Winzeler EA, Shoemaker DD, Astromoff A et al (1999) Functional characterization of the *S. cerevisiae* genome by gene deletion and parallel analysis. *Science* **285**: 901-906.

7. Walker M, Nguyen T, Liccioli T et al (2014) Genome-wide identification of the Fermentome; genes required for successful and timely completion of wine-like fermentation by *Saccharomyces cerevisiae*. *BMC Genomics* **15**: 552.

8. Roncoroni M (2014) Quantitative trait loci mapping in winemaking yeast. *PhD thesis*, University of Auckland.
# Increase of fruity aroma during mixed
*T. delbrueckii* / *S. cerevisiae* wine fermentation
is linked to specific ester enhancement

P. RENAULT¹,²*, J. COULON², G. DE REVEL¹,³, J.C. BARBE¹,³, M. BELLY¹

¹Univ. Bordeaux, ISVV, EA 4577, Unité de Recherche Œnologie, 33140 Villenave d'Ornon, France

²Biolaffort, 33100 Bordeaux, France

³INRA, ISVV, USC Œnologie, 33140 Villenave d'Ornon, France

Non-Saccharomyces – *Torulaspora delbrueckii* – mixed inoculation – esters

## Introduction

The fermentation of grape must is a complex microbial process involving sequential development of various yeast communities including *Saccharomyces* and non-*Saccharomyces* species. The dominance of non-*Saccharomyces* yeasts during the early stages of the reaction has a major impact on the aromatic composition and sensory properties of wine [1, 2].

*Torulaspora delbrueckii*, one of the few non-*Saccharomyces* yeast species currently commercialized, has been described as having a positive impact on the organoleptic quality of wines thanks to its low production of off-flavour compounds such as acetic acid and ethyl acetate [3, 4] and its production of few specific esters in more important concentrations than *S. cerevisiae* [5, 6].

The aim of this work was to study the specific ester formation of *T. delbrueckii* and its aromatic impact when used in association with *S. cerevisiae* during the alcoholic fermentation of wine.

## Materials and methods

### Strains and culture conditions

In this study, two commercial strains from Laffort company (France) were used: *S. cerevisiae* Zymaflore® FX10 and *T. delbrueckii* Zymaflore® Alpha<sup>TD N. SACCH</sup>. Single and mixed cultures were inoculated with 1 x 10<sup>7</sup> viable cells/mL for *T. delbrueckii* and 2 x 10<sup>6</sup> viable cells/mL for *S. cerevisiae*. Yeast growth was determined by plate counts.

### Ester analysis

The concentration of 20 esters was determined using a head space solid phase microextraction (HS-SPME) followed by gas chromatography-mass spectrometry (GC-MS) [7]. Seven esters (ethyl butanoate, ethyl hexanoate, ethyl octanoate, ethyl decanoate, isoamyl acetate, hexyl acetate and phenylethyl acetate), found at higher concentrations in *S. cerevisiae* pure culture than in *T. delbrueckii* pure culture, were named "major esters", and the others were named "minor esters".

### Sensory analysis

Sensory analyses were performed as described by Martin and de Revel [8]. The panel was composed of 33 professional tasters (17 women and 16 men) aged between 24 and 60 years selected for their experience in assessing fruity aromas in red wine.

### Data analysis

Experiments were performed in triplicate in laboratory scale and in duplicate for winery scale. In order to compare modalities, data were analyzed by single-factor variance analysis (ANOVA, p < 0.05) after the verification of variance homogeneity (Levene test, p > 0.05). Thereafter a multiple comparison test (Duncan) was applied to classify the different modalities (p < 0.05).
# Results and discussion

## Laboratory scale

Four different fermentations were carried out: two pure cultures and two mixed cultures. For simultaneous mixed culture, *T. delbrueckii* and *S. cerevisiae* were inoculated at the same time while *T. delbrueckii* was inoculated 24 h before *S. cerevisiae* in sequential mixed culture.

When comparing single species inoculations, the sum of all measured esters at the end of fermentation was 5-fold higher with *S. cerevisiae* than with *T. delbrueckii* as shown in Table 1. However, few specific esters: ethyl propanoate, ethyl isobutanoate and ethyl dihydrocinnamate were respectively found at 5-, 3- and 67-fold higher concentrations in *T. delbrueckii* pure fermentation than in *S. cerevisiae* pure fermentation.

<table><caption>**Table 1** - Final ester concentrations in pure and mixed *T. delbrueckii* and *S. cerevisiae* cultures (laboratory scale conditions)</caption><thead><tr><th>Compounds</th><th>T. delbrueckii<br>pure culture</th><th>Sequential<br>mixed culture</th><th>Simultaneous<br>mixed culture</th><th>S. cerevisiae<br>pure culture</th></tr></thead><tbody><tr><td colspan="5"><strong>Major esters (µg/L)</strong></td></tr><tr><td>Ethyl butanoate</td><td>29 ± 4 <sup>a</sup></td><td>220 ± 14 <sup>b</sup></td><td>343 ± 16 <sup>d</sup></td><td>286 ± 20 <sup>c</sup></td></tr><tr><td>Ethyl hexanoate</td><td>120 ± 10 <sup>a</sup></td><td>671 ± 27 <sup>b</sup></td><td>1195 ± 60 <sup>c</sup></td><td>1301 ± 96 <sup>c</sup></td></tr><tr><td>Ethyl octanoate</td><td>187 ± 13 <sup>a</sup></td><td>580 ± 41 <sup>b</sup></td><td>1364 ± 91 <sup>c</sup></td><td>1407 ± 130 <sup>c</sup></td></tr><tr><td>Ethyl decanoate</td><td>458 ± 69 <sup>a</sup></td><td>533 ± 17 <sup>a</sup></td><td>878 ± 38 <sup>c</sup></td><td>614 ± 41 <sup>b</sup></td></tr><tr><td>Isoamyl acetate *</td><td>64 ± 1 <sup>a</sup></td><td>3834 ± 101 <sup>c</sup></td><td>4957 ± 84 <sup>d</sup></td><td>1828 ± 63 <sup>b</sup></td></tr><tr><td>Hexyl acetate</td><td>3,9 ± 1,0 <sup>a</sup></td><td>179 ± 17 <sup>b</sup></td><td>219 ± 16 <sup>c</sup></td><td>236 ± 16 <sup>c</sup></td></tr><tr><td>Phenylethyl acetate</td><td>53 ± 10 <sup>a</sup></td><td>455 ± 69 <sup>b</sup></td><td>540 ± 31 <sup>c</sup></td><td>435 ± 3 <sup>b</sup></td></tr><tr><td><strong>Sum</strong></td><td><strong>915 ± 104 <sup>#</sup></strong></td><td><strong>6472 ± 287 <sup>b</sup></strong></td><td><strong>9496 ± 336 <sup>c</sup></strong></td><td><strong>6107 ± 369 <sup>b</sup></strong></td></tr><tr><td colspan="5"><strong>Minor esters (µg/L)</strong></td></tr><tr><td>Ethyl propanoate</td><td>195 ± 12 <sup>c</sup></td><td>233 ± 8 <sup>d</sup></td><td>110 ± 14 <sup>b</sup></td><td>37 ± 10 <sup>a</sup></td></tr><tr><td>Ethyl isobutanoate *</td><td>24 ± 2 <sup>b</sup></td><td>41 ± 3 <sup>c</sup></td><td>24 ± 2 <sup>b</sup></td><td>8,8 ± 1,1 <sup>a</sup></td></tr><tr><td>Ethyl 2-methylbutanoate</td><td>0.49 ± 0,08 <sup>a</sup></td><td>1,4 ± 0,2 <sup>c</sup></td><td>0.87 ± 0,04 <sup>b</sup></td><td>0.87 ± 0,08 <sup>b</sup></td></tr><tr><td>Ethyl isovalerate</td><td>0.34 ± 0,05 <sup>a</sup></td><td>1,3 ± 0,1 <sup>b</sup></td><td>1,1 ± 0,1 <sup>b</sup></td><td>2,0 ± 0,1 <sup>c</sup></td></tr><tr><td>Ethyl trans-2-hexenoate</td><td>0.25 ± 0,04 <sup>a</sup></td><td>1,7 ± 0,1 <sup>d</sup></td><td>1,2 ± 0,1 <sup>c</sup></td><td>0.71 ± 0,02 <sup>b</sup></td></tr><tr><td>Ethyl dodecanoate</td><td>9,3 ± 0,9 <sup>a</sup></td><td>15 ± 3 <sup>b</sup></td><td>41 ± 3 <sup>c</sup></td><td>43 ± 3 <sup>c</sup></td></tr><tr><td>Ethyl phenylacetate</td><td>0.10 ± 0,00 <sup>a</sup></td><td>0.27 ± 0,05 <sup>b</sup></td><td>0.20 ± 0,00 <sup>b</sup></td><td>0.43 ± 0,06 <sup>c</sup></td></tr><tr><td>Ethyl dihydrocinnamate</td><td>14 ± 1 <sup>c</sup></td><td>1,3 ± 0,2 <sup>b</sup></td><td>0.34 ± 0,04 <sup>a</sup></td><td>0.20 ± 0,01 <sup>a</sup></td></tr><tr><td>Ethyl cinnamate</td><td>1,1 ± 0,0 <sup>a</sup></td><td>1,1 ± 0,0 <sup>a</sup></td><td>1,0 ± 0,0 <sup>a</sup></td><td>1,1 ± 0,0 <sup>a</sup></td></tr><tr><td>Propyl acetate</td><td>11 ± 2 <sup>a</sup></td><td>56 ± 11 <sup>c</sup></td><td>53 ± 3 <sup>c</sup></td><td>36 ± 4 <sup>b</sup></td></tr><tr><td>Isobutyl acetate *</td><td>12 ± 1 <sup>a</sup></td><td>99 ± 9 <sup>c</sup></td><td>103 ± 5 <sup>c</sup></td><td>51 ± 3 <sup>b</sup></td></tr><tr><td>Butyl acetate</td><td>0.12 ± 0,01 <sup>a</sup></td><td>2,7 ± 0,2 <sup>b</sup></td><td>5,1 ± 0,6 <sup>c</sup></td><td>2,8 ± 0,1 <sup>b</sup></td></tr><tr><td>Octyl acetate</td><td>0.05 ± 0,04 <sup>a</sup></td><td>0.21 ± 0,03 <sup>a</sup></td><td>0.71 ± 0,06 <sup>b</sup></td><td>0.81 ± 0,16 <sup>b</sup></td></tr><tr><td><strong>Sum</strong></td><td><strong>266 ± 3 <sup>b</sup></strong></td><td><strong>454 ± 35 <sup>d</sup></strong></td><td><strong>340 ± 30 <sup>c</sup></strong></td><td><strong>182 ± 22 <sup>a</sup></strong></td></tr><tr><td><strong>Total sum (µg/L)</strong></td><td><strong>1181 ± 107 <sup>#</sup></strong></td><td><strong>6926 ± 322 <sup>b</sup></strong></td><td><strong>9836 ± 366 <sup>c</sup></strong></td><td><strong>6289 ± 392 <sup>b</sup></strong></td></tr></tbody></table>

Average values of three experiments ± standard deviation

<sup>a,b,c,d</sup> represent significantly different statistical groups (p < 0.05)

* Isoamyl acetate, ethyl isobutanoate and isobutyl acetate are also called 3-methylbutyl acetate, ethyl 2-methylpropanoate and 2-methylpropyl acetate, respectively

These three esters were also found in higher concentrations in mixed inoculations in comparison to *S. cerevisiae* pure culture. Furthermore, these esters, specifically produced by *T. delbrueckii*, were significantly correlated (Spearman test, p < 0.05) to the maximal *T. delbrueckii* population reached in mixed cultures (Table 1 and 2). Thus, ethyl propanoate, ethyl isobutanoate and ethyl dihydrocinnamate were considered as activity markers of *T. delbrueckii* during mixed inoculations. On the other hand, isobutyl acetate and isoamyl acetate concentrations were systematically increased during mixed inoculations although not correlated with the development of either species but rather due to positive interactions between these species.

This study indicates the major role of the inoculation procedure when performing mixed *T. delbrueckii* / *S. cerevisiae* inoculations, as it drastically impacts the population dynamics of the species, thereafter involving modifications in ester production. Favouring *T. delbrueckii* development when performing
sequential inoculation will enhance the concentration of esters linked to *T. delbrueckii* activity. On the contrary, simultaneous inoculation restricted the growth of *T. delbrueckii*, limiting the production of its activity markers, but involved a very important production of numerous esters due to more important positive interactions between species.

<table><caption>**Table 2** - Maximal cell populations in pure and mixed *T. delbrueckii* and *S. cerevisiae* cultures (laboratory scale conditions)</caption><thead><tr><th></th><th><em>T. delbrueckii</em><br>pure culture</th><th>Sequential<br>mixed culture</th><th>Simultaneous<br>mixed culture</th><th><em>S. cerevisiae</em><br>pure culture</th></tr></thead><tbody><tr><td><em>T. delbrueckii</em><br>(viable cells/mL)</td><td>8.110<sup>7</sup> ± 2.810<sup>6</sup> c</td><td>6.110<sup>7</sup> ± 7.110<sup>6</sup> b</td><td>4.310<sup>7</sup> ± 3.510<sup>6</sup> a</td><td>/</td></tr><tr><td><em>S. cerevisiae</em><br>(viable cells/mL)</td><td>/</td><td>2.410<sup>7</sup> ± 7.110<sup>6</sup> a</td><td>4.410<sup>7</sup> ± 2.310<sup>6</sup> b</td><td>7.610<sup>7</sup> ± 1.810<sup>6</sup> c</td></tr></tbody></table>

Average values of three experiments ± standard deviation

a, b, c, d represent significantly different statistical groups (p < 0.05)

*Winery scale: sensory impact of minor esters generated by mixed inoculations*

Fermentations were carried out at temperatures between 18 and 22°C in 200-L tanks filled with Merlot must from the Bordeaux area (pH: 3.54, sugar concentration: 258 g/L, available nitrogen concentration: 210 mg/L). Two different fermentations were carried out: a pure culture of *S. cerevisiae* and a sequential mixed culture where *T. delbrueckii* was inoculated 24 h before *S. cerevisiae* yeast.

Winery scale results confirmed the ones obtained with laboratory fermentations. Total ester concentration in sequentially mixed wines was slightly higher than in the *S. cerevisiae* fermented wine (Table 3). As in the laboratory scale fermentations, this difference is mostly due to minor esters and in particular ethyl propanoate, ethyl isobutanoate and to a lesser extend ethyl dihydrocinnamate and isobutyl acetate.

<table><caption>**Table 3** - Final ester concentrations in pure *S. cerevisiae* (adjusted or not with esters) and sequential *T. delbrueckii* and *S. cerevisiae* cultures (winery scale conditions)</caption><thead><tr><th>Compounds</th><th>S. cerevisiae<br>pure culture</th><th>Sequential<br>mixed culture</th><th>S. cerevisiae<br>+ esters</th></tr></thead><tbody><tr><td><strong>Major esters (µg/L)</strong></td><td></td><td></td><td></td></tr><tr><td>Ethyl butanoate</td><td>146 ± 6</td><td>169 ± 1</td><td>146 ± 6</td></tr><tr><td>Ethyl hexanoate</td><td>338 ± 13</td><td>355 ± 4</td><td>338 ± 13</td></tr><tr><td>Ethyl octanoate</td><td>409 ± 25</td><td>377 ± 7</td><td>409 ± 25</td></tr><tr><td>Ethyl decanoate *</td><td>273 ± 14</td><td>126 ± 6</td><td>273 ± 14</td></tr><tr><td>Isoamyl acetate *</td><td>318 ± 13</td><td>536 ± 10</td><td>318 ± 13</td></tr><tr><td>Hexyl acetate *</td><td>1,0 ± 0,1</td><td>1,6 ± 0,1</td><td>0,98 ± 0,10</td></tr><tr><td>Phenylethyl acetate *</td><td>29 ± 2</td><td>36 ± 1</td><td>29 ± 2</td></tr><tr><td><strong>Sum</strong></td><td><strong>1514 ± 112</strong></td><td><strong>1601 ± 88</strong></td><td><strong>1514 ± 112</strong></td></tr><tr><td><strong>Minor esters (µg/L)</strong></td><td></td><td></td><td></td></tr><tr><td>Ethyl propanoate *</td><td>143 ± 11</td><td>267 ± 8</td><td>267 ± nd</td></tr><tr><td>Ethyl isobutanoate *</td><td>49 ± 5</td><td>110 ± 6</td><td>110 ± nd</td></tr><tr><td>Ethyl dihydrocinnamate</td><td>0,39 ± 0,10</td><td>0,53 ± 0,10</td><td>0,53 ± nd</td></tr><tr><td>Isobutyl acetate *</td><td>16 ± 1</td><td>38 ± 2</td><td>38 ± nd</td></tr><tr><td>9 other minor esters</td><td>52 ± 20</td><td>58 ± 14</td><td>52 ± 20</td></tr><tr><td><strong>Sum *</strong></td><td><strong>260 ± 24</strong></td><td><strong>474 ± 19</strong></td><td><strong>468 ± nd</strong></td></tr><tr><td><strong>Total sum (µg/L)</strong></td><td><strong>1774 ± 142</strong></td><td><strong>2075 ± 115</strong></td><td><strong>1981 ± nd</strong></td></tr></tbody></table>

Average values of two experiments ± standard deviation

*Significant differences (p < 0.05) between *S. cerevisiae* pure culture and sequential mixed culture and not determined
<figure><img src="image_98.png" /><figcaption>**Fig. 1** - Descriptive sensorial analysis of red wines after blending of duplicates. Grades ranked from 0 (poorly intense) to 7 (very intense).</figcaption></figure>

In order to evaluate the aromatic impact involved by the increase of these four minor esters during mixed inoculation, their pure chemical compounds were added to *S. cerevisiae* pure treatment to reach concentrations found in sequential mixed treatment. This new modality named "*S. cerevisiae* + esters" presented the same major ester concentration as *S. cerevisiae* pure culture but higher minor ester concentrations (Table 3).

First, sequentially mixed and pure *S. cerevisiae* wines were compared by triangular sensory analysis. They were perceived as significantly different by the sensory panel. Then the *S. cerevisiae* adjusted wine was introduced and it was perceived as significantly different from the control *S. cerevisiae* wine but not from the sequentially mixed one. Hence, the addition of the four minor esters impacted the sensory profile of the wine.

The panel was then asked to rate the three wines according to the following descriptors: aromatic complexity, fruitiness and vegetal notes. The sequentially inoculated wine was perceived significantly more fruity and complex than the pure *S. cerevisiae* (Fig. 1). These results are in accordance with those from other authors [9]. The "*S. cerevisiae* + esters" wine was also perceived significantly more fruity and complex than the pure *S. cerevisiae* (Fig. 1). Furthermore, no significant differences were found for these characters between the sequential modality and the "*S. cerevisiae* + esters" wine. In other words, the addition of these four esters restored the more fruity and complex intensities perceived in the sequentially inoculated wine. Although the vegetal note was perceived more intensively for the pure *S. cerevisiae* wine, the differences with the two other modalities were not significant.

These results hence show that the higher levels of ethyl propanoate, ethyl isobutanoate, ethyl dihydrocinnamate and isobutyl acetate found in mixed *T. delbrueckii* / *S. cerevisiae* wines were responsible for the increase of fruitiness and complexity in comparison to pure *S. cerevisiae* wine. In other words, these four esters can be considered as aromatic markers of *T. delbrueckii* during mixed inoculations.

Among these specific esters, only ethyl isobutanoate is present above its perception threshold (15 µg/L) [10]. There is hence no doubt about its fruity contribution (strawberry, kiwi, fruity) to wines fermented partially with *T. delbrueckii*. However, the three other esters: ethyl propanoate (ripe strawberry; perception threshold: 2100 µg/L) [11], ethyl dihydroxycinnamate (fruity, pineapple, almond; perception threshold: 1.1 µg/L) [10] and isobutyl acetate (solvent, fruity; perception threshold: 1600 µg/L) [12] presented concentrations below their perception threshold, questioning the effective contribution of these molecules to the wine aroma. However, it has been recently shown that through particular perceptive interactions certain esters can have an impact on wine aroma, even if present far below their perception threshold [11, 13]. Furthermore, slight single ester variations among complex mixtures can lead to aroma modulation [14]. This is the case for ethyl propanoate, clearly involved in black fruit/jammy notes in red wines in spite of its concentration being below its perception threshold. Furthermore, according to other authors, isobutyl acetate and ethyl propanoate are particularly implicated in this perceptive interaction phenomenon and enhance the intensity of fresh fruit and black fruit notes [15]. As a consequence, their omission among a mix of different esters can significantly impact the aroma perception of the matrix, and their presence (even if below their perception thresholds) in this ester mix, lowers the perception threshold of this aromatic pool, suggesting that isobutyl acetate and ethyl propanoate can be considered as aroma enhancers. In our experiments, the systematic increase of ethyl propanoate and isobutyl acetate during mixed inoculation allowed the enhancement of fruity notes in comparison to *S. cerevisiae* pure fermentation. Concerning ethyl dihydrocinnamate, no information on its aroma impact can be found in the literature.
Conclusion

Ethyl propanoate, ethyl isobutanoate and ethyl dihydrocinnamate are activity markers of *T. delbrueckii* during mixed inoculations with *S. cerevisiae*. Indeed, on the one hand they are produced in higher concentrations in pure *T. delbrueckii* culture than in pure *S. cerevisiae* culture, and on the other hand they are significantly correlated to the maximal *T. delbrueckii* population reached in mixed cultures. Furthermore, isobutyl acetate concentration was systematically increased during mixed inoculations although not correlated with *T. delbrueckii* development.

Finally, sensory analyses, with and without artificial ester concentration adjustments, showed that mixed
inoculations between *T. delbrueckii* and *S. cerevisiae* allowed to increase wine's complexity and fruity
notes in comparison to *S. cerevisiae* pure culture and demonstrated that the higher levels of ethyl
propanoate, ethyl isobutanoate, ethyl dihydrocinnamate and isobutyl acetate in mixed wines were
responsible for the increase of fruitiness and complexity. However, this aromatic impact depends on the
inoculation procedure which drastically impacts the population dynamics of the species, thereafter
involving modifications in ester production.

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# Effects of yeast cultures on sensory properties and volatile esters in white wines

M.J. RUIZ-MORENO<fnref n="1" />*, B. PUERTAS<fnref n="2" />, A. MARRUFO<fnref n="2" />, E. CANTOS-VILLAR<fnref n="2" />

*Torulaspora delbrueckii* – yeasts – esters – sensory analysis

## Introduction

Wine aroma is one of the most influential parameters in consumer preference. The relationship between chemical composition and the fruity aromatic fraction of wines is a key topic in wine research. Many volatile compounds are enzymatically synthesized during alcoholic fermentation by yeasts. Esters have been shown to impact wine fruity aroma. Even at subthreshold level and low concentrations, esters seem to play an important role in wine aroma perception. This complex synergism between flavours emphasizes the importance of accurate and precise quantitative data [1].

Spontaneous alcoholic fermentation of grape must is a complex process carried out by sequential action of several yeast genera and species. Usually, *Saccharomyces* (the strongest ethanol-tolerant species) take over the alcoholic fermentation. Furthermore, co-inoculation with non-*Saccharomyces* yeast species such as *Torulaspora delbrueckii* is considered as a strategy to increase wine flavour diversity. In recent years, the utilization of non-*Saccharomyces* yeasts has been considered for industrial wine production due its high oenological potential.

In this study, three yeast fermentation experiments were compared: conventional inoculation with *Saccharomyces cerevisiae*, co-inoculation of *Saccharomyces* with *Torulaspora delbrueckii*, and spontaneous fermentation. The study was developed on two white grape varieties (Chardonnay and Verdejo) in harvest 2014. Wines were characterized by oenological quality parameters, volatile compounds and sensory analyses.

## Materials and methods

### Fermentation conditions

Chardonnay and Verdejo grapes were harvested, destemmed and crushed. Sulphur dioxide (40 mg/L) and Lallzyme HC enzyme were added (1g/HL) into the must. After dejuicing, musts were divided into three batches (in 20-L stainless steel tank each), and total acidity was corrected. Alcoholic fermentation was started as follows: (i) Control assay (CT): *Saccharomyces cerevisiae* was inoculated (Lallemand Lalvin YSEO QA23®); (ii) Co-inoculation assay (CI): *T. delbrueckii* (Td) was inoculated first and *S. cerevisiae* (Sc) was added when density decreased by 15 points; and (iii) Spontaneous assay (SP): no inoculation of yeast was carried out. Once alcoholic fermentation was finished, wines were racked, supplemented with sulphur, clarified, cold stabilized, and finally bottled and analyzed.

### Oenological quality parameters

For determinations, OIV international analytical methods were used [2].

### Ester analysis

The samples were extracted and analyzed by headspace-solid-phase microextraction (HS-SPME) and gas chromatography-mass spectrometry (GC-MS) according to Antalick et al. [1] with modifications. A method for quantification of esters in white wines was previously developed and validated.

<footnote n="1">¹Instituto de Investigación y Formación Agraria y Pesquera (IFAPA), Centro Alameda del Obispo, Consejería de Agricultura, Pesca y Desarrollo Rural (CAPDA, Junta de Andalucía), Avd. Menéndez Pidal, 14004 Córdoba, Spain</footnote>

<footnote n="2">²IFAPA, Centro Rancho de la Merced, Consejería de Agricultura, Pesca y Desarrollo Rural (CAPDA, Junta de Andalucía), Ctra.Trebujena, Km 2.1, 11471, Jerez de la Frontera, Spain</footnote>
### Sensory analysis

Descriptive sensory analysis was performed following the Sensory Profile Method according to standard ISO 6564:1985 (ISO, 1985) by 11 judges. The descriptors were scored on a scale of 1 to 10 (1: absence of a descriptor, 10: maximum intensity).

### Statistical analysis

One-way analysis of variance (ANOVA) and least significant difference test (LSD) were carried out on data with a significance level of p< 0.05. Statistic version 9.0 (Analytical Software, Tallahassee, FL, USA) was used.

## Results and discussion

Regarding oenological parameters (Table 1), in both varieties “CI” wines presented the lowest values of ethanol content and total acidity. “SP” wines showed the highest ethanol contents. Moreover, “CI” fermentation in Chardonnay wines presented the highest glycerol amount.

<table><caption>Table 1 - Mean values of oenological parameters</caption><thead><tr><th rowspan="2"></th><th colspan="4">Chardonnay</th><th colspan="4">Verdejo</th></tr><tr><th>CT</th><th>CI</th><th>SP</th><th>p-value</th><th>CT</th><th>CI</th><th>SP</th><th>p-value</th></tr></thead><tbody><tr><td>Ethanol (% v/v)</td><td>12.59b</td><td>12.23c</td><td>12.73a</td><td>***</td><td>11.96b</td><td>11.70c</td><td>12.07a</td><td>***</td></tr><tr><td>Sugars (g/L)</td><td>1.04</td><td>0.71</td><td>1.26</td><td>n.s</td><td>2.35a</td><td>1.75b</td><td>1.57b</td><td>*</td></tr><tr><td>Total acidity (g/L TH<sub>2</sub>)</td><td>5.72a</td><td>4.59c</td><td>5.00b</td><td>***</td><td>5.80a</td><td>4.63c</td><td>4.83b</td><td>***</td></tr><tr><td>Volatile acidity (g/L AcH)</td><td>0.42b</td><td>0.43b</td><td>0.51a</td><td>***</td><td>0.41</td><td>0.44</td><td>0.45</td><td>n.s</td></tr><tr><td>pH</td><td>3.27c</td><td>3.38a</td><td>3.32b</td><td>***</td><td>3.21b</td><td>3.28a</td><td>3.18b</td><td>***</td></tr><tr><td>Glycerol (g/L)</td><td>6.37b</td><td>7.37a</td><td>6.07b</td><td>***</td><td>6.52a</td><td>6.27b</td><td>6.24b</td><td>*</td></tr></tbody></table>

Regarding major volatile compounds (Table 2), non-significant differences were found in ethyl acetate and methanol. “CI” wines presented higher content of isobutanol. “SP” wines showed the lowest content of isoamyl alcohols in both varieties. Finally, “CI” in Verdejo wines showed the lowest values in acetaldehyde contents.

<table><caption>Table 2 - Mean values of major volatile compounds (mg/L)</caption><thead><tr><th rowspan="2"></th><th colspan="4">Chardonnay</th><th colspan="4">Verdejo</th></tr><tr><th>CT</th><th>CI</th><th>SP</th><th>p-value</th><th>CT</th><th>CI</th><th>SP</th><th>p-value</th></tr></thead><tbody><tr><td>Acetaldehyde</td><td>28</td><td>30</td><td>45</td><td>n.s</td><td>61a</td><td>14b</td><td>66a</td><td>*</td></tr><tr><td>Ethyl acetate</td><td>30</td><td>36</td><td>42</td><td>n.s</td><td>25</td><td>32</td><td>32</td><td>n.s</td></tr><tr><td>Methanol</td><td>36</td><td>36</td><td>36</td><td>n.s</td><td>38</td><td>38</td><td>38</td><td>n.s</td></tr><tr><td>Propanol</td><td>32b</td><td>35a</td><td>20c</td><td>***</td><td>31a</td><td>31a</td><td>24b</td><td>*</td></tr><tr><td>Isobutanol</td><td>21b</td><td>39a</td><td>24b</td><td>***</td><td>22b</td><td>40a</td><td>29ab</td><td>***</td></tr><tr><td>Isoamyl alcohols</td><td>255a</td><td>243a</td><td>214b</td><td>**</td><td>236 b</td><td>262a</td><td>143c</td><td>***</td></tr><tr><td>ΣHigher Alcohols</td><td>308a</td><td>317a</td><td>259b</td><td>**</td><td>289b</td><td>333a</td><td>247c</td><td>***</td></tr></tbody></table>

Data on ester content are shown in Table 3. On both varieties, esters were significantly affected by the fermentation process. Compared to “CT”, “CI” wines were characterized by the lowest concentrations of methyl fatty acid esters (MFAEs), ethyl esters of straight chain fatty acids (EEs) and isoamyl esters of fatty acids (IEFAs) and the highest concentrations of ethyl esters of branched acids (EEBAs). Our results are in agreement with results obtained by Renault et al. [3] in fermentations stopped at midway using mixed starters of *T. delbrueckii* with *S. cerevisiae*. “SP” assays were characterized by the highest levels of higher alcohol acetates (HAAs).

Regarding sensory analyses (Figure 1), “CI” Chardonnay wines showed increased complexity and fruity notes such as “stone fruit” and “mature fruit”. “CI” wines resulted less acid than “CT” wines. “SP” wines had increased “citrus” and “white fruit” notes. In Verdejo wines, aromatic differences between “CI” and “CT” wines were lower. “CI” wines presented increased “mature fruit” notes in comparison with “CT” ones, while “SP” wines showed higher “citrus” and “white fruit” notes than the other wines. Both “SP” and “CI” wines were less acid and more balanced than “CT” ones.
<table><caption>Table 3 - Mean values of ester contents (µg/L) in fermentation assays</caption><thead><tr><th rowspan="2"></th><th colspan="4">Chardonnay</th><th colspan="4">Verdejo</th></tr><tr><th>CT</th><th>CI</th><th>SP</th><th>p-value</th><th>CT</th><th>CI</th><th>SP</th><th>p-value</th></tr></thead><tbody><tr><td>Isobutyl hexanoate</td><td>0.13a</td><td>0.06b</td><td>0.12a</td><td>***</td><td>0.12ab</td><td>0.07b</td><td>0.16a</td><td>*</td></tr><tr><td>ΣMEs</td><td>0.13a</td><td>0.06b</td><td>0.12a</td><td>***</td><td>0.12ab</td><td>0.07b</td><td>0.16a</td><td>*</td></tr><tr><td>Propyl acetate</td><td>54.6b</td><td>24.0c</td><td>64.4a</td><td>***</td><td>58.62a</td><td>24.6b</td><td>59.0a</td><td>**</td></tr><tr><td>Isobutyl acetate</td><td>45.7b</td><td>20.1c</td><td>114a</td><td>***</td><td>47.6b</td><td>19.5c</td><td>82.6a</td><td>***</td></tr><tr><td>Isoamyl acetate</td><td>2707b</td><td>564c</td><td>6992a</td><td>***</td><td>2557b</td><td>355c</td><td>3877a</td><td>***</td></tr><tr><td>Hexyl acetate</td><td>235b</td><td>22.6c</td><td>392a</td><td>***</td><td>210b</td><td>8.69c</td><td>329a</td><td>***</td></tr><tr><td>Octyl acetate</td><td>1.23a</td><td>0.03c</td><td>1.06b</td><td>***</td><td>0.86a</td><td>0.04b</td><td>0.90a</td><td>**</td></tr><tr><td>Phenylethyl acetate</td><td>230b</td><td>145b</td><td>516a</td><td>***</td><td>194b</td><td>84c</td><td>256a</td><td>***</td></tr><tr><td>ΣHAAs</td><td>3274b</td><td>775c</td><td>8081a</td><td>***</td><td>3068b</td><td>491c</td><td>4604a</td><td>***</td></tr><tr><td>Methyl hexanoate</td><td>0.88a</td><td>0.37c</td><td>0.72b</td><td>***</td><td>1.13a</td><td>0.51b</td><td>1.12a</td><td>***</td></tr><tr><td>Methyl octanoate</td><td>1.67ab</td><td>0.12b</td><td>2.34a</td><td>*</td><td>1.11a</td><td>0.16b</td><td>1.26a</td><td>***</td></tr><tr><td>Methyl decanoate</td><td>0.28a</td><td>0.03b</td><td>0.21a</td><td>**</td><td>0.26a</td><td>0.08b</td><td>0.31a</td><td>***</td></tr><tr><td>ΣMFAEs</td><td>2.83a</td><td>0.52b</td><td>3.26a</td><td>**</td><td>2.50a</td><td>0.74b</td><td>2.68a</td><td>***</td></tr><tr><td>Ethyl isobutyrate</td><td>32.62b</td><td>87.4a</td><td>24.4b</td><td>***</td><td>39.1b</td><td>113a</td><td>24.1c</td><td>***</td></tr><tr><td>Ethyl 2-methylbutyrate</td><td>3.24b</td><td>4.16a</td><td>1.57c</td><td>***</td><td>3.80a</td><td>4.48a</td><td>1.65b</td><td>***</td></tr><tr><td>Ethyl isovalerate,</td><td>9.88a</td><td>6.58b</td><td>5.54b</td><td>***</td><td>11.26a</td><td>7.55b</td><td>5.26c</td><td>***</td></tr><tr><td>Ethyl phenylacetate</td><td>1.45a</td><td>0.78b</td><td>0.63b</td><td>***</td><td>1.85a</td><td>0.69b</td><td>0.77b</td><td>**</td></tr><tr><td>ΣEEBAs</td><td>47.2b</td><td>98.9a</td><td>32.2c</td><td>***</td><td>56.0b</td><td>125a</td><td>31.8c</td><td>***</td></tr><tr><td>Ethyl butyrate</td><td>339a</td><td>186b</td><td>316a</td><td>***</td><td>327a</td><td>191b</td><td>317a</td><td>***</td></tr><tr><td>Ethyl octanoate</td><td>1270a</td><td>390b</td><td>1241a</td><td>***</td><td>111a</td><td>385b</td><td>1164a</td><td>***</td></tr><tr><td>Ethyl hexanoate</td><td>1053a</td><td>342b</td><td>980a</td><td>***</td><td>974a</td><td>360b</td><td>1043a</td><td>***</td></tr><tr><td>Ethyl decanoate</td><td>355a</td><td>75.4b</td><td>370a</td><td>**</td><td>292</td><td>152</td><td>409</td><td>n.s</td></tr><tr><td>ΣEEs</td><td>3100a</td><td>994b</td><td>2908a</td><td>***</td><td>2606a</td><td>1087b</td><td>2933a</td><td>***</td></tr><tr><td>Ethyl valerate</td><td>0.18b</td><td>0.32a</td><td>0.16b</td><td>**</td><td>0.30ab</td><td>0.42a</td><td>0.20b</td><td>**</td></tr><tr><td>Ethyl nonanoate</td><td>0.29b</td><td>0.41ab</td><td>0.47a</td><td>*</td><td>0.35a</td><td>0.26ab</td><td>0.25b</td><td>*</td></tr><tr><td>Ethyl heptanoate</td><td>0.44a</td><td>0.29ab</td><td>0.28b</td><td>*</td><td>0.56</td><td>0.29</td><td>0.60</td><td>n.s</td></tr><tr><td>ΣMEEs</td><td>0.91</td><td>1.03</td><td>0.91</td><td>n.s</td><td>1.22</td><td>0.96</td><td>1.04</td><td>n.s</td></tr><tr><td>Isoamyl butanoate</td><td>0.37a</td><td>0.27b</td><td>0.30b</td><td>**</td><td>0.33a</td><td>0.29ab</td><td>0.27b</td><td>*</td></tr><tr><td>Isoamyl hexanoate</td><td>1.74a</td><td>0.46c</td><td>1.39b</td><td>***</td><td>1.21a</td><td>0.47b</td><td>1.10a</td><td>***</td></tr><tr><td>Isoamyl octanoate</td><td>2.54a</td><td>0.41b</td><td>2.50a</td><td>***</td><td>1.08a</td><td>0.36b</td><td>1.24a</td><td>*</td></tr><tr><td>ΣIEFAs</td><td>4.65a</td><td>1.14b</td><td>4.18a</td><td>***</td><td>2.62a</td><td>1.12b</td><td>2.60a</td><td>**</td></tr><tr><td>ΣTotal Esters</td><td>6429b</td><td>1871c</td><td>11028a</td><td>***</td><td>5737b</td><td>1707c</td><td>7576a</td><td>***</td></tr></tbody></table>
<figure><img src="image_99.png" /><figcaption>Fig. 1 - Sensory analyses</figcaption></figure>

Conclusion

This work contributes to our understanding of the impact of multi-starter fermentation on chemical
composition, aroma profile and sensory characteristics of white wines. According to literature, "CI" assays
produced wines with lower ethanol content (on both Chardonnay and Verdejo varieties). Higher glycerol
production was found in "CI" Chardonnay wines and lower acetaldehyde content was found in "CI"
Verdejo wines. Regarding ester content, "CI" wines showed the highest contents of EEBAs and the lowest
significant quantities of total esters, while "SP" wines contained the highest concentrations of HAAs and
total esters. From a sensory perspective, "SP" assays produced wines with increased fruity flavour in
comparison with "CT" wines. The higher contents of HAAs found in "SP" wines may be responsible for
those aromatic differences. Furthermore, "CI" experiments provided white wines with increased
complexity and more distinctive aromatic characteristics on Chardonnay musts than on Verdejo ones.
Therefore, esters seem to play an important role in aromatic perception of "SP" wines, while other
compounds seem to make a higher contribution to aromatic characteristics of "CI" Verdejo wines.

Acknowledgements

Ruiz-Moreno thanks the European Social Fund (ESF) 2007-2013 “Andalucía se mueve con Europa” for financial support. This work has been funded by (IFAPA) through the project PP.AVA.AVA201301.3 (Proyecto AVA Vid y Vino) including European Social (ESF) and Rural Development (ERDF) Funds.

Abbreviations

"CT", control assay; "CI", co-inoculation assay; "SP", spontaneous assay; "Td", *Torulaspora delbrueckii*; EEBAs, ethyl esters of branched acids; HAAs, higher alcohol acetates; MEs, miscellaneous esters; MFAEs, methyl fatty acid esters; EEs, ethyl esters of straight chain fatty acids; MEEs, minor ethyl esters of straight chain fatty acids; IEFAs, isoamyl esters of fatty acids; n.s, not significant, \* p<0.05, \*\* p<0.05, \*\*\* p<0.001.

References

1. Antalick G., Perello M.C. de Revel G. (2010). Development, validation and application of a specific method for the quantitative determination of wine esters by headspace-solid-phase microextraction-gas chromatography-mass spectrometry. *Food Chem.*, **121**: 1236-1245.

2. OIV (2014) *Recueil des Méthodes Internationales d'Analyse des Vins*. Paris: Office International de la Vigne et du Vin.

3. Renault P., Miot-Sertier C., Marullo P. et al (2009). Genetic characterization and phenotypic variability in *Torulaspora delbrueckii* species: potential applications in the wine industry. *Int. J. Food Microbiol.* **134**: 201-210.
# Importance of lactic acid bacteria strain on hydroxylated branched ester level variation

M. GAMMACURTA<fnref n="1" />,<fnref n="2" />,<fnref n="3" />*, S. MARCHAND<fnref n="1" />,<fnref n="2" />, G. LYTRA<fnref n="1" />,<fnref n="2" />, J.C. BARBE<fnref n="1" />,<fnref n="2" />, V. MOINE<fnref n="3" />, G. DE REVEL<fnref n="1" />,<fnref n="2" />

Lactic acid bacteria – fruity aroma – hydroxylated branched esters

## Introduction

Malolactic fermentation (MLF) allows, among other things, microbial stability of wines by substrate depletion. These secondary bacterial metabolisms are responsible for chemical modifications affecting the olfactory and gustative perception of wine. The most frequently reported aromatic compound associated with MLF is diacetyl (2,3-butandione), largely released by lactic acid bacteria (LAB) [1] and associated with an increase of wine buttery character [2]. Ethyl lactate is another marker of bacterial activity [3], however, its impact on fruity aroma modulation is quite limited, contrary to other esters. Indeed, these aromatic compounds are considered as one of the most important families of fruity compound in wines [4]. For that reason, several studies have investigated the influence of microorganisms, especially of LAB, on ester levels in red wines. However, no real consensus has been established; some authors reported that MLF coincides with a significant increase in ester concentration [5], while others highlighted opposite findings [6]. These diverging results could be explained on the one hand by the use of different LAB strains between these studies and on the other hand by a matrix effect resulting of both cultivar and yeast strain used to carry on alcoholic fermentation (AF) [7].

In order to better understand the contribution of LAB strain to the ester composition of red wines, the purpose of this study was to investigate the influence of MLF with different *Oenococcus oeni* strains on ester levels. To evaluate the impact of the matrix on ester LAB metabolism, this work was conducted over two vintages, on two cultivars, Merlot and Cabernet Sauvignon. To complete this investigation, AF was made by inoculating different *Saccharomyces cerevisiae* starters.

## Materials and methods

### Winemaking conditions

Six single-varietal red wines from 2011 and 2012 vintages were sampled before MLF, after MLF and during wine aging in five different wineries. AF was initiated by inoculation with rehydrated dry yeast; three *S. cerevisiae* strains were tested. MLF were carried out using starters or indigenous strains in triplicate for all experimentations. The winemaking conditions varied following the samples as summarized in Table 1.

<table><caption>Table 1 - Origin of samples</caption><thead><tr><td>Vineyard</td><td>Cultivar</td><td>Vintage</td><td>Yeast strains</td><td>LAB strains</td></tr></thead><tbody><tr><td>WEC11</td><td>Cabernet Sauvignon</td><td>2011</td><td>522D, FX10, XR</td><td>B28, 450</td></tr><tr><td>MRGX11</td><td>Cabernet Sauvignon</td><td>2011</td><td>522D, FX10</td><td>Indigenous</td></tr><tr><td>MDC11</td><td>Merlot</td><td>2011</td><td>522D, XR</td><td>B28, 450, indigenous</td></tr><tr><td>WEC12</td><td>Cabernet Sauvignon</td><td>2012</td><td>522D, FX10, XR</td><td>B28, 450</td></tr><tr><td>PCLN12</td><td>Cabernet Sauvignon</td><td>2012</td><td>522D, FX10</td><td>B28, 450</td></tr><tr><td>STEM12</td><td>Merlot</td><td>2012</td><td>522D, XR</td><td>B28, 450</td></tr></tbody></table>

<footnote n="1">¹Univ. de Bordeaux, ISVV, EA 4577, Unité de Recherche Œnologie, 33882 Villenave d'Ornon, France</footnote>

<footnote n="2">²INRA, ISVV, USC 1366 Œnologie, 33882 Villenave d'Ornon, France</footnote>

<footnote n="3">³Biolaffort, BP 17, F-33072 Bordeaux Cedex 15, France</footnote>
### Ester analysis

Thirty-two esters were quantified by HS-SPME-GC/MS according to the method validated by Antalick *et al* [8]. The concentration of two hydroxylated branched ethyl esters (ethyl 2-hydroxy-3-methylbutanoate (E2H3MB) and ethyl 2-hydroxy-4-methylpentanoate or ethyl leucate (E2H4MP)) as well as the levels of their enantiomeric forms were measured by liquid-liquid extraction and GC/MS analysis based on the method developed by Lytra *et al* [9] and Gammacurta [10], respectively. Olfactory thresholds of E2H3MB and E2H4MP were determined orthonasally in red wine, using triangular tests (AFNOR NF EN ISO 4120, 2007) with ascending concentrations.

### Data analysis

Data were tested with an ANOVA followed by a Tukey post hoc test to identify differences between groups using a 95 % confidence interval. Statistical analyses were performed using XLSTAT 2015.1.03.15659 (Addinsoft, Paris, France).

## Results and discussion

Statistical analyses do not permit to highlight yeast x LAB interaction effect, indicating that the yeast strain used to conduct AF does not seem to influence ester content in wines after MLF regardless of the bacteria strain [11]. Therefore, and for more clarity, results presented here will only focus on one yeast strain (522D).

The concentration of principal odorant esters such as ethyl esters or acetates decreased following MLF, regardless of the experiment considered. The decrease of these compounds was more important, in proportion, for long chain acetates, as observed for ethyl esters. Results were less clear for minor compounds. In particular, the variation in ethyl propanoate level was highly dependent on the experimentation considered. The concentration of other odd number ethyl esters (5 to 9 carbons) and methyl esters (4 to 10 carbons) remained stable in WEC-11 and LP-11 wines, whereas a decrease of their concentration was observed after MLF in the other wines.

In contrast with linear esters, the concentration of four branched esters (ethyl 2-methylpropanoate, ethyl 2-methylbutanoate, ethyl 3-methylbutanoate, ethyl 2-phenylacetate) quantified in this study increased after MLF in wines from all experimentations. The quantification of these compounds during wine aging showed that their concentration still increased over time (Figure 1a, b) in a range of 20 to 40 %, depending on the esters and the matrix considered. Moreover, no significant difference could be highlighted regarding to the LAB strain, no matter what experimentation regarded. Thus, the LAB strain used to conduct MLF does not seem to be an important factor for the synthesis of these aromatic molecules in the present study.

<figure><img src="image_100.png" /><figcaption>**Fig. 1** - Evolution of mean concentration (µg/L, n=3) in ethyl 2-methylpropanoate (a), ethyl 2-methylbutanoate (b), ethyl 2-hydroxy-3-methylbutanoate (c) and ethyl 2-hydroxy-4-methylpentanoate (d) in WEC12, PCLN12 and STEM12 wines fermented with the yeast 522D and two different bacteria strains.</figcaption></figure>
Two other branched ethyl esters with a hydroxyl group, E2H3MB and E2H4MP, were also quantified. Their concentration increased after MLF in all wines from all experimentations, as observed for branched esters. The observed variations in ester levels were influenced by the matrix but also by the LAB strain used during MLF. The quantification of these two aromatic compounds during wine aging showed that their concentration increased over time (Figure 1c, d).

<figure><img src="image_101.png" /></figure>

<figure><img src="image_102.png" /></figure>

<figure><img src="image_103.png" /></figure>

<figure><img src="image_104.png" /></figure>

**Fig. 2** - Chromatograms obtained after the analysis on chiral column of ethyl 2-hydroxy-3-methylbutanoate (a, c; E2H3MB) and ethyl 2-hydroxy-4-methylpentanoate (b, d; E2H4MP) commercial racemic mixture (a, b) and of a PCLN wine extract after MLF (c, d).

E2H3MB and E2H4MP have one asymmetrical carbon atom in position 2, indicating the possibility of two different enantiomers. The analysis of chromatograms showed that only the R form of E2H3MB (Figure 2c) or E2H4MP (Figure 2d) were found in all wines of this study after MLF. Lytra *et al* [9] had already observed that young red wines contained only the R form, whereas aged wines presented both enantiomeric forms in various ratios, according to age. However, this is the first time that the influence of the LAB strain on the concentration of these two aromatic compounds is clearly demonstrated. Campo *et al* [12] revealed the presence in wine of these two compounds, and hypothesized that they could be important contributors to some of the specific fruity notes of wines. Falcão *et al* [13] then assessed the organoleptic impact of E2H4MP, suggesting that this compound contributed to fresh blackberry aromas. Results of sensory analyses carried on WEC, PCLN12 and STEM12 wines, presented in a previous study [11], highlighted the impact of the yeast strain on fruity aroma modulation, whereas no LAB strain impact could ever be demonstrated. Moreover, the perception thresholds of R-E2H3MB (51 000 µg/L) and R-E2H4MP (10 000 µg/L) in red wine were largely higher than the levels found in these wines. Despite a clear significant difference in E2H3MB and E2H4MP concentration observed between wines fermented with B28 or 450 LAB strain, these two compounds seem to have a limited direct impact on overall red wine flavor.

## Conclusion

In summary, our findings indicate that MLF can significantly influence the ester composition of red wines from different Bordeaux regions, made with different cultivars over two vintages. The concentration of principal esters as acetates and ethyl esters decreased after MLF, whereas levels of branched esters increased, irrespective of the LAB strain considered. Conversely, LAB strain has a strong influence on the concentration of hydroxylated branched esters. These results also demonstrate that LAB seems to synthesize only the R enantiomer of E2H3MB and E2H4MP. Further experiments are needed to elucidate the mechanisms involved in the biosynthesis of these odorous compounds by *O. oeni*, as well as the impact
of different bacterial starters to definitively confirm their interest as a remarkable marker of the MLF. Sensory investigations are also needed to elucidate their impact on red wine fruity aroma, in purpose to establish a correlation between the synthesis by *O. oeni* and the flavor modifications associated with MLF.

## Acknowledgements

The authors thank Château Goudichaud, Château des Graviers, Château Léoville-Poyferré, Château Latour-Martillac and the Union des Producteurs de Saint-Emilion for providing samples.

## Abbreviations

AF, alcoholic fermentation; MLF, malolactic fermentation; LAB, lactic acid bacteria; E2H3MB, ethyl 2-hydroxy-3-methylbutanoate; E2H4MP, ethyl 2-hydroxy-4-methylpentanoate.

## References

1. Bertrand A, Zmirou-Bonnamour C, Lonvaud-Funel A (1984) Aroma compounds formed by malolactic bacteria, pp. 39-49. *In: Foundation for Biotechnical and Industrial Fermentation Research*. Nykanen L, Lehtonen P (eds.), Helsinki.

2. Bartowsky EJ, Henschke PA (2004) The “buttery” attribute of wine - diacetyl - desirability, spoilage and beyond. *Int J Food Microbiol* **96**: 235-252.

3. Boido E, Carrau F, Dellacassa E et al (1999) Modificaciones producidas por la fermentacion malolactica en la composicion aromatica de les fracciones libre y ligada para la variedad Tannat. Mendoza, Argentina.

4. Antalick G, Perello MC, de Revel G (2014) Esters in wines: new insight through the establishment of a database of french wines. *Am J Enol Vitic* **65**: 293-304.

5. Delaquis P, Cliff M, King M et al (2000) Effect of two commercial malolactic cultures on the chemical and sensory properties of Chancellor wines vinified with different yeasts and fermentation temperatures. *Am J Enol Vitic* **51**: 42-48.

6. Gámbaro A, Boido E, Zlotejablko A et al (2001) Effect of malolactic fermentation on the aroma properties of Tannat wine. *Aust J Grape Wine Res* **7**: 27-32.

7. Antalick G, Perello MC, de Revel G (2012) Characterization of fruity aroma modifications in red wines during malolactic fermentation. *J Agric Food Chem* **60**: 12371-12383.

8. Antalick G, Perello MC, de Revel G (2010) Development, validation and application of a specific method for the quantitative determination of wine esters by headspace-solid-phase microextraction-gas chromatography-mass spectrometry. *Food Chem* **121**: 1236-1245.

9. Lytra G, Tempere S, de Revel G et al (2012) Distribution and organoleptic impact of ethyl 2-hydroxy-4-methylpentanoate enantiomers in wine. *J Agric Food Chem* **60**: 1503-1509.

10. Gammacurta M (2014) Approches sensorielle et analytique de l'arôme fruité des vins rouges : influence relative des levures et des bactéries lactiques. *Thèse de doctorat*, Université de Bordeaux.

11. Gammacurta M, Marchand S, Albertin W et al (2014) Impact of yeast strain on ester levels and fruity aroma persistence during aging of Bordeaux red wines. *J Agric Food Chem* **62**: 5378-5389.

12. Campo E, Cacho J, Ferreira V (2006) Multidimensional chromatographic approach applied to the identification of novel aroma compounds in wine. Identification of ethyl cyclohexanoate, ethyl 2-hydroxy-3-methylbutyrate and ethyl 2-hydroxy-4-methylpentanoate. *J Chromatogr A* **1137**: 223-230.

13. Falcao LD, Lytra G, Darriet P et al (2012) Identification of ethyl 2-hydroxy-4-methylpentanoate in red wines, a compound involved in blackberry aroma. *Food Chem* **132**: 230-236.
# Residual fungicides compromise the beginning of alcoholic fermentation in grape juice with a considerable impact on wine quality and safety

P. RUSSO<fnref n="1" />, T. NARDI<fnref n="2" />, N. SIECZKOWSKI<fnref n="3" />, G. SPANO<fnref n="4" />*, V. CAPOZZI<fnref n="1" />

Fungicide – wine – yeast – alcoholic fermentation

## Introduction

Grey mould (Botrytis cinerea), powdery mildew (Erysiphe necator) and downy mildew (Plasmopara viticola) are responsible for serious yield loss in the wine sector. They are the most damaging diseases of cultivated grapes (Vitis vinifera) worldwide [1, 2], leading to severe injuries and resulting in significant commercial losses. The use of phytosanitary preparations in order to reduce the incidence of these viticulture pests can lead to the presence of fungicide residues under the legal limit from the grapes up to the corresponding wines [3-5]. Moreover, it is necessary to consider the possibility that the fungicides could be added without respecting the prescription of fungicide producers, with residues over the limits. Although chemical fungicides have been used to combat these problems, they can be nonspecific and therefore act on organisms other than the target fungus, including other naturally occurring beneficial organisms [6, 7]. Fungicide residues can lead to modifications in the structure of the cellular membranes of the yeast and affect their specific function. In Figure 1, an overview of the main effects of fungicides on yeast performances in fermented beverages is reported. Metalaxyl-M is the main optical isomer of metalaxyl. The EU pesticide residues and maximum residue levels (mg/kg) of metalaxyl and metalaxyl-M is 1 mg/kg for wine grapes [8]. To the best of our knowledge, no studies have been conducted to assess the impact of commercial preparation of metalaxyl-M on yeast performances in fermented beverages.

## Materials and methods

### Fungicide and media preparation

A commercial fungicide preparation was added to YPD and MRS broth to achieve a final concentration of 1 mg/L, a dose equal to the maximum residue levels (MRLs) established for metalaxyl-M by European legislation in wine grapes [8].

### Yeasts and lactic acid bacteria inoculation

In this study we used four commercial strains of Saccharomyces cerevisiae (Fervens SLC, Elegance, Enartis Ferm SB, Lalvin RBS133) and two indigenous S. cerevisiae strains (I6 and E4). Non-Saccharomyces spp. yeasts used in this study were Hanseniaspora guilliermondii, Hanseniaspora uvarum, Hanseniaspora pulcherrima, Issatchenkia orientalis, Issatchenkia terricola, Kluyveromyces thermotolerans, Candida zemplinina, Torulaspora delbrueckii, Metschnikowia pulcherrima, Pichia B05A36, and Pichia B05A29. Lactic Acid Bacteria (LAB) investigated in this study were strains belonging to the species Lactobacillus brevis, Lactobacillus hilgardii, Lactobacillus plantarum, Leuconostoc mesenteroides, and Pediococcus parvulus. Yeasts and LAB were routinely grown at 30 °C on YEPD and MRS (Oxoid, Italia), respectively.

<footnote n="1">¹Promis Biotech srl, via Napoli 25, 71122, Foggia, Italy</footnote>

<footnote n="2">²Lallemand Italia, via Rossini 14/B, Castel d'Azzano, VR, 37060, Italy</footnote>

<footnote n="3">³Lallemand SAS, R&D Oenology, Blagnac, France</footnote>

<footnote n="4">⁴Department of Agriculture, Food and Environment Sciences, University of Foggia, via Napoli 25, 71122, Foggia, Italy</footnote>
*Growth curves in media contaminated with the pesticide*

The growth of *Saccharomyces cerevisiae*, non-*Saccharomyces* spp. yeasts, and LAB was monitored during 30 h of incubation at 30 °C, on YEPD and MRS artificially contaminated or not with the commercial fungicide preparation.

*Small-volume vinification assay*

For the small-volume vinification assay, the amount of the commercial fungicide preparation required to achieve the metalaxil-M EU limit in grapevine was added in a total volume of 150 mL of must. The trials were performed using unfiltered “Nero di Troia” must without the grape skins. Commercial and autochthonous *S. cerevisiae* strains were inoculated at an initial concentration of about 1 x 10⁷ cfu/mL in must supplemented or not with the pesticide. Fermentation progress was monitored by weight loss (indicative of CO₂ generation) measurement at 20 °C.

*Small-volume vinification assay in complex microbial ecosystem*

Small-volume vinification assay was repeated at the same conditions reported above by co-inoculating each *S. cerevisiae* strain with two mixtures of non-*Saccharomyces* and LAB strains. The non-*Saccharomyces* yeasts were divided in two panels. Panel A was formed by *H. pulcherrima*, *K. thermotolerans*, *I. orientalis*, *C. zemplinina*, *M. pulcherrima*, and *T. delbrueckii*. Panel B was formed by *H. guilliermondii*, *H. uvarum*, *I. terricola*, *T. delbrueckii*, *Pichia B05A36*, and *Pichia B05A29*. Commercial and autochthonous *S. cerevisiae*, and non-*Saccharomyces* strains were inoculated at an initial concentration of about 1 x 10⁷ cfu/mL. Both yeast mixtures were also contaminated with a mix of LAB containing a concentration of 10⁴ cfu/mL of each culture. Fermentation was monitored by weight loss (indicative of CO₂ generation) measurement at 20 °C.

**Results and discussion**

In this work we studied the influence of a commercial fungicide preparation containing metalaxyl-M (4.85%) as main active principle, a pesticide commonly used in viticulture to fight the oomycete pathogen downy mildew (*Plasmopara viticola*), on the growth of *Saccharomyces* and non-*Saccharomyces* yeasts and LAB of oenological interest, with the aim to highlight its effects on the fermentation process.

<figure><img src="image_105.png" /><figcaption>**Fig. 1** - Growth curves of *S. cerevisae* was monitored in synthetic media supplemented or not with a fungicide commercial preparation to obtain a final concentration of 1 mg/L of metalaxil.</figcaption></figure>

In the past, this molecule was investigated for their actions on wine microorganisms [9-11], but at the best of our knowledge, no study has considered the negative implications of this commercial formulation on wine microbiology.
In a preliminary assay, the growth of *Saccharomyces* spp., non-*Saccharomyces* spp. and LAB was monitored in a synthetic culture media supplemented or not with the pesticide at a concentration corresponding to the EU limit in grapevine. Growth of *S. cerevisiae* was always lower in pesticide-enriched media when compared with the pesticide-free media. In particular, Elegance and Fervens SLC were the strains mainly affected by the occurrence of Ridomil (Figure 1). Similarly, non-*Saccharomyces* strains showed a reduction in their growth rate in media with the pesticide. Interestingly, the growth of *H. uvarum*, *H. guillermondi*, *I. terricola*, *T. delbrueckii*, and *Pichia* spp. was completely inhibited in media containing Ridomil, while *H. pulcherrima*, *K. thermotolerans*, *I. orientalis*, and *C. zempilina* were more resistant at the same conditions (data not shown). In contrast, the presence of Ridomil in MRS broth did not affect in any way the growth of the tested LAB (data not shown).

In a subsequent step, we monitored the fermentation process of must artificially contaminated with the same concentration of Ridomil. Trials were performed by using "Nero di Troia", a wine made from "Uva di Troia" grapes, an autochthonous black grape variety from the Apulian region (south of Italy). Commercial and indigenous strains of *S. cerevisiae* were inoculated to start the fermentation in microvinification assays. As reported in Table 1A, we found that in uncontaminated must, fermentation took place regularly, resulting in approximately 14 g of weight reduction within 2 weeks. In contrast, when must was supplemented with the pesticide, a slower and incomplete fermentation occurred (Table 1B). The weight loss after 2 weeks was less than 5 g for all the investigated strains (Table 1B).

<table><caption>**Table 1** - Weight reduction (g) monitored during 14 days in must not (A) or contaminated (B) with a Ridomil preparation containing 1 mg/L of metalaxil and inoculated with *S. cerevisiae* strains</caption><thead><tr><th>Strain</th><th>1</th><th>4</th><th>5</th><th>6</th><th>7</th><th>8</th><th>11</th><th>12</th><th>13</th><th>14</th><th>Total</th></tr></thead><tbody><tr><td>I6</td><td>1.555</td><td>8.839</td><td>1.170</td><td>0.765</td><td>0.672</td><td>0.510</td><td>0.522</td><td>0.070</td><td>0.000</td><td>0.000</td><td>14.103</td></tr><tr><td>E4</td><td>1.501</td><td>8.739</td><td>0.991</td><td>0.657</td><td>0.534</td><td>0.429</td><td>0.453</td><td>0.102</td><td>0.053</td><td>0.059</td><td>13.518</td></tr><tr><td>Ferm HT</td><td>1.435</td><td>8.544</td><td>1.023</td><td>0.641</td><td>0.454</td><td>0.283</td><td>0.046</td><td>0.000</td><td>0.000</td><td>0.000</td><td>12.426</td></tr><tr><td>Suprared HG</td><td>1.180</td><td>9.162</td><td>1.514</td><td>0.894</td><td>0.539</td><td>0.579</td><td>0.910</td><td>0.202</td><td>0.189</td><td>0.061</td><td>15.230</td></tr><tr><td>MS-08</td><td>1.296</td><td>8.501</td><td>0.859</td><td>0.590</td><td>0.529</td><td>0.596</td><td>0.861</td><td>0.200</td><td>0.200</td><td>0.064</td><td>13.696</td></tr><tr><td>PDM</td><td>1.438</td><td>8.487</td><td>1.196</td><td>0.835</td><td>0.746</td><td>0.598</td><td>0.894</td><td>0.152</td><td>0.103</td><td>0.029</td><td>14.478</td></tr></tbody></table>

<table><thead><tr><th>Strain</th><th>1</th><th>4</th><th>5</th><th>6</th><th>7</th><th>8</th><th>11</th><th>12</th><th>13</th><th>14</th><th>Total</th></tr></thead><tbody><tr><td>I6</td><td>0.163</td><td>0.451</td><td>0.970</td><td>0.727</td><td>0.676</td><td>0.433</td><td>0.264</td><td>0.229</td><td>0.201</td><td>0.158</td><td>4.272</td></tr><tr><td>E4</td><td>0.153</td><td>0.732</td><td>1.232</td><td>0.776</td><td>0.579</td><td>0.295</td><td>0.064</td><td>0.144</td><td>0.108</td><td>0.101</td><td>4.184</td></tr><tr><td>Ferm HT</td><td>0.185</td><td>0.696</td><td>1.078</td><td>0.843</td><td>0.759</td><td>0.384</td><td>0.256</td><td>0.190</td><td>0.046</td><td>0.073</td><td>4.510</td></tr><tr><td>Suprared HG</td><td>0.156</td><td>0.602</td><td>1.091</td><td>0.801</td><td>0.685</td><td>0.344</td><td>0.220</td><td>0.167</td><td>0.092</td><td>0.086</td><td>4.244</td></tr><tr><td>MS-08</td><td>0.164</td><td>0.737</td><td>1.172</td><td>0.743</td><td>0.604</td><td>0.341</td><td>0.000</td><td>0.072</td><td>0.071</td><td>0.126</td><td>4.030</td></tr><tr><td>PDM</td><td>0.197</td><td>0.624</td><td>1.235</td><td>0.743</td><td>0.610</td><td>0.391</td><td>0.141</td><td>0.174</td><td>0.111</td><td>0.110</td><td>4.336</td></tr></tbody></table>

If incomplete fermentations occur, microbial communities other than *Saccharomyces*, including non-*Saccharomyces*, spoilage yeasts, or lactic acid bacteria, could proliferate. A recent study has investigated the non-*Saccharomyces* biodiversity during the early steps of spontaneous alcoholic fermentation of "Nero di Troia" wines, providing the basis for an improved management of non-*Saccharomyces* in typical Apulian wines [12]. However, fermentation under uncontrolled conditions is a risk from a technological and safety point of view. Therefore, in order to investigate the effect of the presence of residual pesticides on the fermentation in a more complex ecosystem resampling the microbial conditions of the must, "Nero di Troia" must be co-inoculated with each *S. cerevisiae* strain and two mixtures containing commercial and autochthonous non-*Saccharomyces* yeasts and LAB strains. In particular, the non-*Saccharomyces* yeasts were divided in two panels on the basis of their tolerance to the pesticide: Panel A formed by the more RG resistant species and Panel B formed by the more RG susceptible species.

The results showed that in uncontaminated must, fermentation took place regularly (Table 2A). In contrast, fermentations always stucked when must was supplemented with a Ridomil preparation containing 1 mg/L metalaxyl-M (Table 2B). The higher weight reduction was observed for the co-inoculum Elegance-PB and E4-PB in untreated and treated samples, respectively (Table 2).
<table><caption>**Table 2** - Weight reduction (g) monitored during 14 days in must not (A) or contaminated (B) with a Ridomil preparation containing 1 mg/L of metalaxil and inoculated with *S. cerevisiae* strains and two mixtures (PA and PB) of non-*Saccharomyces* + LAB</caption><thead><tr><th></th><th>Strain</th><th>1</th><th>3</th><th>7</th><th>8</th><th>9</th><th>10</th><th>11</th><th>14</th><th>Total</th></tr></thead><tbody><tr><td rowspan="14">A</td><td>Must</td><td>0.419</td><td>1.649</td><td>6.345</td><td>0.662</td><td>0.555</td><td>0.416</td><td>0.255</td><td>0.699</td><td>11.000</td></tr><tr><td>Must</td><td>0.810</td><td>2.020</td><td>3.671</td><td>0.246</td><td>0.233</td><td>0.202</td><td>0.158</td><td>0.469</td><td>7.809</td></tr><tr><td>I6</td><td>1.189</td><td>2.233</td><td>5.287</td><td>0.479</td><td>0.373</td><td>0.225</td><td>0.191</td><td>0.675</td><td>10.652</td></tr><tr><td>E4</td><td>1.221</td><td>1.936</td><td>3.967</td><td>0.349</td><td>0.264</td><td>0.195</td><td>0.112</td><td>0.063</td><td>8.107</td></tr><tr><td>Lallemand</td><td>1.211</td><td>2.177</td><td>5.512</td><td>0.738</td><td>0.616</td><td>0.53</td><td>0.451</td><td>1.268</td><td>12.503</td></tr><tr><td>ENARTIS</td><td>1.041</td><td>1.908</td><td>6.075</td><td>1.064</td><td>0.907</td><td>0.785</td><td>0.574</td><td>1.446</td><td>13.800</td></tr><tr><td>DAL CIN</td><td>1.162</td><td>2.066</td><td>5.409</td><td>0.518</td><td>0.326</td><td>0.219</td><td>0.203</td><td>0.798</td><td>10.701</td></tr><tr><td>MAURIVIN</td><td>1.295</td><td>2.345</td><td>7.281</td><td>1.243</td><td>1.048</td><td>0.861</td><td>0.805</td><td>2.500</td><td>17.378</td></tr><tr><td>Must</td><td>1.063</td><td>1.953</td><td>6.056</td><td>0.985</td><td>0.882</td><td>0.735</td><td>0.577</td><td>1.743</td><td>13.994</td></tr><tr><td>I6</td><td>1.213</td><td>1.901</td><td>5.716</td><td>0.817</td><td>0.721</td><td>0.635</td><td>0.612</td><td>1.971</td><td>13.586</td></tr><tr><td>E4</td><td>1.294</td><td>2.064</td><td>6.142</td><td>1.004</td><td>0.930</td><td>0.819</td><td>0.690</td><td>2.170</td><td>15.113</td></tr><tr><td>Lallemand</td><td>1.432</td><td>2.355</td><td>6.827</td><td>1.083</td><td>0.993</td><td>0.882</td><td>0.747</td><td>2.338</td><td>16.657</td></tr><tr><td>ENARTIS</td><td>1.326</td><td>2.184</td><td>6.895</td><td>1.215</td><td>1.104</td><td>0.926</td><td>0.747</td><td>2.182</td><td>16.579</td></tr><tr><td>DAL CIN</td><td>1.304</td><td>2.074</td><td>6.632</td><td>1.126</td><td>0.981</td><td>0.842</td><td>0.710</td><td>1.827</td><td>15.496</td></tr><tr><td>MAURIVIN</td><td>1.462</td><td>2.552</td><td>8.108</td><td>1.586</td><td>1.473</td><td>1.269</td><td>1.098</td><td>3.209</td><td>20.757</td></tr><tr><td rowspan="14">B</td><td>Must</td><td>0.262</td><td>0.272</td><td>2.250</td><td>0.677</td><td>0.287</td><td>0.156</td><td>0.129</td><td>0.754</td><td>4.787</td></tr><tr><td>Must</td><td>0.276</td><td>0.305</td><td>2.550</td><td>0.724</td><td>0.335</td><td>0.213</td><td>0.184</td><td>0.853</td><td>5.440</td></tr><tr><td>I6</td><td>0.214</td><td>0.225</td><td>2.396</td><td>0.635</td><td>0.232</td><td>0.090</td><td>0.058</td><td>0.427</td><td>4.277</td></tr><tr><td>E4</td><td>0.203</td><td>0.206</td><td>2.121</td><td>0.614</td><td>0.227</td><td>0.120</td><td>0.078</td><td>0.478</td><td>4.047</td></tr><tr><td>Lallemand</td><td>0.173</td><td>0.188</td><td>2.052</td><td>0.668</td><td>0.230</td><td>0.075</td><td>0.080</td><td>0.478</td><td>3.944</td></tr><tr><td>ENARTIS</td><td>0.176</td><td>0.184</td><td>1.851</td><td>0.590</td><td>0.181</td><td>0.056</td><td>0.049</td><td>0.488</td><td>3.575</td></tr><tr><td>DAL CIN</td><td>0.233</td><td>0.251</td><td>2.055</td><td>0.686</td><td>0.255</td><td>0.104</td><td>0.102</td><td>0.750</td><td>4.436</td></tr><tr><td>MAURIVIN</td><td>0.252</td><td>0.274</td><td>2.133</td><td>0.847</td><td>0.341</td><td>0.177</td><td>0.162</td><td>0.780</td><td>4.966</td></tr><tr><td>Must</td><td>0.250</td><td>0.272</td><td>2.098</td><td>0.685</td><td>0.286</td><td>0.162</td><td>0.146</td><td>0.877</td><td>4.776</td></tr><tr><td>I6</td><td>0.258</td><td>0.298</td><td>2.323</td><td>0.788</td><td>0.295</td><td>0.175</td><td>0.138</td><td>0.683</td><td>4.958</td></tr><tr><td>E4</td><td>0.348</td><td>0.389</td><td>2.675</td><td>0.789</td><td>0.388</td><td>0.229</td><td>0.347</td><td>1.599</td><td>6.764</td></tr><tr><td>Lallemand</td><td>0.264</td><td>0.295</td><td>2.095</td><td>0.724</td><td>0.340</td><td>0.185</td><td>0.136</td><td>1.004</td><td>5.043</td></tr><tr><td>ENARTIS</td><td>0.269</td><td>0.297</td><td>2.149</td><td>0.742</td><td>0.334</td><td>0.140</td><td>0.109</td><td>0.642</td><td>4.682</td></tr><tr><td>DAL CIN</td><td>0.227</td><td>0.263</td><td>1.634</td><td>0.793</td><td>0.410</td><td>0.131</td><td>0.102</td><td>0.714</td><td>4.274</td></tr><tr><td>MAURIVIN</td><td>0.276</td><td>0.326</td><td>2.018</td><td>0.839</td><td>0.379</td><td>0.195</td><td>0.141</td><td>0.745</td><td>4.919</td></tr></tbody></table>

Interestingly, after about 7 days of fermentation, a thick gelatinous coating appeared on the surface of treated must, while in untreated must this coating was never detected. This finding suggested that the arrest of fermentation deviated the winemaking towards alterations of the wine, probably due to the metabolism of spoilage LAB or acetic acid bacteria (data not shown).

## Conclusion

Fungicides are becoming less accepted by the wine industry because of the rising awareness of science, policies, and consumers for the increase of resistant strains and for their negative impact on human health and on the environment [2]. In contrast, their use might increase: recent scientific publications suggest that climate changes may increase vineyard's susceptibility to fungal pests [13-14].

We assessed the impact of a commercial fungicide formulation containing metalaxil-M on the alcoholic fermentation performances of five commercial preparation of dried yeast used in oenology. Our results, for the first time, demonstrated that fungicide preparations might cause complete inhibition of alcoholic fermentation. These findings suggest a major attention to fungicide management in viticulture/oenology, given that the economic losses avoided using chemical pesticides to fight grey mould, powdery mildew, and downy mildew have to be compared with the risks of economic losses due to fermentation stuck.
## Acknowledgements

This research was supported by the Apulian Region in the framework of the “OenoMicroManagement” project (PIF - Progetti Integrati di Filiera) and by a grant from the project PON02_00186_3417512, “S.I.Mi.S.A.”

## Abbreviations

MLF, malolactic fermentation; AF, alcoholic fermentation; LAB, lactic acid bacteria; WL, Wallestein laboratory.

## References

1. Weng K, Li Z-Q, Liu R-Q et al (2014) Transcriptome of *Erysiphe necator*-infected *Vitis pseudoreticulata* leaves provides insight into grapevine resistance to powdery mildew. *Hort Res* **1**: 14049.

2. Jacometti MA, Wratten SD, Walter M (2010) Review: alternatives to synthetic fungicides for *Botrytis cinerea* management in vineyards. *Aust J Grape Wine Res* **16**: 154-172.

3. Edder P, Ortelli D, Viret O et al (2009) Control strategies against grey mould (*Botrytis cinerea* Pers.: Fr) and corresponding fungicide residues in grapes and wines. *Food Addit Contam* **26**: 719-725.

4. González-Rodríguez RM, Cancho-Grande B, Simal-Gándara J (2011) Decay of fungicide residues during vinification of white grapes harvested after the application of some new active substances against downy mildew. *Food Chem* **125**: 549-560.

5. Vaquero-Fernández L, Sanz-Asensio J, Fernández-Zurbano P et al (2013) Determination of fungicide pyrimethanil in grapes, must, fermenting must and wine. *J Sci Food Agric* **93**: 1960-1966.

6. Ochiai N, Fujimura M, Oshima M et al (2002) Effects of iprodione and fludioxonil on glycerol synthesis and hyphal development in *Candida albicans*. *Biosci Biotechnol Biochem* **66**: 2209-2215.

7. Calhelha RC, Andrade JV, Ferreira IC et al (2006) Toxicity effects of fungicide residues on the wine-producing process. *Food Microbiol* **23**: 393-398.

8. European Commission (2008) *Commission Regulation (EC) No. 149/2008 of 29 January 2008 amending Regulation (EC) No. 396/2005 of the European Parliament and of the Council by establishing Annexes II, III and IV setting maximum residue levels for products covered by Annex I thereto*. Official Journal, L58, 1-398.

9. Cabras P, Angioni A (2000) Pesticide residues in grapes, wine, and their processing products. *J Agric Food Chem* **48**: 967-973.

10. Cabras P, Angioni A, Garau VL et al (1997) Fate of some new fungicides (Cyprodinil, Fludioxonil, Pyrimethanil, and Tebuconazole) from vine to wine. *J Agric Food Chem* **45**: 2708-2710.

11. Oliva J, Cayuela M, Paya P et al (2007) Influence of fungicides on grape yeast content and its evolution in the fermentation. *Commun Agric Appl Biol Sci* **72**: 181-189.

12. Garofalo C, Russo P, Beneduce L et al (2015) Non-*Saccharomyces* biodiversity in wine and the “microbial terroir”: a survey on Nero di Troia wine from the Apulian region, Italy. *Ann Microbiol* **66**: 143-150.

13. Caffarra A, Rinaldi M, Eccel E et al (2012) Modelling the impact of climate change on the interaction between grapevine and its pest and pathogens: European grapevine moth and powdery mildew. *Agric Ecosyst Environ* **148**: 89-101.

14. Mozell MR, Thach L (2014) The impact of climate change on the global wine industry: challenges and solutions. *Wine Econ Policy* **3**: 81-89.
# Effect of water activity on grape berry microbiota from vineyards subject to different microclimates

G. MARTINS<fnref n="1" />,<fnref n="2" />*, C. CASINI<fnref n="3" />, L. GENY<fnref n="1" />, A. LONVAUD<fnref n="1" />, I. MASNEUF-POMAREDE<fnref n="1" />,<fnref n="2" />

Grape berries – water activity – microbial population – metabolic activity

## Introduction

Grape berries are a primary source of microbial communities that play a prominent role in crop health and also interfere with the winemaking process [1]. The epiphytic microbial community of grape berries is influenced by several factors, biotic and abiotic [2]. Despite previous studies, the impact of factors such as the microclimate has never been addressed till now.

The climate and/or the microclimate (the distinctive climate of a small-scale area) are important components involved in the viticulture concept of terroir [3], which posits that the microclimate plays an essential role in grape composition and quality, and can differentiate dramatically the quality of grapes within the same wine region. To date, no study has analyzed the impact of the microclimate on grape berry microbiota.

In this work, we studied four vineyards in the same geographic area (sharing similar characteristics but in two different regions characterized by specific microclimate). We characterized and compared the microbial communities of those two wine appellations and investigated the factors which could explain the differences between them.

## Materials and methods

### *Sampling design*

This study was conducted on 2010 vintage, in the Libourne wine area (southwest France), in two different wine appellations: Pomerol (44°55'52'' N, 0°12'16'' W, 34 m altitude) and Puisseguin St Emilion (44°55'26'' N, 0°04'38'' W, 91 m altitude). These two wine growing regions are characterized by specific climatological conditions. In 2010, Pomerol had an annual average temperature of 19.36 °C, average annual relative humidity of 69.55 % and average annual precipitation of 74.50 mm. In Puisseguin, the values were 18.03 °C, 74.47 % and 76.00 mm, respectively.

Three sampling points, each corresponding to five vines, were selected in each vineyard. Samples were collected at harvest ripe. At each sampling, approximately 1 kg of undamaged grapes with their pedicels attached were aseptically removed from several bunches and put in sterile bags. Grapes were transported to the laboratory in refrigerated boxes and analyzed within 12 h after collection.

### *Microbial biomass recovery and DNA extraction*

250 undamaged berries were dispersed in an isotonic solution [4] and used to inoculate the medium, and the rest was filtered through 0.2-µm pore, 47-mm ø cellulose acetate filter and DNA was extracted.

### *Colony isolation, counting and identification*

Cell suspensions were cultured on 1/10 diluted LB and LT medium. From each medium, 30 colonies were picked. 16S rDNA for bacteria and the D1/D2 variable domains of the ribosomal DNA large-subunit for yeasts were sequenced [4].

<footnote n="1">¹USC Œnologie - INRA, Université Bordeaux Segalen, ISVV, Villenave d'Ornon, France</footnote>

<footnote n="2">²Bordeaux Sciences Agro, Gradignan cedex, France</footnote>

<footnote n="3">³Department of Agriculture, Forestry and Food Science, University of Turin, Italy</footnote>

<footnote>*Corresponding author: guilherme.martins@agro-bordeaux.fr</footnote>
## Water activity (Aw) in grape skins

For each sample, 14 berries were chosen at random (the pedicel was surrounded with paraffin). We used an Aw-Sprint (Novasina) as Aw-meter.

## Microbial community structure analysis

T-RFLP: a nested PCR: 8F and 1406R, secondly 8F (5': 6-FAM) and 1063R [6]. Digestion with *Hae* III or *Hinf* I and profiles were normalized [5, 6]. CE-SSCP: D1/D2 domain of the LSU rRNA gene was amplified using NL3A and NL4 primers [5].

## Results and discussion

Results revealed that sizes of the cultivable communities (bacteria, yeast and filamentous fungi) were higher in Puisseguin samples (Table 1 B). The identification of isolates showed that some of the fungal genera were specific to a wine appellation: *Penicillium* and *Metschnikowia* in Puisseguin and *Cryptococcus* in Pomerol (Figure 1 A). For bacteria, we also observed that some genera were specific to a wine appellation: *Brevibacterium*, *Cellulomonas*, *Microbacterium*, *Paenibacillus*, *Rhizobium*, *Rhodococcus* and *Sphingomonas* in Puisseguin, and *Arthrobacter*, *Burkholderia*, *Curtobacterium*, *Pantoea*, *Streptomyces* and *Staphylococcus* in Pomerol (Figure 1 B).

We characterized the bacterial and fungal community structure by T-RFLP and CE-SSCP respectively; the results indicated that the diversity of the microbial community profiles was, in all samples, higher on Puisseguin vineyards (Table 1 A).

<table><caption>**Table 1** - Diversity of microbial community structure and cultivable population determined by plate counting</caption><thead><tr><th rowspan="2"></th><th colspan="2">A - Microbial community structure</th><th colspan="2">B - Cultivable Population</th></tr><tr><th>CE-SSCP (H')</th><th>T-RFLP (H')</th><th>LT medium</th><th>LB -10 medium</th></tr></thead><tbody><tr><td>Pomerol</td><td>3.59<br>(+/- 0.01)</td><td>1.22<br>(+/- 0.04)</td><td>3.89<br>(+/- 0.02)</td><td>3.95<br>(+/- 0.07)</td></tr><tr><td>Puisseguin</td><td>4.01**<br>(+/- 0.02)</td><td>1.62***<br>(+/- 0.01)</td><td>4.25*<br>(+/- 0.05)</td><td>4.40*<br>(+/- 0.01)</td></tr></tbody></table>

A) Shannon diversity index (H') obtained from CE-SSCP and T-RFLP profiles, values in parentheses represent standard deviation (n = 6). B) Cultivable populations, results are expressed as log 10 CFU/g of fresh weight, determined by plate counting on LT medium and LB 1/10 medium. Significant differences (one way ANOVA) between Pomerol and Puisseguin samples are represented as follows: * p < 0.05, ** p < 0.005, *** p < 0.001.

<figure><img src="image_106.png" /><figcaption>**Fig. 1** - Abundance of cultivable fungal (A) and microbial (B) genera in grape samples in the different vineyards. Bar plots show genus relative percentages of isolates in each vineyard.</figcaption></figure>
Due to its importance in microbial growth [7], we assayed the water activity (Aw) in grape skins of Puisseguin and Pomerol samples (Figure 2). The Aw analyses revealed significant higher values in Puisseguin samples (0.945 ± 0.004) compared to Pomerol samples (0.908 ± 0.001). We compared these values with the plate counting on LT and 1/10 diluted LB medium, and we noted that Aw had a significant positive effect on the cultivable population of bacteria (Pomerol samples: R2 = 0.90 p < 0.01; Puisseguin samples: R2 = 0.96 p < 0.01) and yeast (Pomerol samples: R2 = 0.87 p < 0.01; Puisseguin samples: R2 = 0.89 p < 0.01) (Figure 3).

<figure><img src="image_107.png" /><figcaption>**Fig. 2** - Water activity (Aw) on grape skins. Color code: black, Pomerol; gray, Puisseguin. Asterisks indicate categories that are significantly different between the two subjects (p < 0.05).</figcaption></figure>

<figure><img src="image_108.png" /><figcaption>**Fig. 3** - Correlation between water activity and population. Blue diamonds and blue triangles represent Pomerol and Puisseguin samples from organic and conventional farming system, respectively. A) LT population (Pomerol samples: R2 = 0.87 p < 0.01; Puisseguin samples: R2 = 0.89 p < 0.01), B) 1/10 diluted LB population (Pomerol samples: R2 = 0.90 p < 0.01; Puisseguin samples: R2 = 0.96 p < 0.01).</figcaption></figure>

Our results showed that microclimate could impact the epiphytic microbial population of grapes. The cooler and humid vineyards of Puisseguin presented a higher cultivable population compared to Pomerol (drier and warmer). Some genera were specific to a wine appellation. T-RFLP and CE-SSCP data showed that the population structure in Puisseguin was more diverse. The analyses of Aw revealed to be one factor responsible for the differences between these two wine appellations. These results suggest that climatic conditions can impact the grape skin Aw, and consequently the microbial communities.

## Acknowledgements

The authors wish to acknowledge funding from the Conseil Interprofessionnel du Vin de Bordeaux (CIVB), and the owners of Puisseguin and Pomerol vineyards for their kind cooperation in sampling.
Abbreviations

Aw, water activity; T-RFLP, terminal restriction fragment length polymorphism; CE-SSCP, capillary electrophoresis single strand conformation polymorphism; LSU, large subunit.

References

1. Pretorius IS (2000) Tailoring wine yeast for the new millennium: novel approaches to the ancient art of winemaking. *Yeast* **16**: 675-729.

2. Barata A, Malfeito-Ferreira M, Loureiro V (2012) The microbial ecology of wine grape berries. *Int J Food Microbiol* **153**: 243-259.

3. Van Leeuwen C, Seguin G (2006) The concept of terroir in viticulture. *J Wine Res* **17**: 1-10.

4. Martins G (2012) Communautés microbiennes de la baie de raisin : Incidence des facteurs biotiques et abiotiques. *Thèse de doctorat*, École doctorale des Sciences de la vie et de la santé, Bordeaux, France.

5. Dunbar J, Ticknor LO, Kuske CR (2001) Phylogenetic specificity and reproducibility and new method for analysis of terminal restriction fragment profiles of 16S rRNA genes from bacterial communities. *Appl Environ Microbiol* **67**: 190-197.

6. Smith CJ, Danilowicz BS, Clear AK et al (2005) T-Align, a web-based tool for comparison of multiple terminal restriction fragment length polymorphism profiles. *FEMS Microbiol Ecol* **54**: 375-380.

7. Deytieux-Belleau C, Geny L, Roudet J et al (2009) Grape berry skin features related to ontogenic resistance to Botrytis cinerea. *Eur J Plant Pathol* **125**: 551-563.
# Copper and sulfur in vitro tolerance of wine relevant strains: a focus on the genera Candida and Metschnikowia

C. GRANGETEAU*, C. VOELCKER, T. BOIRON, H. ALEXANDRE, M. GUILLOUX-BENATIER, S. ROUSSEAUX

UMR Procédés Alimentaires et Microbiologiques, Equipe VAIMIS (Vin, Aliment, Microbiologie, Stress), AgroSup Dijon-Université de Bourgogne, Institut Universitaire de la Vigne et du Vin, Jules Guyot, BP 27877 21078 Dijon Cedex, France

Non-Saccharomyces yeasts – copper – sulfur

## Introduction

The most common products used for vine protection are copper and sulfur, whatever the type of farming (conventional, organic, etc.). The effectiveness of these two non-specific products has been widely studied on their respective targets but the effects on other organisms such as yeasts present on grapes remain poorly documented. Impacts of chemical fungicides and biological treatments (based on copper and/or sulfur) were compared and reported by Milanovic et al. [1]. The diversity of yeast species isolated in the organic vineyard seems weak compared to the diversity of yeasts isolated in conventional vineyard: for example, the species *Metschnikowia pulcherrima*, widely found in conventional samples, has been found occasionally in biological samples.

Few studies have focused on the resistance to copper or sulfur of yeasts isolated from the environment. Vadkertiová et al. [2] studied the resistance to copper of yeast strains belonging to 15 species isolated from water, soil and tree leaves and showed a very high intraspecific and interspecific variation regarding the resistance to copper. No strain is able to grow at a concentration greater than 10 mM, but this concentration is tolerated by *M. pulcherrima*, *Pichia americana* and *Cryptococcus laurentii*. Amiranashvili et al. [3] studied the ability of more than 170 yeast strains isolated on vine from flowering to maturity to degrade 2 pesticides containing copper oxychloride (Cihom Blue and Neoram). These authors showed that the impact on yeast growth is different depending on the concentration of copper oxychloride. Only 22% of strains tested were grown in the presence of Cihom Blue (5 g/L) and 44% in the presence of Neoram (4 g/L). Cordero-Bueso et al. [4] evaluated the sulfur resistance of yeast strains belonging to 9 species isolated from grape must and spontaneous fermentations. *Torulaspora delbrueckii*, *Rhodotorula mucilaginosa* and *Hanseniaspora guilliermondii* species are able to resist to 10 mg/L of sulfur. In contrast, the species *Candida apicola* and *M. pulcherrima* are inhibited by a sulfur concentration of 2.5 mg/L and 10 mg/L, respectively.

The objective of this work was to study i) the tolerance to copper and sulfur of non-Saccharomyces yeasts isolated from grape must and ii) the intraspecific variability of the tolerance to copper and sulfur of strains tested.

## Materials and methods

### *Yeast strains and culture conditions*

The yeast isolates studied are from the IUVV collection and have been isolated from a plot of Chardonnay in a Burgundy vineyard (Mâcon, France). A total of 37 isolates of the *Candida* and 76 isolates of the genus *Metschnikowia* were studied. Initially, a growth curve was performed for all isolates in order to normalize the experiment to study the resistance to copper and sulfur. After a preculture of 24 to 72 hours at 30°C in YPD medium, a defined volume of each preculture was deposited in liquid YPD medium to a final volume of 2 mL and an absorbance at 600 nm of 0.05 and incubated at 30°C. The growth of each isolate was carried out by spectrophotometry readings at 660 nm (A<sub>660nm</sub>), after a linear agitation for 30 seconds, every four hours for 32 hours. This growth monitoring was performed in 24-well microtiter plates.
*Copper and sulfur resistance*

For the experimentation in presence of copper, after a preculture of 24 to 72 hours, a defined volume of each preculture was deposited in liquid YPD medium to a final volume of 2 mL and an A<sub>660nm</sub> of 0.05 and incubated at 30°C. Copper was added to YPD liquid medium as CuSO<sub>4</sub> (0 to 10 mM Cu), and yeast growth was measured by monitoring the absorbance during 30 hours (standard inoculation: A<sub>660nm</sub> = 0.05) at 30°C.

For the effect of sulfur, yeasts were cultivated on YPD solid medium containing increasing amounts (0 to 500 mM S) of Thiovit (Syngenta, France). Combined effect between sulfur and copper was also tested for strains able to grow with 250 mM sulfur. The experiment was conducted in YPD solid medium in the presence of 250 mM sulfur and increasing concentrations of copper (0 to 5 mM).

**Results and discussion**

Copper and sulfur tolerance of oenological interest non-*Saccharomyces* yeasts belonging to the genera *Candida* and *Metschnikowia* was evaluated. Figure 1 shows the percentage of isolates inhibited in function of different copper concentrations tested. All tested strains are resistant to more than 2 mM, demonstrating a high resistance to copper compared to other yeasts isolated from different environments. Indeed, Vadkertiová et al. [2] have shown that depending on the environment studied, between 60% and 90% of yeasts are not tolerant to a copper concentration of 2 mM.

<figure><img src="image_109.png" /><figcaption>Fig. 1 - Percentage of isolates of the genera *Candida* and *Metschnikowia* whose growth is inhibited by various copper concentrations.</figcaption></figure>

Yeast resistance to copper is variable depending on the genus. More than 20% of the *Candida* isolates are resistant to 6.5 mM and 40% to 5 mM, while no isolates of the genus *Metschnikowia* are able to grow at 6.5 mM and only 25% at 5 mM. Moreover, a high variability exists between isolates of the same genus. For the genus *Metschnikowia*, differentiation could not be performed between the 2 species *M. fructicola* and *M. pulcherrima*. For the genus *Candida*, all isolates belong to the species *Candida zemplinina*, now renamed *Starmerella bacillaris*. The use of FT-IR spectroscopy allowed to discriminate isolates at the strain level and demonstrated a strain-dependent tolerance to copper (data not shown).

The sulfur resistance of the tested yeasts also varies depending on the genus (Figure 2). The genus *Metschnikowia* is less affected by sulfur than the genus *Candida*. Indeed, more than 95% of the *Metschnikowia* isolates are able to grow with 500 mM sulfur and all isolates with 250 mM sulfur, while only 10% and 57% of isolates of the genus *Candida* are able to grow with 500 mM and 250 mM sulfur, respectively. These results confirm those obtained by Cordero-Bueso et al. [4] which reported a greater resistance to sulfur for the species *Metschnikowia pulcherrima* compared to different species of the genus *Candida*. In our work, resistance to sulfur is very high for all isolates of the genus *Metschnikowia*, whereas for the species *Starmerella bacillaris* it is strain-dependent like for the copper (data not shown).

Concerning the combined effect of sulfur and copper, the presence of sulfur does not change the copper resistance of most yeasts. However, 4% of the isolates tested, all belonging to the genus *Metschnikowia*, are not able to grow in the presence of the two products simultaneously.
<figure><img src="image_110.png" /><figcaption>**Fig. 2** - Percentage of isolates of the genera *Candida* and *Metschnikowia* whose growth is inhibited by various sulfur concentrations.</figcaption></figure>

These results show that the application of these fungicides in the vineyard could change the diversity of the genera, species or strains of yeasts present on grapes and/or found in musts. This impact could notably explain the differences in yeast diversity reported by several authors [1, 5] between organic vineyards and conventional vineyards. The use of these fungicides seems to select more resistant yeast strains compared to isolates of other environments [2].

## Conclusion

The effect of the two main fungicides used in viticulture (copper and sulfur) on yeast of oenological interest belonging to the genus *Candida* and *Metschnikowia* was demonstrated. The genus *Candida* seems to be more resistant to copper than the genus *Metschnikowia*, although it may be noted that the resistance is strain-dependent. Conversely, all isolates belonging to the genus *Metschnikowia* are very resistant to sulfur. For the genus *Candida*, this sulfur resistance seems very strain-dependent, and a low percentage of strains are highly resistant to sulfur. Further works are needed to study the resistance mechanisms of these non-*Saccharomyces* yeasts, mechanisms currently little studied and described. Moreover, the study of the resistance of other yeast genera could help to better understand the effects of the use of copper and sulfur fungicides on the yeast communities on berries or in grape musts.

## Acknowledgements

This work was funded by the Regional Council of Burgundy and the Interprofessional Office of Burgundy wines.

## References

1. Milanović V, Comitini F, Ciani M (2013) Grape berry yeast communities: influence of fungicide treatments. *Int J Food Microbiol* **161**: 240-246.

2. Vadkertiová R, Sláviková E (2006) Metal tolerance of yeasts isolated from water, soil and plant environments. *J Basic Microbiol* **46**: 145-152.

3. Amiranashvili LL, Gagelidze N, Varsimashvili K et al (2011) Screening of yeasts from Georgia to degrade copper oxychloride containing pesticides. *In 34th World Congress of Vine and Wine*.

4. Cordero-Bueso G, Arroyo T, Valero E (2014) A long term field study of the effect of fungicides penconazole and sulfur on yeasts in the vineyard. *Int J Food Microbiol* **189**: 189-194.

5. Martins G, Vallance J, Mercier A et al (2014) Influence of the farming system on the epiphytic yeasts and yeast-like fungi colonizing grape berries during the ripening process. *Int J Food Microbiol* **177**: 21-28.
# Effect of grape must saturation with carbon dioxide at the beginning of alcoholic fermentation: valorisation of species of interest

L. CHASSERIAUD<sup>1,2*</sup>, J. COULON<sup>1</sup>, M. BELY<sup>2</sup>

Wine yeast – carbon dioxide – fermentation – organoleptic properties

## Introduction

During the early stages of winemaking, non-*Saccharomyces* yeasts are usually predominant. However, due to their low ethanol tolerance and microbial competition, *Saccharomyces cerevisiae* develops rapidly and becomes predominant and the main agent of alcoholic fermentation [1]. Among the non-*Saccharomyces* species frequently isolated from must, the species *Hanseniaspora uvarum* and *Candida zemplinina* are considered as being the most predominant. Less frequently, *Metschnikowia pulcherrima* can also be present at fairly high populations. In addition, several minor species have also been isolated, such as *Torulaspora delbrueckii*, *Pichia kluyveri*, *Hanseniaspora guilliermondi*, *Issatchenkia orientalis*, *Zygosaccharomyces bailii*, *Pichia anomala*, etc. [2-5].

The impact of CO₂ on microorganisms has been widely studied, essentially on bacteria and particularly when it is used at high pressure [6, 7]. The presence of CO₂ in culture media is required for the growth and survival of microorganisms. Indeed, CO₂ is necessary for several biological processes like amino and tricarboxylic acid formation. If it is totally absent from the culture media, yeast growth is inhibited [8]. In yeast, CO₂ fixation by *S. cerevisiae* was observed in several culture conditions [9, 10]. Nevertheless, CO₂ is also known to have an inhibitory effect. Gaseous CO₂ is widely used in the food industry to prevent the proliferation of spoilage microorganisms. Inhibition is much higher when CO₂ concentration and pressure increase, temperature decreases and in presence of glucose and ethanol [11-13]. Plasmic membrane is the first target of CO₂ where it can diffuse easily causing anaesthesia. Anaesthesia occurs when foreign molecules occupy a critical volume of the membrane [14, 15]. This leads to structural and functional modifications. Then, accumulation of CO₂ in the cytoplasm disturbs enzyme activities by decreasing intracellular pH or modifying the equilibrium between carboxylation and decarboxylation reactions [7, 16-18]. In 1976, Chen and Gutmanis [19] observed the effect on *S. cerevisiae* of unpressured CO₂ under conditions favourable for biomass production in bioreactors. In this study, portions of nitrogen in the airstream of the bioreactor were replaced by CO₂. A drop in population occurred when 40% of nitrogen was replaced by CO₂. At 80% (1.1 g/L of dissolved CO₂) a strong decrease in fermentation rate was observed.

Non-*Saccharomyces* yeasts are known to contribute either positively or negatively to the organoleptic characteristics of wine. Some factors are known to influence yeast growth in oenological conditions: temperature, dissolved oxygen, turbidity, etc. [5, 20]. The aim of this work is to see how the addition of gaseous carbon dioxide (CO₂) in must could affect wine yeast and particularly if this factor can help us to eliminate undesirable non-*Saccharomyces* (*H. uvarum*, *C. zemplinina*) and favour species of interest (*T. delbrueckii*, *Metschnikowia* spp., etc.).

## Materials and methods

### *Yeast strains*

Five non-*Saccharomyces* species were used in this study: *Torulaspora delbrueckii*, *Metschnikowia* spp. (99% of identity with *M. andauensis*), *Candida zemplinina*, *Pichia kluyveri* and *Hanseniaspora uvarum*. One *Saccharomyces cerevisiae* strain was also included (Table 1).

<footnote n="1"><sup>1</sup>BioLaffort, 33100 Bordeaux, France</footnote>

<footnote n="2"><sup>2</sup>Univ. Bordeaux, ISVV, EA 4577, Œnologie, F-33140 Villenave d'Ornon, France</footnote>
## Preculture conditions

Except for *S. cerevisiae* and *T. delbrueckii*, two preculture steps were necessary before fermentation. First, strains were grown at 24°C for 24h in YPD-based medium containing 1% yeast extract (w/v, Difco Laboratories, Detroit, MI), 1% Bacto peptone (w/v, Difco), and 2% glucose (w/v). Then, cultures were transferred in half diluted grape must and grown at 24°C for 24h. For *S. cerevisiae* and *T. Delbrueckii*, only growth in half diluted grape must was performed.

<table><caption>**Table 1 - Oenological strains used for alcoholic fermentations**</caption><thead><tr><th>Species</th><th>Strain</th><th>Collection</th></tr></thead><tbody><tr><td>Torulaspora delbrueckii</td><td>Alpha</td><td>Laffort, Bordeaux, France</td></tr><tr><td>Metschnikowia spp.</td><td>L0563</td><td></td></tr><tr><td>Candida zemplinina</td><td>PE401</td><td>Centre de Ressources Biologiques Œnologie, Bordeaux, France</td></tr><tr><td>Pichia kluyveri</td><td>Pk.31</td><td></td></tr><tr><td>Hanseniaspora uvarum</td><td>L0666</td><td></td></tr><tr><td>S. cerevisiae</td><td>X5</td><td>Laffort, Bordeaux, France</td></tr></tbody></table>

## Alcoholic fermentations

Alcoholic fermentations were carried out in Sauvignon Blanc grape must from the Bordeaux region (sugars 197 g/L, assimilable nitrogen 121 mg/LN, free SO₂ 15 mg/L, total acidity 4.02 g/L H₂SO₄) at 18°C with agitation and in 125-mL bioreactors. The different strains were inoculated at 10⁶ viable cells/mL in pure cultures. Two conditions were tested for each strain: saturation with gaseous CO₂ (added in bioreactors to obtain a dissolved concentration of 1.2 g/L corresponding to saturation in grape must) and without saturation (control). Saturation was done just after inoculation.

## Cell enumeration

Yeast growth during fermentation was monitored by flow cytometry with a Cell Lab Quanta™ (Beckman Coulter, USA) equipped with a 488-nm laser. Viability was measured using propidium iodide.

## Growth parameters

Several growth and fermentation parameters were calculated for each species: lag phase (time between inoculation and beginning of growth, h), growth rate (g.L⁻¹.h⁻¹), maximum population size (viable cells. mL⁻¹).

## Data analysis

All fermentations were performed in triplicate. R software was used for statistical analysis (R Development Core Team, 2008). Variance heteroscedasticity led us to apply non-parametric test (Kruskal-Wallis) to determine the difference between the fermentations. The statistical level of significance was set at p≤0.05.

# Results and discussion

No variation of pH was noticed in the fermentation medium when CO₂ was added. All growth kinetics parameters are presented in table 2. All fermentations with *S. cerevisiae*, with and without CO₂, were completed. Nevertheless, in the presence of CO₂, *S. cerevisiae* growth is slightly inhibited with a decrease in growth rate and maximum population size.

Concerning non-*Saccharomyces* species, in terms of growth kinetics, two behaviours can be distinguished. First, *T. delbrueckii* and *M. pulcherrima* are stimulated in the presence of CO₂, the growth rate and maximum population being enhanced. On the other hand, CO₂ has a negative impact on *C. zemplinina*, *P. kluyveri* and *H. uvarum*.

The use of carbon dioxide could be interesting in winemaking. Indeed, *C. zemplinina* and *H. uvarum* are very sensitive to CO₂ and are known to produce undesirable compounds such as H₂S, volatile acidity, etc. In contrast, *T. delbrueckii* and *M. pulcherrima* are stimulated by this gas and can impact positively wine aroma [21-24]. Moreover, CO₂ also delays the growth and the beginning of alcoholic fermentation in *S. cerevisiae*, which could allow non-*Saccharomyces* yeast to develop easily.

As for *T. delbrueckii* and *M. pulcherrima*, *P. kluyveri* can enhance wine aroma but saturation with CO₂ does not seem to be a good way to stimulate this species.
<table><caption>Table 2 - Growth parameters</caption><thead><tr><th>Species</th><th>Condition</th><th>Lag phase (h)</th><th>Maximum growth rate (h<sup>-1</sup>)</th><th>Maximum population size (viable cells/mL)</th></tr></thead><tbody><tr><td rowspan="2">S. cerevisiae</td><td>Control</td><td>2.3<sup>a</sup> ± 0.4</td><td>0.15<sup>a</sup> ± 2.9x10<sup>-2</sup></td><td>1.6x10<sup>8a</sup> ± 1.1x10<sup>7</sup></td></tr><tr><td>CO<sub>2</sub></td><td>2.6<sup>a</sup> ± 0.6</td><td>0.08<sup>b</sup> ± 1.2x10<sup>-3</sup></td><td>9.6x10<sup>7b</sup> ± 2.4x10<sup>6</sup></td></tr><tr><td rowspan="2">T. delbrueckii</td><td>Control</td><td>1.8<sup>a</sup> ± 1.0</td><td>0.15<sup>b</sup> ± 1.5x10<sup>-2</sup></td><td>2.1x10<sup>8b</sup> ± 9.0x10<sup>6</sup></td></tr><tr><td>CO<sub>2</sub></td><td>1.6<sup>a</sup> ± 0.8</td><td>0.19<sup>a</sup> ± 4.0x10<sup>-3</sup></td><td>2.8x10<sup>8a</sup> ± 2.2x10<sup>6</sup></td></tr><tr><td rowspan="2">Metschnikowia spp.</td><td>Control</td><td>1.7<sup>a</sup> ± 0.3</td><td>0.11<sup>b</sup> ± 8.8x10<sup>-3</sup></td><td>1.0x10<sup>8b</sup> ± 5.4x10<sup>6</sup></td></tr><tr><td>CO<sub>2</sub></td><td>1.5<sup>a</sup> ± 0.1</td><td>0.13<sup>a</sup> ± 6.1x10<sup>-3</sup></td><td>1.5x10<sup>8a</sup> ± 7.7x10<sup>6</sup></td></tr><tr><td rowspan="2">P. kluyveri</td><td>Control</td><td>2.4<sup>b</sup> ± 0.2</td><td>0.18<sup>a</sup> ± 1.5x10<sup>-2</sup></td><td>4.6x10<sup>7a</sup> ± 1.9x10<sup>6</sup></td></tr><tr><td>CO<sub>2</sub></td><td>3.4<sup>a</sup> ± 0.1</td><td>0.09<sup>b</sup> ± 4.5x10<sup>-4</sup></td><td>2.5x10<sup>7b</sup> ± 1.2x10<sup>6</sup></td></tr><tr><td rowspan="2">C. zemplinina</td><td>Control</td><td>1.1<sup>b</sup> ± 0.2</td><td>0.12<sup>b</sup> ± 1.3x10<sup>-3</sup></td><td>1.8x10<sup>8a</sup> ± 4.8x10<sup>6</sup></td></tr><tr><td>CO<sub>2</sub></td><td>2.0<sup>a</sup> ± 0.1</td><td>0.10<sup>b</sup> ± 5.8x10<sup>-3</sup></td><td>8.7x10<sup>7b</sup> ± 1.2x10<sup>6</sup></td></tr><tr><td rowspan="2">H. uvarum</td><td>Control</td><td>3.4<sup>b</sup> ± 0.3</td><td>0.09<sup>a</sup> ± 2.3x10<sup>-4</sup></td><td>4.2x10<sup>7a</sup> ± 1.2x10<sup>6</sup></td></tr><tr><td>CO<sub>2</sub></td><td>7.1<sup>a</sup> ± 0.2</td><td>0.03<sup>b</sup> ± 1.8x10<sup>-4</sup></td><td>7.9x10<sup>6b</sup> ± 1.9x10<sup>6</sup></td></tr></tbody></table>

a,b: groups resulting from Kruskal-Wallis test, p≤0.05

## Conclusion

Saturating grape must with CO₂ at the beginning of alcoholic fermentation could be an interesting way to inhibit undesirable species and favour the interesting ones. The effects observed on growth are not due only to a lack in oxygen concentration as saturation with nitrogen does not have the same impact on yeasts (results not shown). More experiments need to be carried out with more strains and in mixed cultures to confirm the impact of CO₂ on the modulation of non-*Saccharomyces* yeasts.

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2. Jolly NP, Augustyn OPH, Pretorius IS (2003) The occurrence of non-*Saccharomyces cerevisiae* yeast species over three vintages in four vineyards and grape musts from four production regions of the Western Cape, South Africa. *S Afr J Enol Vitic* **24**: 35-42.

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4. Combina M, Mercado L, Borgo P et al (2005) Yeasts associated to Malbec grape berries from Mendoza, Argentina. *J Appl Microbiol* **98**: 1055-1061.

5. Zott K, Miot-Sertier C, Claisse O et al (2008) Dynamics and diversity of non-*Saccharomyces* yeasts during the early stages in winemaking. *Int J Food Microbiol* **125**: 197-203.

6. Garcia-Gonzalez L, Geeraerd AH, Spilimbergo S et al (2007) High pressure carbon dioxide inactivation of microorganisms in foods: the past, the present and the future. *Int J Food Microbiol* **117**: 1-28.

7. Jones RP, Greenfield PF (1982) Effect of carbon dioxide on yeast growth and fermentation. *Enzyme Microb Technol* **4**: 210-223.

8. Rockwell GE, Higberger JH (1927) The necessity of carbon dioxide for the growth of bacteria, yeasts and molds. *J Infect Dis* **40**: 438-446.

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12. Gill CO, Tan KH (1979) Effect of carbon dioxide on growth of *Pseudomonas fluorescens*. *Appl Environ Microbiol* **38**: 237-240.

13. King AD, Nagel CW (1975) Influence of carbon dioxide upon the metabolism of *Pseudomonas aeruginosa*. *J Food Sci* **40**: 362-366.
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# Metabolic impact of redox perturbations on the formation of aroma compounds during alcoholic fermentation

A. BLOEM<sup>1,2,3*</sup>, I. SANCHEZ<sup>1,2,3</sup>, S. DEQUIN<sup>1,2,3</sup>, C. CAMARASA<sup>1,2,3</sup>

*Saccharomyces cerevisiae* – redox balance – alcoholic fermentation – aroma compounds

## Introduction

During alcoholic fermentation, *S. cerevisiae* produces a variety of volatile metabolites, including acetate esters, ethyl fatty acid esters, higher alcohols, volatile fatty acids and volatile sulfur compounds that contribute to the fruity and floral profile of wine. These volatile compounds are formed through a complex and dynamic metabolic process during fermentation and many factors have been reported as controlling their production: grape variety, yeast strain and fermentation conditions (temperature, micronutrients and nitrogen availability in the must) [1].

The redox status of yeast cells could be an additional parameter involved in the qualitative and quantitative modulation of the profile of fermentative compounds. Maintaining the balance between NAD/NADH and NADP/NADPH, which are the main contributors to redox metabolism, is a fundamental requirement to sustain metabolism and growth in *S. cerevisiae*. These cofactors, involved in more than 300 reactions in yeast, are among the most highly connected molecules in metabolic networks [2]. Understanding the links between the cell redox status and the concentrations of volatile compounds in wine has important implications with respect to winemaking parameter such as oxygen and its impact on wine quality. The objective of this study was to gain a better understanding of the effect of the redox balance on the synthesis and regulation of volatile compounds. To this end, the NAD(P)H oxidation was increased under fermentative conditions by the reduction of acetoin to 2,3-butanediol in strains overexpressing native NADH- or engineered NADPH-dependent butanediol dehydrogenase (Bdh) in the presence of acetoin.

## Materials and methods

### Strains

The strains used were derived from the *S. cerevisiae* haploid strain 59A. Strains NADH-Bdh and NADPH-Bdh were obtained by overexpressing the NADH-dependent 2,3-butanediol dehydrogenase (Bdh1p) or a mutated version of this protein [3] with a NADPH cofactor specificity [4].

### Fermentation medium and growth conditions

Batch fermentations were performed in 1-L bioreactors equipped to maintain anaerobiosis, at 24°C, with permanent stirring. Fermentation experiments were carried out in synthetic medium, as described previously [5], containing 100 g L⁻<fnref n="1" /> glucose and 100 g L⁻<fnref n="1" /> fructose at pH 3.4. Acetoin was added at 200 mM at the beginning of the fermentation. Fermentations were performed at 24°C. CO₂ release was determined by automatic measurement of fermentor weight loss every 20 min.

### Metabolite quantification

Glucose, acetate, fructose, glycerol and succinate were analyzed by HPLC using an Aminex HPX-87 ion exchange column. Volatile compounds were determined by GC/MS after dichloromethane extraction procedure with the Siebert GC/MS method [6].

<footnote n="1"><sup>1</sup>INRA, UMR1083 *Sciences pour l'Œnologie, Montpellier, France*</footnote>

<footnote n="2"><sup>2</sup>SupAgro, UMR1083 *Sciences pour l'Œnologie, Montpellier, France*</footnote>

<footnote n="3"><sup>3</sup>Université de Montpellier, UMR1083 *Sciences pour l'Œnologie, Montpellier, France*</footnote>
## Statistical analysis

Statistical analyses were performed using R software, version 3.1.1 [7]. To obtain a general overview of the final production of volatile compounds, a principal component analysis (PCA) was performed using the FactoMineR package [8]. Each variable was then tested using a one-way analysis of variance (ANOVA) with the conditions (strains overexpressing NADH or NADPH-Bdh with 0 or 200 mM acetoin) as a factor to detect a global effect. In the figures, conditions sharing the same letter are not significantly different at a 0.05 threshold.

# Results and discussion

## Impact of NADPH and NADH perturbations on aroma compounds

We investigated the impact of an increased demand of cofactors (NADH and NADPH) on aroma compounds whose synthesis involves several aldehyde/alcohol dehydrogenases sensitive to NAD(P)/NAD(P)H ratios. Fermentations with the strains NADH-Bdh and NADPH-Bdh grown without acetoin addition were used as control for the analysis of the incidence on aroma compound formation of NADH and NADPH perturbation, respectively. We found that an increased NADH or NADPH oxidation markedly changed the production of fermentative aroma. Furthermore, the metabolic variations were more important in response to a NADH imbalance compared with a modification of the NADPH demand. A principal component analysis (PCA) was carried out to get a general overview of the incidence of redox modifications on the formation of aroma (Figure 1).

<figure><img src="image_111.png" /></figure>

**Fig. 1** - Principal component analysis of 59A-NADH-Bdh or 59A-NADPH-Bdh strains supplemented or not with 200 mM acetoin for 26 volatile aroma compounds, based on mean values calculated from 3 replicates. (A) Individual factor map: NADH-Bdh in the presence of 200 mM acetoin (blue) or without acetoin (orange), and for NADPH-Bdh in the presence of 200 mM acetoin (green) or without acetoin (violet); (B) Variable factor map. On the x axis is the percentage of variation explained by principal component 1 (Dim 1; 56.98%); on the y axis is the percentage of variation explained by principal component 2 (Dim 2; 12.84%). ISO: Isobutanol, IBA: Isobutyric acid, BA: butyric acid, EA: ethyl acetate, EP: ethyl propionate, ISA: isobutyl acetate, PRA: propyl acetate, PR: propanol, PA: propionic acid, DA: decanoic acid, OA: octanoic acid, DDA: dodecanoic acid, EH: ethyl hexanoate, EB: ethyl butanoate, ED: ethyl decanoate, EO: ethyl octanoate, EDD: ethyl dodecanoate, EIB: ethyl isobutyrate, MBA: 2-methyl butanoic acid, PEA: phenyl ethyl acetate, ME: methionol, PHE: phenylethanol, IAA: isoamyl acetate, IA: isoamyl alcohol, IVA: isovaleric acid, HA: hexanoic acid.

The final productions of volatile compounds by the strains NADH-Bdh and NADPH-Bdh grown with or without 200 mM acetoin addition were used as quantitative variables. The first two axes accounted for 70% of the total variations and independently separated fermentations with NADH or NADPH imbalance from
the control cultures (NADH-Bdh and NADPH-Bdh without acetoin). The strain NADH-Bdh grown in the presence of acetoin was differentiated along the first axis on the basis of a higher formation of propanol and isobutanol and their derivatives as well as a lower synthesis of medium chain fatty acids and their ethyl esters. This axis also contributed to the discrimination of the NADPH-perturbation modality but to a lesser extent and together with the second axis, mainly defined by the variables isoamyl alcohol, isoamyl acetate, methionol and 2 phenyl ethyl acetate.

Effect of the redox perturbation on the medium chain fatty acid (MCFA) and (MCFA) ethyl ester formation

We then focused on the production of medium chain fatty acids (MCFA) and their ethyl ester derivatives. Contrary to other MCFA, the formation of both butanoic acid (the shortest MCFA) and ethylbutanoate were almost unchanged due to a redox imbalance (Figure 2). These results suggest the existence of other enzymes that can support their synthesis or the existence of several metabolic pathways behind the formation of butyric acid. Indeed, Rollero *et al.* [9] suggested isobutyryl-CoA could be converted to butanoyl-CoA, the precursor of ethyl butanoate.

<figure><img src="image_112.png" /><figcaption>**Fig. 2** - Average contents in µM/g DW of medium chain fatty acids and ethyl esters produced by NADH-Bdh and NADPH-Bdh strains in response to redox perturbation (200 mM acetoin). Conditions with the same letter do not differ significantly at Tukey's test (p<0.05). Metabolite concentrations for NADH-Bdh without acetoin (orange) or in the presence of 200 mM acetoin (blue), and for NADPH-Bdh without acetoin (violet) or in the presence of 200 mM acetoin (green).</figcaption></figure>
Further synthesis of MCFA consists in C2-unit elongation of the carbon chain provided by the active malonyl-CoA that requires NADPH. In line with the cofactor requirements for MCFA elongation metabolism, the formation of MCFA (hexanoic, octanoic, decanoic acids) was reduced by 30% on average, irrespective of the nature of the cofactor. In the same way, fatty acid esters deriving from the combination of MCFA with ethanol exhibited similar patterns in relation to redox demand. The NADPH specificity of fatty acid synthase excludes a direct regulation of MCFA production by NADH cofactor availability. One of the most likely explanations for the decrease in MCFA formation with an increased NADH and NADPH demand is a limitation of the intracellular pool of their acetyl-CoA precursor. Moreover, the down-regulation of the gene *EEB1* encoding for the main acyl-CoA-ethanol O-acyltransferase, responsible for ethyl ester formation [10] in response to an increased NAD(P)H oxidation, may also contribute to the reduced production of this family of volatile compounds [11].

Conclusion

These data provide a wide picture of the effects of NADH or NADPH on the aroma network. As esters are central components of the fruity character, a better understanding of the regulation of this network will help to better manage the aroma levels in fermented beverages with improved sensorial quality.

References

1. Swiegers J.H., Bartowski EJ, Henschke PA et al (2005) Yeast and bacterial modulation of wine aroma and flavour. *Aust J Grape Wine Res* **11**: 139-173.

2. Nielsen J (2003) It is all about metabolic fluxes. *J Bacteriol* **185**: 7031-7035.

3. Ehsani M, Fernandez MR, Biosca JA et al (2009) Reversal of coenzyme specificity of 2, 3-butanediol dehydrogenase from *Saccharomyces cerevisiae* and in vivo functional analysis. *Biotechnol Bioeng* **104**: 381-389.

4. Celton M, Goelzer A, Camarasa C et al (2012) A constraint-based model analysis of the metabolic consequences of increased NADPH oxidation in *Saccharomyces cerevisiae*. *Metab Eng* **14**: 366-379.

5. Crépin L, Nidelet T, Sanchez I et al (2012) Sequential use of nitrogen compounds by *Saccharomyces cerevisiae* during wine fermentation: a model based on kinetic and regulation characteristics of nitrogen permeases. *Appl Environ Microbiol* **78**: 8102-8111.

6. Siebert TE, Smyth HE, Capone DL et al (2005) Stable isotope dilution analysis of wine fermentation products by HS-SPME-GC-MS. *Anal Bioanal Chem* **381**: 937-947.

7. R Core Team (2014) *R: a language and environment for statistical computing*. R Foundation for Statistical Computing, Vienna, Austria. http://www.R-project.org/.

8. Husson F, Josse J, Le S et al (2014) *FactoMineR: multivariate exploratory data analysis and data mining with R*. R package version 1.27. http://CRAN.R-project.org/package=FactoMineR.

9. Rollero S, Bloem A, Camarasa C et al (2014) Combined effects of nutrients and temperature on the production of fermentative aromas by *Saccharomyces cerevisiae* during wine fermentation. *Appl Microbiol Biotechnol* **99**: 2291-2304.

10. Saerens SM, Verstrepen KJ, van Laere SD et al (2006) The *Saccharomyces cerevisiae* EHT1 and EEB1 genes encode novel enzymes with medium-chain fatty acid ethyl ester synthesis and hydrolysis capacity. *J Biol Chem* **281**: 4446-4456.

11. Celton M, Sanchez I, Goelzer A et al (2012) A comparative transcriptomic, fluxomic and metabolomic analysis of the response of *Saccharomyces cerevisiae* to increases in NADPH oxidation. *BMC Genomics* **13**: 1-14.
# Mixed fermentations with *Starmerella bacillaris* (synonym *Candida zemplinina*) and *Saccharomyces cerevisiae* for the reduction of alcohol in wine

V. ENGLEZOS¹, K. RANTSIOU¹, F. TORCHIO², A. ORTIZ-JULIEN³, F. CRAVERO¹, L. ROLLE¹, L. COCOLIN¹*

¹University of Torino, Dipartimento di Scienze Agrarie, Forestali e Alimentari, Agricultural Microbiology and Food Technology Sector, Largo Paolo Braccini 2, 10095 Grugliasco, Torino, Italy

²Istituto di Enologia e Ingegneria Agro-Alimentare, Università Cattolica del Sacro Cuore, Via Emilia Parmense 84, 29122 Piacenza, Italy

³Lallemand SAS, Blagnac, France

Non-*Saccharomyces* yeast – *Starmerella bacillaris* – mixed fermentations – low ethanol wines

## Introduction

In the last 20 years, there has been an increasing interest toward the alcohol content in wines, influenced mainly from the media and the government programs due to the marketing, social and health associated reasons [1]. This global observation has been also linked with the climate change associated with the trend to delay the harvests in order to achieve a “phenolic maturity”, as well as to obtain a rich and ripe fruit flavor in the wine. Several techniques have been proposed so far in order to control the alcohol produced by *Saccharomyces cerevisiae* during fermentation, ranging from viticulture to oenological practises [2]. Among these techniques, the fermentation of must with selected non-*Saccharomyces* yeasts in combination with *S. cerevisiae* strains is gaining attention due to the ability of the first to consume higher amounts of sugar to produce 1% (v/v) of alcohol [3, 4]. Among the non-*Saccharomyces* yeast species of oenological interest, the use of *Starmerella bacillaris* (synonym *Candida zemplinina*) [5] is gaining attention since their ability to produce less ethanol from sugar consumed [6, 7] and persist until the last stages of the alcoholic fermentations [8]. In this context, the aim of this study was to understand the potential use of *Starm. bacillaris* in combination with *S. cerevisiae* in sequential inoculation cultures, in order to achieve a high level of alcohol reduction.

## Materials and methods

Three *Starm. bacillaris* from the yeast culture collection of DISAFA (Dipartimento di Scienze Agrarie, Forestali e Alimentari, University of Torino, Italy) and the commercial *S. cerevisiae* strain Uvaferm BC® (Lallemand, Montreal, Canada) were used in this study. Fermentations were carried out in pasteurized Barbera variety must containing about 230 g/L of sugars. Musts were inoculated with an initial cell density of about 1.0 x 10⁶ cells/mL, which was determined by plate counts on WL nutrient medium (Biogenetics, Milan, Italy). Two inoculation protocols were investigated: firstly, evaluation of the fermentation performance of each strain in pure culture fermentations and secondly, inoculation of the *S. cerevisiae* strain Uvaferm BC®, 2 days after the *Starm. bacillaris* addition. Fermentations were performed in a final volume of 25 mL, under static conditions for 21 days.

## Results and discussion

The fermented musts by the *Starm. bacillaris* strains in pure culture were characterized by the presence of residual sugars, mainly glucose (22 – 33 g/L), confirming the fructophilic character of this species [9]. Complete sugar consumption was observed only for the *S. cerevisiae* strain Uvaferm BC®. The chemical composition of these wines was characterized also by the presence of significant levels of glycerol
(> 11.7 g/L) and lower levels of ethanol (up to 14% by volume) than the *S. cerevisiae* strain in pure culture, in accordance with the results demonstrated by Giaramida et al. [10].

When we investigated the possibility of using *Starm. bacillaris* as a partner of *S. cerevisiae* in mixed fermentation cultures, a completely different picture has emerged. Mixed culture fermentations produced wines with 0.7% (v/v) less ethanol, compared to wine produced by *S. cerevisiae* Uvaferm BC® in pure culture. Specifically, the strains in the mixed fermentations used more sugars (17.0 – 17.5 g/L) to produce 1% (v/v) of alcohol, during the fermentation progress, highlighting the impact of *Starm. bacillaris* on the chemical composition of the final product [10]. Glycerol production was also influenced in the mixed culture fermentations. Compared to the wines produced from the strain Uvaferm BC® in pure culture, wines produced from the mixed culture fermentations contained more glycerol (increase of about 4.0 g/L) than the control wine (8.3 g/L), in agreement with previous studies [11]. Finally, the acetic acid content was not influenced by the inoculation strategy and all the couples used in this study maintained the values at low levels (< 0.5 g/L).

Conclusion

In conclusion, we have shown that the addition of *S. cerevisiae* with 2 days delay could guarantee a decrease of about 0.7% by volume of alcohol and an increase of almost 4.0 g/L of glycerol. The impact of this inoculation on the chemical composition of the wine in terms of aroma should be further investigated.

References

1. Saliba AJ, Ovington LA, Moren CC (2013) Consumer demand for low-alcohol wine in an Australian sample. *Int J Wine Res* 5: 1-8.

2. Pickering GJ (2000) Low-and reduced-alcohol wine: a review. *J Wine Res* 11: 129-144.

3. Quirós M, Rojas V, Gonzalez R et al (2014) Selection of non-*Saccharomyces* yeast strains for reducing alcohol levels in wine by sugar respiration. *Int J Food Microbiol* **181**: 85-91.

4. Contreras A, Hidalgo C, Schmidt S et al (2015) The application of non-*Saccharomyces* yeast in fermentations with limited aeration as a strategy for the production of wine with reduced alcohol content. *Int J Food Microbiol* **205**: 7-15.

5. Duarte FL, Pimentel NH, Teixeira A et al (2012) *Saccharomyces bacillaris* is not a synonym of *Candida stellata*: reinstatement as *Starmerella bacillaris* comb, nov. *Antonie van Leeuwenhoek* **102**: 653-658.

6. Magyar I, Nyitrai-Sardy D, Lesko A et al (2014) Anaerobic organic acid metabolism of *Candida zemplinina* in comparison with *Saccharomyces* wine yeasts. *Int J Food Microbiol* **178**: 1-6.

7. Englezos V, Rantsiou K, Torchio F et al (2015) Exploitation of the non-*Saccharomyces* yeast *Starmerella bacillaris* (synonym *Candida zemplinina*) in wine fermentation: physiological and molecular characterizations. *Int J Food Microbiol* **199**: 33-40.

8. Cocolin L, Hersey A, Mills DA (2001) Direct identification of the indigenous yeasts in commercial wine fermentations. *Am J Enol Vitic* **52**: 49-53.

9. Suzzi G, Schirone M, Sergi M et al (2012) Multistarter from organic viticulture for red wine Montepulciano d'Abruzzo production. *Front Microbiol* **3**: 1-10.

10. Giaramida P, Ponticello G, Di Maio S et al (2013) *Candida zemplinina* for production of wines with less alcohol and more glycerol. *S Afr J Enol Vitic* **34**: 204-211.

11. Zara G, Mannazzu I, Del Caro A et al (2014) Wine quality improvement through the combined utilisation of yeast hulls and *Candida zemplinina/Saccharomyces cerevisiae* mixed starter cultures. *Aust J Grape Wine Res* 20: 199-207.
# Comparaison des phénomènes d'interactions directes/indirectes lors de co-cultures entre des levures *Saccharomyces* et non-*Saccharomyces*

S. BEAUFORT², N. LUYT¹,², F. BAUER¹, C.L. FERNANDEZ LOPEZ², C. BRANDAM², B. DIVOL¹, P. TAILLANDIER²*

¹Institute for Wine Biotechnology, University of Stellenbosch, Private Bag X1, Matieland 7602, South Africa

²Laboratoire de Génie Chimique UMR 5503, CNRS, INPT, UPS, Université de Toulouse, 4 Allée Emile Monso, 31432 Toulouse, France

Interactions – levures non-*Saccharomyces* – contact direct/contact indirect

## Introduction

Dans le domaine des boissons fermentées (vin, tequila,...) l'association de levures non-*Saccharomyces*, du genre Hanseniaspora, Pichia, Lachancea, Kluyveromyces... , à la traditionnelle levure *Saccharomyces cerevisiae* a permis l'obtention de caractéristiques organoleptiques optimisées et diversifiées, en modifiant la composition de molécules aromatiques et/ou en diminuant la concentration de composés néfastes pour le vin [1, 2]. L'existence d'interactions microbiennes a pu être mise en évidence, entrainant une inhibition de la croissance et parfois la mort d'une des populations, modifiant ainsi la qualité du produit final obtenu [3, 4]. A l'exception du mécanisme killer existant entre certaines souches de levure [5], ou des phénomènes de compétition pour le substrat [6], ces interactions ne sont pas encore bien connues et sont fortement dépendantes des couples de levures utilisées et des conditions de culture mises en œuvre.

Cette étude a pour objectif de comparer le comportement de deux couples de levures, en culture pures et en cultures mixtes afin de mettre en évidence ou non un phénomène d'interaction entre des levures *Saccharomyces* et non-*saccharomyces*, et de préciser ensuite l'origine de l'interaction, par un contact physique direct et/ou une interaction sans contact (= indirecte) via un ou des métabolites. Un couple de levures isolées du vin, *S. cerevisiae* et *Lachancea thermotolerans*, et un couple de levures isolées de la tequila, *S. cerevisiae* et *Kluyveromyces marxianus*, ont été étudiés en co-culture sur des jus synthétiques. Pour cela, des co-cultures en double bioréacteur à membrane spécifique développé au laboratoire [7] dans lesquelles les populations sont physiquement séparées de part et d'autres d'une membrane (contact indirect) ont été comparées à des cultures pures puis des co-cultures en absence de membrane (contact direct).

## Matériel et méthodes

Les cultures pures, mixtes directes et mixtes indirectes ont été réalisées dans un double bioréacteur à membrane, spécifiquement conçu pour les études d'interaction [7]. Les cultures mixtes ont été menées avec membrane pour les cultures mixtes en contact indirecte et sans membrane pour les cultures pures en contact direct.

*Conditions de culture pour les études sur milieu « vin »*

- Souches : *L. thermotolerans* IWBT Y1240 est issue de la collection de l'Institut pour la Biotechnologie du vin de l'Université de Stellenbosch et la souche de *S. cerevisiae* EC1118 est issue de la collection Lallemand (Lallemand Inc.).

- Chaque fermentation a été inoculée pour obtenir un total de 2 x 10⁶ cells/mL (en cultures pures 2 x 10⁶ cells/mL pour *L. thermotolerans* ou *S. cerevisiae* et en cultures mixtes, co-inoculation à 1 x 10⁶ cells/mL pour chaque levure). Le milieu de culture est le milieu synthétique AWRI proche du jus de raisin. Les cultures ont été aérées à l'air deux fois par jour pendant 30 minutes à un débit de 0.5 vvm.

- Les cellules viables ont été mesurées par culture sur milieu WL Nutrient Agar à 30 °C.

<footnote>*Auteur principal : patricia.taillandier@ensiacet.fr</footnote>
### *Conditions de culture pour les études sur milieu « tequila »*

- Souches : Les souches *S. cerevisiae* AR5 et *K. marxianus* DU3 sont issues de la collection CIATEJ (Mexique) et ont été isolées au Mexique respectivement de la tequila et du mezcal.

- Chaque fermentation a été inoculée pour obtenir un total de $5 \times 10^6$ cells/mL (en cultures pures $5 \times 10^6$ cells/mL pour *L. thermotolerans* ou *S. cerevisiae* et en cultures mixtes, co-inoculation à $2.5 \times 10^6$ cells/mL pour chaque levure). Le milieu de culture est un milieu synthétique proche du jus de tequila [8]. Les cultures n'ont pas été aérées.

- Les cellules viables ont été mesurées par culture sur milieu solide WL Agar ou WL différentiel Agar à 30 °C.

## Résultats et discussion

Une série de fermentations a été réalisée pour chaque couple de levure afin de mettre en évidence la présence ou non de phénomène d'interaction et de préciser le type d'interaction(s) observée(s), c'est-à-dire dû à un contact physique entre les cellules ou via un métabolite excrété dans le milieu. Pour cela des cultures pures ont été réalisées pour chaque couple, ainsi que des co-cultures dans un double bioréacteur à membrane en présence ou non d'une membrane permettant d'empêcher le contact direct entre les deux populations de levures étudiées.

### *Mise en évidence de l'interaction : comparaison culture pure/culture mixte*

Pour les deux couples de levures, l'interaction a pu être mise en évidence en comparant les résultats issus des cultures pures et des cultures mixtes en contact direct, réalisées dans le même milieu (milieu vin ou milieu tequila). En effet, une modification du comportement d'au moins une des deux souches a été mise en évidence dans les deux cas.

Pour chacun des couples, une interaction négative a été observée, menant à la mort prématurée des levures non-*Saccharomyces* (Figure 1a et 1b).

Néanmoins pour les deux couples, l'impact du phénomène d'interaction a été différent :

- Pour le couple de levures issues du vin (Figure 1a), la concentration des deux levures semble affectée, avec une diminution des concentrations des deux levures *S. cerevisiae* et *L. thermotolerans*, montrant un double effet négatif des levures l'une envers l'autre.

- Pour le couple de levures issues de la tequila (Figure 1b), le comportement de la levure *S. cerevisiae* reste inchangé en culture pure et mixte, les concentrations finales et les cinétiques de croissance de *S. cerevisiae* obtenues sont très proches. Seule la concentration de la levure *K. marxianus* semble avoir été affectée. On peut remarquer également que bien que les milieux de culture soient différents, le phénomène d'interaction est observé plus rapidement pour le couple de levure issue de tequila que sur le couple de levures issues du vin.

<figure><img src="image_113.png" /><figcaption>**Fig. 1** - Couple de levure du vin (A) et de la tequila (B). Cinétique de croissance représentée en ufc/mL pour *S. cerevisiae* (losanges bleus) en culture pure (trait plein) et en culture mixte contact direct (tirets), et *L. thermotolerans* (carrés rouges) en culture pure (trait plein) et en culture mixte contact direct (tirets).</figcaption></figure>
Mise en évidence des différents mécanismes d'interaction : comparaison culture mixte direct/culture mixte indirect

L'utilisation d'un double bioréacteur spécifique à l'étude des interactions a été développé au laboratoire et permet de pouvoir mener une co-culture de levures sur un même milieu, qui reste homogène tout au long de la cinétique, tout en évitant le contact physique entre les deux populations de levures grâce à l'utilisation d'une membrane en fibre creuse laissant par contre passer le milieu de culture et les métabolites produits par les levures. Ce système permet ainsi de pouvoir mettre en évidence un phénomène d'interaction lié à l'excrétion dans le milieu d'un ou plusieurs métabolites produit par l'une ou l'autre des levures. En effet, si les cinétiques obtenues sont différentes de celles obtenues en culture sans membrane (donc avec contact), c'est qu'un phénomène d'interaction lié au contact physique a lieu au sein du couple.

Les expérimentations menées pour le couple de levures issues du vin (Figure 2a) montrent à la fois des cinétiques et des concentrations en levures différentes en culture mixtes avec et sans contact, et ce pour chacune des levures *Saccharomyces* et non-*Saccharomyces*, mettant ainsi en évidence la présence d'une une interaction de type « cell-to-cell contact » pour ce couple. Ceci a pu déjà être observé par d'autres chercheurs [3]. Néanmoins, ce phénomène n'est pas le seul observé car on note également un comportement différent des levures en culture mixte sans contact et en culture pure : un ou des phénomènes d'interaction via des métabolites du milieu ont donc également lieu pour ce couple de levure de vin. Grâce au bioréacteur à membrane, ce type d'interaction avait pu être mis en évidence sur d'autres couples de levures [8].

<figure><img src="image_114.png" /><figcaption>**Fig. 2** - Couple de levure du vin (A) et de la tequila (B). Cinétique de croissance représentée en ufc/mL pour *S. cerevisiae* (losange bleus) en culture mixte indirect (pointillés) et en culture mixte contact direct (tirets), et *L. thermotolerans* (carrés rouges) en culture mixte indirect (pointillés) et en culture mixte contact direct (tirets).</figcaption></figure>

<figure><img src="image_115.png" /></figure>

Pour le couple de levures issues de la tequila, les résultats obtenus en culture mixte sans contact (avec membrane) montrent au contraire des cinétiques de croissance et des concentrations en levures très proches de celles obtenues en cultures sans membrane, donc avec contact. Ce résultat se vérifie aussi bien pour la levure *S. cerevisiae* que pour la levure non-*Saccharomyces* (*K. marxianus*). Ces résultats mettent donc en évidence que l'interaction observée pour ce couple est essentiellement dû à un métabolite excrété dans le milieu.

Conclusion

L'objectif de cette étude était de comparer le comportement de deux couples de levures, l'un issu de vin, et l'autre issu de tequila, en culture pures et en cultures mixtes afin de mettre en évidence ou non un phénomène d'interaction. Le comportement des deux couples a été différent. Dans chaque cas, des interactions négatives ont été observées en culture mixte, menant à une mort prématurée des levures non-*Saccharomyces* comparativement aux cultures pures réalisées dans le même milieu. Néanmoins pour les levures issues du vin, les deux populations ont été affectées par l'interaction avec la seconde, alors que pour les levures issues de la tequila, seule la levure non-*Saccharomyces* a été affectée. Ces résultats suggèrent que le phénomène d'interactions sont dépendants des souches utilisées.
Dans un second temps, grâce à un réacteur spécifique développé au laboratoire, nous avons pu dissocier les phénomènes d'interaction, à savoir ceux dus à un simple contact physique entre cellules ou ceux dus à une interaction via un métabolite excrété dans le milieu, ou encore les deux phénomènes cumulés.

Ces résultats suggèrent que les phénomènes d'interactions peuvent être dus à plusieurs mécanismes, notamment à des contacts entre cellules ou à l'excrétion de métabolites dans le milieu. Il a été également mis en évidence que les mécanismes d'interaction étaient fortement dépendants des souches ou couples de levures utilisés.

Références

1. Amaya-Delgado L, Herrera-López EJ, Arrizon J et al (2013) Performance evaluation of *Pichia kluyveri*, *Kluyveromyces marxianus* and *Saccharomyces cerevisiae* in industrial tequila fermentation. *World J Microbiol Biotechnol* **29**: 875-881.

2. Gobbi M, Comitini F, Domizio P et al (2013) *Lachancea thermotolerans* and *Saccharomyces cerevisiae* in simultaneous and sequential co-fermentation: a strategy to enhance acidity and improve the overall quality of wine. *Food Microbiol* **33**: 271-281.

3. Nissen P, Nielson D, Arneborg N (2003) Viable *Saccharomyces cerevisiae* cells at high concentrations cause early growth arrest of non-*Saccharomyces* yeasts in mixed cultures by a cell-cell contact-mediated mechanism. *Yeast* **20**: 331-341.

4. Taillandier P, Lai QP, Julien-Ortiz A et al (2014) Interactions between *Torulaspora delbrueckii* and *Saccharomyces cerevisiae* in wine fermentation: influence of inoculation and nitrogen content. *World J Microbiol Biotechnol* **30**: 1959-1967.

5. Schmitt MJ, Breinig F (2002) The viral killer system in yeast: from molecular biology to application. *FEMS Microbiol Rev* **26**: 257-276.

6. Nissen P, Nielson D, Arneborg N (2004) The relative glucose uptake abilities of non-*Saccharomyces* yeasts play a role in their coexistence with *Saccharomyces cerevisiae* in mixed cultures. *Appl Microbiol Biotechnol* **64**: 543-550.

7. Albasi C, Riba JP, Salgado E et al (1998) Brevet déposé sous le No. 98.09563, le 27/07/1998.

8. Lopez CL, Beaufort S, Brandam C et al (2014) Interactions between *Kluyveromyces marxianus* and *Saccharomyces cerevisiae* in tequila must type medium fermentation. *World J Microbiol Biotechnol* **30**: 2223-2229.
# Metabolomics pipeline for monitoring alcoholic fermentation: a tool to reveal the “wine-like aroma” volatile feature

A.C. DA SILVA FERREIRA<fnref n="1" />,<fnref n="2" />*, A.R. MONFORTE<fnref n="1" />, C.S. SILVA<fnref n="1" />, R. MARTINS<fnref n="2" />, S. FAIRBAIRN<fnref n="2" />, F. BAUER<fnref n="2" />

Alcoholic fermentation – metabolomics – scale – aroma

## Introduction

Alcoholic fermentation is affected by multiple factors such as yeast strain, temperature, oxygen, nutrients, among others, all of which impact wine quality and commercial value [1-5]. Several studies have highlighted the importance of grape must nitrogen content, in terms of both nitrogen composition and concentration [2-8], as nitrogen directly impacts the final aroma composition in terms of the production of higher alcohols, volatile fatty acids, esters, and sulphur and carbonyl compounds [7,8]. However, this effect is highly complex and the link between a specific nitrogen composition and the aromatic profile is not well understood [2, 6]. Model fermentations are routinely conducted under controlled conditions in order to study yeast metabolism and its associated metabolome. These fermentations, with replicates, are generally conducted in volumes ranging between 100 and 500 mL, and are mostly analyzed at the end of alcoholic fermentation [1, 6].

The aim of this study is to build a metabolic pipeline for the online monitoring of fermentations, by profiling the volatile fraction analyzed by HS-SPME/GC-MS using an untargeted approach and various multivariate techniques. A high-throughput method, using only pre-existing equipment, following the evolution of the volatile fraction during the course of fermentation would greatly contribute to our understanding of yeast metabolism.

## Materials and methods

### Strain, media, and fermentation conditions

The yeast (VIN13, Anchor yeast, Cape Town, South Africa) was pre-cultured in a model grape juice medium adapted from Jiranek et al [9] and with $NH_4Cl$ (1 g/L) as the nitrogen source. This pre-culture was harvested and used to inoculate fermentations to an optical density (OD, 600 nm) of 0.1 (final cell density of approximately $10^6$ cells/mL). The same model grape juice medium was used in subsequent fermentations (M1-5) containing 200 mg/L of nitrogen but varied in its composition (Table 1). The effect of scale was evaluated in two volumes, 5 and 500 mL. Yeast growth was monitored by measuring the OD at 600 nm. To synchronize all the fermentations, two additional sets of the 5-mL fermentations were conducted for OD and weight measurements. The 500-mL fermentations were also monitored for both OD and weight measurements.

### Volatile analysis

The small-scale fermentations (5 mL) were conducted directly in the 15-mL SPME vials (CTC Analytics AG, Zwingen, Switzerland) on the sampling tray. A SPME (CAR/DVB Carboxen) fiber (Supelco, Bellefonte, PA) was used to collect headspace volatiles every 24 h from each 5-mL fermentation. Samples taken daily from the 500-mL fermentation were analyzed in the same manner. Sampling took place from the headspace of the fermentation vials with an extraction time of 10 min at ambient temperature (20 ± 2°C). The SPME fiber was thermally desorbed in the injection port of the GC-MS instrument (Agilent Technologies, Little Falls, Wilmington, U.S.A.). The MS was operated in full-scan mode, scanning from 35 to 650 amu, with and ionization energy of 70 eV and an electron multiplier voltage of 1541 V, as described by Ferreira et al [10].

<footnote n="1">¹Escola Superior de Biotecnologia, Universidade Católica Portuguesa, Porto, Portugal</footnote>

<footnote n="2">²IWBT, DVO University of Stellenbosch, South Africa</footnote>
## Data analysis

The normalized Total Ion Count (TIC-Fingerprint) matrix underwent baseline correction and the first stage of peak alignment using correlation optimized warping (Unscrambler X 10.1 (Camo, Norway)). MarkerLynx XS (version 4.1, Waters, U.S.A.) was used for peak deconvolution, peak integration (using apex track peak detection), and sample alignment. The list of features originated was then subjected to multivariate analysis. The matrix resulted from MarkerLynx underwent correlational analysis using Excel. Data was analyzed using PCA (principal component analysis) and OPLS-DA (using SIMCA-P+ (version 12.0.1, Umetrics, Sweden)). The relative growth rates and CO2 liberation in different volumes and different media were evaluated via two-way analysis of variance (ANOVA), using Microsoft Office Excel, version 2010.

<table><caption>**Table 1 - Nitrogen treatments**</caption><thead><tr><td>Medium</td><td>Description</td></tr></thead><tbody><tr><td>M1</td><td>NH<sub>4</sub>Cl (200 mg N/L, control)</td></tr><tr><td>M2</td><td>NH<sub>4</sub>Cl (50 mg N/L)<br>All amino acids: cysteine, alanine, arginine, asparagine, aspartic acid, glutamine, glutamic acid, glycine, histidine, isoleucine, leucine, lysine, methionine, phenylalanine, proline, serine, threonine, tryptophan, tyrosine, and valine (150 mg N/L)</td></tr><tr><td>M3</td><td>NH<sub>4</sub>Cl (50 mg N/L)<br>Preferred amino acids: arginine, asparagine, aspartic acid, glutamine and glutamic acid (150 mg N/L)</td></tr><tr><td>M4</td><td>NH<sub>4</sub>Cl (50 mg N/L)<br>Aromatic and branch chained amino acids: isoleucine, leucine, phenylalanine, tyrosine, and valine (150 mg N/L)</td></tr><tr><td>M5</td><td>NH<sub>4</sub>Cl (50 mg N/L)<br>Sulphur containing amino acids: cysteine and methionine (150 mg N/L)</td></tr></tbody></table>

## Results and discussion

### Fermentation kinetic parameters

Irrespective of the fermentation media or volume, fermentations proceeded at similar relative fermentation rates (data not shown). Additionally, yeast growth rates were similar for all fermentation treatments, however, differences were observed between fermentation volumes (data not shown). Rossouw et al also reported improved yeast growth in smaller volume fermentations, and attributed this to the presence of more oxygen [11].

### Screening by TIC

The metabolic information was subjected to preprocessing through algebraic normalization (Pareto) using MarkerLynx, both in one dimension (1D), where the total ion chromatogram per sample was used, and in two dimensions (2D), where the retention time vs m/z per sample was used. The PCA plots of the TIC data (Figure 1a, b) show that the second component separates M4 (aromatic and branch chained amino acids) from the rest of the data, with M5 (sulphur containing amino acids) separating at the third component.

### 2D "landscape" data preprocessing

In the 2D PCA plots, each chromatogram and all its information is compressed and projected onto a single point, thus representing the entire exogenous volatile metabolome. The PCA (Figure 1c) shows that yeast metabolism is affected by the amino acids supplied but not by the fermentation volume. For both volumes, the first component (x vector) describes the fermentations over time. The second component shows the separation of M5, M4, and a cluster consisting of M1, M2, and M3. Therefore the 2D procedure captured the relevant information more efficiently than the 1D.
<figure><img src="image_116.png" /><figcaption>**Fig. 1** - (A) PCA score plots of normalized raw data for (i) PC1 vs PC2, (ii) PC2 vs PC3. (B) PCA loading plots of normalized raw data for (i) PC1, (ii) PC2, (iii) PC3 for (1) ethyl acetate; (2) ethanol; (3) unknown 1; (4) isobutyl acetate; (5) ethyl butanoate; (6) methyl thiolacetate; (7) 2-methyl-1-propanol; (8) ethyl thiolacetate; (9) isoamyl acetate; (10) 3-methyl-1-butanol; (11) unknown 3; (12) ethyl hexanoate; (13) acetoin; (14) unknown 4; (15) ethyl octanoate; (16) benzaldehyde; (17) dihydro-2-methyl-3(2H)-thiophenone; (18) ethyl decanoate; (19) 3-methylsulfanylprop-1-ene; (20) unknown (74//60/41); (21) methionol; (22) phenylethyl acetate; (23) benzyl alcohol; (24) 2-phenylethanol. (C) PCA score plots of normalized data after preprocessing with MarkerLynx and normalization by Pareto (i) 5 mL, (ii) 500 mL. (D) Biplot of the first two PC after preprocessing with MarkerLynx and normalization by Pareto (5-mL fermentations).</figcaption></figure>
A biplot was constructed in order to understand and analyze how samples cluster together (scores) and identify which metabolites most contributed to their separation (loadings) (Figure 1d). This may lead to metabolic discovery through the identification of biomarkers. The biomarkers identified, for each media, were the same in both volumes. The clustering of M1, M2 and M3 was due to ethyl acetate and ethanol. Ethanol is a product of fermentation and ethyl acetate is formed from ethanol and acetyl-CoA by alcohol acyltransferase and is the most abundant ester produced during alcoholic fermentation [6]. This cluster contains media that placed fewer nutrient constraints on yeast growth as they contained the principal precursors for the synthesis of nitrogenous compounds [8]. This may somewhat explain the similarities in the volatile profiles. The compounds responsible for the clustering of M5 were methyl thioacetate, ethyl thiolacetate, dihydro-2-methyl-3(2H)-thiophenone, and methionol [12]. These may have a negative contribution to aroma due to the formation of thiols and sulfides [13]. The clustering of M4 was due to products of the Ehrlich pathway (isoamyl alcohol, 2-phenylethanol, hexanoic acid and benzyl alcohol). The higher alcohols isoamyl alcohol and 2-phenylethanol are formed by the decarboxylation and subsequent reduction of α-keto acids produced as intermediates of the amino acids leucine, isoleucine, and phenylalanine catabolism (Ehrlich pathway) [14]. These results are in agreement with literature [15].

Overall, the results show that fermentation volume may impact yeast growth, but has no influence on the quality of the volatile fraction. The application of this online pipeline would allow one to track yeast metabolism, as previously reported by Ferreira et al [10].

## Conclusion

This work culminates with the development of a new metabolic profiling pipeline that relies on an unsupervised and untargeted approach. Additionally, the small fermentation volume allows the high-throughput analyses and “real time” monitoring of the metabolic changes of a perturbed system and the identification of molecular candidates that are later validated in a biochemical context. The pipeline proposed has several practical applications such as yeast phenotyping studies or exploring the impact of nutrient availability on yeast metabolism and subsequent volatile fraction quality. These results do require sensory validation on a larger scale.

## Acknowledgements

This research was funded by the project "Wine Metrics: Revealing the Volatile Molecular Feature Responsible for the Wine Like Aroma a Critical Task Toward the Wine Quality Definition" (PTDC/AGR-ALI/121062/2010) and partially supported by ESB/UCP pluriannual funds through the POS-Conhecimento Program that includes FEDER funds through the program COMPETE (Programa Operacional Factores de Competitividade), national funds through FCT (Fundação para a Ciência e a Tecnologia) and Winetech, the Technology and Human Resources Program and the South African National Science Foundation.

## Abbreviations

HS-SPME/GC-MS, headspace solid phase microextraction / gas chromatography mass spectrometry; OD, optical density; ANOVA, analysis of variance; TIC, total ion count; PCA, principal component analysis.

## References

1. Howell KS, Cozzolino D, Bartowsky EJ et al (2006) Metabolic profiling as a tool for revealing *Saccharomyces* interactions during wine fermentation. *FEMS Yeast Res* 6: 91-101.

2. Hernández-Orte P, Cacho J, Ferreira V (2002) Relationship between varietal amino acid profile of grapes and wine aromatic composition. Experiments with model solutions and chemometric study. *J Agric Food Chem* 50: 2891-2899.

3. Hernández-Orte P, Ibarz MJ, Cacho J et al (2006) Addition of amino acids to grape juice of the Merlot variety: effect on amino acid uptake and aroma generation during alcoholic fermentation. *Food Chem* 98: 300-310.

4. Hernández-Orte P, Ibarz MJ, Cacho J et al (2005) Effect of the addition of ammonium and amino acids to musts of Airen variety on aromatic composition and sensory properties of the obtained wine. *Food Chem* 89: 163-174.
5. Bisson L (1999) Stuck and sluggish fermentations. *Am J Enol Vitic* **50**: 107-119.

6. Ugliano M, Travis B, Leigh Francis I et al (2010) Volatile composition and sensory properties of Shiraz wines as affected by nitrogen supplementation and yeast species: rationalizing nitrogen modulation of wine aroma. *J Agric Food Chem* **58**: 12417-12425.

7. Vilanova M, Ugliano M, Varela C et al (2007) Assimilable nitrogen utilisation and production of volatile and non-volatile compounds in chemically defined medium by *Saccharomyces cerevisiae* wine yeasts. *Appl Microbiol Biotechnol* **77**: 145-157.

8. Bell S, Henschke P (2005) Implications of nitrogen nutrition for grapes, fermentation and wine. *Aust J Grape Wine Res* **11**: 242-295.

9. Jiranek V, Langridge P, Henschke P (1995) Amino acid and ammonium utilization by *Saccharomyces cerevisiae* wine yeasts from a chemically defined medium. *Am J Enol Vitic* **46**: 75-83.

10. Ferreira AC, Monforte AR, Teixeira CS et al (2014) Monitoring alcoholic fermentation: an untargeted approach. *J Agric Food Chem* **62**: 6784-6793.

11. Rossouw D, Jolly N, Jacobson D et al (2012) The effect of scale on gene expression: commercial versus laboratory wine fermentations. *Appl Microbiol Biotechnol* **93**: 1207-1219.

12. Perpète P, Duthoit O, De Maeyer S et al (2006) Methionine catabolism in *Saccharomyces cerevisiae*. *FEMS Yeast Res* **6**: 48-56.

13. Mestres M, Busto O, Guasch J (2000) Analysis of organic sulfur compounds in wine aroma. *J Chromatogr A* **881**: 569-581.

14. Remize F, Andrieu E, Dequin S (2000) Engineering of the pyruvate dehydrogenase bypass in *Saccharomyces cerevisiae*: role of the cytosolic Mg(2+) and mitochondrial K(+) acetaldehyde dehydrogenases Ald6p and Ald4p in acetate formation during alcoholic fermentation. *Appl Environ Microbiol* **66**: 3151-3159.

15. Hazelwood LA, Daran JM, van Maris AJ et al (2008) The Ehrlich pathway for fusel alcohol production: a century of research on *Saccharomyces cerevisiae* metabolism. *Appl Environ Microbiol* **74**: 2259-2266.
# Enzymatic characterization of an oenological relevant protease isolated from *Metschnikowia pulcherrima*

L.W. THERON¹,², M. BELY², B. DIVOL¹*

¹Institute for Wine Biotechnology, University of Stellenbosch, Private Bag X1, Matieland, 7602, South Africa

²Institut des Sciences de la Vigne et du Vin, Université de Bordeaux, CS 50008, 33882 Villenave d'Ornon CEDEX, France

*Proteins – haze – protease – *Metschnikowia pulcherrima*

## Introduction

The formation of haze during the making of white wine occurs when grape proteins become unstable and aggregate into light dispersing particles. The proteins can sediment or flocculate resulting in an unsightly haze [1] that is perceived as a fault leading to a reduction in the commercial quality of wine.

In order to remove these proteins before bottling, winemakers usually resort to bentonite fining. This adsorbent clay has several adverse effects on wine quality and the alternative application of proteolytic enzymes offers promising advantages [2].

Aspartic proteases are active between pH 2 and 5 and are classified as endopeptidases. Several aspartic proteases have been used in industry and show promising results in the wine industry for the removal of haze forming proteins. In a previous study, an aspartic protease-encoding gene (*MpAPr1*) was isolated from *Metschnikowia pulcherrima* IWBT Y1123. This particular strain showed strong extracellular protease activity against skim milk and was also active against grape proteins [3].

The aim of this study was to express the *MpAPr1* gene in *Komagataella pastoris* X33 and characterize the enzyme within the extracellular crude extract.

## Materials and methods

### Cloning and expression of MpAPr1

The *MpAPr1* gene was amplified from gDNA extracted from *M. pulcherrima* IWBT Y1123. The PCR product was cloned into pGEM®-T easy and transformed into *E. coli* DH5α for amplification. After plasmid extraction, the gene was subcloned into the pGAPZαA vector and the construct was transformed into *K. pastoris* X33. Positive transformants were selected and plated on skim milk plates to confirm activity [4]. The cells were then cultured in 1% yeast extract, 2% peptone and 4% glucose for 72 h at 20°C. Extracellular protein crude extracts were collected from the culture supernatant and concentrated 10 times using an Amicon stirred cell Model 8200. It was then used in downstream experiments to test for aspartic protease activity and to characterize the enzyme.

### Enzyme activity assay

Protease activity was determined by measuring the release of acid-soluble material from Azo-Casein. Briefly, 1350 µl substrate (5 mg/ml Azo-Casein) was incubated with 150 µl crude extract for 12 h after which 300-µl samples were removed at specific time points. In order to stop the reaction, 300 µl 20% trichloroacetic acid was added and the reaction was allowed to stand for 5 min. The mixture was centrifuged at 10,000 rpm for 10 min and the supernatant collected for absorbance measurement at 440 nm. The effect of pH on the crude extract was tested by measuring activity at 40°C over the pH range 3 – 8 using 20 mM McIlvaine's buffer. Optimal temperature for protease activity was tested by measuring activity over a temperature range between 10°C and 70°C in 20 mM McIlvaine's buffer (pH 4.5).
*Enzyme kinetics and effects of possible inhibitors*

The crude extract was assayed against increasing amounts of substrate between 0 and 20 mg/ml Azo-Casein and the K_m and V_max were determined by the Lineweaver-Burk double reciprocal plot. Pepsin and acid protease from *Aspergillus saitoi* was included in the experiment to serve as a comparison. The K_i was determined by measuring the hydrolytic activity of the crude extract incubated with concentrations of Azo-Casein between 2.5 and 20 mg/ml in the presence of pepstatin A ranging from 2.5 – 25 nM. K_i was calculated by plotting substrate concentration over substrate velocity against inhibitor concentration [5].

Proteolytic activity was also tested in the presence of various metal ions in order to further characterize the crude extract. Metal ions were added at a final concentration of 1 mM and included CaCl₂, CuCl₂, FeCl₂, MgCl₂, MnCl₂, NiCl₂ and ZnCl₂. These ions are naturally found in grape juice at concentrations lower than 1 mM. Additionally, activity was measured in the presence of 2 mM CaCl₂ and 5 mM MgCl₂, representing the average amounts occurring in grape juice.

Furthermore, the effects of sugar and ethanol on the activity of the crude extract were also tested. Different amounts of sugar (2, 100, 200 and 300 g/l) and ethanol (6, 12 and 15% v/v) were added to the reaction, representing concentrations typically found throughout grape juice fermentation.

**Results and discussion**

*Metschnikowia pulcherrima* IWBT Y1123 was previously shown to display strong extracellular proteolytic activity and the gene responsible isolated [3]. The *MpAPr1* gene was amplified from gDNA and cloned into *K. pastoris* X33 for heterologous expression. Successful transformation was confirmed by skim milk plate assays (data not shown). Cells were cultured and a crude extract was obtained. Milk clotting assays were performed on the crude extract and it was determined that protease activity was retained (data not shown). A crude extract preparation from untransformed cells was also included and no activity could be observed, indicating that protease activity is from the recombinant enzyme.

Optimal working temperature was in the range of 30°C to 50°C with a maximum activity at 40°C (Figure 1). Up to 50% activity was still retained at lower temperatures, but at temperatures above 60°C, activity was dramatically affected. Identical temperature optima have been found in other *Metschnikowia* species [6] and are similar to proteases from filamentous fungi [7, 8]. Moreover, pH optima was measured over a range of 3 – 8 and optimal activity could be observed at 4.5 (Figure 1). Similar pH optima have been observed for other aspartic proteases from yeast species [9-11]. In contrast, an aspartic protease from *Metschnikowia reukaufii* had a pH optima of 3.4 [6].

<figure><img src="image_117.png" /><figcaption>Fig. 1 - The effect of temperature and pH on MpAPr1 activity.</figcaption></figure>

K_m and V_max values were calculated using the Lineweaver-Burk reciprocal plot method and compared to that of pepsin and an acid protease from *Aspergillus saitoi* (Table 1). A K_m value of 3.09 mg/ml was obtained which was lower than both that obtained for the other two commercial acid proteases, indicating a higher affinity to the substrate. Moreover, inhibition kinetics of the crude extract were calculated using pepstatin which is a well-known inhibitor of aspartic proteases. The K_i value was calculated by plotting substrate concentration over substrate velocity against inhibitor concentration and it was also determined that the enzyme follows uncompetitive binding.
<table><caption>**Table 1 - Kinetic rate constants**</caption><thead><tr><th>Source</th><th>Kinetic rate constant</th><th>Value</th></tr></thead><tbody><tr><td rowspan="2">Crude extract (MpApr1)</td><td>K<sub>m</sub></td><td>3.09 mg/ml</td></tr><tr><td>V<sub>max</sub></td><td>0.00494 mg/ml per hour</td></tr><tr><td rowspan="2">Acid protease from <i>Aspergillus saitoi</i></td><td>K<sub>m</sub></td><td>3.26 mg/ml</td></tr><tr><td>V<sub>max</sub></td><td>0.0027 mg/ml per hour</td></tr><tr><td rowspan="2">Pepsin</td><td>K<sub>m</sub></td><td>4.44 mg/ml</td></tr><tr><td>V<sub>max</sub></td><td>0.0218 mg/ml per hour</td></tr></tbody></table>

<table><caption>**Table 2 - Effects of various metal ions, ethanol and sugar on MpAPr1 activity**</caption><thead><tr><th>Compound</th><th>Concentration</th><th>Relative activity (%)</th></tr></thead><tbody><tr><td>None</td><td></td><td>100</td></tr><tr><td rowspan="2">CaCl<sub>2</sub></td><td>1 mM</td><td>92.2 ± 0.8</td></tr><tr><td>2 mM</td><td>104.4 ± 0.4</td></tr><tr><td>CuCl<sub>2</sub></td><td>1 mM</td><td>52.7 ± 1.7</td></tr><tr><td>FeCl<sub>2</sub></td><td>1 mM</td><td>99.1 ± 2.5</td></tr><tr><td rowspan="2">MgCl<sub>2</sub></td><td>1 mM</td><td>95.2 ± 1.5</td></tr><tr><td>5 mM</td><td>103.0 ± 0.8</td></tr><tr><td>MnCl<sub>2</sub></td><td>1 mM</td><td>95.3 ± 1.9</td></tr><tr><td>NiCl<sub>2</sub></td><td>1 mM</td><td>74.7 ± 3.6</td></tr><tr><td>ZnCl<sub>2</sub></td><td>1 mM</td><td>93.3 ± 1.3</td></tr><tr><td rowspan="3">Ethanol</td><td>6%</td><td>46.8 ± 2.2</td></tr><tr><td>12%</td><td>17.1 ± 0.8</td></tr><tr><td>15%</td><td>9.1 ± 0.3</td></tr><tr><td rowspan="4">Sugar</td><td>2 g/l</td><td>101.7 ± 1.1</td></tr><tr><td>100 g/l</td><td>87.2 ± 0.4</td></tr><tr><td>200 g/l</td><td>71.7 ± 1.8</td></tr><tr><td>300 g/l</td><td>60.4 ± 1.1</td></tr></tbody></table>

The effect of various metal ions on protease activity within the crude extract was examined at 40°C and pH 4.5 (Table 2). The addition of CaCl₂, MgCl₂, MnCl₂ and ZnCl₂ at a final concentration of 1 mM had a slight inhibitory effect whereas FeCl₂ had no effect. However, both CuCl₂ and NiCl₂ had a strong inhibitory effect on activity. Finally, addition of CaCl₂ and MgCl₂ at a final concentration similar to those found in grape juice had an enhancing effect on activity. Moreover, the effect of sugar and ethanol concentrations typically found during fermentation was also tested at 40°C and pH 4.5 (Table 2). Inhibition of protease activity was in both cases more severe with increasing concentrations, suggesting that, for optimal activity, the enzyme should be applied during the middle stage of alcoholic fermentation when sugar and ethanol concentrations are moderately low.

## Conclusion

In this study, the acid protease-encoding gene *MpAPr1* from the wine yeast *M. pulcherrima* IWBT Y1123 was cloned into the pGAPZαA vector and transformed into *K. pastoris* X33 for heterologous expression. A protein crude extract was obtained and after verification of protease activity the properties were characterized. Optimal temperature and pH for activity were observed at 40°C and pH 4.5, respectively. A high affinity toward casein was observed when compared to that of other commercially available aspartic proteases. The effects of various metal ions where found to be similar to that of other fungal proteases. Sugar and ethanol concentrations typically present during fermentation were also tested and it was discovered that both have an inhibitory effect on enzyme activity. However, the results show that, overall, MpAPr1 should retain good activity during fermentation. Purification and further analysis of MpAPr1 are currently in progress to elucidate its potential use in industrial applications.
**Acknowledgements**

The authors thank the South African National Research Foundation, Winetech, THRIP, the Oenoviti network and the French embassy in South Africa for their financial support.

**Abbreviations**

MpAPr1, *Metschnikowia pulcherrima* Aspartic Protease 1; gDNA, Genomic Deoxyribonucleic Acid; IWBT, Institute for Wine Biotechnology.

**References**

1. Pocock KF, Waters EJ (2006) Protein haze in bottled white wines: how well do stability test and bentonite fining trails predict haze formation during storage and transport? *Aust J Grape Wine Res* **12**: 212-220.

2. Waters EJ, Alexander G, Muhlack R et al (2005) Preventing protein haze in bottled white wine. *Aust J Grape Wine Res* **11**: 215-225.

3. Reid VJ, Theron LW, du Toit M et al (2012) Identification and partial characterization of extracellular aspartic protease genes from *Metschnikowia pulcherrima* IWBT Y1123 and *Candida apicola* IWBT Y1384. *Appl Environ Microbiol* **78**: 6838-6849.

4. Charoenchai C, Fleet GH, Henschke PA et al (1997) Screening of non-*Saccharomyces* wine yeasts for the presence of extracellular hydrolytic enzymes. *Aust J Grape Wine Res* **3**: 2-8.

5. Cornish-Bowden A (1974) A simple graphical method for determining the inhibition constants of mixed, uncompetitive and non-competitive inhibitors. *Biochem J* **137**: 143-144.

6. Li J, Chi Z, Wang X (2010) Cloning of the SAP6 gene of *Metschnikowia reukaufii* and its heterologous expression and characterization in *Escherichia coli*. *Microbiol Res* **165**: 173-182.

7. Marcial J, Pérez de los Santos AI, Fernandez FL et al (2011) Characterization of an aspartic protease produced by *Amylomyces rouxii*. *Rev Mex Ing Quim* **10**: 9-16.

8. Liu Y, Yang Q (2007) Cloning and heterologous expression of aspartic protease SA76 related to biocontrol in *Trichoderma harzianum*. *FEMS Microbiol Lett* **277**: 173-181.

9. Aoki W, Kitahara N, Miura N et al (2011) Comprehensive characterization of secreted aspartic proteases encoded by a virulence gene family in *Candida albicans*. *J Biochem* **150**: 431-438.

10. Rao S, Mizutani O, Hirano T et al (2011) Purification and characterization of a novel aspartic protease from basidiomycetous yeast *Cryptococcus* sp. S-2. *J Biosci Bioeng* **112**: 441-446.

11. Yamada T, Ogrydziak DM (1983) Extracellular acid proteases produced by *Saccharomycopsis lipolytica*. *J Bacteriol* **154**: 23-31.
# Network reconstruction: a tool for yeast strain selection (preliminary results)

A.R. MONFORTE<fnref n="1" />*, J. DOMINGUES<fnref n="1" />, C.S. SILVA<fnref n="1" />, S. FAIRBAIRN<fnref n="2" />, M.J. SOUSA<fnref n="3" />, A.C. SILVA FERREIRA<fnref n="2" />

Strains – alcoholic fermentation – network reconstruction

## Introduction

The vast majority of chemicals found in wine are the metabolic byproducts of yeast activity during fermentation. Therefore, it is important to characterize the winemaking abilities of wine yeast to ensure reproducible fermentation performance and product quality. The fermentation properties of wine yeasts have been genetically defined. Global gene expression analyses have been carried out in recent years to characterize the physiological properties of natural and commercial yeasts as well as genetically and metabolically engineered yeasts. However, the impact of different strains in an untargeted approach has not been very explored.

Metabolomics is defined as the comprehensive and quantitative analysis of all metabolites and makes it possible to visualize metabolic differences and identify the metabolites contributing to the differentiation [1]. Metabolomics highlights the comprehensive understanding of metabolic pathways or provides potential biomarkers; however, the huge amount of candidates makes metabolomics contextualization very difficult. Network reconstruction has proven to be a useful tool in the study of chemical changes occurring during wine aging. This study addresses the potential use of chromatographic signals coupled with network reconstruction for screening the biochemical modifications of the volatile fraction of fermentations performed with 4 different strains (one non-wine (A) and three wine strains (B, C and D)).

## Materials and methods

### *Strain, media and culture conditions*

The yeast strains used in this study were *Saccharomyces cerevisiae* A, B, C and D strains. Fermentation vessels were inoculated with pre-culture in the logarithmic growth phase (OD640≈1) to an OD640 of 0.1 (final cell density of approximately 106 cfu/mL). Fermentations were conducted in synthetic grape juice (SGJ) adapted from Ciani and Ferraro [2]. All fermentations were performed in Schott bottles modified with a plastic tube and a syringe in order to allow the sampling without opening the flask, thus avoiding media aeration. The ratio liquid/gas was maintained (1:1) in all replicates in order to avoid any deviation. The 1000-mL fermentations were sampled daily for sensory analysis, GC-MS and GC-FID analysis, optical density measurements, sugar consumption and ethanol production. All fermentations were conducted in an incubator at a controlled temperature (22°C) and were agitated before sampling.

### *Growth measurement*

The yeast growth in the fermentations was monitored by measuring the optical density using a UV/VIS spectrophotometer Nicolet evolution 100 (Thermo electron corporation, Cambridge, UK). The absorbance of the homogenized sample was read at 640 nm in acrylic cuvettes with 1 cm of light pathlength.

### *Sugar and ethanol monitoring*

Glucose, fructose, ethanol and glycerol measurements were performed using a HPLC equipment (Beckman, System Gold) with an Aminex HPX-87H (ion exclusion column – 300 mm x 7.8 mm) connected to a refraction index detector. A H₂SO₄ 2.5 mM solution at 0.6 mL/min flow rate was used as mobile phase and 20 µL of sample was injected. The column was kept at a constant temperature of 40°C.

<footnote n="1">¹Escola Superior de Biotecnologia – Universidade Católica Portuguesa, Porto, Portugal</footnote>

<footnote n="2">²IWBT – DVO University of Stellenbosch, South Africa</footnote>

<footnote n="3">³Centre of Molecular and Environmental Biology, University of Minho, Braga</footnote>
## Higher alcohols

Higher alcohols were analyzed using a Hewlett-Packard 5890 gas chromatograph equipped with a flame ionization detector and connected to a HP 3396 Integrator. 50 µL of 4-methyl-2-pentanol, at 10 g/L, were added to 5 mL of wine as internal standard. The sample (1 µL) was injected (split, 1:60) into a CP-WAX 57 CB column (Chrompack) of 50 m x 0.25 mm and 0.2-µm phase thickness. The temperature program was 40°C (5 min) to 180°C at 3°C/min. Injector and detector temperatures were set at 250°C. Carrier gas was H₂ at 1–2 mL/min.

## Volatile analysis

Volatile extraction was performed by SPME using a SPME grey (DVB/CAR/PDMS) fiber (Supelco, Bellefonte, PA) and the volatiles were analyzed using a Varian 450 Gas Chromatograph equipped with a mass spectral detector, Varian 240-MS and a CombiPAL auto sampler (CTC Analytics AG, Zwingen, Switzerland). The column used was a VF-WAXms (15 m x 0.15 mm, 0.15 µm) (Varian). A 5-mL sample was spiked with 20 µL of 3-octanol (50 mg/L) as the internal standard. Anhydrous sodium sulphate (0.5 g) was added to increase ionic strength.

Samples were pre-incubated in the CombiPAL oven at 40°C and 500 rpm for 2 min, then the fiber was exposed on the sample headspace for 10 min at 250 rpm and 40°C for extraction. The fiber was kept at 10 mm from the bottom. Desorption of volatiles took place in the injector in splitless mode, at 220°C for 15 min in both methods. The oven temperature was 40°C (1 min), and then increased 4°C/min to 220°C and held for 2.5 min (48.5 min). The carrier gas was helium at 1 mL/min, with a constant flow. The injector port was heated to 220°C. The injection volume was 1 µL in splitless mode and the split vent was opened after 30 sec. All mass spectra were acquired in the electron impact (EI) mode (ionization energy, 70 eV; source temperature, 180°C). The analysis temperatures were: trap 170°C, manifold 50°C, transfer line 190°C and ion source 210°C.

Compound identification was achieved by comparing retention times and mass spectra obtained from a sample containing pure, authentic standards. Compound quantification was performed based on standard calibration curves.

## Results and discussion

Four *Saccharomyces cerevisiae* strains were used in this study: three wine strains and one cachaça strain. Triplicate fermentations were carried out with each of the four strains using SGJ. For each strain, a daily sample was collected in order to monitor fermentation metabolic changes. All the three strains present a typical fermentative profile with a Vmax between 0.7 and 0.9 g/L/h of CO₂ released.

The fermentation kinetics differed in a strain dependent manner. Strains A and B show a faster fermentation profile and present a higher maximum fermentation rate in comparison with strains C and D.

In order to obtain a characterization of their metabolic profile, HPLC, GC-MS and GC-FID analyses were performed. In particular, we quantified: higher alcohols, ethyl esters, acetates and volatile fatty acids. Network correlation projections were used to screen for compounds related to target molecules. The network obtained applying specific thresholds in the correlation values was different for each strain as well as the number of edges and nodes.

It has been observed a correlation between CO₂ release and acetaldehyde formation, being the maximum concentration related with the Vmax.

The ASCII file of GC-MS data obtained from each time point in each fermentation sample has been extracted and all the m/z channels were normalized to total intensity to yield a total ion current (RIC) chromatogram for each sample. The chromatograms were aligned with icoshift, wavelets were used for peak calling, and the resulting matrix was used to perform network reconstruction via correlation networks. Network correlation projections were used to screen for compounds related to target molecules (sugars, ethanol, higher alcohol acetates and fatty acid ethyl esters) according to Jacobson et al. [3].

In the faster fermentations (strains A and B), the ethanol concentration was connected with ethyl esters and acetates but the same trend was not observed with the slower strains (C and D). These differences may be linked to differences in the gene expression of each strain.
**Conclusion**

The network reconstruction could be a potential tool for yeast strain selection based on the volatile
metabolism characteristic of each strain and in the identification of the molecules/pathways with impact on
the wine quality.

**Acknowledgements**

This research was funded by the project "Wine Metrics: Revealing the Volatile Molecular Feature Responsible for the Wine Like Aroma a Critical Task Toward the Wine Quality Definition" (PTDC/AGR-ALI/121062/2010) and partially supported by ESB/UCP pluriannual funds through the POS-Conhecimento Program that includes FEDER funds through the program COMPETE (Programa Operacional Factores de Competitividade) and national funds through FCT (Fundação para a Ciência e a Tecnologia).

**References**

1. Cevallos-Cevallos JM, Reyes-De-Corcuera JI, Etxeberria E et al (2009) Metabolomic analysis in food science: a review. *Trends Food Sci Technol* **20**: 557-566.

2. Ciani M, Ferraro L (1996) Enhanced glycerol content in wines made with immobilized *Candida stellata* cells. *Appl Environ Microbiol* **62**: 128-132.

3. Jacobson D, Monforte AR, Ferreira ACS (2013) Untangling the chemistry of port wine aging with the use of GC-FID, multivariate statistics, and network reconstruction. *J Agric Food Chem* **61**: 2513-2521.
# Genetic background for second fermentation in sparkling wine production: QTL detection and validation

A. MAS<fnref n="1" />*, M. MARTI-RAGA<fnref n="1" />,<fnref n="2" />, G. BELTRAN<fnref n="1" />, P. MARULLO<fnref n="2" />,<fnref n="3" />

Phenotypic divergence – genotyping – fermentative performance – sparkling wine

## Introduction

Sparkling wine production using the traditional method involves a second fermentation that takes place inside the bottle. This second fermentation presents several stress sources such as low nutrient availability, increasing CO₂ pressure and high ethanol concentration. Thus, the *Saccharomyces cerevisiae* strain selected to conduct the second fermentation must have specific characteristics to ensure the completion of the second fermentation [1]. Recently, the effect of the yeast strain on the second fermentation development was found to be of relevance; however, the genetic basis of the phenotypic variability exhibited during the second fermentation by different strains has not been deciphered [2]. In that sense, quantitative trait loci (QTL) mapping has proved to be a useful approach to find the genetic causes behind the phenotypic diversity of different wine strains. Several studies have used the QTL mapping approach in winemaking conditions to decipher traits of relevance for the oenology field such as the duration of the lag-phase [3, 4], the differences in nitrogen assimilation profiles [5] or the residual sugar and production of succinic acid [6]. The aim of the present study is to decipher the genetic basis behind the phenotypic divergence in the fermentative performance during sparkling wine production.

## Materials and methods

### *Strains and phenotyping*

We used 117 haploid segregants derived from a cross between the strains SB and GN previously obtained [3]. For QTL validation, a reciprocal hemizygous assay was realized on the F1 hybrid HOLV3 according to [8]. The haploid segregants were phenotyped for their fermentative behaviour during the second fermentation. Before inoculation, yeast cells were acclimated following a protocol described elsewhere [2]. At the end of the proliferation phase, yeasts were inoculated at 0.2 OD (2x10⁶ cells/ml) into natural base wine supplemented with sucrose (22 g/l) and bentonite (30 mg/l). The mixture was introduced into bottles (750 ml) that were hermetically closed, and the second fermentation took place at 16°C. The CO₂ production was monitored over time by measuring the pressure inside the bottle with an aphrometer (L sensor, CO₂, L PRO SRL, Camisano Vicentino, Italy). Fermentation kinetics data were fitted using the 5PL model [7] to extract the relevant parameters.

### *Genotyping and QTL mapping*

The haploid segregants were genotyped using NGS approaches. Briefly, total DNA was extracted and DNA libraries were constructed using the Illumina Nextera XT kit (Illumina, California, USA). DNA libraries were then pooled and sequenced (MiSeq) using MiSeq standard kit v2 (Illumina, California, USA). All sequencing data filtering and mapping was performed using the available tools at the public Galaxy server (https://usegalaxy.org). Filtered reads were mapped to the reference genome of *S. cerevisiae* strain s288c, using the BWA software. We extracted all the mapped positions using SAMTools', and the dataset with all the mapped positions was then processed with a custom written R script to obtain the genotype of each segregant at the position of the non-common SNP of the parental strains.

QTL mapping was performed using a custom written R script applying a Wilcoxon test and retaining the LOD score. Significance was estimated using permutation tests (FDR = 5%).

<footnote n="1">¹Dept. Bioquímica i Biotecnologia, Universitat Rovira i Virgili, Tarragona, Spain</footnote>

<footnote n="2">²EA Oenologie ISVV, Université Bordeaux, Villenave d'Ornon, France</footnote>

<footnote n="3">³Biolaffort, Bordeaux, France</footnote>
## Hemizygosity analysis

To determine if the allelic variation in the selected candidate genes was responsible for the phenotypic divergence we tested its effect using reciprocal hemizygosity analysis [8]. *PMA1* and *VMA13* reciprocal hemizygotes were obtained by deleting one of the parental alleles in the hybrid strain (HOLV3). Briefly, ~500bp of the flanking regions of the gene to delete were added to the *Kan-Mx4* cassette. The hybrid yeast strain was then transformed with the constructed cassette. The correct allele deletion was verified by PCR and the remaining allele was assessed by RFLP. The transformants were evaluated for second fermentation traits following the protocol described before. Furthermore, we adjusted the pH of the base wine at 2.8, 3 and 3.3 to assess the effect of the pH on the fermentative traits of the transformant strains.

## Results and discussion

We evaluated 117 haploid segregants for second fermentation traits such as the maximum pressure achieved, the fermentative rate, or the time to reach two or five bars (pressure) inside the bottle (middle and end fermentation). High variation among segregants was observed. For example, the fastest segregant finished the fermentation in 12 days while the slowest needed 40 days to reach 5.25 bars. For second fermentation traits some transgression in the phenotypes could be observed. As an example, the values obtained for the time needed to reach 5.25 bars among the progenies and the parental strains can be observed in Figure 1A.

The sequencing and genotyping of all the progenies resulted in the construction of a genetic marker map of approximately 1000 markers, evenly distributed, with a segregation of 1:1 among the progenies and for which we had at least 50% of the progeny genotype at every position. The resulting marker map can be observed in Figure 1B.

<figure><img src="image_118.png" /><figcaption>Fig. 1 - (A) Duration of the fermentation for the haploid segregants (grey), the parental strains (black), and the hybrid strain (white). (B) Genetic marker map. Every marker considered is represented as a black point in its position along the chromosome.</figcaption></figure>

Linkage analysis was conducted applying the Wilcoxon test to assess the relation between every phenotype with the genotype of all the progenies at every marker position. We detected two major significant QTLs (Figure 2). One QTL located in chromosome VII related to the fermentative rate and the time needed to reach 2 bars during the second fermentation, and the other QTL located in chromosome XVI related to the time needed to reach the 4 and 5.25 bars, thus controlling the time needed to finish the second fermentation. Several candidate genes located in the QTL region were selected considering their function and the presence of the non-synonymous SNP in the sequence of the parental stains. To validate the QTL found in chromosome VII (QTL1) we selected *PMA1*, and to validate the QTL mapped in chromosome XVI (QTL2) we selected *VMA13*. The validation of the detected QTL was carried out using reciprocal hemizygosity analysis (Figure 3A).
<figure><img src="image_119.png" /><figcaption>**Fig. 2** - QTL detection. Two major QTL related to fermentative traits were mapped.</figcaption></figure>

We could verify the effect of the allelic variation of the genes *PMA1* and *VMA13* in the fermentative rate and the time needed to achieve 5.25 bars (Figure 3B and 3C). The hybrid strain containing only the allele of the parental strain SB achieved a significantly higher fermentative rate. The fermentative rate of the hybrid strain containing the GN allele and the fermentative rate of the hybrid strain were not different, indicating the recessive character of the SB allele. For the time needed to achieve 5.25 bars, we could also verify the effect of the allelic variance existing between the parental strains as responsible for the phenotypic divergence. In this case, the hybrid strain containing only the GN allele needed significantly less time to complete the second fermentation, while the hybrid strain presented an intermediate phenotype and the strain containing only the SB allele of *VMA13* needed significantly more time to reach 5.25 bars.

Additionally, taking in consideration that *PMA1* and *VMA13* encode for the cytosolic ATPase and a subunit of the vacuolar ATPase, respectively [9], and the low pH of the base wine used to produce sparkling wine (cava) (2.8<pH<3.5), we thought that the effect of the allelic variations within the gene sequences may be conditioned by the base wine pH. To test this hypothesis, we assessed the fermentative behaviour of the hybrid strains and the transformants for *PMA1* and *VMA13* genes, by fermenting the same base wine at different pH (2.8, 3, and 3.3). We could detect a significant effect of the pH on the fermentative behaviour of the strains (data not shown). Furthermore, significant allele-pH interactions were found. The difference between the fermentative rate of PMA1<sup>SB</sup> and PMA1<sup>GN</sup> strains was accentuated at low pH: while the strain containing only the SB allele (PMA1<sup>SB</sup>) did not present changes in its fermentative rate regardless of the pH, the strain with the GN allele (PMA1<sup>GN</sup>) was strongly affected and its fermentative rate was even slower at lower pH. The duration of the fermentation was also found to be affected in an allele dependent way. The positive effect of the GN allele of *VMA13* was lost at low pH, while it was accentuated at higher pH.

<figure><img src="image_120.png" /><figcaption>**Fig. 3** - (A) Reciprocal hemizygosity analysis scheme. (B) Fermentative rate of the reciprocal diploid hemizygotes, retaining only one copy of the *PMA1* gene. The value represented is the average of three different transformants, the error bar is the standard deviation. (C) Time needed to reach 5.25 bars of reciprocal diploid hemizygotes, retaining only one copy of the *VMA13* gene. The value represented is the average of three different transformants, the error bar is the standard deviation.</figcaption></figure>
**Conclusion**

The application of a QTL mapping approach on the second fermentation performance of *S. cerevisiae* strains allowed us to identify the allelic variation within *PMA1* and *VMA13* sequences as responsible for the observed phenotypic diversity. Furthermore, a strong interaction with the environment was detected. The pH of the base wine modulated the effect of the identified alleles. The genetic variants identified could be used to select new yeast strains suitable for conducting the second fermentation or to improve already existing strains via marker assisted introgression.

**Acknowledgements**

The authors thank Biolaffort for supporting this work and providing the strains, VITEC for allowing the use of the aphrometer, and Juve&Camps for providing the base wine.

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