<table><caption>**Table 3** - Antioxidant activity characterisation in EAQ and EA70 grape pomace seed extracts</caption><thead><tr><th></th><th></th><th>ORAC<sup>2</sup></th><th>FRAP<sup>2</sup></th><th>ABTS<sup>2</sup></th><th>DPPH<sup>2</sup></th></tr></thead><tbody><tr><td rowspan="3">Seeds-EAQ</td><td>GRE1<sup>1</sup></td><td>1466.4 ± 29.6<sup>a</sup></td><td>0.63 ± 0.02<sup>a</sup></td><td>1203.2 ± 24.1<sup>a</sup></td><td>410.8 ± 43.3<sup>a</sup></td></tr><tr><td>SYR1<sup>1</sup></td><td>2230.7 ± 101.7<sup>b</sup></td><td>1.33 ± 0.08<sup>c</sup></td><td>2432.6 ± 56.0<sup>c</sup></td><td>1037.1 ± 64.0<sup>b</sup></td></tr><tr><td>CAR<sup>1</sup></td><td>2058.6 ± 85.1<sup>b</sup></td><td>1.06 ± 0.08<sup>b</sup></td><td>1948.8 ± 61.1<sup>b</sup></td><td>1050.6 ± 30.1<sup>b</sup></td></tr><tr><td rowspan="3">Seeds-EA70</td><td>GRE1<sup>1</sup></td><td>1926.7 ± 108.6<sup>a</sup></td><td>1.28 ± 0.01<sup>a</sup></td><td>2813.2 ± 90.0<sup>a</sup></td><td>1277.6 ± 54.7<sup>a</sup></td></tr><tr><td>SYR1<sup>1</sup></td><td>2614.0 ± 150.9<sup>a</sup></td><td>1.45 ± 0.16<sup>a</sup></td><td>3601.2 ± 88.6<sup>b</sup></td><td>1685.9 ± 130.7<sup>b</sup></td></tr><tr><td>CAR<sup>1</sup></td><td>2332.9 ± 91.9<sup>a</sup></td><td>1.20 ± 0.06<sup>a</sup></td><td>3495.6 ± 66.4<sup>b</sup></td><td>1536.8 ± 38.9<sup>b</sup></td></tr></tbody></table>

<table><caption>**Table 4** - Antioxidant activity characterisation in EAQ and EA70 grape pomace skin extracts</caption><thead><tr><th></th><th></th><th>ORAC<sup>2</sup></th><th>FRAP<sup>2</sup></th><th>ABTS<sup>2</sup></th><th>DPPH<sup>2</sup></th></tr></thead><tbody><tr><td rowspan="6">Skins-EAQ</td><td>GRE2<sup>1</sup></td><td>1190.7 ± 183.6<sup>ab</sup></td><td>0.56 ± 0.01<sup>c</sup></td><td>934.1 ± 11.9<sup>b</sup></td><td>99.5 ± 10.8<sup>a</sup></td></tr><tr><td>SYR1<sup>1</sup></td><td>1345.9 ± 19.2<sup>ab</sup></td><td>0.88 ± 0.01<sup>e</sup></td><td>1428.0 ± 54.8<sup>c</sup></td><td>690.3 ± 147.0<sup>bc</sup></td></tr><tr><td>SYR2<sup>1</sup></td><td>1066.0 ± 84.2<sup>a</sup></td><td>0.14 ± 0.02<sup>a</sup></td><td>668.3 ± 30.0<sup>a</sup></td><td>263.9 ± 71.5<sup>ab</sup></td></tr><tr><td>CAR<sup>1</sup></td><td>1077.8 ± 60.2<sup>a</sup></td><td>0.67 ± 0.02<sup>d</sup></td><td>1048.8 ± 101.6<sup>b</sup></td><td>591.0 ± 85.6<sup>abc</sup></td></tr><tr><td>MOU<sup>1</sup></td><td>1033.8 ± 77.6<sup>a</sup></td><td>0.32 ± 0.01<sup>b</sup></td><td>965.6 ± 16.6<sup>b</sup></td><td>279.4 ± 61.7<sup>ab</sup></td></tr><tr><td>ALI<sup>1</sup></td><td>1714.6 ± 14.8<sup>b</sup></td><td>1.13 ± 0.00<sup>f</sup></td><td>1760.1 ± 91.0<sup>d</sup></td><td>1057.1 ± 45.2<sup>c</sup></td></tr><tr><td rowspan="6">Skins-EA70</td><td>GRE2<sup>1</sup></td><td>1828.3 ± 40.4<sup>bc</sup></td><td>1.32 ± 0.03<sup>c</sup></td><td>2612.1 ± 130.9<sup>a</sup></td><td>877.0 ± 74.3<sup>a</sup></td></tr><tr><td>SYR1<sup>1</sup></td><td>1912.6 ± 6.1<sup>bc</sup></td><td>1.52 ± 0.05<sup>d</sup></td><td>2614.5 ± 10.4<sup>a</sup></td><td>1391.7 ± 37.2<sup>bc</sup></td></tr><tr><td>SYR2<sup>1</sup></td><td>1701.8 ± 88.3<sup>bc</sup></td><td>0.94 ± 0.03<sup>a</sup></td><td>2010.6 ± 147.0<sup>a</sup></td><td>1164.9 ± 55.6<sup>ab</sup></td></tr><tr><td>CAR<sup>1</sup></td><td>1238.4 ± 11.1<sup>a</sup></td><td>1.34 ± 0.03<sup>c</sup></td><td>2555.9 ± 146.0<sup>a</sup></td><td>1075.4 ± 46.2<sup>ab</sup></td></tr><tr><td>MOU<sup>1</sup></td><td>2070.0 ± 60.6<sup>c</sup></td><td>1.03 ± 0.02<sup>ab</sup></td><td>2674.8 ± 187.3<sup>a</sup></td><td>833.3 ± 26.4<sup>a</sup></td></tr><tr><td>ALI<sup>1</sup></td><td>1628.5 ± 82.6<sup>b</sup></td><td>1.13 ± 0.01<sup>b</sup></td><td>1923.4 ± 87.0<sup>a</sup></td><td>1749.3 ± 112.7<sup>c</sup></td></tr></tbody></table>

Regression analyses (correlation coefficient R²) were attempted in order to correlate the results obtained with different methods. The best correlations with total phenolic contents were obtained with EAQ extracts both for seed and skin extracts: from R² = 0.87 for DPPH to R² = 0.99 for FRAP and from R² = 0.79 for DPPH to R² = 0.97 for ABTS in seed and skin extracts, respectively. Our results illustrated that antioxidant activity is more related to the total constituent levels than to the concentration of any individual compound, despite the fact that some compounds may contribute more than the others. Actually, the antioxidant activities of EAQ and EA70 followed the same trend as the phenol content of the extracts. EA70 extracts exhibited higher potential and proved to be more effective than EAQ extracts. Because of bioavailability, metabolism, biotransformation and chemical reactivity, *in vitro* capacity cannot be simply extrapolated. Therefore, in order to evaluate the health effects of these extracts, *in vivo* experiments need to be performed and the effects of antioxidant may be evaluated using appropriate biomarkers in biological fluids and tissues.

### *In vivo* results

Spontaneously hypertensive rats (SHR) selected for this study is frequently used to carry out studies on the antihypertensive effect of functional food ingredients. This strain represents nowadays the best experimental model for essential hypertension in humans and has shown its efficiency in many studies [14, 15].

In order to evaluate the *in vivo* effect of grape pomace extracts and their potential effect on hypertension, rats were fed with different grape pomace extracts at a dose of 21 mg/kg/day, equivalent to a daily dose of 70 kg human consumption of 0.5 L of wine. Study was conducted during six weeks including three weeks of treatments, one week of treatment resumption followed again by two weeks of treatment.

<footnote n="1">¹ GRE1, Grenache; SYR1, Syrah; CAR, Carignan. Data are expressed as the mean of triplicate ± SD. ² ORAC, ABTS and DPPH are expressed as µmol Trolox/g DW and FRAP as mmol Fe²⁺/g DW. a, b, c Anova was made to compare values obtain between varieties for the same test. Same letters indicate no significant differences between the value (Tukey's test, *p*<0.05).</footnote>

<footnote n="1">¹ GRE2, Grenache; SYR1 and SYR2, Syrah; CAR, Carignan; MOU, Mourvèdre; ALI, Alicante. Data are expressed as the mean of triplicate ± SD. ² ORAC, ABTS and DPPH are expressed as µmol Trolox/g DW and FRAP as mmol Fe²⁺/g DW. a, b, c, d, e, f Anova was made to compare values obtain between varieties for the same compound. Same letters indicate no significant differences between the value (Tukey's test, *p*<0.05).</footnote>
Concerning the first experiment, as expected, the growth of WKY (3.3 ± 0.09 g/day) were higher than those observed in SHR rats (2.49 ± 0.16 g/day) without significant influence of grape pomace extracts on weight gain. The mean systolic blood pressure of SHR rats was comprised between 150 mmHg at the beginning of the experiment and 190 mmHg after five weeks (Figure 1).

<figure><img src="image_67.png" /><figcaption>Fig. 1 - Effect of polyphenolic extracts on the mean systolic blood pressure during the 6-week study.</figcaption></figure>

<figure><img src="image_68.png" /><figcaption>Fig. 2 - Systolic blood pressure measured at week 2 (after 14 days of treatment with pomace extracts) and at week 4 (after 7 days of therapeutic interruption) (*p<0.05).</figcaption></figure>

Polyphenolic extracts gave to SHR rats have little effect on systolic blood pressure which increased gradually (except for the SHR1 after 2 weeks of treatments) (Figure 2). However, after three weeks, gavage intolerance were observed and caused difficult the administration of polyphenolic extracts, forcing the interruption of the treatment during one week. This treatment interruption was followed by an increase of the systolic blood pressure in SHR1 (Grenache seed pomace extract), SHR2 (Syrah seed pomace extract) and SHR6 (Alicante skin pomace extract) group compared to the SHR control group. This phenomenon can be interpreted as a « rebound effect » commonly observed with anti-hypertensive drugs and may reveal an antihypertensive effect of grape pomace extracts (Figure 3). However, the treatment resumption at week 5 and 6 was not followed by a decrease of the systolic blood pressure nor another « rebound effect » during the re-interruption of the treatment (data not shown).
<figure><img src="image_69.png" /><figcaption>**Fig. 3** - Variations of systolic blood pressure during interruption and resumption period
(*p*<0.05, **p**<0.01).</figcaption></figure>

Conclusion

This investigation screened the phenolic composition of by-products obtained after vinification of different
Mediterranean grape varieties, in order to assess their potential for nutraceutical applications. Despite
extraction during vinification, grape seed and skin pomace extracts contained appreciable amounts of
flavan-3-ols and anthocyanins. The quantitative and qualitative distribution of polyphenols in grape
pomaces showed significant differences through varieties. Seeds from Grenache (GRE1), Syrah (SYR1)
and Alicante and skins from Syrah (SYR1), Carignan and Alicante were evidenced as the most interesting
fractions because of their richest polyphenol content. The *in vivo* study also showed the efficiency of
Grenache seed pomace and Alicante skin pomace extracts to regulate blood pressure which was illustrated
by a rebound effect. Thus, this study showed that substantial levels of polyphenols, especially flavan-3-ols,
procyanidins and anthocyanins remained in sufficient quantities in pomace after the winemaking process to
exert anti-hypertensive effects.

Acknowledgements

The authors thank the Chateau de Beaucastel for providing grape, pomace and wine samples.

References

1. Llobera A, Cañellas J (2007) Dietary fibre content and antioxidant activity of Manto Negro red grape (*Vitis vinifera*): pomace and stem. *Food Chem* **101**: 659-666.

2. Scalbert A, Williamson G (2000) Dietary intake and bioavailability of polyphenols. *J Nutr* **130**: 2073-2085.

3. Scalbert A, Johnson IT, Saltmarsh M (2005) Polyphenols: antioxidants and beyond. *Am J Clin Nutr* **81**: 215S-217S.

4. Teissandre PL, Frankel EN, Waterhouse AL et al (1996) Inhibition of in vitro human LDL oxidation by phenolic antioxidants from grapes and wines. *J Sci Food Agric* **70**: 55-61.

5. Gorelik S, Ligumsky M, Kohen R et al (2008) A novel function of red wine polyphenols in humans: prevention of absorption of cytotoxic lipid peroxidation products. *FASEB J* **22**: 41-46.

6. Chong MFF, MacDonald R, Lovegrove JA (2010) Fruit polyphenols and CVD risk: a review of human intervention studies. *Br J Nutr* **104**: S28-S39.

7. He FJ, Nowson CA, Lucas M et al (2007) Increased consumption of fruit and vegetables is related to a reduced risk of coronary heart disease: meta-analysis of cohort studies. *J Hum Hypertens* **21**: 717-728.
8. Ky I, Lorrain B, Kolbas N et al (2014) Wine by-products: phenolic characterization and antioxidant activity evaluation of grapes and grape pomaces from six different French grape varieties. *Molecules* **19**: 482-506.

9. Singleton VL, Rossi JA (1965) Colorimetry of total phenolics with phosphomolybdic-phosphotungstic acid reagents. *Am J Enol Vitic* **16**: 144-158.

10. Ribéreau Gayon P, Stonestreet E (1966) Le dosage des tanins dans le vin rouge et détermination de leur structure. *Chim Anal* **48**: 188-196.

11. Ribéreau Gayon P, Stonestreet E (1965) Le dosage des anthocyanes dans le vin rouge. *Bull Soc Chim Fr* **9**: 2649-2652.

12. Ky I, Teissedre PL (2015) Characterisation of Mediterranean grape pomace seed and skin extracts: polyphenolic content and antioxidant activity. *Molecules* **20**: 2190-2207.

13. Ribéreau-Gayon P (1959) Recherches sur les anthocyanes des végétaux. Application au genre *Vitis*. *Unpublished PhD*, Université de Paris, Paris.

14. Al-Awwadi NA, Bornet A, Azay J et al (2004) Red wine polyphenols alone or in association with ethanol prevent hypertension, cardiac hypertrophy, and production of reactive oxygen species in the insulin-resistant fructose-fed rat. *J Agric Food Chem* **52**: 5593-5597.

15. Wada T, Sanada T, Ojima M et al. (1996) Combined effects of the angiotensin II antagonist candesartan cilexetil (TCV-116) and other classes of antihypertensive drugs in spontaneously hypertensive rats. *Hypertens Res* **19**: 247-254.
# Tannin composition of Merlot and Cabernet Sauvignon grapes from seven vineyards of Bordeaux in vintage 2014

W. MA<sup>1,2,3</sup>, M. JOURDES<sup>1,2</sup>, P.L. TEISSEDRE<sup>1,2*</sup>

<sup>1</sup>Univ. Bordeaux, ISVV, EA 4577, Œnologie, 210 Chemin de Leysotte, F-33140 Villenave d'Ornon, France

<sup>2</sup>INRA, ISVV, USC 1366 Œnologie, 210 Chemin de Leysotte, F-33140 Villenave d'Ornon, France

<sup>3</sup>College of Enology, Northwest A & F University, Shaanxi 712100, China

Tannins – proanthocyanins – grapes – Bordeaux

## Introduction

Cabernet Sauvignon and Merlot are popular and globally recognized red grape varieties in the world. In Bordeaux, the worldwide prestigious wine-producing area, these two varieties are widely grown in its numerous appellations. Bordeaux red wine is famous for its intense and complex mouth-feel. The tannins, which are extracted from the grape during the winemaking, are one of the most important contributors to this impressive perception property [1]. Therefore, the qualification and the quantification analysis of grape and wine tannin have been attracting great attention for a long time.

Condensed tannins (proanthocyanidins) are the oligomers or polymers of flavon-3-ols, which are mostly located in grape seed and skin. The flavon-3-ol subunits of tannins discovered in grape involve (+)-catechin and its isomers (-)-epicatechin, (-)-epicatechin 3-gallate, catechin 3-gallate and gallocatechin 3-gallate. Their tannin structures vary in the nature of their constitutive sub-units, degree of polymerization and linkage position according to the grape varieties, vineyard management and terroir. The chemical characteristics of tannins are generally believed to affect wine perception property. Therefore, the present article aims to assess the effect of appellation and variety on tannins, in order to supplement the data of grape tannin composition in the Bordeaux vineyards.

## Materials and methods

### Chemicals and samples

Acetonitrile (HPLC grade), formic acid (HPLC grade), methanol (HPLC grade), glacial acetic acid (HPLC grade), L-ascorbic acid, and sodium acetate were purchased from Prolabo-VWR (Fontenays/Bois, France). Deionized water was purified with a Milli-Q water system.

The studied samples are from seven vineyards (six appellations) located in the Bordeaux vine-growing region. They are situated in Pauillac (Château Lafite Rothschild), Margaux (Château Brane Cantenac), Saint Emillion (Château Figeac, Château Clos Courtet), Côtes de Bourg (Château Brulesécaille), Grave (Château Carbonnieux), and Entre deux Mers (Château Thieuley). The vineyards are all planted with *Vitis vinifera* L. cv. Cabernet Sauvignon (CS) and Merlot (M). All grape samples were collected at maturity in September 2014.

### Preparation of extract

Seeds and skins were removed by hand from grapes, lyophilized for 2 days and stored at -20°C. The frozen seeds and skins were finally ground in a ball grinder. An ASE 350 Accelerated Solvent Extraction System (Dionex Corporation, Sunnyvale, CA) was used to extract the phenolic compounds from the obtained powder. The ground grape seed and skin (~10 g) were submitted to eight solid/liquid consecutive extractions with acetone/water (80:20, v/v) as solvent systems and three times with methanol/water (60:40, v/v). The ASE experimental variables were pressure (1500 psi), temperature (40 °C), static time (4 min),
preheat time (5 min), rinse volume (30%), Purge (100s), Cycles (1) by using a N₂ flush to prevent oxidation during extraction. The volume of all collection bottles was combined after extraction and then evaporated under reduced pressure. The obtained solid residue was dissolved in 30 mL of water and lyophilized, prior to final storage as a dry powder under dark conditions until analysis. All samples were extracted in duplicate. A crude polyphenol extract was obtained and reserved at -20°C.

### Global analysis of polyphenols

Total phenol concentration was determined according to the Folin Ciocalteu method. Total tannins were estimated spectrophotometrically through the Bate-Smith reaction, in accordance with Ribéreau-Gayon and Stonestreet [2]. Total anthocyanidins were determined by the SO₂ bleaching procedure [3].

### Mean Degree of Polymerization (mDP)

The proanthocyanidin mDP concentrations were quantified by phloroglucinolysis reaction as previously described [4].

### Procyanidin analysis by HPLC

The grape seed and skin crude extracts were dissolved in methanol/water (50:50, v/v) as the concentration of 4 mg/mL and 20 mg/mL, respectively. Analysis was carried out on a Surveyor HPLC-UV-Fluo-MS system consisting of an autosampler cooler maintained at 4°C, a Thermo-Finnigan quaternary pump, a Thermo-Finnigan autosampler, a Thermo-Finnigan UV-Vis detector (Surveyor PDA Plus), a Thermo-Finnigan fluorescence detector (Surveyor FL Plus) spectrometer and a mass spectrometer (Finnigan LCQ Deca XP Max). Fluorescence detection was set respectively at an excitation and emission wavelengths of 230 nm and 320 nm By ESI operating in full-scan negative ionization mode scanning from m/z 200 to 2000.

Separation was performed on a normal phase 250 × 4.6 mm i.d. 5-µm HILIC 200Å column (Phenomenex). The method uses a binary gradient with mobile phases containing 2% acetic acid in acetonitrile (mobile phase A) and 95:3:2 methanol/water/acetic acid (mobile phase B). The separation was achieved using a linear gradient from 7% to 37.6% B in 57 min and then to 100% B over the next 3 min. The conditions were held at 100% B for 7 min prior to returning to 7% B over 10 min. The flow rate was set at 1 mL/min. Identification of peaks was confirmed by mass spectrometry in full-scan negative ionization. Peak detection and quantification were performed by fluorescence. Procyanidins were quantified as percentage of the CF-M concentration.

### Data analysis

Statistical data analysis was performed by Statistica V. 10.

## Results and discussion

### Seed tannin composition in seven vineyards

The phenol global analysis procedures, widely used in wine, were applied to grape tannin crude extracts with appropriate concentrations in model wine. As shown in Table 1, the TPC and total tannin amounts in seeds varied from 60.4 to 94.9 GAE mg and from 97.44 to 171.07 mg per g dried seed for the seven samples, respectively. One-ANOVA statistical analyses (*p* < 0.05) revealed that both TPC and tannin amounts per g dried seed samples in Merlot were significantly higher than in Cabernet Sauvignon.

The mDP and the percentage of galloylation (%G), Catechin (%C), Epicatechin (%EC) and Epicatechin-O-gallate (%ECG) of the seed proanthocyanidin extracts are presented in Table 1. The HPLC-Fluo analysis of proanthocyanidins according to their degree of polymerization is shown in Figure 1. Figure 2 reveals the composition of oligomeric proanthocyanidins (monomer, monomer-O-gallate, dimers, trimers, tetramers) in seed. In Table 1, the mDP of grape seed varied between 8 and 15. The mDP of CS in Château Lafite Rothschild was significant higher than the other samples, while the total oligomer concentration of CS and M in Château Lafite Rothschild was lower than the other samples (Figure 1). It illustrated that Lafite Rothschild did a good performance in seed DP when it was harvested in the 2014 vintage.

Although there was no significant difference between the percentage of galloylation in Cabernet Sauvignon and in Merlot (Table 1), the concentration of monomer-O-gallate in CS was generally higher than in M in the same vineyard (Figure 1). It was reported that the esterification of proanthocyanidins with addition of
<table><caption>**Table 1** - Seed tannin sub-unit content and total phenol analysis</caption><thead><tr><th>Appellation<br>& varieties</th><th>mDP</th><th>% G</th><th>%C</th><th>% EC</th><th>TPC<br>[GA] (mg/g dried sample)</th><th>Total tannins<br>(mg/g dried sample)</th></tr></thead><tbody><tr><td>CF-M-SD</td><td>10.07±0.01ab</td><td>59.71±4.64ab</td><td>8.57±0.09ab</td><td>31.72±4.73abc</td><td>94.90±2.17d</td><td>171.07±0.73i</td></tr><tr><td>CF-CS-SD</td><td>8.94±1.18a</td><td>60.86±1.46ab</td><td>8.34±0.63ab</td><td>30.80±0.83abc</td><td>75.72±2.98abc</td><td>111.44±2.90de</td></tr><tr><td>CAR-M-SD</td><td>10.47±0.58ab</td><td>63.16±0.24b</td><td>8.28±0.49ab</td><td>28.55±0.25ab</td><td>97.83±4.70d</td><td>121.89±6.56efg</td></tr><tr><td>CAR-CS-SD</td><td>13.42±0.91cd</td><td>61.15±0.72ab</td><td>6.75±0.46a</td><td>32.09±0.26abc</td><td>83.95±3.45bcd</td><td>110.42±3.06cde</td></tr><tr><td>BC-M-SD</td><td>11.01±0.56abc</td><td>59.00±0.48ab</td><td>7.86±0.47ab</td><td>33.13±0.01abc</td><td>94.38±5.09d</td><td>149.97±1.43h</td></tr><tr><td>BC-CS-SD</td><td>11.02±0.05abc</td><td>58.78±0.68ab</td><td>7.49±0.29ab</td><td>33.73±0.38bc</td><td>69.16±4.34ab</td><td>107.55±0.87bcd</td></tr><tr><td>FIG-M-SD</td><td>11.11±1.32abc</td><td>59.72±0.05ab</td><td>7.65±0.75ab</td><td>32.63±0.80abc</td><td>94.52±3.60d</td><td>131.50±1.72g</td></tr><tr><td>FIG-CS-SD</td><td>10.39±0.61ab</td><td>59.61±0.75ab</td><td>8.22±0.25ab</td><td>32.17±0.49abc</td><td>78.41±3.38bc</td><td>110.27±6.07cde</td></tr><tr><td>BS-M-SD</td><td>9.47±0.06a</td><td>55.92±0.89a</td><td>8.57±0.19ab</td><td>35.52±0.70c</td><td>99.36±3.45d</td><td>119.08±5.89def</td></tr><tr><td>BS-CS-SD</td><td>12.44±0.22bc</td><td>63.83±0.55b</td><td>8.49±0.07ab</td><td>27.69±0.48a</td><td>95.88±5.44d</td><td>107.59±6.03bcd</td></tr><tr><td>LAF-M-SD</td><td>11.71±0.21abc</td><td>61.62±0.45b</td><td>9.16±0.53b</td><td>29.22±0.07ab</td><td>87.95±5.83cd</td><td>98.70±4.74abc</td></tr><tr><td>LAF-CS-SD</td><td>15.38±0.25d</td><td>60.99±0.44ab</td><td>6.55±0.24a</td><td>32.46±0.69abc</td><td>60.40±0.21a</td><td>97.44±8.98ab</td></tr><tr><td>TH-M-SD</td><td>11.23±1.29abc</td><td>62.63±1.23b</td><td>8.22±0.68ab</td><td>29.15±0.55ab</td><td>118.97±3.11e</td><td>123.62±1.99fg</td></tr><tr><td>TH-CS-SD</td><td>10.97±0.77abc</td><td>62.61±0.08b</td><td>8.33±0.99ab</td><td>29.07±0.90ab</td><td>86.35±2.11cd</td><td>91.61±1.93a</td></tr></tbody></table>

<table><caption>**Table 2** - Skin tannin sub-unit content and total phenol analysis</caption><thead><tr><th>Appellation<br>& varieties</th><th>mDP</th><th>% P</th><th>% C</th><th>% EC</th><th>Total phenols<br>[GA] (mg/g dried sample)</th><th>Total tannins<br>(mg/g dried sample)</th><th>Total anthocyanidins<br>(mg/g dried sample)</th></tr></thead><tbody><tr><td>CF-M-SK</td><td>25.33±0.21ab</td><td>37.23±0.20d</td><td>9.90±0.06h</td><td>52.87±0.15d</td><td>28.25±1.22bc</td><td>78.50±1.67b</td><td>15.57±0.66e</td></tr><tr><td>CF-CS-SK</td><td>41.61±3.37e</td><td>49.44±0.24h</td><td>5.73±0.33bc</td><td>44.84±0.57a</td><td>28.09±1.00bc</td><td>81.45±2.67b</td><td>18.90±0.33f</td></tr><tr><td>CAR-M-SK</td><td>24.72±1.27ab</td><td>26.46±0.47a</td><td>10.62±0.01h</td><td>62.92±0.48i</td><td>28.59±1.29c</td><td>87.80±0.28cd</td><td>12.21±0.63b</td></tr><tr><td>CAR-CS-SK</td><td>40.95±4.03e</td><td>42.52±0.45e</td><td>7.13±0.06ef</td><td>50.35±0.39c</td><td>21.30±2.17a</td><td>70.31±0.67a</td><td>13.30±0.02bc</td></tr><tr><td>BC-M-SK</td><td>19.25±0.17a</td><td>34.00±0.12c</td><td>7.96±0.32fg</td><td>58.04±0.20ef</td><td>22.79±1.09ab</td><td>81.76±3.50b</td><td>10.64±0.46a</td></tr><tr><td>BC-CS-SK</td><td>40.22±0.68de</td><td>30.15±0.32b</td><td>8.31±0.15g</td><td>61.54±0.17h</td><td>24.60±2.64abc</td><td>82.82±1.91bc</td><td>16.18±0.14e</td></tr><tr><td>FIG-M-SK</td><td>22.32±1.75a</td><td>31.11±0.46b</td><td>6.86±0.32de</td><td>62.03±0.14hi</td><td>29.75±2.53cd</td><td>110.43±0.34f</td><td>14.01±0.33cd</td></tr><tr><td>FIG-CS-SK</td><td>51.77±2.86f</td><td>48.14±0.21gh</td><td>3.61±0.00a</td><td>48.25±0.20b</td><td>41.47±2.14e</td><td>143.14±4.59h</td><td>24.25±0.77h</td></tr><tr><td>BS-M-SK</td><td>25.06±0.96ab</td><td>33.55±0.61c</td><td>7.00±0.09e</td><td>59.45±0.53g</td><td>28.72±0.17c</td><td>125.27±2.37g</td><td>19.04±0.52f</td></tr><tr><td>BS-CS-SK</td><td>22.73±0.49a</td><td>36.05±0.12d</td><td>6.99±0.06e</td><td>56.96±0.06e</td><td>25.85±1.32abc</td><td>97.52±1.83e</td><td>15.00±0.03de</td></tr><tr><td>LAF-M-SK</td><td>19.40±0.20a</td><td>33.14±0.06c</td><td>8.09±0.05g</td><td>58.77±0.01fg</td><td>34.78±1.48d</td><td>124.54±3.06g</td><td>17.74±0.64f</td></tr><tr><td>LAF-CS-SK</td><td>30.71±1.07bc</td><td>46.29±0.19f</td><td>6.05±0.32cd</td><td>47.66±0.13b</td><td>20.32±1.12a</td><td>65.92±0.16a</td><td>13.62±0.10c</td></tr><tr><td>TH-M-SK</td><td>33.01±1.09cd</td><td>47.26±0.34fg</td><td>5.13±0.25b</td><td>47.61±0.59b</td><td>47.43±4.64f</td><td>182.09±4.79i</td><td>33.86±1.74i</td></tr><tr><td>TH-CS-SK</td><td>40.53±1.78e</td><td>42.62±0.63e</td><td>5.09±0.35b</td><td>52.28±0.27d</td><td>28.61±0.56c</td><td>92.23±1.45d</td><td>21.09±0.64g</td></tr></tbody></table>
gallic acid played a significant role in the protein binding capability [5] and the tannins with lower Dp were
easier to extract in the aqueous medium like wine [6]. This result may partly be due to the wine astringency
of Cabernet Sauvignon which is usually stronger than that of Merlot, although both TPC and tannin
amounts per g dried seed samples in Merlot were significantly higher than in Cabernet Sauvignon.
Additionally, it was observed in seed that there were more epicatechin serving as the procyanidin units than
catechin.

<figure><img src="image_70.png" /><figcaption>**Fig. 1** - HPLC-Fluo chromatogram of Clos Fourtet Merlot grape seed extract with excitation and emission wavelengths at 230 nm and 320 nm using different sensitivity ("medium" model in 0-13 min; "superhigh" model in 14-83 min).</figcaption></figure>

<figure><img src="image_71.png" /><figcaption>**Fig. 2** - Relative content of monomers, dimers, trimers and tetramers in different samples (the contents of monomers, dimers, trimers and tetramers in Clos Fourtet merlot grape seed were regarded as 100%, respectively.).</figcaption></figure>

Skin tannin composition in seven vineyards

The TPC, total tannin amounts, total anthocyanidins, mDP, %EC, %C and the percentage of prodelphinidins (%P) of the skin proanthocyanidin extracts are presented in Table 2. Total anthocyanidins ranged from 91.61 to 171.07 mg per g dried seed. Both TPC and total tannin amounts in skins were much lower than the amounts in seeds. According to the results of procyanidin composition, it was found that both mDP and the percentage of prodelphinidins in CS were significantly higher than that in M. This observation well illustrated the higher astringency intensity in CS wine than in M wine, attributed to DP [7] and the B-ring trihydroxylation [8]. Similarly to the observation in seed, there were more epicatechin than catechin as the procyanidin units.
**Conclusion**

The present article studied the characterization of tannins in M and CS grapes from seven vineyards of Bordeaux in vintage 2014. A higher amount of total phenol in all grapes was discovered in seeds than in skins, while a lower mDP value was observed in seeds than in skins. In seeds, the concentration of monomer-O-gallate in CS was generally higher than in M in the same vineyard. The seed mDP of CS in Château Lafite Rothschild was significantly higher than the other samples, while the total oligomer concentration of CS and M in Château Lafite Rothschild was lower than the other samples. In skins, the results revealed significant differences between Cabernet Sauvignon and Merlot in Bordeaux in the level of both mDP and %P. In future investigations, identification and quantification of polymeric tannins in grapes is expected to better illustrate the tannin characteristics in grape.

**Acknowledgements**

The authors thank the China Scholarship Council for providing financial support.

**Abbreviations**

CS, Cabernet Sauvignon; M, Merlot; mDP, Mean degree of polymerization; HPLC, High-performance liquid chromatography; TPC, total phenol concentrations; CF, Château Clos Fourtet; CAR, Château Carbonnieux; BC, Château Brane Cantenac; FIG, Château Figeac; BS, Château Brulesécaille; LAF, Château Lafite; TH, Château Thieuley; %G, percentage of galloylation; %C, percentage of Catechin; %EC, percentage of Epicatechin; %P, percentage of prodelphinidins.

**References**

1. Ma W, Guo A, Zhang Y et al (2014) A review on astringency and bitterness perception of tannins in wine. *Trends Food Sci Technol* **40**: 6-19.

2. Ribéreau-Gayon P, Stonestreet E (1966) Dosage des tanins du vin rouge et détermination de leur structure. *Chim Anal* **48**: 188-196.

3. Ribéreau-Gayon P, Stonestreet E (1965) Le dosage des anthocyanes dans le vin rouge. *Bull. Soc. Chim Fr* **9**: 2649-2652.

4. Drinkine J, Lopes P, Kennedy JA et al (2007) Analysis of ethylidene-bridged flavan-3-ols in wine. *J Agric Food Chem* **55**: 1109-1116.

5. Kawamoto H, Nakatsubo F, Murakami K et al (1996) Stoichiometric studies of tannin-protein co-precipitation. *Phytochemistry* **41**: 1427-1431.

6. Rinaldi A, Iturmendi N, Jourdes M et al (2015) Transfer of tannin characteristics from grape skins or seeds to wine-like solutions and their impact on potential astringency. *LWT - Food Sci Technol* **63**: 667-676.

7. Peleg H, Gacon K, Schlich P et al (1999) Bitterness and astringency of flavan-3-ol monomers, dimers and trimers. *J Sci Food Agric* **79**: 1123-1128.

8. Schwarz B, Hofmann T (2008) Is there a direct relationship between oral astringency and human salivary protein binding? *Eur Food Res Technol* **227**: 1693-1698.
<figure><img src="image_72.png" /></figure>
# THÈME 2

## Microorganismes du raisin et du vin

*Grape and wine microorganisms*
<figure><img src="image_73.png" /></figure>
# New insights into the adaptation of yeast to wine making using comparative genomics

J.-L. LEGRAS<fnref n="1" />*, A.L. COI<fnref n="2" />, F. BIGEY<fnref n="1" />, V. GALEOTE<fnref n="1" />, S. MARSIT<fnref n="1" />, A. COULOUX<fnref n="3" />, J. GUY<fnref n="3" />, R. FRANCO-DUARTE<fnref n="4" />, D. SCHULLER<fnref n="4" />, J.P. SAMPAIO<fnref n="5" />, M. BUDRONI<fnref n="2" />, S. DEQUIN<fnref n="1" />

Yeast – adaptation – genome – mutation

## Introduction

Fermented beverages and foods have a unique place in most societies because of their economical and cultural importance, and the development of fermentation technologies is deeply rooted in their history. Archaeologists have found evidence for the production of a fermented beverage at the Neolithic period in China at 7000 bc [1] and of wine in Hajji Firuz in Iran at 6000 bc and later in Egypt at 3000 bc respectively [2, 3]. In addition to the diversity of these fermented beverages and foods, technological practices have evolved over time history, especially for wine making.

The yeast *Saccharomyces cerevisiae* is the main microorganism responsible for these alcoholic fermentations, and it can also be isolated from leavening dough or cheese as well as from natural sources such as fruits and from oak bark or litter. Despite the numerous investigations on yeast diversity, yeast population structure has been unravelled only recently [4, 5]. These recent studies have shown that yeast populations were tightly associated with their ecological origin and have been deeply impacted by human activities, suggesting domestication for wine and saké strains [4]. The development of wine making all over the world since the Neolithic, which has also favoured the development of wine yeast from a unique origin, very likely from the Middle East [5], as well as the extension of grape cultures from different grape stocks including one from the Caucasus mounts [6], is a fascinating example of evolution.

Wine fermentation is a fluctuating environment that exposes yeast to a variety of stresses, including high osmolarity reflecting sugar concentrations, high sulphite content, high acidity, nutrient depletion (nitrogen, vitamins) and anaerobiosis [7]. The comparison of the phenotypic properties of wine, oak as well as other source strains has revealed that wine strains have a better ability to cope with these multiple stresses and a remarkable better ability to perform alcoholic fermentation than oak strains [8]. In addition, small scale fermentation of Chardonnay grape must performed by wine and oak strains can be differentiated by humans, with wines produced by wine yeast being more fruity and those produced by oak strains having earthy and sulphurous characteristics [9]. Surprisingly, very few examples of the mechanisms involved in the adaptation of wine yeast to its environment have been evidenced. The first example to be described was the adaptation to sulphite permitted by a translocation between chromosome VIII and XVI, which is widespread among wine yeast. This translocation results in an increase of the production of the Ssulp sulphite pump and provides resistance [10, 11]. Recently, another translocation between chromosome XV and XVI was identified in several wine strains through quantitative trait loci (QTL) mapping for lag phase duration [12] and revealed a similar increase in the expression of gene *SSU1* providing an improved resistance to sulphite. Another example of adaptation to a chemical compound has been reported for copper. In this case, a different mechanism has been reported: the increase in the number of copies of the *CUP1* gene [13, 14]. Flor strains, which are also isolated from wine, but are able to build a velum, possess a mutation in the promoting region of gene *FLO11* coding for parietal protein that provides a higher expression, and specific alleles with increased protein length, which favour their aggregation [15].

<footnote n="1">¹SPO, INRA, SupAgro, Université de Montpellier, 34060, Montpellier, France</footnote>

<footnote n="2">²Dipartimento di Agraria, Università di Sassari, Sassari, Italy</footnote>

<footnote n="3">³Genoscope – Centre National de Séquençage, UMR CNRS 8030,
2 Gaston Crémieux, CP 5706, 91507 Evry, France</footnote>

<footnote n="4">⁴Universidade do Minho, Braga, Portugal</footnote>

<footnote n="5">⁵CREM, DCV – Universidade Nova de Lisboa, Portugal</footnote>
The recent genomic approaches performed on *S. cerevisiae* isolated from wine have revealed that they present a specific genomic content [16, 17] with large regions introgressed from distant species by horizontal gene transfer. However, the first large scale genomic studies failed to detect more genes involved in the adaptation to wine making [18].

In order to provide new insights into the adaptation of yeast to anthropic niches, we have obtained high quality genome sequences of 82 yeast strains. The genomes have been compared in order to obtain the inventory of genetic variation. Because of their contrasted life style (pelagic fermentation growth on glucose and fructose contained in the grape must for wine strains, and aerobic biofilm growth utilizing ethanol and glycerol of the wine for flor strains), we will focus on the adaptation of wine and flor yeast to their environment.

## Materials and methods

We gathered a set of 82 strains from different origins: strains from different yeast collections (27 wine strains, 8 flor strains) as well as 47 strains from rum, fermented dairy product, bakery, and oak trees. The DNA was extracted with the phenol chloroform method [19] and submitted to Illumina HiSeq2000 sequencing with 100bp paired reads. A mean sequencing depth of 100x was achieved (20-400) and genomes were assembled de novo. The list of genetic variation was obtained by genotyping using the genome of S288C as reference.

## Results and discussion

A phylogeny of the different strains drawn from these genomic data confirmed that wine strains represent a family of closely related strains; however, few strains presented an atypical localization in the tree, indicating that they result from the hybridization of wine strains with strains of more distant origins (Fig. 1). Flor strains appeared as a well-identified group closely related to wine strains. Distance between flor strains measured by branch length is half lower between flor strains than between wine strains, indicating a more recent divergence between strains. However, the distance between flor strains and wine strains is twice as the divergence between wine strains, indicating that the two groups were separated before the expansion of each cluster, which suggests that these groups separated at the very beginning of wine making. Interestingly, several strains such as EC1118, QA23 and VIN13 have a median position between flor and wine, which suggests that these strains result from a cross between a wine and a flor strain, and may explain their specific properties of resistance to ethanol and the ability of EC1118 to perform the second fermentation of the Champagne "prise de mousse" step. This phylogeny revealed also an important new group of oak strains isolated from Mediterranean countries and Romania. This group of wild isolates presents a high interest, as it is the closest to wine and can be considered as a member of the wild population from which wine stains have emerged [20].

<figure><img src="image_74.png" /><figcaption>**Fig. 1** - Phylogenic tree presenting the diversity and the relationships between *Saccharomyces cerevisiae* strains from different origins.</figcaption></figure>
Different mechanisms have been described in order to explain adaptation to specific environment: accumulation of mutation, gene duplication, and horizontal gene transfer. Variation in the number of gene copies has been shown to explain the adaptation of yeast to new harsh environment [21, 22], and has been proposed as a mechanism explaining the adaptation of flor yeast to the specific environment of biological ageing [23]. Our results indicate that flor yeast do not present duplication shared by all strains, and only MCH2 and its adjacent gene YKL222C are duplicated in several strains [24]. In contrast, the gene CUP1 is significantly amplified in the genome of wine strains as already described [14], which results very likely from the use of copper sulphate treatment of vine for pest management (Table 1) but this duplication is also observed in the genome of rum strains. We could also notice the amplification of several genes coding for parietal proteins (PAU3, PAU21, 22) and ADH7 coding for an alcohol dehydrogenase gene among wine strains. Other genes such as IMD2 and PHO12 are also found amplified both in flor and wine strains.

<table><caption>**Table 1** - Variations in the copy number of different regions in the genome of wine and flor strains in comparison to oak strains. Results are given on 1kb windows and expressed as the log ratio of sequence reads mapped in this 1kb region divided by the mean number of reads mapped along the genome per 1kb window (Chr: chromosome).</caption><thead><tr><th>Chr</th><th>Coordinates</th><th>Med Oak</th><th>Flor</th><th>Rum</th><th>Wine</th><th>Genes contained in the 1kb region</th></tr></thead><tbody><tr><td>I</td><td>222000</td><td>0.62</td><td>0.89</td><td>0.60</td><td>1.32</td><td>YAR064W, YAR066W</td></tr><tr><td>I</td><td>225000</td><td>0.48</td><td>1.01</td><td>0.08</td><td>1.05</td><td>YAR068W, YAR069C, YAR070C</td></tr><tr><td>III</td><td>309000</td><td>0.49</td><td>0.13</td><td>-2.34</td><td>1.24</td><td>PAU3</td></tr><tr><td>III</td><td>310000</td><td>0.39</td><td>0.04</td><td>-3.23</td><td>1.19</td><td>ADH7</td></tr><tr><td>VIII</td><td>554000</td><td>1.28</td><td>2.10</td><td>1.55</td><td>2.36</td><td>PHO12, IMD2</td></tr><tr><td>VIII</td><td>213000</td><td>-0.71</td><td>-0.59</td><td>0.92</td><td>0.97</td><td>CUP1_1</td></tr><tr><td>XI</td><td>8000</td><td>0.59</td><td>1.72</td><td>1.37</td><td>0.38</td><td>MCH2</td></tr><tr><td>XI</td><td>653000</td><td>0.72</td><td>0.61</td><td>0.57</td><td>-0.27</td><td>NFT1</td></tr><tr><td>XIV</td><td>774000</td><td>1.08</td><td>0.23</td><td>0.41</td><td>0.88</td><td>HXT17</td></tr><tr><td>XIV</td><td>775000</td><td>1.20</td><td>0.38</td><td>0.03</td><td>0.58</td><td>YNR073C</td></tr><tr><td>XV</td><td>1080000</td><td>0.79</td><td>1.31</td><td>1.13</td><td>1.31</td><td>YOR392W</td></tr><tr><td>XV</td><td>1083000</td><td>0.03</td><td>0.42</td><td>0.10</td><td>0.51</td><td>PAU21</td></tr><tr><td>XVI</td><td>9000</td><td>0.03</td><td>0.20</td><td>0.06</td><td>0.51</td><td>PAU22</td></tr></tbody></table>

<figure><img src="image_75.png" /><figcaption>**Fig. 2** - Genomic scan presenting nucleotidic diversity calculated by 1kb window, along the genome, and displaying divergent regions between wine and flor strains. Each colour corresponds to a different chromosome (from I left to XVI and mitochondrial DNA right), and for each chromosome, nucleotidic diversity is given according to the coordinates on the chromosome from left to right. The dashed line represents the threshold separating the 2% highest values.</figcaption></figure>

The accumulation of mutations in certain genomic regions may also explain phenotypic variations. We calculated the nucleotidic diversity combining flor and wine data in order to detect such regions. The result of the genomic scan presented in Figure 2 reveals regions that present a high diversity. Among the genes contained in these divergent regions, several coded for parietal proteins (*FLO9*, *FLO1*, *FLO5*, *FLO10*, *FLO11* as well as most PAU genes). In addition, we could detect genes involved in the regulation
of *FLO11* which is essential for the film forming ability, and for one of them, the replacement of the flor allele by the wine allele led to a decrease of the ability to produce a velum. But this genomic investigation revealed also unsuspected features such as the fructophily of flor strains and specific metal homeostasis. The fructophily of flor strains is caused by the presence of a specific allele of *HXT3* with a high affinity for fructose. Another surprising point is the accumulation of variation in numerous genes involved in metal transport such as gene *ZRT1*, suggesting a specific metal homeostasis. It must be pointed out that the amplification of *CUP1* gene of wine strains is not observed among flor strains, which might be permitted by the detoxification of the media performed by wine yeast during alcoholic fermentation resulting in a chelation of copper.

Finally, regarding the three large chromosomal regions named A, B and C, discovered in EC1118 [17] and originated from horizontal gene transfer (HGT) from distant yeasts, the complete region C was only found among flor strains.

Conclusion

These genomic data represent a unique resource for understanding the adaptation of yeast to wine making and thus for elucidating the basis of technological properties. Our first results indicate the adaptation of flor yeast has involved the accumulation of convergent mutations. But our work has also revealed some specific features.

References

1. McGovern PE, Zhang J, Tang J et al (2004) Fermented beverages of pre- and proto-historic China. *Proc Natl Acad Sci USA* **101**: 17593-17598.

2. McGovern PE, Hartung U, Badler V et al (1997) The beginnings of winemaking and viniculture in the ancient Near East and Egypt. *Expedition* **39**: 3-21.

3. Cavalieri D, McGovern PE, Hartl DL et al (2003) Evidence for *S. cerevisiae* fermentation in ancient wine. *J Mol Evol* **57**: S226-S232.

4. Fay JC, Benavides JA (2005) Evidence for domesticated and wild populations of *Saccharomyces cerevisiae*. *PLoS Genet* **1**: 66-71.

5. Legras J-L, Merdinoglu D, Cornuet J-M et al (2007) Bread, beer and wine: *Saccharomyces cerevisiae* diversity reflects human history. *Mol Ecol* **16**: 2091-2102.

6. This P, Lacombe T, Thomas MR (2006) Historical origins and genetic diversity of wine grapes. *Trends Genet* **22**: 511-519.

7. Marsit S, Dequin S (2015) Diversity and adaptive evolution of *Saccharomyces* wine yeast: a review. *FEMS Yeast Res* **15**:fov067.

8. Camarasa C, Sanchez I, Brial P et al (2011) Phenotypic landscape of *Saccharomyces cerevisiae* during wine fermentation: evidence for origin-dependent metabolic traits. *PLoS One* **6**: e25147.

9. Hyma KE, Saerens SM, Verstrepen KJ (2011) Divergence in wine characteristics produced by wild and domesticated strains of *Saccharomyces cerevisiae*. *FEMS Yeast Res* **11**: 540-551.

10. Pérez-Ortín JE, Querol A, Puig S et al (2002) Molecular characterization of a chromosomal rearrangement involved in the adaptive evolution of yeast strains. *Genome Res* **12**: 1533-1539.

11. Yuasa N, Nakagawa Y, Hayakawa M et al (2004) Distribution of the sulfite resistance gene SSU1-R and the variation in its promoter region in wine yeasts. *J Biosci Bioeng* **98**: 394-397.

12. Zimmer A, Durand C, Loira N et al (2014) QTL dissection of lag phase in wine fermentation reveals a new translocation responsible for *Saccharomyces cerevisiae* adaptation to sulfite. *PLoS One* **9**: e86298.

13. Fay JC, McCullough HL, Sniegowski PD et al (2004) Population genetic variation in gene expression is associated with phenotypic variation in *Saccharomyces cerevisia*. *Genome Biol* **5**: R26.

14. Warringer J, Zörgö E, Cubillos FA et al (2011) Trait variation in yeast is defined by population history. *PLoS Genet* **7**: e1002111.

15. Fidalgo M, Barrales RR, Ibeas JI (2006) Adaptive evolution by mutations in the FLO11 gene. *Proc Natl Acad Sci USA* **103**: 11228-11233.

16. Borneman AR, Forgan AH, Pretorius IS (2008) Comparative genome analysis of a *Saccharomyces cerevisiae* wine strain. *FEMS Yeast Res* **8**: 1185-1195.

17. Novo M, Bigey F, Beyne E et al (2009) Eukaryote-to-eukaryote gene transfer events revealed by the genome sequence of the wine yeast *Saccharomyces cerevisiae* EC1118. *Proc Natl Acad Sci USA* **106**: 16333-16338.
18. Liti G, Carter DM, Moses AM et al (2009) Population genomics of domestic and wild yeasts. *Nature* **458**: 337-341.

19. Hanna M, Xiao W (2006) Isolation of nucleic acids. *Methods Mol Biol* **313**: 15-20.

20. Almeida P, Barbosa R, Zalar P et al (2015) A population genomics insight into the Mediterranean origins of wine yeast domestication. *Mol Ecol* **24**: 5412-5427.

21. Dunham MJ, Badrane H, Ferea TL et al (2002) Characteristic genome rearrangements in experimental evolution of *Saccharomyces cerevisiae*. *Proc Natl Acad Sci USA* **99**: 16144-16149.

22. Gresham D, Desai MM, Tucker CM et al (2008) The repertoire and dynamics of evolutionary adaptations to controlled nutrient-limited environments in yeast. *PLoS Genet* **4**: e1000303.

23. Infante JJ, Dombek KM, Rebordinos L et al (2003) Genome-wide amplifications caused by chromosomal rearrangements play a major role in the adaptive evolution of natural yeast. *Genetics* **165**: 1745-1759.

24. Legras J-L, Erny C, Charpentier C (2014) Population structure and comparative genome hybridization of European flor yeast reveal a unique group of *Saccharomyces cerevisiae* strains with few gene duplications in their genome. *PLoS One* **9**: e108089.
# Biodiversity of wine yeasts: new insights from population genetics

W. ALBERTIN<sup>1,2*</sup>, P. MARULLO<sup>1,3</sup>, E. PELTIER<sup>1,3</sup>, S. WINDHOLTZ<sup>1</sup>, M. BELY<sup>1</sup>, I. MASNEUF-POMARÈDE<sup>1,4</sup>

Non-conventional yeast – non-*Saccharomyces* – wine – microsatellite

## Introduction

Yeast plays a key role in oenology: they are responsible for alcoholic fermentation (AF), an essential step in winemaking. Only two yeast species are able to complete AF in winemaking (i.e. are able to consume all sugars present in grape must): *Saccharomyces cerevisiae*, and a closely related species (*S. uvarum*) which has been isolated in northern French vineyards, in Italian wines and botrytized grape must [1].

Besides the *Saccharomyces* genus, other yeasts species, frequently designed as ‘non-*Saccharomyces*’ yeasts (NS), are present in grape must and may contribute to the wine’s flavour and taste by producing secondary metabolites including esters, higher alcohols, acids, volatile thiols [2-6] and extracellular enzymes like beta-glucosidases [7-10]. In addition, some NS species could be of interest for alcohol level reduction in wine [2, 3]. Indeed, some NS species are now proposed as starter cultures for winemakers as it is the case for *T. delbrueckii*, *Metschnikowia pulcherrima*, *Pichia kluyveri*, and *Lachancea thermotolerans*. It has to be noted that these NS are not able to complete AF, so they must be used in mixed or sequential fermentation with *S. cerevisiae* [2, 4] to secure AF completion. The oenological potential of other NS species (*Candida zemplinina/Starmerella bacillaris*, *Hanseniaspora uvarum*, etc.) is currently being investigated [2, 5-13].

Finally, spoilage yeasts are also known in winemaking, the most famous one being *Brettanomyces bruxellensis*, responsible for the ‘brett’ character in wines. This species has detrimental effects on wine quality by producing ‘phenolic’ off-flavours [14], acetic acid [15] and ‘mousy’ off-flavours [16].

The knowledge of these wine yeasts has greatly increased this last decade, particularly with the revolution triggered by genomics and genetics tools. Here we review the recent advances in the biology, evolution, adaptation, and dissemination of wine yeasts.

## Genetic diversity to understand wine yeasts

*S. cerevisiae* is the most extensively studied yeast. From a genetic viewpoint, the species is divided in subpopulations from wild ecosystems and also from human environments: some populations are particularly associated with wine, bread, beer, sake, etc. [17-20]. Comparative genomics analyses suggested the occurrence of multiple domestication events, i.e. multiple and independent selection of strains for different human purposes [21]. In addition, genomic studies show the occurrence of cross-breeding (probably driven by human) between these specialized genetic groups [17], and the species could be disseminated by insects & human activities [22]. Various mechanisms are involved in *S. cerevisiae* evolution and may have an impact on its technological abilities for winemaking: gene transfer from other species [23], chromosomal rearrangements such as translocations [24], polyploidy & hybridization [25], allelic variation [26], etc. Ecological studies during winemaking show that indigenous *S. cerevisiae* populations can participate in AF even in the presence of selected starter cultures [27, 28]. The genetic diversity during AF varies greatly: Shannon's equitability index (EI) is a diversity indicator (EI=0 meaning fully clonal population and EI=1 meaning fully diversified population). EI was computed for a few

<footnote n="1"><sup>1</sup>Univ. Bordeaux, ISVV, Unité de recherche Œnologie EA 4577, USC 1366 INRA,
Bordeaux INP, 33140 Villenave d'Ornon, France</footnote>

<footnote n="2"><sup>2</sup>ENSCBP, Bordeaux INP, 33600 Pessac, France</footnote>

<footnote n="3"><sup>3</sup>Biolaffort, 126 Quai de Souys, 33100 Bordeaux, France</footnote>

<footnote n="4"><sup>4</sup>Bordeaux Sciences Agro, 33170 Gradignan, France</footnote>
spontaneous fermentations (Table 1) and ranged from 0.39 to 0.65. The precise impact of such diversity (or absence of diversity) on wine quality is still debated/studied [29, 30].

<table><caption>**Table 1** - Genetic diversity from winemaking environments for several yeast species. EI= Shannon's equitability index (EI). EI=0 means fully clonal population, EI=1 means fully diversified population.</caption><thead><tr><th>Species</th><th>Sample description</th><th>EI (number of strains analyzed)</th><th>References</th></tr></thead><tbody><tr><td rowspan="3">S. cerevisiae</td><td>France, Gers, wine (end of spontaneous ferm. F3)</td><td>0.64 (10)</td><td>[31]</td></tr><tr><td>France, Gers, wine (end of spontaneous ferm. F4)</td><td>0.39 (10)</td><td>[31]</td></tr><tr><td>France, Gers, wine (end of spontaneous ferm. F43)</td><td>0.65 (10)</td><td>[31]</td></tr><tr><td rowspan="4">T. delbrueckii</td><td>France, Burgundy, wine (end of AF)</td><td>0.35 (9)</td><td>Unpublished data</td></tr><tr><td>France, Burgundy (Cote de Beaune vineyard), grapes</td><td>0.80 (17)</td><td>Unpublished data</td></tr><tr><td>France, Loire Valley, bottling chain</td><td>0.89 (12)</td><td>Unpublished data</td></tr><tr><td>New Zealand, Hawke's Bay, grape must</td><td>1.00 (17)</td><td>Unpublished data</td></tr><tr><td rowspan="3">C. zemplinina</td><td>France, Sauternes, grape must</td><td>0.90 (7)</td><td>[32]</td></tr><tr><td>France, Bordeaux, grape must</td><td>0.94 (10)</td><td>[32]</td></tr><tr><td>France, Burgundy, grape must</td><td>0.97 (16)</td><td>[32]</td></tr><tr><td rowspan="2">B. bruxellensis</td><td>France, Bordeaux, red wine (winery A, 2006)</td><td>0.31 (7)</td><td>Unpublished data</td></tr><tr><td>France, Bordeaux, red wine (winery C)</td><td>0 (8)</td><td>Unpublished data</td></tr></tbody></table>

S. uvarum is a sister species of S. cerevisiae, whose genome has recently been published [33, 34]. Comparative genomics approaches show that domestic strains (from wine & cider) display lower genetic diversity than their wild counterpart. However, domestic strains harbor several genomic introgressions, i.e. genome portions coming from other species, mainly from S. eubayanus species. This suggests that S. uvarum specialization for winemaking & cidermaking could be related to the gain of such introgressions, an hypothesis that remains to be addressed.

T. delbrueckii is one of the first NS yeast whose population genetics was studied [35]. Microsatellite markers reveal that T. delbrueckii strains are clustered in four populations: two wild populations from Americas & Old World, and the two others associated with winemaking & other bioprocesses (bakery, dairy and other food & beverage fermentations). Preliminary results suggest that in winemaking environments, T. delbrueckii populations are quite diverse: Shannon's equitability index was computed and ranged from 0.35 to 1.00 for various winemaking isolates, indicating diversified populations.

The same approach was recently applied to C. zemplinina. C. zemplinina is frequently isolated from alcoholic fermentation of grapevine, with occasional colonization of other favorable niches. Its oenological use in mixed fermentation with S. cerevisiae has been extensively investigated these last years, and several interesting features, including low ethanol production, fructophily, glycerol & other metabolites production, have been described. Populations isolated from winemaking environments are quite diverse at the genetic level (EI from 0.90 to 0.97) and suggest that C. zemplinina is not under selective pressure in wine [32].

Finally, the genetic diversity of the spoilage yeast B. bruxellensis was also investigated using microsatellite markers [36]. Our results suggest that B. bruxellensis is a highly disseminated species, with some strains isolated from different continents being closely related at the genetic level. We show that one clonal population may cause spoilage over decades in wineries [36]. Comparative genomics was also conducted and revealed the co-existence of diploid and triploid strains [37], with the occurrence of independent triploidization events [38] involving closely related species of Brettanomyces. By contrast with other wine species, the genetic diversity of 'Bretts' within one sample seems quite low (EI from 0.00 to 0.31, Table 1).
Conclusion

The different yeast species associated with winemaking harbor different features at the genetic level: *S. cerevisiae*, *S. uvarum* and *T. delbrueckii* show the coexistence of both wild and domestic populations. There are numerous mechanisms driving *S. cerevisiae* evolution (gene transfer, chromosomal rearrangements, polyploidy, hybridization, allelic variation, etc.), while *S. uvarum* shows low genetic diversity except for genomic introgression. Most studied yeasts seem to display medium to high genetic diversity in winemaking environments, except for *B. bruxellensis* that shows high level of clonality and dissemination. Variation of ploidy level seems to play an important role in *B. bruxellensis* evolution, with the existence of triploid individuals, an extremely uncommon feature in yeasts and also in eukaryotes. Wine yeast species are thus dissimilar when comparing their genetic diversity and evolutionary mechanisms.

In conclusion, recent advances in genomics (particularly with the revolution of Next Generation Sequencing) have greatly increased data availability and subsequently our knowledge of wine yeast species. However, there are still many questions to answer, particularly for NS yeasts that are less studied. Sequencing their genome is now the next step toward the launching of subsequent genomic approaches. Understanding extensive population structure of wine yeast will improve our understanding of their evolutionary history and may rationalize selection programs, in order to improve wine quality, in the case of species with interesting oenological features. Conversely, for spoilage yeasts, a better understanding of their dissemination model and their mechanisms of adaptation to wine may help winemakers to limit their development during winemaking.

References

1. Masneuf-Pomarede I, Bely M, Marullo P et al (2010) Reassessment of phenotypic traits for *Saccharomyces bayanus* var. *uvarum* wine yeast strains. *Int J Food Microbiol* **139**: 79-86.

2. Bely M, Renault P, da Silva T et al (2013) Non-conventional yeasts and alcohol levels reduction, pp. 33-37. *In Alcohol level reduction in wine*. Bordeaux: Ed. Vigne et Vin Publications Internationales.

3. Ciani M, Canonico L, Oro L et al (2014) Sequential fermentation using non-*Saccharomyces* yeasts for the reduction of alcohol content in wine. *BIO Web Conf 3*: 02015.

4. 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.

5. Wang C, Mas A, Esteve-Zarzoso B (2015) Interaction between *Hanseniaspora uvarum* and *Saccharomyces cerevisiae* during alcoholic fermentation. *Int J Food Microbiol* **206**: 67-74.

6. Hong Y-A, Park H-D (2013) Role of non-*Saccharomyces* yeasts in Korean wines produced from Campbell Early grapes: potential use of *Hanseniaspora uvarum* as a starter culture. *Food Microbiol* **34**: 207-214.

7. Ciani M, Comitini F (2015) Yeast interactions in multi-starter wine fermentation. *Curr Opin Food Sci* **1**: 1-6.

8. Di Maio S, Genna G, Gandolfo V et al (2012) Presence of *Candida zemplinina* in Sicilian musts and selection of a strain for wine mixed fermentations. *S Afr J Enol Vitic* **33**: 80-87.

9. 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.

10. 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.

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.

12. Magyar I, Toth T (2011) Comparative evaluation of some oenological properties in wine strains of *Candida stellata*, *Candida zemplinina*, *Saccharomyces uvarum* and *Saccharomyces cerevisiae*. *Food Microbiol* **28**: 94-100.

13. Tofalo R, Schirone M, Torriani S et al (2012) Diversity of *Candida zemplinina* strains from grapes and Italian wines. *Food Microbiol* **29**: 18-26.

14. Chatonnet P, Dubourdieu D, Boidron J-N et al (1992) The origin of ethylphenols in wines. *J Sci Food Agric* **60**: 165-178.

15. Freer SN, Dien B, Matsuda S (2003) Production of acetic acid by *Dekkera/Brettanomyces* yeasts under conditions of constant pH. *World J Microbiol Biotechnol* **19**: 101-105.
16. Grbin PR, Henschke PA (2000) Mousy off-flavour production in grape juice and wine by Dekkera and Brettanomyces yeasts. *Aust J Grape Wine Res* **6**: 255-262.

17. Liti G, Carter DM, Moses AM et al (2009) Population genomics of domestic and wild yeasts. *Nature* **458**: 337-341.

18. Legras JL, Merdinoglu D, Cornuet JM et al (2007) Bread, beer and wine: *Saccharomyces cerevisiae* diversity reflects human history. *Mol Ecol* **16**: 2091-2102.

19. Sicard D, Legras JL (2011) Bread, beer and wine: yeast domestication in the *Saccharomyces* sensu stricto complex. *CR Biol* **334**: 229-236.

20. Fay JC, Benavides JA (2005) Evidence for domesticated and wild populations of *Saccharomyces cerevisiae*. *PLoS Genet* **1**: 66-71.

21. Schacherer J, Shapiro JA, Ruderfer DM et al (2009) Comprehensive polymorphism survey elucidates population structure of *Saccharomyces cerevisiae*. *Nature* **458**: 342-345.

22. Goddard MR, Anfang N, Tang R et al (2010) A distinct population of *Saccharomyces cerevisiae* in New Zealand: evidence for local dispersal by insects and human-aided global dispersal in oak barrels. *Environ Microbiol* **12**: 63-73.

23. Novo M, Bigey F, Beyne E et al (2009) Eukaryote-to-eukaryote gene transfer events revealed by the genome sequence of the wine yeast *Saccharomyces cerevisiae* EC1118. *Proc Natl Acad Sci USA* **106**: 16333-16338.

24. Zimmer A, Durand C, Loira N et al (2014) QTL dissection of lag phase in wine fermentation reveals a new translocation responsible for *Saccharomyces cerevisiae* adaptation to sulfite. *PLoS One* **9**: e86298.

25. Albertin W, Marullo P (2012) Polyploidy in fungi: evolution after whole-genome duplication. *Proc Biol Sci* **279**: 2497-2509.

26. Marullo P, Aigle M, Bely M et al (2007) Single QTL mapping and nucleotide-level resolution of a physiologic trait in wine *Saccharomyces cerevisiae* strains. *FEMS Yeast Res* **7**: 941-952.

27. Egli CM, Edinger WD, Mitrakul CM et al (1998) Dynamics of indigenous and inoculated yeast populations and their effect on the sensory character of Riesling and Chardonnay wines. *J Appl Microbiol* **85**: 779-789.

28. Beltran G, Torija MJ, Novo M et al (2002) Analysis of yeast populations during alcoholic fermentation: a six year follow-up study. *Syst Appl Microbiol* **25**: 287-293.

29. 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.

30. King ES, Swiegers JH, Travis B et al (2008) Coinoculated fermentations using *Saccharomyces* yeasts affect the volatile composition and sensory properties of *Vitis vinifera* L. cv. Sauvignon Blanc wines. *J Agric Food Chem* **56**: 10829-10837.

31. Albertin W, Miot-Sertier C, Bely M et al (2014) Oenological prefermentation practices strongly impact yeast population dynamics and alcoholic fermentation kinetics in Chardonnay grape must. *Int J Food Microbiol* **178**: 87-97.

32. Masneuf-Pomarede I, Juquin E, Miot-Sertier C et al (2015) The yeast *Starmerella bacillaris* (synonym *Candida zemplinina*) shows high genetic diversity in winemaking environments. *FEMS Yeast Res* **15**:fov045.

33. Almeida P, Gonçalves C, Teixeira S et al (2014) A Gondwanan imprint on global diversity and domestication of wine and cider yeast *Saccharomyces uvarum*. *Nat Commun* **5**: 4044.

34. Scannell DR, Zill OA, Rokas A et al (2011) The awesome power of yeast evolutionary genetics: new genome sequences and strain resources for the *Saccharomyces sensu stricto* genus. *G3 (Bethesda)* **1**: 11-25.

35. Albertin W, Chasseraud L, Comte G et al (2014) Winemaking and bioprocesses strongly shaped the genetic diversity of the ubiquitous yeast *Torulaspora delbrueckii*. *PLoS One* **9**: e94246.

36. Albertin W, Panfili A, Miot-Sertier C et al (2014) Development of microsatellite markers for the rapid and reliable genotyping of *Brettanomyces bruxellensis* at strain level. *Food Microbiol* **42**: 188-195.

37. Curtin CD, Borneman AR, Chambers PJ et al (2012) De-Novo assembly and analysis of the heterozygous triploid genome of the wine spoilage yeast *Dekkera bruxellensis* AWRI1499. *PLoS One* **7**: e33840.

38. Borneman AR, Zeppel R, Chambers PJ et al (2014) Insights into the *Dekkera bruxellensis* genomic landscape: comparative genomics reveals variations in ploidy and nutrient utilisation potential amongst wine isolates. *PLoS Genet* **10**: e1004161.
# Phylogenomic analysis of *Oenococcus oeni* reveals specific domestication of strains to cider and wines

H. CAMPBELL-SILLS<fnref n="1" />,<fnref n="2" />*, M. EL KHOURY<fnref n="1" />, M. FAVIER<fnref n="3" />, A. ROMANO<fnref n="2" />, F. BIASIOLI<fnref n="2" />, G. SPANO<fnref n="4" />,
D. SHERMAN<fnref n="5" />,<fnref n="6" />, O. BOUCHEZ<fnref n="7" />,<fnref n="8" />, E. COTON<fnref n="9" />, M. COTON<fnref n="9" />, S. OKADA<fnref n="10" />, N. TANAKA<fnref n="10" />,
M. DOLS-LAFARGUE<fnref n="1" />,<fnref n="11" />, P. LUCAS<fnref n="1" />

*Oenococcus oeni* – malolactic fermentation – genomics – population structure

## Introduction

Oenococcus oeni is a lactic acid bacteria (LAB) species encountered particularly in wine, where it achieves the malolactic fermentation (MLF) [1, 2]. MLF consists in the decarboxylation of malic acid into lactic acid by the action of the malolactic enzyme. This process contributes to the deacidification of wine, improving the microbiological stability and the organoleptic properties of the final product. The microbiological activity of O. oeni has a direct impact on the aromatic profile of wine, mainly through modifications on the concentration of odorant molecules such as esters and thiols [1-3]. Because of this, mastering the MLF is an important issue for winemakers. Although other lactic acid bacteria species have also been documented to perform the MLF, O. oeni is the most predominant and is often the only species detected during spontaneous MLF [1, 2]. Molecular typing methods have previously revealed that the species is made of several genetic groups of strains, some being specific to certain types of wines, ciders or regions [4]. The first fully sequenced O. oeni genome was published the last decade [5], and 55 more strains coming from different products (wine, cider, champagne) and geographical origins (France, Italy, Australia, Lebanon, USA) have been sequenced since [6-10]. Here, we present a genomic comparison of the 56 strains, revisiting their genetic structure, phylogeny and diversity at the genomic level.

## Materials and methods

All the genomes are available on NCBI's public databases (table 1). Whole genome alignments were done with JSpecies software using ANIm algorithm [11]. Distances matrices were transformed into the Newick format using custom scripts. The tree was calculated with MEGA5 by NJ method [12].

SNP and indels were mapped against PSU-1 reference genome using MUMmer software [13]. The structure of the population was calculated from the SNP data using structure software [14]. The molecular effect of each SNP and indel was determined with snpEff [15]. Definition of the ortholog groups was done using orthoMCL running under default parameters [16]. Cluster analysis was done using pheatmap R package [17].

<footnote n="1">¹Univ. Bordeaux, ISVV, EA 4577 Œnologie, F-33140, Villenave d'Ornon, France</footnote>

<footnote n="2">²Research and Innovation Centre, Fondazione Edmund Mach, San Michele all'Adige, Italy</footnote>

<footnote n="3">³BioLaffort, research subsidiary of the Laffort group, Bordeaux, France</footnote>

<footnote n="4">⁴University of Foggia, SAFE Department, 71100, Foggia, Italy</footnote>

<footnote n="5">⁵INRIA, project team MAGNOME, Univ. Bordeaux, Talence, France</footnote>

<footnote n="6">⁶UMR 5800 LaBRI (CNRS, Univ. Bordeaux), Talence, France</footnote>

<footnote n="7">⁷INRA, UMR444, Laboratoire de Génétique Cellulaire, Castanet-Tolosan, France</footnote>

<footnote n="8">⁸GeT-PlaGe, Genotoul, INRA Auzeville, Castanet-Tolosan, France</footnote>

<footnote n="9">⁹Université de Brest, EA 3882, Laboratoire Universitaire de Biodiversité et Ecologie Microbienne,
ESIAB, Technopôle Brest-Iroise, 29280 Plouzané, France</footnote>

<footnote n="10">¹⁰NODAI Culture Collection Center, Tokyo University of Agriculture, Tokyo, Japan</footnote>

<footnote n="11">¹¹Bordeaux INP, ISVV, EA 4577 Œnologie, F-33140, Villenave d'Ornon, France</footnote>
Unique genetic elements (genes, SNP and indels) were searched by calculating the Shannon's Entropy (H) for each element with the formula $H = -\sum p(xi) \log_2 p(xi)$, where $p(xi)$ represents the probability of finding the allele xi in an arbitrarily defined group of strains. Enrichment analysis of SNP was done using geneAnswers R package [17].

<table><caption>Table 1 - List of the analyzed *O. oeni* strains and their origins</caption><thead><tr><td>Strain</td><td>Origin</td><td>Reference</td></tr></thead><tbody><tr><td>PSU-1</td><td>USA, Red wine</td><td>[5]</td></tr><tr><td>ATCC_BAA-1163</td><td>France, Red wine</td><td>NCBI</td></tr><tr><td>AWRIB129</td><td>France</td><td>[7]</td></tr><tr><td>AWRIB202</td><td>Australia</td><td>[7]</td></tr><tr><td>AWRIB304</td><td>Australia</td><td>[7]</td></tr><tr><td>AWRIB318</td><td>Australia</td><td>[7]</td></tr><tr><td>AWRIB418</td><td>USA</td><td>[7]</td></tr><tr><td>AWRIB419</td><td>France</td><td>[7]</td></tr><tr><td>AWRIB422</td><td>France, Champagne</td><td>[7]</td></tr><tr><td>AWRIB429</td><td>Italy</td><td>[6]</td></tr><tr><td>AWRIB548</td><td>France, Champagne</td><td>[7]</td></tr><tr><td>AWRIB553</td><td>France</td><td>[7]</td></tr><tr><td>AWRIB568</td><td>Australia</td><td>[7]</td></tr><tr><td>AWRIB576</td><td>Australia</td><td>[7]</td></tr><tr><td>IOEB_0205</td><td>France, Champagne</td><td>[10]</td></tr><tr><td>IOEB_0501</td><td>France, Red wine</td><td>[10]</td></tr><tr><td>IOEB_0502</td><td>France, Red wine</td><td>[10]</td></tr><tr><td>IOEB_0607</td><td>France, Red wine</td><td>[10]</td></tr><tr><td>IOEB_0608</td><td>France, Red wine</td><td>[10]</td></tr><tr><td>IOEB_1491</td><td>France, Red wine</td><td>[10]</td></tr><tr><td>IOEB_8417</td><td>France</td><td>[10]</td></tr><tr><td>IOEB_9304</td><td>France, Cider</td><td>[10]</td></tr><tr><td>IOEB_9517</td><td>France</td><td>[10]</td></tr><tr><td>IOEB_9803</td><td>France</td><td>[10]</td></tr><tr><td>IOEB_9805</td><td>France</td><td>[10]</td></tr><tr><td>IOEB_B10</td><td>NA</td><td>[10]</td></tr><tr><td>IOEB_B16</td><td>France, Champagne</td><td>[10]</td></tr><tr><td>IOEB_C23</td><td>France, Cider</td><td>[10]</td></tr><tr><td>IOEB_C28</td><td>France, Cider</td><td>[10]</td></tr><tr><td>IOEB_C52</td><td>France, Cider</td><td>[10]</td></tr><tr><td>IOEB_CiNe</td><td>NA</td><td>[10]</td></tr><tr><td>IOEB_L18_3</td><td>Lebanon, Red wine</td><td>[10]</td></tr><tr><td>IOEB_L26_1</td><td>Lebanon, Red wine</td><td>[10]</td></tr><tr><td>IOEB_L40_4</td><td>Lebanon, Red wine</td><td>[10]</td></tr><tr><td>IOEB_L65_2</td><td>Lebanon, Red wine</td><td>[10]</td></tr><tr><td>IOEB_S277</td><td>France</td><td>[10]</td></tr><tr><td>IOEB_S436a</td><td>NA</td><td>[10]</td></tr><tr><td>IOEB_S450</td><td>France</td><td>[10]</td></tr><tr><td>IOEB_VF</td><td>France</td><td>[10]</td></tr><tr><td>OM22</td><td>Italy, Red wine</td><td>[9]</td></tr><tr><td>OM27</td><td>Italy, Red wine</td><td>[8]</td></tr><tr><td>OT25</td><td>Italy, Red wine</td><td>[9]</td></tr><tr><td>OT3</td><td>Italy, Red wine</td><td>[9]</td></tr><tr><td>OT4</td><td>Italy, Red wine</td><td>[9]</td></tr><tr><td>OT5</td><td>Italy, Red wine</td><td>[9]</td></tr><tr><td>S11</td><td>France, White wine</td><td>[10]</td></tr><tr><td>S12</td><td>France, White wine</td><td>[10]</td></tr><tr><td>S13</td><td>France, Red wine</td><td>[10]</td></tr><tr><td>S14</td><td>France, Red wine</td><td>[10]</td></tr><tr><td>S15</td><td>France, Red wine</td><td>[10]</td></tr><tr><td>S19</td><td>France, Red wine</td><td>[10]</td></tr><tr><td>S22</td><td>France, White wine</td><td>[10]</td></tr><tr><td>S23</td><td>England, White wine</td><td>[10]</td></tr><tr><td>S25</td><td>France, Red wine</td><td>[10]</td></tr><tr><td>S28</td><td>France, Red wine</td><td>[10]</td></tr><tr><td>S161</td><td>Red wine</td><td>[10]</td></tr></tbody></table>
## Results and discussion

In order to understand the genetic relationships between the *O. oeni* strains, the distance between the genomes was calculated by whole-genome alignment using ANIm algorithm and a neighbour-joining tree was reconstructed. The tree shows that the 56 strains are divided into 2 genetic groups, namely A and B, plus an isolated strain, IOEB_C52, belonging to a putative group C (fig. 1). In order to confirm the phylogenetic groups A, B and C, an analysis of the population structure was done using SNP data. A total of 53,717 SNP and indels were mapped in 52,502 different loci of PSU-1 reference chromosome. A SNP matrix was generated and analyzed with structure software. The analysis confirms the presence of three populations, matching the groups A, B, and an isolated strain of a putative group C (data not shown). The latter strain was isolated from cider, while strains of group B were isolated both from wine and cider and strains of group A from different types of wine, including champagne. Strains from specific products (champagne, cider) form distinct clusters. There is not enough evidence to tell that there is a direct link between the geographical origin of the strains and the genetic groups; while three strains isolated in Italy form a distinct cluster, the remaining three are dispersed in groups A and B. The strains from Lebanon are all in different clusters, while there is a cluster of 4 genetically-close strains coming from different geographical origins and products. The fact that a more ancestral strain coming from cider gave birth to two new genetic groups, more adapted to wine, seems to be plausible since group B strains are more diverse and can be found both in wine and cider, while group A strains are more numerous, genetically closer, and are present only in wine.

<figure><img src="image_76.png" /><figcaption>**Fig. 1** - Phylogenomic reconstruction of the 56 *O. oeni* strains. Colours indicate the genetic groups depicted (A, B, C). Strains from cider and from champagne form distinct clusters.</figcaption></figure>

In order to evaluate whether the pangenome of the species has been fully represented, we determined the ortholog groups, analyzed the composition of the pangenome, and plotted the evolution of the coregenome versus the pangenome from 1 to 56 strains (fig. 2). The tendency of the curves suggests that the pangenome of the species has not yet been exhaustively pictured, since the pangenome of the species keeps growing when adding new strains to the analysis. The pangenome of the 56 strains is composed of 3,155 CDS distributed in 2,918 ortholog groups. From this set, 576 correspond to the coregenome, 1,877 to the shellgenome, and 702 to the cloud genome, being distributed in 574, 1,688 and 688 ortholog groups, respectively. These results are consistent with previous analyses for 14 genomes [7]. When we calculate the core and pangenome for the same 14 strains, we get a set of 1,067 and 2,549 genes, respectively, compared to the 1,165 and 2,846 genes found previously. These divergences in the results might be due to the method for determining the ortholog groups and the parameters of the algorithms. Regarding the pangenome of groups A and B separately, the set of group A strains is composed of 829 and 2,733 genes for the core and pangenome, respectively, while that of group B is composed of 914 and 2,370 genes. However, when only 13 random strains of group A are analyzed, in order to compare a group of equal size to group B, the core and pangenome are composed of 1,122 and 2,329 genes, respectively. Because group A strains have a more conserved coregenome, it is possible that they have been further domesticated than group B strains, since domestication is characterized by a diminished genetic diversity and a loss of
functions [18], both reflected in the fact that group A strains have a bigger coregenome and a smaller pangenome. Although domestication of *O. oeni* had already been reported [18], we propose that the domestication is not homogeneous for all the genetic groups.

<figure><img src="image_77.png" /><figcaption>**Fig. 2** - Progression of the pangenome of the 56 *O. oeni* strains. Genomes were added sequentially from 1 to 56, plotting the core and pangenome, and iterating each step 55 times.</figcaption></figure>

To explore the distribution of the ortholog groups, a cluster analysis of the pangenome was done with pheatmap package on R. The generated heatmap reveals that strains being closely related genetically tend to share similar sets of CDS. Strains from groups A and B, strain C, and strains from champagne share sets of 5, 1, 65 and 13 genes respectively that differentiate them from the rest of the strains, i.e. they are systematically present or absent in contrast to all the remaining strains. Although most of the genes that characterize the groups of strains are of viral origin, some of them are not (data not shown).

Regarding the mutations (SNP and indels), an entropy analysis reveals that strains of different genetic groups or products share specific sets of mutations that distinguish them from each other. Although most of these mutations seem to be expected (i.e. N/S ≈ 1/3), some others might be the product of environmental pressure. For example, the strains coming from champagne show an inverted ratio of N/S mutations, and they share a set of 21 unique mutations that truncate the genes at less than 1/3 of their total length, plus 3 mutations that affect the start codon of genes (fig. 3). An enrichment analysis of the characteristic mutations for each group of strains reveals that these mutations are not randomly distributed all over the genome on group B and champagne strains, but they are concentrated in some specific pathways (data not shown).

<figure><img src="image_78.png" /><figcaption>**Fig. 3** - Molecular effect of the characteristic SNP of the groups of *O. oeni* strains. The molecular effect of the unique SNP of each group was calculated.</figcaption></figure>

## Conclusion

A holistic analysis of the population structure of the *O. oeni* species by comparative genomics has confirmed the distribution of strains reported in previous studies, i.e. two major groups, namely A and B, each hosting some subgroups. The major presence of group A strains in wine could be an evidence of the existence of subspecies, but previous studies show that group B strains are occasionally detected in wine and there is not a clear phenotypic divergence between strains from both groups, making this subdivision
still premature. A phylogenomic reconstruction revealed one strain isolated from cider that might be a member of an ancestral group from which originated the others. This analysis, along with the distribution of orthologs and the presence of unique genes and SNP, agrees with the idea that *O. oeni* is a species that has been domesticated to cider and wine. Group A might have appeared as a new group with better fitness than group B and C to dominate wine-like environments. The narrowness of its pangenome in comparison to that of group B might be an evidence that group A strains have been further domesticated than the others. The presence of unique genes and mutations could possibly explain some phenotypic features of certain groups of strains (e.g., those coming from champagne).

## Acknowledgements

This work was supported in parts by the European commission (FP7-SME project Wildwine, grant agreement no. 315065) and the Regional Council of Aquitaine (project SAGESSE 2010). HCS was recipient of a GIRACT bursary award for promoting flavour research among PhD students in Europe. Authors thank Andrés Aravena for providing the script for calculating the Shannon Entropy.

## References

1. Fleet GH, Lafon-Lafourcade S, Ribéreau-Gayon P (1984) Evolution of yeasts and lactic acid bacteria during fermentation and storage of Bordeaux wines. *Appl Environ Microbiol* **48**: 1034-1038.

2. Lonvaud-Funel A (1999) Lactic acid bacteria in the quality improvement and depreciation of wine. *Antonie van Leeuwenhoek* **76**: 317-331.

3. Bartowsky EJ (2005) *Oenococcus oeni* and malolactic fermentation-moving into the molecular arena. *Aust J Grape Wine Res* **11**: 174-187.

4. Bilhère E, Lucas PM, Claisse O et al (2009) Multilocus sequence typing of *Oenococcus oeni*: detection of two subpopulations shaped by intergenic recombination. *Appl Environ Microbiol* **75**: 1291-1300.

5. Mills DA, Rawsthorne H, Parker C et al (2005) Genomic analysis of *Oenococcus oeni* PSU-1 and its relevance to winemaking. *FEMS Microbiol Rev* **29**: 465-475.

6. Borneman AR, Bartowsky EJ, McCarthy J et al (2010) Genotypic diversity in *Oenococcus oeni* by high-density microarray comparative genome hybridization and whole genome sequencing. *Appl Microbiol Biotechnol* **86**: 681-691.

7. Borneman AR, McCarthy JM, Chambers PJ et al (2012) Comparative analysis of the *Oenococcus oeni* pan genome reveals genetic diversity in industrially-relevant pathways. *BMC Genomics* **13**: 373.

8. Lamontanara A, Orru L, Cattivelli L et al (2014) Genome sequence of *Oenococcus oeni* OM27, the first fully assembled genome of a strain isolated from an Italian wine. *Genome Announc* **2**: e00658-14.

9. Capozzi V, Russo P, Lamontanara A et al (2014) Genome sequences of five *Oenococcus oeni* strains isolated from Nero Di Troia wine from the same terroir in Apulia, Southern Italy. *Genome Announc* **2**: e01077-14.

10. Campbell-Sills H, El Khoury M, Favier M et al (2015) Phylogenomic analysis of *Oenococcus oeni* reveals specific domestication of strains to cider and wines. *Genome Biol Evol* **7**: 1506-1518.

11. Richter M, Rosselló-Móra R (2009) Shifting the genomic gold standard for the prokaryotic species definition. *Proc Natl Acad Sci USA* **106**: 19126-19131.

12. Tamura K, Peterson D, Peterson N et al (2011) MEGA5: molecular evolutionary genetics analysis using maximum likelihood, evolutionary distance, and maximum parsimony methods. *Mol Biol Evol* **28**: 2731-2739.

13. Kurtz S, Phillippy A, Delcher AL et al (2004) Versatile and open software for comparing large genomes. *Genome Biol* **5**: R12.

14. Hubisz MJ, Falush D, Stephens M et al (2009) Inferring weak population structure with the assistance of sample group information. *Mol Ecol Resour* **9**: 1322-1332.

15. Cingolani P, Platts A, Wang LL et al (2012) A program for annotating and predicting the effects of single nucleotide polymorphisms, SnpEff: SNPs in the genome of Drosophila melanogaster strain w1118; iso-2; iso-3. *Fly* **6**: 80-92.

16. Li L (2003) OrthoMCL: identification of ortholog groups for Eukaryotic genomes. *Genome Res* **13**: 2178-2189.

17. R Core Team (2013) *R: a language and environment for statistical computing*. Vienna, Austria: R Foundation for Statistical Computing.

18. Douglas GL, Klaenhammer TR (2010) Genomic evolution of domesticated microorganisms. *Annu Rev Food Sci Technol* **1**: 397-414.
# Influence of nutrients and temperature on the production of fermentative aromas in winemaking conditions by an evolved strain of *Saccharomyces cerevisiae*

S. ROLLERO<sup>1,2,3,4*</sup>, J.R. MOURET<sup>1,2,3</sup>, I. SANCHEZ<sup>1,2,3</sup>, A. ORTIZ-JULIEN<sup>4</sup>, J.M. SABLAYROLLES<sup>1,2,3</sup>, S. DEQUIN<sup>1,2,3</sup>

Adaptive evolution – Box-Behnken design – online monitoring – fermentative aromas

## Introduction

Fermentative aromas (especially higher alcohols and esters) play an important role in the organoleptic profile of young wines. Their production depends both on yeast strain and on fermentation conditions. Within the wine industry, there is a will to develop innovative strategies for enhancing and controlling the fruity character of the wines. To achieve this purpose, two strategies can be considered: the control of fermentation conditions (including sustainable addition of nutrients) and the development of yeast strains with aromatic properties. The main objective of this work is to quantify and better understand the impact of three environmental parameters (nitrogen, phytosterol and temperature) on the fermentative aroma production. This study was performed with two *Saccharomyces cerevisiae* strains having different aromatic profiles, Lalvin EC1118® and Affinity™ ECA5, obtained by evolutionary engineering of Lalvin EC1118® and presenting an enhanced aroma production [1].

## Materials and methods

### *Yeast strains*

Fermentations were performed using two commercial wine yeast strains: Lalvin EC1118® and Affinity™ ECA5, obtained by evolutionary engineering of Lalvin EC1118®. In these culture media, the lipid fraction was represented by a commercial mixture of phytosterols (mainly β-sitosterol, campesterol and stigmasterol) similar to the sterols present in grape solids.

### *Treatments and volatile compound analysis*

First, fermentations were performed in 330-ml fermenters in synthetic media with different initial concentrations of nitrogen (70, 200 and 330 mg/l) and phytosterols (2, 5 and 8 mg/l) at three different temperature (20, 24 and 28°C). The effect of temperature and initial nitrogen and lipid contents on the production of volatile compounds was determined using a Box-Behnken experimental design [2]. The effects of these three factors were studied in a single block of 16 sets of test conditions and 4 central points. The order of the experiments was fully randomized. Statistical analysis was performed with R software, version 2.14.2. A quadratic polynomial model was defined to fit the response.

Then, four fermentations (70 and 330 mg/l nitrogen, 2 and 8 mg/l phytosterol) leading to very different productions of aromas were run in 10-l tanks at 24°C. The release of CO₂ was measured using a mass flow meter. The concentrations of volatile compounds in the headspace of the fermenter were measured with an online GC (gas chromatography) device [3]: the headspace gas was pumped through heated transfer lines. Carbon compounds were concentrated in a cold trap and analyzed by GC (Figure 1). Thanks to the high measurement frequency, we can calculate rates and specific rates of production of fermentative aromas.

<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 Montpellier, UMR1083 Sciences pour l'œnologie, Montpellier, France</footnote>

<footnote n="4"><sup>4</sup>Lallemand SAS, Blagnac, France</footnote>
<figure><img src="image_79.png" /><figcaption>Fig. 1 - Online monitoring of aroma compounds by gas chromatography.</figcaption></figure>

## Results and discussion

### Relative performances of Affinity™ ECA5 compared to Lalvin EC1118®

In previous studies [1, 4, 5], the capabilities of Affinity™ ECA5 and of its parental strain Lalvin EC1118® were compared and major differences were identified. Especially, the production of fermentative aromas was enhanced for the evolved strain [1].

In this work, to evaluate the generality of these observations, the productions of higher alcohols and esters of the two yeast strains were compared in various fermentation conditions. The combined effects of nitrogen, phytosterols and temperature were evaluated using a Box-Behnken design [2]. For each aroma compound, we plotted the ratio of final concentration obtained with the strain Affinity™ ECA5 divided by the one obtained for Lalvin EC1118® (Figure 2). These ratios were systematically greater than 1 for higher alcohols (except propanol) and acetate esters, indicating that these compounds were systematically overproduced by the evolved strain whatever the fermentation conditions. These molecules were thus identified as metabolic markers of the evolved yeast strain. Conversely, we found that for the ratios for the fatty acids and ethyl esters, 1 was included in the confidence interval. Thus, these compounds were not systematically overproduced by Affinity™ ECA5.

<figure><img src="image_80.png" /><figcaption>Fig. 2 - Ratios between final liquid concentrations of fermentative aromas produced by Affinity™ ECA5 and Lalvin EC1118®. PR: propanol; ISO: isobutanol; IA: isoamyl alcohol; HE: hexanol; ME: methionol; PHE: 2-phenylethanol; EA: ethyl acetate; ISA: isobutyl acetate; AA: amyl acetate; IAA: isoamyl acetate; PEA: 2-phenylethylacetate; PA: propanoic acid; BA: butanoic acid; IBA: isobutanoic acid; IVA: isovaleric acid; MBA: 2-methylbutanoic acid; VA: valeric acid; HA: hexanoic acid; OA: octanoic acid; DA: decanoic acid; EB: ethyl butanoate; DS: diethyl succinate; EL: ethyl lactate; EV: ethyl valerate; EH: ethyl hexanoate; EO: ethyl octanoate; ED: ethyl decanoate; EDD: ethyl dodecanoate.</figcaption></figure>
## Kinetics profiles of fermentative aromas

First, we observed an interaction between nitrogen and phytosterol production for both strains; but this interaction was more marked in the media containing 330 mg/l of assimilable nitrogen and for Affinity™ ECA5, as detailed in Table 1.

<table><caption>**Table 1** - Total production (mg/l) of fermentative aromas by Lalvin EC1118® and Affinity™ ECA5</caption><thead><tr><th rowspan="2">Initial nitrogen concentration (mg/l)</th><th rowspan="2">Initial phytosterol concentration (mg/l)</th><th colspan="2">Propanol</th><th colspan="2">Isobutanol</th><th colspan="2">Isoamyl alcohol</th></tr><tr><th>Total production (mg/l) by EC1118</th><th>Total production (mg/l) by ECA5</th><th>Total production (mg/l) by EC1118</th><th>Total production (mg/l) by ECA5</th><th>Total production (mg/l) by EC1118</th><th>Total production (mg/l) by ECA5</th></tr></thead><tbody><tr><td>70</td><td>2</td><td>5.04</td><td>4.52</td><td>25.8</td><td>36.1</td><td>165</td><td>276</td></tr><tr><td>70</td><td>8</td><td>5.25</td><td>4.63</td><td>28.8</td><td>36.5</td><td>175</td><td>270</td></tr><tr><td>330</td><td>2</td><td>25.6</td><td>22.2</td><td>19.9</td><td>42.1</td><td>103</td><td>148</td></tr><tr><td>330</td><td>8</td><td>27.6</td><td>21.2</td><td>36.3</td><td>54.9</td><td>156</td><td>221</td></tr><tr><th rowspan="2">Initial nitrogen concentration (mg/l)</th><th rowspan="2">Initial phytosterol concentration (mg/l)</th><th colspan="2">Ethyl acetate</th><th colspan="2">Isobutyl acetate</th><th colspan="2">Isoamyl acetate</th></tr><tr><th>Total production (mg/l) by EC1118</th><th>Total production (mg/L) by ECA5</th><th>Total production (mg/l) by EC1118</th><th>Total production (mg/L) by ECA5</th><th>Total production (mg/l) by EC1118</th><th>Total production (mg/l) by ECA5</th></tr><tr><td>70</td><td>2</td><td>37.7</td><td>27.1</td><td>0.05</td><td>0.23</td><td>0.45</td><td>3.29</td></tr><tr><td>70</td><td>8</td><td>39.7</td><td>22.9</td><td>0.05</td><td>0.19</td><td>0.43</td><td>2.14</td></tr><tr><td>330</td><td>2</td><td>67.7</td><td>124</td><td>0.36</td><td>1.79</td><td>3.83</td><td>13.0</td></tr><tr><td>330</td><td>8</td><td>52.8</td><td>73.3</td><td>0.31</td><td>1.45</td><td>2.87</td><td>12.0</td></tr><tr><th rowspan="2">Initial nitrogen concentration (mg/l)</th><th rowspan="2">Initial phytosterol concentration (mg/l)</th><th colspan="2">Ethyl hexanoate</th><th colspan="2">Ethyl octanoate</th><th colspan="2"></th></tr><tr><th>Total production (mg/l) by EC1118</th><th>Total production (mg/L) by ECA5</th><th>Total production (mg/l) by EC1118</th><th>Total production (mg/l) by ECA5</th><th></th><th></th></tr><tr><td>70</td><td>2</td><td>0.67</td><td>0.72</td><td>0.64</td><td>0.62</td><td></td><td></td></tr><tr><td>70</td><td>8</td><td>0.63</td><td>0.68</td><td>0.57</td><td>0.55</td><td></td><td></td></tr><tr><td>330</td><td>2</td><td>1.32</td><td>1.71</td><td>1.71</td><td>2.05</td><td></td><td></td></tr><tr><td>330</td><td>8</td><td>0.88</td><td>1.07</td><td>1.26</td><td>1.18</td><td></td><td></td></tr></tbody></table>

We then compared the kinetic profiles of production of fermentative aromas of the evolved and ancestral strains using the online GC system. The interaction between factors resulted in differences in the kinetic profiles of synthesis of aroma compounds (example of isoamyl acetate, Figure 3). The shapes of these specific rates were dependent on the strain but also on the environmental parameters. At high nitrogen content, the profiles of the specific rates of acetate ester production were mostly dependent on the lipid dose, showing the predominance of the environmental effects. At 2 mg/l of lipids, the specific production rate peaked and then decreased, whereas at 8 mg/l of phytosterols, once the maximum value was reached the decrease was much slower (Figure 3). The flux of ester synthesis was affected in the same way for both strains. Conversely, in SM70, the strain effect was predominant. When comparing the total production and the specific rates of these esters, it was interesting to note that the ranking of the fermentation conditions according to these two parameters was different (Figure 3, Table 1). This observation illustrates the interest of online monitoring of the kinetics of production to better understand the regulation of the metabolic pathways of aroma production.

## Bioconversion between higher alcohol and its acetate ester

We wondered if the systematic overproduction of acetate esters by the evolved strain was solely due to the overproduction of their higher alcohol precursor or if the activity of the alcohol acetyltransferases (Atf1p and Atf2) responsible for this bioconversion was also involved.
<figure><img src="image_81.png" /><figcaption>**Fig. 3** - Changes in the specific rates of production of isoamyl acetate in SM70 (A) or SM330 (B).</figcaption></figure>

We studied two couples of higher alcohol / acetate ester: isobutanol and isobutyl acetate; and isoamyl alcohol and isoamyl acetate. For these two couples and for both strains, the conversion yield was dependent on the initial nitrogen and phytosterol contents: the highest yields were obtained at high nitrogen (330 mg/l) and low phytosterol contents (2 mg/l) (Figure 4). For Lalvin EC1118®, the yield always remained constant throughout the fermentation. By contrast, for Affinity™ ECA5, there were generally two production phases, with a much higher yield in the second phase. The transition occurred during the stationary phase and was particularly visible with 330 mg/l nitrogen and 8 mg/l phytosterols (Figure 4). This transition could be related to the presence of lipids, which are known to repress the expression of *ATF1* [6]. To confirm this hypothesis, we added phytosterols at different stages of fermentation (Figure 5). These additions had no impact on the bioconversion of higher alcohol into acetate ester by Lalvin EC1118®: the bioconversion yields remained constant despite the additions. On the other side, the additions of phytosterols dramatically lowered the bioconversion of higher alcohols for Affinity™ ECA5. Indeed, after each lipid addition, the yield was divided by two (Figure 5). This result clearly illustrated a modified management of phytosterols by the evolved strain.

<figure><img src="image_82.png" /><figcaption>**Fig. 4** - Changes in total isoamyl acetate production as a function of total isoamyl alcohol in SM70 (A) and SM330 (B).</figcaption></figure>

Conclusion

This study demonstrated the major impact of environmental parameters; in particular, an important interaction between nitrogen and phytosterol was highlighted. Overall, the two strains presented a similar response to the changes in fermentation conditions; however, the productions of aroma compounds were
generally higher for the evolved strain. In addition, substantial differences in the chronology of production of fermentative aromas between the two strains were observed, which suggest modifications in the regulation of aroma synthesis.

<figure><img src="image_83.png" /><figcaption>**Fig. 5** - Changes in total isoamyl acetate production as a function of total isoamyl alcohol production in SM330 with 8 mg/l of phytosterols for Lalvin EC1118® (in blue) and Affinity™ ECA5 (in purple and red). The arrows indicate the phytosterol additions.</figcaption></figure>

This kinetic monitoring of production of aroma compounds allowed highlighting some mechanisms, which differed between the two strains. Indeed, we noted substantial differences in the management of the lipid source for the evolved strain. These metabolic modifications were particularly visible on the bioconversion of higher alcohols into acetate esters.

These results have already a practical interest. They provide additional knowledge on the management of nutrition in oenological fermentation. Nevertheless, the study of the role of oxygen and the quality of nitrogen source should greatly supplement these contributions. In the longer term, the data generated will allow the development of new fermentation driving modes to better control the aromatic quality of wines.

**Acknowledgements**

We thank Evelyne Aguera, Magaly Angenieux and Christian Picou for technical assistance.

**References**

1. Cadiere A, Ortiz-Julien A, Camarasa C et al (2011) Evolutionary engineered *Saccharomyces cerevisiae* wine yeast strains with increased in vivo flux through the pentose phosphate pathway. *Metab Eng* **13**: 263-271.

2. Rollero S, Bloem A, Camarasa C et al (2015) Combined effects of nutrients and temperature on the production of fermentative aromas by *Saccharomyces cerevisiae* during wine fermentation. *Appl Microbiol Biotechnol* **99**: 2291-2304.

3. Mouret JR, Perez M, Angenieux M et al (2014) Online-based kinetic analysis of higher alcohol and ester synthesis during winemaking fermentations. *Food Bioprocess Technol* **7**: 1235-1245.

4. Cadière A, Aguera E, Caillé S et al (2012) Pilot-scale evaluation of the enological traits of a novel, aromatic wine yeast strain obtained by adaptive evolution. *Food Microbiol* **32**: 332-337.

5. Mouret JR, Cadiere A, Aguera E et al (2015) Dynamics and quantitative analysis of the synthesis of fermentative aromas by an evolved wine strain of *Saccharomyces cerevisiae*. *Yeast Chichester Engl* **32**: 257-269.

6. Fujii T, Kobayashi O, Yoshimoto H et al (1997) Effect of aeration and unsaturated fatty acids on expression of the *Saccharomyces cerevisiae* alcohol acetyltransferase gene. *Appl Environ Microbiol* **63**: 910-915.
# Hybridization within *Saccharomyces* genus results in homeostasis, heterosis and phenotypic novelty in winemaking conditions

T. DA SILVA<fnref n="1" />, W. ALBERTIN<fnref n="2" />,<fnref n="3" />*, M. BLEIN-NICOLAS<fnref n="4" />, C. DILLMANN<fnref n="4" />, M. BELY<fnref n="3" />, S. LA GUERCHE<fnref n="5" />, D. SICARD<fnref n="4" />, I. MASNEUF-POMAREDE<fnref n="3" />,<fnref n="6" />, M. ZIVY<fnref n="4" />, P. MARULLO<fnref n="3" />,<fnref n="7" />, D. DE VIENNE<fnref n="4" />

Interspecific hybrids – aromas – proteomics – yeast selection

## Introduction

The benefits of hybridization have been described in a large range of plant and animal species in an agronomical context. One of the most striking consequences of hybridization results in the phenotypic superiority of hybrid over its two progenitors (i.e. *heterosis*). Hybridization may also confer an improved phenotypic stability over environmental change (i.e. *homeostasis*). In the *Saccharomyces sensu stricto* clade, hybrids can be easily obtained both at the intra- or interspecific level, allowing yeast improvement. Although hybrids are readily used for selection, the heterosis and homeostasis phenomena have scarcely been studied in yeast. Measuring 35 enological traits, we investigated the physiological and technological properties of a collection of four *S. uvarum* and seven *S. cerevisiae* parental strains and all their 55 possible hybrids, namely 27 intraspecific hybrids and 28 interspecific hybrids, under winemaking conditions at two temperatures. We analyzed the sources of phenotypic variation – genetic and/or environmental – for various categories of traits (fermentation kinetics, life-history, volatile composition, proteome expression), compared the intra- and interspecific hybrids and assessed the extent to which hybridization increased homeostasis at a multi-trait level.

## Materials and methods

### *Yeast strains*

Fully homozygous diploid strains were used as parental strains. W1, D1, D2, E2, E3, E4 and E5 are *S. cerevisiae* strains; U1, U2, U3 and U4 are *S. uvarum* strains. With this genetic material, a complete diallele of 55 hybrids (6 *S. uvarum* hybrids, 21 *S. cerevisiae* hybrids and 28 interspecific hybrids) was constructed by mating all the strains together.

### *Fermentations and trait analysis*

Fermentations were carried out in Sauvignon blanc grape juice and run at two temperatures (18°C and 26°C) in triplicate for a total of 370 experiments. Thirty-five traits were collected and grouped into four classes (Fermentation Kinetics Traits, Life-History Traits, Basic Enological Parameters and Aromatic Traits) according to da Silva et al. [1]. Fermentation Kinetics Traits (FKT) were calculated from weight loss raw data. Life-history traits were obtained from growth curves, cell size and viability data measured by flow cytometry. Aromatic traits and Enological parameter traits correspond to the concentration of end point compounds measured by the SARCO laboratory. At 40 g/L of CO2 produced, a biomass sample was collected, frozen and total proteins were extracted in order to quantify the yeast proteome by an LC-MS approach [2]. Figure 1 summarizes the overall experiment.

<footnote n="1">¹INRA, UMR 0320 / UMR 8120 Génétique Végétale, Gif-sur-Yvette, France</footnote>

<footnote n="2">²ENSCBP - Bordeaux INP Pessac, France</footnote>

<footnote n="3">³Univ. de Bordeaux, ISVV, EA 4577, Unité de Recherche Œnologie, Villenave d'Ornon, France</footnote>

<footnote n="4">⁴Univ Paris-Sud, UMR 0320 / UMR 8120 Génétique Végétale, Gif-sur-Yvette, France</footnote>

<footnote n="5">⁵SARCO, Bordeaux, France</footnote>

<footnote n="6">⁶Bordeaux Sciences Agro, Gradignan, France</footnote>

<footnote n="7">⁷Biolaffort, Bordeaux, France</footnote>
<figure><img src="image_84.png" /><figcaption>Fig. 1 - Experimental design.</figcaption></figure>

## Results and discussion

*Impact of temperature and species effect on the alcoholic fermentation traits and yeast proteome*

A total of 396 alcoholic fermentations were performed (66 strains * 2 temperatures * 3 replicates). The sources of variation of each phenotypic trait were studied by analysis of variance (ANOVA) to estimate the *Strain*, *Temperature*, and *Strain*\*Temperature interaction effects. Broadly, fermentation kinetics parameters (*lag phase*, *biomass production*, *maximal CO2 rate*) are strongly affected by a temperature elevation, while the concentrations of volatiles (esters, higher alcohols, volatile thiols...) are more affected by the yeast strain used. The total phenotypic variation (35 traits) was analyzed by a Principal Component Analysis (PCA). As shown in Figure 2, PCA axis 1 discriminates the fermentation according to temperature, while PCA axes 2 and 3 (25 % of total inertia) well discriminate the fermentations according to the “hybrid type” (*S. cerevisiae*, *S. uvarum* or interspecific hybrids). Interestingly, this strong “hybrid type” effect was also found at the proteomic level (615 common proteins) as shown in the PCA (Figure 3).

<figure><img src="image_85.png" /><figcaption>Fig. 2 - Fermentation PCA. Each point represents one of the 55 hybrid strains: *S. cerevisiae* intraspecific hybrids (in red), *S. uvarum* intraspecific hybrids (in blue) and interspecific hybrids (in green), at 18°C (circles) and 26°C (triangles). The names of the parental strains are noted in italic (18°C) or regular (26°C) characters. A: axes 1 and 2 (33 % of the total inertia). B: axes 2 and 3 (25 % of the total inertia). C: correlation of the variables to discriminant axes PCA1, PCA2 and PCA3. Only variables showing a significant correlation (p-value < 0.0001) are shown. The four-color palette corresponds to the four variable categories (FK: Fermentation Kinetics, LHT: Life-history Trait, BEP: Basic Enological Parameters, AT: Aromatic Traits).</figcaption></figure>
<figure><img src="image_86.png" /><figcaption>**Fig. 3** - Proteome PCA. Principal component analysis based on 615 protein abundances. Parental strains are shown in regular (18°C) or italic characters (26°C).</figcaption></figure>

*Interspecific hybrids showed heterosis for ethyl ester production and for the expression level of specific protein set*

Both fermentation and proteome PCA showed that interspecific hybrids constitute a distinct cluster compare to *S. uvarum* and *S. cerevisiae* pure species. This result demonstrates that interspecific hybridization creates an important phenotypic novelty. For the fermentation traits, we observed that interspecific hybrids showed a global heterosis producing higher quantity of ethyl esters (Figure 4). At the proteome level, we identified a set of 104 proteins that are quantitatively more expressed in interspecific hybrids than in their parental strains. This *heterotic* protein set is enriched in essential genes as well in genes involved in central or energetic metabolism. These findings suggest that interspecific hybridization reshapes proteome expression in a narrowed way by increasing the expression of important proteins [2].

<figure><img src="image_87.png" /><figcaption>**Fig. 4** - Heterosis effect of interspecific hybrids for ethyl ester production. Each bar plot represents the mean of all the Sc (21), INTER (28) and Su hybrids (6). Significant differences within groups were assayed by multiple non-parametric comparisons test (Campbell and Skillings analysis).</figcaption></figure>

*According to the hybrid type, hybridization creates more robustness to environmental changes*

To assess the relative position of the hybrids and their parents we performed another PCA including in silico hybrids (data not shown). The phenotypic values of in silico intraspecific and interspecific hybrids were computed assuming additivity (i.e. mid-parent value) for all 35 traits. For each hybrid type, we computed the multi-trait distance between 18°C and 26°C (Figure 5). The smaller the distance, the higher the homeostasis with respect to temperature. The *S. cerevisiae* hybrids globally showed a higher homeostasis than the other two groups. Moreover, homeostasis is significantly higher for in vivo hybrids than for in silico hybrids for both *S. uvarum* and interspecific hybrids.
<figure><img src="image_88.png" /><figcaption>**Fig. 5** - Homeostasis effect of interspecific and S. *uvarum* hybrids with respect to temperature changes. Multi-trait phenotype distance between the two temperatures was measured for each hybrid type. Bar plot represents the mean and the standard error for the six groups of hybrids. Differences between in silico and in vivo hybrids were tested using a non-parametric test (Wilcoxon test).</figcaption></figure>

**Conclusion**

By constructing a unique diallel design of 55 hybrids we demonstrated that hybridization could generate phenotypic novelties, heterosis and homeostasis for fermentation and proteome.

**References**

1. Da Silva T, Albertin W, Dillmann C et al (2015) Hybridization within *Saccharomyces* genus results in homoeostasis and phenotypic novelty in winemaking conditions. *PloS One* **10**: e0123834.

2. Blein-Nicolas M, Albertin W, da Silva T et al (2015) A systems approach to elucidate heterosis of protein abundances in yeast. *Mol Cell Proteomics* **14**: 2056-2071.
# Genome-scale metabolic model of *Oenococcus oeni* and its application to the analysis of malolactic fermentation

S. MENDOZA, P. CAÑÓN, E. AGOSIN*

*Department of Chemical and Bioprocess Engineering, School of Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile*

*Oenococcus oeni* – malolactic fermentation – wine must – genome-scale metabolic model

## Introduction

The microbiology of wine involves more than just yeasts. Lactic Acid Bacteria (LAB) are also present in wine must. They metabolize numerous substrates, playing an important role in the final quality of wine [1]. Five genera of LAB can be found in grape must: *Lactobacillus*, *Pediococcus*, *Leuconostoc*, *Oenococcus* and *Weissella*. However, *Oenococcus oeni* is the predominant species at the end of primary fermentation and after. Its remarkable metabolism makes it resistant to high ethanol concentrations (15% v/v), low pH (as low as 2.9), limited nutrient availability [2] and high SO₂ concentrations (50 ppm) [3]. These characteristics make *O. oeni* the main organism responsible of carrying out malolactic fermentation (MLF).

The MLF (also called secondary fermentation) is responsible for the decarboxylation of L-malic acid to L-lactic acid, which has three main consequences. First, MLF reduced the acidity of wine, increasing wine pH and decreasing titratable acidity. Second, it confers microbial stability to the wine through the removal of malic acid, which could act as a carbon source for other microorganisms. Finally, MLF impacts sensory properties, providing organoleptic complexity [2]. MLF generally occurs once the primary fermentation has finished, but could also start earlier, or several months after. The reasons for this are not clear yet, which transforms FML in an unpredictable and problematic process for winemakers.

Metabolic networks at the genome scale (so called GENREs or GENome-scale metabolic REconstructions) are built systematically using genome annotation, knowledge databases, "omics", and primary literature. The resulting networks provide an accurate representation of the metabolic capabilities of the target organisms at the time of reconstruction [4]. Integrating this information in a structured fashion enables its translation into computational models, called GEnome-scale Metabolic models or GEMs, that can be used to simulate metabolic phenotypes [5]. In 2005, the genome sequence and annotation of *O. oeni* PSU-1 strain was released [6], allowing the study of the metabolic pathways of this organism. The latter provides an opportunity to generate a high-quality GEM that allows a comprehensive understanding of possible phenotypic states that are generated during MLF.

The objective of the present work is to develop a genome-scale metabolic model of the malolactic bacterium *O. oeni* PSU-1 capable of properly identifying and evaluating the nutritional requirements and specific rates of this unique bacterial species under MLF culture conditions. This will allow in the near future to infer more effective strategies for the development of appropriate and predictable malolactic fermentation conditions.

## Materials and methods

### Procedure

The GEM of *O. oeni* PSU1 strain was constructed following the protocol for generating a high-quality genome-scale metabolic reconstruction proposed by Thiele and Palsson [7]. This protocol comprises five stages. The first one consists of generating a draft reconstruction, based on the genome annotation of the target organism and biochemical databases. In the second one, the entire draft reconstruction is re-evaluated
<figure><img src="image_89.png" /><figcaption>**Fig. 1** - Metabolic networks at the genome scale of *O. oeni* PSU1 strain, which contains 694 reactions and 528 metabolites. This model includes most of the reactions describing the primary metabolism of this strain and 138 membrane transporters.</figcaption></figure>
and refined. The metabolic functions and reactions collected in the draft reconstruction are individually evaluated against organism-specific literature and expert opinion. In the third stage, the reconstruction is converted into a mathematical format and condition-specific models are defined. System boundaries are defined, converting the general reconstruction into a condition-specific model. The fourth stage consists of network verification, evaluation and validation. This evaluation generally leads to the identification of missing metabolic functions in the reconstruction – or network gaps – and the consequent reparation of these gaps. In the fifth stage, the final reconstruction is made available to the research community in two formats: as a spreadsheet containing all information collected during the reconstruction process; and as a SBML file, a transportable format of the models which can be used with other modeling tools.

## Software and databases

Pathway Tools v. 16.5 [8, 9] was employed to create a draft reconstruction of *O. oeni*, and as a platform to refine the reconstruction. TransportDB [10] was used to incorporate information on cytoplasmic membrane transporters and outer membrane channels of *O. oeni*. Omix v. 1.8, an editing and visualization tool for biochemical networks [11], was employed to re-evaluate and perform additional refinement of the draft reconstruction. The Cobra Toolbox for MATLAB [12, 13] was used to simulate, analyze and predict metabolic phenotypes using the resulting *O. oeni*´ GEM. Specifically, we carried out Flux Balance Analysis (FBA) based on linear optimization of the biomass formation under different sets of constraints.

## Results and discussion

We built a GEM of *O. oeni* PSU1 strain which contains 694 reactions and 528 metabolites (Figure 1). This model includes most of the reactions describing the primary metabolism of this strain – including the malolactic reaction and reactions of the heterolactic fermentation pathway – and 138 membrane transporters, as well. 86% of the reactions in the model are associated with at least one gene.

For model validation, an in silico single omission experiment was performed using the chemically defined medium determined by Terrade *et al*. [14]. This experiment consisted in removing each nutrient from the medium separately and analyzing if the microorganism is able to grow after its removal. Nutrients that inhibit growth, when removed, were considered essential. The model showed an accuracy of 89%, pointing to its high performance.

Amino acids have been the most studied nutrients in *O. oeni*, by far. Of the twenty amino acids, the essentiality of eighteen of them was correctly predicted by the model. Indeed, the model predicted that L-arginine, L-asparagine, L-glutamic acid, L-histidine, L-isoleucine, L-leucine, L-methionine, L-phenylalanine, L-tryptophan, L-tyrosine and L-valine were essential for growth, in agreement with experimental results of most *O. oeni* strains [15-17]. On the other hand, L-alanine, L-aspartic acid, L-glutamine, L-glycine and L-proline have been shown to be non-essential nutrients in vivo [15-17], which is also correctly predicted by the model. L-cysteine, which is present in low concentrations in wine must, is essential to most strains of *O. oeni*. Nevertheless, Mills [6] concluded that the strain PSU-1 has all the genes of the L-cysteine biosynthesis pathway. Interestingly, the model predicts that this strain is indeed able to synthesize L-cysteine.

Vitamin requirements have also been widely studied in *O. oeni*. For example, pantothenate and nicotinic acid have been shown to be essential, while biotin, aminobenzoic acid, choline chloride, cabalamin, folic acid, pyridoxine, riboflavin and thiamine are not [14, 16, 18]. With the exception of biotin, the model predicts correctly the essentiality of these vitamins.

Minerals also play a critical role in *O. oeni* metabolism. Manganese is strictly necessary for growth [14, 19], probably due to its participation in the activation of the malolactic enzyme [20, 21], allowing the generation of a proton gradient which produces ATP and leads to growth stimulation [22]. The necessity for manganese is correctly predicted by the model. Other minerals, such as calcium, copper, iron, magnesium and zinc are not required for growth, which is also correctly simulated by the model.

The resulting model was employed to predict growth rate in continuous and batch cultures. Zhang *et al*. [23] determined glucose and fructose consumption rates, as well as lactate, mannitol, acetate and ethanol production rates under five different dilution rates in continuous culture. However, with these few measurements as inputs to the model, it was not possible to predict growth rate accurately, mainly due to
the absence of measurements of major metabolites like carbon dioxide, for example. Interestingly, we discovered that if we fix the rates of carbon dioxide production and adenine consumption, the model is able to predict growth rate with a mean difference of 11%, with respect to the in vivo growth rates.

We also carried out FBA to predict the specific growth rate in batch cultures. Employing the data of Olguin and co-workers [24], we calculated the specific consumption and production rates from batch cultures in exponential phase at different pH and ethanol concentrations. When ethanol is absent in the medium, the model is able to accurately predict specific growth rate at pH 4 and 3.5. We are currently working on the calibration of the model for ethanol rich cultures, and further model refinement will require determining biomass composition and maintenance coefficients under increasing ethanol culture conditions.

## Conclusion

In this work, we built a GEM of the wine malolactic bacterium *O. oeni* containing 694 reactions and 528 metabolites. The resulting metabolic model was validated by comparing nutrient requirement predictions with literature. For a total of 44 growth/non growth experiments, the model showed an accuracy of 89% in its predictions, suggesting a high overall performance.

Additionally, the model was employed to predict specific growth rates in continuous and batch cultures in exponential phase. In continuous culture, an average percentage difference of 11% was observed between experimental and predicted specific growth rates. In batch cultures in exponential phase, a percentage difference of 23% and 19% was observed at pH 4 and 3.5, respectively. Predictions were not achieved under ethanol growing conditions, probably due to the limited information concerning activation/deactivation of genes/reactions of *O. oeni* growing in a medium containing ethanol. Further model refinement will require determining biomass composition and whole gene expression of *O. oeni* under ethanol growing conditions.

Finally, this model is the first step towards the generation of a comprehensive understanding of *O. oeni* metabolism and the consequent control of malolactic fermentation during winemaking. This model could be useful to predict the rate at which malolactic fermentation occurs in wineries, helping winemakers to predict the putative duration of this process. Challenges remain in improving specific growth rate predictions of *O. oeni* under ethanol growing conditions and in incorporating high throughput data and regulatory mechanisms.

## Acknowledgements

This work was supported by FONDECYT (Grant 1130822), FONDEF (Grant D11i1139), and a CONICYT Grant to pursue doctoral studies in Chile (P. Cañón).

## Abbreviations

COBRA, Constraint-based reconstruction and analysis; FBA, Flux Balance Analysis; GEM, Genome-Scale Model; LAB, Lactic Acid Bacteria.

## References

1. Ribéreau-Gayon P, Dubourdieu D, Doneche B et al (2006) *Handbook of Enology Volume 1: The Microbiology of Wine and Vinifications 2nd Edition*. John Wiley & Sons, Ltd.

2. Bartowsky EJ (2005) *Oenococcus oeni* and malolactic fermentation–moving into the molecular arena. *Aust J Grape Wine Res* **11**: 174-187.

3. Bauer R, Dicks L (2004) Control of malolactic fermentation in wine. A review. *S Afr J Enol Vitic* **25**: 74-88.

4. Monk J, Nogales J, Palsson BO (2014) Optimizing genome-scale network reconstructions. *Nat Biotechnol* **32**: 447-452.

5. McCloskey D, Palsson BO, Feist AM (2013) Basic and applied uses of genome-scale metabolic network reconstructions of *Escherichia coli*. *Mol Syst Biol* **9**: 661.
6. Mills DA, Rawsthorne H, Parker C et al (2005) Genomic analysis of *Oenococcus oeni* PSU-1 and its relevance to winemaking. *FEMS Microbiol Rev* **29**: 465-475.

7. Thiele I, Palsson BØ (2010) A protocol for generating a high-quality genome-scale metabolic reconstruction. *Nat Protoc* **5**: 93-121.

8. Karp PD, Riley M, Paley SM et al (2002) The MetaCyc database. *Nucleic Acids Res* **30**: 59-61.

9. Karp PD, Paley SM, Krummenacker M et al (2009) Pathway tools version 13.0: integrated software for pathway/genome informatics and systems biology. *Brief Bioinform* **11**: 40-79.

10. Ren Q, Chen K, Paulsen IT (2007) TransportDB: a comprehensive database resource for cytoplasmic membrane transport systems and outer membrane channels. *Nucleic Acids Res* **35**: D274-D279.

11. Droste P, Miebach S, Niedenfuhr S et al (2011) Visualizing multi-omics data in metabolic networks with the software Omix: a case study. *Biosystems* **105**: 154-161.

12. Becker SA, Feist AM, Mo ML et al (2007) Quantitative prediction of cellular metabolism with constraint-based models: the COBRA toolbox. *Nat Protoc* **2**: 727-738.

13. Schellenberger J, Que R, Fleming RMT et al (2011) Quantitative prediction of cellular metabolism with constraint-based models: the COBRA toolbox v2.0. *Nat Protoc* **6**: 1290-1307.

14. Terrade N, Mira de Orduña R (2009) Determination of the essential nutrient requirements of wine-related bacteria from the genera *Oenococcus* and *Lactobacillus*. *Int J Food Microbiol* **133**: 8-13.

15. Fourcassie P, Makaga-Kabinda-Massard E, Belarbi A et al (1992) Growth, D-glucose utilization and malolactic fermentation by *Leuconostoc ænos* strains in 18 media deficient in one amino acid. *J Appl Bacteriol* **73**: 489-496.

16. Garvie EI (1967) The growth factor and amino acid requirements of species of the genus *Leuconostoc*, including *Leuconostoc paramesenteroides* (sp. nov.) and *Leuconostoc oenos*. *J Gen Microbiol* **48**: 439-447.

17. Remize F, Gaudin A, Kong Y et al (2006) *Oenococcus oeni* preference for peptides: qualitative and quantitative analysis of nitrogen assimilation. *Arch Microbiol* **185**: 459-469.

18. Richter H, Vlad D, Unden G (2001) Significance of pantothenate for glucose fermentation by *Oenococcus oeni* and for suppression of the erythritol and acetate production. *Arch Microbiol* **175**: 26-31.

19. Theobald S, Pfeiffer P, Konig H (2005) Manganese-dependent growth of *Oenococci*. *J Wine Res* **16**: 171-178.

20. Lonvaud-Funel A, Strasser de Saad AM (1982) Purification and properties of a malolactic enzyme from a strain of *Leuconostoc mesenteroides* isolated from grapes. *Appl Environ Microbiol* **43**: 357-361.

21. Spettoli P, Nuti MP, Zamorani A (1984) Properties of malolactic activity purified from *Leuconostoc oenos* ML34 by affinity chromatography. *Appl Environ Microbiol* **48**: 900-901.

22. Cox DJ, Henick-Kling T (1989) Chemiosmotic energy from malolactic fermentation. *J Bacteriol* **171**: 5750-5752.

23. Zhang D, Lovitt R (2006) Performance assessment of malolactic fermenting bacteria *Oenococcus oeni* and *Lactobacillus brevis* in continuous culture. *Appl Microbiol Biotechnol* **69**: 658-664.

24. Olguin NT, Bordons A, Reguant C (2009) Influence of ethanol and pH on the gene expression of the citrate pathway in *Oenococcus oeni*. *Food Microbiol* **26**: 197-203.
# Proteomic study of the adaptation of *Oenococcus oeni* to wine conditions

M. MARGALEF*, I. ARAQUE, A. BORDONS, C. REGUANT

Departament de Bioquímica i Biotecnologia, Facultat d'Enologia, Universitat Rovira i Virgili, Spain

## *Oenococcus oeni* – proteomic – stress – iTRAQ and 2D-DIGE

### Introduction

*Oenococcus oeni* is the main lactic acid bacteria responsible for malolactic fermentation (MLF). Malic conversion plays an important role in the improvement of the organoleptic quality and sensorial properties of wines. Although *O. oeni* can survive the harsh conditions of wine (low pH, ethanol and sulfur compounds), sometimes the survival is low and the MLF may stop. This bacteria is studied in order to understand the genetic diversity and furthermore the enological potential.

The proteomic studies on *O. oeni* are quite scarce. The first of them was from Silveira et al. [1] in which they used 2D gel electrophoresis (without the DIGE application) and showed that both ethanol stress and adaptation changed the protein profiles of *O. oeni* cells. Other authors have been studying enological starters from a proteomic approach in order to understand better the adaptation and changes into wine [2-4].

Nowadays, different proteomic techniques are available. 2D-DIGE technique was first described in 1997 [5]. It relies on a pre-electrophoretic labeling, allowing multiplexing of samples into the same gel.

There is another gel-free technique which uses isobaric tags for relative and absolute quantification (iTRAQ). It is combined with liquid chromatography (LC) and electrospray ionization tandem mass spectrometry (MS/MS) analysis, and nowadays it is emerging as a powerful methodology. It enables quantification and comparison of protein levels directly from samples with greater efficiency and accuracy.

Here, we use the iTRAQ technique to analyze absolute protein expression changes in *O. oeni* PSU-1 during the first hours after the inoculation to wine. Using the 2D-DIGE technique and iTRAQ method in tandem with LC-MS/MS, we have identified several proteins that were either up- or down-regulated. This is the first proteomic analysis using these techniques with *O. oeni* to elucidate proteins involved in the adaptation to wine.

### Materials and methods

#### *Strain and inoculation*

The strain used in this study was *O. oeni* PSU-1 (CP000411.1), whose complete genome sequence is available. Strain PSU-1 was taken from cryopreserved culture and inoculated in modified MRS medium (Fluka, Switzerland) supplemented with 4 g/L D,L-malic acid and 5 g/L fructose (Sigma-Aldrich, USA) pH 5 at 28°C. Bacterial growth was monitored by optical density measurement at 600 nm with spectrophotometer UV2 (Unicam, United Kingdom) until reaching OD=1.6. At this point, the wine-like medium (WLM) with 12% ethanol and at pH 3.4 [6] was inoculated at 2% of culture and kept at 20°C.

#### *Protein extraction*

After the inoculation into WLM, samples were taken at different time points for protein extraction (1h, 2h, 4h, 6h, 8h) and at 0h from MRS broth to make the control sample. Regarding the amount of pellet needed, 800 mL of WLM were collected and centrifuged at 4500 rpm for 15 min, the supernatant was removed and the pellet was washed with Tris-Cl pH 8 10 mM, frozen in liquid nitrogen and kept at -80°C until protein extraction.
### Proteomic analysis

The proteomic extracts were obtained by a cell disruptor. Then the samples were taken to the Centre for Omic Sciences from Servei de Recursos Científics i Tècnics de la Universitat Rovira i Virgili (Reus, Spain) where both proteomic characterizations were carried out.

To perform 2D-DIGE, the Gorg et al. [7] protocol was followed and the analysis of the spots displaying a ≥1 average-fold increase or decrease in abundance with a p-value <0.05 was selected for protein identification [8]. The peptides were analyzed on a MALDI TOF/TOF (Ultraflex extreme, Bruker Daltonics, Bremen, Germany).

Peptide analysis was also performed by iTRAQ labelling, using a nano LC II (Thermo Fisher Scientific, Bremen, Germany), coupled to an LTQ-Orbitrap Velos Pro mass analyzer (also from Thermo Fisher Scientific). All MS/MS samples were analyzed using Mascot (Thermo Fisher Scientific; version 2.4.1.0). Mascot was set up to search the NCBInr database (2.4, 46742655 entries), following the application of the restriction for Firmicutes taxonomy. Proteins that resulted with p>0.05 and ≥1.5 average-fold from the analysis of the variance were selected as differentially expressed between times 0h vs 1h and 0h vs 6h.

## Results and discussion

All the proteomic samples were taken before the start of MLF and characterized by 2D-DIGE. 33 proteins that were significantly up- or down-regulated were selected to be identified by mass spectrometry (MS). The maximum differences were seen comparing points 0h vs 1h and 6h, from which 25 proteins were unequivocally identified by PMF (Peptide Mass Fingerprinting) and MS/MS. The analysis of all the spots from the 1h and 6h samples was repeated taking all the possible spots in order to obtain the maximum number of proteins related to these samples. Some proteins were detected in different spots because of post-translation modifications or the identification of fragments. In general, the amount of proteins up-regulated was higher than the down-regulated: for the analysis 0h vs 1h, 22 different proteins could be identified as up-regulated and 13 down-regulated; for the analysis 0h vs 6h, 30 different proteins were up-regulated (15 of them were in the 1h analysis) and 10 proteins down-regulated (6 of them were in the 1h analysis).

To make an overview of the proteins that have appeared in the analysis, the Clusters of Orthologous Groups (COGs) were used. Fig. 1A shows the different number of proteins obtained using 2D-DIGE analysis and the corresponding COGs. Some proteins are annotated in more than one COG because of their multifunctionality. The existence of few proteomic analyses in *O. oeni* makes this step sometimes difficult due to the lack of information.

In both times (1h and 6h), it can be seen an increase of COGs related to energy production, amino acid and carbohydrate metabolism. At 6h there are more proteins related to energy production, so possibly part of this energy will be needed for active transport of substrate, mainly L-malate. On the other hand, COGs F and J (nucleotide metabolism and translation) are present in the down-regulated proteins in both times. The same proteins clustered in post-translation modification (COG O) are up-regulated at 1h and 6h and both correspond to molecular chaperones.

Samples at 1h and 6h were used for iTRAQ quantification because these were the times where more proteins were differently regulated regarding 0h. With this gel-free technique, 23 different proteins could be identified as up-regulated and 27 down-regulated for time 1h; for the analysis 0h vs 6h, 28 different proteins were up-regulated and 29 proteins down-regulated. In total, there were 105 proteins because some of them are differentially regulated at both times analyzed.

It could be seen, as with the other technique, the relevance of different alcohol dehydrogenases involved in energy production. In Fig. 1B, the COG more relevant is translation (J) and energy production (C) for up- and down-regulated proteins, respectively. However, this technique reveals other information comparing to the 2D-DIGE, the proteins obtained are different.

Proteins up-regulated with the two techniques are alcohol dehydrogenase and an S-ribosylhomocysteinase. The former is related to energy production and the latter is involved in amino acid metabolism and inorganic ion transport. S-ribosylhomocysteinase is involved in the synthesis of autoinducer 2 (AI-2) which is secreted by bacteria and is used to communicate both the cell density and the metabolic potential of the environment.
With this technique some stress proteins like two thioredoxins and several peptidases are differentially regulated. Since peptides account for the largest proportion of total nitrogen in wine, these results suggest the relevant relationship between wine nitrogen composition and the ability of *O. oeni* to cope with its environment.

<figure><img src="image_90.png" /><figcaption>**Fig. 1** - COG functional annotation of differentially regulated proteins. A, proteins obtained from 2D-DIGE technique. B, proteins obtained from iTRAQ technique. For each COG category the red bars represent the number of up-regulated and green bars the down-regulated proteins. COG annotation: A (RNA processing and modification), C (Energy production and conversion), E (Amino acid transport and metabolism), F (Nucleotide transport and metabolism), G (Carbohydrate transport and metabolism), H (Coenzyme transport and metabolism), I (Lipid transport and metabolism), J (Translation, ribosomal structure and biogenesis), K (Transcription), L (Replication, recombination and repair), M (Cell wall/membrane/envelope biogenesis), O (Post-translational modification, protein turnover and chaperones), P (Inorganic ion transport and metabolism), R (General function prediction only), S (Function unknown), T (Signal transduction mechanisms), V (Defense mechanisms).</figcaption></figure>
## Conclusion

O. oeni is the main species to carry on the MLF and although this transformation can be spontaneous, the inoculation with a commercial starter strain increases the control of MLF [9]. After the inoculation into wine, the bacteria must deal with different stress factors and give a cellular response. Proteomic techniques give us a great number of cytosolic proteins that are differentially regulated after the inoculation and before the MLF and it is a great approach to know what is happening in the cell.

In this period of adaptation to this new environment, we have obtained several proteins which are up- and down-regulated using two different techniques, 2D-DIGE and iTRAQ. It has been seen that from all the samples analyzed, the ones with more proteins differentially regulated between control (0h) were at 1h and 6h. We have revealed more up- than down-regulated proteins, three relevant COGs related to metabolism (C, E and G) in both techniques, and proteins involved in translation.

Different approaches have been obtained from the results of the two complementary proteomic techniques used in this work. However, we think that this is the path to unveil the complex process of adaptation of O. oeni to wine.

## Acknowledgements

This work has been supported by the grant AGL2012-07369 of the Spanish Ministry of Science and Innovation. M. Margalef is thankful for the predoctoral grant of the University of Rovira i Virgili.

## References

1. Silveira MG, Baumgärtner M, Rombouts FM et al (2004) Effect of adaptation to ethanol on cytoplasmic and membrane protein profiles of *Oenococcus oeni*. *Appl Environ Microbiol* **70**: 2748-2755.

2. Cecconi D, Milli A, Rinalducci S et al (2009) Proteomic analysis of *Oenococcus oeni* freeze-dried culture to assess the importance of cell acclimation to conduct malolactic fermentation in wine. *Electrophoresis* **30**: 2988-2995.

3. Napoli A, Aiello D, Aiello G et al (2014) Mass spectrometry-based proteomic approach in *Oenococcus oeni* enological starter. *J Proteome Res* **6**: 2856-2866.

4. Cafaro C, Bonomo MG, Rossano R et al (2014) Efficient recovery of whole cell proteins in *Oenococcus oeni* – a comparison of different extraction protocols for high-throughput malolactic starter applications. *Folia Microbiol* **59**: 399-408.

5. Unlü M, Morgan ME, Minden JS (1997) Difference gel electrophoresis: a single gel method for detecting changes in protein extracts. *Electrophoresis* **18**: 2071-2077.

6. Olguín N, Bordons A, Reguant C (2009) Influence of ethanol and pH on the gene expression of the citrate pathway in *Oenococcus oeni*. *Food Microbiol* **26**: 197-203.

7. Gorg A, Weiss W, Dunn MJ (2004) Current two-dimensional electrophoresis technology for proteomics. *Proteomics* **4**: 3665-3685.

8. Lin ST, Chou HC, Chang SJ et al (2012) Proteomic analysis of proteins responsible for the development of doxorubicin resistance in human uterine cancer cells. *J Proteomics* **75**: 5822-5847.

9. Nielsen JC, Prahl C, Lonvaud-Funel A (1996) Malolactic fermentation in wine by direct inoculation with freeze-dried *Leuconostoc oenos* cultures. *Am J Enol Vitic* **47**: 42-48.
# Involvement of yeast-derived peptides in the inhibition of malolactic fermentation and impact of these peptides on malolactic enzyme activity

Z. RIZK<sup>1,2*</sup>, Y. EL RAYESS<sup>3,4</sup>, C. GHANEM<sup>1,3</sup>, F. MATHIEU<sup>5</sup>, P. TAILLANDIER<sup>4,6</sup>, N. NEHME<sup>2,7</sup>

Malolactic fermentation – malolactic enzyme – yeast antimicrobial peptides – inhibitory kinetic

## Introduction

In the context of winemaking, it is usually known that after the accomplishment of alcoholic fermentation (AF) carried out mainly by *Saccharomyces cerevisiae*, most of the red wines and certain white and sparkling wines are spontaneously, or purposely, taken through a malolactic fermentation (MLF) step, mostly by indigenous or inoculated lactic acid bacteria of the *Oenococcus oeni* species. The MLF, an enzymatic decarboxylation of L-malic acid into L-lactic acid and CO₂, diminishes wine acidity and improves its sensorial characteristics and its microbial stability [1, 2]. However, it is often difficult to trigger and accomplish because of the individual or synergistic antibacterial activity of several physical chemical wine parameters and yeast inhibitory metabolites. Some of these factors have been intensively investigated such as low pH [3, 4], inadequate temperature [3], nutrient depletion [5], endogenous and exogenous SO₂ [6], phenolic compounds [7], high ethanol content [3, 4] and medium chain fatty acids [8]. While the anti-MLF role of the previous compounds is already well established, there are gradually growing evidences suggesting the involvement of yeast peptides/proteins in the inhibition of *O. oeni* growth and L-malic acid consumption. Few authors such as Dick et al. [9], Comitini et al. [10], Osborne and Edwards [11], Mendoza et al. [12] and Branco et al. [13] demonstrated that some *S. cerevisiae* strains were able to produce extracellular proteinaceous compounds of different MW active against some *O. oeni* strains with no conclusive results on their mechanism of action. Most of the time, the impact of these compounds on the growth of *O. oeni* was the main parameter evaluated and few data concerning the malate consumption was given. Besides, the effect of these compounds on the malolactic enzyme activity was never studied. In a previous work [14], we demonstrated that *S. cerevisiae* D strain was able to produce a 5-10 kDa peptidic fraction responsible for the strong inhibition of *O. oeni* X strain growth and malate consumption during sequential cultures. In the present work, we intended to further characterize this fraction by measuring its inhibitory effect on the malolactic enzyme activity.

## Materials and methods

The *S. cerevisiae* D strain and the *O. oeni* X strain used in this work were kindly provided by Lallemand Inc. (Blagnac, France).

<footnote n="1"><sup>1</sup>Lebanese Agricultural Research Institute (LARI), Fanar Station, P.O. Box 90-1965, Jdeidet El-Metn Fanar, Lebanon</footnote>

<footnote n="2"><sup>2</sup>Faculty of Sciences, Holy Spirit University of Kaslik, P.O. Box 446, Jounieh, Lebanon</footnote>

<footnote n="3"><sup>3</sup>Faculty of Agricultural and Food Sciences, Holy Spirit University of Kaslik, P.O. Box 446, Jounieh, Lebanon</footnote>

<footnote n="4"><sup>4</sup>Centre de Viticulture et d'Œnologie de Midi-Pyrénées, Avenue de l'Agrobiopôle, 31320 Castanet-Tolosan, France</footnote>

<footnote n="5"><sup>5</sup>Laboratoire de Génie Chimique, INPT, UPS, Université de Toulouse, 1 Avenue de l'Agrobiopôle, F-31326 Castanet-Tolosan, France</footnote>

<footnote n="6"><sup>6</sup>Laboratoire de Génie Chimique, INPT, UPS, Université de Toulouse, 4 Allée Emile Monso, F-31432 Toulouse, France</footnote>

<footnote n="7"><sup>7</sup>Faculty of Agricultural Engineering and Veterinary Medicine, Lebanese University, Dekwaneh, Lebanon</footnote>

<footnote>*Corresponding author: ziadrizk ce@hotmail.com</footnote>
## Growth media

- Synthetic grape juice medium (SGJ medium): The medium composition that simulated the natural grape juice consisted of D-glucose 100 g l⁻¹, D-fructose 100 g l⁻¹, yeast extract 1 g l⁻¹, (NH₄)₂SO₄ 2 g l⁻¹, citric acid 0.3 g l⁻¹, L-malic acid 3 g l⁻¹, L-tartaric acid 5 g l⁻¹, MgSO₄ 0.4 g l⁻¹, and KH₂PO₄ 5 g l⁻¹, pH 3.5. The medium was autoclaved before use (121°C, 20 min).

- Modified MRS medium: The medium composition consisted of MRS 51 g l⁻¹, 1 ml of Tween 80, L-malic acid 3 g l⁻¹, ethanol 10 % (v/v), pH 3.5. The medium lacking ethanol was first autoclaved at 121°C for 15 min. After cooling, ethanol was added aseptically through sterile filter membranes of 0.22 µm cut-off (Elvetec services, Meyzieu-France) and the pH was readjusted to 3.5 using an 85 % orthophosphoric acid solution.

## Isolation of the 5-10 kDa peptidic fractions from the yeast fermented medium by ultrafiltration

The SGJ medium was first inoculated by *S. cerevisiae* D strain at an initial concentration of 3 x 10⁶ cells ml⁻¹ and the alcoholic fermentation (AF) was carried out at 22°C with stirring at 150 rpm in Erlen-Meyer flasks for 120 h to allow complete sugar consumption (< 2 g l⁻¹). In order to isolate the proteinaceous compounds, the yeast fermented medium was fractionated each 24 h by ultrafiltration through centrifugal filter units (3500 g, 45 min, 4°C) having cut-offs of 10 kDa (Amicon® Ultra-15 with ultracel-10 membrane) and 5 kDa (Corning® Spin-X UF 20 Sigma-Aldrich). This yielded five peptidic fractions with a MW between 5 and 10 kDa, 75 times concentrated, collected at 24, 48, 72, 96 and 120 h of the AF.

## In vitro evaluation of the inhibitory effect of the 5-10 kDa peptidic fractions on the malolactic enzyme activity

- Preparation of the bacterial enzymatic extract comprising the malolactic enzyme: A bacterial culture of *O. oeni* X strain was performed in a modified MRS medium. An inoculum of 2 x 10⁶ cells ml⁻¹ was used and the culture was followed for 5 days at 22°C with stirring at 150 rpm. After a centrifugation at 3500 rpm for 20 min at 4°C, the supernatant was removed and 0.4 g of the bacterial pellet were weighed and suspended in 800 µl of a phosphate buffer (67 mmol l⁻¹; pH 6.7). The bacterial cells of the suspension were then lysed by vortexing them with glass beads presenting a diameter of 300 µm (Sigma-Aldrich) in a FastPrep® FP120 bead-beater device (BIO 1010/Savant) at 6 m s⁻¹ for 3 x 45 s at 4°C. Between 2 consecutive runs, the cells were kept on ice for 1 min. The cell debris was removed by centrifugation at 14000 g for 10 min at 4°C [15]. The supernatant containing the cell-free enzymatic extract was recovered and the volume completed to 2 ml using the same buffer.

- Monitoring of the L-malic acid consumption in vitro: The 2 ml of each enzymatic extract were divided into 2 aliquots; one used as a control and the other one as a test medium containing the 5-10 kDa fractions. The reaction mixture is given in Table 1. The enzymatic reaction was performed at 30°C for 40 min. The inhibitory kinetic of the 5-10 kDa fractions on the malolactic enzyme activity was evaluated. The five peptidic fractions of 5-10 kDa collected each 24 h of the AF were tested separately. At the end of each assay, the concentrations of the L-malic acid consumed and the L-lactic acid produced were measured in both aliquots (control and test) and compared.

- L-malic acid consumption and L-lactic acid production by bacteria: The L-malic acid and the L-lactic acid concentrations were determined using two enzymatic assays (Boehringer Mannheim/R-Biopharm, kit. No 10 139 068 035 and kit. No 10 139 084 035 respectively, Darmstadt-Germany) and the results were expressed in g l⁻¹.

## Results and discussion

The pair *S. cerevisiae* D strain/*O. oeni* X strain was previously tested by applying the sequential culture strategy that simulated the natural winemaking process. In other words, the MLF started after the accomplishment of the AF. The AF lasted 120 h and its stationary phase started after 50 h. The MLF results showed a strong inhibition of both the bacterial growth and the L-malic acid consumption. The MLF was followed for more than one month and no L-malic acid was consumed. The results were reproducible both in the synthetic grape juice and the natural grape juice media fermented by the yeasts. Investigations indicated the involvement of a yeast peptidic fraction with a MW between 5 and 10 kDa in the MLF inhibition.
<table><caption>**Table 1** - Reaction mixture for the monitoring of the L-malic acid consumption *in vitro*</caption><thead><tr><th>Aliquot 1 (Control)</th><th>Aliquot 2 (Test)</th><th>Amounts</th></tr></thead><tbody><tr><td>Enzymatic extract</td><td>Enzymatic extract</td><td>1 ml</td></tr><tr><td>Mn<sup>2+</sup></td><td>Mn<sup>2+</sup></td><td>0.1 mg</td></tr><tr><td>NAD<sup>+</sup> (35 g l<sup>-1</sup>)</td><td>NAD<sup>+</sup> (35 g l<sup>-1</sup>)</td><td>0.1 ml</td></tr><tr><td><sup>a</sup>phosphate buffer</td><td><sup>b</sup>ABP (10.5 x)</td><td>0.14 ml</td></tr><tr><td>L-malic acid (1 g l<sup>-1</sup>)</td><td>L-malic acid (1 g l<sup>-1</sup>)</td><td>1 mg</td></tr></tbody></table>

In an attempt to determine the mechanism of action of the 5-10 kDa fractions, an *in vitro* enzymatic reaction was designed and the fractions of 5-10 kDa collected each 24 h of the AF were tested as described in Table 1.

Table 2 shows the amounts of L-malic acid consumed and the corresponding L-lactic produced for each 5-10 kDa fraction tested. First, the results clearly show that the 5-10 kDa fractions were able to reduce the amount of L-malic acid consumed of 28 % (D1: 24 h) to 91 % (D5: 120 h) when compared to the control, thus exhibiting a direct inhibitory effect on the malolactic enzyme activity. Second, the kinetic of the inhibition showed that the 5-10 kDa peptidic fraction was gradually released during the AF with the highest concentration reached at the end of the stationary phase. Third, it is known that during the MLF, each mole of L-malic acid decarboxylated by the malolactic enzyme releases one mole of L-lactic acid and one mole of CO₂. The amounts of L-lactic acid produced were measured and found to be stoichiometrically equivalent to the amounts of L-malic acid consumed. As an example, in the presence of the fraction D1, 0.72 g l⁻¹ of L-malic acid equivalent to 5.4 mmol l⁻¹ were consumed and gave 0.48 g l⁻¹ of L-lactic acid which corresponded to 5.3 mmol l⁻¹. Consequently, the gradual decrease in the amount of L-lactic acid produced was an additional indicator of the malolactic enzyme inhibition by the 5-10 kDa peptidic fractions. Therefore, we suggest that *in vivo*, these peptides would enter the bacterial cells and directly inhibit the malolactic enzyme.

<table><caption>**Table 2** - Amount of L-malic acid consumed (g l⁻¹) and L-lactic acid produced (g l⁻¹) during the enzymatic reaction *in vitro* in the presence of the 5-10 kDa fractions collected each 24 h of the AF</caption><thead><tr><th>Sampling time of the 5-10 kDa fractions during the AF</th><th>Consumed L-malic acid (g l<sup>-1</sup>)</th><th>Produced L-lactic acid (g l<sup>-1</sup>)</th><th>Inhibition % of malate consumption</th></tr></thead><tbody><tr><td>Control (absence of *ABP)</td><td>1 (± 0.02)</td><td>0.67 (± 0.013)</td><td>0 (± 0)</td></tr><tr><td>After 24 h of the AF (D1)</td><td>0.72 (± 0.015)</td><td>0.48 (± 0.01)</td><td>28 (± 0.6)</td></tr><tr><td>After 48 h of the AF (D2)</td><td>0.63 (± 0.013)</td><td>0.42 (± 0.009)</td><td>37 (± 0.8)</td></tr><tr><td>After 72 h of the AF (D3)</td><td>0.56 (± 0.011)</td><td>0.38 (± 0.008)</td><td>44 (± 1)</td></tr><tr><td>After 96 h of the AF (D4)</td><td>0.1 (± 0.002)</td><td>0.07 (± 0.002)</td><td>90 (± 2)</td></tr><tr><td>After 120 h of the AF (D5)</td><td>0.09 (± 0.002)</td><td>0.06 (± 0.002)</td><td>91 (± 2)</td></tr></tbody></table>

Inhibition % of malate consumption = (consumed L-malic acid in the control - consumed L-malic acid in the presence of ABP) x 100 / (consumed L-malic acid in the control)

No previous works have shown the involvement of yeast proteinaceous metabolites in the direct inhibition of the malolactic enzyme activity. Few have attempted to explain the mechanism of action of the yeast antibacterial peptides/proteins by evaluating mainly their impact on the bacterial growth. The proteinaceous compound ≥ 10 kDa found by Comitini et al. [10] was dose dependent and was able to reduce the bacterial growth with a typical saturation kinetic, thus suggesting the presence of a receptor on the bacterial cell. Therefore its bacteriostatic or bactericidal effect depended on its concentration and the incomplete MLF was correlated to its bactericidal effect. Mendoza et al. [12] found that their peptidic fraction having a MW between 3 and 10 kDa inhibited the bacterial growth but not the ability to consume L-malic acid with a typical saturation kinetic similar to that suggested by Comitini et al. [10]. Osborne and Edwards [11] suggested that their antibacterial peptide presenting a MW of 5.9 kDa inhibited the bacteria along with SO₂. The mechanism proposed was that of bacteriocins forming membrane pores and facilitating the entry of SO₂ inside the cells therefore leading to the bacterial death and arrest of MLF. The mechanism of action of the GAPDH-derived peptides identified by Branco et al. [13] and that inhibited the growth of *O. oeni* was not elucidated. In addition, no data concerning the malate consumption was shown [13].

<footnote><sup>a</sup>phosphate buffer: 67 mmol l<sup>-1</sup>; pH 6.7</footnote>

<footnote><sup>b</sup>ABP: antibacterial peptides (5-10 kDa fractions, 10.5 times concentrated)</footnote>
**Conclusion**

In conclusion, the results of the present work revealed that *S. cerevisiae* D strain was able to produce an anti-MLF proteinaceous compound with a MW between 5 and 10 kDa that was mainly released at late stages of the AF stationary phase. It was shown for the first time that yeast proteinaceous compounds were able to inhibit the malic acid consumption by directly targeting the malolactic enzyme activity. Current work is carried out in order to purify and sequence the putative bioactive peptides of the 5-10 kDa fraction. Future work should investigate their antimicrobial range of action, their biochemical and inhibitory properties and the possibility of using them as natural alternative biopreservatives in food products.

**Acknowledgements**

The authors would like to thank LARI (Lebanese Agricultural Research Institute) for their financial support and Lallemand Inc. for providing the strains.

**Abbreviations**

AF, Alcoholic Fermentation; ABP, Antibacterial Peptides/Proteins; GAPDH, Glyceraldehyde 3- Phosphate Dehydrogenase; kDa, Kilo Dalton; MLF, Malolactic Fermentation; MW, Molecular Weight.

**References**

1. Bartowski E, Costello P, Henschke P (2002) Management of malolactic fermentation-wine flavour manipulation. *Aust. NZ Grapegrow Winemak* **461a**: 10-12.

2. Lonvaud-Funel A (1999) Lactic acid bacteria in the quality improvement and depreciation of wine. *Antonie Van Leeuwenhoek* **76**: 317-331.

3. Britz TJ, Tracey RP (1990) The combination effect of pH, SO₂, ethanol and temperature on the growth of *Leuconostoc oenos*. *J Appl Bacteriol* **68**: 23-31.

4. Vaillant H, Formysin P, Gerbaux V (1995) Malolactic fermentation of wine: study of the influence of some physico-chemical factors by experimental design assays. *J Appl Bacteriol* **79**: 640-650.

5. Terrade N, Mira de Orduna R (2009) Determination of the essential nutrient requirements of wine-related bacteria from the genera *Oenococcus* and *Lactobacillus*. *Int J Food Microbiol* **133**: 8-13.

6. Carreté R, Vidal MT, Bordons A et al (2002) Inhibitory effect of sulfur dioxide and other stress compounds in wine on the ATPase activity of *Oenococcus oeni*. *FEMS Microbiol Lett* **211**: 155-159.

7. Reguant C, Bordons A, Arola L et al (2000) Influence of phenolic compounds on the physiology of *Oenococcus oeni* from wine. *J Appl Microbiol* **88**: 1065-1071.

8. Capucho I, San Romao MV (1994) Effect of ethanol and fatty acids on malolactic activity of *Leuconostoc oenos*. *Appl Microbiol Biotechnol* **42**: 391-395.

9. Dick KJ, Molan PC, Eschenbruch R (1992) The isolation from *Saccharomyces cerevisiae* of two antibacterial cationic proteins that inhibit malolactic bacteria. *Vitis* **31**: 105-116.

10. Comitini F, Ferretti R, Clementi F et al (2005) Interactions between *Saccharomyces cerevisiae* and malolactic bacteria: preliminary characterization of a yeast proteinaceous compound(s) active against *Oenococcus oeni*. *J Appl Microbiol* **99**: 105-111.

11. Osborne JP, Edwards CG (2007) Inhibition of malolactic fermentation by a peptide produced by *Saccharomyces cerevisiae* during alcoholic fermentation. *Int J Food Microbiol* **118**: 27-34.

12. Mendoza L, Manca de Nadra M, Farias M (2010) Antagonistic interaction between yeasts and lactic acid bacteria of oenological relevance. Partial characterization of inhibitory compounds produced by yeasts. *Food Res Int* **43**: 1990-1998.

13. Branco P, Francisco D, Chambon C et al (2014) Identification of novel GAPDH-derived antimicrobial peptides secreted by *Saccharomyces cerevisiae* and involved in wine microbial interactions. *Appl Microbiol Biotechnol* **98**: 843-853.

14. Nehme N, Mathieu F, Taillandier P (2010) Impact of the co-culture of *Saccharomyces cerevisiae*-*Oenococcus oeni* on malolactic fermentation and partial characterization of a yeast-derived inhibitory peptidic fraction. *Food Microbiol* **27**: 150-157.

15. Larsen N, Boye M, Siegumfeldt H et al (2006) Differential expression of proteins and genes in the lag phase of *Lactococcus lactis* subsp. *lactis* grown in synthetic medium and reconstituted skim milk. *Appl Environ Microbiol* **72**: 1173-1179.
*Candida pyralidae* secretes killer toxins active
*against *Brettanomyces bruxellensis*-*

N.N. MEHLOMAKULU, M.E. SETATI, B. DIVOL*

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

Biocontrol – killer toxins – *Brettanomyces bruxellensis* – *Candida pyralidae*

**Introduction**

The contamination of red wine by the yeast *Brettanomyces bruxellensis* is one of the major problems for winemakers worldwide. This yeast produces volatile phenols that give characteristic off-flavours and off-odours in contaminated wines [1]. The persistence of *B. bruxellensis* in wine is mainly attributed to its characteristic slow growth, ability to enter into a viable but not culturable (VNBC) state and thrive under conditions of low pH, high ethanol, low sugar concentration and low nutrient levels [2, 3].

Winemakers use the chemical preservative sulphur dioxide (SO₂) to control microbial spoilage in wine as well as for its antioxidant properties. However, the antimicrobial property of SO₂ is dependent on the *B. bruxellensis* strain to be eliminated and also on the pH of the wine, which influences the concentration of molecular SO₂ [4]. Biological compounds termed killer toxins have been postulated to be used as partial alternatives to SO₂. The killer toxins Kwkt, Pikt and PMKT secreted by non-Saccharomyces yeasts have indeed been shown to control the growth of *B. bruxellensis* under winemaking conditions [5].

Killer toxins execute their lethal effect by binding to receptors located on the cell wall or cell membrane of the target cells. Once the killer toxins are bound to the receptors, they kill the target cells by either permeating the cell membrane, inhibiting DNA synthesis, arresting cell cycle completion, inhibiting β-1,3-glucan synthase activity or hydrolysing the cell wall components β-1,3-glucans and β-1,6-glucans [6].

The aim of this study was to investigate and characterize antimicrobial compounds secreted by two strains of *Candida pyralidae* against *B. bruxellensis*.

**Materials and methods**

*Strains and culture conditions*

Two strains, IWBT Y1140 and IWBT Y1057, of the wine yeast *C. pyralidae* were screened for their killer activity against 22 *B. bruxellensis* strains. The *C. pyralidae* strains were cultured in YPD broth and the extracellular protein crude extracts were used to test for killer activity. The crude extracts were characterized for their proteolytic nature, environmental stability and activity by exposing them to proteolytic enzymes and high temperature, and to conditions found during winemaking in terms of temperature (15 - 25°C), pH (3.4 - 4.5), ethanol and sugar concentrations between 0 - 14% and 2 - 230 g/L, respectively. Residual activity after exposure to these conditions was tested against the sensitive strain *B. bruxellensis* IWBT Y169 using the seeded agar method.

*Killer toxin activity*

The effect of the crude extracts on *B. bruxellensis* IWBT Y169 was tested in red table grape juice adjusted to pH 4.5. The medium was inoculated with this sensitive strain to a final concentration of 10⁵ cfu/mL and 100 AU/mL of each crude extract was added to the medium. Hundred microliters from each test tube was immediately sampled, serially diluted and plated on YPD agar plates. Sampling was also carried out after 6, 12, 24, 48 and 120 h. The YPD agar plates were incubated at 30°C for 5 days. The experiments were performed on biological triplicates.
Fluorescence microscopy

B. bruxellensis IWBT Y169 cells exposed to the crude extracts were stained with the fluorescent dyes propidium iodide, aniline blue and concanavalin A Alexa Fluor® to test whether the killer toxins target the cell membrane, β-glucans or mannoproteins, respectively. All the stained cells were analysed at the Central Analytical Facility at Stellenbosch University and visualized using the Olympus IX-81 fluorescent microscope and the Carl Zeiss Confocal LSM 780 Elyra SI microscope. The experiment was carried out on biological duplicates and cell imaging was performed on statistical triplicates defined as three different visual areas of each biological sample. Cell wall morphology damage was investigated on the sensitive cells after exposure to the crude extracts for 56 h. The cells were fixed with 2.5% glutaraldehyde at 4°C for 2.5 h and dehydrated with increasing amount of ethanol (i.e. 30, 50, 70, 90, 95 and 100%) for 3 min for each concentration of ethanol. The cells were dried with a Critical Point Drier under CO2, mounted on a stub and coated with a thin layer of gold. The cells were then viewed using a Leo® 1430VP Scanning Electron Microscope.

**Results and discussion**

The *C. pyralidae* strains IWBT Y1140 and IWBT Y1057 inhibited 16 of the 22 *B. bruxellensis* strains screened. The crude extracts secreted by the *C. pyralidae* strains were confirmed to be of proteolytic nature as their antimicrobial activity was inhibited by Proteinase K and after boiling at 100°C. The antimicrobial compounds contained within the crude extracts were regarded as killer toxins and designated CpKT1 and CpKT2 for the strains IWBT Y1140 and IWBT Y1057, respectively. The killer toxins exhibited optimum activity between 20 and 25°C, pH 3.5 and 4.5 for CpKT1 and pH 4.0 for CpKT2. Both killer toxins were stable at 15 and 20°C, pH 4.5 for CpKT1 and pH 3.5 - 4.5 for CpKT2 (Figure 1). The activity and stability of the killer toxins CpKT1 and CpKT2 correlates well with the biochemical characteristics, activity and stability of killer toxins as described by Golubev [7]. Furthermore, sugar and ethanol concentrations representing conditions found during beginning, middle and end stages of fermentation did not affect the stability of these killer toxins (Figure 1). These results clearly show the potential application of these killer toxins in winemaking.

<figure><img src="image_91.png" /><figcaption>**Fig. 1** - Temperature, pH, Ethanol and Sugar stability of CpKT1 and CpKT2; (a) CpKT2 temperature stability, (b) CpKT1 pH stability, (c) CpKT2 ethanol stability, (d) CpKT1 sugar stability.</figcaption></figure>

Sensitive cells exposed to the killer toxin CpKT1 resulted in 100% loss of viability within 12 h in contrast to cells exposed to CpKT2 which lost viability within 24 h (Figure 2). Killer activity of CpKT1 and CpKT2 was similar to that of Kwkt and Pikt as reported by Comitini *et al.* [8]. The cells treated with CpKT1 and CpKT2 exhibited 79.73% and 97% infiltration with the propidium iodide dye, respectively. These results indicate that CpKT1 and CpKT2 exert their killer activity through membrane permeabilization which is
similar to the mode of action of the killer toxins PMKT, K1 and K2 [9, 10]. The killer toxins were found to target the cell wall components β-glucan and mannoproteins of the sensitive cells. Binding of the killer toxins to the mannoprotein layer resulted in diffusion of the concanavalin A dye around the cell periphery, while binding to the β-glucan layer resulted in diffusion of aniline blue dye within the cell periphery and within the sensitive cells. This indicated the disturbance of the mannoprotein layer and degradation of the β-glucan layer. Indeed, β-glucans and mannoproteins are identified as the primary receptors for killer toxins [6] serving as translocation intermediates for the killer toxins. Electron microscopy revealed that *B. bruxellensis* cells treated with CpKT1 and CpKT2 exhibited a wrinkled cell surface, punctures, indentations, cracks and peeling off the cell surface. These effects are the first to be reported on *B. bruxellensis* cells as such morphological changes have only been reported on *S. cerevisiae* cells [11, 12]. The results obtained suggest that the mode of action of CpKT1 and CpKT2 is a stepwise process, initiated by binding followed by hydrolysis of the cell wall components by the killer toxins. These killer toxins then permeate the exposed plasma membrane of the sensitive cells leading to the death of the sensitive cells.

<figure><img src="image_92.png" /><figcaption>Fig. 2 - Growth response of *B. bruxellensis* cells following exposure to CpKT1 and CpKT2 in red grape juice medium.</figcaption></figure>

## Conclusion

This study showed that the killer toxins CpKT1 and CpKT2 are able to control the proliferation of *B. bruxellensis*. The killer toxins also exhibited activity and stability under conditions found during winemaking. The killer toxins were shown to target the cell surface and the plasma membrane of *B. bruxellensis* cells. Future work will include testing higher concentrations of the killer toxins under real winemaking conditions.

## Acknowledgements

The financial assistance of the National Research Foundation, South Africa (Grant specific unique reference numbers (UID) 70999 and 91977) towards this research is hereby acknowledged. Opinions expressed and conclusions arrived at, are those of the authors and are not necessarily to be attributed to the NRF.

## Abbreviations

VNBC, Viable But Non-Culturable state; YPD, Yeast Peptone Dextrose; Pikt, *Pichia anomala* killer toxin; PMKT, *Pichia membranifaciens* killer toxin; CpKT1, *Candida pyralidae* killer toxin 1; CpKT2, *Candida pyralidae* killer toxin 2.
References

1. Dias L, Dias S, Sancho T et al (2003) Identification of yeasts isolated from wine-related environments and capable of producing 4-ethylphenol. *Food Microbiol* **20**: 567-574.

2. Oelofse A, Pretorius IS, du Toit M (2008) Significance of *Brettanomyces* and *Dekkera* during winemaking: a synoptic review. *S Afr J Enol Vitic* **29**: 128-144.

3. Millet V, Lonvaud-Funel A (2000) The viable but non-culturable state of wine micro-organisms during storage. *Lett Appl Microbiol* **30**: 136-141.

4. Barata A, Caldeira J, Botelheiro R et al (2008) Survival patterns of *Dekkera bruxellensis* in wines and inhibitory effect of sulphur dioxide. *Int J Food Microbiol* **121**: 201-207.

5. Ciani M, Comitini F (2011) Non-*Saccharomyces* wine yeasts have a promising role in biotechnological approaches to winemaking. *Ann Microbiol* **61**: 25-32.

6. Liu G-L, Chi Z, Wang C-Y et al (2013) Yeast killer toxins, molecular mechanisms of their action and their application. *Crit Rev Biotechnol* **26**: 257-276.

7. Golubev WI (2000) *Mycocins (killer toxins)*. Elsevier.

8. Comitini F, De Ingeniis J, Pepe L et al (2004) *Pichia anomala* and *Kluyveromyces wickerhamii* killer toxins as new tools against *Dekkera/Brettanomyces* spoilage yeasts. *FEMS Microbiol Lett* **238**: 235-240.

9. Santos A, Marquina D (2004) Ion channel activity by *Pichia membranifaciens* killer toxin. *Yeast* **21**: 151-162.

10. Novotná D, Flegelová H, Janderová B (2004) Different action of killer toxins K1 and K2 on the plasma membrane and the cell wall of *Saccharomyces cerevisiae*. *FEMS Yeast Res* **4**: 803-813.

11. Vadasz AS, Jagganath DB, Pretorius IS et al (2000) Electron microscopy of the K2 killer effect of *Saccharomyces cerevisiae* T206 on a mesophilic wine yeast. *Antonie van Leeuwenhoek* **78**: 117-122.

12. Comitini F, Mannazzu I, Ciani M (2009) *Tetrapisispora phaffii* killer toxin is a highly specific β-glucanase that disrupts the integrity of the yeast cell wall. *Microb Cell Fact* **8**: 55.
# The "Terroir Yeast" does not exist!

R. TOURDOT-MARECHAL*, C. LONGIN, V. CIRILLO, M. SADOUDI, A. DUCERF-GAUDIN, J. DRINKINE-MAGNEUX

Institut Universitaire de la Vigne et du Vin, Université de Bourgogne Franche-Comté
Cellule d'Expertise Scientifique en Œnologie CESEO, Derttech Université de Bourgogne
Campus Montmuzard BP 27877, 21078 Dijon Cedex, France

Yeast – biodiversity – terroir – fermentation

## Introduction

Must inoculation with a selected strain of *Saccharomyces cerevisiae* is a well-known practice by winemakers and represents a technological breakthrough for the safety of fermentations. In Europe, approx. 50% of the wine would be inoculated with Active Dry Yeasts (ADY). But, since the late 90s, many studies have focused on the potential negative impact of the implementation of a single “exogenous” strain to the full expression of the aromatic complexity of the wine [1, 2].

Based on these data, new questions emerge from some winemakers. In the case of a choice of fermentation with indigenous flora, how to overcome an organoleptic drift in an uncontrolled start in fermentation? Is inoculation of ADY starter the only solution? On the contrary, if the yeast starter is an integrated step in the winemaking process, why not consider an inoculation with a yeast strain previously isolated in the production area? Faced with this emerging demand, new companies offer winemakers a micro-area selection of *S. cerevisiae* strains and the development of “home-made” starter cultures.

In 2010, in relation with four Burgundy wineries, we initiated the program “Florigène” (2011 to 2013) supported by the Regional Council of Burgundy and the Inter-professional Office of Burgundy Wines. The objectives of this program were to study *Saccharomyces* yeast biodiversity at different stages of alcoholic fermentation during three years, in order to potentially identify winery specific yeast strains and analyze the influence of the origin of the grapes on the biodiversity of these yeasts. In other words: Do “winery” yeasts exist? Do “Terroir” yeasts exist? The results presented here were obtained with one of the four partner areas.

## Materials and methods

### *Experiment design and sampling*

The partner had never used commercial ADY in winemaking. The different modalities were clearly separated in the cellar in order to limit the spread of the yeasts from one modality to another by the material used. The partner had also committed not to change cultural practices and the winemaking process during the three years of studying. Two modalities were identified with the partner for red winemaking: M2 (Pinot noir grapes from the plot “Côte de Beaune”) and M3 (Pinot noir grapes from the plot “Corton”).

Sampling was conducted at four steps: S1: grapes on parcel 24 hours before the harvest; S1': after homogenization and putting into tank (only in 2012 and 2013); S2: at mid-alcoholic fermentation (mid-AF); S3: end AF.

### *Sample analysis*

The samples were analyzed in the laboratory as soon as possible, to avoid cold storage of samples. Grape samples were crushed directly in sterile bags. First, we estimated the sample total population (juice and fermenting musts) by microscopy direct counting in order to apply the ideal dilution for a maximum of 100 colonies per Petri dish. Approx. 50 colonies per modality and step were inoculated and stored at -80 °C in glycerol YPD medium.
The technique of PCR ITS [3] was carried out to discriminate *Saccharomyces* from non-*Saccharomyces* yeasts. The strains belonging to the genus *Saccharomyces* were distinguished by a characteristic 880-bp band. Then, an intra-specific identification of *Saccharomyces* yeasts was performed by inter-delta PCR analysis [4]. Delta sequences are 0.3-kb elements found in the genome of *Saccharomyces* yeasts at different positions. Conducting a PCR amplification of the inter-delta sequences allows the establishment of a characteristic and unique profile of one strain (or one clone) analyzed.

Comparison of all profiles obtained required: i- an image analysis for each gel giving the size bands for each colony; ii- the development for each profile in a binary matrix (values 0 or 1 for the absence or presence respectively were assigned according to band size); and iii- the statistical treatment of the data set allowing the construction of a phylogenetic tree. Two colonies are considered identical (same clone) if they have a dissimilarity less than or equal to 10%. However, the results were sometimes ambiguous and the 10% threshold could be sometimes slightly exceeded. In this case, only gel visual observation allows validation of the result.

## Results and discussion

### Inventory of *Saccharomyces* colonies

Table 1 lists the number of isolated colonies of the genus *Saccharomyces*. The frequency of detection of *Saccharomyces* yeasts on the grapes was low, with only one *S. cerevisiae* strain detected among 296 colonies. These results agreed with previous data [5-7] that highlight the predominance of non-*Saccharomyces* yeasts on grapes. The predominance of *Saccharomyces* yeasts was observed during alcoholic fermentation. However, we note, particularly in 2012, the exclusive presence of *Saccharomyces* before the fermentation (putting into tank), which can be explained by a late sampling compare to 2013. However, during alcoholic fermentation, non-*Saccharomyces* yeasts were isolated especially for the 2012 harvest. The persistence of non-*Saccharomyces* yeasts during the fermentation has already been reported [8].

<table><caption>Table 1 - Frequency of detection of *Saccharomyces* colonies</caption><thead><tr><th rowspan="2">Sampling time (S)</th><th colspan="3">2011</th><th colspan="3">2012</th><th colspan="3">2013</th></tr><tr><th>M2*</th><th>M3*</th><th>Total</th><th>M2</th><th>M3</th><th>Total</th><th>M2</th><th>M3</th><th>Total</th></tr></thead><tbody><tr><td>Grapes S1</td><td>0/50</td><td>0/50</td><td><strong>0/100</strong></td><td>0/49</td><td>1/50</td><td><strong>1/99</strong></td><td>0/47</td><td>0/50</td><td><strong>0/97</strong></td></tr><tr><td>Tank S1'</td><td>/</td><td>/</td><td>/</td><td>50/50</td><td>50/50</td><td><strong>100/100</strong></td><td>49/50</td><td>0/50</td><td><strong>49/100</strong></td></tr><tr><td>Mid-AF S2</td><td>50/50</td><td>50/50</td><td><strong>100/100</strong></td><td>18/48</td><td>14/50</td><td><strong>32/98</strong></td><td>49/50</td><td>49/50</td><td><strong>98/100</strong></td></tr><tr><td>End AF S3</td><td>50/50</td><td>50/50</td><td><strong>100/100</strong></td><td>50/50</td><td>48/50</td><td><strong>98/100</strong></td><td>/</td><td>/</td><td>/</td></tr><tr><td>Total by modality</td><td><strong>100/150</strong></td><td><strong>100/150</strong></td><td><strong>200/300 (67%)</strong></td><td><strong>118/197</strong></td><td><strong>113/200</strong></td><td><strong>231/397 (58%)</strong></td><td><strong>98/147</strong></td><td><strong>49/150</strong></td><td><strong>147/297 (49%)</strong></td></tr></tbody></table>

### 2011 vintage analysis

The distribution of the number of clones for each occurrence (number of colonies per clone) relative to the total number of identified clones is presented Figure 1. 89.4% of the identified clones were represented by less than four colonies, with 67.3% of mono-clones. This reflected a high biodiversity of *S. cerevisiae* strains on two studied modalities. No predominant strain could be detected, even during mid-FA. The maximal number of occurrences was 9. We focused the analysis by modality and sampling step only on clones whose number of occurrences was greater than or equal to 4 (14 clones). Two clones were identified specifically on the modality "Côte de Beaune" and one clone was identified specifically on the modality "Corton". Eleven clones were common to both conditions, with five predominant clones on the modality "Côte de Beaune" and five predominant clones on the modality "Corton". One clone could not be assigned to a specific modality. For the same modality, no clone covered two sampling steps (mid-AF and end-AF), which suggests a strong blending of strains during the progress of the alcoholic fermentation.

### Data comparison between the three vintages

The results obtained in 2012 and 2013 confirmed previous data, with a significant biodiversity of *S. cerevisiae* clones during fermentations. More than 65% mono-clones relative to the total number of

<footnote>*M2 (modality "Côte de Beaune"); M3 (modality "Corton")</footnote>
clones were counted (Table 2). The years 2012 and 2013 were also characterized by small rates (approx. 5%) of clones whose numbers of occurrences were greater than or equal to 4, with twelve clones identified in 2012 and one in 2013 for both modalities studied. No dominant clone could be counted. Moreover, only two clones were identified on one modality at two sampling steps in 2012, supporting the hypothesis of a succession of different strains during the fermentation process.

<figure><img src="image_93.png" /><figcaption>**Fig. 1** - Distribution of the number of clones for each occurrence in relation with the total number of clones – Vintage 2011.</figcaption></figure>

Statistical analysis of 578 profiles obtained on the modalities “Côte de Beaune” and “Corton” led to the following conclusions. We have identified 28 clones whose numbers of occurrence were greater than or equal to 4. Fourteen clones were identified only on a single vintage, eleven clones were found in two vintages and three clones were identified on three successive years. None of these three clones was affiliated with a modality (origin of the plot of vines). However, a higher detection rate was obtained for both clones from the modality “Corton”.

<table><caption>**Table 2** - Comparison of key data collected during three vintages</caption><thead><tr><th></th><th>2011</th><th>2012</th><th>2013</th></tr></thead><tbody><tr><td>Rate of identified mono-clones</td><td>67.3%</td><td>65.5%</td><td>80.0%</td></tr><tr><td>Rate of clones whose number of occurrence was greater than or equal to 4</td><td>10.6%</td><td>8.5%</td><td>0.9%</td></tr><tr><td>Maximum number of occurrence</td><td>9</td><td>9</td><td>4</td></tr></tbody></table>

## Conclusion

The origin of *S. cerevisiae* strains remains a controversial topic [9]. Some authors argue that *Saccharomyces* yeasts, although they are a minority, come from the surface of the grapes. Their presence depends on vine or sanitary quality of grapes. Other studies estimate that *S. cerevisiae* strains are linked to the environment of the winery. Other authors even suggest that the yeast has not an identified habitat. Yeasts appear in the vineyard and in the winery with different vectors such as insects or birds [10, 11], and yeast development becomes significantly quantifiable in winery that represents a favorable environment for yeast growth.

Do “winery” yeasts exist? Our data highlight only three yeast strains detected on three successive vintages, which is very low. Moreover, none of these strains was dominant, which minimizes the impact of a “resident” strain in the winery. Do “Terroir” yeasts exist? No strain was identified exclusively on one vine plot. Our data underlined also the absence of dominant strains during alcoholic fermentation. The fermentation process with indigenous flora is provided by a consortium of different yeasts, whose dynamics changes during the fermentation process.
References

1. Egli C, Edinger W, Mitrakul C et al (1998) Dynamics of indigenous and inoculated yeast populations and their effect on the sensory character of Riesling and Chardonnay wines. *J Appl Microbiol* **85**: 779-789.

2. Henick-Kling T, Edinger W, Daniel P et al (1998) Selective effects of sulfur dioxide and yeast starter culture addition on indigenous yeast populations and sensory characteristics of wine. *J Appl Microbiol* **84**: 865-876.

3. White T, Brown T, Lee TS et al (1990) Direct amplification and sequencing of fungal ribosomal RNA genes for phylogenetics, pp. 315-322. In: Inns MA, Gefland DH, Sninsky JJ & White TJ (eds), *PCR Protocols: A Guide to Methods and Applications*. Academic Press, Inc., New York.

4. Legras J-L, Karst F (2003) Optimization of Interdelta analysis for *Saccharomyces cerevisiae* strain characterization. *FEMS Microbiol Lett* **221**: 249-255.

5. Mateo J, Jimenez M, Huerta MT et al (1991) Contribution of different yeasts isolated from musts of Monastrell grapes to aroma of wines. *Int J Food Microbiol* **14**: 153-160.

6. Esteve-Zarzoso B, Belloch C, Uruburu F et al (1999) Identification of yeasts by RFLP analysis of the 5.8S rRNA gene and the two ribosomal internal transcribed spacers. *Int J Syst Bacteriol* **49**: 329-337.

7. 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.

8. Xufre A, Albergaria H, Inácio J et al (2006) Application of fluorescence in situ hybridization (FISH) to the analysis of yeast population dynamics in laboratory and winery grape must fermentations. *Int J Food Microbiol* **108**: 376-384.

9. Bisson F, Joseph L (2009) Yeasts, pp. 47-60. In: König H et al (eds.), *Biology of Microorganisms on Grapes, in Must and in Wine*. Springer-Verlag Berlin Heidelberg.

10. Mortimer R, Polsinelli M (1999) On the origins of wine yeast. *Res Microbiol* **150**: 199-204.

11. Clavijo A, Calderón I, Paneque P (2010) Diversity of *Saccharomyces* and non-*Saccharomyces* yeasts in three red grape varieties cultured in the Serrania de Ronda (Spain) vine-growing region. *Int J Food Microbiol* **143**: 241-245.
# Reducing alcohol levels in wines through adaptive evolution of wine yeast

V. TILLOY¹,², J. NOBLE¹,², A. ORTIZ-JULIEN², S. DEQUIN¹*

¹SPO, INRA, SupAgro, Université Montpellier, Montpellier, France

²Lallemand SAS, Blagnac, France

Evolved yeast – fermentation – ethanol yield – glycerol

## Introduction

Over the past two decades, the level of ethanol in wine has increased in most wine-producing regions, raising a number of issues related to consumer health, prevention policies, fermentation efficiency and wine sensorial quality. While different approaches have been proposed at all stages of the winemaking process to reduce this trend, microbial strategies have the advantage of being inexpensive and easy to implement. Many research efforts during the last two decades have therefore focused on developing novel yeast strains that produce less ethanol during wine fermentation [1]. Because the diversity between *S. cerevisiae* isolates for ethanol yield is very low [2, 3], the development of strains producing less ethanol requires a marked modification of yeast metabolism. This is a challenging task that requires considering a number of important constraints, such as maintaining carbon, redox and energetic balances, preserving wine quality and yeast performance.

Metabolic engineering approaches have been first used to generate strains producing less ethanol. Among various strategies, rerouting carbons towards glycerol has emerged as the best option to reduce ethanol yield [4-7]. As a proof of concept, wine yeast strains overproducing glycerol and 2,3-butanediol with a lower ethanol yield and without accumulation of unwanted by-products have been successfully constructed by metabolic engineering strategies [8]. These strains have the potential to decrease alcohol levels in wines by up to 3% (vol/vol).

Evolutionary engineering is a GM-free strategy that has proven its effectiveness to reshape yeast metabolism. The concept of adaptive evolution is that microorganisms tend to evolve their intrinsic characteristics to adapt to new conditions. During this process of evolution, random genetic mutations occur, and if a selection pressure is applied, strains having one or more beneficial mutations in the selective medium will dominate in the culture medium and can thus be selected. This approach is based on the extended cultivation of a strain in controlled selective medium to select for natural genetic variants having beneficial mutations under the conditions used. Since the emergence of mutations is a rare event, several hundred generations are usually necessary before observing an evolution, which can last several months.

We present here our recent work based on adaptive evolution for developing wine yeast strains with reduced ethanol yield. Different strategies to redirect carbons towards other by-products than ethanol were used to reprogram the metabolism of a commercial *S. cerevisiae* wine yeast strain, and resulted in the selection of an evolved strain producing less ethanol. These strategies and the characteristics of the wines produced at lab-scale and pilot-scale by the selected evolved strains will be discussed. Overall, this work shows that adaptive evolution is a valuable alternative to rational modification for reducing ethanol yield in wine yeasts.

## Materials and methods

### *Strain*

The commercial *S. cerevisiae* wine yeast strain Lalvin EC1118® was used as the ancestral strain. This strain is diploid and heterozygous.
### Adaptive evolution experiments

Long-term serial transfer procedure was performed in capped tubes (13-mL), each containing 5 mL of selective medium. The cultures were incubated at 28°C under agitation at 225 rpm. Such serial transfers were repeated for up to 450 generations. Every 50 generations, 1-mL samples of the evolving populations were collected and stored at 80°C in 20% glycerol for subsequent analysis. Duplicate evolution experiments were performed. The selective media used were (i) SD gluconate medium (0.67% nitrogen base, 2% gluconate) pH 6 (adjusted with a solution of NaOH 32%); (ii) YPD 8% glucose (1% Bacto yeast extract, 2% bactopeptone) containing KCl or sorbitol. KCl and sorbitol concentrations were sequentially increased from 1.25 M (KCl) or 1.5 M (sorbitol) to 2.4 M and thereafter maintained at 2.4 M; and (iii) SD containing methylglyoxal in concentrations sequentially increased from 30 to 200 mM.

### Fermentation medium and growth conditions

Evolved populations and strains of these populations were characterized in a synthetic must at laboratory scale (1 L). Batch fermentations were carried out in synthetic medium (MS), which mimics standard grape juice [9], containing 240-260 g/L glucose, in 1-L bioreactors equipped to maintain anaerobiosis, at 28°C, with permanent stirring. The CO₂ release was determined by automatic measurement of fermentor weight loss every 20 min.

Selected KCl-evolved strains were characterized in pilot-scale fermentations (1 hL) performed in cylindrical stainless-steel tanks with natural grape must from Syrah 2013, 2014 or Merlot 2013. Grape musts were flash pasteurized and stored at 2°C before fermentation.

### Metabolite quantification

Glucose, acetate, fructose, and glycerol were analyzed by HPLC using an Aminex HPX-87 ion exchange column. Acetoin and 2,3-butanediol were assayed by gas chromatography (GC). Volatile aroma compounds were extracted according to the method described by Rollero et al [10] and determined by GC/MS analysis after dichloromethane extraction. Extracts were analyzed with a Hewlett Packard (Agilent Technologies, Santa Clara, California, USA) 6890 gas chromatograph coupled to a HP 5973 mass spectrometer.

## Results and discussion

### Amplification of the pentose phosphate pathway

A first approach was based on the amplification of the pentose phosphate (PP) pathway [11]. Rerouting carbon towards the PP pathway was expected to reduce the availability of carbon for ethanol production. For each sugar molecule entering this pathway, there is a loss of 1 carbon out of 6 in the form of carbon dioxide. Using long-term batch cultivation of a commercial wine yeast strain on gluconate, a substrate poorly assimilated by *S. cerevisiae* and metabolized by the PP pathway, several variants with increased capacity to assimilate gluconate were obtained. The evolved strain Affinity™ECA5 had a 1.5-fold increase in flux through the PP pathway compared to the ancestral strain (17% of the carbon flux was channeled into the PP pathway versus 11%). As a result, this strain produced slightly less ethanol than the EC1118 strain, which exactly matched the additional excretion of 6.9 mmol of carbon as CO₂. However, such a decrease is expected to reduce the ethanol concentration in wine of about 2g/L, which is hardly detectable. A 5-fold increase in flux through the PP pathway would have been necessary to reduce the ethanol level of wine by approximately 1% (vol/vol). Nevertheless, the evolved strains had various other commercially relevant properties, including reduced acetic acid production and remarkably increased ester production [11, 12].

### Glycerol overproduction

We then considered strategies aiming at rerouting sugars towards the formation of glycerol. In addition to its key role in redox balance, glycerol is also the main compatible solute produced by yeast in response to hyperosmotic stress. This response is controlled by the HOG (high osmolarity glycerol pathway) signaling pathway. We therefore carried our evolution experiments using various agents known to stimulate the HOG pathway, such as methylglyoxal, sorbitol or potassium chloride [13]. After prolonged cultures on rich medium containing these stressors, we observed a marked overproduction of glycerol on KCl medium after 200 generations (Figure 1). This overproduction was observed simultaneously for 2 evolution replicates carried out in parallel on this medium.
<figure><img src="image_94.png" /><figcaption>**Fig. 1** - Glycerol production of EC1118® populations cultivated for 100 (green), 200 (blue) or 300 (red) generations in the presence of methylglyoxal (YNB medium), sorbitol (YPD) or KCl (YPD). (a) and (b) correspond to two evolution experiments conducted in parallel.</figcaption></figure>

Strains were isolated from the evolved population and characterized during wine fermentation on synthetic must. The evolved strains had a marked increased glycerol production and produced less ethanol. These strains also produced more succinic acid and 2,3-butanediol than the ancestral strain and did not accumulate undesirable organoleptic compounds, such as acetic acid, acetaldehyde, or acetoin. In a second step, this evolutionary strategy was coupled to a genetic approach in order to amplify the metabolic shift. A population of 150 haploid derivatives from the evolved strain was produced, and the haploid strains with the highest capacity to produce glycerol were selected and mated, generating intra-breed diploid strains [13].

Pilot-scale fermentations (1 hL) on Syrah using the improved evolved strain (H2) resulted in wines with 1.3% (vol/vol) lower ethanol level (9% reduction in ethanol yield), more glycerol (17.9 g/L versus 10.8 g/L for the ancestral strain), 2,3-butanediol, a polyol having no sensorial impact in wine (3.96 g/L versus 1.11 g/L) and succinic acid (1.5 g/L versus 1.3 g/L) compared to the ancestral strain (EC1118). By contrast, the level of acetic acid was markedly reduced (below 10 mg/L versus 0.5 g/L for the ancestral strain) and the production of other undesirable by-products such as acetaldehyde and acetoin remained very low. Other pilot-scale trials in different grape musts gave consistent results with 0.6 to 0.9% (vol/vol) less ethanol, depending on the initial sugar amount and the grape and conditions used, and marked reduction of the volatile acidity.

Conclusion

Today, health concerns and consumer expectations for wines that are easy to drink have made ethanol reduction a focal point of research. Due to the poor public acceptance of GM-based foods, non-GM strategies such as evolutionary engineering have been recently used for generating wine yeast with reduced ethanol yields. For the first time, unprecedented diversion of sugars towards the formation of glycerol and 2,3-butanediol without additional unwanted effects was obtained by adaptive evolution of a commercial wine yeast. This approach can lead to a reduction of up 1.3% (vol/vol) ethanol in wines and results in a similar, although less pronounced, flux redistribution towards glycerol and 2,3-butanediol than in a GM strain previously obtained. Pilot-scale trials confirmed the reduced alcohol yield and acidification properties of the evolved strain, a combination of traits of particular interest in the context of climate change.

Current work focuses on the elucidation of the genetic bases of the evolved phenotypes. Whole-genome sequencing, combined with gene expression and metabolic flux analysis, are key approaches for elucidating the genetic basis of improved phenotypes, enabling their transfer to other strains. The power of adaptive evolutionary strategies is expected to increase with the rapid development of these approaches.
References

1. Tilloy V, Cadière A, Ehsani M et al (2015) Reducing alcohol levels in wines through rational and evolutionary engineering of *Saccharomyces cerevisiae*. *Int J Food Microbiol* **213**: 49-58.

2. Camarasa C, Sanchez I, Brial P et al (2011) Phenotypic landscape of *Saccharomyces cerevisiae* during wine fermentation: evidence for origin-dependent metabolic traits. *PloS One* **6**: e25147.

3. Palacios García AT, Raginel F, Julien-Ortiz A (2007) Can the selection of *Saccharomyces cerevisiae* yeast lead to variations in the final alcohol degree of wines? *Aust NZ Grapegrow Winemak* **527**: 71-75.

4. Michnick S, Roustan JL, Remize F et al (1997) Modulation of glycerol and ethanol yields during alcoholic fermentation in *Saccharomyces cerevisiae* strains overexpressed or disrupted for GPD1 encoding glycerol 3-phosphate dehydrogenase *Yeast* **13**: 783-793.

5. Remize F, Roustan J-L, Sablayrolles J-M et al (1999) Glycerol overproduction by engineered *Saccharomyces cerevisiae* wine yeast strains leads to substantial changes in by-product formation and to a stimulation of fermentation rate in stationary phase. *Appl Environ Microbiol* **65**: 143-149.

6. Cambon B, Monteil V, Remize F et al (2006) Effects of GPD1 overexpression in *Saccharomyces cerevisiae* commercial wine yeast strains lacking ALD6 genes. *Appl Environ Microbiol* **72**: 4688-4694.

7. Varela C, Kutyna DR, Solomon MR et al (2012) Evaluation of gene modification strategies for the development of low-alcohol-wine yeasts. *Appl Environ Microbiol* **78**: 6068-6077.

8. Ehsani M, Fernández MR, Biosca JA et al (2009) Engineering of 2,3-butanediol dehydrogenase to reduce acetoin formation by glycerol-overproducing, low-alcohol *Saccharomyces cerevisiae*. *Appl Environ Microbiol* **75**: 3196-3205.

9. Bely M, Sablayrolles JM (1990) Automatic detection of assimilable nitrogen deficiencies during alcoholic fermentation in oenological conditions. *J Ferment Bioeng* **70**: 246-252.

10. 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.

11. Cadière A, Ortiz-Julien A, Camarasa C et al (2011) Evolutionary engineered *Saccharomyces cerevisiae* wine yeast strains with increased in vivo flux through the pentose phosphate pathway. *Metab Eng* **13**: 263-271.

12. Cadière A, Aguera E, Caillé S et al (2012) Pilot-scale evaluation of the enological traits of a novel, aromatic wine yeast strain obtained by adaptive evolution. *Food Microbiol* **32**: 332-337.

13. Tilloy V, Ortiz-Julien A, Dequin S (2014) Reduction of ethanol yield and improvement of glycerol formation by adaptive evolution of the wine yeast *Saccharomyces cerevisiae* under hyperosmotic conditions. *Appl Environ Microbiol* **80**: 2623-2632.
# Generation and characterization of improved wine microbes

V. JIRANEK<fnref n="1" />*, T. LICCIOLI<fnref n="2" />, A. BETTERIDGE<fnref n="2" />, K. SUMBY<fnref n="2" />, M. WALKER<fnref n="2" />, J. GARDNER<fnref n="2" />, J. SUNDSTROM<fnref n="2" />

Yeast – bacteria – strain optimisation – fermentation – directed evolution – high-throughput screening

## Introduction

Fermentations, whether the ethanolic or malolactic, are not always completed in a timely manner or with minimal winemaker intervention. Similarly, the contribution by the microbes to the sensory properties of the wine is not always predictable or desirable. The source of difficulties is often the limited capabilities of the microbes in terms of their fermentation robustness or their propensity for a given metabolic output. Our efforts centre on i) the generation or isolation of more robust organisms to ensure smooth and efficient fermentations and ii) predictable or desirable sensory contributions to the finished wine. With the aim of providing strains that can be readily adopted by the wine industry, the approaches taken have deliberately been non-recombinant and include classical strain isolation and screening, EMS mutagenesis and directed evolution. Recombinant methods have been utilised to better understand or confirm the genetic basis of desired attributes in superior strains as well as to identify genes and cellular processes linked to attributes of interest. This information in turn informs further non-recombinant strain generation efforts. A summary of some of the main activities undertaken by the group in recent years is presented below.

## Materials and methods

Most materials and methods have been reported previously. In the interests of brevity, the reader is directed to the appropriate references mentioned in the text. Details of any unpublished materials or methods are included in the text.

## Results and discussion

### New yeast through directed evolution

In our first publication detailing the application of directed evolution to wine yeast [1], we described a strain capable of completing fermentation of a defined medium (200 g/L sugar) in ~60% of the time of the parent strain (L2056). In subsequent work we have sought to define the basis of this dramatic improvement. Specifically, efforts have utilised whole genome sequencing and lipidomic [2] and metabolomic analysis. In addition, the suitability of the strain for real juice fermentation has been assessed at a number of scales in recent vintages. As an example, fermentations at 22 °C of 10-L aliquots of 2012 Chardonnay juice reiterated the superior performance of the evolved strain, which completed fermentation in ~350 hours compared to almost 500 hours for strain EC1118 and the parent, L2056 (Fig. 1).

Additional directed evolution experiments have been conducted to produce a suite of potentially useful yeast strains. Targets for improvement have included traits such as the enhanced utilisation of fructose, the predominant monosaccharide with glucose found in grape juice, or reduced ethanol yield. Unlike glucose, fructose utilisation is characteristically delayed, resulting in a shift from the near equimolar mix of these sugars to a predominance of fructose late in fermentation. Completion of fermentation is often predicated on the ability of the inoculated yeast to ferment remaining fructose, a sugar that most *Saccharomyces cerevisiae* strains inherently utilise less effectively, and whilst experiencing harsh conditions (high ethanol content, low nutrition, declining viability, etc).

<footnote n="1">¹Australian Research Council Training Centre for Innovative Wine Production</footnote>

<footnote n="2">²Department of Wine and Food Science, University of Adelaide,
PMB 1 Glen Osmond, SA 5064, Australia</footnote>
<figure><img src="image_95.png" /><figcaption>**Fig. 1** - Evaluation of evolved strain FM16-C7 H in 10-L fermentations (triplicate, 22 °C) of 2012 Chardonnay juice from Padthaway, South Australia. Juice parameters: 287 mg L⁻¹ YAN, 11.8 Bé, pH 3.85, 4.5 g L⁻¹ TA, 7 mg L⁻¹ free SO₂.</figcaption></figure>

Strain AWRI 796 was cultured continuously on limited amounts of fructose (~5 g/L) as sole carbon source [3]. At regular intervals the mixed culture and individual isolates were screened in micro-fermentations using sacrificial sampling (i.e. 5-6 replicate plates prepared, and one destructively sampled at each chosen time point for enzymatic residual sugar determination [Boehringer Mannheim; Cat: 139106]). Figure 2 shows the fermentation performance of several isolates from such a screening exercise. Fermentation by the parent strain (AWRI 796) required approximately 80 hours. The mixed, evolved population took a similar amount of time. The individual isolates spread in their fermentation durations between ~65 and 80 hours. The fastest isolate being “mutant 43”.

<figure><img src="image_96.png" /><figcaption>**Fig. 2** - Evaluation of candidates evolved for enhanced 'fructophilicity'. Panels show the removal of glucose and fructose (TOTAL SUGAR). The kinetics of removal of individual sugars from the same fermentations is shown in the bottom panels. See text and [3] for details.</figcaption></figure>

Examination of the fermentation kinetics for each sugar indicated that the basis for the accelerated fermentation by “mutant 43” was not an increase in the utilisation of monosaccharides generally, but rather was due to a more rapid utilisation of fructose specifically. These results indicate the effectiveness of the directed evolution strategy and provide candidate strains for further evaluation, characterisation and as breeding material (i.e. to incorporate rapid fructose utilisation into other strain backgrounds).