Next Article in Journal
Is There Any Difference in the In Situ Immune Response in Active Localized Cutaneous Leishmaniasis That Respond Well or Poorly to Meglumine Antimoniate Treatment or Spontaneously Heal?
Previous Article in Journal
The Mystery of Piezophiles: Understudied Microorganisms from the Deep, Dark Subsurface
Previous Article in Special Issue
Characterization of Zygosaccharomyces lentus Yeast in Hungarian Botrytized Wines
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Microorganisms
Volume 11
Issue 7
10.3390/microorganisms11071630
microorganisms-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Endogenous CO2 Overpressure Effect on Higher Alcohols Metabolism during Sparkling Wine Production

by
María del Carmen González-Jiménez
1,
Juan Carlos Mauricio
1,*,
Jaime Moreno-García
1,
Anna Puig-Pujol
2,
Juan Moreno
1 and
Teresa García-Martínez
1
1
Department of Agricultural Chemistry, Edaphology and Microbiology, Agrifood Campus of International Excellence CeiA3, University of Cordoba, Ctra. N-IV-A, km 396, 14014 Cordoba, Spain
2
Department of Enological Research, Institute of Agrifood Research and Technology, Catalan Institute of Vine and Wine (IRTA, INCAVI), Plaça Àgora 2, 08720 Barcelona, Spain
*
Author to whom correspondence should be addressed.
Microorganisms 2023, 11(7), 1630; https://doi.org/10.3390/microorganisms11071630
Submission received: 28 April 2023 / Revised: 30 May 2023 / Accepted: 19 June 2023 / Published: 22 June 2023
(This article belongs to the Special Issue Wine Yeast 3.0)
Browse Figures
Review Reports Versions Notes

Abstract

:
Higher alcohols produced by yeast during the fermentation of sparkling wine must have the greatest impact on the smell and taste of wine. At present, the metabolic response to methanol and higher alcohols formation of Saccharomyces cerevisiae under endogenous CO2 overpressure has not been fully elucidated. In this work, a proteomics and metabolomics approach using a OFFGEL fractionator and the LTQ Orbitrap for the protein identification, followed by a metabolomic study for the detection and quantification of both higher alcohols (GC-FID and SBSE-TD-GC-MS) and amino acids (HPLC), was carried out to investigate the proteomic and metabolomic changes of S. cerevisiae in relation to higher alcohols formation under a CO2 overpressure condition in a closed bottle. The control condition was without CO2 overpressure in an open bottle. Methanol and six higher alcohols were detected in both conditions, and we have been able to relate to a total of 22 proteins: 15 proteins in the CO2 overpressure condition and 22 proteins in the control condition. As for the precursors of higher alcohols, 18 amino acids were identified in both conditions. The metabolic and proteomic profiles obtained in both conditions were different, so CO2 overpressure could be affecting the metabolism of higher alcohols. Furthermore, it was not possible to establish direct correlations in the condition under CO2 overpressure; however, in the condition without pressure it was possible to establish relationships. The data presented here can be considered as a platform that serves as a basis for the S. cerevisiae metabolome–proteome with the aim of understanding the behavior of yeast under conditions of second fermentation in the production of sparkling wines.

1. Introduction

The quality of alcoholic beverages such as wine is determined by the production of volatile aromatic compounds during fermentation [1,2]. The most important volatile aromatic compounds produced by yeast are esters, fatty acids, and higher alcohols, and their syntheses involve a highly interconnected metabolic network [3]. These compounds and their derived metabolites contribute significantly to the taste and aroma of wine [4,5,6,7,8,9]. Additionally, its amount depends on the variety of the grape, the type of yeast, the composition of the must, the fermentation temperature, winemaking practices, and the aging time [10,11,12].
Higher alcohols are quantitatively the largest group of flavor-contributing compounds in alcoholic beverages, which are present at concentrations of 10 mg/L or higher in wine [13]. Depending on the concentration in which the fusel alcohols are found, they can have a positive or negative sensory impact. Its concentrations range between 100 and 400 mg/L [4]; concentrations above 400 mg/L give a pungent solvent-like aroma to the wine, while concentrations of less than 300 mg/L are often described as imparting a desirable fruity characteristic [14,15]. Propanol, butanol, and isobutanol provide an alcoholic odor, active amyl alcohol and isoamyl alcohol have a marzipan or banana aroma, while tyrosol and 2-phenylethanol contribute with floral and honey aromas, respectively [4].
In general, the synthesis of higher alcohols takes place from the metabolism of amino acids via the Ehrlich pathway in the mitochondria and in the cytoplasm [16] or from the anabolic pathway from glucose via pyruvate [15]. Both synthesis pathways converge in the formation of an α-keto acid that can be decarboxylated and reduced to generate a higher alcohol. The Ehrlich pathway directly involves the amino acids leucine, valine, isoleucine, phenylalanine, tyrosine, and tryptophan. So, the formation of these alcohols depends on the concentration of nitrogen and amino acids [4,17,18]. The metabolic network involved in the production of these compounds during still wine fermentation has been well studied [3] but less so during second fermentation in closed bottles for sparkling wine production. The relationship between volatile compounds and the proteome of the yeast Saccharomyces cerevisiae is complex. In recent years, our research group has focused on the sensory and metabolic study of sparkling wines [3,19,20,21,22]. Recently, the metabolism of esters was studied, and the first metabolome–proteome relationships of S. cerevisiae were established during the second fermentation of sparkling wines [23]. Continuing with this purpose, the main objective of this work was to study the metabolism of higher alcohols and their relationship with the S. cerevisiae proteome during the second fermentation of sparkling wine production, due to the great contribution of these volatile compounds to the organoleptic properties of the wine and, therefore, to its final quality. In addition, this work has been complemented by the study of the CO2 overpressure that yeasts encounter during the production process and how it affects the metabolism of higher alcohols.

2. Materials and Methods

2.1. Microorganism, Grape Must, and Fermentation Conditions

In this work, the typical S. cerevisiae P29 strain (CECT11770) in the elaboration of sparkling wines was used. It was isolated from the designation of origin (DO) of the Penedès (Barcelona, Spain). To obtain a starter culture with a high concentration of yeast cells, a medium rich sugar was used—a pasteurized must of the Macabeo grape variety (174.9 g/L of sugar, 18.5° Bx, 3.6 g/L of total acidity and 3.43 pH) using gentle agitation of 100 rpm for 5 days and at 21 °C. When an ethanol content of 10.39% (v/v) was reached, yeast cells were used as inoculum for second fermentation. To carry out the second fermentation in the bottle using the traditional method, a base wine (Macabeo:Chardonnay (6:4), 10.21% (v/v) of ethanol, 0.3 g/L of sugar, pH 3.29, 5.4 g/L of total acidity, and 0.21 g/L of volatile acidity) previously made using traditional alcoholic fermentation was used, to which sugar (21 g/L) and 1.5 × 106 yeast cells/mL were added. The second fermentation was carried out in a thermostatic chamber (Grand Cru WK/GWK 708, Liebherr, Baden, Germany) at 14 °C in bottles with a volume of 750 mL.
The bottles were divided into two groups to study the effect of CO2 overpressure on the metabolism of methanol and higher alcohols. For the CO2 overpressure condition, P (+), half of the bottles were hermetically sealed with a stopper and with a metal crown cap. The rest of the bottles were closed with a perforated shutter constituting the condition without CO2 overpressure, P (-), and used as a control condition.
For greater monitoring and control of the second fermentation, two sampling points were taken: at middle of fermentation (MF) when the CO2 overpressure in sealed bottles, measured using a pressure gauge (Mei-Heca Group, Zaragoza, Spain), reached around 3.3 atmospheres (atm) (day 8), and at the end of second fermentation (EF), when CO2 overpressure inside the bottles reached 6.5 atm (day 26). Residual sugars were 0.3 g/L in both cases (P (+) and P (-)). The two sampling moments were chosen based on the growth curve and metabolism of the yeasts: when yeasts are in the middle of the exponential phase of growth and express their maximum metabolism (MF samples) and when the yeasts are in the stationary phase, in a situation of maximum stress, without fermentable sugars and with the maximum pressure exerted by CO2 inside the bottle (EF).

2.2. Metabolic Analysis

2.2.1. Analysis of Methanol and Higher Alcohols

Two different chromatographic methods were used to identify the largest possible number of higher alcohols. Methanol, 1-propanol, isobutanol, isoamylic alcohols, and 2-phenylethanol were detected using the direct injection method in an Agilent 6890 Series II gas chromatograph (Agilent Technologies, Palo Alto, CA, USA) equipped with a fused silica capillary column CP-WAX 57 CB (60 m long, 0.25 mm internal diameter, and 0.4 μm film thickness, from Agilent J&W, Santa Clara, CA, USA) attached to an FID detector. A solution of 1 g/L of 4-methyl-2-pentanol in pure ethanol was used as an internal standard. The chromatographic conditions are described in detail in Peinado et al. (2004) [24].
Hexanol and 2-ethyl-1-hexanol were analyzed by extraction by magnetic stir bar stirring, followed by thermal desorption and gas chromatography coupled to a mass spectrometer (SBSE-TD-GC-MS). The SBSE-TDU-GC-MS analytical platform consisted of an Agilent-7890A chromatograph, an MSD 5975 mass detector (Agilent Technologies, Wilmington, DE, USA), and the Gerstel thermal desorption unit (TDU) coupled to a CIS-4 injection system, Agilent, and an HP-5 capillary column 30 m long, 0.25 mm internal diameter and 0.25 μm film thickness. This method consisted of introducing 1 mL of sparkling wine sample into a 10 mL vial with a 12% hydroalcoholic solution in ethanol (v/v) buffered to pH 3.5, together with 0.1 mL of internal standard solution (0.446 mg/L of ethyl nonanoate in ethanol). The samples were then shaken at 1200 rpm for 100 min at 20 °C with a stirrer bar coated with polydimethylsiloxane (PDMS) (Twister) with 0.5 mm film thickness and 10 mm length, by Gerstel (GmbH, Mülheim an der Rühr, Germany). The Twister was removed from the vial, rinsed with distilled water, dried, and finally transferred to a thermal desorption unit (TDU) of Gerstel for analysis by GC/MS. The chromatographic conditions are detailed in Vararu et al. (2016) [25].

2.2.2. Analysis of Amino Acids

The determination of amino acids was performed using high-performance liquid chromatography, HPLC (Agilent Technologies, mod. 1260 Infinity, Santa Clara, CA, USA), in the reverse phase by precolumn derivatization reaction with orthophthaldehyde (OPA) in the presence of mercaptoethanol. The liquid chromatograph used consisted of a 1525 pump system, a 2475 fluorescence detector, 356 nm excitation and 445 nm emission wavelength (λ), and a 717 automatic injector (Waters, Milford, Massachusetts, USA). Equipment monitoring and data acquisition and processing were performed through Waters’ Breeze program. Separations were carried out using a Nova-Pack C18 column (3.9 × 150 mm) from Waters. OPA was used as a derivatizing reagent; 750 mg of OPA was dissolved in 5 mL of methanol and 0.5 mL of 2-mercaptoethanol was added. The protocol was followed, and the chromatographic conditions used are detailed in Pripis-Nicolau et al. (2001) [26].

2.3. Proteomic Analysis

The yeast cells of each condition and in each time were collected by 4500× g for 10 min in a Routine-38 centrifuge at 4 °C. The pellet that was formed was washed twice with sterile cold distilled water. For the extraction of the proteins, the yeast cells were broken using a mechanical technique in Vibrogen Cell Mill V6 (Edmund Bühler, Bodelshausen, Germany) with glass beads of 500 μm in diameter (Sigma, St. Louis, MO, USA). These cells were resuspended in 1 mL of extraction buffer (100 mM Tris-HCl pH 8, 0.1 mM EDTA, 2 mM DTT, and 1 mM PMSF) and a cocktail of protease inhibitors provided by Roche. Then, 10% trichloroacetic acid and 4 volumes of cold acetone were added to the obtained supernatant, so that the proteins precipitated. It was left to incubate overnight at −20 °C. After the incubation period, the samples were centrifuged at 16,000× g for 30 min and the resulting pellet was resuspended using solubilization buffer (8 M urea, 2 M thiourea, 4% CHAPS, and 1% dithiothreitol). Later, the protein concentration was estimated using the Bradford test (1976) [27] to subsequently proceed to protein analysis. For this, 500 µg of total protein from each condition and replica was loaded into the tray of the well of the OFFGEL 3100 fractionator from Agilent Technologies (Palo Alto, CA, USA). Once the proteins were separated according to their isoelectric point, the fractions were collected from each well and their identification was carried out on an LTQ Orbitrap XL (Thermo Fisher Scientific, San Jose, CA, USA) mass spectrometer equipped with a nano-LC Ultimate 3000 system (Dionex, Germering, Germany), at the Central Support Service to Research (SCAI) of the University of Córdoba. The proteins have to be digested with trypsin beforehand. The analysis techniques described above have been published in several articles by our research team [23,28,29].
Finally, the proteins identified were quantified following the Exponentially Modified Protein Abundance Index, EmPAI, a method described by Ishihama et al. (2005) [30].

2.4. Statistical Analysis

All experiments were carried out in triplicate. The data was previously normalized with the square root and then scaled using the Pareto method, to avoid the differences introduced by the units of measurement [31].
The data were processed using the Statgraphics Centurion XVI.II statistical package, from STSC, Inc. (Rockville, MD, USA). The statistical tests applied were the ANOVA and Fisher’s test for the establishment of homogeneous groups (HG), with a significance level of p ≤ 0.05; a principal component analysis (PCA) was performed. In addition, a correlation analysis to establish significant relationships between metabolites and proteins was carried out according to the Metaboanalyst database (https://www.metaboanalyst.ca/, accessed on 30 May 2023).

3. Results

A total of methanol and six higher alcohols were identified in both study conditions; these were 1-propanol, isobutanol, isoamyl alcohols (2-methyl-1-butanol and 3-methyl-1-butanol), 2-phenylethanol using GC-FID, and hexanol and 2-ethyl-1-hexanol using the SBSE-TD-GC-MS technique. The data are shown in Table 1. Most of the identified higher alcohols are synthesized through the Ehrlich pathway from amino acid precursors [15], while methanol can come from the hydrolysis of pectins in the grape cell walls [32,33].
In this work, the metabolic profile obtained from the evolution of the concentration of methanol and higher alcohols was different under each condition. Under CO2 overpressure conditions, a decrease in the amount of methanol, 1-propanol, isobutanol, isoamyl alcohols, and 2-phenylethanol was obtained. Hexanol concentration remained constant during the second fermentation. Under the control condition, the concentrations remained constant except for propanol that increased and 1-hexanol, which decreased. The reduction in the content of 1-hexanol may have occurred due to the formation of the corresponding ester, hexyl acetate, as a product of yeast metabolism [33,34]. Tao et al. (2008) [35] found that the main higher alcohols in wine are isobutanol, phenylethanol, and isoamyl alcohol. These results obtained differ from those obtained by other authors who observe an increase in propanol, isoamyl alcohol, and isobutanol [35,36], while others obtain a decrease in these compounds [37,38]. These differences in concentrations could be due to adsorption–desorption processes in the cell walls during the aging of the wine in contact with the lees [39,40], to variations in the intracellular composition of the substrates and intermediates involved in its synthesis [41], or could be caused by the effect of CO2 overpressure, due to conditions of CO2 overpressure, P (+); the concentrations obtained decreased with respect to the control condition, P (-).
Isoamyl alcohols and 2-phenylethanol show contents, respectively, that are ten- and five-times higher than their Odor Perception Threshold, which is consistent with their classification as powerful active odorants of sparkling and still wine aromas [21]. In accordance with these authors, the isoamyl alcohols, whose aroma descriptors are wine, whiskey, ripe fruit, and nail polish, contribute in a higher extension to smell intensity than it does to wine aroma quality. Nevertheless, 2-phenyl-ethanol, with rose or honey smells, contributes pleasant notes to the wine aroma. Because of this difference, the aromatic profile of the final wine obtained could be different; total production of higher alcohols increases as concentrations of the corresponding amino acids increase. Regarding this, a preliminary sensorial analysis made by the tasters of our research team classifies these wines as a good example of the traditional sparkling cava wines from Spain. Regarding this, a preliminary organoleptic analysis made by tasters of our research team classifies these wines as representatives of the traditional sparkling wines from Spain.
With the aim of contrasting this hypothesis and relating the total production of higher alcohols to the concentrations of their corresponding amino acids, the amino acid precursors and the S. cerevisiae proteome were analyzed under both conditions.
A total of eighteen amino acids were identified and quantified during the second fermentation using the OPA method. The data are shown in Table 2.
Under CO2 overpressure conditions, an increase in the concentration of all the amino acids studied, except valine and methionine which did not show significant differences, was reported. Despite the amino acids alanine, arginine, asparagine, aspartate, glutamate, glutamine, and serine nitrogen sources being preferred by yeasts [42], an increase in the concentration of these amino acids was observed. In this condition, significant inverse correlations could be established between the amino acids identified with isoamyl alcohol, methanol, and propanol (Figure 1A). By increasing the concentration of amino acids, there is a decrease in the concentration of these alcohols. However, Belda et al. (2017) [43] reported that as the concentration of amino acids increases, that of alcohols increases.
Under the condition without CO2 overpressure, the amount of GABA, glutamine, asparagine, threonine, glycine, serine, isoleucine, aspartate, and tryptophan increased during the second fermentation, while the concentration of leucine and glutamate decreased (Figure 2). This release of amino acid toward the end of the fermentation and during aging is a widely documented phenomenon associated with the progressive loss of viability and later autolysis of the yeast [44]. The yeast may be using these amino acids to continue the development of other biological processes such as protein synthesis. Due to the increased concentration of alcohol in the medium, yeasts may be activating their transcription and translation machinery to try to cope with cell death and autolysis, allowing the survival of younger yeast cells by recycling the compounds released by dead yeasts [45].
In this condition, a direct and significant correlation was established between threonine and propanol. Meanwhile, inverse relationships were obtained between 2-phenylethanol and tyrosine; hexanol and GABA; glutamine, asparagine, threonine, glycine, and serine; and 2-ethyl-1-hexanol and alanine (Figure 1B).
On the other hand, alcohols were related to a total of 22 proteins involved in their metabolism: 15 proteins in the CO2 overpressure condition, P (+), and 22 proteins in the control condition, P (-). The data are shown in Table 3. Regarding the proteins involved in the metabolism of these alcohols, a lower number of proteins were identified under the CO2 overpressure condition than under the nonoverpressure condition. Under the CO2 overpressure condition, P (+), fourteen proteins were identified in the middle (MF) and seven proteins at the end of the fermentation (EF). Meanwhile, under nonoverpressure conditions, P (-), 22 and 14 proteins were identified in MF and EF, respectively.
In general, under the CO2 overpressure condition, the protein content of the identified proteins was lower with respect to the condition without CO2 overpressure; in addition, during the second fermentation, a decrease in the protein content was reported, except for Adh1p, Bat1p, Pdc1p, and Pdc5p. These proteins could be responsible for the increase in the concentration of higher alcohols observed at the end of the second fermentation. These proteins catalyze a different stage in the Ehrlich pathway, and they are enough to carry out the final stage in the formation of fusel alcohols in S. cerevisiae. In the catabolism of the amino acids phenylalanine, tryptophan, valine, isoleucine, and leucine, the Pdc1p, Pdc5p, or Pdc6p proteins perform the decarboxylation [46]. Furthermore, direct correlations were established between the proteins involved in the metabolism of higher alcohols, isoamyl alcohol, and methanol except for these four proteins (Figure 1A).
On the other hand, under the CO2 non-overpressure condition, the proteins Adh1p, Ald4p, Ald6p, Aro8p, Aro9p, and Pdc1p increased their protein content in EFP (-). On the contrary, the rest of the proteins decreased their content. As in the previous condition, proteins belonging to each stage of the formation of fusel alcohols have been identified. Because no increase in the concentration of higher alcohols was observed, as expected, the formation of fusel acids could take place, since the Ald4p and Ald6p proteins were identified. Ald4p and Ald6p are responsible for reducing the aldehyde of fusel to a fusel acid. These proteins were directly correlated with propanol and inversely with hexanol (Figure 1B). This could suggest the possible formation of propanol via the Ehrlich pathway from threonine.
It should be noted that in both study conditions the Adh2 and Thi3p proteins were not identified, which have been related to a reduced production of acetaldehyde or fusel alcohols, respectively [47]. These data may also be indicative of the reduction in alcohols found in our work.
Generally, the amine group of most amino acids enters the central nitrogen metabolism through transamination, thereby releasing the carbon skeleton as a 2-oxoacid. These compounds can directly enter the central carbon metabolism, while the 2-oxoacids from aromatic and branched-chain amino acids can decarboxylate through the so-called Ehrlich pathway and produce the corresponding aldehyde [48]. Depending on the availability of oxygen, the aldehyde can be oxidized to a carboxylic acid, called fusel acid, or reduced to a fusel alcohol [49]. Both acids and fusel alcohols and their derivatives contribute greatly to the flavor of the wine.
To carry out the analysis of principal components (PCA), the number of variables was reduced according to the multiple analysis of homogeneous groups, staying with those that presented HG ≥ 3. The resulting PCA (Figure 3) explains 96.84% of the total variance. Component 1 (PC 1) explains 43.99% and helps to discriminate the sampling times, that is, half of the fermentation with negative values and the end of the second fermentation in positive values. This component is strongly correlated with Adh5p (−0.262423) for half the fermentation and asparagine (0.266257), tryptophan (0.26102), and glutamine (0.259309) for the end of fermentation. These have been surrounded by green color. Component 2 (PC 2), in yellow color, explains 29.30% of the variability and distinguishes, with positive values, the end of the fermentation with pressure and, in negative values, the end of the fermentation without pressure; four proteins contribute to this component, Ald2p (−0.326691), Ald4p (−0.319366), Ald6p (−0.319599), and Aro8p (−0.302044), for the condition without CO2 overpressure and the amino acid leucine (0.292128) and Pdc5p (0.276056) for the CO2 overpressure condition at the end of the second fermentation. Meanwhile 1-propanol (0.311078), Adh7p (−0.312687), Ald5p (−0.317511), and Bat2p (−0.317433) are related to component 3, which explains 23.55% of the variance. This component differentiates half of the fermentation with pressure in positive values and the fermentation half without CO2 overpressure with negative values. This component has been represented with the red color.

4. Conclusions

This work is focused on establishing correlations between the metabolite of higher alcohols and the proteome of S. cerevisiae during the second fermentation in the production of Spanish sparkling wine (cava). We investigated the effect of CO2 overpressure in a typical yeast to produce sparkling wines, through the study of the metabolism of higher alcohols, due to its influence on the organoleptic properties of sparkling wines.
In general, under the CO2 overpressure condition, the concentration of the higher alcohols decreased during the second fermentation, while in the condition without pressure the concentration remained constant for most of these compounds. In protein terms, in the pressure condition, a smaller number of proteins were identified than in the non-pressure condition.
It was not possible to establish significant direct correlations between amino acids, alcohols, and proteins in the condition under CO2 overpressure; on the contrary in the condition without overpressure, propanol may be synthesized through threonine via the Ehrlich pathway.
Regarding the CO2 overpressure to which the yeasts are subjected, it could be concluded that the CO2 overpressure affects the metabolism of the alcohols in the yeast.
The wines did not show sensorial defects and were positively evaluated by the researchers.
The results presented in this work are the beginning in the search for metabolome-proteome relationships of the yeast S. cerevisiae during the production of Spanish sparkling wine (cava). However, additional research involving transcriptomic and genetic analyzes would be necessary to reach more robust conclusions.

Author Contributions

M.d.C.G.-J.: Investigation, Methodology, and Writing—original draft. J.M.-G.: Data curation, Formal analysis, Investigation, Methodology, Validation, and Writing—original draft. J.C.M.: Conceptualization, Formal analysis, Funding acquisition, Investigation, Supervision, and Writing—review and editing. J.M.: Supervision, Validation, and Visualization. A.P.-P.: Methodology. T.G.-M.: Formal analysis, Investigation, Methodology, Supervision, and Writing—review and editing. All authors have read and agreed to the published version of the manuscript.

Funding

This research was granted funding from the “Consejería de Economía, Conocimiento, Empresas y Universidad”, Programa Operativo FEDER de Andalucía 2014–2020 (T García-Martínez), Reference 1380480-R.

Institutional Review Board Statement

“Not applicable” for studies not involving humans or animals.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data are available upon request to the authors.

Acknowledgments

The kind help of the staff at the Central Research Support Service (SCAI) of the University of Cordoba with the protein analysis is gratefully acknowledged.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Zhu, F.; Du, B.; Li, J. Aroma compounds in wine. In Grape and Wine Biotechnology; Morata, A., Loira, I., Eds.; Intech Open Journals: Rijeka, Croatia, 2016. [Google Scholar]
  2. de Castilhos, M.B.; de Queiroga, A.P.; Sabino, L.L.; dos Santos Júnior, J.R.; Santiago-Urbina, J.A.; Nolasco-Cancino, H.; Ruíz-Terán, F.; Del Bianchi, V.L. Flavor Biochemistry of Fermented Alcoholic Beverages. In Natural Flavours, Fragrances, and Perfumes: Chemistry, Production, and Sensory Approach; Wiley: Hoboken, NJ, USA, 2023; pp. 91–114. [Google Scholar]
  3. Rollero, S.; Mouret, J.R.; Bloem, A.; Sanchez, I.; Ortiz-Julien, A.; Sablayrolles, J.M.; Dequin, S.; Camarasa, C. Quantitative13C-isotope labelling-based analysis to elucidate the influence of environmental parameters on the production of fermentative aromas during wine fermentation. Microb. Biotechnol. 2017, 10, 1649–1662. [Google Scholar] [CrossRef] [PubMed]
  4. Lambrechts, M.G.; Pretorius, I.S. Yeast and its Importance to Wine Aroma—A Review. S. Afr. J. Enol. Vitic. 2000, 21, 97–129. [Google Scholar] [CrossRef] [Green Version]
  5. Van Rooyen, P.C.; De Wet, P.; Van Wyk, C.J.; Tromp, A. Chening Blanc wine volatiles and intensity of guava-like flavour. S. Afr. J. Enol. Vitic. 1982, 3, 1–7. [Google Scholar]
  6. Gonzalez-Ramos, D.; Gonzalez, R. Genetic determinants of the release of mannoproteins of enological interest by Saccharomyces cerevisiae. J. Agric. Food Chem. 2006, 54, 9411–9416. [Google Scholar] [CrossRef] [PubMed]
  7. Martínez-Rodríguez, A.J.; Polo, M.C.; Carrascosa, A.V. Structural and ultrastructural changes in yeast cells during autolysis in a model wine system and in sparkling wines. Int. J. Food Microbiol. 2001, 71, 45–51. [Google Scholar] [CrossRef] [PubMed]
  8. Penacho, V.; Valero, E.; Gonzalez, R. Transcription profiling of sparkling wine second fermentation. Int. J. Food Microbiol. 2012, 153, 176–182. [Google Scholar] [CrossRef] [PubMed]
  9. Molina, A.M.; Guadalupe, V.; Varela, C.; Swiegers, J.H.; Pretorius, I.S.; Agosin, E. Differential synthesis of fermentative aroma compounds of two related commercial wine yeast strains. Food Chem. 2009, 117, 189–195. [Google Scholar] [CrossRef]
  10. Rankine, B.C. Formation of higher alcohols by wine yeasts, and relationship to taste thresholds. J. Sci. Food Agric. 1967, 18, 583–589. [Google Scholar] [CrossRef]
  11. Mauricio, J.C.; Moreno, J.; Zea, L.; Ortega, J.M.; Medina, M. The effects of grape must fermentation conditions on volatile alcohols and esters formed by Saccharomyces cerevisiae. J. Sci. Food Agric. 1997, 75, 155–160. [Google Scholar] [CrossRef]
  12. Bozdogan, A.; Canbaç, A. The effect of yeast strain, immobilisation and aging time on the amount of free amino acids and amino acids in peptides of sparkling wines obtained from cv. Dimrit Grapes. S. Afr. J. Enol. Vitic. 2012, 33, 257–263. [Google Scholar]
  13. Amerine, A.M.; Berg, H.V.; Kunkee, R.E.; Ough, C.S.; Singleton, U.L.; Weeb, A.D. The Technology of Winemaking; AVI Technical Books Inc.: Westport, CT, USA, 1980. [Google Scholar]
  14. Swiegers, J.H.; Pretorius, I.S. Yeast modulation of wine flavor. Adv. Appl. Microbiol. 2005, 57, 131–175. [Google Scholar] [PubMed]
  15. Dickinson, J.R.; Lanterman, M.M.; Danner, D.J.; Pearson, B.M.; Sanz, P.; Harrison, S.J.; Hewlins, M.J.E. A 13C Nuclear Magnetic Resonance Investigation of the Metabolism of Leucine to Isoamyl Alcohol in Saccharomyces cerevisiae. J. Biol. Chem. 1997, 272, 26871–26878. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  16. Hazelwood, L.A.; Daran, J.-M.; van Maris, A.J.; Pronk, J.T.; Dickinson, J.R. The Ehrlich Pathway for Fusel Alcohol Production: A Century of Research on Saccharomyces cerevisiae Metabolism. Appl. Environ. Microbiol. 2008, 74, 2259–2266. [Google Scholar] [CrossRef] [Green Version]
  17. Badura, J.; Medić, M.; van Wyk, N.; Krause, B.; Semmler, H.; Brezina, S.; Pretorius, I.S.; Rauhut, D.; von Wallbrunn, C. Synthesis of Aroma Compounds as a Function of Different Nitrogen Sources in Fermentations Using Non-Saccharomyces Wine Yeasts. Microorganisms 2022, 11, 14. [Google Scholar] [CrossRef]
  18. Su, Y.; Heras, J.M.; Gamero, A.; Querol, A.; Guillamón, J.M. Impact of nitrogen addition on wine fermentation by Saccharomyces cerevisiae strains with different nitrogen requirements. J. Agric. Food Chem. 2021, 69, 6022–6031. [Google Scholar] [CrossRef]
  19. Martínez-García, R.; García-Martínez, T.; Puig-Pujol, A.; Mauricio, J.C.; Moreno, J. Changes in sparkling wine aroma during the second fermentation under CO2 pressure in sealed bottle. Food Chem. 2017, 237, 1030–1040. [Google Scholar] [CrossRef] [PubMed]
  20. Martínez-García, R.; Roldán-Romero, Y.; Moreno, J.; Puig-Pujol, A.; Mauricio, J.C.; García-Martínez, T. Use of a flor yeast strain for the second fermentation of sparkling wines: Effect of endogenous CO2 over-pressure on the volatilome. Food Chem. 2019, 308, 125555. [Google Scholar] [CrossRef] [PubMed]
  21. Martínez-García, R.; Mauricio, J.C.; García-Martínez, T.; Peinado, R.A.; Moreno, J. Towards a better understanding of the evolution of odour-active compounds and the aroma perception of sparkling wines during ageing. Food Chem. 2021, 357, 129784. [Google Scholar] [CrossRef]
  22. Martínez-García, R.; Moreno, J.; Bellincontro, A.; Centioni, L.; Puig-Pujol, A.; Peinado, R.A.; Mauricio, J.C.; García-Martínez, T. Using an electronic nose and volatilome analysis to differentiate sparkling wines obtained under different conditions of temperature, ageing time and yeast formats. Food Chem. 2020, 334, 127574. [Google Scholar] [CrossRef]
  23. González-Jiménez, M.D.C.; Moreno-García, J.; García-Martínez, T.; Moreno, J.J.; Puig-Pujol, A.; Capdevilla, F.; Mauricio, J.C. Differential analysis of proteins involved in ester metabolism in two Saccharomyces cerevisiae strains during the second fer-mentation in sparkling wine elaboration. Microorganisms 2020, 8, 403. [Google Scholar] [CrossRef] [Green Version]
  24. Peinado, R.A.; Moreno, J.A.; Muñoz, D.; Medina, M.; Moreno, J. Gas Chromatographic Quantification of Major Volatile Compounds and Polyols in Wine by Direct Injection. J. Agric. Food Chem. 2004, 52, 6389–6393. [Google Scholar] [CrossRef] [PubMed]
  25. Vararu, F.; Moreno-García, J.; Zamfir, C.-I.; Cotea, V.V.; Moreno, J. Selection of aroma compounds for the differentiation of wines obtained by fermenting musts with starter cultures of commercial yeast strains. Food Chem. 2016, 197, 373–381. [Google Scholar] [CrossRef] [PubMed]
  26. Pripis-Nicolau, L.; De Revel, G.; Marchand, S.; Anocibar Beloqui, A.; Bertrand, A. Automated HPLC method for the measurement of free aminoacids including cysteine in must and wines, first application. J. Sci. Food Agric. 2001, 81, 731–738. [Google Scholar] [CrossRef]
  27. Bradford, M.M. A Rapid and Sensitive Method for the Quantitation of Microgram Quantities of Protein Utilizing the Principle of Protein-Dye Binding. Anal. Biochem. J. 1976, 72, 248–254. [Google Scholar] [CrossRef]
  28. Moreno-García, J.; García-Martínez, T.; Moreno, J.; Millán, M.C.; Mauricio, J.C. A proteomic and metabolomic approach for understanding the role of the flor yeast mitochondria in the velum formation. Int. J. Food Microbiol. 2014, 172, 21–29. [Google Scholar] [CrossRef] [PubMed]
  29. Moreno-García, J.; Garcia-Martinez, T.; Millán, M.C.; Mauricio, J.C.; Moreno, J. Proteins involved in wine aroma compounds metabolism by a Saccharomyces cerevisiae flor-velum yeast strain grown in two conditions. Food Microbiol. 2015, 51, 1–9. [Google Scholar] [CrossRef] [PubMed]
  30. Ishihama, Y.; Oda, Y.; Tabata, T.; Sato, T.; Nagasu, T.; Rappsilber, J.; Mann, M. Exponentially Modified Protein Abundance Index (emPAI) for Estimation of Absolute Protein Amount in Proteomics by the Number of Sequenced Peptides per Protein. Mol. Cell. Proteom. 2005, 4, 1265–1272. [Google Scholar] [CrossRef] [Green Version]
  31. Seisonen, S.; Vene, K.; Koppel, K. The current practice in the application of chemometrics for correlation of sensory and gas chromatography data. Food Chem. 2016, 210, 530–540. [Google Scholar] [CrossRef]
  32. Merín, M.G.; Mendoza, L.M.; Farías, M.E.; Morata de Ambrosini, V.I. Isolation and selection of yeasts from wine grape ecosystem secreting cold-active peptinolytic activity. Int. J. Food Microbiol. 2017, 147, 144–148. [Google Scholar] [CrossRef] [Green Version]
  33. Wei, X.; Francoise, U.; Qin, M.; Chen, Q.; Li, Y.; Sun, X.; Fang, Y.-L. Effects of different fermentation and storage conditions on methanol content in Chinese spine grape (Vitis davidii Foex) wine. CyTA J. Food 2020, 18, 367–374. [Google Scholar] [CrossRef]
  34. Dennis, E.G.; Keyzers, R.A.; Kalua, C.M.; Maffei, S.M.; Nicholson, E.L.; Boss, P.K. Grape Contribution to Wine Aroma: Production of Hexyl Acetate, Octyl Acetate, and Benzyl Acetate during Yeast Fermentation Is Dependent upon Precursors in the Must. J. Agric. Food Chem. 2012, 60, 2638–2646. [Google Scholar] [CrossRef] [PubMed]
  35. Tao, Y.; Li, H.; Wang, H.; Zhang, L. Volatile compounds of young Cabernet Sauvignon red wine from Changli County (China). J. Food Compos. Anal. 2008, 21, 689–694. [Google Scholar] [CrossRef]
  36. Coelho, E.; Coimbra, M.A.; Nogueira, J.M.F.; Rocha, S.M. Quantification approach for assessment of sparkling wine volatiles from different soils, ripening stages, and varieties by stir bar sorptive extraction with liquid desorption. Anal. Chim. Acta 2009, 635, 214–221. [Google Scholar] [CrossRef] [PubMed]
  37. Loyaux, D.; Roger, S.; Adda, J. The evolution of champagne volatiles during ageing. J. Sci. Food Agric. 1981, 32, 1254–1258. [Google Scholar] [CrossRef]
  38. Pozo-Bayón, M.; Martín-Álvarez, P.; Moreno-Arribas, M.; Andujar-Ortiz, I.; Pueyo, E. Impact of using Trepat and Monastrell red grape varieties on the volatile and nitrogen composition during the manufacture of rosé Cava sparkling wines. LWT 2010, 43, 1526–1532. [Google Scholar] [CrossRef] [Green Version]
  39. Sawyer, S.; Longo, R.; Solomon, M.; Nicolotti, L.; Westmore, H.; Merry, A.; Gnoinski, G.; Ylia, A.; Dambergs, R.; Kerslake, F. Autolysis and the duration of ageing on lees independently influence the aroma composition of traditional method sparkling wine. Aust. J. Grape Wine Res. 2021, 28, 146–159. [Google Scholar] [CrossRef]
  40. Pueyo, E.; Martín-Alvarez, P.J.; Polo, M.C. Relationship Between Foam Characteristics and Chemical Composition in Wines and Cavas (Sparkling Wines). Am. J. Enol. Vitic. 1995, 46, 518–524. [Google Scholar] [CrossRef]
  41. Procopio, S.; Sprung, P.; Becker, T. Effect of amino acid supply on the transcription of flavour-related genes and aroma compound production during lager yeast fermentation. LWT 2015, 63, 289–297. [Google Scholar] [CrossRef]
  42. Ljungdahl, P.O.; Daignan-Fornier, B. Regulation of amino acid, nucleotide, and phosphate metabolism in Saccharomyces cerevisiae. Genetics 2012, 190, 885–929. [Google Scholar] [CrossRef] [Green Version]
  43. Belda, I.; Ruiz, J.; Esteban-Fernández, A.; Navascués, E.; Marquina, D.; Santos, A.; Moreno-Arribas, M. Microbial contribution to wine aroma and its intended use for wine quality improvement. Molecules 2017, 22, 189. [Google Scholar] [CrossRef] [Green Version]
  44. Marsit, S.; Sanchez, I.; Galeote, V.; Dequin, S. Horizontally acquired oligopeptide transporters favour adaptation of Saccharomyces cerevisiae wine yeast to oenological environment. Environ. Microbiol. 2016, 18, 1148–1161. [Google Scholar] [CrossRef] [PubMed]
  45. Cebollero, E.; Gonzalez, R. Induction of Autophagy by Second-Fermentation Yeasts during Elaboration of Sparkling Wines. Appl. Environ. Microbiol. 2006, 72, 4121–4127. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  46. Dickinson, J.R.; Salgado, L.E.; Hewlins, J.E. The catabolism of amino acids to long chain and complex alcohols in Saccha-romyces cerevisiae. J. Biol. Chem. 2003, 278, 8028–8034. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  47. Wu, L.; Wen, Y.; Chen, W.; Yan, T.; Tian, X.; Zhou, S. Simultaneously deleting ADH2 and THI3 genes of Saccharomyces cerevisiae for reducing the yield of acetaldehyde and fusel alcohols. FEMS Microbiol. Lett. 2021, 368, fnab094. [Google Scholar] [CrossRef]
  48. Hazelwood, L.A.; Tai, S.L.; Boer, V.M.; de Winde, J.H.; Pronk, J.T.; Daran, J.M. A new physiological role for Pdr12p in Saccharomyces cerevisiae: Export of aromatic and branched-chain organic acids produced in amino acid catabolism. FEMS Yeast Res. 2006, 6, 937–945. [Google Scholar] [CrossRef] [Green Version]
  49. Vuralhan, Z.; Morais, M.A.; Tai, S.L.; Piper, M.D.; Pronk, J.T. Identification and characterization of phenylpyruvate decarboxylase genes in Saccharomyces cerevisiae. Appl. Environ. Microbiol. 2003, 69, 4534–4541. [Google Scholar] [CrossRef] [Green Version]
Disclaimer/Publisher’s Note: The statements, opinions, and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions, or products referred to in the content.
Microorganisms 11 01630 g001 550
Figure 1. Matrix resulting from the analysis of correlations made for the condition with CO2 overpressure (A) and condition without CO2 overpressure (B) with a level of significance of 95%.
Figure 1. Matrix resulting from the analysis of correlations made for the condition with CO2 overpressure (A) and condition without CO2 overpressure (B) with a level of significance of 95%.
Microorganisms 11 01630 g001
Microorganisms 11 01630 g002 550
Figure 2. Determination of the concentration (mM) of amino acids using high-performance liquid chromatography, HPLC, in the reverse phase via the precolumn derivatization reaction with orthophthaldehyde (OPA), under the CO2 overpressure condition (above) and without CO2 overpressure condition (below). MFP (+): middle of fermentation with pressure; EFP (+): end of pressure fermentation; MFP (-): middle of fermentation without pressure; EFP (-): end of fermentation without pressure.
Figure 2. Determination of the concentration (mM) of amino acids using high-performance liquid chromatography, HPLC, in the reverse phase via the precolumn derivatization reaction with orthophthaldehyde (OPA), under the CO2 overpressure condition (above) and without CO2 overpressure condition (below). MFP (+): middle of fermentation with pressure; EFP (+): end of pressure fermentation; MFP (-): middle of fermentation without pressure; EFP (-): end of fermentation without pressure.
Microorganisms 11 01630 g002
Microorganisms 11 01630 g003 550
Figure 3. Representation in the defined plane of three main components (PCA). MFP (+): middle of fermentation with pressure; MFP (-): middle of fermentation without pressure; EFP (+): end of fermentation with pressure; EFP (-): end of fermentation without pressure.
Figure 3. Representation in the defined plane of three main components (PCA). MFP (+): middle of fermentation with pressure; MFP (-): middle of fermentation without pressure; EFP (+): end of fermentation with pressure; EFP (-): end of fermentation without pressure.
Microorganisms 11 01630 g003
Table
Table 1. Determination of the concentration (mg/L) of methanol and higher alcohols in four conditions (MFP (+), MFP (-), EFP (+), EFP (-)) using the GC-FID and SBSE-TD-GC-MS methods. Odor Perception Threshold (OPT).
Table 1. Determination of the concentration (mg/L) of methanol and higher alcohols in four conditions (MFP (+), MFP (-), EFP (+), EFP (-)) using the GC-FID and SBSE-TD-GC-MS methods. Odor Perception Threshold (OPT).
Concentration (mg/L)
OPT (mg/L)MFP (+)EFP (+)MFP (-)EFP (-)
Methanol66830.85 c ± 1.1126.80 a ± 1.6026.82 ab ± 1.3929.10 bc ± 1.81
1-Propanol83015.01 c ± 1.2012.89 ab ± 0.4911.97 a ± 0.6214.12 bc ± 0.51
Isobutanol4021.03 b ± 0.0418.96 a ± 0.9619.69 a ± 0.3319.41 a ± 0.42
Isoamylic Alcohols30296.33 b ± 3.29260.97 a ± 10.01288.52 b ± 15.98295.61 b ± 0.25
2-phenylethanol1058.06 b ± 2.9951.11 a ± 3.0952.99 ab ± 2.0052.28 ab ± 1.14
Hexanol2.50.69 a ± 0.720.68 a ± 0.420.82 b ± 0.330.69 a ± 0.40
2-ethyl-1-hexanol80.91 ns ± 0.270.72 ns ± 0.170.60 ns ± 0.170.49 ns ± 0.13
Different letters (a–c) in the same row are significantly different (p ≤ 0.05) according to Fisher’s test; ns = non-significant. MFP (+): middle of fermentation with pressure; MFP (-): middle of fermentation without pressure; EFP (+): end of fermentation with pressure; EFP (-): end of fermentation without pressure.
Table
Table 2. Determination of the concentration (mM) of the different amino acids via the OPA method using HPLC.
Table 2. Determination of the concentration (mM) of the different amino acids via the OPA method using HPLC.
Concentration (mM)
MFP (+)EFP (+)MFP (-)EFP (-)
GABA7.1 a ± 0.725.4 d ± 0.015.4 b ± 0.123.7 c ± 0.7
GLN5.0 a ± 1.217.8 c ± 1.07.2 b ± 0.315.9 c ± 0.3
TYR11.0 a ± 0.416.6 c ± 0.813.2 b ± 0.813.6 b ± 0.7
CYS5.0 a ± 0.711.8 b ± 1.511.9 b ± 1.49.5 b ± 1.1
LEU5.1 a ± 0.411.0 c ± 0.58.0 b ± 1.34.1 a ± 0.3
ASN3.0 a ± 0.210.0 d ± 0.43.7 b ± 0.38.7 c ± 0.8
THR6.5 a ± 0.99.7 c ± 0.47.8 b ± 0.410.5 c ± 0.3
GLY5.3 a ± 0.18.9 b ± 0.75.0 a ± 0.28.3 b ± 0.4
GLU4.8 b ± 0.27.2 a ± 0.46.7 b ± 0.55.6 a ± 0.6
VAL3.8 ns ± 0.26.9 ns ± 0.23.1 ns ± 2.63.0 ns ± 0.7
ARG3.6 a ± 0.46.1 b ± 0.14.2 a ± 0.13.9 a ± 0.2
SER1.2 b ± 0.55.0 c ± 0.12.1 a ± 0.14.8 c ± 0.4
ILE2.0 a ± 0.64.5 b ± 0.32.6 a ± 0.37.1 c ± 0.5
ALA2.0 a ± 0.63.5 b ± 0.03.4 b ± 0.53.7 b ± 0.2
HIS1.7 a ± 0.13.3 c ± 0.12.1 b ± 0.12.3 b ± 0.2
ASP0.4 a ± 0.41.9 b ± 0.20.6 a ± 0.11.8 b ± 0.1
TRP0.8 a ± 0.01.8 c ± 0.10.9 a ± 0.21.5 b ± 0.1
MET0.7 ns ± 0.00.7 ns ± 0.00.8 ns ± 0.30.5 ns ± 0.1
n.d. = not detected. Different letters (a–d) in the same row are significantly different (p ≤ 0.05) according to Fisher’s test. MFP (+): middle of fermentation with pressure; MFP (-): middle of fermentation without pressure; EFP (+): end of fermentation with pressure; EFP (-): end of fermentation without pressure; ns = non-significant.
Table
Table 3. Determination of protein content (mol%) using the abundance index (EmPAI) of the proteins identified in each condition (MFP (+), MFP (-), EFP (+), and EFP (-)) related to the synthesis of fusel alcohols using the Ehrlich pathway.
Table 3. Determination of protein content (mol%) using the abundance index (EmPAI) of the proteins identified in each condition (MFP (+), MFP (-), EFP (+), and EFP (-)) related to the synthesis of fusel alcohols using the Ehrlich pathway.
Protein NameProtein Content (mol%)
MFP (+)EFP (+)MFP (-)EFP (-)
1 AAD14Pn.d. an.d. a0.0355 b ± 0.0004n.d. a
1 AAD4P0.0149 b ± 0.0001n.d. a0.0503 c ± 0.0005n.d. a
1 ADH1P0.424 a ± 0.0040.841 c ± 0.0080.200 b ± 0.0020.539 d ± 0.005
1 ADH3P0.122 c ± 0.0010.078 a ± 0.0010.121 c ± 0.0010.0356 b ± 0.0004
1 ADH4P0.0195 b ± 0.0002n.d. a0.0675 c ± 0.0007n.d. a
1 ADH5P0.0404 b ± 0.0004n.d. a0.0578 c ± 0.0004n.d. a
1 ADH6Pn.d. an.d. a0.0567 b ± 0.0006n.d. a
1 ADH7Pn.d. an.d. a0.120 b ± 0.0010.0187 c ± 0.0002
2 ALD2P0.0102 c ± 0.0001n.d. a0.0091 b ± 0.00010.0399 d ± 0.0004
2 ALD4P0.0365 b ± 0.00040.0120 a ± 0.00010.0519 c ± 0.00050.109 d ± 0.001
2 ALD5Pn.d. an.d. a0.0280 c ± 0.00030.0110 b ± 0.0001
2 ALD6P0.0295 d ± 0.0003n.d. a0.0768 c ± 0.00080.370 b ± 0.004
3 ARO10Pn.d. an.d. a0.00389 b ± 0.00004n.d. a
4 ARO8P0.0317 ab ± 0.0003n.d. a0.111 b ± 0.0010.211 c ± 0.002
4 ARO9P0.0280 d ± 0.0003n.d. a0.0061 b ± 0.00010.0176 c ± 0.0002
4 BAT1Pn.d. a0.0930 b ± 0.00090.103 c ± 0.001n.d. a
4 BAT2Pn.d. an.d. a0.0496 c ± 0.00050.0187 b ± 0.0002
3 PDC1P0.397 b ± 0.0040.430 c ± 0.0040.246 a ± 0.0030.453 d ± 0.005
3 PDC5P0.377 c ± 0.0040.408 d ± 0.0040.253 b ± 0.0030.197 a ± 0.0002
3 PDC6Pn.d. an.d. a0.0488 b ± 0.0005n.d. a
1 SFA1P0.0913 c ± 0.00090.0800 b ± 0.00080.0793 b ± 0.00080.0556 a ± 0.0006
1 YPL088WP0.0234 b ± 0.0002n.d. a0.0443 d ± 0.00040.0298 c ± 0.0003
n.d. = not detected, for statistics it has been replaced by half the minimum value. Different letters (a–d) in the same row are significantly different (p ≤ 0.05) according to Fisher’s test. The superscripts indicate the stage of the route in which they are involved: 1 oxidation, 2 reduction, 3 decarboxilation and 4 transamination. MFP (+): middle of fermentation with pressure; MFP (-): middle of fermentation without pressure; EFP (+): end of fermentation with pressure; EFP (-): end of fermentation without pressure.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

González-Jiménez, M.d.C.; Mauricio, J.C.; Moreno-García, J.; Puig-Pujol, A.; Moreno, J.; García-Martínez, T. Endogenous CO2 Overpressure Effect on Higher Alcohols Metabolism during Sparkling Wine Production. Microorganisms 2023, 11, 1630. https://doi.org/10.3390/microorganisms11071630

AMA Style

González-Jiménez MdC, Mauricio JC, Moreno-García J, Puig-Pujol A, Moreno J, García-Martínez T. Endogenous CO2 Overpressure Effect on Higher Alcohols Metabolism during Sparkling Wine Production. Microorganisms. 2023; 11(7):1630. https://doi.org/10.3390/microorganisms11071630

Chicago/Turabian Style

González-Jiménez, María del Carmen, Juan Carlos Mauricio, Jaime Moreno-García, Anna Puig-Pujol, Juan Moreno, and Teresa García-Martínez. 2023. "Endogenous CO2 Overpressure Effect on Higher Alcohols Metabolism during Sparkling Wine Production" Microorganisms 11, no. 7: 1630. https://doi.org/10.3390/microorganisms11071630

APA Style

González-Jiménez, M. d. C., Mauricio, J. C., Moreno-García, J., Puig-Pujol, A., Moreno, J., & García-Martínez, T. (2023). Endogenous CO2 Overpressure Effect on Higher Alcohols Metabolism during Sparkling Wine Production. Microorganisms, 11(7), 1630. https://doi.org/10.3390/microorganisms11071630

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop