Mass Spectrometry-Based Proteomic Profiling of a Silvaner White Wine

The comprehensive identification of the proteome content from a white wine (cv. Silvaner) is described here for the first time. The wine protein composition isolated from a representative wine sample (250 L) was identified via mass spectrometry (MS)-based proteomics following in-solution and in-gel digestion methods after being submitted to size exclusion chromatographic (SEC) fractionation to gain a comprehensive insight into proteins that survive the vinification processes. In total, we identified 154 characterized (with described functional information) or so far uncharacterized proteins, mainly from Vitis vinifera L. and Saccharomyces cerevisiae. With the complementarity of the two-step purification, the digestion techniques and the high-resolution (HR)-MS analyses provided a high-score identification of proteins from low to high abundance. These proteins can be valuable for future authentication of wines by tracing proteins derived from a specific cultivar or winemaking process. The proteomics approach presented herein may also be generally helpful to understand which proteins are important for the organoleptic properties and stability of wines.


Introduction
The white grape Silvaner (synonym Grüner Silvaner) is an autochthonous cultivar from Austria, originating from a genetic crossing of the cultivars Traminer and Österreichisch-Weiß [1]. Being only marginally important in today's Austria, the grape variety is of highest importance in the region of Franconia (Franken, in German), where it was introduced at the end of the seventeenth century. Thus, Silvaner can be considered as a very old grape variety [2]. In 2021, 4535 ha of vineyards in Germany are planted with Silvaner, which corresponds to 6.5% and 4.4% of the white (70,138 ha) and total wine growing area (103,421 ha) in Germany, respectively [3]. Moreover, Silvaner is cultivated in various countries, including France (Alsace), Romania, Slovakia, Croatia, Italy (Trentino-Alto Adige), Austria, the United States and Australia. Wines of the cultivar are generally characterized to have mild acidity and subtle aromas. The on-going climate change has also been shown to affect the quality of Franconian Silvaner wines, particularly increasing sugar levels and decreasing acidity, thereby altering the wines' sensory characteristics [4]. Furthermore, increased temperatures and decreased precipitation amounts, a frequent consequence of climate change in many wine growing regions, increased the risk for protein haze formation in the wine [5]. Proteins that survive the vinification process can interact with other wine components (e.g., ethanol) to influence the wine aroma, flavor, texture astringency, and color [6]. Additionally, wine proteins and

Silvaner Wine
Silvaner grapes were harvested from the "Würzburger Pfaffenberg" vineyard (Würzburg, Germany) on 19 September 2018 and processed to must and wine by the Bavarian State Institute for Viticulture and Horticulture (LWG, Veitshöchheim, Germany). The pH of the must and wine sample was measured using a titrator (TitroLine alpha plus with TA20 plus, TM 125 and Titrisoft 3.1 SI Analytics, Mainz, Germany). The must had a measured weight of 99 • Oe (DMATM 35, Anton Paar, Graz, Austria), a total acidity of 5.0 g/L (as tartaric acid) and a pH value of 3.5 (after adding 1.5 g/L tartaric acid to lower the pH). The grapes were not destemmed and only lightly crushed (crush roller, Scharfenberger Maschinenbau, Bad Dürkheim, Germany). The maceration time was 4 h at 16 • C. The solid-liquid separation was performed using a pneumatic, partially slotted tank press with a volume of 900 L (Europress P9, Scharfenberger Maschinenbau). Pectinase treatment was carried out at the must stage with 2 mL/hL (Trenolin Rapid, Erbslöh, Geisenheim, Germany). After enzymation, the must sedimented for 12 h at 16 • C and then the clear supernatant was drawn off and used for fermentation. For better nutrition of the yeast, 200 mL/hL of Vitamon Liquid (Erbslöh) was added as a yeast nutrient (combination nutrient of vitamin B 1 and diammonium phosphate). The commercial yeast strain "Oenoferm Terra" (Erbslöh) was used at 20 g/hL to ferment the must for 21 days at 17 • C, while in the last third of fermentation the temperature was increased to 18 • C to obtain a safe final fermentation. The obtained wine had an alcohol content of 11.31%, fermentable sugars of 3.4 g/L, total acidity of 5.1 g/L (calculated as tartaric acid), a pH of 3.35, volatile acid content of 0.24 g/L, free SO 2 (incling reductones) content of 102 mg/L, reductone levels of 66 mg/L, and an effective content of free SO 2 at 36 mg/L. The bentonite (NaCalit PORE-TEC, Erbslöh) requirement, determined by a heat test (4 h at 80 • C in a drying oven (UNB 200, Memmert, Büchenbach, Germany), subsequent cooling and then evaluation with turbidity meter (Turb 430 IR, WTW, Weilheim, Germany)) was extremely high (450 g/hL), which indicated a high content in proteins and proteinaceous colloids.

Technical Scale Isolation and Analysis of Silvaner Wine Colloids
The ultrafiltration of the protein-rich colloid of the Silvaner wine (250 L) was performed as described by Albuquerque et al. [32]. Briefly, the wine was firstly sheet-filtered by using a stainless steel sheet filter (40 cm × 40 cm, Pall-Seitz-Schenk, Bad Kreuznach, Germany) packed with 5 filter sheets (K 250, Pall-Seitz-Schenk). Ultrafiltration was subsequently performed with a Sartocon beta system (Sartorius, Göttingen, Germany) equipped with two 0.6 m 2 Sartocon Hydrosart cassettes with a molecular mass cut-off (MWCO) of 10 kDa. A subsequent diafiltration step, performed with citrate buffer (5 g citric acid per L, pH 4) and water, aimed to remove low molecular weight substances. However, still low molecular weight wine components bound to the colloids may remain in the isolated colloids. After the lyophilization of the retentate, the resulting powder was hygroscopic and, thus, stored in airtight containers at room temperature.
The carbohydrate content of the isolated colloids was determined by quantitation of neutral sugars and uronic acids released after hydrolysis with sulphuric acid by high performance anion exchange chromatography with pulsed amperometric detection (HPAEC-PAD) as described beforehand [32]. Additionally, the total protein content of the isolated colloids was determined after colloid hydrolysis by measuring the released amino acids by anion exchange chromatography according to Ahlborn et al. [33]. The wine colloids contained 47.1% of carbohydrates and 34.7% of protein in the dry matter. Residual moisture, determined by a moisture analyzer (ML-50, AND, Tokyo, Japan) at 120 • C with 0.5 g sample, was 8.9%. Based on the yield of the ultrafiltration and the residual moisture, the studied Silvaner wine contained 0.63 g colloid per L wine [20].

Protein Content and Visualization
Protein in the isolated colloid and from chromatographic runs (see Section 2.5.1) were quantified according to Bradford [34], with bovine serum albumin (Carl Roth) as standard. Proteins were separated by sodium dodecyl sulfate polyacrylamide gel electrophoresis SDS-PAGE (12% polyacrylamide gel) according to Laemmli [35] under denaturing conditions. After separation, protein spots were visualized by Coomassie blue staining (Thermo Fisher Scientific).

MS-Based Proteomics Analysis of Proteins from a Silvaner Wine
The aforementioned isolated colloid was submitted to size exclusion chromatography (SEC) and subsequent in-solution and in-gel digestion, as described in Figure 1.
However, still low molecular weight wine components bound to the colloids may remain in the isolated colloids. After the lyophilization of the retentate, the resulting powder was hygroscopic and, thus, stored in airtight containers at room temperature.
The carbohydrate content of the isolated colloids was determined by quantitation of neutral sugars and uronic acids released after hydrolysis with sulphuric acid by high performance anion exchange chromatography with pulsed amperometric detection (HPAEC-PAD) as described beforehand [32]. Additionally, the total protein content of the isolated colloids was determined after colloid hydrolysis by measuring the released amino acids by anion exchange chromatography according to Ahlborn et al. [33]. The wine colloids contained 47.1% of carbohydrates and 34.7% of protein in the dry matter. Residual moisture, determined by a moisture analyzer (ML-50, AND, Tokyo, Japan) at 120 °C with 0.5 g sample, was 8.9%. Based on the yield of the ultrafiltration and the residual moisture, the studied Silvaner wine contained 0.63 g colloid per L wine [20].

Protein Content and Visualization
Protein in the isolated colloid and from chromatographic runs (see Section 2.5.1) were quantified according to Bradford [34], with bovine serum albumin (Carl Roth) as standard. Proteins were separated by sodium dodecyl sulfate polyacrylamide gel electrophoresis SDS-PAGE (12% polyacrylamide gel) according to Laemmli [35] under denaturing conditions. After separation, protein spots were visualized by Coomassie blue staining (Thermo Fisher Scientific).

MS-Based Proteomics Analysis of Proteins from a Silvaner Wine
The aforementioned isolated colloid was submitted to size exclusion chromatography (SEC) and subsequent in-solution and in-gel digestion, as described in Figure 1.  Illustrative scheme of the methods applied for the isolation and identification of proteins from a Silvaner wine. After fractionation via size exclusion chromatography (SEC), the wine proteins were subjected to distinct methods of digestion: (a) in-solution, in which the samples were directly tryptically digested and submitted to LC-MS analyses after the SEC fractionation step; and (b) in-gel, whereby the proteins were further fractionated by SDS-PAGE and then tryptically digested prior to the LC-MS analysis.

In-solution Digestion: Protein Fractionation by SEC Chromatography
The proteins present in the isolated wine colloid with 0.5 ± 0.1 mg/mL were fractionated using a HiLoad 16/60 Superdex 200 prep grade size-exclusion chromatography column (GE Healthcare Biosciences, Uppsala, Sweden) on a fast protein liquid chromatography (FPLC) system (Bio-rad NGC™ Quest Plus, Feldkirchen, Germany), using 50 mM Tris-HCl (pH 7, containing 150 mM NaCl) as eluent at 1 mL/min. Proteins were detected at 280 nm and automatically collected by a fraction collector (BioFrac™, Bio-Rad). The % of the yield from the protein fractions after FPLC fractionation is shown in Figure S1. The retention time was correlated to the molecular mass based on gel filtration protein standards (from 1350 kDa to 670,000 kDa, Bio-Rad) using the software ChromLab version 6.1.29 (Bio-Rad).
To perform the in-solution digestion, aliquots of 25 µL of wine proteins collected from the SEC (standardized at 1 µg/µL by vacuum concentration or dilution) were mixed with 5 µL of a 50 mM ammonium bicarbonate solution and 20 µL of a RapiGest solution (0.1% dissolved in ABC) and vortexed. Subsequently, the mixture was incubated with 5 µL of 5 mM dithiothreitol dissolved in ABC at 60 • C for 15 min. Protein alkylation was performed by incubation with 5 µL of 200 mM iodoacetamide dissolved in ABC for 30 min at 25 • C. Trypsin digestion was performed by the addition of 1.25 µL trypsin/Lys-C mix (0.5 µg/µL in ABC buffer), further incubation at 37 • C for 16 h, and then stopped by the addition of 2 µL of 100% formic acid. The samples were centrifuged (15 min at 4 • C and about 13,000× g) and concentrated using a vacuum concentrator (Eppendorf, Hamburg, Germany). The obtained digestates were resuspended in 100 µL of ultrapure water, desalted by ZipTip C18 pipette tips (Merck), vacuum concentrated and stored for further analysis. The gel pieces were dried again and a trypsin solution (0.5 µg/µL solved in 50 mM ABC) was added for protein digestion for 16 h at 37 • C. Afterwards, the samples were centrifuged (13,000× g, 10 min, 4 • C) and the supernatants were used for further analysis.

Liquid Chromatography Mass Spectrometry (LC-MS) Analysis
The digested peptides were separated using a UHPLC system (UltiMate 3000 RSLC HPLC system, Ultra-High-Performance Liquid Chromatography, Thermo Fisher Scientific). A Kinetex C18 (2.1 mm × 100 mm, 2.6 µm 100 Å particle size) column (Phenomenex, CA, Torrance, USA) was used to separate the digests at a flow rate of 250 µL/min following an optimized gradient of the solvents A (aqueous 0.1% (v/v) water) and B (ACN/0.1% formic acid): isocratic flow (2% B) for 5 min, followed by a gradient of 2-40% (B) for 70 min, 40-50% (B) over 5 min and 50-98% (B) for 2 min. Re-equilibration was obtained by an isocratic flow at 2% of B for 10 min. The HPLC system was coupled to a Q Exactive HF-X (Thermo Fisher Scientific) mass spectrometer. The MS device was operated in data-dependent acquisition (top-10 DDA) mode with the following parameters for full MS scans: mass range of m/z 350 to 1800, resolution of 120,000 (at m/z 200), automatic gain control (AGC) target of 3 × 10 6 , injection time (IT) of 50 ms; and MS/MS scans: mass range of m/z 200 to 2000, mass resolution of 30,000 (at m/z 200), AGC target of 1 × 10 5 , IT of 120 ms, isolation window m/z 1.3 and dynamic exclusion duration set to 60 s.

MS Data Analysis
Protein sequences were obtained through shotgun searching performed by the software Proteome Discoverer (PD) version 2.4 (Thermo Fisher Scientific). The organisms Vitis vinifera and Saccharomyces cerevisiae were taxonomically set for the search. Protein sequences from both organisms were downloaded from the UniProt protein database [36] and used as a personal database. Other organisms, which are pathogens or participate in the fermentative process, were included in the database search (see Section 3.2., i.e., the methylotrophic bacterium Methylobacterium sp., which has epiphytic interactions with grapes and can survive during the wine production [37]). The peptide precursor and fragment ion mass tolerance in PD were set to 10 and 0.5 ppm, respectively. The pa-rameters were assigned to a maximum of two missed cleavage sites of trypsin digestion and a minimum peptide length of 6. The dynamic modification was set to an oxidation (+15.995 Da (M)) and static modification to carbamidomethyl (+57.021 Da (C)). Percolator node was used to validate the identified peptide-spectrum matches (PSMs) and filter the data with parameters of a strict target FDR (false discovery rate) of 0.01 and a relaxed target FDR of 0.05. The MaxQuant contaminant database was used to mark the contaminants in the results file and proteins with at least one identified unique peptide were considered in the survey. "Characterized" proteins were considered those with annotated functional information in the database.

Protein Fractionation and Visualization
Proteins ( Figure 2a) separated by size exclusion chromatography (SEC) were collected in four main fractions (A, B, C and D), with the proteins represented by the largest peak in the range of 20-70 kDa and collected in fraction C. The collected proteins from each chromatographic peak were subjected to in-solution digestion bottom-up MS-based proteomics and were further separated according to their molecular mass (also described as molecular weight (MW)) by SDS-PAGE, resulting in a total of 16 protein bands ( Figure 2b). Fraction A from SEC showed a single protein band greater than 170 kDa, fraction B showed two bands between 130 and 55 kDa, fraction C showed the densest protein bands, with a total of 12 spots from 72 to 20 kDa and finally fraction D revealed two bands from 17 to 10 kDa.

MS-Based Proteomics Analysis
A total of 154 proteins (with different identification numbers, but not 154 proteins with different functions) were identified by combining the data obtained from the in-solution and in-gel protein digestion methods. The identified proteins were further classified as "characterized" (with characteristics or functions described in the database) and "uncharacterized" (when no properties or functions were found in the database). Among these proteins, 88 were only identified with the in-gel digestion method (48 characterized

MS-Based Proteomics Analysis
A total of 154 proteins (with different identification numbers, but not 154 proteins with different functions) were identified by combining the data obtained from the in-solution and in-gel protein digestion methods. The identified proteins were further classified as "characterized" (with characteristics or functions described in the database) and "uncharacterized" (when no properties or functions were found in the database). Among these proteins, 88 were only identified with the in-gel digestion method (48 characterized and 40 uncharacterized), while 45 other proteins were exclusively found with the in-solution digestion approach (38 characterized and seven uncharacterized). Moreover, 21 further proteins were commonly found after both digestion methods (16 characterized and five uncharacterized) ( Figure 3). Table 1 (characterized) and Table 2 (uncharacterized) list all identified proteins, according to the respective digestion method applied. Some proteins were repeatedly found; therefore, only the those with the highest coverage and identified unique peptides are presented. The complete protein list is available as Supplementary Data S1. The proteins had molecular masses ranging from 6.4 to 372.2 kDa. Figure 2 shows the correlation of each spot in the gel (spots 1 to 16) with some of the identified proteins by MS proteomics analysis (in-gel analysis). The complete list of identified proteins for each gel spot ( Figure 2) is available in the Supplementary Data S1. The organism source and MW for each protein are given and the characterized proteins have a description associated with their accession numbers. Proteins from 10 additional organisms were included in the database of Saccharomyces cerevisiae, because they are eventually found as grape pathogens or fermentative organisms. Among them, we identified proteins from Ashbya gossypii (n = 5), Cyberlindnera fabianii (n = 4), Kazachstania saulgeensis (n = 2), Methylobacterium sp. (n = 2), Novosphingobium sp. (n = 2), Pichia kudriavzevii (n = 2), Geotrichum candidum (n = 1), Aspergillus niger (n = 1) and Penicillium citrinum (n = 1). Table 1. Characterized proteins identified by MS-based proteomics of a colloid isolated from a Silvaner wine.   Biomolecules 2023, 13, x FOR PEER REVIEW 8 of and 40 uncharacterized), while 45 other proteins were exclusively found with the in-sol tion digestion approach (38 characterized and seven uncharacterized). Moreover, 21 fu ther proteins were commonly found after both digestion methods (16 characterized an five uncharacterized) (Figure 3).

Discussion
With the availability of high-throughput and rapid screening methods and HR-MS techniques, the evaluation of wine processing and an overview of the metabolism and defense mechanisms of grapes are feasible [26]. Therefore, MS-based proteomics may be applied to authenticate wines as a "proteome signature" to avoid fraudulent products in the wine market [12] in addition to other methods such as polyphenolic profiling (based on HPLC coupled with ultraviolet (UV) and MS analysis (HPLC-UV-MS/MS)) [40] and fluorescence spectroscopy [41]. The proteomics data reported here might serve in the future (after authenticity requirements) for a comparative authentication of Silvaner wine based on identifying particular proteins. A comparative analysis of wine proteomes showed that some proteins are commonly reported, and generally present across different cultivars. These include proteins from the vine plant V. vinifera (TLPs, CHIs, vacuolar invertase, (1,3)-β-glucanase, lipid transfer protein), from fermentative organisms, i.e., S. cerevisiae (acid phosphatase, seripauperin, protein YGP1, glycosidases, protein Tos1p, daughterspecific expression-related protein, and cell wall proteins) and from grape pathogens such as A. niger (pectin lyase).
Eventually, the reported proteins might be useful for a comparative analysis between cultivars (similarly to the analyses presented in the Tables 1 and 3) and therefore, protein matches with at least one unique peptide were considered in the present study. In this study, the combination of two different protein fractionation steps, the HR-MS analysis and the complementary in-solution and in-gel digestion techniques allowed for a high-score level of identified proteins. In total, from the 154 proteins identified from a Silvaner wine, 80% orig-inated from V. vinifera and S. cerevisiae, and roughly 20% from other organisms, which are frequently found to be associated with wine and grapes (Figure 4a). Protein species, which can survive the vinification process may influence the wine organoleptic properties and haze formation in wines [17]. Similar compositions of proteins from different organisms have been reported in the literature. However, the methods and the HR-MS analysis in this study provided a higher number of identified proteins compared to other studies (Table 3). Marangon et al. [38] combined hydrophobic interaction chromatography with reversedphase liquid chromatography using HPLC and nano-LC-MS/MS analyses to improve the protein purity and the quality of the proteomics analysis of Semillon grape juice and wine. The in-gel digestion allowed the identification of proteins after an additional step of separation (gel electrophoresis) and had the advantage of reducing the mixture of proteins that are digested by trypsin and further fragmented during the MS analysis. However, some proteins were still detected in unexpected molecular masses (Supplementary Data S1). The number of identified proteins after in-gel digestion was higher than that after the in-solution method, which was also observed by Choksawangkarn et al. [31]. In contrast, the in-solution approach allowed the direct LC-MS/MS analysis of the digested peptide mixtures, avoiding the risk of protein losses during further fractionation steps. Approximately one-third of the identified proteins in this present study were exclusively found using the in-solution digestion method. Additionally, methods of protein extraction are compared in Table 3. Sample isolation such as the MWCO of membranes, precipitation method and pellet resuspension can reduce the final protein content and influence the proteome analysis.  Proteomic profile might be comparatively used to detect differences in products from different wineries and years and validate authentication marker proteins. Proteins suc as TLP, CHI, vacuolar Invertase, and protein Ygp1, detected in the Silvaner wine, are reg ularly found in other wine samples. Other low-abundance proteins identified in this stud could be characterized as protein markers from now on. Some examples could be a cyste ine proteinase inhibitor (A5ANX3) and a plasma membrane ATPase (A0A438EWP8 which are originated from the plant V. vinifera (to evidence a protein from the cultiva Silvaner and not from fermentative organisms), they were found here with three and tw unique peptides (respectively) and were not previously identified in literature-reported wine proteomics. However, to validate the hypothesis that these proteins may be used a qualitative markers, several wines from different cultivars and geographical regions and years have to be analyzed by the same method described herein. A comparison of protein reported from different white wines, which were also identified in the present study, ca be found in the Supplementary Data 2 (Table S2). Rešetar et al. [58] emphasized the in crease in fraud on the wine market in recent years and discussed the need for guideline and laws to regulate standard production procedures and ensure quality parameters suc as geographical origin. Chambery et al. [59] presented the concepts of food traceabilit based on the EU General Food Law Regulation as a form to guarantee food quality and safety. Recent advances and the availability of MS techniques could be applied in the pro teomics analyses of different wines and become a powerful tool to provide informatio about food additives, allergenic proteins, fining agents, and haze potential to validat products and prevent commercial counterfeiting. Such methods are also recommended for the validation of suitable marker proteins based on the evaluation of many differen vineyards, cultivars, years, drought, grape pathogens, and plant stress conditions. The identification of low-abundance proteins originated from eventual grape infections, contaminations, distinct fermentative organisms and others are difficult to reproduced in different wine analyses, even if these grapes are from the same cultivar. The eventual presence of organisms such as pathogens [37,42], fermentative bacteria or yeasts [17,43] and factors such as differential gene expression induced by abiotic and biotic stress including climatic aspects [44,45] or protein contaminants [46,47] can greatly influence the variability of the proteomic analysis of wine. Righetti et al. [48] discussed that the wine composition and age might be affected by the presence of additives and, therefore, lowabundance proteins can evidence the vinification process. In addition, proteins from the fermentation process or added as fining agents such as egg white, as potential allergens, can influence the protein composition and may participate in the formation of haze particles [49]. The proteomics of wines has already been established as a tool for product authentication and avoiding food fraud. Ortea et al. [50] highlighted that not only vintages or cultivars, but also protein additives could be traced and characterized by proteomics analysis. Since such proteins were not identified, their absence in the clarification process of the analyzed Silvaner wine was confirmed. Table 4 shows the classification of the characterized proteins based on their cellular functions. In total, eleven proteins were related to gene regulations and nucleotide metabolism: eight, five, and four proteins were described as participating in the metabolism of carbohydrates, proteins and lipids, respectively. Six proteins were identified as participating in the cell defense of V. vinifera and S. cerevisiae, including the pathogenesis-related TLPs and CHIs. Six proteins were related to cell structural functions, and 14 proteins (the most abundant group) are responsible for metabolic and cell signaling functions. Several proteomics studies have classified wine proteins in different classes, including the proteins involved in sugar metabolism (such as vacuolar invertases) and in stress response or plant defense (such as the pathogenesis-related proteins TLPs, CHIs and osmotin-like proteins) as well as proteins from yeast and other fungal origins [22]. In general, the distribution of the proteins of berries is known to vary with the stages of their development. In late growth stages (i.e., at full maturity, during harvesting periods), an increase in the levels of proteins involved in stress response, metabolism, plant defense, and cytoskeleton formation is significant [51]. A graphical comparison of the number of identified proteins (classified by their cellular functions) and the digestion method used (in-gel, in-solution, and in-gel+in-solution) is presented in Figure 4b. In our findings, the highest number of proteins was associated with basic cellular functions related to metabolism and cell signaling. According to Kuang et al. [51], such protein profiles are more related to late stages of berry development, which is in agreement with the fact that wines are produced from ripe fruit. Proteins related to basic cellular functions were also found by Marsoni et al. [52], when they isolated and identified 15 proteins from different grape tissues and verified that most of them were involved in the regulatory and secondary metabolism such as energy metabolism. The classes of proteins or enzymes participating in the metabolism of proteins, nucleotides and lipids were also well represented in our findings. Sarry et al. [53] identified 67 proteins from six V. vinifera grape varieties and classified the proteins by their cellular functions: 34% of them were involved in energy metabolism, 19% had functions in the cell defense and in the response to stress, while 13% participated in the primary metabolism.
Particularly important for the deleterious haze formation are the pathogenesis-related (PR) proteins, which exert defensive functions in diverse plant species [17]. In V. vinifera, they are commonly expressed on a basal level during ripening or mechanical stress, while their expression level is upregulated during plant infection [54]. The highest fraction of these PR proteins is represented by TLPs and CHIs [18,45]. These two protein species are often reported as the main contributors for haze formation and wine instability [8,55]. Many isoforms of heat unstable proteins (HUPs), such as TLPs and CHIs, as well as other proteins such as β-glucanases [56] are also involved in haze formation and they are often reported to have molecular masses in the range of 20-30 kDa [17].
We previously used top-down proteomics to detect peptides obtained by tryptic digestion of the same proteinaceous substance studied herein [32]. A total of nine proteins (including high and low-abundances) from our earlier study could be identified in the present study (Supplementary Data S2, Table S1). Kwon [30] found a total of 20 proteins from a Sauvignon blanc wine by nano-LC-MS analysis. From these, five proteins were from grape, twelve from yeast, two from bacteria and one of fungal origin. The author emphasized that the MS analysis provided a sensitive and selective analysis for the protein identification. Okuda et al. [57], for example, detected vacuolar invertases (with a MW of approximately 66 kDa) and a lipid transfer protein (LTP, with 13 kDa) in Chardonnay wines by sequencing the N-terminal amino acid sequences of protein spots from 2D electrophoresis gels (electroblotted onto a Polyvinylidene fluoride (PVDF) membrane). Although the authors found approximately 150 protein spots on a 2D electrophoresis gel, most of which were related to TLP, osmotin-like protein, invertase, LTP, and their hydrolysis products. As expected, yeast proteins were also often reported as part of the wine proteome. Cilindre et al. [22] reported ten different proteins in a wine from healthy grapes and eight different proteins in a wine from grapes infected with Botrytis sp. (two protein bands probably secreted by B. cinerea), including a cell-wall mannoprotein from S. cerevisiae and two pectinolytic enzymes from Botryotinia fuckeliana (teleomorph of B. cinerea).
Proteomic profile might be comparatively used to detect differences in products from different wineries and years and validate authentication marker proteins. Proteins such as TLP, CHI, vacuolar Invertase, and protein Ygp1, detected in the Silvaner wine, are regularly found in other wine samples. Other low-abundance proteins identified in this study could be characterized as protein markers from now on. Some examples could be a cysteine proteinase inhibitor (A5ANX3) and a plasma membrane ATPase (A0A438EWP8), which are originated from the plant V. vinifera (to evidence a protein from the cultivar Silvaner and not from fermentative organisms), they were found here with three and two unique peptides (respectively) and were not previously identified in literature-reported wine proteomics. However, to validate the hypothesis that these proteins may be used as qualitative markers, several wines from different cultivars and geographical regions and years have to be analyzed by the same method described herein. A comparison of proteins reported from different white wines, which were also identified in the present study, can be found in the Supplementary Data S2 (Table S2). Rešetar et al. [58] emphasized the increase in fraud on the wine market in recent years and discussed the need for guidelines and laws to regulate standard production procedures and ensure quality parameters such as geographical origin. Chambery et al. [59] presented the concepts of food traceability based on the EU General Food Law Regulation as a form to guarantee food quality and safety. Recent advances and the availability of MS techniques could be applied in the proteomics analyses of different wines and become a powerful tool to provide information about food additives, allergenic proteins, fining agents, and haze potential to validate products and prevent commercial counterfeiting. Such methods are also recommended for the validation of suitable marker proteins based on the evaluation of many different vineyards, cultivars, years, drought, grape pathogens, and plant stress conditions.

Conclusions
The two-step protein fractionation and subsequent HR-MS techniques allowed the analysis of the comprehensive proteome profiling of a Silvaner wine for the first time. In addition, combining in-solution and in-gel protein digestion techniques enabled sufficient sensitivity to detect a high number (154 different accession numbers) of identifiable proteins. The functions of 50 proteins were described and classified according to their roles in cell metabolism, signaling, defense and structure. Such a combination of methods can improve the characterization of wine proteomes and be helpful to obtain traces of wine's origin and processing as an authentication method for future applications.
Supplementary Materials: All data needed to evaluate the conclusions are presented in the paper and in the Supplementary Materials and can be found in the online version, at https://www.mdpi.com/ article/10.3390/biom13040650/s1. Supplementary data are available as Supplementary Data S1 (A list of all identified proteins from the Silvaner wine) and Supplementary Data S2 (Table S1. Comparison of proteins from the same Silvaner wine identified in the present study and proteins from a Silvaner wine identified by ; Table S2. Comparison of proteins from different white wines reported in the literature and identified in the present study; and Figure S1. Quantification of the % of yield from the sample to protein fraction after FPLC fractionation based on the relative area calculated by the software ChromLab v6.1.29). The raw MS data were deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD040172.
Author Contributions: W.A. was responsible for the experimental design, conceptualization, investigation, data curation and writing. P.G. participated in the sample preparation, investigation and data curation. L.S. and J.B. participated in the sample preparation and data curation. B.S. was involved with resources and review. F.W., R.S. and H.Z. were involved with resources, funding acquisition, supervision, review, and editing of the final manuscript. M.G. was responsible for the project administration, conceptualization, supervision and writing. All authors have read and agreed to the published version of the manuscript.