A Multivariate Approach Using Attenuated Total Reflectance Mid

Article pubs.acs.org/JAFC

A Multivariate Approach Using Attenuated Total Reflectance Midinfrared Spectroscopy To Measure the Surface Mannoproteins and β‑Glucans of Yeast Cell Walls during Wine Fermentations John P. Moore,*,† Song-Lei Zhang,† Hélène Nieuwoudt,† Benoit Divol,† Johan Trygg,§ and Florian F. Bauer† †

Institute for Wine Biotechnology, Department of Viticulture and Oenology, Faculty of AgriSciences, Stellenbosch University, Matieland 7602, South Africa § Computational Life Science Cluster (CLiC), Department of Chemistry, Umeå University, Umeå 901 87, Sweden ABSTRACT: Yeast cells possess a cell wall comprising primarily glycoproteins, mannans, and glucan polymers. Several yeast phenotypes relevant for fermentation, wine processing, and wine quality are correlated with cell wall properties. To investigate the effect of wine fermentation on cell wall composition, a study was performed using mid-infrared (MIR) spectroscopy coupled with multivariate methods (i.e., PCA and OPLS-DA). A total of 40 yeast strains were evaluated, including Saccharomyces strains (laboratory and industrial) and non-Saccharomyces species. Cells were fermented in both synthetic MS300 and Chardonnay grape must to stationery phase, processed, and scanned in the MIR spectrum. PCA of the fingerprint spectral region showed distinct separation of Saccharomyces strains from non-Saccharomyces species; furthermore, industrial wine yeast strains separated from laboratory strains. PCA loading plots and the use of OPLS-DA to the data sets suggested that industrial strains were enriched with cell wall proteins (e.g., mannoproteins), whereas laboratory strains were composed mainly of mannan and glucan polymers. KEYWORDS: yeast cell wall, wine fermentation, mannoproteins, glucans, spectroscopy, multivariate analysis

■

investigate component parts10 we refer to as alcohol-insoluble residues common in the plant cell wall community, we use aqueous solvents and freeze-drying to obtain a residue containing proteins (glycoproteins, e.g., AGPs) and polysaccharides (e.g., xyloglucan and cellulose), but we chose not to use disruption procedures9 to obtain whole yeast cells with walls intact for surface spectroscopy. Such time-consuming degradative approaches include gas chromatography coupled with mass spectrometry analysis of cell wall monomers after hydrolysis.10 A further approach was to assay what was present in the wine samples released by the yeast, by collecting mannoproteins using gel filtration cartridges and subjecting the samples to acid hydrolysis, followed by the quantification of mannose released by ion exchange chromatography.11 These approaches are also often limited to a single class of compounds such as amino acids, sugars, or lipids. Contact spectroscopy such as attenuated total reflectance (ATR) mid-infrared (MIR) instruments provides a noninvasive approach to evaluate the overall cell wall surface chemistry, including mannoproteins and glucans, without significant demands on sample pretreatment.12,13 It is possible from wavescan data sets and reference spectral literature sources to assign major vibrational band regions and identify broad shifts in chemistry of samples.12 Furthermore, as contact methods are rapid, spectra can be acquired in a few minutes and collated per sample, offering a convenient means to evaluate many different strains simulta-

INTRODUCTION The commercial production of white wines such as Chardonnay or sparkling wines such as Champagne is critically dependent on using the correct strain of yeast (i.e., Saccharomyces cerevisiae), which has been selected (i.e., bred) for appropriate properties such as strong fermentor, nitrogen efficiency, and alcohol tolerance.1 However, many strains of S. cerevisiae exist in nature (wild yeasts) and in the laboratory that are able to impart less desirable properties such as off-flavors and/or haziness to wine.2 The phenotypic diversity of S. cerevisiae is remarkable, and strains exist that are flor forming, flocculate under specific fermentative conditions, and produce viscous velums (reviewed in Moore and Divol3). In the laboratory a range of phenotypes exist including modified colony morphology, enhanced adherence to plastics, and pseudohyphal growth under nutrient limiting conditions.4 Many of these properties are linked with modifications in cell wall properties (e.g., mannoprotein rich) of specific S. cerevisiae strains, such an example being the presentation of Flo proteins on the surface of flocculating yeast cells.5 The complexity and context of many of these phenotypes are suspected to be related to the general yeast cell wall environment and composition.6 The yeast cell wall consists of proteins (including structural mannoproteins and enzymes), mannans, lipids, and glucans (β-1,3-, β-1,4-, and β-1,6-linked), which form an intercross-linked composite architectural network.6 Many studies have addressed specific aspects of the organizational complexity such as the gas1 mutants, which comprise defects in glycosylphosphoinositol anchoring cell proteins to the wall matrix.7,8 To prepare cell wall samples and thereafter analyze them using mostly degradative techniques that break down linkages and © 2015 American Chemical Society

Received: Revised: Accepted: Published: 10054

July 1, 2015 October 21, 2015 October 21, 2015 October 21, 2015 DOI: 10.1021/acs.jafc.5b03154 J. Agric. Food Chem. 2015, 63, 10054−10063

Article

Journal of Agricultural and Food Chemistry

Table 1. Yeast Species and Strains Used in This Study, Classified According to Code Number, Species, Strain, Genotype/ Description, and Appropriate Literature Reference no.

strain

Saccharomyces cerevisiae spp. 1 VIN13 2 VIN13-F1A 3 VIN13-F11A 4 VIN13-F5A 5 VIN13-F1H 6 VIN13-F11H 7 BM45 8 BM45-F1A 9 BM45-F11A 10 BM45-F5A 11 BM45-F1H 12 BM45-F11H 13 FY23 14 FY23-F1A 15 FY23-F11A 16 FY23-F5A 17 s288c 18 s288c FLO8 19 s288c FLO8 gas1 20 BY4742 knr4Δ 21 BY4742 gas1Δ 22 BY4742 gpi7Δ 23 Σ1278b 24 Σ1278b mss11 25 WE372 26 DV10 27 W303 28 Fermicru XL 29 R088 30 N96 31 EC1118 Saccharomyces paradoxus 32 Phaff 01-161 33 Phaff 01-146 34 Prexotic non-Saccharomyces spp. 35 Brettanomyces bruxellensis Y121 36 Hanseniaspora uvarum Y1133 37 Dekkera bruxellensis ISA1649 38 39 40

Dekkera bruxellensis CB63 Kluyveromyces wickerhamii CBS8526 Torulaspora spp.

genotype/description industrial wine yeast strain (unknown genotype) FLO 1p::SMR1-ADH 2p FLO11p::SMR1-ADH 2p FLO 5p::SMR1-ADH 2p FLO 1p::SMR1-HSP30p FLO11p::SMR1-HSP30p industrial wine yeast strain (unknown genotype) FLO 1p::SMR1-ADH 2p FLO11p::SMR1-ADH 2p FLO 5p::SMR1-ADH 2p FLO 1p::SMR1-HSP30p FLO11p::SMR1-HSP30p MATa leu2 trp1 ura3 f lo8-1 MATa leu2 trp1 ura3 f lo8-1 FLO 1p::SMR1-ADH 2p MATa leu2 trp1 ura3 f lo8-1 FLO11p::SMR1-ADH 2p MATa leu2 trp1 ura3 f lo8-1 FLO 5p::SMR1-ADH 2p MATa f lo8-1 his3 leu2 lys2 ura3 MATa f lo8-1 his3 leu2 lys2 ura3 flo8-1Δ::FLO8-LEU2 MATa f lo8-1 his3 leu2 lys2 ura3 f lo8-1Δ::FLO8-LEU2 gas1Δ::KanMX4 MATa f lo8-1 his3 leu2 lys2 ura3 knr4Δ::KanMX4 MATa f lo8-1 his3 leu2 lys2 ura3 gasΔ::KanMX4 MATa f lo8-1 his3 leu2 lys2 ura3 gpi7::KanMX4 MATa ura3-52 trpΔ::hisG leu2Δ::hisG his3Δ::hisG MATa ura3-52 trpΔ::hisG leu2Δ::hisG his3Δ::hisG mss11Δ::ura3 industrial wine yeast strain (unknown genotype) industrial wine yeast strain (unknown genotype) MATa, leu2-3, 112 trp1-1 can1-100 ura3-1 ade2-1 his3-11,15 industrial wine yeast strain (unknown genotype) trp1 ade1 his7 ura1 gal1 industrial wine yeast strain (unknown genotype) industrial wine yeast strain (unknown genotype) industrial wine yeast strain (unknown genotype) industrial wine yeast strain (unknown genotype) industrial wine yeast strain (unknown genotype) isolated strain from barrel fermented Cabernet Sauvignon isolated in from grape juice during harvest. isolated from lambic beer, Belgium isolated 2005, FOEB collection type Strain from CBS collection isolated from Chardonnay juice during 2011 harvest, (unknown genotype)

reference Anchor Yeast, Cape Town, South Africa Govender, 200920 Govender, 200920 Govender, 200920 Govender, 200920 Govender, 200920 Lallemand Inc., Montreal, Canada Govender, 200920 Govender, 200920 Govender, 200920 Govender, 200920 Govender, 200920 Winston et al., 199528 Govender, 200920 Govender, 200920 Govender, 200920 Brachmann et al., 199814 Bester et al., 200629 Bester et al., 200629 EUROSCARF EUROSCARF EUROSCARF Van Dyk et al., 200515 Van Dyk et al., 200515 Anchor Yeast, Cape Town, South Africa Lallemand Inc., Montreal, Canada Yeast Genetic Stock Center (Berkeley, CA, USA) DSM Food Specialties B.V. none Anchor Yeast, Cape Town, South Africa Lallemand Inc., Montreal, Canada DBVPG, Coconcelli Luca, Italy Phaff Institute, USA DBVPG, Perugia, Italy Phaff Institute, USA Lallemand Inc., Montreal, Canada Oelofse, 200830 IWBT, Cape Town, South Africa Instituto Superior de Agronomica, Lisbon, Portugal FOEB Collection, France CBS Collection, http://www.cbs.knaw.nl/ IWBT, Cape Town, South Africa

strains such as BM45 and VIN13; and those engineered to flocculate (i.e., FLO11p::SMR1-ADH 2p),7 along with nonSaccharomyces such as Brettanomyces bruxellensis and Hanseniaspora uvarum.19 This broad diversity of strains was chosen to assess the ability of ATR-FT-MIR spectroscopy to evaluate the diversity and/or conservation of yeast cell wall composition across the species, the genus, and possibly at the strain level under fermentative conditions. The use of multivariate analyses including principal component analysis (PCA), hierarchical cluster analysis (HCA) (Ward’s distance), and orthogonal projections to latent structures discriminant analysis (OPLSDA) modeling permitted separation and clustering of the

neously under identical fermentative conditions. Few studies have evaluated a broad range of yeast species and strains under wine fermentative conditions to evaluate how cell wall properties differ or are modulated by genetic background in similar/conserved environmental AF conditions. In the current study we used ATR-FT-MIR spectroscopy to analyze the cell wall residues prepared after individual fermentation using 40 different strains inoculated into both synthetic (MS300) and Chardonnay grape must. The strains selected included S. cerevisiae species, such as laboratory strains s288c14 and Σ1278b15, and the cell wall mutants BY4742 knr4Δ,16,17 BY4742 gas1Δ9, and BY4742 gpi7Δ;18 industrial 10055

DOI: 10.1021/acs.jafc.5b03154 J. Agric. Food Chem. 2015, 63, 10054−10063

Article


Diamond ATR accessory was used to record spectra (128 co-added scans per analysis) between 4000 and 650 cm−1 using a Geon-KBr beamsplitter and DTGS/Csl detector. Multivariate and Univariate Statistics. Univariate descriptive statistics and one-way ANOVA (analysis of variance) were performed (with P = 0.05) with the guidance of the Centre for Statistical Consultation (CSC) at Stellenbosch University (Prof. Martin Kidd). Software packages used include Statistica 10 (Statsoft) and Excel 2010 (Microsoft). Multivariate methods, including PCA, were performed using SIMCA 14 software (MKS Umetrics). Spectra (FT-IR) were imported into SIMCA 14 using conversion algorithms. Spectral data were averaged after baseline correction smoothing (Savitsky−Golay and multiplicative scatter correction). All of the tests were performed at P = 0.05. The spectral wall protein, lipid, and carbohydrate (1768− 770 cm−1) region was further divided into three spectral windows, 1185−937 cm−1 (region 1), 1478−1185 cm−1 (region 2), and 1768− 1478 cm−1 (region 3) (W3), for ease of interpretation in SIMCA 14 software. PCA, HCA (Ward’s distance), and OPLS-DA were performed using SIMCA 14. HCA is a method of cluster analysis that uses an agglomerative “bottom-up” approach using a series of observations to start a pair of clusters. These cluster pairs are then merged as the method moves up the hierarchy; such an approach is slow and time-consuming with large data sets. In this case Ward’s method was used as a criterion and uses objective functioning to assign clusters such as error sum of squares and minimum variance within the data set. It is also important to note that the algorithms used to perform HCA are not the same as for PCA; and although there are often similarities in the resulting patterns, this does not need to be the case. Data were prior to PCA and OPLS-DA modeling column centered and scaled to unit variance (UV). Cross-validation was used to assess model significance.

different species. These models were interpreted by spectral wavenumber/region assignments with reference to the literature to permit spectral wavenumber/region assignments.

■

MATERIALS AND METHODS

Yeast Strains and Growth Conditions. A total of 40 strains consisting of S. cerevisiae, Saccharomyces paradoxus, and nonSaccharomyces spp. were used for the analysis, including laboratory, industrial, and genetically modified strains (see Table 1 for strain information). Freeze-cultures were streaked out on YPD (yeast extract 2%, peptone 4%, and dextrose 4%) agar plates, and after 48 h of incubation at 30 °C, colonies were selected for confirmation of strain identity using molecular typing techniques. Confirmed strains were grown in an overnight culture of YPD at 30 °C with shaking at 80 rpm and used as seed cultures for subsequent experiments. Cells were washed twice in a sterile physiological buffer (10 mM phosphate buffered saline, 0.9% NaCl) prior to inoculation. Overnight cultures were evaluated for optical density at 600 nm using a UV−visible spectrophotometer (Lamda 25, PerkinElmer, USA). Fermentation bottles were inoculated with YPD cultures in the logarithmic growth phase (around OD600 = 1) to an OD600 of 0.1 (i.e., a final cell density of approximately 106 cfu mL−1). Experiments consisted of three biological data sets with duplicate technical repeats for all strains fermented. Effectively this means that three flasks per strain were seeded and fermented for each biological experiment (three separate biological experiments) and two technical repeats (which consisted of rescanning the same sample twice) per flask were undertaken. For each analysis of a strain sample, 128 co-added scans were integrated into a single spectrum. Must and Wine Alcoholic Fermentation Conditions. MS300 is a model synthetic must that approximates to a natural grape must. The sugar concentration was altered, as laboratory strains are not able to ferment at high sugar concentrations, to 50 g/L glucose and 50 g/L fructose. Similarly for fermentations using grape must, Chardonnay grapes (approximately 20 kg) were destemmed, crushed, and pressed to obtain a must of 21.9 °Brix, titratable acidity of 7.59 g/L, and pH of 3.5 at a temperature of 25 °C. Again due to the high sugar concentration the Chardonnay must, after filter sterilization using 0.45 μm filters and confirmation via agar plating for contamination, was diluted 1:2 with sterile distilled water and thereafter supplemented with oligo-elements, vitamins, and amino acids, and the nitrogen concentration was adjusted. Fermentations were performed in 80 spice bottles (preautoclaved) and capped using a rubber stopper with a CO2 outlet. Monitoring of fermentation progress was performed by measuring the daily weight loss of the bottles (using an electronic balance) relative to the unfermented starting weight. Most strains completed fermentation within 10−14 days of initial inoculation (data not shown). Fermentation cultures were collected after the completion of fermentation, transferred into 50 mL Falcon tubes, and centrifuged at 4000 rpm for 5 min; the supernatant was discarded; and the cell pellet was washed twice with physiological solution (0.9% NaCl). Washed cell pellets were frozen at −80 °C prior to further manipulations. Preparation of Alcohol-Insoluble Residues (AIR) from Yeast Samples. Pelleted cells were suspended in 5 mL of distilled water to which 40 mL of absolute ethanol was added and placed on a rotating wheel for 2 h at room temperature. After centrifugation at 4000 rpm for 5 min, fresh absolute ethanol was added to the tubes and further rotated for another 2 h. This was repeated with a methanol/ chloroform (1:1) mixture, absolute chloroform, chloroform acetone (1:1) mixture, and absolute acetone followed by final centrifugation at 4000 rpm for 5 min. The AIR were suspended in distilled water, frozen at −80 °C, and then freeze-dried. Vibrational Reflectance Spectroscopy of Cell Wall Samples. A complete wavelength absorbance spectrum in the infrared region from each sample was obtained directly from AIR. Powdered samples were placed directly (in a contact mode) onto the diamond window and clamped in position to obtain reflectance spectra. The NEXUS 670 (Thermo, USA) instrument equipped with a Golden Gate

■

RESULTS AND DISCUSSION In previous studies it has been shown that the cell wall of S. cerevisiae retains its integrity even in the acidic wine Table 2. Main Absorption Bands and Assignments for the ATR-FT-IR Spectra of Yeast Species Based on Reference 1 and References Therein spectral region

absorption band (cm−1)

main assignments −1

1

1185−937

∼972 cm mannans ∼998 cm−1 β-1,6 glucans ∼1025 cm−1 β-1,4 glucans ∼1035 and 1050 cm−1 mannans ∼1108 cm−1 β-1,3 glucans

2

1478−1185

∼1200 cm−1 carbohydrates ∼1240 cm−1 phosphate in DNA and RNA ∼1300 cm−1 amide III ∼1350 cm−1 CH2 lipids ∼1390 cm−1 CH2 lipids ∼1440 to ∼1470 cm−1 lipids

3

1768−1478

∼1550 cm−1 amide II: N−H and C−H ∼1670 cm−1 amide I: carbonyls ∼1740 cm−1 carbonyls in lipid esters

environment, and although it undergoes morphological alterations (i.e., wrinkling), the overall composition remains unaltered.1,12 Further to this, FT-IR spectroscopy used in ATR mode has been used to successfully track autolysis of EC1118 (a wine yeast used in sparkling wines) during fermentation and aging.1 This technique is able to monitor the global cell wall composition, that is, lipid, carbohydrate, and protein content, in 10056


Article


Figure 1. (A) PCA plot showing the distribution of different yeast species based on the fingerprint spectral region (1768−770 cm−1) where principal component 1 (PC-1) accounts for 63% of the variation and PC-2 accounts for 19% of the variation in the data set. PC-1 appears to be driving the separation of Saccharomyces spp. (samples 1−34, represented as circles) from non-Saccharomyces spp. (samples 35−40, represented as triangles). (B) PCA plot showing the distribution of different yeast species and Saccharomyces genotypes (e.g., BM45, VIN13, etc.) based on the fingerprint spectral region (1768−770 cm−1) where PC-1 accounts for 63% of the variation and PC-2 accounts for 19% of the variation in the data set. Laboratory strains are represented as circles, industrial strains as boxes, and non-Saccharomyces spp. as triangles.

an unbiased manner. A typical wavescan contains a fingerprint region (1768−770 cm−1), which is rich in information on lipid, protein (including CHO and amide groups), mannans, nucleotide, phospholipid, and glucan functional chemistry groups (see Table 2 for main assignments). We subdivided for ease of interpretation this fingerprint region into region 1 (1185−937 cm−1), composed primarily of glucans and mannans; region 2 (1478−1185 cm−1), which includes a mixture of lipid moieties, amides, protein carboxyls, nucleotides (DNA and RNA), and phospholipids; and finally region 3 (1768−1478 cm−1), indicating the presence of amide I and II bands as well as carbonyl groups of lipid esters. As data sets were highly similar, and identical conclusions drawn between MS300 and Chardonnay fermentations, only Chardonnay data are reported.

First, PCA was performed on all 40 yeast strains including their overall fingerprint region (Figure 1A). The resulting PCA scores plot shows that S. cerevisiae and S. paradoxus21,22 (plotted as circles) are mainly located in the left (half) and top (right) clusters, whereas all of the non-Saccharomyces spp. (plotted as triangles) are found in the bottom (left) of the plot (Figure 1A). The first principal component (PC-1) appears important for separation of the different species, explaining 63% of the variation in the data, whereas PC-2 accounts for a further 19% of the variation (Figure 1A). The combination of PC-1 and PC-2 appears necessary for the separation of Saccharomyces spp. from non-Saccharomyces spp. (Figure 1A). The same PCA plot as Figure 1A but now annotated/colored for genotype provides further insight into the separation significance (Figure 1B). Furthermore, non-Saccharomyces spp. are plotted as triangles, whereas Saccharomyces spp. are replotted as laboratory 10057


Article


Figure 2. PCA plot showing the distribution of different laboratory, industrial, and non-Saccharomyces yeast strains based on the fingerprint spectral region (1768−770 cm−1) where PC-1 accounts for 63% of the variation and PC-2 accounts for 19% of the variation in the data set. Laboratory strains are represented as circles, industrial strains as boxes, and non-Saccharomyces spp. as triangles. Absorance wavescans: (A) 1768−770 cm−1 of sample 8 representing genotype BM45-F1A; (B) 1768−770 cm−1 of sample 36 representing Hanseniaspora uvarum; (C) 1768−770 cm−1 of sample 18 representing s288c FLO8; (D) 1768−770 cm−1 of sample 33 representing Phaff 01-146. For panels A−D the y-axis denotes absorbance units and the x-axis represents wavenumbers in cm−1.

Clearly the fingerprint region allows separation of nonSaccharomyces spp. from S. cerevisiae and S. paradoxus strains; furthermore, the genotypes also showed clear clustering, indicating that cell wall composition shows value as a means to classify yeast samples at genus, species, and potentially strain level. To determine the corresponding loading variables, sourced from region 1 (1185−937 cm−1), region 2 (1478− 1185 cm−1), and finally region 3 (1768−1478 cm−1) of the wavescan profile, a PCA biplot was performed and inspected (not shown). A PCA plot (Figure 2) showing the distribution of laboratory (green circles), industrial (blue boxes), and nonSaccharomyces spp. (red triangles) is provided with PC-1 and PC-2 corresponding to 63 and 19% of the variation,

strains (circles) and industrial strains (boxes) (Figure 1B). Now it is clear the laboratory strains (circles) cluster to the left of center, whereas PC-2 at 19% is driving a separation of industrial strains (boxes) from the center to the top half of the PCA plot (Figure 1B). The coloring indicates the genotypes; for the laboratory strains common clusters appear for s288c and FY23 to the left of center plot; similarly the industrial strains VIN13 and BM45 generally cluster together, as well as the S. paradoxus strains. The use of S. paradoxus strains has been investigated by Ndlovu et al.22 due to their interesting cell wall properties and haze-protecting features in experimental and commercial wine fermentations. 10058


Article


Figure 3. Hierarchical cluster analysis (HCA) of all 40 samples using squared Euclidean distance and Ward’s method. Laboratory, industrial, and non-Saccharomyces groupings are indicated by brackets and labels.

three sample clusters, individual representative wavescans are provided for four selected yeast strains (Figure 2A−D). Sample 8 representing industrial strain BM45-F1A has a glucan to amide ratio of 3.5:1.5 (Figure 2A). At the opposite end of the plot is sample 36, representing Hanseniaspora uvarum Y1133, a non-Saccharomyces sp. that in strong contrast has a glucan to amide ratio of almost 1:1, suggesting a protein-rich surface matrix (Figure 2B). Sample 18 wavescan represents s288c FLO8 laboratory yeast strain and exhibits a ratio of 3:1, suggesting a glucan-rich matrix (Figure 2C). Sample 33 representing the central cluster of samples and the industrial strain Phaff 01-146, shows a glucan to amide ratio of 2.5:1 (Figure 2D). Hence, glucan-rich samples cluster to the left of the plot (Figure 2) mainly representing laboratory strains, whereas protein (amide) rich samples mostly representing non-

respectively, as performed for Figure 1. It is clear from the plot (Figure 2) that laboratory strains (green circles) cluster with region 1 loading variables (mainly glucans and mannans); in contrast, the non-Saccharomyces spp. represented as red triangles strongly co-localize with region 3 wavenumbers, which represent amide bands of proteins and carboxyl groups (see Table 2). The top portion of the plot where mostly industrial strains separate from the central axis corresponds to region 2 wavenumbers, which represent a mixture of lipid moieties, amides, protein carboxyls, nucleotides (DNA and RNA), and phospholipids, and appears to drive the differences; interestingly, BY4742 knr4Δ, BY4742 gas1Δ, and BY4742 gpi7Δ, the three cell wall deletion mutants in the BY4742 laboratory strain background, also cluster here. To further emphasize the differences between the central axis from the 10059


Article


Figure 4. (A) PCA plot showing the distribution of 27 different laboratory (blue circles) and industrial (green boxes) strains based on the fingerprint spectral region (1768−770 cm−1) where PC-1 accounts for 64% of the variation and PC-2 accounts for 15% of the variation in the data set. (B) PCA plot as in (A) but now showing the distribution of 27 different strains colored according to genotype.

Saccharomyces spp. cluster to the right-hand side, and the bulk of the samples cluster in the center (Figure 2). Region 2 (1478−1185 cm−1), representing a mixture of lipid moieties, amides, protein carboxyls, nucleotides (DNA and RNA), and phospholipids, appears to be able to discriminate within the industrial strain cluster pattern (Figure 2). It should be noted that region 2 appears linked to PC-2 regularly in the analysis, a very difficult region to interpret; hence, PC-1 is referred to much more often. Alternative methods were then sought to determine the separation of laboratory, industrial, and nonSaccharomyces spp. from the data sets obtained. HCA was able to assign five broad clusters, and these could be assigned to either laboratory, industrial, or non-Saccharomyces spp. strains (Figure 3). The first cluster belongs to laboratory genotypes s288c and Σ1278b, and the second cluster groups all of the non-Saccharomyces spp. (e.g., Dekkera bruxellensis) together in one cluster. The third cluster contains a mixture of mainly VIN13 strains, S. paradoxus, and the

BY4742 deletion mutants. The fourth cluster contains the FY23 laboratory genotypes as well as other laboratory strains such as W303. The fifth cluster contains all industrial strains, including many BM45 strains with EC1118, N96, and WE372 among others. The clustering is not clearly able to separate all strains based on their genetic relatedness, but the broad groupings are logical and given that the PCA was also not able to separate all strains perfectly, these results are still noteworthy. However, unlike PCA with scores and loading plots, this classification approach does not give insight into the variables driving the variation as the algorithms used to derive the clusters are not the same as the equations used to develop the PCA models. We wished to return to PCA, to further tease out the differences between selected industrial and laboratory strains and to probe the underlying causality for the different clustering/grouping observed thus far by removing the non-Saccharomyces spp. and remodeling the data. 10060


Article


Figure 5. (A) OPLS-DA plot showing the distribution of 27 different laboratory (green circles) and industrial (blue circles) strains based on the fingerprint spectral region (1768−770 cm−1) where the orthogonal variation accounts for 36% of the variation in the data set. (B) OPLS-DA loading plot showing the wavenumbers of the fingerprint spectral region (1768−770 cm−1), which contribute to the main separation observed in (A) along PC-1. The y-axis denotes p(corr) absorbance units, and the x-axis denotes wavenumbers in cm−1.

RNA), and phospholipids. However, we wished to determine if the wavescan data contain variation that will allow a complete and distinct separation between industrial and laboratory strains. OPLS-DA is a technique that sharpens the interpretation of the discriminant variation (laboratory vs industrial strains) by separating it from the variation in data that is not correlated to class separation. The OPLS-DA score plot (Figure 5A) for the 27 objects shows all industrial strains clustering to the left of the central axis, whereas the laboratory strains (including the BY4742 mutants) now group to the right-hand side of the chart. Interestingly, the VIN13, BM45, and S. paradoxus strains show clear groupings now based on genotype characteristics (Figure 5A). Similarly, laboratory strains cluster to the righthand side with FY23 and s288c genotypes grouping; the BY4742 strains now group in the top-right quadrant of the OPLS-DA score chart (Figure 5A). The corresponding loading plot (Figure 5B) shows that industrial strains are enriched in

Replotting the PCA but excluding all non-Saccharomyces spp. and selected industrial and laboratory strains produces a plot containing 27 objects (Figure 4A). In this replotted score chart, all industrial strains cluster to the left of the central axis, and, except for the BY4742 deletion mutants, all laboratory strains cluster to the right of the central axis (Figure 4A). The industrial VIN13, BM45, and S. paradoxus strains show a mixed clustering with no distinct subclusters evident; the loading variables for the right-hand side of the chart correspond mainly to region 3 (1768−1478 cm−1), suggesting enrichment in amide I and II bands of proteins (Figure 4B). To the left-hand side of the plot the laboratory strains (apart from BY4742) show clusters for the FY23 and s288c strains; the driving loading variables are correlated with mannans and glucans (Figure 4B). The broad distribution of industrial strains from the bottom-middle to the top-right appears also to be driven by region 2 (1478−1185 cm−1), which is a mixture of lipid moieties, amides, protein carboxyls, nucleotides (DNA and 10061


Article


copy, albeit within the limitations of the technique measuring surface chemistry via spectroscopy, is able to discriminate at the genus, species, and potentially strain taxonomic levels for S. cerevisiae. Furthermore, application of such FT-IR spectroscopic technology in industrial processes, such as wine fermentations, offers a means to chemotype strains and to monitor fermentations in a winemaking environment providing insights into the contribution provided by the changing nature of the yeast cell wall.

amides I and II of proteins (region 1) and some mannans (region 2) as well as β-1,6-glucans (region 3), whereas laboratory strains are dominated by a significant proportion of mannans and β-1,3;β-1,4-glucans (region 3) in their cell wall structures. When considering the method described here using ATR-FT-MIR spectroscopy of processed yeast cell walls using multivariate data analysis to analyze mannoprotein to glucan (and mannan) relative proportion exposed on the surface the cells, in relation to other methods,1011 a number of points must be borne in mind. These methods use a form of acid hydrolysis (e.g., ref 10) to measure the amount of mannose to glucose ratio of the yeast cell wall from must (and applicable to wine but residual glucose remains); hence, the method in ref 11 addresses this by collecting the mannoprotein fraction released into wine and measuring the mannose released after hydrolytic degradation. Both methods are valid1011 in that they measure total mannoproteins (and possibly glucans); however, in the spectroscopic method of this paper we measure the surface chemistry of the yeast cell walls, which indicates the exposure of mannoproteins and glucans, where acid hydrolysis measures total monomers present in the sample bulk matter; hence, the spectroscopic approach, albeit on processed cell walls, complements the approach of Quirόs et al.,1011 and also provides additional insights; it is important to note that the methods need not provide the same data, suggesting further hypotheses on the functional properties of these glycopolymers in yeast fermentation biology. It is clearly evident from this study that non-Saccharomyces yeasts are generally richer in the protein component of their cell walls in relation to the carbohydrate component; this is not surprising given the diversity of habitats these species inhabit in the natural environment (e.g., insects such as wasps and bees, various soil habitats, wine cellars, etc.) where properties such as surface adhesion may be important.19 These non-Saccharomyces yeasts also play a distinct and important role in winemaking and under scientifically controlled conditions are able to impart distinctive taste and aroma contributing to the specific style of wine being produced.19 Relative to the laboratory yeasts, industrial strains were also rich in protein in their wall structures, confirming previous studies showing that mannoproteins are released into wine, modifying haze susceptibility and mouthfeel,23,24 whereas aroma volatile composition is influenced by yeast wall composition.25 In the context of yeast selection and evolution it is generally thought that Flo8p regulates filamentous growth in wild yeasts26 and that a mutation prevents expression of this gene in laboratory strains. Indeed, the FLO gene family includes structural proteins such as FLO11,15,27 which are involved in pseudohyphal formation and invasive growth phenotypes. Similarly, although the cell wall mutants BY4742 knr4Δ, BY4742 gas1Δ, and BY4742 gpi7Δ all initially clustered with the industrial strains, application of OPLS-DA showed that fundamental aspects of their cell wall architecture were not affected by the mutations and were identifiable with laboratory strains. These observations raise interesting questions regarding the domestication of laboratory S. cerevisiae strains from wild progenitors and suggests fundamental aspects of cell wall architecture have been modified by human-mediated selection processes. However, the reasons behind these differences are far from clear, and we can only speculate on function; further work is clearly necessary to understand this observed diversity in the context of yeast selection and evolution. Overall, the cell wall data presented in this study confirm that ATR-MIR spectros-

■

AUTHOR INFORMATION

Corresponding Author

*(J.P.M.) Phone: +27 21 808 2773. Fax: +27 21 808 3771. Email: [email protected]. Author Contributions

J.P.M. and F.F.B. designed the study. J.P.M. and S.-L.Z. collected and analyzed the data. F.F.B. and B.D. assisted with provision of the yeast strains and their authentication. H.H.N. and J.T. assisted with multivariate data analysis. All authors read and approved the final manuscript. Funding

Funding for this study was provided by the Wine Industry Network of Expertise and Technology (Winetech), the Institute for Wine Biotechnology (IWBT), Stellenbosch University, and the South African Technology and Human Resources for Industry Programme (THRIP). Notes

The authors declare no competing financial interest.

■

ABBREVIATIONS USED AIR, alcohol-insoluble residue; ATR, attenuated total reflectance; FT-IR, Fourier transform−infrared spectroscopy; HCA, hierarchical cluster analysis; MIR, mid-infrared spectroscopy; PCA, principal component analysis; OPLS-DA, orthogonal projections to latent structures−discriminant analysis; YPD, yeast peptone dextrose

■

REFERENCES

(1) Cavagna, M.; Dell’Anna, R.; Monti, F.; Rossi, F.; Torriani, S. Use of ATR-FTIR microspectroscopy to monitor autolysis of Saccharomyces cerevisiae cells in a base wine. J. Agric. Food Chem. 2010, 58, 39−45. (2) Lomolino, G.; Curioni, A. Protein haze formation in white wines: effect of Saccharomyces cerevisiae cell wall components prepared with different procedures. J. Agric. Food Chem. 2007, 55, 8737−8744. (3) Moore, J. P.; Divol, B. Tracking the careers of grape and wine polymers using biotechnology and systems biology. In Biotechnology in Functional Foods and Nutraceuticals; Bagchi, D., Lau, F. C., Ghosh, D. K., Eds.; CRC Press/Taylor and Francis Publishers: Boca Raton, FL, USA, 2011; pp 389−406. (4) Verstrepen, K. J.; Klis, F. M. Flocculation, adhesion and biofilm formation in yeasts. Mol. Microbiol. 2006, 60, 5−15. (5) Govender, P.; Bester, M.; Bauer, F. F. FLO gene-dependent phenotypes in industrial wine yeast strains. Appl. Microbiol. Biotechnol. 2010, 86, 931−945. (6) Klis, F. M.; Mol, P.; Hellingwerf, K.; Brul, S. Dynamics of cell wall structure in Saccharomyces cerevisiae. FEMS Microbiol. Rev. 2002, 26, 239−256. (7) Popolo, L.; Vai, M. The Gas1 glycoprotein, a putative wall polymer cross-linker. Biochim. Biophys. Acta, Gen. Subj. 1999, 1426, 385−400. (8) Rolli, E.; Ragni, E.; Calderon, J.; Porello, S.; Fascio, U.; Popolo, L. Immobilization of the glycosylphosphatidylinositol-anchored Gas1

10062


Article

Journal of Agricultural and Food Chemistry protein into the chitin ring and septum is required for proper morphogenesis in yeast. Mol. Biol. Cell 2009, 20, 4856−4870. (9) Dallies, N.; Francois, J.; Paquet, V. A new method for the quantitative determination of polysaccharides in the yeast cell wall. Application to the cell wall defective mutants of Saccharomyces cerevisiae. Yeast 1998, 14, 1297−1306. (10) Quirós, M.; Morales, P.; Pérez-Través, L.; Barcenilla, J. M.; Gonzalez, R. A new methodology to determine cell wall mannoprotein content and release in wine yeasts. Food Chem. 2011, 125, 760−766. (11) Quirós, M.; Gonzalez, R.; Morales, P. A simple method for the total quantification of mannoprotein content in real wine samples. Food Chem. 2012, 134, 1205−1210. (12) Galichet, A.; Sockalingum, G. D.; Belarbi, A.; Manfait, M. FTIR spectroscopic analysis of Saccharomyces cerevisiae cell walls: study of an anomalous strain exhibiting a pink-colored cell phenotype. FEMS Microbiol. Lett. 2001, 197, 179−186. (13) Burattini, E.; Cavagna, M.; Dell’Anna, R.; Malvezzi Campeggi, F.; Monti, F.; Rossi, F.; Torriani, S. A FTIR microspectroscopy study of autolysis in cells of the wine yeast Saccharomyces cerevisiae. Vib. Spectrosc. 2008, 47, 139−147. (14) Brachmann, C. B.; Davies, A.; Cost, G. J.; Caputo, E.; Li, J.; Hieter, P.; Boeke, J. D. Designer deletion strains derived from Saccharomyces cerevisiae S288C: a useful set of strains and plasmids for PCR-mediated gene disruption and other applications. Yeast 1998, 14, 115−132. (15) Van Dyk, D.; Pretorius, I. S.; Bauer, F. F. Mss11p is a central element of the regulatory network that controls FLO11 expression and invasive growth in Saccharomyces cerevisiae. Genetics 2004, 169, 91− 106. (16) Dagkessamanskaia, A.; Martin-Yken, H.; Basmaji, F.; Briza, P.; Francois, J. Interaction of Knr4 protein, a protein involved in cell wall synthesis, with tyrosine tRNA synthetase encoded by TYS1 in Saccharomyces cerevisiae. FEMS Microbiol. Lett. 2001, 200, 53−58. (17) Basmaji, F.; Martin-Yken, H.; Durand, F.; Dagkessamanskaia, A.; Pichereaux, C.; Rossignol, M.; Francois, J. The ‘interactome’ of the Knr4/Smi1, a protein implicated in coordinating cell wall synthesis with bud emergence in Saccharomyces cerevisiae. Mol. Genet. Genomics 2006, 275, 217−230. (18) Benachour, A.; Sipos, G.; Flury, I.; Reggiori, F.; CanivencGansel, E.; Vionnet, C.; Conzelmann, A.; Benghezal, M. Deletion of GPI7, a yeast gene required for addition of a side chain to the glycosylphosphatidylinositol (GPI) core structure, affects GPI protein transport, remodeling, and cell wall integrity. J. Biol. Chem. 1999, 274, 15251−15261. (19) Jolly, N. P.; Varela, C.; Pretorius, I. S. Not your ordinary yeast: non-Saccharomyces yeasts in wine production uncovered. FEMS Yeast Res. 2014, 14, 215−237. (20) Govender, P. Industrial Yeast Strains Engineered for Controlled Flocculation. Doctoral dissertation, Stellenbosch University, Stellenbosch, South Africa, 2009. (21) Johnson, L. J.; Koufopanou, V.; Goddard, M. R.; Hetherington, R.; Schafer, S. M.; Burt, A. Population genetics of the wild yeast Saccharomyces paradoxus. Genetics 2004, 166, 43−52. (22) Ndlovu, T. Mannoprotein Production and Wine Haze Reduction by Wine Yeast Strains. Doctoral dissertation, Stellenbosch University, Stellenbosch, South Africa, 2012. (23) Gonzales-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. (24) Gonzales-Ramos, D.; Cobollero, E.; Gonzalez, R. A recombinant Saccharomyces cerevisiae strain overproducing mannoproteins stabilizes wine against protein haze. Appl. Environ. Microbiol. 2008, 74, 5533− 5540. (25) Pradelles, R.; Alexandre, H.; Ortiz-Julien, A.; Chassagne, D. Effects of yeast cell-wall characteristics on 4-ethylphenol sorption capacity in model wine. J. Agric. Food Chem. 2008, 56, 11854−11861. (26) Liu, H.; Styles, C. A.; Fink, G. R. Saccharomyces cerevisiae S288C has a mutation in FLO8, a gene required for filamentous growth. Genetics 1996, 144, 967−978.

(27) Lambrechts, M. G.; Bauer, F. F.; Marmur, J.; Pretorius, I. S. Flo11, a mucin-like protein that is regulated by Mss10, is critical for pseudohyphal differentiation in yeast. Proc. Natl. Acad. Sci. U. S. A. 1996, 93, 8419−8424. (28) Winston, F.; Dollard, C.; Ricupero-Hovasse, S. L. Construction of a set of convenient Saccharomyces cerevisiae strains that are isogenic to S288C. Yeast 1995, 11, 53−55. (29) Bester, M. C.; Pretorius, I. S.; Bauer, F. F. The regulation of Saccharomyces cerevisiae FLO gene expression and Ca2+-dependent flocculation by Flo8p and Mss11p. Curr. Genet. 2006, 49, 375−383. (30) Oelofse, A. Investigating the Role of Brettanomyces and Dekkera during Winemaking. Doctoral dissertation, Stellenbosch University, Stellenbosch, South Africa, 2008.

10063


A Multivariate Approach Using Attenuated Total Reflectance Mid

Recommend Documents