Microarray analysis of DV10 Saccharomyces cerevisiae during Second Fermentation under Oenological Conditions

by

Andrew Fletcher

A Thesis Presented to The University of Guelph

In partial fulfilment of requirements for the degree of Master of Science in Bioinformatics

Guelph, Ontario, Canada

© Andrew S. Fletcher, May, 2015

ABSTRACT

Microarray Analysis of DV10 Saccharomyces cerevisiae During Second Fermentation Under Oenological Conditions

Andrew S. Fletcher Advisors: University of Guelph, 2014 Dr. G van der Merwe Dr. P McNicholas

During aging of high quality sparkling wine, yeasts that have finished fermentation slowly die and release metabolites into the wine that are responsible for characteristic taste and foaming properties. Two of the main processes that aid in aging are autolysis and autophagy but their regulation during secondary fermentation remains largely unknown. Microarray analysis of the Saccharomyces cerevisiae strain DV10 during secondary fermentation yielded some potential targets for future studies regarding these processes. Several stress pathways were identified as well as the GATA transcription factors GAT2, GAT3, and GAT4. Many proposed targets of GATA transcription factors are part of the ATG family of autophagy genes, which had one or more copies of the GATA consensus sequence in their promoters. This possible relationship is of particular interest due to the poorly understood roles of these GATA factors and could lead to better understanding of the regulatory processes of autophagy.

ACKNOWLEDGMENTS

I did not expect to be writing a thesis during post secondary studies. For this experience I owe thanks to my two advisors. Without them I would have graduated a long time ago. Instead I was given the “opportunity” to transfer into a M.Sc. program. But kidding aside, I would do it all again. My project was the envy of everyone I met. So thank you Dr. George van der Merwe, for choosing me to be the lucky grad student, and for introducing me to a world I always wanted to be a part of. Also, for not laughing in my face when I insisted I could work full time on the other side of the country and still write a thesis (even though I obviously couldn’t).

Thank you to Paul McNicholas for offering to take me on, and providing funding to give me an opportunity I wouldn’t have otherwise had. Despite being impossibly busy, I could always get the help I needed. Every meeting we had left me feeling more encouraged. I also must recognize the efforts of Gavin Robertson and the crew at the Niagara College Teaching Winery for producing the sparkling wine necessary for my experiments. A big thanks, also, to Angus Ross for getting me started in the van der Merwe lab, and always being there to help me find things. And to Richard

Priess for the brainstorming help.

Finally, I have to thank my friends and family for keeping me alive and sane through the whole ordeal. Most importantly, I must express my appreciation for

Mel. Without her I would have been a model grad student. However, she showed me that pursuing a master’s degree and the rest of life’s goals needn’t be mutually exclusive.

iii Table of Contents

ABSTRACT ...... i Table of Contents ...... iv List of Tables ...... vii List of Figures ...... x List of Abbreviations ...... xii Chapter 1 – Introduction ...... 1 1.1. Overview of sparkling wine production ...... 1 1.1.1. Methods of production ...... 2 1.1.2. Base wine production ...... 2 1.1.3 Secondary fermentation ...... 5 1.1.4. Alternative methods of sparkling wine production ...... 8 1.2. Overview of Saccharomyces cerevisiae ...... 9 1.2.1 Yeast strains used in sparkling wine production ...... 10 1.2.2. Carbon Metabolism ...... 11 1.2.2.1. Glycolysis ...... 12 1.2.2.2. Fermentation ...... 13 1.2.3. Stress response and consequences ...... 15 1.2.4. Osmotic stress response ...... 16 1.2.5. Autolysis ...... 17 1.2.6. Yeast cell wall ...... 19 1.2.7. Autophagy ...... 20 1.2.8. Autophagy regulation ...... 23 1.2.9. GATA family of transcription factors ...... 25 1.3. Microarrays ...... 26 1.3.1. In-situ synthesized oligonucleotide arrays ...... 27 1.3.2. Hybridization ...... 27 1.4 Data Analysis ...... 28 1.4.1. Pre-processing ...... 28 1.4.2. Background noise and correction ...... 29 1.4.3. Normalization ...... 31 1.4.4. Cluster analysis ...... 34 1.4.5. Model-based clustering ...... 35 1.4.6. Longitudinal Clustering ...... 36 1.4.7. Gene ontology ...... 38 1.5. Previous microarray studies of S. cerevisiae during secondary sparkling wine fermentation ...... 38 1.6. Hypothesis and Objectives ...... 39 Chapter 2 - Materials and Methods ...... 40 2.1. Sparkling wine production: ...... 40 2.1.1. Base wine fermentation ...... 40 2.1.2. Second Fermentation ...... 41 2.2. Yeast collection ...... 42 2.2.1. Cell viability analysis ...... 43 2.2.2. RNA isolation ...... 44 2.3. Microarray Methods ...... 45 2.4. Metabolite Analysis ...... 46 iv 2.5. Data Analysis ...... 47 2.5.1. HPLC Analysis ...... 47 2.5.2. Microarray Analysis ...... 48 Chapter 3 – Results ...... 50 3.1. Yeast culture growth and liqueur de tirage followed industry standards ...... 50 3.2. Abnormal fructose metabolism but standard fermentation product concentrations ...... 52 3.3. Substantial loss of cell viability during fermentation ...... 54 3.4. Conclusions and discussion ...... 58 3.5. Microarray analysis shows most genes have altered regulation during the sparkling wine production process ...... 59 3.6. Genes can be divided into 15 groups based on similar expression profiles ...... 60 3.7. Cluster 1 shows increased transcription toward the end of fermentation ...... 66 3.7.1. Transcription factor properties in Cluster 1 ...... 69 3.7.2. DNA binding activity in Cluster 1 ...... 72 3.7.3. Glycosyl hydrolyase activity in Cluster 1 ...... 76 3.7.4. Unfolded protein binding activity in Cluster 1 ...... 79 3.7.5. Spermine transporter activity in Cluster 1 ...... 82 3.7.6. Adenyl-nucleotide exchange factor activity in Cluster 1 ...... 83 3.7.7. Enzyme regulation activity in Cluster 1 ...... 85 3.7.8. Carbohydrate binding activity in Cluster 1 ...... 87 3.7.9. Hydrolase activity in Cluster 1 ...... 89 3.8. Cluster 1 Conclusions ...... 94 3.9. Cluster 7 upregulated during late fermentation ...... 95 3.9.1. Genes with zinc ion binding activity upregulated in cluster 7 ...... 97 3.10. Similar expression patterns in clusters 2, 3, and 15 ...... 98 3.10.1. Cluster 2 genes have ribosomal functions ...... 99 3.10.2. Cluster 3 genes have ribosomal and biosynthetic functions ...... 100 3.10.3. Cluster 15 genes have ribosomal and biosynthetic functions ...... 102 3.10.3.1. Mannosyl transferase genes have opposing expression profiles...... 104 3.11. Expression patterns in cluster 4 ...... 107 3.12. Expression patterns in cluster 5 ...... 109 3.13. Genes from clusters 6, 9, 10, 11, and 12 have immediate and sustained upregulation ...... 111 3.13.1. Protein degradation, actin binding, and trehalose synthesis activity in cluster 6 ...... 112 3.13.2. Cluster 9 genes have roles in fermentation response and meiosis/cell division ...... 115 3.13.3. Cluster 10 genes have poorly characterized functions ...... 116 3.13.4. Cluster 11 genes are part of carbohydrate metabolism and cellular reproduction ...... 118 3.13.4. Cluster 12 genes have aerobic respiration and metabolite biosynthetic functions ...... 120 3.14. Cluster 8 shows erratic expression ...... 121 3.15. Cluster 13 expression drops briefly during mid fermentation ...... 124 3.15.1. Cluster 13 genes have roles in stress response and nutrient transport ...... 125 3.16. Cluster 14 genes have RNA processing and osmosensing functions ...... 127 3.17. Conclusions of cluster analysis ...... 129 3.18. Autophagy genes have GATA consensus sequence in promoters ...... 131 v 3.18.1. GATA consensus sequences are associated with specific expression profiles . 135 3.18.2. GATA family of transcription factors undergo similar transcription changes as some ATG genes ...... 136 3.19. Conclusions ...... 138 General Discussion ...... 139 References ...... 144 Web References ...... 151

vi List of Tables

Table 1.1 The covariance structures of each model from longclust...... 37 Table 3.1 Tirage for sparkling wine production. Two batches of yeast were acclimatized to ethanol and nutrient stress for second fermentation at Niagara College Teaching Winery. Acclimatization occurred over approximately 60 hours and the liqueur de tirage was fermented fermented until available sugars were almost depleted...... 51 Table 3.2 Clusters and gene membership for microarray data from sparkling wine yeast. Clusters were assigned by longitudinal cluster analysis using the longclust package form R...... 61 Table 3.3 Properties of the optimal multivariate normal and multivariate t models determined from cluster analysis, as well as the local maximum used for analysis in this thesis...... 62 Table 3.4 Molecular function gene ontology results for cluster 1 genes. Several genes fit into multiple categories. Largely redundant categories were omitted...... 68 Table 3.5 Genes with transcription factor activity from cluster 1. These genes were identified using gene ontology and belong to the accession category GO:0043565 (sequence-specific DNA binding transcription factor activity)...... 70 Table 3.6 Genes with DNA binding activity from cluster 1. These genes were identified using gene ontology and belong to the accession category GO:0003677 (DNA binding). Some genes are not present in this table due to redundancy...... 74 Table 3.7 Genes with gylcosyl bond hydrolysis activity from cluster 1. These genes were identified using gene ontology and belong to the accession category GO:0016798 (hydrolase activity, acting on glycosyl bonds)...... 77 Table 3.8 Genes with products that bind unfolded proteins activity from cluster 1. These genes were identified using gene ontology and belong to the accession category GO:0051082 (unfolded protein binding)...... 80 Table 3.9 Cluster 1 Genes with products that are spermine/H+ antiporters. These genes were identified using gene ontology and belong to the accession category GO:0000297 (spermine transmembrane transporter activity)...... 82 Table 3.10 Cluster 1 nucleotide exchange factor genes. These genes were identified using gene ontology and belong to the accession category GO:0000774 (Adenyl- nucleotide exchange factor activity)...... 84 Table 3.11 Genes with enzyme regulation activity from cluster 1. These genes were identified using gene ontology and belong to the accession category GO:0030234 (enzyme regulator activity)...... 86 Table 3.12 Genes with carbohydrate binding activity from cluster 1. These genes were identified using gene ontology and belong to the accession category GO:0030246 (carbohydrate binding)...... 88 Table 3.13 Genes with hydrolase activity from cluster 1. These genes were identified using gene ontology and belong to the accession category

vii GO:0016787 (hydrolase activity). Several genes were omitted as they are present in previous tables and their hydrolase activity is mentioned...... 92 Table 3.14 Biological and molecular function gene ontology results for cluster 7 genes. The “molecular_function” GO term is indicative of genes with largely uncharacterized molecular functions or GO classification...... 97 Table 3.15 Gene form cluster 7 with zinc ion binding activity according to gene ontology analysis ...... 98 Table 3.16 Molecular function gene ontology results for cluster 2 genes...... 100 Table 3.17 Biological function gene ontology results for cluster 3 genes. Several GO categories omitted due to redundancy ...... 101 Table 3.18 Biological function gene ontology results for cluster 15 genes. Several GO categories omitted due to redundancy ...... 103 Table 3.19 Genes with mannoprotein biosynthesis activity from cluster 15. These genes were identified using gene ontology and belong to the accession category GO:0000032 (cell wall mannoprotein biosynthetic process)...... 105 Table 3.20 Molecular function gene ontology results for cluster 4 genes. Several GO categories omitted due to redundancy ...... 109 Table 3.21 Molecular function gene ontology results for cluster 5 genes. Several GO categories omitted due to redundancy. The “molecular_function” GO term is indicative of genes with largely uncharacterized molecular functions or GO classification...... 111 Table 3.22 Molecular function gene ontology results for cluster 6 genes. Several GO categories omitted due to redundancy. The “molecular_function” GO term is indicative of genes with largely uncharacterized molecular functions or GO classification...... 114 Table 3.23 Biological function gene ontology results for cluster 9 genes. Several GO categories omitted due to redundancy...... 116 Table 3.24 Molecular function gene ontology results for cluster 10 genes. Two GO categories were omitted due to redundancy. The “molecular_function” GO term is indicative of genes with largely uncharacterized molecular functions or GO classification...... 117 Table 3.25 Molecular and biological function gene ontology results for cluster 11 genes. Some GO categories were omitted due to redundancy. The “molecular_function” GO term is indicative of genes with largely uncharacterized molecular functions or GO classification...... 119 Table 3.26 Biological function gene ontology results for cluster 12 genes. Some GO categories were omitted due to redundancy...... 121 Table 3.27 Biological and molecular function gene ontology results for cluster 8 genes. Several GO categories were omitted due to redundancy. The “molecular_function” GO term is indicative of genes with largely uncharacterized molecular functions or GO classification...... 123 Table 3.28 Biological function gene ontology results for cluster 13 genes. Several GO categories were omitted due to redundancy. The “biological_function” GO term is indicative of genes with largely uncharacterized biological functions or GO classification...... 126

viii Table 3.29 Molecular and biological function gene ontology results for cluster 14 genes. Several GO categories were omitted due to redundancy...... 129 Table 3.30 GATA consensus sequence locations for all ATG genes with two-fold or greater changes in transcript level. Sequence location is recorded in base pairs from the start codon...... 133 Table 3.31 proportions of ATG genes by cluster that have a specific GATA consensus sequence. Proportions are calculated for individual sequences within each cluster...... 135

ix List of Figures

Figure 1.1 The steps of secondary fermentation for the method champenoise. Adapted from Buxaderas and Lopez-Tamames...... 8 Figure 1.2 Ethanol fermentation and cyclic reaction of NAD+. Image was partially generated by chemspider, Royal Society of Chemistry...... 14 Figure 1.3 model for non cvt autophagy...... 21 Figure 1.4 Example of quantile normalisation with respect to genes on each array . 33 Figure 3.1 Metabolites found in sparkling wine over the course of second fermentation. Metabolites were measured using HPLC and included glucose, fructose, glycerol and ethanol...... 54 Figure 3.2 Viability of DV10 S. Cerevisiae over the course of second fermentation and aging. Viability was measured by serial dilution of yeast collected from bottled sparkling wine and plated in triplicate on YPD agar...... 56 Figure 3.3 Combined sparkling wine metabolite and cell viability over the course of second fermentation and aging. Abundance is expressed as the proportion by which metabolites concentrations have changed compared to day 7. Metabolites were measured using HPLC and viability was measured by plating yeast samples on YPD agar...... 57 Figure 3.4 Mean expression of genes in their respective clusters. Cluster analysis was done using the longclust package from R and bioconductor. RNA samples for analysis were taken during yeast rehydration, acclimatization, and days 1- 39 after bottling...... 63 Figure 3.5 Individual plots of cluster means found from longitudinal cluster analysis of microarray data...... 65 Figure 3.6 Gene expression of cluster 1 as determined by longitudinal clustering of microarray data. Expression is measured in terms of log2 of the fluorescent intensity ...... 67 Figure 3.7 Gene expression from cluster 1 with transcription factor activity as determined by gene ontology analysis. Gene expression is transformed to log2 values and was generated by DNA microarray...... 71 Figure 3.8 Gene expression from cluster 1 with DNA binding activity as determined by gene ontology analysis. Gene expression is transformed to log2 values and was generated by DNA microarray ...... 75 Figure 3.9 Gene expression from cluster 1 with hydrolysis of gycosyl bond as determined by gene ontology analysis. Gene expression is transformed to log2 values and was generated by DNA microarray...... 78 Figure 3.10 Gene expression from cluster 1 with unfolded-protein binding activity as determined by gene ontology analysis. Gene expression is transformed to log2 and was generated by DNA microarray...... 81 Figure 3.11 Gene expression from cluster 1 with spermine transporter activity as determined by gene ontology analysis. Gene expression is transformed to log2 values and was generated by DNA microarray...... 83

x Figure 3.12 Gene expression from cluster 1 with Adenyl-nucleotide exchange factor activity as determined by gene ontology analysis. Gene expression is transformed to log2 values and was generated by DNA microarray...... 84 Figure 3.13 Gene expression from cluster 1 with enzyme regulator activity as determined by gene ontology analysis. Gene expression is transformed to log2 values and was generated by DNA microarray...... 87 Figure 3.14 Gene expression from cluster 1 with carbohydrate binding activity as determined by gene ontology analysis. Gene expression is transformed to log2 values and was generated by DNA microarray...... 89 Figure 3.15 Gene expression from cluster 1 with hydrolase activity as determined by gene ontology analysis. Gene expression is transformed to log2 values and was generated by DNA microarray. Several genes from this GO category have been omitted due to redundancy or relevancy...... 93 Figure 3.16 Gene expression of cluster 7 as determined by longitudinal clustering of microarray data. Expression is measured in terms of log2 of the fluorescent intensity...... 96 Figure 3.17 Gene expression of clusters 2, 3 and 15 as determined by longitudinal clustering of microarray data. Expression is measured in terms of log2 of the fluorescent intensity...... 99 Figure 3.18 Gene expression from cluster 15 with mannoprotein biosynthesis activity as determined by gene ontology analysis. Gene expression is transformed to log2 values and was generated by DNA microarray...... 106 Figure 3.19 Gene expression of cluster 4 as determined by longitudinal clustering of microarray data. Expression is measured in terms of log2 of the fluorescent intensity...... 108 Figure 3.20 Gene expression of cluster 5 as determined by longitudinal clustering of microarray data. Expression is measured in terms of log2 of the fluorescent intensity...... 110 Figure 3.21 Gene expression of clusters 6, 9, 10, 11, and 12 as determined by longitudinal clustering of microarray data. Expression is measured in terms of log2 of the fluorescent intensity...... 112 Figure 3.22 Gene expression of cluster 8 as determined by longitudinal clustering of microarray data. Expression is measured in terms of log2 of the fluorescent intensity...... 122 Figure 3.23 Gene expression of cluster 13 as determined by longitudinal clustering of microarray data. Expression is measured in terms of log2 of the fluorescent intensity...... 125 Figure 3.24 Gene expression of cluster 14 as determined by longitudinal clustering of microarray data. Expression is measured in terms of log2 of the fluorescent intensity...... 128 Figure 3.25 Expression profile of ATG genes with one or more GATA motifs. Expression is measured as log2 intensity from microarray analysis over the course of sparkling wine fermentation...... 134 Figure 3.26 Expression profiles for the GAT family of transcription that each contain the GATA box motif. The cluster affiliation of each was unclustered, cluster 1, cluster 10, and cluster 10 respectively...... 137 xi List of Abbreviations

aRNA amplified complimentary ribonucleic acid (a.k.a. cRNA) ATP adenosine triphosphate BIC Bayesian Information Critiron cDNA complimentary deoxyribonucleic acid csv Comma separated value CVT cytoplasm-to-vacuole targeting DAP diammonium phosphate DEPC diethyl pyrocarbonate DHA1P dihydroxyacetone-1-phosphate DNA deoxyribonucleic acid ESR environmental stress response FBP fructose 1,6-bisphosphate G3P glyceraldehyde 3-phosphate GO gene ontology HOG high osmolarity glycerol HPLC high performance liquid chromatography MM mismatch NAD+/NADH nicotinamide adenine dinucleotide PM perfect match qPCR quantitative polymerase chain reaction RMA robust multiarray average RNA ribonucleic acid SDS sodium dodecylsulphate

xii Chapter 1 – Introduction

1.1. Overview of sparkling wine production

The production of traditional, high quality sparkling wines such as

Champagne is a labour intensive practice. Therefore, moderate and large-scale operations encounter costly difficulties during and after fermentation. Vinification of these wines requires a secondary fermentation to achieve effervescence in the final product. There are two primary methods used to achieve this: the champenoise method and the Charmat method. The latter method was conceived to circumvent some of the more expensive procedures of the former. The result is a lower quality product.

Champenoise sparkling wines are fermented in sealed, pressure rated glass bottles that require individual attention from the winemaker. The yeasts are subjected to greater stress than encountered in still wine fermentation, increasing the likelihood of stuck fermentation. Upon the completion of this bottle fermentation, the bottles must rest and age for several months. The duration depends on local laws, but in general it takes nine months or greater for the typical characteristics of high quality sparkling wines to develop (Charpentier, 2000).

During this time the bottles must be stored and undergo further processing, both adding to the mounting cost of production. The research in this thesis was done in order to find possible regulatory genes responsible for the processes that occur

1 during aging. The end goal is to control the timing and/or magnitude of aging pathways such that aging duration can be reduced, lowering the cost of sparkling wine production.

1.1.1. Methods of production

Sparkling wines are defined simply as wines that contain dissolved CO2 of at least 3 bar at 20oC (Buxaderas and Lopez-Tamames, 2012). The source of which can be naturally produced via fermentation, or exogenous. The origin of effervescence will however affect the overall quality of the wine as well as the cost of production and purchasing price. The highest quality wines are usually the product of the more time consuming and expensive methods. In order of increasing cost and duration of production these methods are: gas injection, the Charmat method, the transfer method, and the champenoise method. The champenoise method, or traditional method, was the first used to produce sparkling wine as we know it today, and little has changed since its conception.

1.1.2. Base wine production

All methods of true sparkling wine production require a base wine. This wine is traditionally made from a blend of Pinot Noire, Pinot Meunier and

Chardonnay grapes, although other varietals are becoming more commonplace outside of the Champagne region of France. Pinot Noire and Pinot Meunier are both red grapes and would normally be used for red wine production. However, the

2 grapes are gently pressed before fermentation preventing extended contact with the grape skins, the source of most pigments found in red wines. Therefore, since there is not a good opportunity for pigment extraction, the final product has characteristics of white or rosé wines (Buxaderas and Lopez-Tamames, 2012).

High quality sparkling wines such as Champagne use only the free run juice, which is juice freed by very gentle pressing. This accounts for approximate 67% of the grapes’ mass. The extraction of the remaining juice requires too much pressure resulting in higher quantities of undesirable metabolites from grape skins and stems being released. The free run juice is then clarified by sedimentation, filtration or centrifugation (Buxaderas and Lopez-Tamames, 2012).

Grape juice is rarely ideal for fermentation after pressing. The sugar and pH level is dependent on climate, with high sugar content in warm/temperate climates and low pH in cool climates. Therefore, the addition of sugar (chaptalization) is often necessary to adjust the initial gravity to achieve a final alcohol content of approximately 10%. If the pH is too high, tartaric and/or citric acid may be added.

At this point the must is ready for fermentation if the winemaker chooses to forgo the addition of sulphur dioxide, an antimicrobial agent. However, they do so at the risk of wild yeast and bacteria contaminating the wine. Thus SO2 is generally used for the sake of consistency, if not the potential loss of product (Ribéreau-Gayon et al., 2006).

Once the wine has been adequately prepared, a yeast culture is introduced or

“pitched”. The strain is chosen based on attributes such as fermentation ability, nutrient requirements, resistance to killer strains of yeast, stress tolerance,

3 flocculation ability, and desirable sensory qualities (e.g. mouthfeel, aroma, etc.).

Like most white vinifications, the base wine is usually fermented in large, insulated steel tanks, and maintained at 20o C or less. Temperature control is critical for the retention of volatile aromatic compounds, maintaining a high quality final product.

As fermentation progresses, yeasts accumulate on the bottom of the tank. These yeasts, known as lees, contribute certain characteristics to the wine via the release of certain metabolites (e.g. mercaptans). In most base wines, time on the lees is limited to avoid the accumulation of too many metabolites. A clean, unremarkable wine is the goal, as most of the characteristics the winemaker would like to impart are produced during secondary fermentation. Therefore the wine is often racked before fermentation is complete. If the chosen yeast strain has poor flocculation ability, fining agents are employed so that suspended yeast will sediment out of the finished base wine (Buxaderas and Lopez-Tamames, 2012).

The base wine is then assessed for quality and pH measurements are taken.

If the acidity is too high, problems can occur further along such as excessive CO2 production by lactic acid bacteria. To circumvent this, malolactic fermentation can be induced before secondary fermentation. The base wine is inoculated with lactic acid bacteria that convert highly tart malic acid to softer tasting lactic acid.

C4H6O5 → C3H6O3 + CO2 malic acid lactic acid carbon dioxide

The malolactic fermentation is advantageous for two reasons. First, it produces microbial stability as it removes a nutrient that could potentially be used by other microorganisms that would spoil the wine. Second, it prevents malolactic

4 fermentation from occurring alongside secondary fermentation, which could result in high CO2 pressure and bottle integrity failure. Malolactic fermentation can have negative consequences as well. Malic acid, while tart, often imparts wines with a crisper, fresher taste. This is in stark contrast to lactic acid, often described as buttery (Buxaderas and Lopez-Tamames, 2012).

1.1.3 Secondary fermentation

Many of the steps required to prepare must for base wine fermentation are also needed to some degree for secondary fermentation. The base wine has had much of its nutrient content depleted and usually cannot support further fermentation. In order for sustained growth, the winemaker must supplement that which the yeasts will require. For example sugar is added, as well as nutrients such as phosphate, some form of bioavailable nitrogen, and other trace minerals. Recent practices have also included growth and survival factors to aid growth after yeast rehydration. The amount of sugar added will depend on the desired final product.

For instance, if a final pressure of 6 bar is required, ~24 g/L of residual sugar is needed (Cavazzani, 1989).

Once the base wine has been prepared, the yeasts are added. This addition does not happen in the same manner as the original grape must. Before pitching, the yeast must be acclimatized to the high ethanol found in the base wine. If this step is skipped, it is likely the yeasts will enter into a stress state that will result in a stuck fermentation. Instead, the yeasts are gradually introduced to high ethanol concentration by creating a liqueur de tirage. First, the yeasts are rehydrated. This 5 is done with warm water (40 oC) and possibly additional nutrients such as GO-

FERM. After rehydration, the winemaker incrementally adds base wine and sugar to raise the ethanol concentration. The winemaker also has the option of adding a flocculating agent (e.g. bentonite) to encourage yeast to settle. This acclimatization process is known as tirage. Once the tirage process is complete, the liqueur de tirage is added to the base wine and individually bottled, then sealed with a crown cap and bidule (plastic insert). Bottles are designed specifically for high-pressure secondary fermentations. The bottles are placed on their side while fermentation progresses. Duration can vary depending on the temperature and yeast strain, but generally secondary fermentation takes place anywhere from 2 to 6 weeks. Yeast viability is reduced up to 99% after the first month. This increases to 100% six months after tirage, and if fermentation had not ceased before, it is no longer possible (Buxaderas and Lopez-Tamames, 2012). The carbon dioxide produced from fermentation raises the pressure in the bottles anywhere from 3 to 6 bar or higher (Buxaderas and Lopez-Tamames, 2012).

After fermentation has ended, the bottles remain sealed. The wine must remain in contact with the dead and dormant yeast (sur lie) for several months to acquire certain sensory characteristics standard in quality sparkling wine. These come from released metabolites such as polysaccharides and mannoproteins that contribute to the foaming properties of sparkling wine, as well as others that give the wines their unique flavour such as sulphur compounds and lactones (Buxaderas and Lopez-Tamames, 2012). For high profile sparkling wines such as Champagne or

Cava, aging can take years (Buxaderas and Lopez-Tamames, 2012). For the average

6 producer, wines will spend at least nine months “sur lie”, which has shown to be when yeast autolysis has caused a notable change in the metabolite profile of the wine (Alexandre and Guilloux-Benatier, 2006). Despite their necessity for wine improvement, the lees are considered a fault if found in finished sparkling wines and therefore must be removed. The method of removal begins with riddling.

Riddling occurs after the wine has been deemed properly aged. The bottles are placed on a rack angled at 25-30o to the ground with the cap pointing down. The bottles are gently agitated by turning them 1/8 rotation ever day or two (depending on the wine). The angle of placement in the rack increases over time. The lees eventually sediment until they rest above the cap. This can take over a month to complete (Buxaderas and Lopez-Tamames, 2012).

Upon the completion of riddling, the sediment and yeast are removed by disgorging. The bottle is held upside down and partially submerged into a freezing bath of 45% ethylene glycol. The bottle contents freeze where exposed. The removal of the crown cap is sufficient to free the frozen sediment as it is forced out by high internal pressure. The bottle now has capacity for additional fluid to be added. These additions are collectively known as dosage and composition can vary greatly. Depending on the winemaker, dosage can consist of grape must, other wines, fortified wines and brandy, sugar, and/or citric and ascorbic acid. Stabilizers are often added at this stage as well such as sulphur dioxide. Since secondary fermentation often ferments to dryness, dosage provides an opportunity to sweeten the wine. The amount of sugar present in the final product will determine how it is categorized (e.g. brut, sec, doux, etc.). The wine is now finished and can be corked

7 for storage. Some are shipped immediately for sale, while others remain in storage for several months to allow time for mercaptans to dissipate. (Buxaderas and Lopez-

Tamames, 2012)

Figure 1.1 The steps of secondary fermentation for the method champenoise. Adapted from Buxaderas and Lopez-Tamames.

1.1.4. Alternative methods of sparkling wine production

Gas injection is exactly that, the injection of CO2 gas. This is achieved by way of a carbonator, the same used in industrial soft drink production. Wines produced by gas injection are usually no more expensive than still wines, but do not acquire

8 characteristics produced by more time consuming techniques. The decreased quality requires the wines to be labelled differently in many regions. For example, gas injection wines are labelled as such in many jurisdictions (European

Commission, 2010).

The Charmat method is named after the man who first saw industrial success using the techniques. It borrows from traditional practices but does not require some of the most expensive components. The secondary fermentation takes place in large, pressurized steel tanks where aging on the lees gives the final product many of the characteristics found in traditional champagne. Costs are also kept low by a bulk filtration process. This requires drastically less effort that the champenoise method. Many of the most popular sparkling wines are produced this way including Italian Prosecco.

The transfer method produces wines very similar to true champagne. All steps are identical until aging is complete. The aged wines are then combined and filtered instead of individually disgorged. The key advantage to this process is that it allows wines to be blended, therefore reducing variation between bottles and producing a product of consistent quality.

1.2. Overview of Saccharomyces cerevisiae

The common denominator in the production of almost any wine is the yeast

Saccharomyces cerevisiae. This eukaryotic microorganism is responsible for fermenting sugars found in grape must to ethanol. S. cerevisiae was one of the first

9 microorganisms identified and remains a target of study to this day. Its genome was one of the first sequenced, and continued revisions make it an excellent candidate for microarray studies. While categorized as the same species, many variations exist with a large discrepancy in genetic structure. These subspecies are known as strains. Since the number of genes varies between strains, this can be problematic for microarray studies, as a standard S. cerevisiae microarray contains probes for

5,841 genes (Affymetrix, 2014).

1.2.1 Yeast strains used in sparkling wine production

Several different yeast strains are used in the wine industry for sparkling wine production. Since secondary fermentation exposes yeast to unfavourable conditions the strain selected must be adapted to continue to ferment under high stress. The base wine has an ethanol concentration of approximately 10% (v/v).

Grape must is also highly acidic with a pH of approximately 3 in many varietals following harvest. As fermentation progresses nitrogen levels fall and CO2 pressure rises to three or more atmospheres (Buxaderas and Lopez-Tamames, 2012; Sefton et al., 1993). Many of these issues can be mitigated by the addition of nutrient supplements, but several others cannot. To avoid a stuck fermentation, yeasts are selected based on their ability to tolerate these stresses.

One particular strain, DV10, has found favour with sparkling wine makers.

DV10 is an industrial strain developed by Lallemand Inc. It was first isolated from vinyards in Champagne, France (Dharmadhikari, 1996). It has since been produced at industrial scale since the 1990s. Its choice for sparkling wine secondary 10 fermentation is due to its lack of aromatic contribution to the wine, high ethanol tolerance (14-15% v/v), and its ability to dominate a wine fermentation over other yeasts (killer activity) (Dharmadhikari, 1996). This strain of S. cerevisiae var. bayanus has been well studied and comparative studies between it and other popular sparkling wine yeast have been performed. It has demonstrated characteristics similar to that of EC1118, one of the most widely used strains of S. cerevisiae bayanus. Some things that set it apart are low SO2 accumulation, increased flocculation, low tolerance to heat shock, and moderate resistance to oxidative stress (Rossouw et al., 2009).

Yeast are also selected based on the metabolites that they produce. For example, mannose and mannoproteins are key metabolites required for proper foaming characteristics of sparkling wines. By selecting yeast that express mannoproteins in higher amounts such as BM45 or 285, winemakers may be able to achieve a higher concentration of these key metabolites during aging (Rossouw et al., 2009).

1.2.2. Carbon Metabolism

The purpose of vinification is to ultimately create ethanol. This is accomplished by yeast fermentation of carbon containing molecules. The array of organic compounds S. cerevisiae can metabolize is diverse. They will however preferentially utilize hexose sugars, particularly glucose, as an energy source. S. cerevisiae is capable of both aerobic and anaerobic carbon metabolism, yet unlike many organisms does not cease fermentation under aerobic conditions. This

11 phenomenon is recognized as the Crabtree effect (Ribéreau-Gayon et al., 2006). The amount of adenosine triphosphate (ATP) produced from one glucose molecule by fermentation can be up to 17 times less than respiration. Thus yeasts lose a potential source of energy due to the Crabtree effect. The reason behind the preference for fermentation is thought to be the antiseptic effect of ethanol production, therefore granting the yeast a competitive advantage (Thomson et al.,

2005).

1.2.2.1. Glycolysis

Like nearly every other organism, S. cerevisiae commence the catabolism of hexose sugars via glycolysis. Glycolysis is a very well understood pathway and will be touched on lightly here. Glycolysis is critical to survival as evidence suggests that it is the only pathway available to yeast for glucose catabolism (Bisson, 1991). The process begins after glucose import, achieved by a number of hexose transporters and is carried out entirely within the cytosol. Glucose molecules are phosphorylated to glucose-6-phosphate by one of two hexokinases. Further on, phosphorylation occurs once more to produce fructose 1,6-bisphosphate (FBP). Both phosphorylation events require ATP; therefore energy must be initially expended.

FBP is split in two by fructose bisphosphate aldolase to create glyceraldehyde 3- phosphate (G3P) and dihydroxyacetone phosphate, the latter of which is an isomer of the former and subsequently converted by triosephosphate isomerase. G3P is phosphorylated once more, this time by organic phosphate and glyceraldehyde phosphate dehydrogenase (this step also reduces NAD+ to NADH). The remaining

12 steps produce pyruvate, removing all phosphate groups and producing four ATPs along the way. Therefore glycolysis results in a net gain of two ATPs. (Ribéreau-

Gayon et al., 2006)

1.2.2.2. Fermentation

Yeasts benefit from fermentation in two ways. The first of which was already discussed; the ethanol produced inhibits the growth of competitive organisms.

Second, and most important is the oxidation of NADH to NAD+. Nicotinamide adenine dinucleotide (NAD+) is an electron carrier required to complete glycolysis and does not exist in infinite supply. If glycolysis progresses, NAD+ is reduced to

NADH, depleting the amount of available NAD+. If the NAD+ is not replaced, glycolysis will cease, possibly resulting in the death of an organism. NAD+ cannot sustainably be synthesized from precursors and must be replaced by oxidized

NADH. This can be accomplished by two primary means, oxidative phosphorylation and fermentation. Oxidative phosphorylation relies on aerobic conditions and is used by S. cerevisiae after the initial pitching into must for rapid growth and reproduction. The yeasts however use fermentation in the presence or absence of oxygen. This reaction uses acetaldehyde as the terminal electron acceptor. The acetaldehyde is produced by decarboxylating pyruvate via pyruvate decarboxylase and thiamine pyrophosphate. It is this step that produces the CO2 required for effervescence in sparkling wine. To generate ethanol the enzyme alcohol dehydrogenase transfers the hydrogen atom from NADH to the acetaldehyde

(Ribéreau-Gayon et al., 2006).

13

Figure 1.2 Ethanol fermentation and cyclic reaction of NAD+. Image was partially generated by chemspider, Royal Society of Chemistry. Another form of fermentation that occurs in yeast cells is glyceropyruvic fermentation. This reaction is common when yeasts are in solution with high amounts of sulphite, a common wine preservative. As such, it is observed during sparkling wine fermentation. This results in higher amounts of glycerol, an important metabolite for enhancing the mouthfeel of wines. It is produced in higher amounts because sulphite can react with acetaldehyde. The product of this reaction can no longer be acted upon by alcohol dehydrogenase. In this scenario another terminal electron receptor is used, dihydroxyacetone-1-phosphate (DHA1P). To produce DHA1P, the yeast must reduce dihydroxyacetone phosphate (an intermediate of glycolysis) or potentially glyceraldehyde-3-phosphate after triosephosphate topoisomerase reaction to dihydroxyacetone phosphate. The

14 resulting glycerol-3-phosphate is dephosphorylated and glycerol is the result

(Ribéreau-Gayon et al., 2006).

Glycerol production can also occur without sulphite during the early stages of fermentation when yeasts are able to undergo respiration. During this period the enzymes required for ethanol fermentation aren’t expressed in high enough quantities to facilitate the required NADH turnover. Glyceropyruvic fermentation serves to fulfil the need of the yeast in this scenario. Approximately 8% of the sugar acquired by the yeast will be used to produce glycerol over the course of fermentation of still wines. It should be noted that glyceropyruvic fermentation is

ATP neutral assuming triosephosphate topoisomerase equilibrium, as dihydroxyacetone phosphate does not proceed to the end of glycolysis to produce further ATP.

1.2.3. Stress response and consequences

Yeast survival under oenological conditions is dependent on each strain’s ability to cope with the harsh conditions of fermentation. In natural environments, the conditions encountered by yeasts can vary greatly. Wild yeasts must be able to maintain relative homeostasis in conditions of drought and starvation. While dissimilar to natural environments, stresses remain present during wine fermentation. Many characteristics of stressed yeast contribute to overall wine quality and have been noted to facilitate the production of desirable metabolites in sparkling wines. Some of the adverse conditions encountered by fermenting yeast include high ethanol concentration, low bioavailable nitrogen, low phosphate, low

15 pH, depleted fermentable carbon sources, osmotic stress, and temperature fluctuation. Despite the diversity of possible stress factors, the response is very similar for each, demonstrating co-regulation. Yeast that encounter one form of stress are better able to withstand another, and slight stress can prepare a cell for a much more severe stress (Lewis, 1995). A group of approximately 900 genes are expressed in response to almost any stress (Gasch, 2003). These genes make up the environmental stress response (ESR) and while many of them are present regardless of the stress source, the timing and magnitude of expression depends on the situation. Approximately one third of all ESR genes have a function related to protein synthesis. These genes mostly experience reduced translation as protein synthesis decreases under many stressful conditions (Gasch, 2003). The remaining portion of ESR genes encode proteins that stabilize the cell. The function of these genes are related to chaperonins, cell wall, cytoskeleton, metabolism of amino acid and carbon sources, DNA replication and nucleotide biosynthesis, glycoprotein synthesis and secretion, and several others (Gasch, 2003).

1.2.4. Osmotic stress response

Grape must is not an ideal substrate for yeast growth and fermentation.

There is a considerably high concentration of osmotically active sugars such as glucose and fructose that induce hypertonic stress in yeast. The hyperosmolarity of grape must results in a net loss of water from the cytoplasm. This interferes with normal cellular processes such as protein folding. While potentially detrimental for the yeast, the reactions to osmotic stress are largely positive for the sensory

16 characteristics of wine, contributing many metabolites that enhance mouthfeel. The mechanisms of action focus on the cell wall and the periplasmic space. The cell wall thickens to provide structural support, building reserves of proteins and polysaccharides that will eventually find their way into a finished sparkling wine.

To negate the effects of high osmotic concentration of must, yeast increase the osmotic concentration of the periplasmic space via glycerol synthesis. The mechanism through which this is achieved is the high osmolarity glycerol (HOG) signal transduction pathway (Albertyn, 1994).

1.2.5. Autolysis

A well-aged wine is defined by many compounds found in dying yeast. These metabolites are released into the wine by a process called autolysis. The duration of autolysis becomes quite costly to winemakers, and therefore many in the industry consider acceleration of autolysis highly desirable. The mechanisms by which autolysis is achieved is largely a result of intracellular protease activity. This protease activity is directed at the cell walls, structures composed largely of polysaccharides and mannoproteins. During stressful conditions, yeast will increase the thickness of the cell wall. By the time autolysis commences, cell walls make up a large amount (between 20% and 30%) of the cell’s dry mass (Alexandre and

Guilloux-Benatier, 2006). Lysis occurs by several means due to the diversity of cell wall components. The polysaccharide component is degraded when β-Glucanase hydrolyses the β-0-glycosidic links of β-glucan chains. The bound mannoproteins, glucose and oligosaccharides held within the cell wall are freed. At this point the

17 cell wall is still intact and will remain that way after the cell membrane has degraded and released its contents into the aging wine. Instead of rupturing and creating a hole through which cellular metabolites rush out, autolysis results in the weakening and withering of the cell. At the beginning of fermentation, cells are healthy and uniformly ovoid. As aging progresses and the cell wall begins to degrade and the cell develops wrinkles. These wrinkles spread, and the cell eventually deflates once the contents of the cytoplasm have been degraded and diffuse though pores in the cell wall. The remaining cell wall is considered undesirable in the final product and is eliminated with other sediment during disgorging (Alexandre and Guilloux-Benatier, 2006).

Autolysis has proven to be a time consuming process during sparkling wine production. Polysaccharide levels in wine have been shown to increase from 366 mg/L to 602 mg/L over nine months (Charpentier, 2000). These polysaccharides such as mannose are required for foaming, mouthfeel and some taste characteristics and thus extended aging is critical to producing higher quality wines.

In the past, attempts have been made to accelerate autolysis. Some attempts have been made to skip autolysis altogether by the direct addition of metabolites produced during autolysis (Feuillat and Charpentier, 1982). These attempts have met with limited success, producing a “toasty” final product. Further metabolomic studies may eventually lead to a breakthrough for this technique. Some headway has been made during attempts to create strains that commence autolysis sooner than current strains. By inducing mutation via UV exposure, new strains have been

18 created, and in some cases undergo autolysis as much as three months sooner

(Nunez, 2005).

1.2.6. Yeast cell wall

The composition of the yeast cell wall will determine the metabolites and in what quantities they will be found in the finished sparkling wine. The purpose of the cell wall for the yeast is to provide structural integrity, shape, and osmotolerance (Kollar et al., 1997). The wall consists of primarily polysaccharides and protein including chitin, β(1,3)-D-glucan, β(1,6)-D-glucan, and mannoprotein.

Through crosslinking, these components are bound together and thus are much more rigid than the plasma membrane (Kollar et at., 1997).

Chitin is a polymer consisting of N-acetylglucosamine subunits bound by β(1-

4) linkages. It creates a strong barrier and each monomer unit is capable of forming multiple hydrogen bonds, facilitating interaction with other cell wall components

(Kollar et al., 1997). The two forms of glucan polysaccharide serve different functions. β(1,3)-glucan has a higher degree of polymerization. It binds chitin and aids in maintaining cell wall shape and rigidity. Its second form (amorphous) binds mannoproteins and contributes some elasticity to the cell wall. β(1,6)-glucan has a lower degree of polymerization and is required for binding cell wall components together (Kollar et al., 1997). Mannoproteins are a glycoproteins composed of mannose polysaccharide branches bound to a peptide. Mannoproteins are recognized as critical to foam formation in sparkling wines. They are found in sparkling wines following cell wall degradation during autolysis.

19 1.2.7. Autophagy

A more recent addition to targets of study to accelerate aging of sparkling wines is the process of autophagy. Autophagy, as its names suggests, is the catabolism of cellular components and organelles while the cell is still living. It is usually induced by a state of cell starvation (Cebollero et al., 2005). Autophagy takes place in vacuole of a yeast cell. The cellular components destined for degradation is usually engulfed in an intracellular membrane structure, called an autophagosome, which delivers its content to the vacuole. The autophagosome fuses with the vacuole where its contents are hydrolysed. The resulting metabolites are then free to be reused by the cell or in the case of yeast autolysis, released into the fermented wine (Reggiori and Klionsky, 2002) .

20 -+.*+')+,

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1'.($, !%+2,

%.!%+2, !#(0,

-&'($%&$.+,

!"#$%&'(34,5$67,

!"#$%&'($)$*+,

Figure 1.3 model for non-CVT autophagy. Atg9 recruits membrane to the phagophore assembly site, where Atg8 plays a role in cargo selection. The complete phagophore is called the autophagosome and transports the cargo for degradation to the vacuole. The outer membrane fuses with the vacuole and the resulting single-membrane vesicle (autophagic body) is enveloped. The prAPE zymogen matures to its active form as aminopeptidase I. Auotophagosome cargo is then degraded. Adapted from Jin and Klionsky, 2014.

Autophagy has become a target of research due to its ability to break down the yeast cell. If factors regulating autophagy can be manipulated, autophagy could be induced much sooner during the aging process. This could shorten the time required for aging to complete. The predominant issue is that autophagy is untargeted, and unregulated autophagy would likely interfere with other necessary steps in fermentation. Some of the work on autophagy is focussed on cytoplasm to vacuole targeting (CVT) mechanisms. CVT directs aminopeptidase I (Ape1) to select targets and avoids mass catabolism of the cell (Tanaka et al., 2014). It requires the 21 assembly of an autophagosome to engulf select targets and deliver them to the vacuole for degradation. While the mechanisms by which the autophagosome are formed are fairly well characterized, the regulation is poorly understood and could provide a tool to control the process in future yeast strains.

Autophagosome and CVT vesicle formation requires the combined action of autophagy related proteins (Atg). Autophagy related 1 (Atg1) is the only known kinase of the family and is regulated by Atg13. There are several upstream pathways of activation, including Target of rapamycin (TOR), protein kinase A

(PKA), and Sch9. These pathways increase Atg1 activity during starvation by associating Atg1 and Atg13 with another complex consisting of Atg17, Atg29, and

Atg31. While there is only one kinase in the Atg group, its targets are still largely unknown (Jin, and Klionsky. 2014). The upstream regulators of Atg1 are involved in a broad array of pathways and therefore would make poor targets for strain improvement. While altering their action might impact autophagy, it would also alter several other pathways in the cell.

The formation of the autophagosome requires the action of the phosphatidylinositol 3-kinase Vps34, and is recruited to the phagophore assembly site (PAS) by the PtdIns3K complex I. This complex is downstream of the Atg1 complex and is made up of Vps34, Vps15, Vps30, Atg14 and Atg38. The PtdIns3K complex is necessary to recruit other components of autophagosome assembly (Jin, and Klionsky. 2014).

Autophagosomes are capable of selective uptake. One of the mechanisms identified for cargo selection is Atg8, an ubiquitin-like protein. While not a true

22 homolog, Atg8 has a folded structure similar to ubiquitin. Just like ubiquitin, it is capable of activation and conjugation with the aid of ubiquitin ligase-like proteins.

These proteins are Atg7 and Atg3. Atg7, a true E1 homolog, activates the Atg8 that is attached to phosphatidylinositol via the E2 homolog Atg3. The E3 ligase in this process is thought to be a complex of Atg12, Atg5, and Atg16. The Atg8 is conjugated on the inside and outside surface of the autophagosome, where it plays a role in selective autophagy and autophagosome size respectively. (Jin, and Klionsky,

2014)

Another Atg protein under study is the transmembrane protein Atg9. While its function has yet to be fully understood, it is thought to transport membrane to the PAS by binding single-membrane vesicles (Yamamoto et al., 2012). It has been observed cycling between sites close to mitochondria and the PAS where it is recruited by a Atg17-Atg29-Atg31 complex to the autophagosome (Jin, and Klionsky.

2014).

1.2.8. Autophagy regulation

Perhaps the most poorly understood aspect of autophagy is how it is regulated at the transcriptional level. One possible transcription factor is Fhl1, the yeast homolog of forkhead box O (FOXO). During nutrient stress FOXO is no longer phosphorylated by AKT1, subsequently translocating to the nucleus where it regulates transcription of Atg3, Atg5, and Atg12. It should be noted that the yeast homolog Fhl1 has never been observed to target Atg promoters in vivo, only during

CHIP-chip experiments (Jin, and Klionsky. 2014).

23 Another non-yeast transcription factor involved with autophagy is transcription factor EB (TFEB). TFEB is a known initiator of lysosome and autophagosome biogenesis (Jin, and Klionsky. 2014). TFEB localizes to the nucleus under starvation conditions where it binds to the promoters of the Coordinated

Lysosomal Expression and Regulation (CLEAR) network genes (Sardiello et al.,

2009; Palmieri et al., 2011). This transcriptional activator works in opposition to

ZKSCAN3, a repressor that inhibits the transcription of the same autophagy genes.

Neither of these transcription factors have a confirmed counterpart in yeast, however the protein sequence of Rtg3 shares 33% identity with TFEB and has shown to play a role in stress response, albeit for mitochondrial signalling

(Srinivasan et al., 2011). Any homologous yeast transcription factors that may share the function of ZKSCAN3 have yet to be elucidated.

One of the few members of the Atg family with a known transcriptional regulator is Atg14. Its expression is upregulated by Gln3, a GATA zinc finger transcription factor. Gln3 in turn is regulated upstream by TOR, which has a well- characterized role in nutrient signalling. Under conditions of adequate nutrition, the action of Gln3 is suppressed and subsequently prevents Atg14 expression. It is also worth noting that TOR is required for downstream suppression of Rtg3. The other GATA proteins related to Gln3 include Gat1, Gat2, Gat3, and Gat4. They all share a conserved zinc finger and may play a role in autophagy regulation (Jin, and

Klionsky. 2014).

Autophagy is a complex process with over 30 Atg proteins known to be involved (Jin, and Klionsky. 2014). However, its mechanism of transcription

24 regulation remains poorly characterized. By identifying these transcriptions factors, autophagy regulation could be altered in new strains of yeast, possibly leading to accelerated wine aging.

1.2.9. GATA family of transcription factors

In mammals, GATA transcription factors have been very well studied. One in particular, GATA-1, was identified as a regulator of haemoglobin and its structure is well known. The DNA binding domain was found to have four cysteine residues that coordinate a zinc ion (Evans and Felsenfeld, 1889). It is this domain that is conserved among all GATA transcription factors. Several GATA transcription factors have been identified in S. cerevisiae and some remain poorly characterised. In particular the functions of Gat2, Gat3, and Gat4 are not very well understood. Those that have been studied (Gln3 and Gat1) have demonstrated regulatory activity of nitrogen starvation pathways as well as autophagy (Jin, and Klionsky. 2014).

The name of these transcription factors alludes to their target sequence in gene promoters. They all contain a conserved zinc-finger domain that binds to consensus sequences with a GATA core motif. There are several variations of this sequence that GATA factors have shown affinity for, but they can be divided into two basic types. The first consensus sequence type is six base pairs long. The GATA sequence makes up the centre and is bordered by a base pair on either side. This sequence is often referred to as WGATAR where W can be either A or T, and R is A or G (Merika and Orkin, 1993). The second sequence type to which GATA factors bind is the palindromic consensus sequence (GATApal). To bind to this sequence

25 two separate zinc finger domains are required (Trainor et al., 1996). The binding site itself is composed of a WGATAR sequence and another partial, overlapping GAT motif. The GATApal motif has been shown to result in stronger binding but occurs infrequently in vertebrates. The prevalence of GATApal motifs in yeast requires further study.

1.3. Microarrays

DNA microarrays are a technology developed to measure the expression of many genes simultaneously. Using results from microarrays we are able to determine which genes are expressed under certain conditions. This is valuable information as it can lead to uncovering the function of genes. The technology is based on northern and southern blotting in which labelled DNA or RNA sequences can be detected after hybridization with oligonucleotide probes.

A common array used for S. cerevisiae studies is the Affymetrix Yeast Genome

2.0 chip. This array was synthesized with target probes specific to S. cerevisiae as well as S. pombe, a species of fission yeast. Since there are probes for two species from the same genus, hybridization between S. cerevisiae RNA and S. pombe probes will occur. While there is a great deal of similarity between the genomes of these two, it is usually not a perfect match, therefore hybridization data for the same gene from probes of either species cannot be reliably used. For this reason, the Yeast

Genome 2.0 chip requires more downstream processing than a species-specific chip.

The probe sets for Affymetrix arrays were designed using S. cerevisiae strain

S288c. While this strain is of the same species as the DV10 strain used for sparkling 26 wines, there are some differences between the genomes. However previous microarray studies have used DV10 and they appear to have no issues with hybridization to Affymetrix arrays (Rossouw et al., 2009).

1.3.1. In-situ synthesized oligonucleotide arrays

The arrays are built on square or rectangular wafers made of either glass or silicon. Oligonucleotide probes are built on the chip one nucleotide at a time. This is accomplished by using nucleotides with a protective group covalently bound to the

5’ position. These protective groups can be selectively altered to allow for the addition of another nucleotide during each stage of synthesis. Protective group removal is executed using light, and is directed to specific probes by masking those to remain protected. Since different probes require unique nucleotide sequences, every step requires a different mask. The entire process of in-situ oligonucleotide synthesis is known as photolithography. This method of microarray production relies on a sequence of masks, each of which is very expensive to produce. It is therefore often prohibitively expensive to produce custom arrays. (Stekel, 2014)

1.3.2. Hybridization

In order to produce the target DNA for hybridization, a sample of cells is collected and lysed. The mRNA must then be isolated and converted to complimentary DNA (cDNA). This cDNA is produced using the enzyme reverse transcriptase. The cDNA is then converted to aRNA, this is the amplification step analogous to the more common polymerase chain reaction. It is during this step

27 that the aRNA is labelled, often with fluorescent dye so that it may be detected at a later stage (Do, J. and Choi, D). In the case of Affymetrix probes, labelling is accomplished with biotin-nucleotide conjugation. Pre-synthesized biotin conjugated nucleotides are used for synthesis of aRNA, and are thus present in the probes at known concentrations. These probes will fluoresce at 520 nm and their presence can be quantified. After labelling, the aRNA is complete and hybridization may begin. mRNA isolated from yeast extracts binds complimentary probes, and excess target RNA is washed away. The chip can then be examined using fluorescent microscopy and transcript levels can be quantified. The industry standard data format for microarray data is the CEL file and is compatible with most well-known microarray analysis software (Affymetrix, 2012).

1.4 Data Analysis

1.4.1. Pre-processing

Raw microarray data cannot be used for analysis until several steps are taken. The components of pre-processing include background correction, normalization and summarization. Collectively, these steps alter the raw data to account for differences between arrays produced by human, mechanical, and random error. Variables introduced by these sources of error result in uneven probe hybridization, and therefore intensity strength and distribution. If this error is not accounted for, gene expression analysis will not return reliable results. For example, a single sample applied to two microarrays with unequal hybridization will show distributions where the mean expression of chip “A” may be an order of 28 magnitude higher than chip “B”. Without pre-processing the data would show that all genes in chip “A” are expressed more highly than chip “B” despite originating from the same source. Pre-processing can modify the distributions such that the mean expression of chips A and B are equal. Analysis can now be performed assuming equal mean expression across all arrays. However, this has the drawback of eliminating the possibility of quantitative analysis. Therefore, when performing analysis, only change in expression can be used and not the actual expression amount.

The first step, background correction, is required to account for discrepancies created by optical noise and mismatched probes. The optical noise is a result of inaccuracies during the measurement of probe intensities. Probe mismatch occurs when probe oligonucleotides are a near match with a particular target gene. This results in a certain percentage of probes binding to the wrong target and therefore must be removed to avoid a false result.

1.4.2. Background noise and correction

Each probe is designed to be a perfect match for a specific gene sequence.

However, on every array a certain amount of non-specific binding occurs as well as some optical “noise”. Collectively, any feature that causes a change in intensity where there should be none is called background. In order to obtain more accurate results from array analysis, this background must be accounted for. There are several methods for doing so. The RMA method is based on the following formula:

29 Where: Y = intensity, B = background, α = gain factor

and S = amount of specific binding

S itself is a random variable and represents error of measurement and probe effects:

Where: θ is the true probe amount, Φ = probe

effects, and ε = measurement error.

The value of S may be equal for two arrays, yet the distribution of B can vary and make for an inaccurate comparison. The purpose of background correction is to equalize the value of B across all arrays, and bring it as close to 0 as possible. There is a notable trade-off when using background corrections. A loss of precision generally occurs when trying to compensate for background. This is one of several reasons why microarray analysis is not considered a completely accurate representation of transcript quantity. Another issue is represented by ϕ. That is not all probes are created equal. In many instances, there is discrepancy among probes and their affinity for their target. One of the reasons for this is that our knowledge of the target sequence is not perfect, and in several cases will not be a perfect match. There also exists a possibility that one probe may bind more than one target (Gentleman et al., 2005).

One of the most used forms of background correction is the robust multiarray average (RMA) method. RMA differs from many other background correction methods due to not using mismatch probes (MM). Every array from

Affymetrix has two probes for every target sequence. One of these probes is considered to be a perfect match (PM) for the target. The other probe has a single substituted base pair; this is the MM probe. The idea behind the two-probe system 30 is that one can determine how much nonspecific binding occurs by subtracting the

MM from PM intensities. However, the RMA method takes a different approach and only uses PM intensities. Instead of performing corrections specific to a single array, RMA corrects intensity values based on data from all arrays. Intensity values are adjusted based on the Gaussian noise component, B (μ, σ2), and the exponential signal component, S with mean α:

Where Y = intensity, S = Signal, ϕ and Φ are the standard normal density and distribution functions, a = s-μ-σ2α, and b = σ. (Gentleman et al., 2005)

1.4.3. Normalization

Background correction is not the only preprocessing requirement for accurate microarray analysis. Intensity data must also be normalized for accurate comparison. Raw data that has not been normalized can show inaccurate variation between different arrays. As mentioned earlier, the mean intensity value for one array can differ significantly from the next due to sources of experimental and human error. To account for this normalization is used. A popular method of normalization is the quantile method. In a nutshell, this method alters the intensity distribution among all arrays to be roughly the same.

The means by which this is achieved is by coercion to fit the same distribution. When two vectors of the same distribution are plotted against each other on a quantile-quantile plot, it yields a straight line with slope of 1 and an

31 intercept of zero. The same idea extends to multidimensional data like microarrays.

The goal is to have the same quantile-quantile slope between all array comparisons.

The way data is coerced is by organizing n vectors (intensity data form each array) into columns of length p, to form a matrix of arrays. Each intensity from columnn is ranked. Intensity values from each column are rearranged from lowest to highest.

Row means are then calculated to generate a new vector, n’ (this vector is already ranked form low to high). Every intensity value from columnn is then assigned by taking the rank from rowp and altering it to the value from n’ with corresponding rank. Since every intensity value from n’ can be found in every array, the distribution between every array is the same. See Figure 1.4 below.

32 Gene Array 1 Array 2 Array 3 RER2 11.5 9.7 9.0 TAF7 8.7 8.4 8.0 PHS1 10.6 10.1 10.8 SPB1 10.1 8.2 8.1

Intensities for each column are ranked

Gene Array 1 Array 2 Array 3 RER2 D C C TAF7 A B A PHS1 C D D SPB1 B A B

Intensity values for each column are rearranged based on rank

Rank Array 1 Array 2 Array 3 A 8.7 8.2 8 B 10.1 8.4 8.1 C 10.6 9.7 9 D 11.5 10.1 10.8

Means are calculated for each row/rank

Rank Mean A 8.3 B 8.9 C 9.8 D 10.8

Rank means are then substituted for corresponding ranks in original arrays

Gene Array 1 Array 2 Array 3 RER2 10.8 9.8 9.8 TAF7 8.3 8.9 8.3 PHS1 9.8 10.8 10.8 SPB1 8.9 8.3 8.9 Figure 1.4 Example of quantile normalisation with respect to genes on each array

33 It should be noted that microarray data has thousands of genes and therefore the coercion of intensity values will not be to the same degree as the example provided. One exception to this is if gene expression is lower for all genes between arrays, for example if there is a time lapse between arrays where the organism is dying. In this scenario, transcription of nearly all genes will be lower. If this data is normalized with a scalar algorithm like the quantile method, it will appear as though overall expression level has remained constant. This data is still useful however, as it will show relative changes in expression.

1.4.4. Cluster analysis

Of the tools available to bioinformaticians, few are as crucial as cluster analysis. Clustering is used to recognize subpopulations within a large dataset and organizes similar entities into discrete groups. Most molecular biologists rely on clustering algorithms, a prime example being the Basic local alignment search tool or BLAST (Altschul, 1990). When applied to microarray studies, genes with similar expression patterns are grouped together into clusters. Genes found in the same cluster may share similar expression mechanisms or co-regulation for a given condition under study. Thus microarray studies are a valuable tool to find regulatory pathways (Gentleman et al., 2005).

34

1.4.5. Model-based clustering

Clustering is a form of unsupervised learning. With respect to microarray analysis it is useful for identifying groups of genes with similar expression under a given circumstance (e.g. fermentation). Genes with similar patterns of expression are found in clusters that correspond to mixture components from a mixture model fitted to the data. Model based clustering often employs Gaussian mixture models with the density function:

where πg is the probability that a gene belongs to group g, and ϕ(x|μg, Σg) is the multivariate Gaussian distribution density and has mean μg and covariance matrix

Σg. Successful clustering of microarray data has previously been accomplished using mixture models (McNicholas and Murphy, 2010). However, until recently no models were available that accounted for dependencies between samples that arise from longitudinal data (McNicholas and Murphy, 2010). The microarray analysis for this study falls into this category since the RNA used for analysis came from the same batch of yeast measured over time.

35

1.4.6. Longitudinal Clustering

Longitudinal data are generated when a study requires the collection of data from an experimental unit repeatedly over time. It is critical to take this dependency into account because failing to do so can yield misleading results. For example, yeast being sampled at day 1 coming from the same population as those sampled at day 7 has undergone the exact same conditions during tirage. The same nutrients were present at the same times, and the final ethanol concentration was exactly the same. This means they began with similar gene expression profiles, and this will likely influence future expression to some degree.

Most cluster models do not account for the dependency found between samples over time, but the longlust package available for R does (McNicholas et al.,

2015). The longclust package decomposes the covariance matrix of a random variable in a different way than other models. It uses a modified Cholesky decomposition that results in a family of mixture models to cluster gene expression time course data (McNicholas and Murphy, 2010). The parameters are estimated with the expectation-maximization (EM) algorithm (Dempster et al., 1977).

Convergence criteria based on the Aitken acceleration are used to determine EM algorithm convergence (for further details see McNicholas and Murphy, 2010). The number of components was determined using the Bayesian information criterion

(BIC) (Schwarz, 1978).

From the modified Cholesky decomposition of covariance follow a lower triangular matrix Tg and a diagonal matrix Dg. Constraining these to be equal across 36 groups as well as imposing an isotropic constraint leads to a family of eight covariance structures (Table 1.1). Constraining Tg to be equal across all groups suggests a correlation structure that is the same across all groups. Constraining Dg to be equal suggests that variability at each time point is the same for each group.

An isotropic constraint suggests that variability is the same at all time points.

Table 1.1 The covariance structures of each model from longclust.

Model Tg Dg Dg EEA Equal Equal Anisotropic VVA Variable Variable Anisotropic VEA Variable Equal Anisotropic EVA Equal Variable Anisotropic VVI Variable Variable Isotropic VEI Variable Equal Isotropic EVI Equal Variable Isotropic EEI Equal Equal Isotropic

The longclust package makes use of two different distributions, multivariate normal and multivariate t. Multivariate normal is related to the one-dimensional

Gaussian distribution. The multivariate normal is applied to random vectors instead of random variables. The multivariate t distribution model uses an additional parameter, degrees of freedom. This parameter is also estimated using the EM algorithm (McNicholas and Subedi, 2012). This changes the distribution shape to have longer tails. The multivariate t therefore is more lenient with respect to the inclusion of some observations in a given group.

37 1.4.7. Gene ontology

Gene ontology is a tool used for rapid identification of gene roles. It describes functions in a broad sense such as transcription factor activity or DNA binding activity. These categories are given a GO term label and accession number.

When combined with cluster analysis, it can identify common functions found within clusters. Since clustering can be used to isolate genes that are co-regulated, gene ontology provides a method of determining which genes within a cluster may be related. The gene ontology results in this thesis were found using Funspec, a web-based platform specific to yeast gene ontology (Robinson et al., 2002).

1.5. Previous microarray studies of S. cerevisiae during secondary sparkling wine fermentation

Although it was our intention to produce original research, Penacho et al. published a study comparable to this one in 2012. Their goals were to observe the global transcriptome of yeast during secondary fermentation. The conditions were very similar to ours as they used an industrial strain of yeast (EC1118), non- synthetic grape must, and bottle fermentation (Penacho et al., 2012). Four sample points were chosen to collect yeast and analyze their transcriptomes. All of these samples were taken during secondary fermentation, and not from acclimatization or aging. Their findings were that genes involving respiration, oxidative stress response, and autophagy were expressed more highly during secondary fermentation (Penacho et al., 2012). Their work had some shortcomings that this study aims to elaborate upon. These include global transcription analysis during

38 acclimatization and aging, searching for mechanisms of autophagy regulation, and using a clustering model appropriate for longitudinal data.

1.6. Hypothesis and Objectives

The aim of this study is to determine which pathways in the DV10 strain of S. cerevisiae undergo significant changes in regulation during the secondary fermentation of sparkling wine. Untargeted studies are not conducive to a standard hypothesis as there is no specific proposal being tested. However, we postulate that several stress related pathways will be significantly upregulated throughout fermentation and aging, cell wall biosynthesis genes will be highly expressed as fermentation progresses, and many genes that are part of autophagy pathways will be upregulated as well.

39 Chapter 2 - Materials and Methods

2.1. Sparkling wine production:

All wine production took place at the Niagara College Teaching Winery under the direction of assistant winemaker Gavin Robertson. Grapes were collected and pressed for three hours to a maximum pressure of 1.1 bar. The pressing consisted of two events, collection of free run juice and a hard pressing. The maximum pressure for free run collection was 0.8 bar, with the juice collection from 0.8-1.1 bar collected separately. Only free run juice was used for base wine production.

Upon collection, FSO2 was added to the juice at a concentration of 25 mg/L. Cinn- free pectinase (Scot Labs) was added after pressing to a concentration of .025 mL/L.

Juice was settled and clarified for three days at 2 oC.

2.1.1. Base wine fermentation

Base wine was composed of three separate batches, later blended. Lot One consisted of Chardonnay grapes with a sugar content of 19.1 °Bx (determined by digital refractometer), pH of 3.17 (determined by pH meter), and titratable acidity of

7.7 g/L (determined by pH meter titration). Lot Two consisted of Pinot Noir grapes with a sugar content of 17.3 °Bx, pH of 3.21, and titratable acidity of 9.2 g/L. Lot

Three also consisted of Pinot Noir grapes with a sugar content of 16.0 °Bx, pH of

2.95, and titratable acidity of 9.6 g/L. Lot one was fermented over 15 days using

DV10 yeast (lavin), pitched at a density of 0.25 g/L. The average temperature of fermentation was 16.5 °C with a range of 13.2 °C-20.1 °C. Lot Two was fermented

40 over 14 days using DV10 yeast (lavin), pitched at a density of 0.25 g/L. The average temperature of fermentation was 16.1 °C with a range of 11.9 °C-18.0 °C. Lot Three was fermented over 15 days using DV10 yeast (lavin), pitched at a density of 0.25 g/L. The average temperature of fermentation was 14.8°C with a range of 13.6 °C-

20.0 °C.

The finished base wine was a blend of the lots One, Two and Three, with proportions of each being 53%, 19%, and 28% respectively. Final alcohol was

10.75% (determined using FOSS analysis), with residual sugar of 3.1 g/L (FOSS), pH of 3.21 (pH meter), titratable acidity of 8.8 g/L (pH meter), volatile acidity of 0.19 g/L (FOSS), and free SO2 of 14 mg/L. Additions were made to the base wine to aid fermentation. These included 0.06 g/L of diammonium phosphate (DAP), as well as

20.9 g/L sucrose. The latter was added to achieve a total residual sugar level of 24 g/L. This concentration is required to produce approximately 6 bar of CO2 pressure from secondary fermentation.

2.1.2. Second Fermentation

Before pitching, yeast were treated with nutrient and acclimatized to ethanol to produce a liqueur de tirage. Water to be used was heated to 41 °C, and supplements were added including 0.3 g/L Go-Ferm (nutrient source) and 0.3 g/L

Dynastart (growth and survival factors). After cooling to 40 °C, yeast were rehydrated at a concentration of 0.5 g/L in nutrient enriched water. Initial sucrose concentration in the liqueur de tirage was raised to 9.5 °Bx. Yeast were left 1.5 hours to ferment until the liqueur de tirage reached 4.5 °Bx and 2.8 % ethanol

41 (average temperature 28 °C). 4 L of base wine with a sucrose concentration of 5 %

(w/v) was added and once again fermented until half of the sucrose was depleted (3 hours, average temperature 27 °C). This step was repeated twice more; the second time with 5.8 L of base wine and the third with 10.4 L added. Each fermentation took 3 hours and average temperatures for each were 23 °C and 17.5 °C respectively. Each addition step was followed by mechanical agitation and aeration via agitation. Liqueur de tirage was then divided between 3000 L of base wine, bottled, and capped. The final concentration of the yeast was determined. Bottles for microarray analysis were transported to a refrigeration facility where they were stored horizontally at 15 °C in sealed boxes to prevent light exposure.

2.2. Yeast collection

Yeasts were collected over the course of second fermentation. Bottles were removed from refrigeration immediately prior to collection. Yeast and wine were homogenized in bottle before opening by inversion and shaking for 30 seconds. If collection took place during late secondary fermentation, bottles were opened over a basin to save ejecta. Four bottles were used for every collection day.

Homogenized wine was collected by pouring 200 mL in a graduated cylinder, then transferred to 500 mL centrifuge tubes and stored on ice. Two collections were done per bottle resulting in a total of 8 per sample point. Centrifugation took place in an ultracentrifuge (Avanti J-30I, Beckman Coulter) at 5000 x g for 5 minutes at 4°

C. Some supernatant (wine) was collected in 50 mL falcon tubes for metabolite analysis. The remainder was disposed of or used to resuspended yeast. The pellets 42 were resuspended in ~ 4mL of settled wine and transferred to 20 mL falcon tubes.

Resuspended yeast were centrifuged once more at 5000 x g for 5 minutes at 4 °C.

The supernatant was discarded and pellets were resuspended in ~2 mL of deionized

H2O (dH2O). Resuspended yeast from each bottle were combined in one falcon tube, then pipetted into three microcentrifuge tubes and centrifuged in a microcentrifuge

(Micromax, Thermo IEC) at 5000 x g for 3 minutes at room temperature.

Supernatant was discarded, and yeasts in each tube were resuspended in 1 mL dH2O, which were once again centrifuged at 5000 x g for 3 minutes at room temperature. Residual supernatant was pipetted from each tube. Pellets were frozen at -80° C. Pellets were kept on ice when not being centrifuged to prevent mRNA degradation.

2.2.1. Cell viability analysis

Some of the homogenized yeast/sparkling wine for RNA collection was set aside for viability analysis. 10 mL samples were collected in 20 mL falcon tubes and serially diluted 9:1 with dH2O to a max dilution that depended on the viability of prior samples. Yeast with an expected viability of 5 x 106 CFU/mL, for example, would be serially diluted up to 107. Dilutions were then plated in triplicate on Yeast

Peptone Dextrose (YPD) agar (50 g/L YPD broth (Difco), 1.5% (w/v) agar (Difco)) and incubated for 2-3 days at 30 °C. Colonies were then counted and averaged.

Viability plots were generated using R.

43 2.2.2. RNA isolation

Yeast pellets were removed from -80 °C storage and placed on ice. All reagents used were ~21 °C unless otherwise stated, and all samples were stored on ice when possible. Pellets were resuspended in 300 μL of STE buffer (10 mM Tris-

Cl, 10 mM EDTA, 0.5% (w/v) SDS), and 200 μL of glass beads (0.5 mm dia., BioSpec) were added. 400 μL of acid phenol chloroform (P:C:I) (5:1, pH 4.5 +\- 0.2, Ambion), was added to each yeast sample which was followed by vortexing (Vortex Genie 2,

Fisher Scientific). Vortexing required 6 minutes or more at maximum rpm, depending on the age of the yeast post-tirage (thicker cell walls required more time). 50 μL of 10% sodium dodecylsulphate (SDS) was added to each vortexed sample. All samples were incubated on ice for 20 minutes, then centrifuged

(Micromax, Thermo IEC) at 13000 rpm for 20 minutes at 4 °C. The supernatant was collected in new 2 mL microcentrifuge tubes and 400 μL of 4 °C P:C:I was added.

Tubes were vortexed for 30 seconds, then centrifuged at 13000 rpm for 5 minutes at

4° C. Supernatant was collected and transferred to new microcentrifuge tubes and

300 μL of chloroform was added. Samples were vortexed for 30 seconds then centrifuged at 13000 rpm for 5 minutes at 4 °C. Chloroform washing was done three times. After the final chloroform wash, the mRNA quality of lysed yeast samples was assessed by spectrometry (Nanodrop 8000, Thermo Scientific). If phenol contamination was deemed too high, samples were again washed with chloroform and reassessed. Once the samples had acceptably low levels of phenol, they were precipitated using 1 mL of 100% ethanol. Samples were centrifuged at

13000 RPM for 20 min at 4 oC. The supernatant was discarded and pelleted RNA

44 was either resuspended in ethanol and stored, or dried by tapping the centrifuge tubes while inverted, followed by air-drying for several minutes. If samples were to be stored, they were resuspended in 80% ethanol and kept at -20 °C for 1-3 days. If samples were to be used immediately, the pellets were resuspended in 100 μL of diethylpyrocarbonate (DEPC) treated dH2O. The addition of DEPC H2O was followed by rigorous agitation for 5-10 minutes to fully dissolve the RNA pellet. This was done by repeatedly dragging the microcentrifuge tubes against a pitted surface

(microcentrifuge rack). Resuspended RNA was then assessed by spectrometry to determine if the final concentration was high enough to proceed with subsequent treatment. RNA purification was done using a Qiagen RNeasy miniprep kit as per manufacturer instructions. However, the provided elution solution was not used,

DEPC H2O was substituted. The purified RNA was assessed for degradation by gel electrophoresis. Acceptable samples were stored at -80 °C until microarray analysis.

2.3. Microarray Methods

Microarray results were generated at the MaRS research center in Toronto,

ON, under the direction of Gurbaksh Basi. In total, 30 RNA samples from 10 time points were shipped for microarray assay. Samples were shipped on dry ice by a frustrated courier to prevent degradation. Quality was assessed using an RNA

Bioanalyzer kit (Agilent technologies). Upon passing inspection, sample RNA were converted to cDNA by reverse transcriptase. Reverse transcriptase reaction required the use of a T7 oligo primer sequence. Single stranded cDNA was 45 converted to double stranded cDNA with DNA polymerase while RNA was degraded using RNase H. The double stranded cDNA was used as a template for producing biotin-labelled aRNA. The aRNA were then purified and fragmented for hybridization with the Yeast Genome 2.0 chip (Affymetrix). Hybridization was performmed following manufacturer directions, and analysis was done using an

Affymetrix GeneChip Scanner. Resulting intensity information was converted to CEL files for further analysis.

2.4. Metabolite Analysis

Wine separated from yeast during previous extraction was stored at -80 °C or 4 °C, depending on time between collection and analysis. Samples extracted at the start of secondary fermentation were stored at -80 °C to prevent ethanol loss, and samples collected toward the end of fermentation were stored at 4 °C. For every wine bottle opened, two 40 mL samples were collected in falcon tubes. This yielded two subsamples per sample totalling eight per sample point. Analysis was done using high performance liquid chromatography (HPLC). Metabolites analyzed included glucose, fructose, glycerol, and ethanol. The two internal standards chosen were lactose and propanol. Standard curves were generated using concentrations of lactose from 10-0.078% (w/v), and propanol from 10-0.078% (v/v). Samples were prepared by spiking with the two internal standards. To prepare samples for analysis, 500 μL of wine and standard were aliquoted into 2 mL HPLC vials. Vials were sealed and stored at 4 °C. The HPLC system (Shimadzu Corp.) maintained sample temperature at 4 °C. The analysis was performed using 5mM H2SO4 and a 46 Biorad HPX87H column. Each sample required a run time of 30 minutes. Peak integration was primarily determined automatically by software, with occasional manual intervention. Intensity values were exported as Excel files for further analysis.

2.5. Data Analysis

Data were analysed primarily using R (R Core Team, 2012) and Excel

(Microsoft, 2011). Two separate analyses were done: HPLC and microarray analysis. Microarray analysis can be further broken into two components: microarray pre-processing, and cluster analysis. Pre-processing was used to format data for analysis while cluster analysis isolated genes following one of several expression patterns in hopes of identifying genes that play a roles during sparkling wine fermentation, particularly acclimatization and aging. Following cluster analysis, gene ontology was used to identify the function of genes in specific clusters.

2.5.1. HPLC Analysis

HLPC data were formatted using Excel and saved as comma separated value

(csv) files. Internal standard data were used to find concentration data using the following formula: where A = area, x = internal standard, y = metabolite to be analysed, and F = a constant with a value that depends on the metabolite. Once the sample concentration was determined, it was

47 adjusted for dilution that occurred during sample preparation. Final concentrations were plotted using R.

2.5.2. Microarray Analysis

Microarrays were pre-processed using the “affy” package for R available from

Bioconductor (Gentleman et al., 2004). Many forms of background correction were attempted, and rma correction was ultimately chosen based on comparative analysis. The method of normalization was similarly chosen, with scalar correction yielding the best result. For these reasons, the RMA algorithm was used for background correction, normalization and summarization. Probe designations were substituted with corresponding gene names provided by Affymetrix. The Yeast

Genome 2.0 chips carry probes for S. pombe as well as S. cerevisiae and therefore the

S. pombe probe data were removed. Genes with two-fold or greater changes in expression over the ten selected sample points were screened using the genefilter package. Selected genes were clustered using longitudinal clustering from the longclust package for R (McNicholas et al., 2015). The arguments selected for the longlustEM function were gaussian=TRUE (Gaussian distribution mixtures), and criteria=”BIC” (model evaluation with Bayesian information criterion

(BIC)(Schwarz, 1978)). The initial analysis had a Gmin value of 1 and a Gmax of 20 to screen models with 1-20 components. This was repeated twice more for alternate seed values to find an estimate of the expected number of components.

Further analyses could then be reduced to models with 13-17 groups. Multivariate

Gaussian analysis was done 32 times using randomly selected seeds. This was

48 followed by 9 analyses using guassian=FALSE (multivariate t distributions) using randomly selected seeds. All plots were generated using R.

49 Chapter 3 – Results

3.1. Yeast culture growth and liqueur de tirage followed industry standards

In an effort to replicate conditions found during commercial wine making,

Sparkling wine production was outsourced to the Niagara College Teaching Winery.

The assistant wine maker carried out the preparation of yeast cultures for secondary fermentation. For this reason, some of the results weren’t recorded and are unknown. Cell viability was one of these results and was not ascertained empirically. However, it was recorded that the yeasts were pitched into the liqueur de tirage at a density of approximately 107 cells/mL. Growth after that point until bottling is not known. What is known are the conditions under which yeast rehydration and acclimatization occurred. Temperatures were kept well within acceptable limits, starting at 31.6 °C and ending at 16.2 °C after refrigeration (Table

3.1). The sugar content also began at 10.8 °Bx, a concentration easily tolerated by yeast, and was reduced to near dryness by the end of acclimatization or 1.2 °Bx

(Table 3.1). In theory, tirage should have provided an opportunity for the yeasts to adjust to the harsh conditions found in the base wine and thereby allow the adapted yeast to perform the secondary fermentation. While conclusions cannot be reached without more evidence, the practices used were standard in the industry.

50 Table 3.1 Tirage for sparkling wine production. Two batches of yeast were acclimatized to ethanol and nutrient stress for second fermentation at Niagara College Teaching Winery. Acclimatization occurred over approximately 60 hours and the liqueur de tirage was fermented fermented until available sugars were almost depleted..

Time (hours) Temp (°C) Specific Gravity Brix (°B) TUB # 1 0 31.6 1.043 10.8 0:44 31.6 1.043 10.8 1:24 31.2 1.043 10.8 2:19 31 1.039 9.6 3:14 31 1.035 8.8

4:09 31.2 1.029 7.4

5:30 32.2 1.024 6.2

6:29 30.2 1.019 4.8 7:26 29.3 1.016 4.1 7:55 27.6 1.011 2.8 8:56 25.4 1.007 1.8 9:46 24.5 1.005 1.2 11:03 21.4 1.003 0.7 TUB # 2 0 19.4 1.026 6.6 0:48 19.1 1.026 6.6 2:55 18.3 1.026 6.6 6:43 16.4 1.026 6.6 11:15 16.2 1.026 6.6 14:05 16.3 1.026 6.6 17:01 16.3 1.024 6 18:50 16.3 1.023 5.7 20:32 16.3 1.023 5.7 22:25 16.4 1.022 5.5 22:59 16.4 1.021 5.2 27:10 16.5 1.019 4.8 31:08 16.5 1.018 4.4 35:37 16.6 1.016 4.1 38:20 16.6 1.015 3.9 41:36 16.6 1.012 3.1 44:36 16.6 1.011 2.9 49:31 16.5 1.009 2.3 50:31 16.4 1.008 2.1 59:16 16.2 1.005 1.2

51 3.2. Abnormal fructose metabolism but standard fermentation product concentrations

Metabolite analysis was performed to measure the change of nutrient concentrations and products of fermentation. It allowed us to determine which stresses might be present over the course of fermentation (e.g. nutrients stress, ethanol stress, osmotic stress, etc.). To achieve this, several wine samples were collected over the course of fermentation and analyzed with HPLC. This HPLC analysis yielded results somewhat atypical of wine secondary fermentation. The duration of fermentation was long, but not extraordinarily so.

Aside from the sluggish fermentation, the metabolite profiles deviated to some degree from expectations. Glucose levels roughly reflected normal fermentation, and was used preferentially by yeast (Berthel et al., 2004; Cebollero and Gonzalez, 2006). There was a rapid drop in glucose concentration from bottling until day 33. The depletion rate of glucose remained almost constant and by the

33rd day after bottling the concentration of glucose was negligible (Figure 3.1).

Fructose levels declined as well although they followed a different pattern than glucose. Initially fructose was used by yeast at approximately the same rate (Figure

3.1). However, between days 10 and 20 fructose levels remained nearly constant.

After day 21, fructose utilization resumed at the prior rate until day 39. At this point, residual fructose remained over 0.3% (w/v) and as of the last sample point, day 66, was still present at over 0.2% (w/v).

The fermentation products were also somewhat abnormal. Glycerol concentrations largely met expectation and increased slightly over the course of

52 fermentation. However, the majority of glycerol production occurred between days

19 and 23 (Figure 3.1). Any glycerol increase outside of this period was negligible.

Ethanol production showed a large increase between days 19-23 (Figure 3.1).

However, the ethanol concentration continued to rise sharply until approximately day 39. The final concentration of glucose, fructose, glycerol and ethanol was 0%

(w/v), 0.248 % (w/v), 0.536 % (v/v), and 11.586 (v/v) % respectively. The change in concentration over the course of secondary fermentation for glucose, fructose, glycerol and ethanol was -100 %, -67.4 %, +11..4 %, and +12.0 % respectively

(Figure 3.1).

These results are not consistent with a normal secondary fermentation. We would expect to see completion much sooner. The end concentrations of these metabolites were more or less expected, but the patterns of change were not. In particular, we observed a substantial lag in ethanol and glycerol production, followed by a sharp rise over a period of 4 days, two weeks after bottling. This occurs as the pace of fructose metabolism increases and suggests that the yeasts may have had difficulty adapting to fructose utilization.

53 Glucose Fructose 0.9 0.8 0.6 0.7 0.4 0.5 0.2 Concentration (%w/v) Concentration (%w/v) Concentration 0.3 0.0

0 10 20 30 40 50 60 0 10 20 30 40 50 60

Days post tirage Days post tirage

Ethanol Glycerol 11.5 0.64 11.0 0.60 10.5 10.0 Concentration (%v/v) Concentration (%v/v) Concentration 0.56

0 10 20 30 40 50 60 0 10 20 30 40 50 60

Days post tirage Days post tirage

Figure 3.1 Metabolites found in sparkling wine over the course of second fermentation. Metabolites were measured using HPLC and included glucose, fructose, glycerol and ethanol.

3.3. Substantial loss of cell viability during fermentation

The analysis of cell viability provided insight regarding the efficiency with which the yeast endured the stresses of secondary fermentation. It was measured by plating serial dilutions of homogenized wine on YPD agar. In some ways cell viability analysis yielded expected results, but just like fructose levels the patterns of change are abnormal. The yeast showed an initial decline in viability as nutrients

54 decreased. However, the degree of this drop is substantial. Figure 3.2 shows a severe decline between days 7 and 10 post-bottling. At the same time, nutrients remained fairly high in the wine, and ethanol concentration did not increase to a level that should be detrimental to the yeast. Between 20 and 30 days after bottling the decline in viability was less substantial. The initial pitching density of yeasts for the liqueur de tirage was approximately 107 cells/mL and may have increased to a higher density by the end of acclimatization. By day 7 after bottling, viability was measured to be 7.2 x 106 CFU/mL, and the last sample point at 66 days post bottling it was 8.3 x 105 CFU/mL (Figure 3.2). Although the exact number is unknown, cell viability decreased a minimum of 88.47% over secondary fermentation.

The extensive loss of viability during early fermentation was unanticipated.

There was a decrease of 26.6% between days 7 and 10 alone (Figure 3.2). With yeast unable to thrive and function normally fermentation is unable to progress.

Therefore this was likely the cause of the extended duration of fermentation. Since the yeast had a period of acclimatization to ethanol stress this should not have occurred and the reason for the loss of viability is unknown.

55 Cell viability 7e+06 5e+06 3e+06 Colony forming units / units mL forming Colony 1e+06

10 20 30 40 50 60

Days post tirage

Figure 3.2 Viability of DV10 S. Cerevisiae over the course of second fermentation and aging. Viability was measured by serial dilution of yeast collected from bottled sparkling wine and plated in triplicate on YPD agar.

56 Metabolite and CFU

Fructose Glucose Glycerol

1.0 Ethanol CFU 0.8 0.6 0.4 Proportional change (%) change Proportional 0.2 0.0

10 20 30 40 50 60

Days post triage

Figure 3.3 Combined sparkling wine metabolite and cell viability over the course of second fermentation and aging. Abundance is expressed as the proportion by which metabolites concentrations have changed compared to day 7. Metabolites were measured using HPLC and viability was measured by plating yeast samples on YPD agar.

57

3.4. Conclusions and discussion

The secondary fermentation in this experiment was atypical for several reasons. The drop in viability shows an exponential decrease from day 7 until the end of fermentation. Although the yeasts encounter several stresses during fermentation, viability does not usually drop so rapidly in other industrial strains

(Mannazzu et al., 2008). The precise cause of this drop is unknown, and viability of the DV10 strain during secondary fermentation has not been studied in depth.

DV10 is however closely related to the EC1118 strain that has shown a rapid decrease in viability around the same time (Orozcoa et al., 2012). The rapid loss of cell viability early during fermentation raises several questions that are currently beyond our ability to answer. For example, is the sudden drop in pressure after uncapping of the bottles and subsequent ejection of wine causing cells to rupture? It may be the case that the viability results do not accurately reflect the condition of the yeast prior to uncapping. If the results are accurate though, it may be tied to abnormal fructose usage and ethanol production observed during early secondary fermentation.

The metabolite analysis is far from consistent with our knowledge of secondary fermentation. The feature that stands out the most is the stalling of ethanol production and fructose metabolism. This is not characteristic of a normal fermentation and looks somewhat like a stuck fermentation. However, if this was a stuck fermentation we would not expect to see glucose metabolism continue

58 uninterrupted. The usual pattern of hexose metabolism has a bias toward glucose use over fructose, but even if fructose is not the preferred carbon source the concentration continues to decrease (Berthel et al., 2004). The absence of oxygen limits the possibilities for glucose utilization as the pathways into which glucose could be directed without ethanol production are largely anabolic under anaerobic conditions. The primary pathways for glucose metabolism are glycolysis and the pentose phosphate pathway. The former generates ethanol and therefore must not be occurring at significant levels. The pentose phosphate pathway uses glucose to synthesize pentose sugars and NADPH for biosynthetic pathways, and while the pathway does not result in ATP loss many pathways that use the products do consume ATP. This leaves us with the possibility that this pathway may have somehow become deregulated and lead to the build-up of biosynthetic intermediates. The results generated in this study cannot explain the lack of ethanol production and fructose metabolism observed shortly after secondary fermentation began. Further testing will be required to determine if this result is reproducible.

3.5. Microarray analysis shows most genes have altered regulation during the sparkling wine production process

Global transcription of yeasts were analyzed using microarrays in order to determine which genes were responsible for physiological changes during sparkling wine productions. Of the 5744 probes present on the Yeast Genome 2.0 chip for S. cerevisiae, 4044 met the criteria to be included in the cluster analysis. Specifically, over the course of fermentation at least one sample point showed gene expression

59 that was a minimum of two-fold or one half that of yeast on day 1. In other words, of the S cerevisiae genes with corresponding probes, 70.4% showed altered regulation during the course of the fermentation. The expression in this instance could not be discreetly divided into groups that were “upregulated” or “downregulated”. Instead, expression yielded profiles that varied in regulation over time (Figure 3.4). Since most genes experienced a notable change in expression, this demonstrates that fermentation causes extreme alterations of yeast physiology.

3.6. Genes can be divided into 15 groups based on similar expression profiles

The gene expression data was analyzed by clustering to identify genes with a higher likelihood of co-regulation. By analyzing the median expression of clustered longitudinal data, we can observe how the yeasts react to particular stresses during fermentation. When clustered, a local maximum was modeled with multivariate

Gaussian distribution, 15 groups, a minimum membership of 101 genes, and a maximum of 715 genes (Table 3.2). For nearly every cluster the most dramatic change in expression occurred between the rehydration of yeast and acclimatization to ethanol. After this point, expression patterns stabilize and more gradual changes take place until the end of sampling (Figure 3.4).

Of the 15 clusters two, cluster 1 and cluster 7, showed an expression profile where little change was present until mid fermentation. These clusters also show the least change in expression from rehydration to acclimatization. At 249 and 101 genes respectively cluster 1 and 7 had moderate to low gene membership as well.

60 For these reasons they were selected as clusters that may contain genes governing autolysis and autophagy and were analyzed further using gene ontology.

Table 3.2 Clusters and gene membership for microarray data from sparkling wine yeast. Clusters were assigned by longitudinal cluster analysis using the longclust package form R.

Cluster 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 Genes 249 138 162 165 345 309 101 238 556 233 259 138 117 317 715

Further analysis revealed better models. The optimal multivariate Gaussian model had a BIC of -51025.36 compared to the local maximum BIC of -51091.83 used for further analysis in this thesis. The best model was found using multivariate t distributions as several observation were likely located at the tails of distributions and therefore had a higher probability of group membership using this model. The

BIC value of -50802.68 was much larger than any found using multivariate normal.

The covariance structure for all models was EVA. This structure constrains the triangular matrix Tg to be equal across all groups and suggests a correlation structure that is the same across all groups. The diagonal matrix Dg is variable and suggests that variability at each time point is the same for each group. An isotropic constraint is not imposed which suggests that variability is not the same at all time points. Both Gaussian models in Table 3.3 have 15 components. The mean expression profile for each group in the local maximum is a close match for one found in the best multivariate normal model. The optimal model found using multivariate t distributions has 14 components. Every component in this model has a matching group in the local maximum, with the exception of clusters 1 and 7 from

61 the local maximum, which were combined into a single cluster in the multivariate t model.

Table 3.3 Properties of the optimal multivariate normal and multivariate t models determined from cluster analysis, as well as the local maximum used for analysis in this thesis.

Analysis Local Maximum Best multivariate Best Multivariate t Gaussian Distribution Gaussian Gaussian Multivariate t Components 15 15 14 Covariance EVA EVA EVA Structure BIC -51091.83 -51025.36 -50802.68

62 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 Clusters D39 D36 D33 D30 D21 Sample point Sample D14 All clusters D7 D1 A

R

12 10 8 6

2 Intensity log Intensity

Figure 3.4 Mean expression of genes in their respective clusters. Cluster analysis was done using the longclust package from R and bioconductor. RNA samples for analysis were taken during yeast rehydration, acclimatization, and days 1-39 after bottling.

63 Cluster 2 has an expression profile that gradually decreases after day 14

(Figure 3.5). It does however show a large drop in expression between rehydration and acclimatization. Expression then increases between acclimatization and one day post bottling. Like clusters 1 and 7, the change in expression is gradual and begins near the end of fermentation. It is therefore likely that any genes that regulate aging in this cluster will be repressors of transcription since their presence decreases as aging progresses.

64 Figure 3.5 Individual plots of cluster means found from longitudinal cluster analysis of microarray data.

65 3.7. Cluster 1 shows increased transcription toward the end of fermentation

The mean expression of genes in cluster 1 showed a general increase toward the end of fermentation and with moderate to low activity during fermentation

(Figure 3.6). The most pronounced change in expression occurred with acclimatization where expression increased nearly two-fold. After which, a period of relative stability for two weeks was observed, followed by increasing transcription into aging. This profile matches the expected regulation of genes required for stress response and therefore merited further investigation. One noteworthy feature is the spike in expression at day 7, the period when fermentation was sluggish and the production of some metabolites stalled. To identify possible co-regulated pathways, a gene ontology analysis was performed to examine the function of these genes in detail.

66 Cluster 1 11.0 2 10.5 Intensity Intensity log 10.0 9.5

R A D1 D7 D14 D21 D30 D33 D36 D39

Sample point

Figure 3.6 Gene expression of cluster 1 as determined by longitudinal clustering of microarray data. Expression is measured in terms of log2 of the fluorescent intensity

Gene ontology analysis showed that several functions and pathways were present in cluster 1, although many of the genes analyzed had DNA binding properties or protein folding function (Table 3.4). Most of these genes show an expression profile similar to the mean of cluster 1, but deviate in some key ways.

One common trait between almost all genes is a rapid initial change in regulation during acclimatization. While there are numerous ways to divide these genes into subcategories, there are two patterns that may explain some of the phenomena

67 observed: gradual increase of expression during fermentation, and gradual expression increases with brief but significant changes during fermentation. The latter pattern is of interest due to the aforementioned anomalous fermentative phenomena. Most notably is the dramatic decrease of cell viability until day 21

(Figure 3.2), and the lack of fructose utilization and ethanol production between days 10 and 21 (Figure 3.1). Many of the abrupt changes in gene expression occur during these time periods and some of them may shed light on why this fermentation deviated from the norm.

Table 3.4 Molecular function gene ontology results for cluster 1 genes. Several genes fit into multiple categories. Largely redundant categories were omitted.

Accession Category Genes GO:0043565 sequence-specific DNA SMP1 RPN4 STP4 GIS1 ACA1 DOT6 SPT15 HAC1 TOS8 RSC30 binding STE12 ASG1 PUT3 IXR1 HAP1 YAP1 GAT2 MET4 RAP1 TYE7 USV1 ROX1 GO:0003700 sequence-specific DNA SMP1 RPN4 UPC2 ACA1 HAC1 TOS8 RSC30 STE12 ASG1 PUT3 binding transcription factor OAF3 HAP1 YAP1 GAT2 MET4 RAP1 TYE7 activity GO:0051082 unfolded protein binding SSA1 HSP42 ECM10 SSA4 SSC1 LHS1 HSP104 CPR6 HSC82 SIS1 YDJ1 APJ1 HSP82 GO:0016798 hydrolase activity, acting on MAL32 AMS1 CRH1 MAL12 UNG1 DFG5 SCW10 ATH1 glycosyl bonds GO:0000297 spermine transmembrane TPO2 TPO1 TPO3 transporter activity GO:0000774 adenyl-nucleotide exchange FES1 LHS1 SSE1 factor activity GO:0005388 calcium-transporting PMC1 PMR1 ATPase activity GO:0030234 enzyme regulator activity RPN1 RPN2 SSC1 TSL1 GO:0030246 carbohydrate binding AMS1 MNL1 ATH1 GO:0016787 hydrolase activity UBP13 RDH54 MAL32 HIS4 POL3 PTP1 DOA4 PHM8 PMC1 AMS1 PMR1 CAX4 CRH1 ADE3 MAL12 ULP2 RHR2 SSL2 MPH1 LHS1 IRC20 PPZ1 UNG1 RPM2 NGL3 DFG5 SCW10 PLB3 HST1 DED1 PTP2 PDE2 EEB1 ATH1

68 3.7.1. Transcription factor properties in Cluster 1

Since one of the topics under study was the regulation of the aging process in sparkling wines, transcription factor activity was a gene ontology category of interest. Transcription factors were isolated using gene ontology and of the 249 genes in cluster 1, 17 have DNA transcription factor properties. As with most genes in cluster one, they can be divided into those that have gradual increases in expression and those that see spikes in expression at certain points (Figure 3.7).

Genes with gradual increases include HAC1, MET4, PUT3, TOS8, and YAP1. Of the remaining genes, all show some degree of sudden change during fermentation.

However, some of these genes do not show a two-fold or greater spike in expression compared to neighbouring time points. These include HAP1, OAF3, RAP1, RPN4,

RSC30, and SMP1. Those with a two-fold or greater spike include ACA1, ASG1, GAT2,

STE12, TYE7, and UPC2.

These genes as a group do not appear to have a common function or pathway other than general stress response (Table 3.5), but relationships can be observed when broken down into smaller groups. Several genes are regulators of metabolite synthesis or catabolism including ACA1, MET4, PUT3, TYE7, and UPC2 (Table 3.5).

Although not directly responsible for ethanol fermentation, they may have varying degrees of impact on its regulation. Some genes with a possible function relating to autophagy and autolysis are GAT2, TOS8, and RSC30 (Table 3.5), but may also only be part of the osmotic stress response (Jin and Klionsky, 2014; Horak et al., 2002;

Angus-Hill et al., 2001). Thus, even though there are no obvious regulators that can

69 explain the fermentation anomalies observed or the aging process, some genes are present in Table 3.5 that could shed light on the mechanisms in play.

Table 3.5 Genes with transcription factor activity from cluster 1. These genes were identified using gene ontology and belong to the accession category GO:0043565 (sequence-specific DNA binding transcription factor activity).

Transcription Gene Function factor ACA1 Regulator of carbon source utilization genes ASG1 Proposed transcription factor with hypothesized role in stress response. Part of the GATA zinc finger family. Has been hypothesized to potentially GAT2 play a role in autophagy regulation HAC1 Unfolded protein response regulator HAP1 Regulator for cytochrome C. Self repressing in a HSP70 dependent manner. MET4 Regulation of sulfur containing amino acid pathways OAF3 Putative transcriptional repressor. Regulates genes in response to oleate Transcriptional regulator of protein utilization. Promoter binding depends PUT3 on available nitrogen sources. Transcription factor with several roles relating to chromatin structure as RAP1 well telomere length. RPN4 Proteasome gene regulator. Upregulated during stress Regulates genes encoding ribosomal proteins by chromatin remodeling. RSC30 Plays a role related to cell wall integrity. SMP1 Transcription during osmotic stress response. Part of the HOG pathway. STE12 Activator of mating genes Putative transcription factor. Thought to play a role in cell cycle, and TOS8 possibly yeast cell wall. TYE7 Contributes to activation of glycolytic genes UPC2 Sterol biosynthetic gene activator YAP1 Active under periods of oxidative stress

70 D36 D36 D21 D21 MET4 SMP1 Sample point Sample point Sample D1 D1

R R

12 10 9 8 13 11 9 8

2 2 Intensity log Intensity log Intensity D36 D36 D36 D21 D21 D21 YAP1 HAP1 RSC30 Sample point Sample point Sample point Sample D1 D1 D1

R R R

11.5 10.5 9.5 11.0 10.0 9.0 11 10 9 8 7

2 2 2 Intensity log Intensity log Intensity log Intensity D36 D36 D36 D21 D21 D21 HAC1 RPN4 UPC2 Sample point Sample point Sample point Sample D1 D1 D1

R R R

14 12 10 13.5 12.0 10.5 12 11 10 9

2 2 2 Intensity log Intensity log Intensity log Intensity D36 D36 D36 D21 D21 D21 TYE7 GAT2 RAP1 Sample point Sample point Sample point Sample D1 D1 D1

R R R

11.0 10.0 9.0 9.5 8.5 7.5 12 11 10 9 8

2 2 2 Intensity log Intensity log Intensity log Intensity D36 D36 D36 D21 D21 D21 PUT3 TOS8 ASG1 Sample point Sample point Sample point Sample D1 D1 D1

R R R

10 9 8 7 11 10 9 8 7 14 12 10

2 2 2 Intensity log Intensity log Intensity log Intensity D36 D36 D36 D21 D21 D21 OAF3 ACA1 STE12 Sample point Sample point Sample point Sample D1 D1 D1

R R R

13 11 9 9.0 8.0 10 9 8 7 6

2 2 2 Intensity log Intensity log Intensity log Intensity Figure 3.7 Gene expression from cluster 1 with transcription factor activity as determined by gene ontology analysis. Gene expression is transformed to log2 values and was generated by DNA microarray.

71 3.7.2. DNA binding activity in Cluster 1

Gene ontology analysis of cluster 1 yielded 18 genes that showed DNA binding activity, but weren’t identified in the transcription factor gene ontology group (Table 3.6). As was the case with the transcription factors, the goal was to identify regulators of both aging and the reason behind the abnormal metabolite patterns observed. The expression patterns of these genes can be divided into the two groups seen from the transcription factors as well: steady increases in expression, and steady increases with abrupt spikes. The steady increase gene group comprises of IRC20, IXR1, RDH54, and USV1 (Figure 3.8). Genes with a spike in expression less than two-fold compared to adjacent time points are DOT6, GIS1,

HAP4, POL30, RPO21, SIS1, SSL2, and SPT4 (Figure 3.8). Genes with a spike in expression greater or equal to two-fold compared to adjacent time points are ROX1,

SWI1 and VHR1 (Figure 3.8). It should also be noted that there are some genes in this cluster that deviate from the cluster mean in unique ways including MPH1,

SPT15 and TFC6 (Figure 3.8). MPH1 expression increases sharply during acclimatization, then slowly decreases until day 14 where it quickly increases and plateaus at day 30. SPT15 expression decreases from rehydration until day 14, then sharply increase from day 21 to 33 where it plateaus. Expression levels in TFC6 remain relatively stable except for a large increase from day 21 to 30 after which it remains high.

Despite not being part of the gene ontology transcription factor activity group, several genes in the DNA binding group have demonstrated transcription factor functions. Other than these, the function of these DNA binding genes appears

72 to be largely related to DNA replication, and chromatin structure. Genes from this group are GIS1, IRC20, MPH1, POL30, RDH54, RPO21, SSL2, SWI1, and TFC6 (Table

3.6). Several genes are part of some type of stress response. IXR1, and ROX1 for example are regulators of hypoxic genes (Vizoso-Vazquez et al., 2012; Kwarst et al,

1997). Many of these genes appear to be upregulated due to nutrient stress. Genes like SWI1, GIS1, USV1 and HAP4 are known regulators of many carbon utilization genes (Côté et al., 1994; Pedruzzi et al., 2000; Hlynialuk et al., 2008; Forsburg and

Guarente, 1989).

As was the case for the transcription factors of cluster 1, there are no obvious dominant pathways present in the DNA binding group other than general stress response. However even if they aren’t the direct cause, some of these genes may play a part in aging or the observed metabolite patterns.

73

Table 3.6 Genes with DNA binding activity from cluster 1. These genes were identified using gene ontology and belong to the accession category GO:0003677 (DNA binding). Some genes are not present in this table due to redundancy.

DNA Gene Function binding rRNA and ribosome biogenesis. Upregulated during stress. Sequence specific DOT6 binding. GIS1 Histone demethylase and transcription factor. Sequence specific binding. HAP4 Regulator of respiratory genes IRC20 E3 ubiquitin ligase and putative helicase. Role in homologous recombination Transcriptional repressor of genes that respond to hypoxia. Sequence IXR1 specific binding. MPH1 DNA helicase. Null mutants cause mutator phenotype POL30 DNA polymerase sliding clamp. Functions in DNA replication and repair RDH54 DNA recombination and repair translocase. DNA coiling ROX1 Repressor of hypoxic genes. Sequence specific binding. RPO21 Subunit of DNA polymerase II Co-chaperone that interacts with HSP70. Required for degradation of SIS1 misfolded proteins. Transcription factor that interacts with others to form preinitiation complex. SPT15 Sequence specific binding. SSL2 Component of TFIIH holoenzyme. Unwinds promoter DNA Regulation of transcription initiation, RNA processing, quality control, and STP4 DNA repair. Sequence specific binding. SWI/SNF chromatin remodelling component. Regulates transcription of SWI1 carbon metabolic genes TFC6 TFIIIC transcription initiator component Putative transcription factor. Plays a role in utilization of non-fermentable USV1 carbon sources and cell wall biosynthesis. Sequence specific binding. Transcriptional activator for vitamin H transport and biotin biosynthesis VHR1 pathways

74 D36 D36 D36 D21 D21 D21 VHR1 MPH1 SPT15 Sample point Sample point Sample point Sample D1 D1 D1

R R R

10.0 9.0 8.0 12.5 11.5 10.5 13.0 11.5 10.0

2 2 2 Intensity log Intensity log Intensity log Intensity D36 D36 D36 D21 D21 D21 SIS1 IXR1 USV1 Sample point Sample point Sample point Sample D1 D1 D1

R R R

12 10 9 14 12 10 12 10 9 8

2 2 2 Intensity log Intensity log Intensity log Intensity D36 D36 D36 D21 D21 D21 TFC6 IRC20 RPO21 Sample point Sample point Sample point Sample D1 D1 D1

R R R

12 10 9 8 7 11.5 10.5 10.5 9.5 8.5

2 2 2 Intensity log Intensity log Intensity log Intensity D36 D36 D36 D21 D21 D21 SWI1 HAP4 ROX1 Sample point Sample point Sample point Sample D1 D1 D1

R R R

15 13 11 13 12 11 10 10 9 8 7

2 2 2 Intensity log Intensity log Intensity log Intensity D36 D36 D36 D21 D21 D21 GIS1 STP4 RDH54 Sample point Sample point Sample point Sample D1 D1 D1

R R R

13 11 9 12 11 10 9 8 12.0 11.0 10.0

2 2 2 Intensity log Intensity log Intensity log Intensity D36 D36 D36 D21 D21 D21 SSL2 DOT6 POL30 Sample point Sample point Sample point Sample D1 D1 D1

R R R

13 12 11 10 12 11 10 9 12.0 11.0 10.0

2 2 2 Intensity log Intensity log Intensity log Intensity Figure 3.8 Gene expression from cluster 1 with DNA binding activity as determined by gene ontology analysis. Gene expression is transformed to log2 values and was generated by DNA microarray 75 3.7.3. Glycosyl hydrolyase activity in Cluster 1

An important gene ontology category to investigate was hydrolase activity acting on glycosyl bonds as it indicates how cell stress is affecting cell wall integrity and tehalose synthesis (an indicator stress). While these genes generally have increased expression over the course of fermentation, they deviate substantially from the cluster mean. Three genes follow a similar pattern of a large increase in expression during acclimatization that quickly stabilizes and gradually increases toward the end fermentation. There is also a noticeable spike of expression on day

7 although this jump never exceeds a two-fold change. These three are AMS1, ATH1, and CRH1 (Figure 3.9). DFG5, a cell wall biogenesis gene (Table 3.7), shows an almost linear increase in expression with a dip form days 1-14 (Figure 3.9). The maltase gene MAL32 (Table 3.7) shows expected patterns with a dramatic upregulation during the initiation of fermentation (Figure 3.9). However there is a sudden drop from day 7 to day 14 that recovers during the next seven days. This drop coincides with the slowing of fermentation over the same period. SCW10 shows almost no change in expression from days 14 onward and is a very poor match for cluster 1 (Figure 3.9). The last gene in the category, UNG1, is a DNA repair gene (Table 3.7). It has two local maxima at days 1 and 30 (Figure 3.6), indicating possible high levels of DNA repair. It remains highly expressed from day 30 onward.

The genes in this category do not appear to yield answers regarding the lack of fructose fermentation toward the middle of fermentation. However, they do contribute toward the overall response to stress in the cell. Some of these genes

76 also play a role in cell wall formation, and while they do not appear to directly impact autolysis they may contribute in some ways.

Table 3.7 Genes with gylcosyl bond hydrolysis activity from cluster 1. These genes were identified using gene ontology and belong to the accession category GO:0016798 (hydrolase activity, acting on glycosyl bonds).

Glycosyl Gene Function hydrolase AMS1 Alpha mannosidase, free oligosaccharide degradation in vacuole ATH1 Trehalase required for tehalose degradation after stress recovery Chitin transglycosylase. Transfers chitin to beta glucans in cell wall during CRH1 stress Putative mannosidase. Required for cell wall biogenesis for bud DFG5 formation/filamentous growth. MAL32 Maltase. Hydrolyses several disaccharides SCW10 Similar properties to glucanases. Found in cell wall. UNG1 Uracil-DNA glycosylase. Uracil repair in DNA

77 D39 D33 D21 DFG5 Sample point Sample D7 A

R

12.5 12.0 11.5 11.0

2 Intensity log Intensity D39 D39 D33 D33 D21 D21 CRH1 UNG1 Sample point Sample point Sample D7 D7 A A

R R

14 13 12 11 10 9 8 12.0 11.5 11.0 10.5 10.0 9.5

2 2 Intensity log Intensity log Intensity D39 D39 D33 D33 D21 D21 ATH1 SCW10 Sample point Sample point Sample D7 D7 A A

R R

12 11 10 9 8 13 12 11 10

2 2 Intensity log Intensity log Intensity D39 D39 D33 D33 D21 D21 AMS1 MAL32 Sample point Sample point Sample D7 D7 A A

R R

13.0 12.0 11.0 10.0 10.0 9.5 9.0 8.5 8.0 7.5

2 2 Intensity log Intensity log Intensity Figure 3.9 Gene expression from cluster 1 with hydrolysis of gycosyl bond as determined by gene ontology analysis. Gene expression is transformed to log2 values and was generated by DNA microarray. 78 3.7.4. Unfolded protein binding activity in Cluster 1

One of the clearest signs of cell stress is the upregulation of heat shock protein genes and others associated with them (Gasch, 2003). Several of these genes and were found in cluster 1 and many share a very similar expression pattern.

Of the 13 unfolded-protein binding genes in cluster 1, five show increasing expression until day 14-21 where expression drops, then resumes increasing again

(Figure 3.10). Genes with this expression pattern include APJ1, CPR6, HSC82, SIS1, and SSA4. Genes with a two-fold or greater drop in expression after day 7 are APJ1,

CPR6, and SSA4. These genes have chaperone and heat shock protein functions

(Table 3.8). The features of expression patterns in this group resemble an increased level of cellular stress until the second week of fermentation. Stress then increases as fermentation progresses.

79

Table 3.8 Genes with products that bind unfolded proteins activity from cluster 1. These genes were identified using gene ontology and belong to the accession category GO:0051082 (unfolded protein binding).

Unfolded protein Gene Function binding APJ1 Chaperone protein with SUMO-mediated protein degradation function Catalysis of cis-trans isomerization of peptide bonds N-terminal to proline CPR6 residues ECM10 Hsp70 family member. Role relates to protein translocation HSC82 Chaperone and member of Hsp90 family. HSP104 Refolds denatured and aggregated proteins Prevents aggregation of unfolded proteins. Response to high pretin HSP42 abundance due to DNA replication stress HSP82 Hsp90 chaperone. LHS1 ER lumen chaperone. Role relates to protein translocation and folding Type II Hsp40 co-chaperone. Interacts with Ssa1p. Plays a role in the SIS1 delivery of misfolded proteins for degradation. HSP70 family member. Functions relate to protein folding, and NLS SSA1 transport SSA4 Heat shock protein. Membrane targeting and translocation of proteins Hsp70 family member. Part of the translocase of the inner mitochondrial SSC1 membrane (TIM23 complex) YDJ1 Type I Hsp40 co-chaperone. Regulator of Hsp90 and Hsp70 function.

80 D33 D33 SSA1 D14 D14 HSP104 Sample point Sample point Sample D1 D1

R R

13 12 11 10 14 13 12 11

2 2 Intensity log Intensity log Intensity D33 D33 SIS1 D14 D14 HSC82 Sample point Sample point Sample D1 D1

R R

12.5 11.5 14 12 10

2 2 Intensity log Intensity log Intensity D33 D33 D33 YDJ1 LHS1 D14 D14 D14 ECM10 Sample point Sample point Sample point Sample D1 D1 D1

R R R

8.5 7.5 6.5 12 11 10 9 14 12 10

2 2 2 Intensity log Intensity log Intensity log Intensity D33 D33 D33 SSC1 CPR6 D14 D14 D14 HSP82 Sample point Sample point Sample point Sample D1 D1 D1

R R R

13 12 11 10 13.0 11.5 10.0 14 12 10

2 2 2 Intensity log Intensity log Intensity log Intensity D33 D33 D33 APJ1 SSA4 D14 D14 D14 HSP42 Sample point Sample point Sample point Sample D1 D1 D1

R R R

12 10 9 12.0 11.0 10.0 11.5 10.5 9.5

2 2 2 Intensity log Intensity log Intensity log Intensity Figure 3.10 Gene expression from cluster 1 with unfolded-protein binding activity as determined by gene ontology analysis. Gene expression is transformed to log2 and was generated by DNA microarray. 81 3.7.5. Spermine transporter activity in Cluster 1

Spermine transport is a mechanism for timing the expression of genes that respond to oxidative stress (Kruger et al., 2013). Spermine transporter genes were found to have significant upregulation from clustering, and their functions were identified from gene ontology analysis. Two of the three, TPO1 and TPO2, have similar expression trends. The transcript levels gradually increase until day 36, after which they dip slightly (Figure 3.11). Expression is somewhat different during mid and early fermentation. TPO1 expression decreased over two-fold between days 7 and 14, whereas TPO2 expression decreased during early fermentation and increased more than two-fold from day 14-21 (Figure 3.11). TPO3 showed erratic expression with more than four-fold drops at day 1 and 21, each of which rebounded by the next measurement (Figure 3.11). Spermine transport activity in yeast is indicative of cellular stress (Table 3.9), and the increased expression helps illustrate the conditions under which fermentation progressed.

Table 3.9 Cluster 1 Genes with products that are spermine/H+ antiporters. These genes were identified using gene ontology and belong to the accession category GO:0000297 (spermine transmembrane transporter activity).

Spermine Gene function transporter Polyamine H+ antiporter that recognizes spermine, putrescine, and TPO1 spermidine. Exports polyamines during stress to aid the timing of stress responsive genes. TPO2 spermine specific H+ antiporter. Regulation is done by Haa1p. TPO3 spermine specific H+ antiporter. Paralog of TPO2.

82 TPO1 TPO2 TPO3 16 10 13 15 2 2 2 9 14 12 13 8 Intensity Intensity log Intensity log Intensity log 11 12 11 7 10 10 6

R A D1 D14 D30 D36 R A D1 D14 D30 D36 R A D1 D14 D30 D36

Sample point Sample point Sample point

Figure 3.11 Gene expression from cluster 1 with spermine transporter activity as determined by gene ontology analysis. Gene expression is transformed to log2 values and was generated by DNA microarray.

3.7.6. Adenyl-nucleotide exchange factor activity in Cluster 1

Three adenyl-nucleotide exchange factors were identified from cluster 1 using gene ontology. The three included FES1, LHS1, and SSE1 (Table 3.10). FES1 and SSE1 have roles relating to heat shock protein function. LHS1 encodes an exchange factor of Kar2, which also has protein folding functions (Seppä and

Makarow, 2005). Other than during acclimatization, FES1 and SSE1 have very similar expression patterns and gradually increase toward the end of fermentation

(Figure 3.12). The most prominent feature of expression for both is a spike in transcript levels at day 7. Both FES1 and SSE1 have a two-fold decrease between day 7 and 14. LHS1 expression decreases sharply during acclimatization and bottling, and then increases at day 7 and although there is less transcript abundance by day 14 the drop is less pronounced than for the other two genes in this GO category (Figure 3.12). These exchange factors are part of heat shock protein and

83 chaperone functions (Table 3.10), and play a role in the cell stress response. They contribute to understanding the reaction of the yeast to this particular fermentation.

Table 3.10 Cluster 1 nucleotide exchange factor genes. These genes were identified using gene ontology and belong to the accession category GO:0000774 (Adenyl-nucleotide exchange factor activity).

Adenyl-nucleotide Gene Function exchange factor Nucleotide exchange factor for Hsp70. Abundance increases during FES1 stress. ER lumen chaperone. Nucleotide exchange factor for Kar2, a protein- LHS1 folding chaperone. SSE1 HSP70 family member and Hsp90 complex ATPase.

FES1 LHS1 SSE1 13.5 14 12 13.0 2 2 2 12.5 13 11 12.0 Intensity Intensity log Intensity log Intensity log 12 10 11.5 11 11.0 9

R A D1 D14 D30 D36 R A D1 D14 D30 D36 R A D1 D14 D30 D36

Sample point Sample point Sample point

Figure 3.12 Gene expression from cluster 1 with Adenyl-nucleotide exchange factor activity as determined by gene ontology analysis. Gene expression is transformed to log2 values and was generated by DNA microarray.

84 3.7.7. Enzyme regulation activity in Cluster 1

Gene ontology analysis found four genes in cluster 1 with enzyme regulation activity. The four include RPN1, RPN2, SSC1, and TSL1. The first two RPN1 and

RPN2 encode regulators of the 26S proteasome (Table 3.11), but have very different expression profiles. RPN1 expression is almost identical to that of SSC1, an HSP70 family member (Table 3.11). For these two genes, expression is erratic until day 14-

21 when transcript levels gradually climb in the pattern characteristic to cluster 1, and eventually dip slightly at day 39 (Figure 3.13). They also have the expression spike at day 7 that is pervasive among cluster 1 genes. RPN2 expression is very stable with the exception of larger than two-fold drop in expression after bottling, which then recovers by day 7 (Figure 3.13). This is similar to TSL1 expression, except the recovery after day 1 is much less (Figure 3.13).

The general patterns observed among this gene ontology group can be divided into two, even though all genes have stress related roles (Table 3.11). Two genes, RPN1 and SSC1, are transcribed in larger quantities over fermentation with a notable reaction to some stimuli at day 7, while the other two react to something else around day 1 and then remain relatively unchanged. This provides further evidence that the yeast used for this fermentation encountered a sudden stress after bottling.

85

Table 3.11 Genes with enzyme regulation activity from cluster 1. These genes were identified using gene ontology and belong to the accession category GO:0030234 (enzyme regulator activity).

Enzyme Gene Function regulator RPN1 Component of the 19S regulator particle for the 26S proteasome. Substrate for the N-acetyltransferase Nat1p and 26S proteasome. Cellular RPN2 stress increases abundance. Hsp70 family member. Part of the translocase of the inner mitochondrial SSC1 membrane (TIM23 complex) Trehalose 6-phosphate complex large subunit. Synthesizes trehalose from TSL1 uridine-5'-diphosphoglucose and glucose 6-phosphate.

86 RPN1 RPN2 13.5 2 2 12.0 12.5 11.0 Intensity Intensity log Intensity log 11.5 10.5 10.0

R A D1 D14 D30 D36 R A D1 D14 D30 D36

Sample point Sample point

SSC1 TSL1 14 13 2 2 13 12 12 11 Intensity Intensity log Intensity log 11 10 10

R A D1 D14 D30 D36 R A D1 D14 D30 D36

Sample point Sample point

Figure 3.13 Gene expression from cluster 1 with enzyme regulator activity as determined by gene ontology analysis. Gene expression is transformed to log2 values and was generated by DNA microarray.

3.7.8. Carbohydrate binding activity in Cluster 1

One of the abnormal observations from the metabolite data in need of an explanation was poor fructose metabolism (Figure 3.1). Therefore the gene ontology category of carbohydrate binding was an obvious target for further analysis. Since the expression mean for cluster 1 trends toward increased

87 expression over fermentation (Figure 3.6), positive regulators of fructose metabolism were unlikely to be found in this particular cluster. The three genes identified were two oligosaccharide degradations enzymes, AMS1 and ATH1, as well as the exomannosidase MNL1 (Table 3.12). None of these genes have functional roles that are directly associated with fructose catabolism. AMS1 and ATH1 cleave disaccharides and are therefore capable of increasing monosaccharide concentrations in the cell including fructose (Chantret et al., 2003; Alizadeh and

Klionsky, 1996). MNL1 function is related to ubiquitin targeted protein degradation and therefore has little to do with fructose concentrations in the cell (Clerc et al.,

2009).

Table 3.12 Genes with carbohydrate binding activity from cluster 1. These genes were identified using gene ontology and belong to the accession category GO:0030246 (carbohydrate binding).

Carbohydrate Gene Function binding AMS1 Alpha mannosidase found in vacuole. Degrades free oligosaccharides. ATH1 Acid trehalase. Degrades trehalose after cellular stress has diminished. ER exomannosidase. Involved in glycoprotein targeting for proteasome MNL1 degradation.

88 AMS1 ATH1 MNL1 12 13.0 11.5 2 2 2 11 12.0 10.5 10 Intensity Intensity log Intensity log Intensity log 11.0 9 9.5 10.0 8 9.0 R D1 D14 D30 D36 R D1 D14 D30 D36 R D1 D14 D30 D36

Sample point Sample point Sample point

Figure 3.14 Gene expression from cluster 1 with carbohydrate binding activity as determined by gene ontology analysis. Gene expression is transformed to log2 values and was generated by DNA microarray.

3.7.9. Hydrolase activity in Cluster 1

While aging, the yeast in the sparkling wine break down, requiring the activity of various hydrolases. A total of 34 genes with hydrolase activity were found from the gene ontology analysis, 10 of which have already been mentioned in other GO categories (AMS1, ATH1, IRC20, LHS1, MPH1, PMC1, PMR1, RDH54, RPM2, and SSL2). The remaining genes are diverse but several can be classified as having functions relating to cell wall synthesis, carbon metabolism, and stress response

(Table 3.13).

The genes with cell wall functions from the hydrolysis activity GO category include CAX4, CRH1, DFG5, and SCW10 (Table 3.13). Cax4 is responsible for the glycosylation of mannoproteins, a crucial component of sparkling wines and it’s expression peaks at the end of fermentation with a brief increase at day 7 (Figure

3.15). CRH1 expression increases more than four-fold during acclimatization and it remains highly expressed for the remainder of fermentation. This same pattern is observed for SCW10 (Figure 3.15). DFG5 shows a more gradual increase in 89 transcript abundance over fermentation. The expression increase is almost linear with the exception of a brief decrease from day 1 to 14 (Figure 3.15). These four genes illustrate a trend of upregulation for genes relating to cell wall growth.

Genes with carbon metabolism related functions are ADE3, EEB1,

MAL12/MAL32, and RHR2 (Table 3.13). Their functions are diverse and part of amino acid, ethanol, fatty acid, glycerol and sugar metabolism. ADE3 is part of the synthesis pathway for several metabolites including amino acids and nucleic acids

(Song and Rabinowitz, 1993). Expression varies substantially until day 14 when it drops nearly two-fold form day 7. It then gradually increases until the end of fermentation (Figure 3.15). EEB1 and MAL12/32 have similar expression profiles.

For both genes the peak level of transcripts occurs at day 7, then falls at day 14 and rebounds progressively until the end of fermentation (Figure 3.15). There is little change in expression for RHR2 after acclimatization. The highest transcript level occurs at rehydration (Figure 3.15). Although these genes are hydrolases, almost all of them function as a part of a biosynthetic pathway.

A large portion of the genes in the GO hydrolase category can be described as having a role in stress response. Those with a known stress response role include

PDE2, PHM8, PPZ1, and PTP2, as well as several of the previously mentioned genes with cell wall or carbon metabolism roles. The two protein phosphatases PPZ1 and

PTP2 show very similar expression patterns. Expression increases during acclimatization and spikes slightly at day 7, then gradually increases until the end of sampling (Figure 3.15). The cAMP phosphodiesterase PDE2 is highly expressed during yeast rehydration, then falls until day 1. There is a spike in transcript levels

90 at day seven, and then expression increases until the end of fermentation with a slight dip at day 39 (Figure 3.15). PHM8 expression differs from the other three in that there is no spike in expression at day 7. After bottling there is only one large change in expression and that occurs between day 21 and day 30. Otherwise, expression is fairly stable throughout fermentation. Although this group of genes have hydrolase functions, they are regulatory genes, and are not directly responsible for the production of desirable sparkling wine metabolites. They do however provide information regarding stress signalling and more specifically the osmotic stress response.

91 Table 3.13 Genes with hydrolase activity from cluster 1. These genes were identified using gene ontology and belong to the accession category GO:0016787 (hydrolase activity). Several genes were omitted as they are present in previous tables and their hydrolase activity is mentioned.

Hydrolase Gene Function Cytoplasmic trifunctional enzyme C1-tetrahydrofolate synthase. Required for ADE3 biosynthesis of adenine and histidine as well as playing a role in the synthesis of purines, thymidylate, and methionine. Dolichyl pyrophosphate (Dol-P-P) phosphatase. Functions are N-glycosylation of CAX4 proteins and Dol-P-P-linked oligosaccharide intermediate synthesis. Chitin transglycosylase that transfers chitin to cell wall beta-glucans and induced CRH1 by cell wall stress. DED1 ATP-dependent RNA helicase. Initiates translation of mRNA. DFG5 Putative mannosidase with cell wall biogenesis functions. Ubiquitin hydrolase for interlumenal vesicle cargo proteins. Functions to reclaim DOA4 ubiquitin after proteasome targeting. Acyl-coenzymeA ethanol O-acyltransferase. Synthesizes fatty acid ethyl esters EEB1 during fermentation. Has lipid and detoxification functions. Enzyme with several properties including histidinol dehydrogenase, HIS4 phosphoribosyl-ATP pyrophosphatase, phosphoribosyl-AMP cyclohydrolase functions. NAD+ dependent histone deacetylase. Functions in origin recognition complex HST1 dependent silencing and mititoic repression. MAL12 Maltase. Hyrdolyzes multiple disaccharides including maltose, sucrose, turanose, /MAL32 and maltotriose. NGL3 Endonuclease. 3'-5' for poly-A RNA. PDE2 cyclic AMP phosphodiesterase. Integral to cAMP cell signalling pathways Lysophosphatidic acid phosphatase/nucleotidase. Plays a role in phosphate PHM8 starvation. Phospholipase B. functions in the hydrolysis of phosphatidylinositol and PLB3 phosphatidylserine. DNA polymerase delta catalytic subunit. Functions in the replication and DNA POL3 repair. PPZ1 Protein phosphatase. Functions in osmotic regulation and cell cycle progression Phosphatase of tyrosine residues. Function still under study. May regulate PTP1 filamentous growth. PTP2 Phosphatase of tyrosine residues. Regulator of the osmosensing MAPK pathway. RHR2 Glycerol-3-phosphate phosphatase. Functions in glycerol synthesis Component of the cell wall similar to glucanases. Speculated role in mating SCW10 conjugation. UBP13 Ubiquitin-specific protease. Peptidase. Functions in chromosome cohesion and recover from DNA damage- ULP2 induced checkpoint arrest. UNG1 Uracil-DNA glycosylase. Functions in cytosine deamination repair.

92 D33 D33 D33 DFG5 PHM8 D14 D14 D14 SCW10 Sample point Sample point Sample point Sample D1 D1 D1

R R R

12.0 11.0 15 13 11 9 12 10

2 2 2 Intensity log Intensity log Intensity log Intensity D33 D33 D33 PDE2 CRH1 RHR2 D14 D14 D14 Sample point Sample point Sample point Sample D1 D1 D1

R R R

14 12 10 8 12 10 9 16 14 12 10

2 2 2 Intensity log Intensity log Intensity log Intensity D33 D33 D33 PTP2 CAX4 D14 D14 D14 Sample point Sample point Sample point Sample MAL12/MAL32 D1 D1 D1

R R R

9.0 8.0 9.5 8.5 7.5 13 11 9

2 2 2 Intensity log Intensity log Intensity log Intensity D33 D33 D33 PPZ1 EEB1 ADE3 D14 D14 D14 Sample point Sample point Sample point Sample D1 D1 D1

R R R

13.5 12.5 11.5 11.5 10.5 12 10 8

2 2 2 Intensity log Intensity log Intensity log Intensity

Figure 3.15 Gene expression from cluster 1 with hydrolase activity as determined by gene ontology analysis. Gene expression is transformed to log2 values and was generated by DNA microarray. Several genes from this GO category have been omitted due to redundancy or relevancy. 93 3.8. Cluster 1 Conclusions

Cluster 1 appears to hold great significance with respect to the cellular stress response. Containing 249 genes, this cluster is not particularly large, yet the majority of these genes have roles in stress regulation or downstream effects of the stress response. The number of uncharacterized genes in cluster 1 is also very low, thus the functions of cluster 1 are generally well understood.

Cluster 1 was a prime target for analysis due to its expression profile. The degree of transcriptional change was minimal during the first half of fermentation, and increased significantly during the latter half. This was indicative of pathways that were largely inactive until the yeast began to age. However, the aging role of the genes in cluster 1 is not immediately apparent as far as autophagy and autolysis are concerned. Gene ontology did not reveal a group of genes with direct impact on these two mechanisms, but further analysis uncovered three ATG genes as well as the GATA factor gene, GAT2. There is also a notably large group of genes that affect the structure and composition of the cells walls, the degradation of which has a profound influence on sparkling wine quality. All of these factors combined makes cluster 1 appear to be one of the most influential with respect to aging.

Despite demonstrating a large effect on cellular stress response, Cluster 1 does not adequately explain the abnormal metabolite profile observed during fermentation. There are few genes with fermentation roles and those with carbohydrate binding activity do not directly interact with hexose sugars critical to glycolysis. The mean expression of cluster 1 does show a small spike in transcript levels at day 7 followed by a decrease seven days later. While this is not a huge 94 change, it is pervasive throughout all of the genes in the cluster and could be indicative of a possible causative or responsive relationship with the fermentation anomalies.

3.9. Cluster 7 upregulated during late fermentation

With respect to aging, Cluster 7 has a promising expression profile.

Expression changes very little during acclimatization indicating a lack of function for fermentation adaptation. Transcript levels then continually increase form day 7 until day 36 where they drop slightly by day 39 (Figure 3.16). Gene ontology analysis was therefore used to identify and candidate genes that may play a role in aging. However, Cluster 7 is also the smallest cluster with only 101 genes (Table

3.2), and over half of these have a poorly characterized function (Table 3.14). The functions aside from molecular_function that were found include mating pheromone activity, zinc ion binding, and meiosis. Due to the largely unknown nature of the genes in cluster 7, it will be of limited usefulness for this analysis.

95 Cluster 7 9.0 2 8.5 Intensity Intensity log 8.0 7.5

R A D1 D7 D14 D21 D30 D33 D36 D39

Sample point

Figure 3.16 Gene expression of cluster 7 as determined by longitudinal clustering of microarray data. Expression is measured in terms of log2 of the fluorescent intensity.

96 Table 3.14 Biological and molecular function gene ontology results for cluster 7 genes. The “molecular_function” GO term is indicative of genes with largely uncharacterized molecular functions or GO classification.

Accession Category Genes GO:0003674 molecular_function YAL037W YBR062C ECM2 YBR182C-A YBR196C-A YBR200W-A CTR86 YDL183C YDR034C-A SPS2 MEI4 YER078W-A AUA1 YFL012W YFL051C YFR012W-A YGL006W-A YGL010W YGL015C YGL041C-B YGL230C YGR121W-A YGR146C-A YGR204C-A RTA1 YIL134C-A IRC8 YJL136W-A YJL193W YJR151W-A MBR1 YKL106C-A YKL107W SLD2 YKL183C-A YLR040C YLR042C RRT15 YLR406C-A YLR412C-A SOV1 YMR105W-A YNL146C-A YNL211C YNL277W-A YOL047C YOL097W-A YOR032W-A FYV12 RRG7 YPL152W-A MCM16 TDA6 YPR159C-A GO:0000772 mating pheromone HMLALPHA1 MATALPHA1 activity GO:0008270 zinc ion binding YBR062C NRG2 GUD1 ADR1 AGE1 YER130C DST1 VPS36 LEU3 PFA4 YPR015C GO:0007126 Meiosis SPS2 SPS1 MEI4 MND1 SPO11 MPC54 MCM16

3.9.1. Genes with zinc ion binding activity upregulated in cluster 7

Of the four GO categories found for cluster 7 the zinc ion-binding group has potential for transcription regulation and was analyzed in further detail. Of the 11 genes in this category, 5 have confirmed transcription regulation properties. None appear to have direct aging roles (Table 3.15). The group includes a transcription factor, YER130C, that may play a role in stress adaptation (Table 3.15), as well as

NRG2, a glucose use regulator (Table 3.15). Since several of the known genes in this cluster are transcription factors and many genes in cluster 7 are uncharacterized, it’s possible that many of these unknown genes are transcription factors as well.

Some of them may play important regulatory roles in sparkling wine fermentation.

97

Table 3.15 Gene form cluster 7 with zinc ion binding activity according to gene ontology analysis

Zinc ion Gene Function binding YBR062C Function is unknown, but may activate filamentous growth NRG2 Repressor of transcription. Has glucose related gene repression function. GUD1 Guanine deaminase responsible for guanine catabolism. Transcription factor responsible for ADH2 regulation and peroxisomal ADR1 genes AGE1 ADP-ribosylation. Has roles in secretion and endocytic pathways. YER130C Transcription factor. May play a role in stress adaptation Transcription elongation factor. Has role relating to transcription during DST1 stress. VPS36 ESCRT-II complex component. Transcription factor, activator and repressor. Has roles in amino acid LEU3 biosynthesis. PFA4 Palmitoyltransferase. Has a role in palmitoylation of amino acid permeases. YPR015C Unknown function

3.10. Similar expression patterns in clusters 2, 3, and 15

Clusters 2, 3 and 15 have expression patterns the opposite of cluster 1. The overall trends present in these clusters are that of down regulation. Average gene expression is the highest during rehydration. Transcript levels then decrease over two-fold by the end of acclimatization. Expression then increases until mid fermentation when transcription diminishes and stabilizes toward the end of fermentation. The lowest average expression occurs during acclimatization (Figure

3.17). Since the trend in cluster 2 is decreased expression over fermentation, it was included in the analysis to identify any genes that could have affected fermentation or aging.

98 Cluster 2 Cluster 3 Cluster 15 11.0 11.5 13.0 11.0 2 2 2 10.5 10.5 12.5 Intensity Intensity log Intensity log Intensity log 10.0 10.0 9.5 12.0 9.0 9.5 R A D1 D14 D30 D36 R A D1 D14 D30 D36 R A D1 D14 D30 D36

Sample point Sample point Sample point

Figure 3.17 Gene expression of clusters 2, 3 and 15 as determined by longitudinal clustering of microarray data. Expression is measured in terms of log2 of the fluorescent intensity.

3.10.1. Cluster 2 genes have ribosomal functions

The genes in cluster 2 total 138, and all but two in this group have ribosomal functions. The molecular functions identified by gene ontology analysis include structural constituent of ribosome, RNA binding, rRNA binding, SSU rRNA binding, and 2-deoxyglucose-6-phosphatase activity (Table 3.16). The two genes in the later category are DOG1 and DOG2. Both of these confer 2-deoxyglucose resistance (Table

3.16). The genes in cluster 2 appear to have little specific impact on fermentation or aging. They do however provide evidence that translation may still be ongoing as of day 39, albeit at much lower levels (Figure 3.16).

99 Table 3.16 Molecular function gene ontology results for cluster 2 genes.

Accession Category Genes GO:0003735 structural RPS8A RPL23A RPS11B RPL19A RPS9B RPL21A RPP1A RPL13A constituent of RPS16B RPP1B RPS11A RPS13 RPP2B RPL12B RPS17B RPS18A ribosome RPL27B RPL34A RPS24A RPS8B RPL23B RPS26B RPL2A RPL30 RPL24A RPL28 RPS2 RPL26B RPL11B RPS23A RPS0A RPS20 RPL8A RPL27A RPL2B RPL34B RPL16A RPS21B RPL17B RPL39 RPL43B RPS5 RPL14A RPL17A RPS21A RPL40B RPL8B RPL15A RPS0B RPL22A RPL37A RPS28B RPL38 RPP0 RPL26A RPS22B RPS29A RPL31B RPS1A RPL6B RPS17A RPS18B RPS1B RPL6A RPL13B RPS16A RPL36A RPS10B RPL20A RPL9B RPL16B RPS7B RPS3 RPP2A RPL18A RPS19A RPL3 RPS7A RPS28A RPL33B RPS10A RPL20B RPL21B RPL5 RPL7B RPL36B RPL11A RPS23B GO:0003723 RNA binding RPS11B RPS9B NHP2 RPS11A RPL12B RPS18A SNU13 RPL2A RPL30 RPL24A RPL28 RPS2 RPL26B RPL11B RPS20 NOP10 GAR1 RPL2B RPL16A ANB1 RPS5 RPL14A RPL15A RPL37A RPL26A RPL6B RPS18B RPL6A RPL36A RPL16B RPS3 RPL5 RPL36B RPL11A GO:0019843 rRNA binding RPS11B RPS9B RPS11A RPS18A RPL2A RPL11B RPL2B RPL37A RPS18B RPL9B RPL5 RPL11A GO:0003850 2-deoxyglucose- DOG2 DOG1 6-phosphatase activity GO:0070181 SSU rRNA RPS13 RPS2 binding

3.10.2. Cluster 3 genes have ribosomal and biosynthetic functions

Gene expression in cluster 3 resembles that of cluster 2. There is an immediate decrease in expression from rehydration to acclimatization. Transcript levels decrease more than four-fold. Gene expression then increases through early fermentation, increasing more than two-fold. The gene ontology analysis of cluster

3 primarily yielded genes with roles relating to the biosynthesis and processing of ribosomes and amino acids (Table 3.17). The expression of these biosynthetic genes rapidly declines after yeast rehydration, then increases until day seven, and remain expressed at steady levels (Figure 3.16). This indicates that toward day seven the yeast encountered conditions in which the cells required high levels of amino acid

100 synthesis and gene translation. The presence of ribosomal genes in cluster 3 provides even more evidence that translation continued well into day 39 and perhaps beyond.

Table 3.17 Biological function gene ontology results for cluster 3 genes. Several GO categories omitted due to redundancy

Accession Category Genes GO: 0008652 cellular amino CYS3 MIS1 MET8 HIS7 ARO4 HOM3 HIS1 MET10 STR3 ASN2 CYS4 acid SER33 MET3 CPA2 MET14 MHT1 MET17 YML096W MET2 ARG8 biosynthetic LEU9 SAM4 MET16 process GO:0042254 ribosome MAK5 PWP2 NOP1 RLI1 UTP4 NSA2 UTP8 RRP3 IMP3 RIX1 MRT4 biogenesis URB1 SOF1 NOC3 DIP2 UTP13 UTP21 ERB1 RRB1 RRP5 HAS1 IMP4 DBP2 RCL1 BRX1 NOC2 NOP58 RPS9A NOG1 NIP7 GO:0006364 rRNA processing MAK5 PWP2 NOP1 RRP1 RLI1 BFR2 UTP4 NSA2 PRP43 UTP8 NSR1 RRP3 IMP3 RIX1 MRT4 URB1 SOF1 NOC3 DIP2 UTP13 UTP21 ERB1 RRB1 RRP5 HAS1 IMP4 DBP2 NOP58 RPS9A NOG1 NIP7 GO:0006164 purine MIS1 IMD2 IMD3 IMD4 GUA1 ADE4 nucleotide biosynthetic process GO:0006541 glutamine URA7 HIS7 ASN2 YML096W GUA1 ADE4 metabolic process GO:0006412 Translation SRO9 RPL4B KRS1 RLI1 RPL12A RPL22B RPL9A MES1 RPS22A RPS29A TIF11 NIP1 IMP4 RPL18B BRX1 RPS9A CDC60 TIF5 GO:0055085 transmembrane FUR4 BAP2 TAT1 AGP1 ADY2 ENA5 MEP1 MUP3 YHK8 YKE4 transport PHO90 OPT1 MMP1 SUL2 YML018C TAT2 NRT1 GO:0032259 Methylation NOP1 RMT2 MHT1 TRM10 TRM11 SAM4 GO:0015696 ammonium ADY2 MEP1 transport

101 3.10.3. Cluster 15 genes have ribosomal and biosynthetic functions

Cluster 15 expression is very similar to cluster 2. Transcript levels plummet more than two-fold from rehydration to acclimatization. This reverse is seen at day

1 at a lower magnitude, and then a steadily decline occurs until the end of the fermentation to levels seen during acclimatization (Figure 3.16). The function of cluster 15 genes is very similar to those found in cluster 2 and 3. The predominant groups observed are those with ribosomal or amino acid biosynthetic roles (Table

3.18). There were also some GO categories not present in clusters 2 or 3 including tRNA processing, transmembrane transport, and mannoprotein synthesis (Table

3.18). The former two are indicative of decreased translation, and the latter is of particular interest due to the importance of mannoproteins with respect to sparkling wine aging.

102 Table 3.18 Biological function gene ontology results for cluster 15 genes. Several GO categories omitted due to redundancy

Accession Category Genes GO:0008033 tRNA POP5 PTA1 NCL1 TRM7 SLM3 LHP1 TRM3 TRM8 TYW3 PUS2 processing NCS6 MTO1 THG1 PUS6 RPP1 UBA4 TAD2 TRM2 TRZ1 DUS4 SEN15 NCS2 POP1 PUS4 SMM1 TRM13 PUS7 TRM44 SEN54 PUS1 GO:0032259 Methylation NCL1 HMT1 TRM7 TAE1 TRM3 TRM8 ADE8 STE14 TMT1 TYW3 YGR001C EFM1 CTM1 SEE1 HPM1 TRM2 MET1 DPH5 EMG1 NNT1 TRM9 NOP2 TRM13 MRM1 TRM44 RKM1 DIM1 GO:0006364 rRNA IPI1 DBP8 UTP9 UTP25 UTP18 UTP10 DHR2 CBT1 SDO1 MDN1 processing EMG1 DBP9 TSR2 UTP14 UTP15 RNT1 NOP2 NAF1 IPI3 POP1 CSL4 DBP6 TSR4 RRP40 RRP6 UTP23 PUS7 YTM1 RVB2 DIM1 TIF6 MRD1 RRP9 NOC4 GO:0042797 tRNA RPB5 RPC11 RPC17 RPC37 RPC19 RPC34 RPO31 RET1 RPC40 transcription RPC82 from RNA polymerase III promoter GO:0042254 ribosome ENP1 REI1 TSR1 FAP7 FCF1 NUG1 TMA20 DBP3 UTP22 CIC1 biogenesis IPI1 DBP8 UTP9 UTP25 UTP18 UTP10 ALB1 DHR2 CBT1 RLP24 SDO1 EMG1 DBP9 UTP14 UTP15 RLP7 NOP2 NAF1 IPI3 RIO2 DBP6 NOG2 UTP23 NOB1 PNO1 PUS7 YTM1 RRS1 DIM1 RRP9 NOC4 GO:0008652 cellular amino LYS2 LEU2 THR4 ARO1 PRO1 TRP4 ILV1 TRP2 TRP5 ARO2 acid YGR012W SER2 ARG4 THR1 HIS6 MET30 MET28 MDE1 TRP3 biosynthetic MET1 ILV5 YML082W ILV2 ORT1 SER1 HIS3 PRO2 process GO:0000032 cell wall LDB7 PSA1 MNN10 PMI40 YUR1 HOC1 KTR2 mannoprotein biosynthetic process GO:0055085 transmembrane FUN26 NUP60 FUI1 YMC2 SSH1 CTP1 PHO89 FEN2 NUP84 transport GGC1 UGA4 SHR3 ENA5 VBA4 SXM1 STL1 SPF1 FCY2 KAP123 NIC96 NUP145 SEH1 TPN1 VRG4 VHT1 HIP1 TNA1 ARN2 ERC1 HXT1 TIM44 FLX1 NUP85 YJR124C OAC1 YBT1 PAM18 POM34 ZRT2 NUP2 SEC61 NUP188 ATR1 POM152 NUP53 ZRC1 PHO91 HOL1 ALR1 DBP5 ATX2 NUP1 ODC2 FSF1 HUT1 MEP3

103 3.10.3.1. Mannosyl transferase genes have opposing expression profiles.

Gene ontology analysis of cluster 15 revealed a number of genes with functions relating to protein mannosylation. Two genes, YUR1 and KTR2 in cluster

15 encode mannosyltransferases that catalyze the transfer of mannose to amino acid residues (Table 3.19). These two also have seemingly contradictory expression profiles.

Both genes show an initial drop in transcript level during acclimatization followed by an increase as secondary fermentation begins. Following a brief drop after day 1, KTR2 expression increases quickly at day 14 (Figure 3.17). Transcript levels plateau and remain at a high level throughout the remainder of fermentation. YUR1 expression is very similar to KTR2 until day 7 when transcript levels slowly decline until the day 39

(Figure 3.17). Both proteins are responsible for the initiation of mannosylation. There appears to be no predominant difference between YUR1 and KTR2 functions. They are paralogous transferases for N- but not O-mannosylation (Hill et al., 1992; Lussieret al.,

1996). Due to the important sensory roles of mannoproteins in sparkling wines, up- regulation of mannosyltransferases is expected. The purpose for the down-regulation of YUR1 remains a mystery.

104

Table 3.19 Genes with mannoprotein biosynthesis activity from cluster 15. These genes were identified using gene ontology and belong to the accession category GO:0000032 (cell wall mannoprotein biosynthetic process).

Manno- protein Gene Function biosynthesis LDB7 Part of the RSC chromatin remodeling complex. PSA1 GDP-Mannose phosphorylase. Mannosyltransferase complex component. Elongates carbohydrate MNN10 component of glycoproteins. PMI40 Mannose-6-phosphate isomerase YUR1 Mannosyltransferase that plays a role in protein N-glycosylation. HOC1 Alpha-1,6-mannosyltransferase. KTR2 Mannosyltransferase with roles in N- and O-glycosylation.

105 LDB7 PSA1 MNN10 13 2 2 2 13.5 11 11 12.5 9 9 Intensity Intensity log Intensity log Intensity log 8 8 11.5

R D1 D14 D30 D36 R D1 D14 D30 D36 R D1 D14 D30 D36

Sample point Sample point Sample point

PMI40 YUR1 HOC1 11.5 2 2 2 13 10.0 12 10.5 9.0 11 Intensity Intensity log Intensity log Intensity log 10 9.5 8.0

R D1 D14 D30 D36 R D1 D14 D30 D36 R D1 D14 D30 D36

Sample point Sample point Sample point

KTR2 11.2 2 10.6 Intensity Intensity log 10.0

R D1 D14 D30 D36

Sample point

Figure 3.18 Gene expression from cluster 15 with mannoprotein biosynthesis activity as determined by gene ontology analysis. Gene expression is transformed to log2 values and was generated by DNA microarray.

106 3.11. Expression patterns in cluster 4

The gene ontology analysis of cluster 4 provided little information with respect to specific pathways. The categories identified are vague, and include oxidoreductase activity, catalytic activity, metal ion binding, and aldehyde dehydrogenase activity (Table 3.20). Furthermore, the expression profile for cluster

4 contains no significant changes other than during acclimatization. Any changes after this point are not two-fold or greater (Figure 3.19). Therefore these genes mostly maintain expression throughout fermentation. Genes that stand out as relevant to wine aging are the ERG genes that maintain membranes via lipid synthesis (Veen et al., 2003).

107 Cluster 4 10.8 10.6 2 10.4 Intensity Intensity log 10.2 10.0 9.8 R A D1 D7 D14 D21 D30 D33 D36 D39

Sample point Figure 3.19 Gene expression of cluster 4 as determined by longitudinal clustering of microarray data. Expression is measured in terms of log2 of the fluorescent intensity.

108 Table 3.20 Molecular function gene ontology results for cluster 4 genes. Several GO categories omitted due to redundancy

Accession Category Genes GO:0016491 oxidoreductase BDH2 ARA1 YCP4 TSC13 YDL124W RIP1 ERG26 AIM14 CTT1 ERG1 activity YHB1 PUT2 ERG9 DOT5 KGD1 GUT2 MCR1 URA1 SDH2 ERG3 ERG27 DUS3 CYB2 NDI1 YML131W ERG5 HFD1 NDE1 ADH6 CIR2 ALD4 GDH1 OYE3 GO:0003824 catalytic activity BDH2 ACH1 PHO13 CCC2 PAD1 GLY1 ERG26 PMA1 IRE1 ERG9 BAT2 URA1 ERG27 ACS2 DUS3 CYB2 ERG13 YML131W SNZ1 ADH6 YOR093C FAA1 GDH1 ERG10 OYE3 QCR2 GO:0046872 metal ion BDH2 CCC2 RIP1 PMA1 LEU1 COX4 CTT1 YHB1 COX6 IRE1 LSB6 binding ERG20 MSN4 OMA1 SDH2 HMX1 DUS3 CYB2 ERG5 YTA12 CEP3 CAT8 ADH6 AAH1 CYT1 NFI1 CIR2 ERG10 IDI1 QCR2 GO:0004030 aldehyde HFD1 ALD4 dehydrogenase activity

3.12. Expression patterns in cluster 5

The gene ontology results for cluster 5 initially looked promising. There is a two-fold drop and immediate recovery of gene expression on day 7 (Figure 3.20).

This coincides with the atypical metabolite profile found (Figure 3.1). However, upon closer examination, the genes identified mostly fall under the category of

“molecular_function”. These genes are generally putative, or have roles that are poorly understood. Other categories for this cluster include ribosomal structure components, RNA polymerase cofactors, and microtubule binding (Table 3.21). The last category in the cluster is SNAP receptor activity (Table 3.21). The genes in this group are responsible for vesicle fusion, a process required for normal cell function, and could provide information relevant to this experiment.

109 Cluster 5 10.5 10.0 2 Intensity Intensity log 9.5 9.0

R A D1 D7 D14 D21 D30 D33 D36 D39

Sample point

Figure 3.20 Gene expression of cluster 5 as determined by longitudinal clustering of microarray data. Expression is measured in terms of log2 of the fluorescent intensity.

110

Table 3.21 Molecular function gene ontology results for cluster 5 genes. Several GO categories omitted due to redundancy. The “molecular_function” GO term is indicative of genes with largely uncharacterized molecular functions or GO classification.

Accession Category Genes GO:0003674 molecular_function ATS1 SAW1 YAL044W-A ECM15 CMC2 YBL059W ATG8 SCS22 YBL107C FMP23 AIM4 YBR230W-A SWC5 AIM5 YCR075W-A YCR076C CSM1 GPM2 SNU23 BUG1 STF1 YDL160C-A UGX2 CIS1 RAD28 MIC14 GIR2 DON1 YDR286C YDR381C-A DYN2 CWC21 MZM1 EAF5 IES6 PET117 YER128W YER137C FMP10 BUD27 AIM13 IRC6 BUD13 MTC3 STF2 YGR021W YGR042W YGR079W SLX9 CBP4 YGR174W-A OKP1 CIR1 YGR235C SPG1 RTT102 OTU2 FYV4 BZZ1 COX23 WSS1 SSP1 CTF8 YIL002W-A YIL024C FIS1 PRM5 OM45 YIL152W AIM21 MND2 COX16 YJL077W-B REC107 PET191 NNF1 DID4 YKL018C-A MRP8 MEH1 NSE1 IRC25 IES3 BUD20 YLR099W-A YLR126C QRI5 COA4 YLR257W TMA7 YLR283W CWC24 YLR361C-A PSY3 ECM19 BLS1 NBP1 RAD33 AIM31 MSC1 COX14 TAP42 YET2 YMR074C YMR111C CUS1 YMR244C-A YMR262W SNN1 YSF3 PGA2 IGO1 MDG1 SLZ1 MIM1 YOL086W-A ZEO1 NOP8 YOR020W-A YOR097C IES4 AIM41 RUD3 YOR223W TMA16 YOR289W YPL071C YPL107W YPL108W UIP4 YPL199C LEA1 GRE1 NSL1 RDS3 YPR148C SUE1 URN1 GO:0003735 structural MRPL16 MRP21 MRPL37 MRPL27 IMG2 MRP10 RPL35A MRP20 constituent of MRPL28 RSM28 RSM18 RSM25 MRPL8 MRP17 MRPL31 ribosome MRPL38 MRPL20 MRPL15 RPL15B MRPL44 MRPL33 SWS2 MRPL50 RSM19 MRP51 MRPL51 MRP2 GO:0001104 RNA polymerase II ROX3 SRB6 MED2 PGD1 MED6 MED11 MED7 transcription cofactor activity GO:0005484 SNAP receptor SFT1 BOS1 VTI1 PEP12 VAM3 SNC2 SSO1 activity GO:0008017 microtubule DAD3 BIK1 DAD1 FIN1 DAD4 ASK1 binding

3.13. Genes from clusters 6, 9, 10, 11, and 12 have immediate and sustained upregulation

Clusters 6, 9, 11, and 12 have similar expression profiles. The most prominent feature is that transcript levels increase substantially during acclimatization, and then remain at this level for the duration of fermentation and

111 into aging. There are some fluctuations in expression that differ between the clusters, but none of these amount to a two-fold or greater change (Figure 3.21).

Since the yeasts are under continual stress during secondary fermentation, this pattern of transcription suggests pathways that may relate to stress tolerance.

Cluster 6 Cluster 9 Cluster 10 9.5 6.5 11.0 2 2 2 6.0 9.0 10.5 Intensity Intensity log Intensity log Intensity log 5.5 10.0 8.5

R A D1 D14 D30 D36 R A D1 D14 D30 D36 R A D1 D14 D30 D36

Sample point Sample point Sample point

Cluster 11 Cluster 12 9.0 13.0 2 2 8.5 12.5 8.0 Intensity Intensity log Intensity log 12.0 7.5 7.0 11.5

R A D1 D14 D30 D36 R A D1 D14 D30 D36

Sample point Sample point

Figure 3.21 Gene expression of clusters 6, 9, 10, 11, and 12 as determined by longitudinal clustering of microarray data. Expression is measured in terms of log2 of the fluorescent intensity.

3.13.1. Protein degradation, actin binding, and trehalose synthesis activity in cluster 6

Most of the genes that are found in cluster 6 fall under the GO term of

“molecular_function” or genes with poorly characterized functions (Table 3.22).

Other than these genes some that fall under the vague term of oxidoreductase activity. These can be part of any number of pathways and the GO term does not provide much information. Several genes fall under the GO term of “peptidase activity” indicating there is an elevated and sustained transcription of genes that 112 play a role in the degradation of proteins (Table 3.22). There are two categories that are relevant for trehalose synthesis, an important metabolite for stress response in yeast. And lastly two genes that belong to the fructose-2,6- bisphosphate 2-phosphatase activity. The functions of genes from cluster 6 do not appear to reveal any large scale transcription events that could explain the metabolite levels observed during fermentation, or genes that have an obvious aging role. However, there are numerous genes with mitochondrial related function such as the AIM genes (Hess et al., 2009).

113

Table 3.22 Molecular function gene ontology results for cluster 6 genes. Several GO categories omitted due to redundancy. The “molecular_function” GO term is indicative of genes with largely uncharacterized molecular functions or GO classification.

Accession Category Genes GO:0003674 molecular_function FUN14 FUN19 AIM2 RFS1 YBR053C YBR056W SIF2 RTC2 APD1 SDS24 OM14 FMP21 YCL012C LSB5 YCL049C YCR024C-B YCR061W YET3 PST2 MRH1 SHU2 TVP23 UPS3 RAV2 COQ4 YDR262W YDR391C JIP4 PEX29 VAB2 YEL073C RRT13 YER079W YFL042C LSB3 SCL1 MDM34 YGR125W YGR127W PHB1 NAS6 RTC3 MDM31 AIM18 YHR202W VID28 MAM33 YIL077C AIM19 COA1 IRC24 YJL016W MPM1 YJL132W YJL163C YJL185C SOP4 YJR030C VPS55 VPS70 IML1 YKL071W YKL091C MTC2 DGR2 YKL151C YKR051W POM33 VPS13 YLR072W YLR149C COQ9 YLR241W SYM1 YLR290C YLR326W ATG33 YLR422W YLR446W SUR7 YML079W AIM33 PRE8 YMR027W YMR118C YIM1 YMR155W AIM36 YMR160W YMR181C YMR196W YMR252C YNL011C AIM37 YNL200C ATG2 SNO2 YNR065C PRE6 ZPS1 IRC23 SIA1 YOR152C YOR186W YOR228C LSP1 YPL119C-A OXR1 YPL247C ISA2 YPR127W YPR172W GO:0016491 oxidoreductase UGA2 ZTA1 RFS1 PDB1 GPD1 SFA1 PST2 YPR1 TSA2 RNR1 LPD1 activity COQ6 GND2 GRE3 TRR2 AYR1 IRC24 HYR1 YJL045W OSM1 YJR096W YKL071W SDH1 YLR290C AIM33 YIM1 YMR315W YNL134C GOR1 ERG24 GRE2 GCY1 CAT5 IDH2 YPR127W GO:0008233 peptidase activity PIM1 YBR139W APE3 PRD1 PRE1 DUG1 SCL1 DAP2 PRE3 CPS1 VPS70 APE2 YPS1 PRE8 UBP15 LAP3 PRE6 PUP1 PRE10 PRE2 GO:0003779 actin binding ARC40 ABP1 ARP2 SAC6 CAP2 ARP3 CAP1 ARC19 MYO3 SRV2 GO:0004805 trehalose- TPS2 TPS3 phosphatase activity GO:0003825 alpha,alpha- TPS1 TPS3 trehalose- phosphate synthase (UDP- forming) activity GO:0004331 fructose-2,6- FBP26 YLR345W bisphosphate 2- phosphatase activity

114 3.13.2. Cluster 9 genes have roles in fermentation response and meiosis/cell division

The mean expression profile of cluster 9 is very close to that of cluster 6. The changes in expression over time are almost identical (Figure 3.21). The main difference is that cluster 9 genes have an average two-fold lower expression, and unlike cluster 6, most of the genes in cluster 9 have known functions (Figure 3.21;

Table 3.23). The GO categories that represent the functions of cluster 9 genes are predominately related to signalling, the cell cycle, mitosis, meiosis and cell division.

Apart from these functions, there is a group of genes that are part of the growth response under limited glucose. The GO functions of cluster 9 appear to have limited use as far as explaining fermentation response and aging.

115 Table 3.23 Biological function gene ontology results for cluster 9 genes. Several GO categories omitted due to redundancy.

Accession Category Genes GO:0006468 protein CDC15 PKC1 AKL1 SLI15 SSK22 MPS1 STE7 KIN1 PKH3 SNF1 phosphorylation PKH1 SAK1 CAK1 CMK1 TOS3 ATG1 BUB1 YCK1 PKP1 PRK1 MOB1 IKS1 IME2 HAL5 SWE1 PTK2 PRR1 YPK1 KKQ8 NNK1 KNS1 STE11 TDA1 MCK1 PSK2 MKK1 SSN3 MKK2 FRK1 GO:0007165 signal transduction GPB2 PKC1 STE50 GPR1 RGT2 RGA2 SNF1 STE2 CMK1 CDC55 PKP1 CYR1 TOR1 TUS1 PSK2 STE4 MKK1 RLM1 MKK2 IQG1 GO:0007049 cell cycle SPC72 CDC15 KIP1 PKC1 HSL7 AMN1 RIF1 BUD3 STE50 CDC9 DBF4 SCC2 CHL4 DSE1 RAD24 CAK1 SMC2 SLD3 CDC55 CSE1 DAM1 BUB1 MOB1 POG1 VHS2 MAD3 SAP185 SWE1 TOR1 SMC4 CLF1 CLB4 RSE1 FAR8 MIH1 MCM1 PDS5 APC1 CNM67 SIN3 DNL4 YOR019W ELG1 APC5 DSE3 MYO2 IQG1 CLB5 KAR3 GO:0007124 pseudohyphal GPB2 BUD5 CDC39 DIA3 GPR1 STE7 PAM1 RGA2 SNF1 CDC55 growth DIA4 FKH1 IME1 STE11 MSS11 FKH2 HMS1 GO:0000196 MAPKKK cascade STE7 PKH3 PKH1 SDP1 STE11 involved in cell wall biogenesis GO:0006281 DNA repair MRC1 CDC9 RAD57 SCC2 DIN7 XRS2 MUS81 LCD1 YEN1 SLX8 RPH1 RAD24 DNA2 RAD26 MLH2 CST9 RAD52 PIF1 PSO2 SGS1 YKU70 PMS1 MKT1 MGS1 RAD50 NTG2 DNL4 EXO1 MLH3 DDC1 MMS1 GO:0007126 Meiosis RAD57 DBF4 CHL4 XRS2 MUS81 SPR6 CAK1 IME4 MDS3 IME2 SWE1 TOR1 IME1 SPO14 MSC3 RED1 CST9 PDS5 CNM67 RAD50 MCK1 SSN3 DDC1 KAR3 GO:0000086 G2/M transition of PIN4 HSL7 RSC3 CAK1 SWE1 CLB4 MIH1 ULP1 CLB5 mitotic cell cycle GO:0001403 invasive growth in GPB2 DIA3 GPR1 STE7 RGA2 SNF1 DSE1 DIA4 STE11 MSN1 STE4 response to RIM20 glucose limitation GO:0051301 cell division SPC72 CDC15 KIP1 HSL7 AMN1 CDC9 DBF4 CHL4 DSE1 SMC2 CSE1 DAM1 MOB1 POG1 MAD3 SWE1 SMC4 CLB4 MIH1 PDS5 APC1 CNM67 SIN3 DNL4 APC5 MYO2 IQG1 CLB5 KAR3

3.13.3. Cluster 10 genes have poorly characterized functions

The genes of cluster 10 have a lower overall expression level than cluster 6,

9, 11, and 12 (Figure 3.21). The gene ontology analysis provided little information regarding the function of these genes (Table 3.24). Most of the genes in cluster 10 are categorized as “molecular_funtion” (Table 3.24). The three categories other

116 than the aforementioned contain few genes and include microtubule motor activity, trans-membrane transport, and endopeptidases (Table 3.24). The mostly unknown nature of genes in this cluster does not make it particularly useful for explaining fermentation phenomena and aging.

Table 3.24 Molecular function gene ontology results for cluster 10 genes. Two GO categories were omitted due to redundancy. The “molecular_function” GO term is indicative of genes with largely uncharacterized molecular functions or GO classification.

Accession Category Genes GO:0003674 molecular_function YAL018C YAL037C-A YAL064W YAR029W PRM9 MST28 SHE1 YBL044W SRO77 YBR072C-A ECM8 YBR138C YSW1 SPP381 ICS2 YBR184W PCH2 YBR197C YBR221W-A SPO23 YBR298C-A YCL001W-B YCR099C YCR100C RAD61 YDR124W ECM18 RAD34 ECM11 YDR506C YEL057C BUD25 YER085C ULI1 YFR057W YGL138C YGL188C-A ZIP2 VEL1 YGL258W-A YGR126W YGR153W YGR273C YHL012W YHR007C-A SPO13 YHR022C YHR022C-A YHR050W-A SAE3 RTT107 KEL1 YIL029C YIL046W-A YIL060W PCI8 YIL102C AIM20 LOH1 YJL043W YJL107C YJR005C- A YJR154W COS5 AIM26 YKR015C YLL007C YLL066W-B YLR012C YLR030W YLR031W YLR132C YLR211C NKP2 REC102 YLR445W YML003W YML037C PRM6 SMA2 YML100W-A YMR001C-A AIM34 YMR018W YMR158C-A SPT21 YMR242W-A YMR247W-A YMR315W-A YMR317W YNL033W YNL034W YNL067W-B DGR1 BSC4 BSC5 CSI2 IRC10 YOL019W-A YOL038C- A YOL155W-A YOR072W-B SSP2 OSW1 YOR293C-A YOR381W-A YOR387C PAU21 SRL4 YPL077C MEI5 SPO19 ATG29 CSM4 YPL277C YPR027C YPR078C YPR153W CUR1 GO:0003777 microtubule motor PAC11 CIN8 CIK1 VIK1 activity GO:0022891 substrate-specific MPH2 HXT13 HXT10 YFL040W HXT14 transmembrane transporter activity GO:0004190 aspartic-type YGL258W-A YPS5 BAR1 endopeptidase activity

117 3.13.4. Cluster 11 genes are part of carbohydrate metabolism and cellular reproduction

The gene ontology analysis of cluster 11 yielded similar results to cluster 10, although gene expression was higher (Figure 3.21). Most genes in this cluster have poorly characterized functions and thus aren’t immediately useful. However there are 13 genes that fall under the GO category of carbohydrate metabolic process.

This group contains a number of genes that play a role in carbohydrate metabolism such as GAL7 and MIG2, and are therefore important for fermentation. This cluster also contains genes that regulate cell wall growth including EXG2, CTS2, CTS1, and

CDA1 indicating a potential role in aging (Table 3.25; Larriba et al., 1993; Dünkler et al., 2005; Christodoulidou et al., 1996).

118

Table 3.25 Molecular and biological function gene ontology results for cluster 11 genes. Some GO categories were omitted due to redundancy. The “molecular_function” GO term is indicative of genes with largely uncharacterized molecular functions or GO classification.

Accession Category Genes GO:0003674 molecular_function MDM10 FRT2 YAR028W SEF1 FIG1 YBR285W YCL021W-A FUS1 ATG22 SPS22 SYP1 FIG2 YCR108C YDL057W YDL114W MSH5 YDR042C AIM7 TVP15 TMN2 YDR182W-A MCM21 UBX5 YDR366C CTS2 YDR379C-A EMI1 FIT1 YEL025C YFL041W-A DDI2 PES4 RPN14 SCY1 VID30 RMR1 YGR035W-A BUD9 YGR066C THI4 TOS2 YGR240C-A YHL044W YHL048C-A COS8 REC104 YHR182W APQ12 SIM1 CSM2 YIL165C YIR007W CHS6 YJL118W YJL160C FMP33 ASG7 NCE101 RAV1 YJR098C ECM27 YKL162C YKR017C BCH2 MSA2 YLR049C OSW2 RAX2 GEP5 GIS3 YLR108C YLR173W MMR1 BOP2 YLR285C-A BUD8 ART10 YML007C-A YML083C YML119W YMR030W-A YMR085W YMR144W ICY1 YNL046W YNL058C SPS18 PRM1 YNR014W YNR068C YNR071C YOL024W OPI10 RRT8 YOL107W YOL131W YOR114W YOR214C YOR292C YOR365C YPL014W SMA1 AIM44 SVS1 PRM3 YPL264C REC8 DIB1 NCA2 GO:0000746 Conjugation FUS3 FIG2 SPH1 PRM1 GO:0005975 carbohydrate GAL7 EXG2 CTS2 SCW11 PKP2 MIG2 XKS1 YIR007W PGM1 CTS1 metabolic process CDA1 YMR085W YNR071C GO:0007155 cell adhesion FIG2 AGA2 SAG1 AGA1 GO:0006032 chitin catabolic CTS2 CTS1 CDA1 process GO:0030435 sporulation SEF1 SPS22 RMD5 CTS2 DIT1 EMI1 GSM1 OSW2 CDA1 FKS3 resulting in SPS18 SMA1 formation of a cellular spore GO:0007049 cell cycle FUS3 PCL9 MCM21 YHP1 ALK1 BUD9 CLB1 NDT80 RAX2 MMR1 BUD8 YOX1 TEM1 CDC5 HOF1 CLN1 PCL1 EGT2 NUD1 KIP2 DIB1 CLB2

119 3.13.4. Cluster 12 genes have aerobic respiration and metabolite biosynthetic functions

The gene ontology results from cluster 12 show a large number of genes with roles in aerobic respiration (Table 3.26). Like cluster 6, 9, 10, and 11, genes in cluster 12 see a significant change in expression over acclimatization, and then remain highly expressed for the remainder of the experiment (Figure 3.21).

However, these genes are expressed at very high levels compared to those from clusters 6, 9, 10, and 11 (Figure 3.21). Due to the function of the genes as well as the magnitude of expression cluster 12 can be considered distinct from all other clusters. There is also a potential role in aging due to the group of genes that fall under the vacuolar protein catabolic process GO function. Two of the three genes in this group also belong to the cellular response to starvation, further solidifying their potential role in aging.

The predominant function of cluster 12 is tied to mitochondrial function.

Genes relating to mitochondria are found in all of the related clusters in Figure 3.21.

These genes are upregulated from the beginning of fermentation and remain that way indicating that the mitochondria plays a large role in sparkling wine secondary fermentation. The prevalence of genes with mitochondrial function raises the possibility that many of the genes with uncharacterised functions in clusters 6, 9, 10,

11, and 12 may also have mitochondria related functions.

120

Table 3.26 Biological function gene ontology results for cluster 12 genes. Some GO categories were omitted due to redundancy.

Accession Category Genes GO:0022900 electron transport COR1 TRX3 SDH4 GRX2 QCR7 CYC7 OLE1 QCR9 TRX2 QCR10 chain QCR8 CYC1 ACP1 TRX1 CYB5 GO: 0006754 ATP biosynthetic ATP1 ATP16 ATP17 ATP2 ATP7 ATP18 ATP19 ATP15 ATP20 process GO:0045454 cell redox PRX1 TRX3 GRX2 TRX2 TRX1 AHP1 TSA1 POR1 homeostasis GO:0055114 oxidation- PRX1 COX9 PDA1 OLE1 ERG11 SOD2 AAD10 MDH1 FAS1 AHP1 reduction process PUT1 COX8 TSA1 COX7 IDH1 COX5A ZWF1 FAS2 GO:0006810 Transport PET9 COR1 ATP1 TRX3 ATP16 CDC48 CPR1 SDH4 ATP17 GRX2 QCR7 CYC7 PIC2 OLE1 QCR9 TRX2 QCR10 QCR8 CYC1 ATP2 ATP7 ACP1 TRX1 ATP18 PBI2 POR1 TOM7 CYB5 ATP19 VPS68 TOM6 GSP2 ATP15 ATP20 TOM5 GO:0006099 tricarboxylic acid CIT2 SDH4 MDH1 ACO1 IDH1 CIT1 cycle GO:0006662 glycerol ether TRX3 TRX2 TRX1 metabolic process GO:0008610 lipid biosynthetic ERG28 OLE1 ERG11 FAS1 ACP1 ACC1 FAS process GO:0007039 vacuolar protein PRB1 PRC1 PEP4 catabolic process GO:0009267 cellular response PRB1 PEP4 to starvation

3.14. Cluster 8 shows erratic expression

Cluster 8 expression is erratic throughout fermentation. There is a slight rise in transcript levels during acclimatization that drops over two-fold until day 7.

From this point expression rises and drops until the fermentation ends (Figure

3.22). The gene ontology analysis did not reveal any pathways specific to cluster 8 that might explain this expression pattern. Most of the GO categories are also present in other clusters including rRNA processing, cell cycle, and protein transport (Table 3.27). Like many other clusters, the largest GO category in cluster

8 is “molecular_fuction”, and therefore many of the genes have poorly characterized functions. One fairly unique category is DNA-dependant transcription. This 121 category is made up of genes that encode for components of the cells’ transcription machinery (Table 3.27). The most relevant category may be “protein targeting to vacuole” which could play a role in targeted protein degradation during autophagy

(Table 3.27).

Cluster 8 9.5 9.0 2 Intensity Intensity log 8.5 8.0

R A D1 D7 D14 D21 D30 D33 D36 D39

Sample point

Figure 3.22 Gene expression of cluster 8 as determined by longitudinal clustering of microarray data. Expression is measured in terms of log2 of the fluorescent intensity.

122 Table 3.27 Biological and molecular function gene ontology results for cluster 8 genes. Several GO categories were omitted due to redundancy. The “molecular_function” GO term is indicative of genes with largely uncharacterized molecular functions or GO classification.

Accession Category Genes GO:0003674 molecular_function MAK16 YBL028C AAR2 YSY6 APM3 KRR1 YCR016W NSE4 RPN5 SAS10 PAR32 YDL177C ADY3 DOS2 CWC15 CSN9 VPS64 CNL1 ESC2 SEM1 NKP1 RRP17 PRP3 KRE28 NOP16 SMB1 MAM1 YFR016C ERJ5 CGR1 MAD1 YGL108C NUT1 YGR053C UPF3 NNF2 NOP7 YGR169C-A YGR174W-A SMI1 SDA1 YGR251W PUP2 EFG1 SPO12 YRB2 FMC1 RRT14 YIL161W YIR024C YJL049W MTC1 AIM23 SGM1 YKL023W VPS24 BLI1 YKL063C RRP14 UTP11 EBP2 MIA40 NTR2 YKR023W DID2 LAS1 RSC58 FCF2 YLR053C YLR057W FYV7 RSA3 FAR10 YLR271W IMH1 YML053C BUD22 RSF1 FAR3 CTF18 SPG4 YMR114C JLP2 ESC1 TRI1 GFD1 TMA23 YNL050C NOP15 FYV6 EAF7 RRG9 IES2 KRI1 RTS2 SPP2 SLK19 RPN8 RRP36 SPS4 YPL038W-A CWC27 RSA1 RRP15 GO:0006364 rRNA processing SPB1 KRR1 NOP14 SAS10 NOP6 ESF1 RRP17 NOP16 LSM4 GLC7 SPB4 CGR1 ROK1 NOP7 EFG1 PXR1 LRP1 RPF1 LSM8 RRP14 UTP11 EBP2 FCF2 FYV7 RMP1 CBF5 LSM3 NOP15 JJJ1 KRI1 MPP6 REX4 BUD21 RRP36 NOP53 RRP15 GO:0006351 transcription, DNA- HPC2 PAF1 HMLALPHA2 HMRA1 BDF2 RPC53 NGG1 YAP6 dependent RTT103 ESC2 BUR6 EPL1 NUT1 HOS2 RTF1 TFG1 SMI1 RRT14 ASF1 RPB4 TFA1 PRI2 DAL80 BRE2 RSC58 SFH1 BDF1 MFT1 RSF1 SUB1 IOC4 ESC1 TAF7 TRI1 BDP1 ARP5 EAF7 CTR9 LEO1 TOA1 CTI6 HDA3 PZF1 GO:0007049 cell cycle DAD2 CBF5 FAR10 NDL1 CDC3 SFH1 FAR3 CTF18 KAR1 NUF2 VHS3 RIO1 SLK19 GO:0006623 protein targeting BSD2 VPS64 SNX4 VPS51 DID2 SRN2 to vacuole GO:0015031 protein transport YSY6 APM3 ATG20 VPS64 YRB30 SEC9 SDA1 YRB2 SNX4 YJL049W VPS24 MIA40 VPS51 DID2 SED5 SRN2 PEP3 YPT6 IMH1 VPS20 GFD1 UFE1 ARL3

123 3.15. Cluster 13 expression drops briefly during mid fermentation

The mean expression pattern observed in cluster 13 is reminiscent of clusters 6, 9, 11, and 12 (Figure 3.23; Figure 3.21). Expression begins low then increases sharply and is maintained at this level until then end. However there are two differences: the increase does not occur until acclimatization has finished, and there is a brief drop in expression between day 7 and 14, which recovers by day 21.

This drop coincides with the fermentation anomalies observed (Figure 3.1). The initially delayed upregulation also indicates that these genes likely have little to do with acclimatization, and have functions relating to fermentation only.

124 Cluster 13 10.5 2 10.0 Intensity Intensity log 9.5

R A D1 D7 D14 D21 D30 D33 D36 D39

Sample point

Figure 3.23 Gene expression of cluster 13 as determined by longitudinal clustering of microarray data. Expression is measured in terms of log2 of the fluorescent intensity.

3.15.1. Cluster 13 genes have roles in stress response and nutrient transport

The gene ontology analysis of cluster 13 revealed several GO categories with possible fermentation and stress related roles. The stress response genes are particularly apparent as they belong to the GO category of “response to stress”

(Table 3.28). What makes this group interesting is the fact that they appear to play

125 no role in acclimatization and are specific to fermentation. Many of them are related such as the PAU genes, which are upregulated by anaerobiosis and therefore fermentation (Rachidi et al., 2000). The TIR genes in the same GO category have been shown to respond to cold shock (Kowalski et al., 1995). The fermentation related genes are sugar transporters, most of which are hexose transporters (Table

3.28). Nutrient transporters account for a large number of the genes found in cluster 13. Aside from sugar transporters, amino acid and phosphate transporters are also regulated in a similar manor. The last group in the cluster may have an aging role since it contains genes with cell wall organization roles (Table 3.28).

Table 3.28 Biological function gene ontology results for cluster 13 genes. Several GO categories were omitted due to redundancy. The “biological_function” GO term is indicative of genes with largely uncharacterized biological functions or GO classification.

Accession Category Genes GO:0006950 response to stress PAU7 AAD4 PAU2 TIR1 PAU5 TIR3 DAN1 SSA2 PAU17 TIR4 TIR2 GO:0006865 amino acid BAP3 GNP1 MUP1 BTN2 ALP1 DIP5 transport GO:0008643 carbohydrate HXT13 MAL11 HXT8 HXT2 HXT17 transport GO:0008150 biological_process PAU7 YAR064W YBR242W BSC1 YDR222W PAU2 DSF1 PAU5 YGR266W YHR033W ANS1 TIR3 YIL169C SET4 YJL213W YJL218W YJR115W YKL187C PRY2 YKR041W PAU17 TMA10 YMR147W YMR230W-A YMR244W TOS6 TIR4 YOR072W-B GO:0007047 cellular cell wall ECM13 ECM12 HPF1 TIR4 SRL1 organization GO:0006817 phosphate ion DIC1 PHO84 transport

126 3.16. Cluster 14 genes have RNA processing and osmosensing functions

The genes in cluster 14 are predominantly expressed during rehydration.

Between rehydration and day 1 expression decreases more than two-fold and remains low for the duration of fermentation (Figure 3.24). There are some slight fluctuations in transcript level, but these are small in magnitude and can’t be established as significant. The gene ontology analysis identified functions that were almost entirely related to transcription and translation (Table 3.29). The one group that doesn’t fit that description is the GO category of osmosensor activity. It is worth noting that although transcript levels for these genes decrease over two-fold, they remain at moderate to high levels throughout the experiment indicating that the osmosensing genes continue to be transcribed at a steady rate.

127 Cluster 14 9.6 9.4 9.2 2 9.0 Intensity Intensity log 8.8 8.6 8.4

R A D1 D7 D14 D21 D30 D33 D36 D39

Sample point

Figure 3.24 Gene expression of cluster 14 as determined by longitudinal clustering of microarray data. Expression is measured in terms of log2 of the fluorescent intensity.

128

Table 3.29 Molecular and biological function gene ontology results for cluster 14 genes. Several GO categories were omitted due to redundancy.

Accession Category Genes GO:0006364 rRNA processing UTP20 POP7 POP4 RRP7 RRP43 DBP10 FAL1 RRP8 RRP45 UTP5 UTP6 UTP7 LCP5 NSA1 RAI1 ENP2 MTR3 NOP9 MPP10 DBP7 UTP30 DRS1 GRC3 PWP1 ECM16 SSU72 SQS1 POP3 DIS3 NOP12 NOP4 MOT1 NAN1 BMS1 GO:0006351 transcription, DNA- RRN6 REB1 ISW1 HCM1 NHP10 INO2 TAF12 SRB7 SWR1 UTP5 dependent SPT2 RAD3 PHO4 TAF6 RAI1 SPT6 GCN5 YAP3 NOT3 YAP5 DAL81 ZAP1 GZF3 CBF1 RRN3 RPC25 IOC2 RRN5 IFH1 BUR2 IKI3 SPT5 RRN11 TAF13 ARG81 TAF8 UBP8 RRN9 HDA1 YAF9 RPC31 GCR2 RTT106 TEX1 RTG1 SWT1 SAS5 LGE1 MOT1 NAN1 AFT2 CCL1 ARP7 FHL1 NUT2 GO:0008033 tRNA processing POP7 POP4 PUS9 TRM1 TRM82 DEG1 SUA5 RPR2 TRL1 GCD14 DUS1 POP3 GO:0006397 mRNA processing ABD1 LUC7 DHH1 CDC40 BRR2 SAD1 EDC1 RAI1 CWC22 CBP2 SNU114 PRP16 SPT5 STO1 SWT21 SSU72 SQS1 CWC25 SNU66 GO:0005034 osmosensor HKR1 SHO1 SLN1 activity

3.17. Conclusions of cluster analysis

The cluster analysis did reveal the activation of many stress pathways. There were several means by which the yeasts respond to stress including transcriptional regulatory mechanisms, metabolite production, and structural changes. Stress related transcription factors were largely present in cluster 1 and encompass regulators with diverse functions. Stress-related metabolite production pathways were found in clusters 1, and 6. The most prominent pathways regulated glycerol production, and trehalose production. Glycerol production regulation genes were found in cluster 1 and are expressed more highly during rehydration and the end of fermentation. The trehalose synthesis pathway was found in cluster 6 and showed sustained upregulation throughout. A number of genes regulated by hypoxia and cold were found in cluster 13. These were highly expressed after acclimatization

129 had concluded and expression remained high throughout fermentation. The most notable structural response is the strengthening of cell walls. Cell wall pathways were found in clusters 1, 13, and 11. The pathway specific to cluster 1 was for beta glucanses, a group on enzymes that are responsible for glycosylation of mannoprotein in the cell wall. Their upregulation demonstrates activity consistent with our expectation of sparkling wine yeasts as these mannoproteins are critical to the foaming properties of the finished sparkling wine.

The related clusters 6, 9, 10, 11, and 12 had a large number of mitochondrial genes. The general trend for these genes was elevated transcription throughout fermentation. This tells us that mitochondrial function remains a priority for yeasts during secondary fermentation. At the same time, tRNA processing genes are downregulated as fermentation progresses telling us that even if transcription remains high translation does not.

Of all the data obtained from clustering and gene ontology analysis, nothing adequately explained the stall in production of ethanol, fructose utilization, and loss of cell viability. No single cluster had a group of genes with roles in glycolysis, fermentation or fructose metabolism. It may be the case that the fermentation cannot be explained by understood pathways. It is possible that the genes responsible are presently uncharacterized, in which case cluster 5 may hold the answers. The genes in the this cluster do not undergo any significant changes except at day 7 where transcript levels plunge to half the level found at day 1. This drop is followed by an immediate recovery by day 14. This drop occurs at the same time as the stall in metabolite use, production and a drop in cell viability, but most of the

130 genes in cluster 5 are poorly characterized. Several other clusters had spikes or drops in transcript levels at this time including clusters 1, 3, 7, 8, 13, and 14.

However, none of these showed any obvious pathways that may influence fermentation and fructose utilization. It is also unknown whether this drop in fructose use is specific to the DV10 strain of S. cerevisiae, or if this result would appear in additional fermentations. The reason for the pause in fructose metabolism may also have nothing to do with transcriptional regulation.

3.18. Autophagy genes have GATA consensus sequence in promoters

Autophagy regulation in yeast is not well understood. However, possible regulators of autophagy were identified by cluster analysis, specifically the GATA family of transcription factors. GATA factors have been previously shown to bind to

ATG promoters so there was precedence for further investigation (Inoue and

Klionsky, 2011). Promoter sequences of upregulated ATG genes were screened for the GATA factor consensus sequence WGATAR (where W = A/T, R = A/G), and the palindromic consensus sequence GATApal where the GATA motif partially overlaps with a GATA palindrome (Merika, and Orkin, 1993; Trainor et al., 1996). Of the 21

ATG genes with two-fold or greater expression, 18 have one or more GATA binding sites (Table 3.30). Although they are found in vertebrates, a single palindromic

GATA site was found in the promoter of an ATG gene investigated here, specifically

ATG21 (Table 3.30; Trainor et al., 1996). Of the 18 promoters with GATA sites, 10 had two or more (Table 3.30). Furthermore, when more than one GATA sequence appears in a promoter, two of the sites usually are identical (Table 3.31). With the 131 exception of ATG22, all promoters with two GATA sequences have a duplicate consensus sequence. For example ATG4 has a duplicate of TGATAA. Orientation does not appear to matter with respect to these duplicates as half of the promoters have GATA sites with matching orientation and the other half are opposing. In vertebrates, the GATA consensus sequences with opposing orientation have been demonstrated stronger attraction to their respective GATA factors (Trainor et al.,

1996). Any yeast homologs with two zinc fingers may behave in a similar fashion.

The single identified palindromic GATA site in the promoter of ATG21 does not follow the GATApal formula found in vertebrate GATA promoters. It is lacking a partial overlap and instead is separated by a single base pair. The prevalence of

GATA sites among the promoters of ATG genes is widespread and may be indicative of regulatory control by promoters with the GATA finger motif including the more poorly characterised GAT2, GAT3, and GAT4 in yeast.

132

Table 3.30 GATA consensus sequence locations for all ATG genes with two-fold or greater changes in transcript level. Sequence location is recorded in base pairs from the start codon.

AGATAA/ TGATAA/ AGATAG/ TGATAG/ Gene AATAGA AATAGT GATAGA GATAGT Palindrome Overlaps Total ATG1 (-180) (-152) (-98) - - - 3 ATG2 (-413), (-345) - - - - - 2 ATG3 - (-589) - - - - 1 ATG4 - (-300), (-522) - - - - 2 ATG7 (-52), (-17) - (-204) - - - 3 ATG8 ------0 ATG9 - (-57), (-343) - - - - 2 ATG10 ------0 ATG13 - (-450) - - - - 1 ATG14 - (-409), (-276) - - - - 2 ATG15 - - - (-354) - - 1 ATG16 ------0 ATG17 - (-76) - (-577), (-576) - 1 3 ATG18 - (-47) - - - - 1 ATG19 - (-89) - - - - 1 ATG20 - (-255) - - - - 1 ATG21 (-435), (-21) - (-14), (-13) - 1 1 5 ATG22 - (-35) - (-199) - - 2 ATG23 (-167) (-426) (-203), (-101) (-202) - 1 5 ATG26 - (-86) - - - - 1 ATG29 - (-301) - - - - 1

133

D36 D36 D36 D21 D21 D21 ATG9 ATG19 ATG29 Sample point Sample point Sample point Sample D1 D1 D1

R R R

11 10 9 8 12 10 9 8 5.0 4.0

7

2 2 2 Intensity log Intensity log Intensity log Intensity

D36 D36 D36 D21 D21 D21

ATG7 ATG18 ATG26 Sample point Sample point Sample point Sample D1 D1 D1

R R R

10 9 8 7 11 10 9 8 11.5 10.5 9.5

2 2 2 Intensity log Intensity log Intensity log Intensity

D36 D36 D36 D21 D21 D21 ATG4 ATG17 ATG23 Sample point Sample point Sample point Sample D1 D1 D1

R R R

9.0 8.5 8.0 11 10 9 8 7 8.5 7.5 6.5

7.5

2 2 2 Intensity log Intensity log Intensity

log Intensity

D36 D36 D36 D21 D21 D21 ATG3 ATG15 ATG22 Sample point Sample point Sample point Sample

D1 D1 D1

R R R

10.5 9.5 8.5 7.5 6.5 5.5 13 11 9

8.5

2 2 2 Intensity log Intensity log Intensity log Intensity

D36 D36 D36 D21 D21 D21 ATG2

ATG14 ATG21 Sample point Sample point Sample point Sample D1 D1 D1

R R R

10.0 9.0 8.0 10 9 8 7 10.0 9.0

2 2 2 Intensity log Intensity log Intensity log Intensity D36 D36 D36 D21 D21 D21 ATG1 ATG13 ATG20 Sample point Sample point Sample point Sample D1 D1 D1

R R R

11 10 9 8 10 9 8 7 9.5 8.5 7.5

2 2 2 Intensity log Intensity log Intensity log Intensity

Figure 3.25 Expression profile of ATG genes with one or more GATA motifs. Expression is measured as log2 intensity from microarray analysis over the course of sparkling wine fermentation.

134 3.18.1. GATA consensus sequences are associated with specific expression profiles

The presence of certain GATA consensus sequences appears to be connected to the expression profile of ATG genes. The ATG genes with two-fold or greater change in expression are found in seven of the fifteen clusters. These clusters are clusters 1, 5, 6, 8, 9, 10, and 11 (Table 3.31). Clusters 6, 9, 10, and 11 share a similar expression profile with genes upregulated after rehydration that remain highly expressed for the remainder of fermentation (Figure 3.21). Most of the ATG genes with significant regulation changes are found in these clusters and generally share the same expression profile as the clusters they belong to (Figure 3.25). The single

ATG gene in cluster 5, ATG8, is expressed somewhat differently from the others and has no GATA binding sites. The two ATG genes with GATA sites in cluster 1 have the same consensus sequence, AATAGT. Clusters 6 and 9 are the only ones that have ATG genes with either AATAGA or GATAGA in the promoter. Clusters 10 and 11 have ATG genes with either AATAGT or GATAGT. Although it requires further investigation, there may be a link between specific sequences and genes in related pathways.

Table 3.31 proportions of ATG genes by cluster that have a specific GATA consensus sequence. Proportions are calculated for individual sequences within each cluster.

Number of AGATAA/ TGATAA/ AGATAG/ TGATAG/ Cluster Genes AATAGA AATAGT GATAGA GATAGT 1 3 - 0.67 - - 5 1 - - - - 6 3 1 - 0.67 - 8 2 - 1 - 0.5 9 5 0.4 1 0.4 0.2 10 3 - 0.33 - 0.33 11 4 - 1 - 0.25

135 3.18.2. GATA family of transcription factors undergo similar transcription changes as some ATG genes

Some of the GATA box transcription factors investigated were clustered with

ATG genes and therefore had similar expression profiles. The GATA genes GAT1,

GAT2, GAT3, and GAT4 all undergo upregulation during the latter part of fermentation (Figure 3.26). Of these transcription factors, only GAT2 was found within a cluster with a general trend of increasing expression, cluster 1. This cluster also contained the genes ATG3, ATG10 and ATG13. GAT1 does have a similar expression profile to the other GAT genes, but did not meet the criteria of two-fold or greater expression (Figure 3.26). GAT3 and GAT4 were found in cluster 10. The mean expression profile of cluster 10 is an immediate increase, moderate decrease then subsequent increase in expression. GAT3 and GAT4 don't fit this pattern perfectly, but in both cases there is an early spike in expression that returns to lower levels before gradually increasing toward the end of fermentation. These

GATA transcription factors are clustered with ATG15, ATG19 and ATG21, but also have a similar expression profile to clusters 6, 9, and 11, which contain the majority of ATG genes with a two-fold or greater change in expression including ATG1, ATG2,

ATG4, ATG7, ATG9, ATG14, ATG16, ATG17, ATG18, ATG20, ATG22, ATG23, ATG26, and

ATG29. The clustering of ATG genes with GATA factors may be indicative of expression and perhaps even possible regulatory action of GATA factors on ATG genes.

136 GAT1 GAT2 13 11.0 12 2 2 11 10 10.0 Intensity Intensity log Intensity log 9 9.5 8 9.0

R A D1 D14 D30 D36 R A D1 D14 D30 D36

Sample point Sample point

GAT3 GAT4 6.0 7.5 5.5 2 2 6.5 5.0 4.5 5.5 Intensity Intensity log Intensity log 4.0 4.5

R A D1 D14 D30 D36 R A D1 D14 D30 D36

Sample point Sample point

Figure 3.26 Expression profiles for the GAT family of transcription that each contain the GATA box motif. The cluster affiliation of each was unclustered, cluster 1, cluster 10, and cluster 10 respectively.

137 3.19. Conclusions

The presence of GATA elements in the promoters of ATG genes merits further study. These elements are found in abundance in promoters of autophagy related genes. All but three of the ATG genes with a two-fold or greater change in transcript level have a minimum of one GATA consensus sequence. In many cases, GATA sequences appear twice or more in an ATG promoter. It still remains to be seen if

GATA transcription factors do indeed interact with these promoters as all analysis was in silico. Since the GATA family consists of several transcription factors it is not known for certain how many (if any) will bind to the GATA consensus sequences found in ATG genes. GATA factors have been observed binding to two distinct types of sequences in vertebrate cells: the previously mentioned WGATAR (W = A/T, R =

A/G) and the overlapping GATA palindrome. The latter of these two is absent in all

S. cerevisiae ATG promoters. This certainly does not disqualify ATG promoters from associating with GATA transcription factors, but it does distinguish this group of genes from their vertebrate counterparts.

The regulation of GAT genes during fermentation provides further support for the idea that they may be linked to ATG gene regulation. Of the four GAT genes in

S. cerevisiae (GAT1, GAT2, GAT3, and GAT4), three saw a two-fold or greater increase in transcript levels. Not only were they up regulated, but the greatest and most sustained upregulation occurred toward the end of fermentation and into aging.

This tells us that they are highly expressed during nutrient stresses present during second fermentation, which is also when autophagy is observed (Cebollero and

Gonzalez, 2006). 138 General Discussion

The results of the experiments performed for thesis cannot explicitly confirm or reject a hypothesis. This is due to the lack of focus inherent to an untargeted microarray study. It would be more accurate to say that the experiments performed revealed the global transcriptome of DV10 S. cerevisiae under industrial conditions of secondary fermentation during sparkling wine production. We were able to show a massive change in gene expression over the course of fermentation. At some point in the experiment well over 4000 genes were transcribed with greater than twice or less than half the amount found at day one of fermentation. This encompasses the vast majority of the genome. The cluster analysis was able to isolate co-regulated genes and stress pathways were observed to be highly upregulated demonstrating their importance in the production of sparkling wines.

A previous microarray study of yeasts during secondary sparkling wine fermentation turned up many of the same pathways found in this thesis. Penacho et al. found that oxidative stress pathways were highly upregulated (Penacho et al.,

2012). This was observed in this study as well as pathways for osmotic stress, cold shock, and nutrient stress. Respiratory metabolism genes were also upregulated in the Penacho study, a result that is mirrored in our study as well. However, our study improved on their findings as samples cover a much longer period in greater detail. We can now see that many of the stress response genes are highly expressed during acclimatization and that they initially decrease as secondary fermentation commences, only to increase once more as aging begins. We also observe that genes for respiration are consistently expressed from the end of acclimatization into aging. 139 Overall, even though the Penacho study found many of the same results as we did, our study adds a new dimension as the timing of expression can now be seen in greater detail. This is valuable because instead of simply knowing that the yeasts will undergo certain physiological changes during secondary fermentation, we can see when they occur, which brings us a step closer to understanding why they occur.

The Penacho gene ontology analysis also identified a group of genes under the GO category for autophagy. While this term did not appear in our study, many autophagy genes did undergo significant changes in expression. In our case they had been dispersed among several clusters after longitudinal clustering. This provides additional information as we can see that they have different expression patterns from acclimatization into aging. This supports current knowledge of regulation of autophagy genes as the few known transcriptional regulators of autophagy in yeasts are part of six or more distinct and separate pathways (Jin and

Klionsky, 2014).

The presence of GATA elements in the promoters of ATG genes suggests a method of regulation that has yet to be reported in the literature in any strain of S. cerevisiase. This is of particular interest due to the widespread upregulation of autophagy related genes. This study identifies fifteen ATG genes that may share a group of related transcriptional regulators. The shortcomings of microarray analysis are evident here since the low replication and other inherent problems make establishing a proper statistical correlation difficult.

If these autophagy genes are indeed regulated by the GATA family of transcription factors, they will lack properties found in some of their vertebrate

140 counterparts. For example, GATA factors bind two distinct types of consensus sequences in vertebrates, the WGATAR sequence (found in yeast) as well as overlapping GATA palindromes (GATApal). None of the ATG promoters in this study contained one of these GATApal sequences. There were some overlaps, but these overlaps encompassed three of the four base pairs found in the central GATA consensus sequence. A non-overlapping palindrome was found in the promoter of

ATG21, but it was separated by a single base pair and the ability of a GATA element to bind to this is unknown. Fifteen genes were identified in this study that have

GATA consensus sequences in their promoters, but there may be more. There are over 30 ATG genes in S cerevisiae, but less than half demonstrate adequate upregulation during sparkling wine fermentation. They were not included in this analysis due to the inability to establish a link between them and sparkling wine secondary fermentation.

The regulation of ATG genes by GATA factors is not a new idea. The mammalian GATA-1 has been linked to autophagy in rodent cells (Kang et al., 2012).

GATA family members in yeast share little homology with the mammalian GATA-1, but both do have the zinc finger domain common to all GATA transcription factors.

Most of the work on GATA-ATG relationships in this study are ongoing and future quantitative PCR analysis should be able to confirm whether or not they do interact.

This is a promising result for an untargeted microarray study, which does not aim to find concrete conclusions, but rather new hypotheses. If true, the implications of this new information could ultimately lead to industrial applications. Autophagy has been speculated to play an important role in the aging of sparkling wines. If the

141 GAT-ATG interactions can be altered such that autophagy is induced earlier without detrimental effects, it could decrease the required duration of sparkling wine aging.

In a nutshell, this study has potential to be a step forward for the production of sparkling wines.

Perhaps the greatest mystery regarding this thesis has been the fermentation itself. The profile of ethanol and fructose appear to contradict that of glucose, i.e. glucose continues to be depleted while ethanol and fructose remain relatively static.

If ethanol is not produced fermentation must have stalled, yet glucose continues to be metabolized. It would appear at first glance that this may be the result of experimental error, but after examining the transcription profiles of some clusters there may be another explanation. Several clusters have sudden dips and spikes in transcription around days 7 and 14, precisely where ethanol and fructose concentration begin to plateau. Cluster 5 for example has a massive drop in transcription at day 7, no significant changes occur at any other time for these genes. Unfortunately the majority of genes in this cluster aren’t characterized.

Those clusters with noticeable changes in transcription during this period did not contribute any useful information after gene ontology analysis. This particular fermentation may not be unusual for the DV10 strain during secondary fermentation in wine and further research will be required to establish whether or not the metabolite profiles seen in this study occur regularly. The lag in fructose consumption can ultimately lead to a sweeter wine due to its presence well after the glucose has been completely metabolised. Since DV10 is often selected to ferment

142 wines to dryness, this particular strain may not be an optimal choice if the type of fermentation observed here occurs with any regularity.

There are a several ways in which this study could be expanded in the future.

Microarray analyses are valuable tools but cannot accurately quantify gene expression. For this reason, the results in this study must be verified by qPCR. The fermentation itself needs to be repeated, as fructose metabolism was not consistent with previous studies. Cell viability analysis showed a rapid decline and while this has been seen before in related yeast strains, it has not been observed in DV10 and should be retested. The cluster analysis should also be altered. The longclustEM function was initially run once and data from that analysis were used. The cluster analysis should have been run several times with different seeds to account for the inherent sensitivity to starting values in longclustEM, and the results used from the best model. This was done in hindsight to test the validity of the cluster analysis used in this thesis. Finally, the work on GATA factors needs to be tested to see if it is more than coincidence that GATA consensus motifs are pervasive among ATG promoters. This can likely be verified by chromatin immunoprecipitation.

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