Devant Le Jury Composé De

Associate Professor, Elke Nevoigt

Ramon Gonzalez

Professeur Sophie Landaud

Professeur, SupAgro Bruno Blondin

Sylvie Dequin
email: sylvie.dequin@inra.fr

Clove-Like

Matthias Eder
email: matthias.eder@inra.fr

Isabelle Sanchez
email: isabelle.sanchez@inra.fr

Claire Brice
email: claire.bricecostal@gmail.com

Carole Camarasa
email: carole.camarasa@inra.fr

Jean-Luc Legras
email: jean-luc.legras@inra.fr

QTL mapping of volatile compound production in Saccharomyces cerevisiae during alcoholic fermentation

I also have to thank all PhD students of UMR SPO, Amandine, Camille, Elisa, Guillaume, Noemie, Souhir, Stéphanie, Thomas and Pauline. You were a great support and an excellent source of entertainment ;) I would like to thank Camille in particular for his willingness to help me with everything related to the pitfalls of the French language or the twisty French bureaucracy. Above all, my thank goes to my brother in spirit David (Dude …or Sweet? I don't know). All work and no play would have made Matthias a dull boy. Luckily, he avoided that in creative ways, including pizza, drinks and computer games.

Last but not least, I must express my very profound gratitude to my parents Edith and Johannes, and to my sister Johanna. Throughout all the years of my studies, they provided me with unfailing emotional (and financial) support and continuous encouragement. Above all, I thank my girlfriend and partner Anna. No matter what, she always made me know that I can count on her unconditional love and support. Her manifold help throughout the process of researching and writing this work made my thesis an accomplishment that could not have been possible without her. Thank you!

The research project behind this thesis received funding from the People Programme (Marie Curie Actions) of the European Union's Seventh Framework Programme FP7/2007-2013/ under REA grant agreement n° 606795, project acronym: YEASTCELL. Table 1: Most dominant aroma and flavor compounds commonly found in wine, with the concentration range, their perception threshold and the associated scent; from [START_REF] Swiegers | Yeast and bacterial modulation of wine aroma and flavour[END_REF]

Abbrevations

List of Figures

List of Tables

Résumé Français

Les composés volatils jouent un rôle important dans la perception de la qualité du vin. Sans ces composés, le vin aurait peu de caractéristiques organoleptiques et ne serait pas apprécié de la même façon. Plus d'un millier de composés différents participent à l'arôme du vin, et interviennent dans des équilibres complexes pour participer aux principales perceptions gustatives et olfactives.

Les consommateurs partagent certaines attentes sur la flaveur du vin, mais leurs préférences peuvent changer au fil du temps. Récemment, le développement de vins fruités est devenu une priorité au niveau commercial. Dans un contexte visant à améliorer la santé publique, les vins allégés et réduits en alcool sont également plus populaires. Ceci est en contradiction avec les changements causés par le réchauffement climatique. La hausse des températures dans les régions viticoles entraîne une augmentation de la teneur en sucre des raisins et, par conséquent, des niveaux élevés d'alcool dans les vins finis. Le commerce du vin est un marché international important. Comme les vignes ne peuvent être cultivées que sous certaines latitudes, les régions productrices sont également impliquées dans l'export, dans un domaine devenant de plus en plus concurrentiel. Cela amène les producteurs de vin à constamment s'efforcer d'améliorer leurs produits afin de répondre aux attentes des consommateurs.

Outre les pratiques viticoles et de vinification, la levure a un impact significatif sur la qualité organoleptique des vins. Au cours de la fermentation, elle modifie et approfondit le profil aromatique en produisant des composés volatils à partir des nutriments présents dans le moût, en transformant les composés aromatiques apportés par les raisins, ou en libérant des molécules aromatiques à partir de molécules précurseurs. Parmi les composés d'arômes, les plus abondants, parmi ceux produits de novo par les levures, sont les alcools supérieurs, les acides gras à courte chaine, les acides gras à moyenne chaîne et leurs esters d'éthyle et d'acétate. En fonction du composé et de sa concentration, ces molécules peuvent produire des notes aromatiques agréables, comme le fruité ou fleuri par exemple, ou désagréables, telles que les odeurs de rance ou de solvant. Leur production est étroitement liée aux propriétés métaboliques des levures et est guidée à la fois par le métabolisme du carbone et de l'azote. Les alcools supérieurs et les acides à courte chaine peuvent dériver de la dégradation des acides aminés, mais aussi des intermédiaires du métabolisme carboné central. La production d'acides gras à moyenne chaîne repose sur l'acétyl-CoA qui est généré par le métabolisme carboné central. Les esters d'acétate et d'éthyle de tous ces composés sont formés par voie enzymatique, reposant sur des acétylations ou acylations. La levure a également un impact sur les arômes dérivés du raisin, soit par métabolisation des précurseurs aromatiques, soit par altération des composés d'arômes du raisin. Les terpénols, par exemple, sont synthétisés et apportés par les raisins. Selon le composé, ils produisent des arômes allant du pin à l'agrume et au fleuri. Des enzymes de levure catalysant des conversions entre différents terpénols pendant la fermentation ont été identifiées. De plus, à travers son activité fermentaire, la levure peut influencer la formation d'arômes post-fermentaires au cours du vieillissement du vin. Un exemple notable est le sulfure de diméthyle (DMS), qui peut conférer des notes de truffe et d'olives et a également un rôle d'exhausteur de l'arôme fruité du vin.

Les précurseurs du DMS (pDMS) sont présents dans les raisins et peuvent être métabolisés par les levures en DMS, ce dernier étant cependant éliminé en partie lors de la fermentation, du fait d'un entraînement par le CO2 produit. Les précurseurs restant présents dans le vin jeune pourront être ensuite transformés chimiquement lors de la maturation.

La production et le métabolisme des composés aromatiques reposant sur les propriétés métaboliques de la levure, les bases génomiques sous-jacentes deviennent importantes à prendre en compte. L'évolution et la sélection humaine ont conduit à une population diversifiée de souches de levures Saccharomyces cerevisiae à forte variation génomique.

Elucider les liens entre les variations génomiques et propriétés métaboliques est primordial pour pouvoir comprendre les mécanismes moléculaires responsables de l'orientation du métabolisme et pour pouvoir mieux exploiter, à terme, cette diversité au sein de l'espèce. Au final, cette connaissance peut permettre d'améliorer l'arôme et la qualité du vin et d'adapter les vins aux demandes des consommateurs.

Les bases génétiques des traits complexes, gouvernés par plusieurs gènes et donc quantitatifs, peuvent être explorées par des approches de cartographie QTL. Cette stratégie est basée sur la présence de marqueurs génétiques définis reposant sur des différences génotypiques, qui sont mis en lien avec des variations phénotypiques. Tout d'abord appliquée à la sélection végétale, cette approche a été adaptée à S. cerevisiae et est devenue un outil puissant pour élucider les bases génétiques de nombreux traits d'intérêt industriels, avec un nombre croissant d'études publiées durant les 10 dernières années y compris dans le domaine oenologique. Les approches de cartographie QTL sont continuellement améliorées afin d'augmenter la puissance des analyses et d'étendre la méthodologie à de nouveaux traits d'intérêt.

Le principal objectif de ce travail est d'identifier les bases génomiques et métaboliques de la formation d'arômes par S. cerevisiae pendant la fermentation du vin. Nous nous sommes intéressés aux esters, alcools supérieurs et acides organiques produits de novo par la levure, ainsi qu'aux terpénols et au DMS, formés à partir de précurseurs du moût de raisin. A cette fin, un croisement a été effectué entre deux souches de levures de vin sélectionnées en raison de leurs besoins différents en azote pendant la fermentation, paramètre considéré comme une indication de différences potentielles dans la formation des arômes. 130 ségrégants de génération F2 ont été obtenus et génotypés par séquençage complet de leur génome. Les données de génotypage ont été utilisées pour identifier l'ensemble des SNP différenciant les souches parentales et pour établir une carte de marqueurs sélectionnés. Les ségrégants ont été phénotypés individuellement pendant la fermentation du vin dans du moût de raisin synthétique, en utilisant des fermenteurs de 300 mL. Les concentrations en métabolites extracellulaires ont été mesurées en utilisant la chromatographie liquide à haute performance et la chromatographie en phase gazeuse associée à la spectrométrie de masse. Une analyse de liaison combinant les données de génotypage et de phénotypage a été réalisée pour identifier les variations alléliques ayant un impact sur la production d'arômes.

Dans la première partie de la thèse, les bases de l'analyse QTL ont été établies. Une cartographie simple-QTL a été réalisée pour trouver des loci dans le génome de la levure ayant une influence sur les paramètres généraux de fermentation, la production de métabolites extracellulaires principaux et la formation de métabolites volatils en fin de fermentation. L'utilisation d'un nombre relativement élevé de ségrégants (130) avec un taux de recombinaison augmenté (génération F2) a permis d'obtenir une puissance analytique élevée.

General Introduction

Volatile aroma compounds play an outstanding role for the quality and perception of wine. They can emerge the liquid in the wine glass to become detectable by the human olfactory receptors. These volatile compounds give wine its organoleptic character and without it, wine would not be enjoyed in the same way it is.

Besides viticultural and winemaking practices, yeast, such as Saccharomyces cerevisiae, has a substantial influence on wine flavor. During fermentation, yeast influences the aromatic profile of wine by producing volatile compounds from nutrients in grape must, by the modification of aroma compound precursors found in the grapes or by releasing potent aroma molecules from odor-less grape precursor molecules. The production of fermentative aroma molecules is closely linked to yeast carbon and nitrogen metabolism. Yeast's ability to affects grape derived flavors, either by metabolism of odor-less precursors to aromas or by modification of grape aroma compounds, was also demonstrated to be dependent on yeast enzymatic properties. In addition, strain dependent influence of yeast on the development of post-fermentative aromas during wine aging was shown.

As the formation of numerous aroma compounds relies on yeast metabolic activities, it is therefore governed by the underlying genomic bases. Human use and selection of yeast for winemaking has led to a wide yeast population containing high genomic variation. In recent years, research has focused on the understanding of wine aroma formation as well as the genetic and phenotypic differences between yeast strains. However, information about the impact of genotypic diversity on phenotypic trait determination is still scarce. Understanding the links between genomic and metabolic properties and measuring the impact of genomic variation on metabolic traits is required for optimally exploiting yeast diversity in various applications.

The main aim of this work is to identify the genomic and metabolic bases for the formation of volatile aroma compounds by S. cerevisiae during wine fermentation. This includes the study of the de novo production of different aroma compounds such as esters, higher alcohols and organic acids as well as the investigation of compounds that are formed from precursors in the must like terpenols and dimethyl sulfide. The study will assess the impact of genomic variation among yeast strains on wine aroma trait determination to evaluate the hidden potential of genetic resources from divergent strains for the optimization of industrially used wine yeasts.

The results of the experimental work are divided in four chapters. Chapter one describes the assessment of two S. cerevisiae wine strains regarding their genomic difference and their ability to produce fermentative aromas such as higher alcohols, esters and acids. Furthermore, the strains were evaluated for general fermentation parameters and their production of main metabolites. Differences in fermentation parameters, main metabolite production and fermentative aroma formation were subsequently linked to genomic variations. Linkage analysis was extended to search for genomic regions that showed interaction among each other regarding their influence on fermentative aroma formation. Allelic gene variants responsible for the phenotypic differences were identified and characterized within these regions.

As the production of yeast fermentative aroma is tightly associated to yeast metabolism, especially the central carbon metabolism, better understanding of the impact of genomic variation on intracellular fluxes is crucial. The second chapter of this thesis evaluates how genetic differences of two wine yeast strains can be associated to differences in intracellular carbon flux distributions. This was achieved by using a constraint-based model that takes determined concentrations of extracellular metabolites to estimate fluxes of the yeast central carbon metabolism. Subsequently, it was assessed if the obtained information can be used to also link differences in metabolic flux distributions to yeast genomic properties and to detect allelic variants that account for a diverse shaping of the metabolic profile.

The third and fourth chapter of the thesis extend the investigation to yeast's influence on the composition of grape derived aroma contributors. In the third chapter, the evaluated strains were characterized for their ability to preserve S-methylmethionine, an odor-less aroma precursor that is chemically transformed to the flavor compound dimethyl sulfide during wine maturation. Genomic bases behind strain differences were identified and linked to the strain's origin. In the fourth chapter, the strains were assessed for their different impact on terpenols, a class of potent grape-derived volatiles that can be altered by yeast metabolism. Phenotypic differences were again linked to the genomic properties of the strains and genomic regions behind terpenol metabolism were identified. These regions were compared to the detected genomic regions influencing other traits, such as fermentative aroma formation, in order to evaluate potential connections within the shaping of wine aromatic profile by yeast.

Chapter one was submitted to the journal "BMC Genomics" on 18.09.2017 and is currently under review. The chapters two and three are completed for submission. For chapter two, this process will be completed until the defense of the thesis. A patentability study is in progress on the results of chapter 3. Chapter four presents preliminary results on the genetic basis of the transformation of terpenols compounds during wine fermentation. Additional experimental work to validate the role of the identified alleles will be necessary before publication.

Introduction 1 The role of fermentation

Fermentation (from the Latin word fervere, which means "to boil") plays a central role for a large share of foods that we consume every day. Fermentation is a natural process and plays a non-negligible role in nature. Usually, when a fruit is ripe it falls on the soil and the sugars that it contains are partly metabolized by microorganisms, particularly yeasts of different genera that are naturally present on the surface of the fruit. Alongside with the production of alcohol, fermentation leads to the production and release of volatile molecules that can be perceived as odors and flavors. In nature, these aromas produced by yeast may signal suited properties of the rotting fruit and may attract (or repel) insects and animals on the search for food. This can lead to the dispersal of both, yeast and fruit seeds.

Fermented Foods and beverages

It is estimated that a third of the food we eat is produced or refined by fermentation. Bread, cheese, yogurt, chocolate, vinegar, beer and wine are just examples of this non-exhaustive list of foods daily consumed worldwide. The use of fermentation by human is an inheritance. Archeology revealed that many civilizations used this process, from the Chinese (cabbage, tea) to the Mesopotamian, and Egyptian (bread, beer) and Romans (wine). But also the Aztec (cocoa) and Vikings (fermented milk) knew food preparation techniques that relied on fermentation.

Fermented foods and beverages have many advantages. The fermentation can allow the food to be more digestible. Contrary to apparent assumptions, the nutritional value is increased as vitamins or antioxidants are accumulated or release by the involved microorganisms [START_REF] Steinkraus | Nutritional significance of fermented foods[END_REF]. Furthermore, the benefits of produced ethanol were probably one reason for early civilizations to develop fermentation techniques. Together with a reduction of pH during fermentation, ethanol promotes the conservation of food, thus making it safer to consume. In addition, it improves digestibility and on top of that acts as a euphorisant (Alba-Lois and Segal-Kischinevzky, 2010).

Fermented beverages play an important role in the diet of past and present mankind.

Regardless the country of origin, their production process is fairly similar. A nutritious juice or liquid suspension is made from carbohydrate rich raw materials like cereals, fruits, milk or sap, which is then covered to protect it from oxidation. Subsequently, microorganisms present on the material or added carry out the fermentation. From this simple recipe, man was able to create a wide variety of alcoholic beverages. While bacteria and molds play the dominant role for the fermentation of foods, e.g. Lactobacillus, Streptococcus and Penicillium genera for the fermentation of dairy products, most fermented beverages are produced by yeast, particularly of the genus Saccharomyces and most often of the species cerevisiae, although bacteria might be additionally involved. In most cases, these yeasts are indigenously present on the raw material, making an addition not necessarily required and leading to the involvement of a community of different species or strains in the fermentation.

History of fermented foods and beverages

It is estimated that the history of fermented foods and beverages began in Mesopotamia.

Archaeologists have found indication of wine production in Iran and Egypt dating back to 6000 and 3000 BC [START_REF] Cavalieri | Evidence for S. cerevisiae fermentation in ancient wine[END_REF][START_REF] Mcgovern | The beginnings of winemaking and viniculture in the ancient Near East and Egypt[END_REF] and more recently, an Armenian archaeological site has revealed the existence of a production unit of wine dating back to 4000 BC [START_REF] Barnard | Chemical evidence for wine production around 4000 BCE in the Late Chalcolithic Near Eastern highlands[END_REF]. However, Chinese archaeological evidences suggest the production of fermented beverages as early as 7000 and 9000 BC [START_REF] Mcgovern | Uncorking the past. Quest Wine, Beer, Other Alcohol[END_REF][START_REF] Mcgovern | Fermented beverages of pre-and proto-historic China[END_REF]. Investigations carried out show that sweetened rice mush was produced probably by mastication of rice. The juice could then have been fermented to an alcoholic beverage by yeasts. The more recent archaeological findings allow [START_REF] Mcgovern | Uncorking the past. Quest Wine, Beer, Other Alcohol[END_REF] to push this theory further. According to him, the Neolithic revolution that began 11000 years ago would be a consequence of the use of alcoholic fermented foods, as consuming alcohol helped to survive in a hostile environment with few natural resources.

The production of fermented food and beverages began to take on bigger proportions in the early Middle Ages. At that time, small communities, usually monasteries, set up small scale production units for fermented foods and beverages such as cheese, bread, wine or beer. Towards the end of the 18th century, the industrial revolution led to the growth of larger units manufacturing these products. The progress of science, notably the work of Pasteur on yeasts in beer and wine, and the development of technologies such as artificial refrigeration and the steam engine prompted an enormous progress in the fermentation industry, especially the brewing industry, by achieving a better control over the fermentation process. On the other side, the development of these industries has made science particularly benefiting from the advances. The knowledge acquired on yeasts, for example, allowed later the use of particular and selected strains and resulted in considerable progress within this sector.

Yeast alcoholic fermentation

Alcoholic fermentation is a process of energy utilisation performed by yeast even under anaerobic conditions. The main reaction of this process is the bioconversion of sugars, such as glucose, fructose or galactose, into ethanol and carbon dioxide (CO2):

C6H12O6 → 2 CO2 + 2 C2H6O
One mole of sugar is converted to two moles of ethanol and two moles of CO2. Two moles of adenosine triphosphate (ATP) are generated in the process, which the cell requires to maintain its metabolism (Figure 1). Fermentation medium is an environment for many different competing species of microorganisms. The species with the best characteristics to survive and outcompete other opponents will proliferate and colonize the environment. The combination of the abilities of Saccharomyces cerevisiae to produce heat and to produce and tolerate ethanol in quantities of more than 5%, a toxic concentration for most other microorganisms, has enabled it to outgrow and eliminate competitors [START_REF] Goddard | Quantifying the complexities of Saccharomyces cerevisiae's ecosystem engineering via fermentation[END_REF]. Other reasons include more efficient uptake of nitrogen sources or vitamins [START_REF] Williams | Evolution of ecological dominance of yeast species in high-sugar environments[END_REF]. Its genomic and metabolic properties are the basis behind the yeast's supremacy in fermentation conditions. During their evolution, Saccharomyces yeast and closely related Saccharomycetaceae have undergone a duplication of their whole genome (Conant and Wolfe, 2007). Together with additional genomic rearrangements, this has allowed S. cerevisiae to maintain a high metabolic activity even under anaerobiosis [START_REF] Hagman | Yeast "make-accumulate-consume" life strategy evolved as a multi-step process that predates the whole genome duplication[END_REF]. The drawback of alcoholic fermentation is a very low energy yield since only 2 molecules of ATP are produced per molecule of glucose, compared to 36 produced ATP molecules through respiration in the presence of oxygen. However, even under aerobic conditions S. cerevisiae and closely related species show the ability to produce and accumulate ethanol, which was termed Crabtree effect [START_REF] De Deken | The Crabtree effect: a regulatory system in yeast[END_REF][START_REF] Postma | Enzymic analysis of the crabtree effect in glucose-limited chemostat cultures of Saccharomyces cerevisiae[END_REF]. It has been suggested that overflow in sugar metabolism is the responsible mechanism behind the Crabtree effect and that this characteristic would have been acquired to enable rapid sugar consumption and to increase energy production rates. Further adaption would have led to the glucose-mediated repression of respiration in order to increase overflow metabolism and ethanol production [START_REF] Hagman | A study on the fundamental mechanism and the evolutionary driving forces behind aerobic fermentation in yeast[END_REF]. As Saccharomyces (and Dekkera) are also efficient in consuming ethanol, this strategy could provide the advantage to competitively dominate other yeasts by rapidly consuming sugars and producing alcohol, which is later consumed after establishing dominance: a makeaccumulate-consume strategy (reviewed by [START_REF] Marsit | Diversity and adaptive evolution of Saccharomyces wine yeast: a review[END_REF].

Oenological importance of alcoholic fermentation

Alcoholic fermentation is an essential step in the winemaking process, leading to the conversion of grape juice into wine. The sugars that are contained in grapes are converted into alcohol and at the same time many other biochemical, chemical and physicochemical processes take place. This leads to the production of other main metabolites such as glycerol and organic acids. In addition, several minor volatile compounds are formed, which are important for the sensory characteristics of wine and, in particular, give wine its vinous character [START_REF] Romano | Function of yeast species and strains in wine flavour[END_REF]. At the same time, grape derived aroma contributors get transformed by yeast's metabolism as well. Without the production of these substances, wine would have little organoleptic interest.

In general, grape must is characterized by an acidic pH (2.9 to 3.8) with high sugar content (150 to 260 g/L of an equimolar mix of glucose and fructose). It furthermore contains essential nutrients for yeast growth, such as nitrogen sources, lipids, vitamins and minerals. However, certain agents, such as sulfur dioxide, can act as growth inhibitors.

Unlike beer or sake, grape must does not undergo any heating stage and therefore contains a population of microorganisms deriving from grape berries and the extraction process. Grape flora is composed of different genera of yeast (Saccharomyces, Kloeckera, Hanseniaspora, Rhodoturula, Candida, Debaryomyces, Brettanomyces, ...), which may potentially contribute to the organoleptic characteristics of wine [START_REF] Fleet | Wine microbiology and biotechnology[END_REF]reviewed by Jolly et al., 2014). This biodiversity depends on several factors, such as the grape variety, the ripening state at harvest, antifungal treatments, the climatic conditions of the year, possible occurrence of fungal plagues and the general viticultural practices [START_REF] Bokulich | Microbial biogeography of wine grapes is conditioned by cultivar, vintage, and climate[END_REF][START_REF] Pretorius | Yeast biodiversity in vineyards and wineries and its importance to the South African wine industry. A review[END_REF]. Some of these yeast strains can enhance the aromatic composition of wine, while others have a deleterious effect on the organoleptic characteristics of the final product (i.e., Dekkera bruxellensis). The aromatic profile and the flavor of a wine are among its most important characteristics and in an increasingly competitive market, the modulation of fermentative aroma production to meet consumers' expectations has become one of the major challenges in winemaking.

Over the last 30 years the inoculation of must with selected yeasts, mainly the budding yeast S. cerevisiae, has become a widespread practice that concerns about 80% of all oenological fermentations today [START_REF] Sablayrolles | Control of alcoholic fermentation in winemaking: Current situation and prospect[END_REF]. The bulk implantation of one or more yeast strains selected for their technological properties such as fermentative capacity or for aromatic potential allows a better control of the fermentation process or the organoleptic characteristics of the wine.

The final flavor and aroma profile of wine is influenced by many viticultural effects and vinification methods used during the winemaking process, however, the impact given by the choice of the yeast strain plays a central role [START_REF] Robinson | Influence of Yeast Strain, Canopy Management, and Site on the Volatile Composition and Sensory Attributes of Cabernet Sauvignon Wines from Western Australia[END_REF]. The various yeast strain marketed for oenology exhibit different abilities to produce or release aromas of different kinds [START_REF] Cadière | Evolutionary engineered Saccharomyces cerevisiae wine yeast strains with increased in vivo flux through the pentose phosphate pathway[END_REF][START_REF] Camarasa | Phenotypic landscape of Saccharomyces cerevisiae during wine fermentation: evidence for origin-dependent metabolic traits[END_REF][START_REF] Patel | Effect of 20 different yeast strains on the production of volatile components in Symphony wine[END_REF][START_REF] Torija | Effects of fermentation temperature and Saccharomyces species on the cell fatty acid composition and presence of volatile compounds in wine[END_REF][START_REF] Torrea | Production of volatile compounds in the fermentation of chardonnay musts inoculated with two strains of Saccharomyces cerevisiae with different nitrogen demands[END_REF][START_REF] Vilanova | Assimilable nitrogen utilisation and production of volatile and non-volatile compounds in chemically defined medium by Saccharomyces cerevisiae wine yeasts[END_REF].

Advances in scientific research have given winemakers the possibility to shape the aroma outcome of their wines in selecting commercially available wine yeasts by their biosynthesis of flavor active compounds and their ability to release or modify grape derived compounds (reviewed by [START_REF] Swiegers | Yeast and bacterial modulation of wine aroma and flavour[END_REF]. However, a smaller share of wineries, especially traditional wine cellars, continue to use spontaneous alcoholic fermentation, as they find it grants their wines a greater complexity. Today, there is increasing interest on the contribution of non-Saccharomyces yeast species to wine sensorial attributes, either as wild microbiota or as mixed starters with S. cerevisiae.

Despite increased knowledge on the impact of S. cerevisiae metabolic properties on the synthesis of fermentative aroma, current information on the production and regulation of aroma biosynthesis is still incomplete. Many questions remain, notably about the influence of yeast genomic diversity on the winemaking process.

Fermentation process

During fermentation, the yeast is subjected to different stresses (high sugar content, acidic pH, nutrient limitation, increase in ethanol concentration, etc.), which require a continuous metabolic adaptation. Monitoring the alcoholic fermentation process is therefore a key step of winemaking. This involves keeping track of the consumption of sugars, which enables forecasting fermentation progression and anticipating possible problems, such as a sluggish or stuck fermentation. It can be achieved by different possible approaches:

-Monitoring the density of the fermentation medium, which correlates to the amount of present sugars (used in many cellars). -Measuring the concentration of alcohol.

-Measuring the release of CO2 (used in the laboratory and in an increasing number of cellars with on-line monitoring systems for fermentations, which allow precise monitoring and give access to the fermentation parameters).

Monitoring the yeast fermentation activity reveals three main phases (Figure 2). The lag phase corresponds to the adaptation of the yeast to a new environment [START_REF] Pérez-Torrado | Study of the first hours of microvinification by the use of osmotic stress-response genes as probes[END_REF]. This phase can vary from a few hours to a few days, depending on the temperature, presence of inhibitors and the condition of the inoculated yeast cells (e.g. the preparation procedure of added active dry yeasts). During the lag phase, the yeast population does not increase, while the medium saturates with CO2 approximately 1.5 g/L). The metabolism of glycerol and ergosterol becomes important during the lag phase, as it leads to the accumulation of reserve molecules and cell protection factors. Ergosterol, for example, will later on protect the yeast from ethanol stress [START_REF] Alexandre | Ethanol adaptation mechanisms in Saccharomyces cerevisiae[END_REF].

The growth phase or exponential phase starts when the cells are adapted to the new environment. It is characterized by an exponential cell multiplication, leading to a high release of CO2 and a high fermentation rate. During this phase, the specific rate of CO2 (dCO2/dt) will reach a maximum value, corresponding to the maximum activity of the fermentation. Simultaneously, the transcriptomic activity remains stable during this period [START_REF] Rossignol | Genome-wide monitoring of wine yeast gene expression during alcoholic fermentation[END_REF]. The duration of the growth phase can last from 2 to 6 days, which is relatively short compared to the duration of the fermentation. It is highly influenced by the concentration of ammonia, amino acids and other nutrients, which are limiting factors [START_REF] Ingledew | Factors influencing sluggish fermentations of grape juice[END_REF] and by the presence of oxygen [START_REF] Bisson | Stuck and sluggish fermentations[END_REF].

The stationary phase in wine fermentation lasts between 3 and 20 days, during which the yeast does not multiply anymore, but retains fermentative activity. Most of the sugars (60-70%) are consumed during this phase. The cell viability remains generally higher than 80-90% and therefore the cell population maintains a maximum level, while the fermentation activity decreases progressively until most of the sugars are depleted. This decrease in activity is mainly due to increasing concentrations of ethanol and other substances produced during alcoholic fermentation, which are toxic for the cells. Furthermore, the nitrogen limitation plays an essential inhibitory role on hexose transporters [START_REF] Salmon | Effect of sugar transport inactivation in Saccharomyces cerevisiae on sluggish and stuck enological fermentations[END_REF]. The stationary phase corresponds to a decrease in biosynthesis activities and a reorganization of the nitrogen metabolism. It is associated with important transcriptomic rearrangements, notably with the implementation of a stress response that is linked to the growth stop. Even though the stationary phase is considered a stress condition, the genes involved in glycolysis are maintained during this phase [START_REF] Rossignol | Genome-wide monitoring of wine yeast gene expression during alcoholic fermentation[END_REF].

Wine aromas

Wine is a complex mixture and many molecules play a role in its organoleptic characteristics. Four senses are involved in defining the organoleptic quality of wine: sight, smell, taste and touch. Wine taste, perceived with the taste buds of the tongue, involves sweetness, acidity, bitterness, saltiness and the taste of umami. The mouth-feel of wine relates to the body and texture of the liquid and is influenced by factors such as the alcohol content or the presence of astringency. The general structure of a wine includes a wide range of perceptions, such as acidity, sweetness, occasional bitterness, tannins, alcohol content, palate weight and length, mouth-feel, the intensity of aroma and flavor and overall complexity (reviewed by [START_REF] Styger | Wine flavor and aroma[END_REF]. These structural elements should be in balance and harmony. They are not assessed in isolation but in relationship to each other. Wine drinkers' senses are not uniformly sensitive to the subtle assortment of changing sensations. Some of the diversity in sensory perception and preferences for different wine styles among individuals and populations is cultural, some learned, some genetic [START_REF] Pretorius | Grape and wine biotechnology: challenges, opportunities and potential benefits[END_REF]. Furthermore, preferences are also influenced by factors such as gender and age (reviewed by [START_REF] Swiegers | Yeast and bacterial modulation of wine aroma and flavour[END_REF].

Wine aroma is related to the presence of compounds with low boiling points, which are, therefore, volatile. These compounds can escape the liquid in the wine glass to become detectable by the human nose (reviewed by [START_REF] Styger | Wine flavor and aroma[END_REF]. While over 1000 aroma compounds were detected to be present in wine [START_REF] Tao | Active volatiles of cabernet sauvignon wine from Changli County[END_REF], only a few compounds actually contribute to the sensory perception of wine flavor (reviewed by [START_REF] Polášková | Wine flavor: chemistry in a glass[END_REF]. Higher alcohols, acids and esters are quantitatively dominant in wine aroma and are important for the sensorial properties and the quality of wine [START_REF] Stashenko | Monitoring volatile chemicals formed from must during yeast fermentation[END_REF].

In general, wine aroma can be divided into classes, depending on its origin. Varietal aroma is synthesized and contributed by the grapes, and is a distinction of grape variety. Wines made from specific grape varieties, like Gewürztraminer or Muscat, display distinctive aromas, which are induced by the corresponding variety [START_REF] Duchêne | Variation of linalool and geraniol content within two pairs of aromatic and non-aromatic grapevine clones[END_REF][START_REF] Guth | Identification of character impact odorants of different white wine varieties[END_REF].

Prefermentative aroma originates during grape processing processes. Fermentative aroma is produced by yeast and bacteria during alcoholic and malolactic fermentations.

Postfermentative aroma develops due to transformations that occur during conservation and aging of wine [START_REF] Vilanova | Correlation between volatile composition and sensory properties in Spanish Albariño wines[END_REF]. Modern advances in scientific research give winemakers tools to shape their wines toward predetermined aroma outcomes. Today, wine yeast and bacteria can be selected to optimally biosynthesize flavor-active compounds and to release or modify grape-derived flavor compounds without affecting the general fermentation performance. The most dominant aroma molecules are listed in Table 1, together with their perception threshold and concentration usually found in wine.

Table 1: Most dominant aroma and flavor compounds commonly found in wine, with the concentration range, their perception threshold and the associated scent; from [START_REF] Swiegers | Yeast and bacterial modulation of wine aroma and flavour[END_REF].

Compound

Varietal aromas

Wines made from specific grape varieties typically display varietal character, e.g. distinctive aromas which evoke that variety. Varietal aroma contributors may exist as free forms or as odorless precursors in the grape berry, which are then released as odorous compounds during harvesting, vinification or ageing. These aromas are representative for the particular grape variety [START_REF] Francis | Determining wine aroma from compositional data[END_REF]. The main compounds responsible for the intense aromas in Sauvignon Blanc wines have been assumed to be varietal thiols, while terpenols are most characteristic for Muscat varieties [START_REF] Darriet | Caractérisation de l'arôme variétal des vins de Sauvignon par couplage chromatographie en phase gazeuseodométrie[END_REF]. The majority of varietal aroma compounds are present in grapes as bound forms, e.g. glycosylated in the case of terpenols and cysteinylated or glutathionylated in the case of varietal thiols, which are then released during fermentation.

Terpenes

Terpenes constitute a large family of molecules and more than 4000 compounds have been identified. These compounds are polymers of isoprene units with five carbons (C5). Each terpene is classified according to the number of units: hemi-(C5), mono-(C10), sesqui-(C15), di-(C20), disesqui-(C25) and tri-(C30) terpenes.

40 and 30 different terpenes have been identified in grapes and wine, respectively. In wine, the most olfactory important terpenes are monoterpene alcohols (terpenols) (reviewed by [START_REF] Strauss | Role of monoterpenes in grape and wine flavor[END_REF]. Geraniol, linalool, nerol and α-terpineol, but also cis/trans rose oxide, which is an oxidized form of citronellol [START_REF] Luan | Metabolism of geraniol in grape berry mesocarp of Vitis vinifera L. cv. Scheurebe: demonstration of stereoselective reduction, E/Z-isomerization, oxidation and glycosylation[END_REF], are characteristic aroma contributors for certain wines, such as Muscat or Gewürztraminer, and give them typical floral or citrusy notes (reviewed by [START_REF] Black | Terpenoids and their role in wine flavour: recent advances[END_REF][START_REF] Ribéreau-Gayon | Aroma of Muscat grape varieties[END_REF][START_REF] Simpson | Aroma composition of bottle aged white wine. Vitis-Berichte ueber Rebenforsch. mit Dokumentation der Weinbauforsch[END_REF].

Their biosynthesis in grapes involves the building block isopentenyl pyrophosphate (IPP), which is produced in the cytosol and in the chloroplast. This intermediate plays a central role in the production of many molecules in plants. It is known that IPP synthesis in the cytosol takes place via the mevalonate pathway [START_REF] Wright | Biosynthesis of isoprenoid compounds[END_REF], allowing the production of sesquiterpenes, sterols and ubiquinone, while the synthesized IPP in the chloroplast is produced by the 2-C-methyl-D-erythritol-4-phosphate pathway, allowing the production of monoterpenes, diterpenes, carotenoids and chlorophyll [START_REF] Schwender | Biosynthesis of isoprenoids (carotenoids, sterols, prenyl side-chains of chlorophylls and plastoquinone) via a novel pyruvate/glyceraldehyde 3-phosphate non-mevalonate pathway in the green alga Scenedesmus obliquus[END_REF]. However, the precursor IPP can also pass from the chloroplast to the cytosol [START_REF] Bick | Metabolic cross talk between cytosolic and plastidial pathways of isoprenoid biosynthesis: unidirectional transport of intermediates across the chloroplast envelope membrane[END_REF]. The isopentenyl diphosphate isomerase produces an isomer of IPP, dimethylallyl pyrophosphate (DMAPP) [START_REF] Banthorpe | Biosynthesis of geraniol and related monoterpenes in Pelargonium graveolens[END_REF]. Subsequently, geranyl pyrophosphate synthases allow the condensation of an IPP molecule on DMAPP to form geranyl pyrophosphate (GPP) (C10), or on GPP to form farnesyl pyrophosphate (FPP) (C15) (Figure 3). There are also other terpenes in wines such as limonene, α-pinene or myrcene, although it is not known whether they are produced in the vine by terpene synthases or if they are derived from a chemical conversion of present terpenols. [START_REF] Cheng | Plant terpenoids: biosynthesis and ecological functions[END_REF][START_REF] Mcgarvey | Terpenoid metabolism[END_REF][START_REF] Nagegowda | Plant volatile terpenoid metabolism: biosynthetic genes, transcriptional regulation and subcellular compartmentation[END_REF].

Although much of the variation in the level of terpenols in the finished wine is determined in the vineyard, yeast influences the levels of these compounds during fermentation [START_REF] Furdíková | Influence of yeast strain on aromatic profile of Gewürztraminer wine[END_REF][START_REF] King | Biotransformation of monoterpene alcohols by Saccharomyces cerevisiae, Torulaspora delbrueckii and Kluyveromyces lactis[END_REF]. Yeast can catalyze the release of terpenols from glycosylated, non-volatile precursors [START_REF] Voirin | Novel monoterpene disaccharide glycosides of Vitis vinifera grapes[END_REF] and even de novo synthesize certain terpenols from geranyl pyrophosphate [START_REF] Carrau | De novo synthesis of monoterpenes by Saccharomyces cerevisiae wine yeasts[END_REF]. [START_REF] Koslitz | Stereoselective formation of the varietal aroma compound rose oxide during alcoholic fermentation[END_REF] showed that S. cerevisiae produces both stereoisomers of rose oxide during fermentation. Furthermore, [START_REF] Steyer | QTL mapping of the production of wine aroma compounds by yeast[END_REF] reported several QTLs involved in the production of citronellol and cis-rose oxide by S. cerevisiae without identifying the underlying genes. Moreover, the study could identify the transcription factor Pdr8 to be responsible for the formation of nerolidol in wine fermentations. Later, [START_REF] Steyer | Genetic analysis of geraniol metabolism during fermentation[END_REF] identified OYE2 as the gene responsible for the reduction of geraniol into citronellol, and the involvement of ATF1 in the acetylation of terpenols could be demonstrated.

The activated form of geraniol, geranyl pyrophosphate, is part of the sterol pathway (Figure 4). This led to the assumption that yeast could synthesize sterols from grape-derived terpenols as a reaction to anaerobiosis during alcoholic fermentation, which inhibits the oxygen dependent first steps of the sterol synthesis [START_REF] Vaudano | Modulation of Geraniol Metabolism During Alcohol Fermentation[END_REF]. [START_REF] King | Biotransformation of monoterpene alcohols by Saccharomyces cerevisiae, Torulaspora delbrueckii and Kluyveromyces lactis[END_REF][START_REF] Zea | Content of free terpenic compounds in cells and musts during vinification with three Saccharomyces cerevisiae races[END_REF].

Volatile Phenols

Various phenolic substances contribute to taste, color and odor of wines [START_REF] Dubois | Volatile phenols in wines[END_REF]. From known classic wine components, vinylphenols (4-vinylguiacol, 4-vinylphenol) and ethylphenols (4-ethylguiacol, 4-ethylphenol) are the most significant phenolic aroma contributors [START_REF] Etievant | Volatile phenol determination in wine[END_REF][START_REF] Singleton | Phenolic substances in grapes and wine, and their significance[END_REF]. Their concentration can range from 0 to 6 mg/L. The derivatives 4-ethylguaiacol and 4-vinylguaiacol may add smoky, vanilla or clovelike odors to wine [START_REF] Singleton | Phenolic substances in grapes and wine, and their significance[END_REF]. On the other hand, vinylphenols and in particular 4-ethylphenol may be responsible for phenolic off-flavors described as animal, horse sweat, stable, medical or "band aid". Their presence is considered undesirable since fruity nuances of the wine can be masked [START_REF] Chatonnet | The origin of ethylphenols in wines[END_REF][START_REF] Dubois | Volatile phenols in wines[END_REF]. This is even the case for concentrations lower than the perception threshold. It was demonstrated that while first the addition of 500 µg/L of 4-ethylphenol to a neutral wine resulted in medicinal, horse sweat or leather odors, the addition of 50 µg/L to the same wine already changed its sensory evaluation. However, this is not the case for 4-vinylguaiacol, which can enhance the varietal character of certain grape varieties, such as Gewürztraminer [START_REF] Dubois | Volatile phenols in wines[END_REF][START_REF] Grando | Selective use of wine yeast strains having different volatile phenols production[END_REF]. The described phenolic off-flavors most often develop in red wines during ageing, particularly when the wines are stored in old barrels and are seldom racked [START_REF] Chatonnet | Synthesis of volatile phenols by Saccharomyces cerevisiae in wines[END_REF][START_REF] Chatonnet | The origin of ethylphenols in wines[END_REF].

Precursors of phenol compounds are produced by certain grape varieties and are often found in glycosylated forms [START_REF] Singleton | Phenolic substances in grapes and wine, and their significance[END_REF]. Coumaric and ferulic acid are predominantly important as they can be transformed into volatiles by the cinnamate decarboxylase of S. cerevisiae during alcoholic fermentation [START_REF] Chatonnet | Synthesis of volatile phenols by Saccharomyces cerevisiae in wines[END_REF]. As this enzyme is inhibited by catechin and catechin tannins, which are abundant in red wine, the levels of volatile phenols formed in red wines are in general lower than those found in white and rosé wines.

The cinnamate decarboxylase of contaminating Brettanomyces or Dekkera species, however, is not inhibited by catechins. If present, these yeasts can reduce vinylphenols to ethylphenols, resulting in significant quantities of later compounds in red wines.

Volatile thiols

Sulfur-containing molecules are generally potent aroma contributors as their perception thresholds are low [START_REF] Mestres | Analysis of organic sulfur compounds in wine aroma[END_REF], e.g., ranging from 0.8 ng/L to 60 ng/L for the volatile thiols 4-mercapto-4-methylpentan-2-one (4MMP), 3-mercaptohexanol (3MH) and its acetylated derivative 3-mercaptohexyl acetate (3MHA), which can impart pleasant smells of black currant/boxwood (4MMP), grapefruit (3MH) and passionfruit (3MHA) [START_REF] Tominaga | Stereoisomeric distribution of 3mercaptohexan-1-ol and 3-mercaptohexyl acetate in dry and sweet white wines made from Vitis vinifera (Var. Sauvignon Blanc and Semillon)[END_REF]. 4MMP and 3MH are released from non-aromatic grape precursors by yeast during fermentation [START_REF] Grant-Preece | Synthesis of wine thiol conjugates and labeled analogues: fermentation of the glutathione conjugate of 3-mercaptohexan-1-ol yields the corresponding cysteine conjugate and free thiol[END_REF][START_REF] Roland | Identification and quantification by LC-MS/MS of a new precursor of 3-mercaptohexan-1-ol (3MH) using stable isotope dilution assay: Elements for understanding the 3MH production in wine[END_REF] and 3MHA is formed by acetylation of the released 3MH. While the production of 4MMP can differ considerably between different strains, the formation of 3MH and 3MHA is less varying and more dependent on grape must composition [START_REF] Lee | Grape juice is the major influence on volatile thiol aromas in Sauvignon Blanc[END_REF].

Fermentative aromas

During wine fermentation, the aromatic profile of must is changed and widened by the involved microorganisms (Figure 5). Besides altering varietal flavor compounds, yeast de novo synthesizes fermentative aromas during alcoholic fermentation. Higher alcohols and esters are hereby the most abundant volatiles synthesized by S. cerevisiae (reviewed by [START_REF] Cordente | Flavor-active wine yeasts[END_REF]. In addition to environmental factors, such as nitrogen composition of the must or fermentation temperature, present yeast strains influence the nature and concentration of fermentative aromas [START_REF] Molina | Influence of wine fermentation temperature on the synthesis of yeast-derived volatile aroma compounds[END_REF]. [START_REF] Gil | Aroma compounds in wine as influenced by apiculate yeasts[END_REF] analyzed the aroma compounds of wines inoculated with pure and mixed cultures of apiculate and S. cerevisiae yeasts, showing that samples fermented with mixed cultures produce total higher amounts of alcohols and acids than wines produced with pure cultures of S. cerevisiae. The production of fermentative aromas is closely linked to yeast central carbon and nitrogen metabolism (Figure 6) and is therefore dependent on the metabolic and enzymatic properties of the yeast (reviewed by [START_REF] Lambrechts | Yeast and its Importance to Wine Aroma -A Review[END_REF].

Higher alcohols

The presence of small amounts of higher alcohols, less than 300 mg/L, contributes to the fruitiness and the aromatic complexity of wines. 2-phenylethanol, for example, may bring floral notes to wine. If concentrations exceed 400 mg/L, however, these alcohols are considered deleterious, as they can impart strong and pungent odors (reviewed by [START_REF] Bell | Implications of nitrogen nutrition for grapes, fermentation and wine[END_REF]. Higher alcohols in yeast are produced by the well-characterized Ehrlich pathway (reviewed by [START_REF] Hazelwood | The Ehrlich Pathway for Fusel Alcohol Production: a Century of Research on[END_REF]) (Figure 7). The production of higher alcohols is carried out by the decarboxylation and reduction of αketo acids. These keto acids are either products of the central carbon metabolism, or they can be formed through transamination of different branched amino acids by Bat1 and Bat2 [START_REF] Dickinson | A study of branched-chain amino acid aminotransferase and isolation of mutations affecting the catabolism of branched-chain amino acids in Saccharomyces cerevisiae[END_REF], respectively aromatic amino acids by Aro8 and Aro9. Other enzymes might be additionally involved in the transamination of branched amino acids [START_REF] Eden | Involvement of branched-chain amino acid aminotransferases in the production of fusel alcohols during fermentation in yeast[END_REF]. Overexpression of BAT1 and BAT2 in wine yeast resulted in increased levels of some higher alcohols, as well as in changes in the level of other aroma compounds in the finished wine (Lilly et al., 2006b). The production of higher alcohols is dependent on the availability of intracellular nitrogen. When the concentration of nitrogen is low, α-keto acids produced from the central carbon metabolism cannot be transaminated to form amino acids, and are metabolized into higher alcohols as a consequence [START_REF] Moreno-Arribas | Wine chemistry and biochemistry[END_REF].

The decarboxylation of α-keto acids is carried out by the pyruvate decarboxylases Pdc1, Pdc5, Pdc6 and broad specificity decarboxylase Aro10. THI3 is involved in leucine degradation, but it is assumed to encode a regulatory element rather than a catalytic enzyme [START_REF] Hazelwood | The Ehrlich Pathway for Fusel Alcohol Production: a Century of Research on[END_REF]. The fusel aldehyde can then be reduced to a higher alcohol by a set of alcohol dehydrogenases (Adh1-Adh7) or by Sfa1. Alternatively, it can be oxidized to the corresponding fusel acid by a set of aldehyde dehydrogenases (Ald1-Ald6). The fate of the fusel aldehyde at this stage of conversion is dependent on the redox balance of the cell. Anaerobic, fermentative cultures favor the production of alcohols over acids to meet the demand for oxidizes nicotineamide adenine dinucleotide (NAD + ) [START_REF] Hazelwood | The Ehrlich Pathway for Fusel Alcohol Production: a Century of Research on[END_REF]. The intermediates of the Ehrlich pathway are shown in Table 2, as well as their precursor amino acids. Even though the Ehrlich pathway is designated as the main synthesis route of higher alcohols [START_REF] Hazelwood | The Ehrlich Pathway for Fusel Alcohol Production: a Century of Research on[END_REF], most of these compounds can also be formed from glucose through the central carbon metabolism (reviewed by [START_REF] Bell | Implications of nitrogen nutrition for grapes, fermentation and wine[END_REF] (Figure 8). Indeed, the majority of the α-keto acids from which the higher alcohols are derived are metabolized from intermediates of the glycolysis or the tricarboxylic acid (TCA) cycle. For example, αketoisovalerate and α-ketoisocaproate, precursors for isobutanol and isoamyl alcohol, can be formed from pyruvate (Figure 8). Nevertheless, fusel alcohol production can not be seen as the result of amino acid imbalances, as recent analysis of the fate of amino acids during alcoholic fermentation revealed a complex of degradation and resynthesis of all amino acids [START_REF] Crépin | Management of Multiple Nitrogen Sources during Wine Fermentation by Saccharomyces cerevisiae[END_REF].

Esters

The presence of esters can have an important effect on the fruity and floral notes of the wine.

The contribution of esters depends on the type of wine. It involves mechanisms of interactions and synergies [START_REF] Escudero | Analytical characterization of the aroma of five premium red wines. Insights into the role of odor families and the concept of fruitiness of wines[END_REF][START_REF] Ferreira | Quantitative determination of the odorants of young red wines from different grape varieties[END_REF]. The two main classes of esters important to wine are acetate esters, such as ethyl acetate, isoamyl acetate and phenylethyl acetate, and ethyl esters, like ethyl hexanoate and ethyl octanoate (reviewed by [START_REF] Styger | Wine flavor and aroma[END_REF].

Most ethyl esters contribute to the aroma of young wines and are the source of pleasant fruity notes, whereas among the acetate esters only amyl acetate, isoamyl acetate and 2phenylethyl acetate contribute to floral and fruity notes. Certain esters, such as ethyl acetate, may provide solvent type aromas at high concentrations.

Acetate esters are produced from acetyl-coenzyme A (acetyl-CoA) and alcohol (ethanol or higher alcohols), by the alcohol acetyltransferases Atf1 and Atf2 (Figure 8). Atf1 is the more important of those two enzymes and is considered to be responsible for the synthesis of 80% of isoamyl acetate, 75% of phenylethyl acetate and about 40% of ethyl acetate. Numerous studies have shown that enzymatic activity is the most important factor in the production of these esters (Lilly et al., 2006a[START_REF] Lilly | Effect of increased yeast alcohol acetyltransferase activity on flavor profiles of wine and distillates[END_REF][START_REF] Mason | Alcohol acetyltransferases and the significance of ester synthesis in yeast[END_REF][START_REF] Verstrepen | Expression levels of the yeast alcohol acetyltransferase genes ATF1, Lg-ATF1, and ATF2 control the formation of a broad range of volatile esters[END_REF]. In a yeast strain overexpressing ATF1, the production of isoamyl acetate is multiplied by more than factor 100, and the syntheses of ethyl acetate and phenylethyl acetate are increased between 10 and 200 times, while the overexpression of the esterase gene IAH1 has a negative effect on ester production (Lilly et al., 2006a). Double deletion mutants of ATF1 and ATF2 still show considerable levels of some esters, suggesting the presence of other unknown estersynthesizing enzymes [START_REF] Verstrepen | Expression levels of the yeast alcohol acetyltransferase genes ATF1, Lg-ATF1, and ATF2 control the formation of a broad range of volatile esters[END_REF]. In conclusion, the production of acetate esters depends on both, the availability of the two co-substrates (acetyl-CoA and higher alcohols) and the enzymatic activities of involved acetyltransferases and esterases.

Ethyl esters are the product of activated fatty acids (fatty acyl-CoA) and ethanol. The acyltransferase genes EHT1 and EEB1 are responsible for both, synthesis and hydrolysis of ethyl esters (Figure 9) [START_REF] Saerens | The Saccharomyces cerevisiae EHT1 and EEB1 genes encode novel enzymes with medium-chain fatty acid ethyl ester synthesis and hydrolysis capacity[END_REF]. It has been shown that overexpression of these two genes does not lead to overproduction of ethyl esters, because these enzymes possess both synthetic activity and esterase activity. Eht1 catalyzes exclusively the synthesis of ethyl hexanoate from ethanol and hexanoyl-CoA, while Eeb1 is an ethanol acyltransferase responsible for most of the ethyl ester biosynthesis during fermentation. This enzyme also possesses esterase activity of short chain esters and may be involved in lipid metabolism [START_REF] Saerens | Parameters affecting ethyl ester production by Saccharomyces cerevisiae during fermentation[END_REF].

The most important factor influencing the production of ethyl esters is the availability of the two precursors, acyl-CoA and ethanol. In addition, the C8 ethyl esters and their precursor fatty acids are not entirely excreted in the medium. A certain share of the product is retained within the cell, about 60% for C8, 20% for 10 and almost 100% for C12 [START_REF] Saerens | Parameters affecting ethyl ester production by Saccharomyces cerevisiae during fermentation[END_REF]. A recent quantitative trait locus (QTL) mapping study by [START_REF] Steyer | QTL mapping of the production of wine aroma compounds by yeast[END_REF] found that allelic variants of PLB2 show different influence on the levels of ethyl esters. Deletion of this gene resulted in a significant drop in the levels of ethyl octanoate and ethyl decanoate. The effect was stronger than for EEB1 deletion mutants.

Fatty acids

Fatty acids have odors usually deemed unpleasant. On the other side, some authors have found positive correlations between fatty acid content and wine quality [START_REF] San-Juan | Quality and aromatic sensory descriptors (mainly fresh and dry fruit character) of Spanish red wines can be predicted from their aroma-active chemical composition[END_REF], however, it is supposed that the ethyl esters, directly derived from fatty acids, are responsible for these correlations [START_REF] Maarse | Volatile compounds in foods and beverages[END_REF]. Among the fatty acids, only octanoic acid could influence the aroma of wines when sole administered. However, if the fatty acids are evaluated as a mixture, their impact on the aroma increases. In certain wines, the perception threshold of 10 mg/L can be reached with a mixture of hexanoic, octanoic and decanoic acids.

Saturated fatty acids with an even number of carbon atoms, which are largely predominant, have two possible origins, the fatty acid synthesis (Figure 9) and the catabolism of long-chain fatty acid chains via β-oxidation [START_REF] Schreier | Flavor composition of wines: a review[END_REF]. The anabolic pathway is the predominant pathway, whereas β-oxidation, which requires the presence of oxygen, is active only at the beginning of fermentation.

During their synthesis, fatty acids are formed successively by adding two carbon atoms from malonyl-CoA to an acyl-CoA, with acetyl-CoA being the first acyl-CoAs of this elongation cycle (Figure 9). The enzymatic reactions are carried out by the multi-enzymatic fatty acid synthase complex, which is composed of two subunits [START_REF] Lynen | On the structure of fatty acid synthetase of yeast[END_REF]. Acetyl-CoA is mainly formed by oxidative decarboxylation of pyruvate. Fatty acid degradation by the β-oxidation pathway consists of a series of reactions, which release a molecule of acetyl-CoA from the fatty acid chain, resulting in a fatty acid reduced by two carbon atoms.

Volatile sulfur compounds

Sulfur dioxide (SO2) and hydrogen sulfide (H2S) are the most common sulfur-containing aroma compounds de novo generated by yeast from sulfur substrates during fermentation [START_REF] Swiegers | Modulation of volatile sulfur compounds by wine yeast[END_REF]. Sulfur is present in grape juice as sulfate, which is contributed by grapes, and sulfite that is added by winemakers for microbial control. Besides prompting a health risk, their presence can impart off-flavors to wine [START_REF] Mendes-Ferreira | The production of hydrogen sulphide and other aroma compounds by wine strains of Saccharomyces cerevisiae in synthetic media with different nitrogen concentrations[END_REF].

Other sulfur compounds with unpleasant smells may be produced during the catabolism of Scontaining amino acids. Methionol, which has cauliflower and cooked cabbage aromas, is produced through the Ehrlich pathway, i.e., the transamination of methionine, decarboxylation of the resulting α-keto acid to methional and subsequent reduction [START_REF] Perpète | Methionine catabolism in Saccharomyces cerevisiae[END_REF]. Furthermore, methionine can be metabolized to methanethiol, which causes a cooked cabbage flavor [START_REF] Perpète | Methionine catabolism in Saccharomyces cerevisiae[END_REF]. The yeast cell can then detoxify methanethiol to methyl thioacetate, which has a cooked vegetable smell [START_REF] Rauhut | Usage and formation of sulphur compounds[END_REF].

Postfermentative aromas

Postfermentative aromas are formed during the maturation of wine due to chemical reactions. Varietal odorless precursors may undergo transformations, producing odor molecules. Fermentative aromas, such as esters, are hydrolysed, resulting in a modification of the fruity notes of the wine. In addition, the volatile substances of the wood diffuse into the wine during aging in oak barrels. These substances will be different depending on wood origin, manufacturing process and wine storage conditions, mainly wood composition and aging time (reviewed by [START_REF] Garde-Cerdán | Review of quality factors on wine ageing in oak barrels[END_REF].

Quantitatively, dimethyl sulfide (DMS) is the most important compound released during the maturation of wine, indicating the presence of DMS precursors in young wine. At low concentrations, it contributes towards the body of aged wines by accentuating berry fruit aromas [START_REF] De Mora | Dimethyl sulphide in some Australian red wines[END_REF][START_REF] Escudero | Analytical characterization of the aroma of five premium red wines. Insights into the role of odor families and the concept of fruitiness of wines[END_REF][START_REF] Lytra | How do esters and dimethyl sulphide concentrations affect fruity aroma perception of red wine? Demonstration by dynamic sensory profile evaluation[END_REF] and even notes of black olives and truffles [START_REF] Segurel | Contribution of dimethyl sulfide to the aroma of syrah and grenache noir wines and estimation of its potential in grapes of these varieties[END_REF]. DMS is known to confer pleasant notes to Cabernet-Sauvignon [START_REF] De Mora | Dimethyl sulphide in some Australian red wines[END_REF] and Shiraz red wines [START_REF] Segurel | Contribution des précurseurs glycosidiques et du sulfure de diméthyle des baies de Vitis vinifera L. Cv Grenache noir et Syrah à l'arôme des vins de la Vallée du Rhône[END_REF]. Furthermore, different white wines with small additions of DMS were preferred over their non-treated counterparts [START_REF] Spedding | The influence of dimethyl sulphide and carbon disulphide in the bouquet of wines[END_REF]. At higher concentrations, the aroma of DMS is described as a fault, and can impart various vegetable flavors. DMS is a characteristic odor molecule found in several raw and processed vegetables [START_REF] Buttery | Characterization of additional volatile components of tomato[END_REF][START_REF] Dignan | DMS levels in the aroma of cooked frozen sweet corn as affected by cultivar, maturity, blanching and packaging[END_REF][START_REF] Tulio | Formation of methanethiol and dimethyl disulfide in crushed tissues of broccoli florets and their inhibition by freeze-thawing[END_REF][START_REF] Ulrich | Contribution of volatile compounds to the flavor of cooked asparagus[END_REF]. It is produced during wine alcoholic fermentation from dimethylsulfoxide (DMSO) or some sulfur amino acids by yeast metabolism [START_REF] Bamforth | The role of dimethyl sulphoxide reductase in the formation of dimethyl sulphide during fermentations[END_REF][START_REF] De Mora | The formation of dimethyl sulphide during fermentation using a wine yeast[END_REF][START_REF] Hansen | Inactivation of MXR1 abolishes formation of dimethyl sulfide from dimethyl sulfoxide in Saccharomyces cerevisiae[END_REF]. However, DMSO levels in must are low [START_REF] Segurel | Contribution des précurseurs glycosidiques et du sulfure de diméthyle des baies de Vitis vinifera L. Cv Grenache noir et Syrah à l'arôme des vins de la Vallée du Rhône[END_REF] and the release of large amounts of CO2 during alcoholic fermentation eliminates most of the DMS from the liquid, accounting for the low DMS concentrations observed just after the fermentation [START_REF] Dagan | Potentiel aromatique des raisins de Vitis Vinifera L. cv. Petit Manseng Gros Manseng[END_REF]. [START_REF] Loscos | Identification of S-methylmethionine in Petit Manseng grapes as dimethyl sulphide precursor in wine[END_REF] revealed that S-methylmethionine (SMM), a methionine derivative, is the principal precursor of DMS in grapes, accounting for more than 70% of the DMS potential (pDMS). Yeast expresses the high-affinity SMM transporter Mmp1 and potentially possesses other transport mechanisms (Rouillon et al., 1999). Intracellular SMM is metabolized to methionine by the methyl-transferase Mht1 [START_REF] Thomas | Reverse methionine biosynthesis from Sadenosylmethionine in eukaryotic cells[END_REF], therefore serving as an amino acid source (Figure 11). Grape pDMS decreases during alcoholic fermentation, and only a small proportion of the pDMS is recovered in young wines. The recovery rate depends on numerous factors, such as the yeast strain, yeast assimilable nitrogen content in must and the general winemaking process [START_REF] Dagan | Le sulfure de diméthyle: quels moyens pour gérer ses teneurs dans les vins en bouteille[END_REF]. Nevertheless, the remaining pDMS leads to the release of DMS through degradation during wine aging, which is dependent on initial pDMS content and wine storage conditions, mostly storage temperature [START_REF] Marais | Effect of storage time and temperature on the formation of dimethyl sulphide and on white wine quality[END_REF]) (Figure 12). In conclusion, the composition and level of listed aroma compounds influenced by yeast strongly determines the type and style of produced wine (Figure 13). Yeast strains producing characteristic flavor profiles were selected accordingly and are commercially available.

Environmental factors affecting aroma formation

Temperature

Fermentation temperature is one of the main parameters affecting final contents of volatile compounds in wines. Temperature has a biological effect on yeast metabolism, as well as a physical effect on wine composition by causing evaporation of certain volatile compounds.

The effect of temperature on the production of higher alcohols is complex. [START_REF] Beltran | Effect of fermentation temperature and culture media on the yeast lipid composition and wine volatile compounds[END_REF] showed that the total concentration of higher alcohols (isobutanol, isoamyl alcohol and 2-phenylethanol) increases with rising temperature, whereas [START_REF] Molina | Influence of wine fermentation temperature on the synthesis of yeast-derived volatile aroma compounds[END_REF] only observed an increase in 2-phenylethanol, while other higher alcohols were decreased. When focusing on isobutanol and isoamyl alcohol, it was found that temperature has different effects on their synthesis kinetics. By using an online gas chromatograph (GC) system to determine the gas-liquid balance and kinetic parameters, [START_REF] Mouret | Kinetic analysis and gas-liquid balances of the production of fermentative aromas during winemaking fermentations: effect of assimilable nitrogen and temperature[END_REF] demonstrated that the total production of isobutanol increases with temperature, whereas production of isoamyl alcohol reaches its maximum value at 24 °C. Conversely, a negative effect of high temperatures (above 20 °C) on the final concentration of acetate and ethyl esters in wines is always observed, justifying the systematic use of low temperatures (between 15 and 20 °C) for the fermentation of white and rosé wines [START_REF] Beltran | Effect of fermentation temperature and culture media on the yeast lipid composition and wine volatile compounds[END_REF][START_REF] Molina | Influence of wine fermentation temperature on the synthesis of yeast-derived volatile aroma compounds[END_REF][START_REF] Mouret | Kinetic analysis and gas-liquid balances of the production of fermentative aromas during winemaking fermentations: effect of assimilable nitrogen and temperature[END_REF][START_REF] Rollero | Combined effects of nutrients and temperature on the production of fermentative aromas by Saccharomyces cerevisiae during wine fermentation[END_REF].

Assimilable nitrogen content

Several studies have examined the impact of nitrogen supplementation of musts on the production of volatile compounds [START_REF] Beltran | Influence of the timing of nitrogen additions during synthetic grape must fermentations on fermentation kinetics and nitrogen consumption[END_REF][START_REF] Jiménez-Martí | The nature of the nitrogen source added to nitrogen depleted vinifications conducted by a Saccharomyces cerevisiae strain in synthetic must affects gene expression and the levels of several volatile compounds[END_REF][START_REF] Mouret | Kinetic analysis and gas-liquid balances of the production of fermentative aromas during winemaking fermentations: effect of assimilable nitrogen and temperature[END_REF]. The results obtained during these various studies are difficult to bring into line and are sometimes even contradictory, resulting from differences in experimental conditions and used strains. However, the synthesis of fermentative aromas is clearly influenced by the quantity and type of assimilable nitrogen, which is present in the musts or is added during the fermentation.

As higher alcohols derive partly from the catabolism of amino acids, their levels are influenced by the content of assimilable nitrogen. However, with the exception of propanol, this relationship is not monotonous. In fact, the addition of nitrogen to a deficient must (less than 60 mgN/L) leads to an increase in the production of higher alcohols, with a peak achieved with concentrations between 200 and 300 mg/L of assimilable nitrogen. Beyond these levels, the trend reverses and the production of higher alcohols decreases when the initial concentration of nitrogen further increases [START_REF] Beltran | Influence of the timing of nitrogen additions during synthetic grape must fermentations on fermentation kinetics and nitrogen consumption[END_REF][START_REF] Jiménez-Martí | The nature of the nitrogen source added to nitrogen depleted vinifications conducted by a Saccharomyces cerevisiae strain in synthetic must affects gene expression and the levels of several volatile compounds[END_REF].

The addition of nitrogen increases the synthesis of acetate esters as well [START_REF] Beltran | Influence of the timing of nitrogen additions during synthetic grape must fermentations on fermentation kinetics and nitrogen consumption[END_REF][START_REF] Mouret | Kinetic analysis and gas-liquid balances of the production of fermentative aromas during winemaking fermentations: effect of assimilable nitrogen and temperature[END_REF], very likely because of a higher expression of genes encoding alcohol acetyltransferases that catalyze the conversion of higher alcohols to acetate esters [START_REF] Verstrepen | Expression levels of the yeast alcohol acetyltransferase genes ATF1, Lg-ATF1, and ATF2 control the formation of a broad range of volatile esters[END_REF]. A similar observation was made for the production of ethyl esters, [START_REF] Mouret | Kinetic analysis and gas-liquid balances of the production of fermentative aromas during winemaking fermentations: effect of assimilable nitrogen and temperature[END_REF].

Lipids

Sterols and fatty acids are not only essential components for maintaining the integrity of the yeast cell membrane, but they also act as precursors for the synthesis of certain aroma compounds [START_REF] Drawert | Über die Biogenese von Aromastoffen bei Pflanzen und Früchten, II. Enzymatische Bildung von Hexen-(2)-al-(1), Hexanal und deren Vorstufen[END_REF]. When exogenous unsaturated fatty acids are available in abundance, the cells incorporate them into their membranes, which results in a significant reduction of de novo fatty acid synthesis. As a consequence, the production of aroma compounds, mostly fatty acid ethyl esters, is reduced [START_REF] Yunoki | Fatty acids in must prepared from 11 grapes grown in Japan: comparison with wine and effect on fatty acid ethyl ester formation[END_REF]. These observations are confirmed by [START_REF] Saerens | Parameters affecting ethyl ester production by Saccharomyces cerevisiae during fermentation[END_REF], who showed that an addition of unsaturated fatty acids to the fermentation medium causes a reduction in the production of ethyl esters. The formation of ethyl decanoate is reduced by 50%, ethyl hexanoate by 33% and ethyl octanoate by 25%, which is sufficient to affect aromatic perception. The addition of unsaturated fatty acids decreases as well the production of acetate esters, e.g., isoamyl acetate is decreased by 32%. This is consistent with previous observations, which show that unsaturated fatty acids repress expression of ATF1 [START_REF] Fujii | Effect of aeration and unsaturated fatty acids on expression of the Saccharomyces cerevisiae alcohol acetyltransferase gene[END_REF][START_REF] Fujiwara | Transcriptional co-regulation of Saccharomyces cerevisiae alcohol acetyltransferase gene, ATF1 and Δ-9 fatty acid desaturase gene, OLE1 by unsaturated fatty acids[END_REF].

Oxygen additions

In enological fermentation, oxygen availability is low. Some oxygen is available when grape must is brought into the fermentation vessel and in the case that oxygen is added during fermentation. This addition of oxygen, either from air or directly, is a popular practice that greatly reduces the risk of a sluggish or stuck fermentation [START_REF] Blateyron | Stuck and slow fermentations in enology: statistical study of causes and effectiveness of combined additions of oxygen and diammonium phosphate[END_REF]. This technique does not affect total ester concentrations, but decreases the ratio of acetate esters to ethyl esters [START_REF] Varela | Effect of oxygen and lipid supplementation on the volatile composition of chemically defined medium and Chardonnay wine fermented with Saccharomyces cerevisiae[END_REF], which might be explained by the repression of ATF1 by oxygen [START_REF] Fujii | Effect of aeration and unsaturated fatty acids on expression of the Saccharomyces cerevisiae alcohol acetyltransferase gene[END_REF]. In agreement with this, [START_REF] Valero | Higher alcohols and esters production by Saccharomyces cerevisiae. Influence of the initial oxygenation of the grape must[END_REF] observed a higher ratio of acetate esters to higher alcohols in a must without initial oxygenation.

Effect of other nutrients

In grape must, vitamins are generally present in sufficient quantities for a successful alcoholic fermentation, but their addition is beneficial for cell growth and may play a role in the production of aroma compounds [START_REF] Hagen | Survey of biotin, pantothenic acid, and assimilable nitrogen in winegrapes from the Pacific Northwest[END_REF].

Minerals and metal ions are essential micronutrients that play important physiological roles during cell growth and alcoholic fermentation [START_REF] Pereira | The importance of metallic elements in wine. A literature survey[END_REF], but these nutritional factors are often neglected regarding their contribution to the aroma of wines [START_REF] Ibanez | Metals in alcoholic beverages: A review of sources, effects, concentrations, removal, speciation, and analysis[END_REF].

4

Other enological important metabolites

Glycerol

Excluding CO2, glycerol is the second most produced metabolite during anaerobic fermentation. Its concentration ranges from 5 -14 g/L in dry and semi-dry wines and can reach up to 25 g/L in botrytised wines, while white wines contain higher concentrations than red wines [START_REF] Nieuwoudt | Glycerol in South African table wines: an assessment of its relationship to wine quality[END_REF]. Glycerol can enhance mouthfeel and perceived sweetness of wine [START_REF] Noble | The contribution of glycerol to perceived viscosity and sweetness in white wine[END_REF], but the effect depends on wine type (higher effect in dry wines) and glycerol concentrations, with a sensory threshold of 5.2 g/L [START_REF] Gawel | The effects of ethanol and glycerol on the body and other sensory characteristics of Riesling wines[END_REF].

Glycerol plays essential roles to maintain the redox balance and to cope with osmotic stress (reviewed by [START_REF] Scanes | Glycerol production by the yeast Saccharomyces cerevisiae and its relevance to wine: a review[END_REF]. During fermentation, the production of glycerol allows to reoxidize the excess of reduced nicotinamide adenine nucleotide (NADH), which is generated during biomass formation and other NAD + -dependent metabolic reactions. The production of glycerol involves two steps: the reduction of dihydroxyacetone phosphate to glycerol-3phosphate, which is then dephosphorylated. Glycerol production is modulated by fermentation conditions, particularly heat, osmotic pressure, SO2, nitrogen content and other factors affecting growth or physiological stress [START_REF] Pigeau | Upregulation of ALD3 and GPD1 in Saccharomyces cerevisiae during Icewine fermentation[END_REF].

In recent research, focus was placed on shifting yeast metabolism from ethanol towards glycerol production (reviewed by [START_REF] Kutyna | Microbiological approaches to lowering ethanol concentration in wine[END_REF][START_REF] Tilloy | Reducing alcohol levels in wines through rational and evolutionary engineering of Saccharomyces cerevisiae[END_REF] as an answer to higher alcohol contents in wine resulting from higher sugar content in grapes as a consequence of climate change [START_REF] De Orduna | Climate change associated effects on grape and wine quality and production[END_REF]. A wine yeast strain producing lower amounts of ethanol and high amounts of glycerol, 2,3-butanediol and succinate has been developed by evolutionary engineering [START_REF] Tilloy | Reduction of ethanol yield and improvement of glycerol formation by adaptive evolution of the wine yeast Saccharomyces cerevisiae under hyperosmotic conditions[END_REF] and has been commercialized.

Organic acids

Certain organic acids are derived from grapes, such as tartaric, malic and citric acid [START_REF] Frayne | Direct analysis of the major organic components in grape must and wine using high performance liquid chromatography[END_REF], while others, such as succinic, acetic and pyruvic acid, are produced during fermentation in comparatively high concentrations [START_REF] Whiting | Organic acid metabolism of yeasts during fermentation of alcoholic beverages-a review[END_REF]. They influence the overall acid-balance of wine and can therefore contribute to wine quality and organoleptic characteristics.

Acetic acid

Acetic acid is produced by yeast (and potentially acetic acid bacteria) as a byproduct during alcoholic fermentation [START_REF] Malherbe | High-resolution mapping of meiotic crossovers and noncrossovers in yeast[END_REF]. It is quantitatively and sensorial the most important volatile organic acid in wine and accounts for more than 90 % of volatile acidity and affects wine quality [START_REF] Eglinton | The occurrence of volatile acidity in Australian wines[END_REF]. Acetic acid concentration depends on wine type and usually ranges from 0.2 -0.8 g/L. In low concentrations, it provides warmth to the palate but can provide a sourness taste and vinegary odor with exceeded levels [START_REF] Lambrechts | Yeast and its Importance to Wine Aroma -A Review[END_REF]. At concentrations greater than 0.8 g/L, it is considered undesirable [START_REF] Fleet | Wine microbiology and biotechnology[END_REF]. Acetic acid is produced as an intermediate of the pyruvate dehydrogenase (PDH) bypass, which converts pyruvate to acetyl-CoA. This pathway is the only source of cytosolic acetyl-CoA required for sterol and lipid synthesis (Pronk et al., 1996a(Pronk et al., , 1996b)). Within the PDH bypass, acetic acid is formed from acetaldehyde by aldehyde dehydrogenases (Ald4, Ald5, Ald6) with Ald6 being the major isozyme [START_REF] Saint-Prix | Functional analysis of the ALD gene family of Saccharomyces cerevisiae during anaerobic growth on glucose: the NADP+-dependent Ald6p and Ald5p isoforms play a major role in acetate formation[END_REF].

The production of acetic acid always increases rapidly at the beginning of fermentation, however, different yeast strains show variation [START_REF] Shimazu | Effects of yeast strains and environmental conditions on formation of organic acids in must during fermentation[END_REF].

Succinic acid

Succinic acid is by quantity the third most produced metabolite during wine alcoholic fermentation. It is present in concentrations of 0.6 -1.2 g/L and contributes significantly to wine acidity. Succinic acid is derived from the TCA pathway. During anaerobiosis, this pathway functions as two branches (reductive and oxidative route) and succinic acid is mainly produced by the reductive branch during wine fermentation [START_REF] Camarasa | Investigation by 13C-NMR and tricarboxylic acid (TCA) deletion mutant analysis of pathways for succinate formation in Saccharomyces cerevisiae during anaerobic fermentation[END_REF].

Pyruvic acid

Pyruvic acid is an important metabolic intermediate in alcoholic fermentation. It is for a large part decarboxylated to acetaldehyde by the pyruvate decarboxylase. Pyruvic acid has the ability to bind SO2 [START_REF] Rankine | Influence of yeast strain on binding of sulphur dioxide in wines, and on its formation during fermentation[END_REF], therefore influencing its germicidial power for which SO2 is widely used in winemaking. The levels of pyruvic acid are initially low and increase with fermentation duration [START_REF] Morata | Pyruvic acid and acetaldehyde production by different strains of Saccharomyces cerevisiae: relationship with vitisin A and B formation in red wines[END_REF]. Final levels of pyruvic acid are dependent on fermenting strain and pH of the must [START_REF] Rankine | Influence of yeast strain on binding of sulphur dioxide in wines, and on its formation during fermentation[END_REF].

Deciphering complex traits

Phenotypic variations between individuals can be either characterized as qualitative or quantitative. Qualitative traits fit into discrete categories. They are shaped by a single locus or a small number of loci. In contrast, quantitative phenotypic traits present continuous ranges of variation and are typically controlled by several genomic regions, called quantitative trait loci or QTLs [START_REF] Valdar | QTL fine-mapping with recombinant-inbred heterogeneous stocks and in vitro heterogeneous stocks[END_REF]. A QTL may contain a single gene or a group of linked genes, which influence the shape of the quantitative trait [START_REF] Mackay | The genetic architecture of quantitative traits[END_REF].

The mapping of QTLs in the genome was first established in the twenties of the last century, with the study of [START_REF] Sax | The association of size differences with seed-coat pattern and pigmentation in Phaseolus vulgaris[END_REF] on the genetic analysis of the size of beans, associated with the color of pigmentation. This concept was then set up in plants [START_REF] Lindstrom | A genetic linkage between size and color factors in the tomato[END_REF] and subsequently in other organisms, especially S. cerevisiae [START_REF] Steinmetz | Dissecting the architecture of a quantitative trait locus in yeast[END_REF][START_REF] Deutschbauer | Quantitative trait loci mapped to single-nucleotide resolution in yeast[END_REF]. As it is one of the main model organisms, the yeast has recently become an important model for elucidating the mechanisms that govern natural genetic and phenotypic variation. This success is partially due to its intrinsic biological features, such as the short sexual generation time, high meiotic recombination rate, and small genome size [START_REF] Malherbe | High-resolution mapping of meiotic crossovers and noncrossovers in yeast[END_REF]. The development of DNA-microarray technology and second-generation sequencing methods enabled the fast and inexpensive genome-scale genotyping of yeast. This resulted in the generation of fully genotyped yeast crosses of homozygous strains that have been made available for QTL mapping [START_REF] Cubillos | Generation of a large set of genetically tractable haploid and diploid Saccharomyces strains[END_REF].

Principle of QTL mapping

QTL mapping is performed with two strain lines of an organism capable of sexual reproduction. It consists in linking the genomic properties of a progeny of these strains, which are obtained by a genotyping step, to phenotypes of interest. The mapping can be divided into several steps, the crossing of two parental strains selected for their specific phenotypes, the sporulation of the cross, the genotyping of the daughter cells and the phenotyping of these strains for traits of interest. With this information, genomic regions that have an impact on the phenotypic traits can be detected and by dissecting these regions, alleles responsible for trait variation can be identified (Figure 14).

Establishment of a population of segregants

QTL mapping starts with the crossing of two parents, who may differ phenotypically in a trait of interest. However, this is not necessary as extreme differences in traits are often explained by major, but rare, loss of function alleles. Instead, crosses can be performed using the knowledge of population structure [START_REF] Liti | Mapping quantitative trait loci in yeast[END_REF]. After crossing, the sporulation of the diploid zygote leads to the production of segregants, the individuals of the F1 progeny, with genomic properties that are a mixture of the parent one's. The genetic diversity of the daughter generation is the result of random chromosomal recombination events during meiosis (~90 events per meiosis determined by [START_REF] Malherbe | High-resolution mapping of meiotic crossovers and noncrossovers in yeast[END_REF]. It is possible to further increase the recombination rate by crossing the F1 strains among each other to produce a second generation of segregants (F2) and eventually more (up to F13 for the production of a highly recombinant population by [START_REF] García-Ríos | The genetic architecture of low-temperature adaptation in the wine yeast Saccharomyces cerevisiae[END_REF]. This increases the resolution of the QTL mapping by shortening the genomic blocks that are inherited by each parent strain. The number of individuals constituting the evaluated population is another important criterion. A higher number of individuals increases the sensitivity of the analysis, as it strengthens the statistical power of the linkage analysis [START_REF] Liti | Mapping quantitative trait loci in yeast[END_REF]. Furthermore, a high number of segregants increases the probability of obtaining strains with extreme phenotypes, which can be advantageous for certain QTL mapping strategies, like bulk segregant analysis [START_REF] Brauer | Mapping novel traits by arrayassisted bulk segregant analysis in Saccharomyces cerevisiae[END_REF].

Phenotyping of segregant population

During the phenotyping of the segregant population for the traits of interest, the reproducibility of the measurements is critical. If the selected traits are strongly influenced by environmental factors, or if the phenotypic determination is not reproducible enough, it can hinder the statistical analysis. The distribution of the phenotype gives an indication of the complexity of a trait. For example, the share of offspring individuals with the same character as the parent strains can give an idea about the number of alleles theoretically involved in the phenotypes [START_REF] Lynch | Genetics and analysis of quantitative traits[END_REF]. The distribution of the phenotype often follows one of the three main distribution laws, the Gaussian distribution, L-distribution or continuous distribution. The continuous and L-distribution correspond to the involvement of genes with major effects, while the Gaussian distribution is more characteristic for a phenotype resulting from the presence of multiple genes with minor effects [START_REF] Griffiths | Modern genetic analysis: integrating genes and genomes[END_REF].

Genetic markers and genotyping

The QTL mapping requires the use of genetic markers -polymorphic deoxyribonucleic acid (DNA) sequences, which determine the parental origin of a genomic region. The identification of markers and the assembly of a genetic map is therefore a key step in the search for QTLs.

The most widely used genetic markers are single nucleotide polymorphisms (SNPs) and the developments of new genome sequencing technologies have open the way for high density and even full genome genotypic maps, in which almost all the genetic variation between parents can be used. Two strategies can be chosen. For the first one, developed for plants by [START_REF] Michelmore | Identification of markers linked to diseaseresistance genes by bulked segregant analysis: a rapid method to detect markers in specific genomic regions by using segregating populations[END_REF] and called bulk segregant analysis, only those individuals that have the most extreme phenotypes are selected and genotyped as a group of individuals. For the second strategy, all individuals are genotyped, which gives the highest information on genetic diversity.

Linkage analysis

When the phenotyping and genotyping steps have been completed, linkage analysis consists in establishing a correlation between the presence of certain alleles and the evaluated quantitative phenotype. If the mean phenotype of segregants with allele A1 is different from those carrying allele A2 at locus A, this locus contains a QTL. Therefore, the simplest method is a variance analysis at each locus (Figure 15). The analytical power of this method is weakened by missing data and a low marker density in the region of the QTL of interest. A more complex methods is interval mapping method [START_REF] Lander | Mapping mendelian factors underlying quantitative traits using RFLP linkage maps[END_REF], which is based on maximum likelihood parameter estimation and uses the information of markers flanking a position to determine the presence of QTLs along the genome and to evaluate a correlation between markers and the phenotype. Regression interval mapping approxes maximum likelihood interval mapping to reduce computation time [START_REF] Haley | A simple regression method for mapping quantitative trait loci in line crosses using flanking markers[END_REF]. The principle of multiple QTL research is to scan the genome for influences on the phenotype, but starting with a predefined assumption. For mapping double QTLs, an already detected single QTL is set as an explanatory variable in the model and a second QTL analysis is redone on the entire genome in the light of this additional variable. For detecting multiple QTLs, the number of QTLs set a priori as additional variables is not stated. Starting from a multiple regression model, the genome is perused step by step, while only markers with an influence on the model are kept [START_REF] Jansen | Interval mapping of multiple quantitative trait loci[END_REF][START_REF] Zeng | Theoretical basis for separation of multiple linked gene effects in mapping quantitative trait loci[END_REF].

In general, the statistical power of the linkage analysis depends on the number of segregants and the quality of the marker map.

Dissecting a QTL and verifying candidate genes

The dissection of a QTL consists in reducing the size of the genetic region identified by the mapping step, and thus identifying the gene or genes present in this region, which have an influence on the examined trait. This step requires the use of databases that provide an annotation of the genes located in this region. The biological function of the genes is evaluated and genes with a potential link to the assessed trait are selected as target genes to verify the QTL. The complexity of this step is increased with the size of the identified region. The larger the genomic region, the more genes have potentially to be assessed. In a first step, the nucleotide sequence of each target gene is compared between the parental strains to determine polymorphisms, especially ones that result in a change of the protein's promoter sequence or amino acid chain. Subsequently, the impact of each target gene variant on the phenotype is tested, e.g. by reciprocal hemizygosity analysis (RHA) or allele swap (Figure 16) [START_REF] Warringer | Yeast reciprocal hemizygosity to confirm the causality of a quantitative trait loci-associated gene[END_REF]. For RHA, a gene or allele is deleted in a heterozygous background, leading to hemizygosity of that sequence. By comparing two heterozygous strains with different hemizygous alleles, the impact of these alleles on the phenotype can be assessed. The impact of target gene variants on the assessed trait can also be validated by exchanging the corresponding DNA sequences between the parent cells. The strains constructed in this way are compared with the corresponding wild type parent strains to assess the change in phenotype caused by the swapped alleles.

Industrial and enological applications of QTL mapping

Numerous QTL analyses have already been performed in yeast. This includes studies of genes involved in general yeast characteristics, e.g., high-temperature growth [START_REF] Sinha | Complex genetic interactions in a quantitative trait locus[END_REF], sporulation [START_REF] Nogami | Genetic complexity and quantitative trait loci mapping of yeast morphological traits[END_REF] and stress tolerance [START_REF] Brauer | Mapping novel traits by arrayassisted bulk segregant analysis in Saccharomyces cerevisiae[END_REF]. Furthermore, an increasing number of QTL studies is carried out on industrial traits, such as traits important for, but not limited to, bio-ethanol production [START_REF] Hu | Genetic dissection of ethanol tolerance in the budding yeast Saccharomyces cerevisiae[END_REF]Hubmann et al., 2013aHubmann et al., , 2013b;;[START_REF] Pais | Comparative Polygenic Analysis of Maximal Ethanol Accumulation Capacity and Tolerance to High Ethanol Levels of Cell Proliferation in Yeast[END_REF][START_REF] Swinnen | Genetic determinants for enhanced glycerol growth of Saccharomyces cerevisiae[END_REF]. In addition to that, the application of QTL mapping has shown to be a powerful tool for deciphering the genomic bases of enological traits. From its first use to map acetic acid production during wine fermentation [START_REF] Marullo | Single QTL mapping and nucleotide-level resolution of a physiologic trait in wine Saccharomyces cerevisiae strains[END_REF] it has recently led to a better understanding of the genomic bases influencing traits such as nitrogen source utilization (Brice et al., 2014a;[START_REF] Cubillos | Identification of nitrogen consumption genetic variants in yeast through QTL mapping and Bulk segregant RNA-seq analyses[END_REF][START_REF] Gutiérrez | Genetic basis of variations in nitrogen source utilization in four wine commercial yeast strains[END_REF][START_REF] Jara | Mapping genetic variants underlying differences in the central nitrogen metabolism in fermenter yeasts[END_REF], sulfite production [START_REF] Noble | Identification of new Saccharomyces cerevisiae variants of the MET2 and SKP2 genes controlling the sulfur assimilation pathway and the production of undesirable sulfur compounds during alcoholic fermentation[END_REF], the production of main metabolites as well as leaving of residual sugars [START_REF] Salinas | The genetic basis of natural variation in oenological traits in Saccharomyces cerevisiae[END_REF] and transcriptomic variation during fermentation [START_REF] Ambroset | Deciphering the molecular basis of wine yeast fermentation traits using a combined genetic and genomic approach[END_REF][START_REF] Brion | Differential adaptation to multi-stressed conditions of wine fermentation revealed by variations in yeast regulatory networks[END_REF]. The formation of wine aroma is a quantitative and complex trait, as it is dependent on several genes, and recent QTL studies were also carried out to decipher the underlying genomic bases [START_REF] Ambroset | Deciphering the molecular basis of wine yeast fermentation traits using a combined genetic and genomic approach[END_REF][START_REF] Steyer | QTL mapping of the production of wine aroma compounds by yeast[END_REF].

The identification of QTLs and the characterization of alleles with different influence on the phenotype can also lead to the determination of molecular markers, which are then used for the selection or generation of new strains. As example, [START_REF] Ambroset | Deciphering the molecular basis of wine yeast fermentation traits using a combined genetic and genomic approach[END_REF], who used a cross between the lab strain S288C and a spore of the widely used winemaking strain EC1118, identified the gene ABZ1 to be responsible for a QTL affecting fermentation rate. Using the same segregant population, [START_REF] Steyer | QTL mapping of the production of wine aroma compounds by yeast[END_REF] mapped QTLs for aroma production in synthetic must and showed that the characterized variant of ABZ1 also influences the production of 2-phenylethyl alcohol. Other QTL studies detected variants of MET2 and SKP2, responsible for differences in the production of SO2, H2S and acetaldehyde [START_REF] Noble | Identification of new Saccharomyces cerevisiae variants of the MET2 and SKP2 genes controlling the sulfur assimilation pathway and the production of undesirable sulfur compounds during alcoholic fermentation[END_REF]. [START_REF] Salinas | The genetic basis of natural variation in oenological traits in Saccharomyces cerevisiae[END_REF] used crosses from divergent yeast strains (wine, North American, West African and sake) and mapped several fermentation traits. They found variants affecting enological traits, such as acetic acid production (ALD6, subtelomeric region of chr II-R), residual sugar concentration (MBR1, HAP4, YJR030C), succinic acid production (FLX1, MDH2) and glycerol production (YFL040W, GAT1).

Materials and Methods

1 Media

Yeast culture medium

Yeast was cultured in Yeast extract Peptone Dextrose (YPD) medium at 28 °C under shaking. The composition of the medium is given in Table 3. Depending on the used resistance, selective YPD media contained 200 µg/mL geneticin (G418), 200 µg/mL nourseothricin (clonNAT) or 200 µg/mL hygromycin B respectively.

Micromanipulation media

The sporulation and dissection of heterozygous cells, which was carried out according to [START_REF] Codon | Factors which affect the frequency of sporulation and tetrad formation in Saccharomyces cerevisiae baker's yeasts[END_REF], relies on the use of different media. The presporulation medium PRE5 allows the cells to reach the exponential phase. The SPO2 medium is a poor environment, leading to a state of nutritional deficiency, causing sporulation of the cells. The individual spores are deposited on YPD micromanipulation solid medium. The characteristic of this medium is its formulation with ultra-pure agar that is low in impurities, which could hinder the visualization of the asci. The media needed for cell sporulation are described in Table 4.

Synthetic must

Fermentations were carried out in synthetic must (SM) that simulates standard grape juice [START_REF] Bely | Automatic detection of assimilable nitrogen deficiencies during alcoholic fermentation in oenological conditions[END_REF]. This medium is characterized by high equimolar concentrations of glucose and fructose, a limiting content of nitrogen and an acid pH of 3.3. Ammonia nitrogen and amino acid nitrogen are added as assimilable nitrogen sources. SM200 with 200 g/L of sugars, 200 mg/L of assimilable nitrogen and 5 mg/L of anaerobic factors (phytosterols) was used for this thesis. The composition of the medium and its components are given in Table 5 to Table 10. For mimicking grape derived aroma precursors, 2.0 mg/L geraniol and 500 µg/L Smethylmethionine (D/L-methionine methylsulfonium chloride) were added to the medium.

SM was stored at -20 °C.

Yeast strains

The haploid S. cerevisiae strains MTF2621 (4CAR1 [ΔHO::Neo r ]) and MTF2622 (T73 [ΔHO::Nat r ]) were selected for the study according to their different need for nitrogen during wine fermentation. This was shown by using an approach based on the addition of nitrogen to keep the CO2 production rate constant during nitrogen limitation (Brice et al., 2014b). The strain 4CAR1 belongs to the group of champagne strains, which originated through crossings between strains of the wine clade and the flor clade. The strain T73, however, belongs to the phylogenetic clade of wine strains. For further analyses both strains were mated to form the parental heterozygote MTF2784.

Molecular biology techniques

DNA extraction

Yeast genomic DNA for sequencing was extracted using the MasterPure Yeast DNA Purification Kit (epicentre). To extract the DNA, 1.5 mL of an overnight yeast culture were transferred to a microcentrifuge tube and pelleted in a microcentrifuge for 5 min at 13000 rpm. The supernatant was removed and the pellet was resuspended in 300 µL of Yeast Cell Lysis Solution. Then, 1 µL of a 5 µg/µL RNase A solution was added to the tube, the suspension was vortexed and incubated at 65 °C for 15 min. The sample was placed on ice for 5 min, 150 µL of MPC Protein Precipitation Reagent were added and the mixture was vortexed for 10 sec. Cellular debris was pelleted by centrifugation in a microcentrifuge at 13000 rpm for 10 min.

The supernatant was transferred to a clean microcentrifuge tube, 500 µL of isopropanol were added and the mixture was mixed by inversion. The DNA was pelleted by centrifugation in a microcentrifuge at 13000 rpm for 10 min and the supernatant was removed by pipetting. The pellet containing the DNA was washed with 0.5 mL of 70% ethanol. Then, the ethanol was carefully removed by pipetting and the microcentrifuge tube was left open at room temperature to evaporate any remaining ethanol. Finally, the DNA was suspended in 35 µL of TE buffer and stored at -20 °C.

Yeast genomic DNA for further analysis was extracted according to the protocol of [START_REF] Lõoke | Extraction of genomic DNA from yeasts for PCRbased applications[END_REF]. One yeast colony was picked from an agar plate and suspended in 100 µL lysis buffer (200 mM LiOAC, 1% SDS). The solution was incubated for 15 min at 70 °C, 300 µL of 96% ethanol were added and the mixture was vortexed. The cell debris was spinned down in a microcentrifuge for 5 min at 13000 rpm and the pellet was subsequently washed with 70% ethanol. The pellet was spinned down in a microcentrifuge for 5 min at 13000 rpm, the ethanol was removed by pipetting and remaining ethanol was evaporated using a vacuum centrifuge. Then, the pellet was dissolved in 80 µL MilliQ water and the cell debris was spinned down in a microcentrifuge for 15 sec at 13000 rpm.

DNA amplification by polymerase chain reaction

Polymerase chain reaction (PCR) allows the amplification of a defined DNA strand, e.g. a genomic region. For this thesis, PCR was used for the construction of transformation cassettes and for the verification of performed transformations. The PCRs were carried out in a thermocycler with a heating lid (T3 thermocycler, Biometra or Mastercycler pro, Eppendorf). Two different polymerase enzymes were used for the PCR, the high fidelity Phusion

Polymerase (New England Biolabs) and the low fidelity Taq Polymerase (Thermo Scientific).

The compositions of the PCR mixes are given in Table 11. The PCR programs were adapted to the chosen enzyme, to the hybridization temperature of the used primers and to the size of the fragment to be amplified. In general, the programs consist of an initial denaturation phase at 95 °C for 5 min, 30 -35 cycles including a denaturation phase between 95 °C and 98 °C for 20 sec to 1 min, a hybridization phase between 52 °C and 70 °C for 20 to 30 sec and an elongation phase at 72 °C for 30 sec to 5 min.

The PCR program ends with a terminal elongation phase at 72 °C for 5 to 10 min. All primers were synthesized by MWG (Eurofins MWG Operon).

DNA purification

DNA samples obtained by PCR were purified using the NucleoSpin Gel and PCR Clean-up kit (Macherey-Nagel). One volume of sample was mixed with 2 volumes of Buffer NTI and 700 µL were loaded into a NucleoSpin Gel and PCR Clean-up Column placed into a collection tube (2 mL). The mixture was centrifuged in a microcentrifuge at 13000 rpm for 30 sec, the flowthrough was discarded and the column was placed back into the collection tube. Then, 700 µL of Buffer NT3 were added to the column. The mixture was centrifuged at 13000 rpm for 30 sec, the flow-through was again discarded and the column was placed back into the collection tube. In order to remove Buffer NT3, the mixture was centrifuged at 13000 rpm for 1 min. Then, the column was placed into a new 1.5 mL microcentrifuge tube. After addition of 15-30 µL 70 °C warm Buffer NE, the tubes were incubated at room temperature for 1 min. Subsequently, the tubes were centrifuged at 13000 rpm for 1 min to eluate the DNA solution.

The elution step was repeated to increase the DNA recovery rate.

Determination of DNA quantity and quality

After DNA extraction or PCR, the quality of the DNA was measured by spectrophotometry with a Nanodrop device (Nanodrop 1000, Thermo Scientific). The spectrophotometer gives certain wavelength signal ratios, which allows the detection of possible contaminations of RNA (260/280 nm higher than 2) and phenol (260/230 nm higher than 1.8).

DNA quantity was determined by fluorometric assay with the Quantus Fluorometer (Promega), using the QuantiFluor dsDNA system (Promega). First the QuantiFluor dsDNA Dye was diluted in 1:400 TE buffer to make the dye working solution. Then 1 µL of unknown DNA sample was added to 200 µL dye working solution into a 0.5 mL PCR tube. The samples were incubated at room temperature for 5 min and the fluorescence of the samples, indicating the DNA concentration, was measured.

Agarose gel electrophoresis

The validation of a PCR was performed by agarose gel electrophoresis, which allows the visualization of an amplification product and the determination of its size. The agarose gel consists of 0.8% agarose dissolved in 1 × TBE buffer (90 mM boric acid, 2 mM EDTA, 90 mM pH 8 Tris), with the addition of 0.2 μg/mL ethidium bromide. The PCR products were supplemented with a loading buffer, containing glycerol to increases the density of the sample and bromophenol blue to visualize its migration. A size marker (1 kb or 100 bp) was run along with each gel and allows the size determinations of the PCR products by comparing the migration distances. Gel electrophoresis was carried out in an electrophoresis device by submerging the gel in TBE buffer and applying a voltage of 120 V for 30 -40 min. After migration, the ethidium bromide, which intercalates with the DNA, allowed the visualization of DNA by fluorescence under UV light. The gel was photographed subsequently (Kodak Gel Logic 100 Imaging system).

DNA sequencing

Genomic DNA samples were submitted to the sequencing platform Genotoul (Toulouse, France) and sequenced using the Illumina technology (paired end, 2 x 100 bp, HiSeq 2500) at a sequencing depth of 20-80 fold.

Yeast transformation

Yeast transformation was carried out according to the lithium acetate transformation method described by [START_REF] Gietz | Studies on the transformation of intact yeast cells by the LiAc/ss-DNA/PEG procedure[END_REF]. Cells to transform were grown overnight in 5 mL of YPD. The optical density (OD) of the culture was determined and 50 mL of YPD were inoculated to an OD of 0.5. The suspensions were grown until the exponential phase was reached, which corresponds to an OD of 2.0 -2.2, to ensure maximal transformation efficiency. The cells were recovered by centrifugation (1960 × g for 5 min), washed with 40 mL of TRIS buffer (10 mM Tris HCl, pH 7.5), resuspended in 50 mL of lithium acetate buffer (0.1 M lithium acetate in 10 mM Tris HCl, pH 7.5) and incubated for 40 min at room temperature under gentle shaking.

After centrifugation (1960 × g for 5 min), the cells were resuspended in 2.2 mL of lithium acetate buffer. In a 13 mL round cap tube, 100 μL of the cell suspension were mixed with 10 μL of salmon sperm DNA (denatured at 100 °C for 20 min and cooled in ice) and 1 -3 µg of the transformation cassette. A second tube without the addition of transforming DNA was prepared as a negative control. After incubation for 10 min at room temperature, 300 μL PEG4000 solution (50 % w/v in TRIS buffer, sterile filtered) were added. The mixture was incubated for 10 min at room temperature. Subsequently a thermal shock was applied by submersion of the tubes in a water bath of 42 °C for 15 min. The thermal shock stimulates DNA uptake into the cells. Afterwards, the mixture was centrifuged (430 × g for 3 min), and the cell pallet was resuspended in 0.5 mL of YPD and incubated overnight without shaking. The cells were then centrifuged (1960 × g for 3 min), resuspended in TRIS buffer and spread on selective solid medium in two concentrations (1/10 and 9/10). The plates were incubated and grown colonies were isolated and verified by PCR.

Yeast sporulation and crossing

Microdissection of asci

The dissection and isolation of yeast asci was carried out using an MSM300 micromanipulator (Singer Instruments, UK). In order to obtain yeasts in the state of sporulation, a diploid yeast strain was transferred to 5 mL of YPD medium and grown overnight. Afterwards, the OD of the culture was determined and 25 mL of PRE5 medium were inoculated with the cells to an OD of 0.05. The culture was incubated for 16 h, the cells were washed with SPO2 medium and 10 mL of SPO2 medium were inoculated with the cells to an OD of 0.1 -0.3. During incubation of the suspensions at room temperature and under gentle shaking, the rate of ascus formation was followed microscopically. When sporulation sufficiently took place (5 to 10 days), asci were treated with 0.2 mL of β-glucuronidase solution (15000 U/mL) for 15 to 30 min, resulting in a deterioration of the walls. 2 -5 µL of cell suspension were spread on a seed strip at the side of the micromanipulation plate. The micromanipulation system was equipped with a needle that manually and individually retrieves the different spores of the same ascus and positions them on a grid, which served as a visual reference. Spore germination was stimulated by incubation for 48 to 72 h at 28 °C. The obtained colonies were subcultured onto solid YPD medium.

Yeast crossing and determination of mating types

Parental heterozygotes and F1 intercrosses were constructed by resuspending cell material of two haploid spores with different antibiotic resistances in 0.5 mL of YPD medium. The suspension was incubated for 4 -8 h and 2 μL of the culture were dropped on double selective solid medium. Only mated heterozygotes with both antibiotic resistances were able to grow after incubation.

The mating type of haploid cells was also determined by yeast crossing. One spore of unknown mating type but known antibiotic resistance was resuspended in 0.5 mL of YPD medium, together with one of two reference strains with known mating type (a and α) and a different antibiotic resistance. The suspension was incubated for 4 -8 h and 2 μL of the culture were dropped on double selective solid medium. Only spores with the opposite mating type of the used reference strain were able to mate and therefore grow after incubation.

Implementation of fermentations and monitoring of fermentation kinetics

The fermentations were carried out in glassware mini-fermenters of 300 mL, filled with 280 mL of SM200. The fermenters were equipped with water-filled airlocks to maintain anaerobiosis for the major part of the fermentation. Yeast strains were grown overnight in a shaking flask with 25 mL of YPD. The cell density was determined and the fermenters were inoculated to a cell density of 1.0 × 10 6 cells/mL. The fermentations were kept at a constant temperature of 24 °C under continuous magnetic stirring at 150 rpm. Fermentation progress was followed by the release of CO2, which was determined by regularly measuring the weight loss, using a scale.

To facilitate the monitoring of the fermentation, a custom built robot (PlateButler) was used for parts of the experiments, which allowed the automatic weight measurement for up to 90 300-mL fermenters in 60 minute frequencies. The fermentation parameters were calculated and saved in a common data base. Cell numbers and sizes were evaluated using an automated cell counter (Counter ZB-2, Beckman Coulter). Cell counting and size determination is based on the detection and measurement of changes in the electrical resistance produced by non-conductive particles. As a diluted suspension of cells in an electrolyte (Isoton solution, Beckman Coulter) is sucked through a small gap of 100 μm, the passage of each individual cell momentarily increases the electrical resistance between two electrodes, which are located on each side of the aperture.

The volume of electrolyte displaced by each particle is measured in the form of a voltage pulse, whose height is proportional to the volume of the particle. The samples were diluted beforehand to remain within the linear range of the measurement between 20000 and 80000 cells/mL. Cell aggregates were disrupted by sonication, using an ultrasonic generator (Branson Sonifier,model 250). This measure allowed us to determine the number of cells per mL for a cell solution and the average cell volume (μm 3 ).

Dry matter

The dry weight was determined in duplicate by filtering 10 mL (V) of a cell culture through a nitrocellulose membrane with a porosity of 0.45 μm (Millipore, France) and known dry weight (P1). The membrane was rinsed twice with 10 mL of distilled water and dried for 48 h on a plate of known weight (P2) at 110 °C, in order to evaporate the water. Subsequently, the plate with the filter membrane was weighed (P3). The dry weight of the culture was calculated according to the formula:

Dry weight (g/L) = (P3 (g) -(P2 (g) + P1 (g)) / V (L)

Determination of extracellular carbon compounds

The determination of remaining sugar substrates (glucose, fructose) and produced extracellular carbon metabolites (ethanol, glycerol, acetic acid, succinic acid, pyruvic acid, αketoglutaric acid) was carried out by high performance liquid chromatography (HPLC) [START_REF] Camarasa | Phenotypic landscape of Saccharomyces cerevisiae during wine fermentation: evidence for origin-dependent metabolic traits[END_REF]. The flow rate of the HPLC (HPLC 1290 Infinity, Agilent Technologies, Santa Clara, California, USA) was set to 0.6 mL/min. Samples were separated by a pre-column and an ion-exclusion column (Phenomenex REZEX™ ROA-Organic Acid H+ (8%)), which was thermostatically controlled at 60 °C. The detection of the compounds was carried out using a refractometer in combination with a UV spectrometer at 210 nm. Chromatograms were processed by Agilent EZChrom software.

To prepare the samples, the cell culture was centrifuged for 5 min at 1360 × g. The supernatant was diluted 1/6 in the mobile phase, a degassed 0.005 N sulfuric acid solution. 25 μL of sample volume were injected automatically into the HPLC.

Determination of nitrogen compounds

Amino acids were derivatized with ninhydrin, which forms a purple compound detectable at 570 nm proportional to the concentration of the amino acid. Proline and hydroxiproline, which do not possess an amine function but an imine function, form a yellow compound detectable at 440 nm. The obtained spectra were analyzed with the Biochrom software.

After elimination of the cells by centrifugation, molecules with high molecular weights, like remaining proteins, were precipitated by adding 4 volumes of sample to one volume of 25% (w/v) sulfosalicylic acid, containing 2500 nmol/mL norleucine as internal standard. The mixture was incubated for 1 h at 4°C and centrifuged for 10 min at 4°C and 1960 × g. Samples were then filtered through a 0.22 μm Millipore nitrocellulose membrane. Amino acids were separated by liquid chromatography on an ion-exchange column (Ultrapac-8 lithium form; Amersham Pharmacia Biotech) on Biochrom HPLC (Serlabo, France) and a temperature gradient from 32 °C to 75 °C, by using several buffers with a pH from 2.8 to 3.55 and a counterion concentration of lithium citrate from 200 to 1650 mM (Table 12, Serlabo, France). The column was then regenerated at 75 °C with a lithium hydroxide buffer of 300 mM. For quantification, a physiological standard was used. This standard contains one volume of acidic and neutral amino acids (A6407, Sigma-Aldrich, France), one volume of basic amino acids (A1585, Sigma-Aldrich, France), one volume of glutamine at 2500 nmol/mL and two volumes of lithium citrate. The standard was then processed as described above.

Determination of volatile compounds

Determination of fermentative aromas

Fermentative aroma samples were analyzed with a HP 6890 GC (Agilent Technologies, Santa Clara, California, USA) equipped with a CTC Combi PAL Autosampler Appl Microbiol Biotechnol AOC-5000 (Shimadzu, Columbia, USA) and coupled to a HP 5973 mass spectrometry (MS) detector (Agilent Technologies, Santa Clara, California, USA). The instrument was controlled and the data analyzed with the HP G1701DA ChemStation software (Agilent Technologies, Santa Clara, California, USA). The GC was fitted with a 60 m × 0.25 mm Phenomenex fused silica capillary column DB-WAX with 0.25 μm film thickness (Agilent Technologies, Santa Clara, California, USA). The carrier gas was helium, linear velocity was 36 cm/s and the flow rate 1.0 mL/min in constant flow mode. The initial oven temperature was 40 °C for 3 min. The temperature was increased by 4 °C/min until it reached 220 °C, and was held at this temperature for 20 min. The injector and the transfer line were held at 250 °C. The sample volume injected was 2 μL, and the splitter was opened after 30 sec, at 10:1. The focus liner (Agilent Technologies, Santa Clara, California, USA) was deactivated and tapered with glass wool (2-4mm). The mass spectrometer quadrupole temperature was set at 150 °C, the source was set at 230 °C, and the transfer line was held at 250 °C. For quantification, mass spectra were recorded in Selected Ion Monitoring (SIM) mode with positive ion electron impact at 70 eV. The ions monitored in SIM runs are shown in Table 13. For each compound, the ion marked in bold was typically used for quantification, as it had the best signal-to-noise ratio and the least interference with other wine components. The other ions were used as qualifiers. The deuterated standard solution used for the quatification of fermentative aromas contained 100 μg/mL of ethyl-d5 butanoate, 100 μg/mL of d5-ethyl hexanoate, 100 μg/mL of d5-ethyl octanoate, 100 μg/mL of d7-butyric acid, 100 μg/mL of d5-ethyl decanoate and 100 μg/mL of d4-phenyl ethanol. For the preperation of standard curves, a synthetic wine (12 % ethanol, 6 g/L malic acid, pH 3.3) was spiked with known concentrations of esters, higher alcohols and organic acids to measure. 10 μL of the deuterated standard solution were added to 5 mL of the synthetic wine in a 15 mL pyrex tube with a Teflon stopper (Supelco, Bellefonte, Pennsylvania, USA). Subsequently, 1 mL of dichloromethane was added and the mixture was gently shaken for 20 min on a shaking table. The samples were then centrifuged for 5 min at 1960 × g and 4 °C. The organic phase was recovered and the extraction was repeated once with the remaining aqueous phase. The resulting 2 mL of organic phase were dried with anhydrous sodium sulfate to remove all traces of the aqueous phase. Subsequently, the organic phase was transferred to a new vial and was concentrated through evaporation under nitrogen flow to a final volume of 0.5 mL. The concentrated sample was transferred to a concentrator insert (Supelco, Bellefonte, Pennsylvania, USA). New calibration standards were prepared with every set of fermentation samples to be measured. For the measurement of fermentation samples, the suspensions were centrifuged for 5 min at 1960 × g and 4 °C. The supernatants were treated as described above.

Determination of terpenols

For the analysis of terpenol composition, the samples were extracted by solid phase micro extraction (SPME) and measured via GC-MS [START_REF] García | Systematic optimization of the analysis of wine bouquet components by solid-phase microextraction[END_REF]. A Thermo Scientific Trace GC Ultra (Thermo Scientific, Waltham, Massachusetts, USA) equipped with a CTC Combi PAL Autosampler Appl Microbiol Biotechnol AOC-5000 (Shimadzu, Columbia, USA) and coupled to a Thermos Scientific ESQ™ single quadrupole MS detector (Thermo Scientific, Waltham, Massachusetts, USA) was used. The instrument was controlled and the data analyzed with the Thermo Xcalibur™ software (Thermo Scientific, Waltham, Massachusetts, USA). The GC was fitted with a 60 m × 0.25 mm Phenomenex fused silica capillary column DB-WAXTR with 0.25 μm film thickness (Agilent Technologies, Santa Clara, California, USA). The carrier gas was helium with a flow rate of 1.0 mL/min in constant flow mode. The injector was held at 270 °C, the source was set at 200 °C and the transfer line was held at 250 °C. The initial oven temperature was 40 °C PDMS/DVB/carboxen fiber was injected and the temperature was held for 5 min. Subsequently, the temperature was increased by 4 °C/min until it reached 180 °C and was then increased by 10 °C/min until it reached 240 °C. This temperature was held for 5 min. For quantification, mass spectra were recorded in Scan mode with positive ion electron impact at 70 eV. The ions monitored in Scan runs are shown in Table 14. For each compound, the ion marked in bold was typically used for quantification, as it had the best signal-to-noise ratio and the least interference with other compounds. The other ions were used as qualifiers.

In the case of cis-rose oxide, geranyl acetate and citronellol, total ion current was used instead of qualifier ions. The deuterated standard solution used for the quantification of terpenols contained 8.35 μg/mL of d2-geraniol/d2-nerol and 0.835 μg/mL of d5-linalool. For the preperation of standard curves, a synthetic wine (12 % ethanol, 6 g/L malic acid, pH 3.3) was spiked with known concentrations of terpenols to measure. In a 20-mL glass flacon, 2.3 g of sodium chloride were dissolved in 7 mL of MilliQ water and the solution was kept on ice. 30 µL of internal deuterated terpenol standard and 1 mL of either calibration standard or sample were added, the tubes were closed and mixed well. The 2 cm long PDMS/DVB/carboxen fiber was inserted in the headspace of the vessel while agitating and incubating the sample for 30 min at 30 °C. Subsequently, the fiber was inserted in the GC-MS and terpenol content was determined as described above.

Determination of pDMS

For the measurement of remaining SMM, 10 mL of fermentation samples were centrifuged for 5 min at 1960 × g and 4 °C. The supernatants were kept at -20 °C and were submitted to Nyséos (Montpellier, France) for determination. The determination method first eliminated DMS already formed in the sample. Afterwards, the contained SMM was reacted to DMS by heat alkaline treatment and the concentration of produced DMS was determined by SPME and GC-MS measurement [START_REF] Segurel | Contribution of dimethyl sulfide to the aroma of syrah and grenache noir wines and estimation of its potential in grapes of these varieties[END_REF].

7

Genotyping and QTL analyses

Calculation of heritability

The term heritability refers to the proportion of phenotypic variation within a population that is of genetic origin. In this thesis, heritability was calculated according to [START_REF] Brem | Genetic dissection of transcriptional regulation in budding yeast[END_REF]. The total variation of a trait within a population of strains (varpop) is composed of the relative contributions of genetic differences (vargen) and environmental influences (varenv).

varpop= vargen + varenv Environmental influences varenv are estimated from the variance of a trait among the replicates of each parent of the study (where n is the number of replicates):

(n4CAR1 -1) var4CAR1 + (nT73 -1) varT73 varenv = n4CAR1 + nT73 -2
The population variance varpop was calculated from the trait variance among the segregant population. Heritability was thus used to separate genetic and environmental components from phenotypic variation. Broad heritability (H2) was equal to the ratio of genetic variance to the total variance: varpop -varenv H2 =

x 100 varpop

Genotyping and generation of marker map

Low-quality reads, obtained by the sequencing, were processed and filtered using the FASTX Toolkit v0.0.13.2 and TRIMMOMATIC v0.30 [START_REF] Bolger | Trimmomatic: a flexible trimmer for Illumina sequence data[END_REF], using a quality threshold of 20. First, reads were aligned to the S288C reference genome (release R64-1-1) using BWA v0.6.2 (Li and Durbin, 2009). Once the reads have been mapped, consensus genotype calling was performed using the tools available in the SAMtools package (Li et al., 2009). The initial variant set of 18155 biallelic variant positions was filtered for insuring a minimum spacing of 2.0 kb between SNPs. This resulted in a genotyping variant dataset of 3727 SNP markers.

Linkage analysis

The statistical analyses were performed using the programming language R v3.2.3 (www.rproject.org) with the program libraries R/qtl v1.40-8 and R/eqtl v1.1-7 [START_REF] Broman | R/qtl: QTL mapping in experimental crosses[END_REF]. QTL mapping was performed with two different phenotype models, the normal model using Haley-Knott regression, and a non-parametric analysis. Two-dimensional, two-QTL scan was performed using the function scantwo. Multiple QTL mapping was performed with the function stepwiseqtl. Single QTL results were grouped as common QTL regions if their peaks were less than 10 cm apart. Proposed models of interaction were assessed with the function fitqtl.

7.4

Validation of QTL

Reciprocal hemizygosity analysis

QTLs were either evaluated as a whole, or single genes with a potential influence on the trait were chosen for evaluation. In both cases a disruption cassette containing the hygromycin B resistance gene (hph r ) from plasmid pAG32 (addgene) was amplified. The used primers del_[GENE]_fw/del_[GENE]_rv or del_[QTL]_fw/del_[QTL]_rv each contained an additional 40 bp overhang sequence, complementary to the upstream respectively downstream region of the target gene or region.

In the case of single gene assessment, these genes were deleted separately in both parent strains by homologous recombination with the disruption cassette. Transformed cells were selected on selective solid medium and correct gene deletion was verified by PCR, using the primer test_[GENE]_fw, which binds in the upstream region of the deleted gene, and the primer Hygro_rv, which binds within the deletion cassette. Deleted parent strains were subsequently mated with the opposite undeleted parent to form a heterozygote that was hemizygous for the target gene. The obtained strains were phenotyped (6) to assess the influence of the target gene respectively allelic variant on the trait.

In the case a QTL region is assessed as a whole, the disruption cassette was transformed into the parental heterozygote. Transformed cells were selected on selective solid medium and correct gene deletion was verified by PCR, using the primer test_[QTL]_fw, which binds in the upstream region of the deleted QTL, and the primer Hygro_rv, which binds within the deletion cassette. Deleted strains were further assessed by allelic PCR to determine the deleted variant. Hereby, the primer test_[QTL]_fw binds in the upstream region of the deleted QTL, and the primers tal_[GENE]_1/tal_[GENE]_2 bind at SNP positions of an allelic gene variant within the targeted region.

Allele swap

In the course of this thesis, allele swap was performed using the CRISPR/Cas9 toolbox developed by [START_REF] Mans | CRISPR/Cas9: a molecular Swiss army knife for simultaneous introduction of multiple genetic modifications in Saccharomyces cerevisiae[END_REF]. This approach has the advantage that genomic regions and even sole SNPs can be rapidly exchanged in a marker-and scarless way. The system consists in three components, the Cas9 enzyme, a spezific guide-ribonucleic acid (RNA) and a repair fragment. The guide RNA is complementary to a nucleotide sequence in the genome and binds to it. Cas9 recognizes the bound guide-RNA and performs a double-strand brake of the genomic DNA, which leads to DNA repair and alteration by the cell, using the repair fragment as a template.

The Cas9 expression cassette and the Hygromycin B resistance cassette were amplified by PCR from plasmids p414-TEF1p-cas9-CYC1t (DiCarlo et al., 2013) and pAG32 (addgene). The primers (Table 15) consisted of overlapping sequences for self-assembly of both cassettes and insertion of the resulting construct into the genomes of parent cells via homologous recombination replacing GAL1. The fragments for assembly of CRISPR transformation plasmids were amplified by PCR using template plasmids pROS11 -pROS17 [START_REF] Mans | CRISPR/Cas9: a molecular Swiss army knife for simultaneous introduction of multiple genetic modifications in Saccharomyces cerevisiae[END_REF] and primers containing allele specific guide sequences (Table 15). The plasmid fragments were transformed in CAS9 carrying parental cells, together with a repair fragment mediating the allelic swap. The CRISPR plasmid was assembled by the cell, the guide RNA was expressed and mediates the double-strand break by Cas9. Positive sequence exchange mediated by the repair fragment was verified by allelic PCR, using a forward primer in the upstream region of the gene and one or more reverse primers on SNP positions within the gene. The obtained strains were phenotyped (6) to validate the impact of the allelic variants on the trait.

Table 15: Primers used for amplification of CAS9 integration cassette (1678-5981) and CRISPR plasmids (6005 and CasGuide_GENE), according to the protocol of [START_REF] Mans | CRISPR/Cas9: a molecular Swiss army knife for simultaneous introduction of multiple genetic modifications in Saccharomyces cerevisiae[END_REF].

Primer name

Nucleotide sequence (5' -> 3')

1678_GAL1DisB AATGAGAAGTTGTTCTGAACAAAGTAAAAAAAAGAAGTATACTTACATAGGCCACTAGTGGATCTGTAT AG 3093_tagA-pUG ACTATATGTGAAGGCATGGCTATGGCACGGCAGACATTCCGCCAGATCATCAATAGGCACCTTCGTACG CTGCAGGTCGA 4653_A-CYC1t-rv GTGCCTATTGATGATCTGGCGGAATGTCTGCCGTGCCATAGCCATGCCTTCACATATAGTCCGCAAATTA AAGCCTTCGAG 5981_Cas9_GAL1_fw TTCACCGGTCGCGTTCCTGAAACGCAGATGTGCCTCGCGCCGCACACCGTATTACCGCCTTTGAGTG 6005_p426-CRISP_rv GATCATTTATCTTTCACTGCGGAGAAG CasGuide_GENE TGCGCATGTTTCGGCGTTCGAAACTTCTCCGCAGTGAAAGATAAATGATC(20bp_homologous_seque nce)GTTTTAGAGCTAGAAATAGCAAGTTAAAATAAG

Results

Chapter 1: Establishment of QTL mapping strategy and QTL analysis of fermentative aroma formation and main metabolite production.

In the first part of this thesis the bases for the performed QTL analysis were established. Two wine yeast strains were chosen as they had previously been demonstrated to have different requirements for nitrogen during fermentation, which was seen as indication of different capacities to produce fermentative aromas. A population of 50 F2-segregants from a cross between these chosen strains was already available from previous studies in the working group. 80 F2-segregants were newly generated during this thesis by repeated crossing, sporulation and dissection. The strains were individually genotyped by whole genome sequencing and phenotyped during small scale (300 mL) wine fermentation in synthetic grape must by determining extracellular metabolites near the end of fermentation. The genotyping data was used to identify all SNPs between the parent cells and to establish a marker map of selected SNP identities for all segregant strains.

Subsequently, the phenotype data and marker map were used to perform linkage analysis in order to detect QTLs in yeast's genome that influence the production of main metabolites and formation of fermentative aroma molecules. Besides mapping single QTLs, several statistical approaches were applied to detect multiple QTLs and their interactions. A selection of detected QTLs was dissected by reciprocal hemizygosity analysis and allelic variants with different impact on aroma formation were described.

The article was published the 01.03.2018 in BMC Genomics 19:166.

Abstract

Background: The volatile metabolites produced by Saccharomyces cerevisiae during alcoholic fermentation, which are mainly esters, higher alcohols and organic acids, play a vital role in the quality and perception of fermented beverages, such as wine. Although the metabolic pathways and genes behind yeast fermentative aroma formation are well described, little is known about the genetic mechanisms underlying variations between strains in the production of these aroma compounds.

To increase our knowledge about the links between genetic variation and volatile production, we performed quantitative trait locus (QTL) mapping using 130 F2-meiotic segregants from two S. cerevisiae wine strains. The segregants were individually genotyped by next-generation sequencing and separately phenotyped during wine fermentation.

Results: Using different QTL mapping strategies, we were able to identify 65 QTLs in the genome, including 55 that influence the formation of 30 volatile secondary metabolites, 14 with an effect on sugar consumption and central carbon metabolite production, and 7 influencing fermentation parameters. For ethyl lactate, ethyl octanoate and propanol formation, we discovered 2 interacting QTLs each. Within 9 of the detected regions, we validated the contribution of 13 genes in the observed phenotypic variation by reciprocal hemizygosity analysis. These genes are involved in nitrogen uptake and metabolism (AGP1, ALP1, ILV6, LEU9), central carbon metabolism (HXT3, MAE1), fatty acid synthesis (FAS1) and regulation (AGP2, IXR1, NRG1, RGS2, RGT1, SIR2) and explain variations in the production of characteristic sensorial esters (e.g., 2-phenylethyl acetate, 2-metyhlpropyl acetate and ethyl hexanoate), higher alcohols and fatty acids.

Conclusions:

The detection of QTLs and their interactions emphasizes the complexity of yeast fermentative aroma formation. The validation of underlying allelic variants increases knowledge about genetic variation impacting metabolic pathways that lead to the synthesis of sensorial important compounds. As a result, this work lays the foundation for tailoring S. cerevisiae strains with optimized volatile metabolite production for fermented beverages and other biotechnological applications.

Keywords: yeast, aroma compounds, metabolites, QTL mapping, fermentation 1 Background

The aroma of fermented beverages is the result of a complex blend of volatile compounds. In wine, these volatiles originate either directly from grape must or are produced de novo by yeast during alcoholic fermentation. Yeast utilizes the nutrients contained in grape must, which are mainly hexoses, nitrogen and lipid sources, for proliferation, whereas ethanol, CO2 and various minor metabolites are produced as byproducts. Many of these metabolites are volatile with sensorial properties, which give wine its vinous character [START_REF] Romano | Function of yeast species and strains in wine flavour[END_REF].

Although the flavor and aroma profile of wine is influenced by vine environment and management techniques, the choice of yeast strain plays a central role [START_REF] Robinson | Influence of Yeast Strain, Canopy Management, and Site on the Volatile Composition and Sensory Attributes of Cabernet Sauvignon Wines from Western Australia[END_REF].

Higher alcohols and esters are the most abundant groups of fermentative aromas that are produced de novo in yeast metabolism [START_REF] Cordente | Flavor-active wine yeasts[END_REF]. Higher alcohols can impart a strong, pungent smell and taste when present in higher concentrations, but they result in a fruity character at low concentrations [START_REF] Lambrechts | Yeast and its Importance to Wine Aroma -A Review[END_REF]. The formation of higher alcohols is carried out through decarboxylation and reduction of α-keto-acids, which derive either from central carbon metabolism or from the transamination of amino acids. Therefore, the synthesis of higher alcohols is linked to both carbon and nitrogen metabolism.

Acetate esters, which are produced by yeast from higher alcohols during fermentation, increase aroma complexity by imparting aromatic notes of pear, apple and banana to general fruitiness [START_REF] Nykänen | Formation and occurrence of flavor compounds in wine and distilled alcoholic beverages[END_REF][START_REF] Nykänen | Aroma of beer, wine and distilled alcoholic beverages[END_REF]. They are synthesized through acetyl transfer from acetyl-CoA to an alcohol by the acetyltransferases Atf1 and Atf2 [START_REF] Mason | Alcohol acetyltransferases and the significance of ester synthesis in yeast[END_REF]. Ethyl esters also contribute to global fruitiness perception and are synthesized through acyl transfer from an acyl-CoA to ethanol by the esterases Eeb1 and Eht1 [START_REF] Saerens | The Saccharomyces cerevisiae EHT1 and EEB1 genes encode novel enzymes with medium-chain fatty acid ethyl ester synthesis and hydrolysis capacity[END_REF]. The carboxylic acid molecules for ethyl ester synthesis predominantly originate from the degradation of α-keto-acids or fatty acid synthesis in lipid metabolism.

As a consequence, fermentative aroma can be seen as a complex mix of volatile compounds intimately associated with yeast metabolism. The diversity of yeast strains and variability in the regulation of yeast metabolism have a large impact on their production [START_REF] Rossouw | Transcriptional Regulation and the Diversification of Metabolism in Wine Yeast Strains[END_REF]. Even though biochemical pathways have been established for most of these compounds and major genetic determinants have been identified, the genetic basis for the variation of volatile compound production between strains remains largely unknown.

The formation of several compounds important for wine aroma has been shown to be a quantitative and complex trait, as it is influenced by the contribution of multiple genes [START_REF] Steyer | QTL mapping of the production of wine aroma compounds by yeast[END_REF]. Quantitative trait locus (QTL) mapping has been demonstrated to be a powerful approach for deciphering the genetic bases of numerous complex traits [START_REF] Deutschbauer | Quantitative trait loci mapped to single-nucleotide resolution in yeast[END_REF][START_REF] Steinmetz | Dissecting the architecture of a quantitative trait locus in yeast[END_REF] and has been applied in several biotechnological applications. From its first use in enological studies to characterize allelic variants influencing acetic acid production [START_REF] Marullo | Single QTL mapping and nucleotide-level resolution of a physiologic trait in wine Saccharomyces cerevisiae strains[END_REF], it has been extended to decipher the genomic bases of fermentation parameters [START_REF] Ambroset | Deciphering the molecular basis of wine yeast fermentation traits using a combined genetic and genomic approach[END_REF], the production of main metabolites and residual sugar concentrations [START_REF] Salinas | The genetic basis of natural variation in oenological traits in Saccharomyces cerevisiae[END_REF], nitrogen utilization (Brice et al., 2014a), sulfite production [START_REF] Noble | Identification of new Saccharomyces cerevisiae variants of the MET2 and SKP2 genes controlling the sulfur assimilation pathway and the production of undesirable sulfur compounds during alcoholic fermentation[END_REF] and secondary fermentation [START_REF] Martí-Raga | Genetic causes of phenotypic adaptation to the second fermentation sparkling wines in[END_REF]. QTL mapping was also used for the detection of genomic regions influencing the production of volatile compounds by yeast during wine alcoholic fermentation [START_REF] Steyer | QTL mapping of the production of wine aroma compounds by yeast[END_REF] using a population of 30 F1-segregants originating from a cross between an S. cerevisiae wine and a lab strain. One major QTL and seven minor genomic regions were found to influence the production of different volatile compounds despite high trait heritability. This result suggested that more analytical power is required in order to decipher the genetic bases of the production of volatiles during alcoholic fermentation. The sensitivity of QTL analysis and the ability to find loci with small contributions to phenotype variations can be increased by assessing a larger number of individuals [START_REF] Bloom | Genetic interactions contribute less than additive effects to quantitative trait variation in yeast[END_REF]. Moreover, the resolution of the mapping can be improved and nearby QTLs can be unlinked by increasing the recombination rate of the segregants [START_REF] García-Ríos | The genetic architecture of low-temperature adaptation in the wine yeast Saccharomyces cerevisiae[END_REF]. When multiple loci influence one trait, their contribution to trait variation can either be additive or interacting. Recent studies with a large yeast cross estimated that more than 40% of trait variations in a set of 20 traits could be explained by additive genetic effects, whereas pair-wise genetic interactions contributed to almost 10% of the phenotypic variance [START_REF] Bloom | Genetic interactions contribute less than additive effects to quantitative trait variation in yeast[END_REF]. Multiple QTL mapping can not only detect linked QTLs but also provides more statistical power to find unlinked QTLs [START_REF] Arends | R/qtl: High-throughput multiple QTL mapping[END_REF].

In this study, we addressed the complexity of the genetic basis underlying volatile metabolite production using a population of 130 F2-segregants obtained from a cross of two wine strains with different requirements for nitrogen (Brice et al., 2014b). In addition to performing a genome search for single QTLs, the large segregant population enabled us to increase the analytical strength by performing a search for multiple QTLs. As far as we know, this study is the first analysis of the interaction between loci influencing fermentative aroma formation.

We identified a total of 65 QTLs in the genome that influence fermentation parameters and the production of metabolites, including 55 QTLs influencing the formation of 30 volatile secondary metabolites. For the production of ethyl lactate, ethyl octanoate and propanol, we could detect interacting QTLs. Finally, we experimentally validated the role of 13 genes in 9 of the identified genomic regions. These findings provide new information about the production of metabolites of interest due to their sensorial properties or other biotechnological value, such as medium chain fatty acids, fusel acids, higher alcohols and their esters. This opens new perspectives for engineering S. cerevisiae strains for broad biotechnological applications.

2 Materials and Methods

Media

Yeast was cultured at 28 °C in yeast extract peptone dextrose (YPD) media containing 10 g/L yeast extract, 20 g/L peptone and 20 g/L glucose. Solid YPD media contained 1.5% agar.

Selective YPD media containing 200 µg/mL geneticin (G418), 200 µg/mL nourseothricin (clonNAT) or 200 µg/mL hygromycin B were used.

Wine fermentations were carried out in synthetic must (SM) described by [START_REF] Bely | Automatic detection of assimilable nitrogen deficiencies during alcoholic fermentation in oenological conditions[END_REF].

The medium contains glucose and fructose (each 100 g/L) and assimilable nitrogen (200 mg/L) in the form of ammonium and free amino acids, which mimics the nitrogen content of standard grape juice.

Yeast strains

The haploid S. cerevisiae strains MTF2621 (4CAR1 [ΔHO::Neo r ]) and MTF2622 (T73 [ΔHO::Nat r ]) exhibit different needs for nitrogen during wine fermentation, which may indicate different formations of aromas associated with nitrogen metabolism. The requirement was previously estimated using an approach based on the addition of nitrogen to keep the CO2 production rate constant during limitation of this substrate (Brice et al., 2014b). The strain T73 belongs to the phylogenetic clade of wine strains, whereas strain 4CAR1 belongs to the group of champagne strains (Additional file 1), which originated through crossings between strains of the wine clade and the flor clade [START_REF] Coi | A set of haploid strains available for genetic studies of Saccharomyces cerevisiae flor yeasts[END_REF].

Generation of F2-segregants

Haploid spores of the strains MTF2621 and MTF2622 were mated to form a zygote, which was selected on YPD-agar containing G418 and clonNAT. The zygote was then sporulated in liquid sporulation media using the protocol of [START_REF] Codon | Factors which affect the frequency of sporulation and tetrad formation in Saccharomyces cerevisiae baker's yeasts[END_REF]. The resulting tetrads were dissected into single spores to obtain the F1-generation using a Singer MSM 400 workstation (Singer Instruments). In most cases, only one spore per tetrad was taken for further experiments to increase genomic independence among the spores. The antibiotic resistance of the obtained spores was determined by growth assay on YPD-agar plates containing G418 or clonNAT. Two spores with different antibiotic properties were mated, and the formed zygotes were subsequently selected on YPD-agar containing G418 and clonNAT. These zygotes were sporulated and dissected again. In total, 130 single spores from the F2-generation were used for this study.

Genotyping of strains

The genomic DNA of all 130 F2-segregants and both parent strains was isolated using the MasterPure™ Yeast DNA Purification Kit (epicentre) according to the protocol. The purity of the DNA was measured using a NanoDrop™ device (Thermo Fisher Scientific), and the concentration was determined by Qubit™ fluorometric quantification (Thermo Fisher Scientific). The DNA samples were then used for sequencing using Illumina technology (HiSeq 2500, paired end, 2x100 bp, sequencing platform Genotoul in Toulouse, France) at a sequencing depth of 20-to 80-fold. For each library, low-quality reads were processed and filtered with the FASTX Toolkit v0.0.13.2 and TRIMMOMATIC v0.30 [START_REF] Bolger | Trimmomatic: a flexible trimmer for Illumina sequence data[END_REF] using a quality threshold of 20. First, reads were aligned to the S288C reference genome (release R64-1-1) using BWA v0.6.2 (Li and Durbin, 2009). Once the reads were mapped, consensus genotype calling was performed using the tools available in the SAMtools package (Li et al., 2009). The global set of variants obtained in VCF format contained 18155 biallelic variant positions with a genotyping quality greater than 100. The effect of SNPs on putative transcription factor binding sites was analyzed using YEASTRACT (release 2017; [START_REF] Teixeira | The YEASTRACT database: an upgraded information system for the analysis of gene and genomic transcription regulation in Saccharomyces cerevisiae[END_REF]. For the location of SNPs in annotated protein domains, information available in the Saccharomyces Genome Database (www.yeastgenome.org) was used. The initial variant set was filtered to ensure a minimum spacing of 2.0 kb between SNPs. This resulted in a genotyping variant dataset of 3727 SNP markers (Additional file 5). To increase the meaningfulness of the analysis, four strains with the most ambiguous markers were excluded.

One strain was excluded because it was too close in genomic proximity to another segregant. This left a population of 125 F2-segregants for statistical analyses.

Phenotyping of strains

Segregants were fermented in duplicate with the parent strains as controls. The strains were grown overnight in 50 mL of YPD media. The cell density was determined using a Multisizer™ 3 Coulter Counter (Beckman Coulter). Sterilized 300-mL glassware mini fermenters were filled with 280 mL of SM200 and closed with an air lock. The fermenters were inoculated to a cell density of 1 × 10 6 cells/mL, weighed and left at 24 °C under stirring (300 rpm).

To determine the concentration of aroma compounds, a sample was taken when approx. 80% of the sugars were depleted. This corresponded to 67.9 -75 g/L produced CO2 and was determined by weighting the fermenters regularly to draw the weight decrease caused by the release of CO2. Volatiles were extracted with dichloromethane according to the method described by [START_REF] Rollero | Combined effects of nutrients and temperature on the production of fermentative aromas by Saccharomyces cerevisiae during wine fermentation[END_REF]. The concentrations of fermentative aromas were measured via GC/MS on full scan mode using a DB-WAX 60 m GC column. Thirty-four compounds were quantified using internal deuterated standards. In addition, the concentrations of extracellular metabolites after 80% of the fermentation were measured using HPLC (REZEX™ ROA-Organic Acid H+ (8%), 0.005 M H2SO4).

QTL mapping

The data obtained from phenotyping and genotyping were used to identify QTLs in the genome of yeast strains that influence the formation of volatile secondary metabolites during wine fermentation. Furthermore, QTLs influencing fermentation parameters, substrate consumption and the production of extracellular main metabolites were examined. The statistical analyses were performed using the programming language R v3.2.3 (www.rproject.org) with the R/qtl v1.40-8 and R/eqtl v1.1-7 libraries [START_REF] Broman | R/qtl: QTL mapping in experimental crosses[END_REF]. QTL mapping was performed with two different phenotype models, the normal model using Haley-Knott regression and a non-parametric analysis, resulting in logarithm of odds (LOD) scores for each marker and pseudo-marker every 2.5 cM (interval mapping method). An interval estimate of the location of each QTL was obtained as the 1-LOD support interval, the region in which the LOD score is within 1 unit of the peak LOD score. If the same locus was detected with both models, the results with the higher LOD score were selected. A two-dimensional, two-QTL scan was performed using the function scantwo. Multiple QTL mapping was performed twice with the function stepwiseqtl, once with strictly additive models and once with models that allowed for interactions. The limit of detected QTLs was set to 5. Newly detected QTL positions were counted when the LOD scores of models including these loci were higher than the added LOD score penalties of combining all loci of the respective model. For each method used, individual LOD score thresholds for a false discovery rate of 0.05 were determined with 1000 permutations. QTL mapping results for single traits were grouped as common QTL regions if their peaks were less than 10 cM apart. Proposed models of interaction were further assessed with the function fitqtl. The support of individual terms was evaluated by dropping each QTL from the proposed model, one at a time, and comparing the resulting models to the full model.

Reciprocal hemizygosity analysis

Validation of found QTLs was performed using reciprocal hemizygosity analysis (RHA) [START_REF] Steinmetz | Dissecting the architecture of a quantitative trait locus in yeast[END_REF]. QTLs were either evaluated as a whole region or single genes with a potential influence on the trait were tested. For the deletion of selected regions, the parent strains were mated to form the heterozygote. Subsequently, one allele of the region was deleted randomly by homologous recombination with a disruption cassette containing the hygromycin B resistance gene (hph r ) that was obtained by PCR of the plasmid pAG32 (addgene) with the primers del_(QTL)_fw and del_(QTL)_rv (Additional file 2). Positive integration was selected by plating the transformed cells on YPD-agar plates containing hygromycin B. Correct deletion of the region was verified by PCR using primer test_(QTL)_fw that binds upstream of the deleted region and primer Hygro_rv that binds within the deletion cassette. The remaining allele of the QTL was identified by allelic PCR using primer test_(QTL)_fw that binds upstream of a selected gene in the hemizygous region and primers tal_(QTL)_1 or tal_(QTL)_2 that bind at a SNP position within the same gene.

For the deletion of single genes, the sequences were deleted in both parent strains by homologous recombination with a disruption cassette containing the hygromycin B resistance gene (hph r ) that was obtained by PCR of the plasmid pAG32 with the primers del_(GENE)_fw and del_(GENE)_rv. Positive integration was selected by plating the transformed cells on YPDagar plates containing hygromycin B. Correct deletion of the gene was verified by PCR using primer test_(GENE)_fw that binds upstream of the deleted gene and primer Hygro_rv that binds within the deletion cassette. Deleted parent strains were subsequently mated with the opposite undeleted parent to form a heterozygote that is hemizygous for the target gene.

Hemizygous constructions were phenotyped in triplicate (2.5). The significance of the influence of an allelic target region or gene variant on the trait was evaluated by student's ttest. If the impact of a variant on several traits was tested, p-values were not adjusted for multiple comparisons.

3 Results and Discussion

Phenotyping of strains

Using small scale fermenters, the F2-segregant population and both parental strains were phenotyped (Additional file 3 and Additional file 4) for the production of 43 extracellular metabolites that originate from nitrogen and central carbon metabolism (Figure 18). Most traits are normally distributed among the population, indicating that they are under polygenic control (Additional file 4). One exception is the ratio of glucose to fructose after 80% of the fermentation (G/F ratio), which shows a biphasic distribution, revealing the major influence of one locus for this trait. The phenotypes of the parental strains are located within the population of segregants, indicating transgression for most traits, which can be explained by the presence of alleles with opposite impacts on these traits in the parental genomes.

Heritability of the traits was calculated according to [START_REF] Brem | Genetic dissection of transcriptional regulation in budding yeast[END_REF] (Additional file 3). With a median of 70.09 and a maximum of 94.35, the determined heritability is high, indicating reproducible phenotyping and a strong genomic influence on trait variations. For the formation of 3-methylbutanol, decanoic acid, diethyl succinate, dodecanoic acid and ethyl dodecanoate, the heritability estimate is almost zero or negative, which might be associated with insufficient analytical reproducibility. We performed a principal component analysis (PCA) to reduce the complexity of the data set for the determined secondary metabolites and to estimate the potential common regulations (Figure 19). The first two dimensions of the PCA together explain 40.8% of global trait variance. It can be seen that several evaluated compounds are grouped according to their chemical family. The production of all higher alcohols (except propanol and 2-methylbutanol) is correlated, together with the formation of their corresponding acetate esters. The synthesis of these molecules shares a common reaction step, i.e., the decarboxylation and reduction of α-ketoacids. Another linked group of volatiles is medium chain fatty acids with their ethyl esters. These compounds share a common pathway, namely, fatty acid synthesis. Interestingly, the formation of fusel acids is not correlated to the production of higher alcohols, although both compounds are metabolized from α-keto-acids. This suggests that the reduction or oxidation reactions, which lead to the formation of these compounds from fusel aldehydes, have a strong impact. The pyruvate yield is strongly negatively correlated to the production of ethyl lactate and loosely negatively correlated to the succinate yield and the formation of diethyl succinate. Pyruvate is a metabolic intermediate of both ethyl lactate and succinate formation (Figure 18).

Genome-wide identification of QTLs influencing fermentation parameters, main and secondary metabolite production

Simple QTL scan

The data obtained from phenotyping and the constructed marker map were used to perform a linkage analysis on 43 quantitative traits, including fermentation parameters, the production of main metabolites and the formation of volatile secondary metabolites. We were able to find a total of 32 QTLs influencing 32 traits (Table 16). The determined logarithm of odds (LOD) scores ranged from 3.41 to 10.49 with a median of 4.35. The highest LOD score was found for chr4@386.5, influencing the G/F ratio. The second highest LOD score of 8.44 was found for a QTL influencing the production of a volatile compound, namely, chr11@127.6 affecting the formation of 2-methylpropanol. Six major QTL regions were detected with LOD scores greater than six for at least one trait, which corresponds to an explained trait variation of more than 20% by these loci.

Globally, these QTLs were distributed over the whole genome, with exception of chromosomes I, V, VI and VIII. The size of the identified regions ranged from 5.2 kb to 65.7 kb with a median of 33.2 kb. The detected regions contained between 4 and 28 genes. Four QTLs were detected for both evaluated traits of the fermentation kinetics, the fermentation time (t80%) and CO2 production rate at t80%. Eight QTLs were found for the concentration of extracellular main metabolites at t80%. These QTLs influenced three traits, which were glycerol yield, pyruvate yield and the G/F ratio. The most QTLs were detected for the formation of volatile secondary metabolites, namely, 28 QTLs influencing the formation of 27 volatiles. This included the production of characteristic sensorial compounds, such as 2-methylbutyl acetate, 3-methylbutanol and 3-methylbutyl acetate, and industrially relevant chemicals, such as higher alcohols and organic acids.

The detected QTLs were compared with loci found in QTL mapping studies of similar traits [START_REF] Abt | Genomic saturation mutagenesis and polygenic analysis identify novel yeast genes affecting ethyl acetate production, a non-selectable polygenic trait[END_REF][START_REF] Ambroset | Deciphering the molecular basis of wine yeast fermentation traits using a combined genetic and genomic approach[END_REF][START_REF] Marullo | Single QTL mapping and nucleotide-level resolution of a physiologic trait in wine Saccharomyces cerevisiae strains[END_REF][START_REF] Salinas | The genetic basis of natural variation in oenological traits in Saccharomyces cerevisiae[END_REF][START_REF] Steyer | QTL mapping of the production of wine aroma compounds by yeast[END_REF]. Only QTL chr7@161.6, which influences ethyl lactate formation, co-localizes with PMA1, a plasma membrane P2-type H+-ATPase that was shown by [START_REF] Abt | Genomic saturation mutagenesis and polygenic analysis identify novel yeast genes affecting ethyl acetate production, a non-selectable polygenic trait[END_REF] to be the responsible gene in a QTL affecting ethyl acetate production. More QTLs were in common with the findings of [START_REF] Rossouw | Linking gene regulation and the exo-metabolome: a comparative transcriptomics approach to identify genes that impact on the production of volatile aroma compounds in yeast[END_REF], who used a comparative approach of combining transcriptomics and exo-metabolome analysis to predict candidate genes with a role in aroma profile modification. Several of the genes proposed by [START_REF] Rossouw | Linking gene regulation and the exo-metabolome: a comparative transcriptomics approach to identify genes that impact on the production of volatile aroma compounds in yeast[END_REF], e.g., ALP1, ILV6, LEU1, LEU2 and LEU9, were included in QTLs that we detected for the same or closely related traits.

Double and multiple QTL scan

To search for additional minor QTLs and to assess genetic interactions between our detected regions, we performed a two-dimensional, two-QTL scan and a multiple QTL search for all traits. The analyses confirmed 24 QTLs that were already found with the single QTL mapping and proposed 36 additional loci (Table 17). With the double QTL mapping, we found significant evidence for an interacting QTL pair at positions chr10@88.5 and chr11@51.7, influencing the formation of ethyl octanoate and accounting for 10.29% of trait variation. Another interacting QTL pair, chr2@181.0 and chr16@304.0, was found to influence the production of ethyl lactate, explaining 10.17% of trait variation. Multiple QTL mapping proposed interacting regions for a wide range of traits. However, their contributions to the respective phenotypes were low, with LOD scores of generally less than two. Due to penalization of the LOD score for more complex models of interaction, solely additive models were found to be more significant for all traits. This indicates that our number of segregants was still insufficient to achieve the statistical power required for the determination of QTL interactions. However, for the production of propanol the three involved QTLs, chr4@176.9, chr12@233.1 and chr14@45.8, could be detected with all three mapping strategies, giving strong evidence for their validity. Although models consisting of viewer QTLs were seen to be more probable by the multiple QTL mapping, the most likely model containing all three loci was an additive model with an interaction between chr4@176.9 and chr12@233.1. This indicates a remaining probability for the proposed interaction, which was calculated to potentially account for 5.58% of trait variation.

Combining the results from the single and multiple QTL mapping, each trait is influenced by a median of 3 QTLs, ranging from 1 to 7. The best explained trait is the G/F ratio with 6 detected loci accounting for 61.0% of determined trait variation. Regarding volatile formation, the difference in 2-methylpropanol production can be best elucidated, with 6 identified QTLs explaining 54.5% of trait variation. Several QTL display pleiotropic effects, as they influence many traits and can therefore be considered "hotspots". The region affecting the most traits is chrXI:77,969..155,100, which influences the fermentation time (t80%) and the formation of eight volatile compounds, namely, hexanoic acid, octanoic acid, decanoic acid, and their corresponding ethyl esters as well as ethyl dodecanoate and 2-phenylethyl acetate. Twentyfour other QTLs were found to influence more than one trait. This is often the case for the production of related compounds and indicates common regulation, which was already concluded by PCA. Examples for jointly influenced traits are sugar consumption and the CO2 production rate, pyruvate yield and the formation of ethyl esters, the production of 2methylpropanol and 3-methylbutanol, and the formation of several acetate esters.

Validation of genomic regions involved in metabolic traits

For the validation of single QTLs and the identification of impacting allelic variants within the corresponding region, 19 genes in 10 QTLs were further evaluated using RHA (Table 18). These target genes were chosen since they contained non-synonymous SNPs between the parent cells and were suspected to play a role in the detected traits, as their biologic functions were mostly connected to central carbon metabolism or nitrogen uptake and metabolism. In 9 QTLs, we could identify 13 genes that influence hexose transport and the formation of medium chain fatty acids, fusel acids, higher alcohols, and their corresponding esters (Table 18).

Hexose transporter Hxt3 influences sugar utilization

Hexose transport is a limiting step for alcoholic fermentation speed [START_REF] Elbing | Role of hexose transport in control of glycolytic flux in Saccharomyces cerevisiae[END_REF]. QTL chr4@386.5, which influences the CO2 production rate and G/F ratio, contains three hexose transporter genes, HXT3, HXT6 and HXT7. We evaluated these genes individually by RHA. As the sequences of HXT6 and HXT7 are nearly identical, we assessed the effect of both genes together. Variation in HXT3 was found as the sole effect influencing the CO2 production rate and the G/F ratio (Additional file 7). The MTF2621 allele of the gene increased the CO2 production rate by a factor of 1.07 and increased the G/F ratio by a factor of 1.86. An effect of this allelic variation on the production of determined volatiles could not be detected. A variant of HXT3 has already been described in the literature by [START_REF] Guillaume | Molecular basis of fructose utilization by the wine yeast Saccharomyces cerevisiae: A mutated HXT3 allele enhances fructose fermentation[END_REF] to have a higher affinity for fructose and was detected among flor strains [START_REF] Coi | A set of haploid strains available for genetic studies of Saccharomyces cerevisiae flor yeasts[END_REF]. This variant originated through recombination between the orthologs HXT1 and HXT3. Except for SNP T1411A, which results in amino acid change L471I, the MTF2621 allele of HXT3 is identical to the variant described by [START_REF] Guillaume | Molecular basis of fructose utilization by the wine yeast Saccharomyces cerevisiae: A mutated HXT3 allele enhances fructose fermentation[END_REF].

The formation of medium chain fatty acids and their ethyl esters is influenced by Agp2, Fas1 and Sir2

Ethyl esters of medium chain fatty acids provide floral and fruity notes to fermented beverages. In QTL chr4@133.6 and QTL hotspot chr11@29.5, which influence the formation of medium chain fatty acids and their ethyl esters, we identified SIR2 and FAS1 as causative genes (Table 18). In chr2@166.4, a QTL impacting ethyl butanoate production with a lower significance (LOD 3.96), AGP2 was found to modulate the formation of butanoic acid, the substrate for ethyl butanoate.

Fatty acids are synthesized from the repeated condensation of malonyl-CoA and acetyl-CoA, which is carried out by fatty acid synthetase (FAS). The FAS complex consists of the beta subunit Fas1 and the alpha subunit Fas2 [START_REF] Kolodziej | Structure-Function Relationships of the Saccharomyces cerevisiae Fatty Acid Synthase Three-Dimensional Structure[END_REF]. Fas1, which was found to regulate the expression of FAS2 [START_REF] Wenz | A downstream regulatory element located within the coding sequence mediates autoregulated expression of the yeast fatty acid synthase gene FAS2 by the FAS1 gene product[END_REF], possesses four independent enzymatic functions, i.e., acetyl transferase, enol reductase, dehydratase and malonyl/palmitoyl transferase [START_REF] Schweizer | The pentafunctional FAS1 gene of yeast: its nucleotide sequence and order of the catalytic domains[END_REF]. The parental allelic variants of FAS1 differ in three nonsynonymous SNPs (Table 19), of which one, SNP I1970V, lies in the malonyl-CoA-acyl carrier protein transacylase domain of the protein. The MTF2621 allele of Fas1 causes a significant decrease in the formation of hexanoic acid, ethyl hexanoate, valeric acid, octanoic acid, decanoic acid and dodecanoic acid by a factor of 0.78 -0.89 (Figure 20). Therefore, we can suggest that the MTF2621 allele of FAS1 is less active than the MTF2622 allele, and thus leads to a decreased synthesis rate of fatty acids.

The gene SIR2 encodes an NAD + -dependent deacetylase involved in chromatin silencing [START_REF] Rine | Four genes responsible for a position effect on expression from HML and HMR in Saccharomyces cerevisiae[END_REF]. The allelic variants of SIR2 differ in two non-synonymous SNPs (Table 19). The MTF2621 allele of the gene causes a decrease in the formation of hexanol, octanoic acid, decanoic acid, dodecanoic acid, ethyl butanoate and ethyl hexanoate up to a factor of 0.57 (Figure 20). In addition, the extracellular concentration of acetate was decreased by a factor of 0.8 (Additional file 7). Sir2 was found to influence the expression of the acetyl-CoA synthase ACS2 [START_REF] Lin | A comprehensive synthetic genetic interaction network governing yeast histone acetylation and deacetylation[END_REF], and a regulating function by Sir2 on the activity of acetyl-CoA synthase enzymes by deacetylation was proposed by [START_REF] Starai | Short-chain fatty acid activation by acyl-coenzyme A synthetases requires SIR2 protein function in Salmonella enterica and Saccharomyces cerevisiae[END_REF]. Furthermore, [START_REF] Casatta | Lack of Sir2 increases acetate consumption and decreases extracellular pro-aging factors[END_REF] demonstrated that a null mutant of SIR2 showed increased acetate metabolism and a lower excretion of acetate to the medium. Based on our observations, we can therefore suggest that the MTF2621 variant of Sir2 has lower deacetylase activity compared to the other variant, which results in decreased expression of ACS2 and reduced activation of acetyl-CoA synthases. Consequently, this reduced activity leads to a lower availability of acetate and acetyl-CoA for fatty acid synthesis and elongation.

Other small but significant influences of SIR2 can be seen in the formation of 2methylpropanol, 3-methylbutanol and other degradation products of α-keto-acids, with the MTF2621 allele leading to a decrease of these compounds (Figure 21). Sir2 was found to modulate the expression of the amino acid permease AGP1 [START_REF] Liu | The polarisome is required for segregation and retrograde transport of protein aggregates[END_REF]. Altered expression of AGP1 could impact the nitrogen assimilation and thus the formation of amino acid related fermentative aromas. Furthermore, as Sir2 is dependent on the cofactor NAD + , its altered activity could influence redox homeostasis of the cell. Redox imbalances were reported to significantly affect the production of fermentative aromas by S. cerevisiae [START_REF] Bloem | Metabolic impact of redox cofactor perturbations on the formation of aroma compounds in Saccharomyces cerevisiae[END_REF].

AGP2 encodes a plasma membrane protein that is involved in the uptake of carnitine and polyamines [START_REF] Schreve | Yeast Agp2p and Agp3p function as amino acid permeases in poor nutrient conditions[END_REF][START_REF] Van Roermund | Molecular characterization of carnitine-dependent transport of acetyl-CoA from peroxisomes to mitochondria in Saccharomyces cerevisiae and identification of a plasma membrane carnitine transporter, Agp2p[END_REF]. Carnitine is important for intracellular acetyl transport between cellular compartments [START_REF] Van Roermund | The membrane of peroxisomes in Saccharomyces cerevisiae is impermeable to NAD (H) and acetyl-CoA under in vivo conditions[END_REF], and the level of carnitine can therefore affect the availability of acetyl-CoA for the fatty acid synthesis. However, carnitine is not present in the synthetic medium used in this study. Agp2 positively regulates the expression of various proteins involved in substrate transport and other biological processes, and might also act as a sensor of environmental signals [START_REF] Aouida | Agp2, a member of the yeast amino acid permease family, positively regulates polyamine transport at the transcriptional level[END_REF]. One non-synonymous SNP was found to distinguish the two parental variants (Table 19), and it is located in the extracellular region of the protein. The MTF2621 allele of AGP2 causes an increase in the formation of butanoic acid, decanoic acid, dodecanoic acid and ethyl dodecanoate up to a factor of 1.63 (Figure 20). We suggest that the reported SNP in AGP2 causes a higher formation rate of fatty acids for MTF2621, although the causative function remains unclear.

The formation of higher alcohols, fusel acids and their esters is influenced by Agp1, Ilv6, Mae1

Higher alcohols, fusel acids and especially their esters are essential fermentative aroma components that provide notes ranging from fruity to flowery. We identified MAE1 in chr11@127.6, the QTL with the highest LOD score for volatile compounds, which influences the formation of five higher alcohols, fusel acids and acetate esters, (Table 18). The enzyme Mae1 catalyzes the oxidative decarboxylation of malate to pyruvate [START_REF] Boles | Identification and Characterization of MAE1, the Saccharomyces cerevisiae Structural Gene Encoding Mitochondrial Malic Enzyme[END_REF].

Pyruvate is a precursor for the synthesis of the amino acids alanine, isoleucine, leucine and valine [START_REF] Umbarger | Amino acid biosynthesis and its regulation[END_REF]). An intermediate of valine and leucine biosynthesis, α-ketoisovalerate, can also be degraded to 2-methylpropanol and 2-methylpropanoic acid or to 3methylbutanol and 3-methylbutanoic acid via α-keto-isocaproate (Figure 21). The allelic variants of MAE1 differ in one non-synonymous SNP (Table 19), which is located in the NADbinding domain of the protein. Furthermore, 5 SNPs in the 1000-bp upstream region of the gene affect predicted binding motifs for the proteins Azf1, Mot3, Rtg1, Rtg3, Stp1 and Stp2 (Additional file 6). The hemizygote carrying only the MTF2621 allele of MAE1 shows increased formation of 2-methylpropanol, 3-methylbutanol, 2-methylpropanoic acid and 3methylbutanoic acid by up to a factor of 1.67 (Figure 21). We can suggest that the MTF2621 allele of MAE1 is superior to the MTF2622 allele and induces an increased flux of malate to pyruvate, leading to higher formation of α-keto-acids and their degradation products. This proposal is further supported by an observed increased formation of ethyl lactate (Additional file 7), which is also derived from pyruvate (Figure 18).

RHA detected several other minor influences of MAE1 on traits that were not found by QTL mapping. The MTF2621 allele of the gene leads to a slightly higher production of 2phenylethanol by a factor of 1.18 (Additional file 7). Mae1 was found to interact with Aro1 [START_REF] Gavin | Proteome survey reveals modularity of the yeast cell machinery[END_REF], an enzyme catalyzing several steps of the chorismate pathway leading to the synthesis of aromatic amino acids, such as phenylalanine [START_REF] Duncan | The pentafunctional arom enzyme of Saccharomyces cerevisiae is a mosaic of monofunctional domains[END_REF]. In addition, the MTF2621 allele of Mae1 leads to a decrease of several acetate esters and medium chain fatty acids up to a factor of 0.8 (Figure 21 and Additional file 7) and to an increase in the extracellular concentration of acetate by a factor of 1.28 (Additional file 7). These effects are consistent with the fact that Mae1 physically interacts with Acc1 [START_REF] Gavin | Proteome survey reveals modularity of the yeast cell machinery[END_REF], an acetyl-CoA carboxylase that is involved in the regulation of acetyl-CoA and in the biosynthesis of medium and long chain fatty acids [START_REF] Galdieri | Acetyl-CoA carboxylase regulates global histone acetylation[END_REF][START_REF] Mishina | Yeast Mutants Defective in Acetyl-Coenzyme A Carboxylase and Biotin: Apocarboxylase Ligase[END_REF].

The gene AGP1, which encodes a low affinity amino acid permease for asparagine and glutamine [START_REF] Schreve | The Saccharomyces cerevisiae YCC5 (YCL025c) gene encodes an amino acid permease, Agp1, which transports asparagine and glutamine[END_REF], was validated in QTL chr3@26.1 with an influence on the formation of 2-methylpropanoic acid. The two allelic variants of AGP1 differ in 8 nonsynonymous SNPs (Table 19), of which three lie in cytoplasmic domains of the protein and 5 in transmembrane domains. Another SNP is located in the 1000-bp upstream region of the gene, affecting the predicted binding motif for Ume6 (Additional file 6). The hemizygote carrying the MTF2621 allele of the gene shows a formation of 2-methylpropanol, 2-methylpropyl acetate, 2-methylpropanoic acid and ethyl 2-methylpropanoate increased by a factor of 1.26 -1.32 (Figure 21). Agp1 was found to transport valine to a lower extent [START_REF] Schreve | The Saccharomyces cerevisiae YCC5 (YCL025c) gene encodes an amino acid permease, Agp1, which transports asparagine and glutamine[END_REF]. Valine can be degraded to α-keto-isovalerate and then to 2-methylpropanol and 2-methylpropanoic acid by the Ehrlich pathway (Figure 21). We hypothesize that the reported SNPs lead to higher affinity of the MTF2621 allele of Agp1 for valine, causing a higher level of this amino acid in the cell. Another possible explanation is a different influence of the alleles on the transport of glutamine, which is important for the transamination of α-keto-acids in the cell. In this scenario, a reduced intracellular level of glutamine could lead to a decreased transamination of α-keto-isovalerate, which can therefore be degraded to 2-methylpropanol and 2-methylpropanoic acid. While this would also affect the transamination of other α-ketoacids and therefore the production of several higher alcohols or fusel acids, a significant, but small, influence of AGP1 could only be additionally detected on the production of 2phenylethanol.

In the same QTL (chr3@26.1), the variants of ILV6 did not show significant differences in the formation of 2-methylpropanoic acid, but they did in the formation of the related higher alcohol 2-methylpropanol. Ilv6 is a regulatory subunit of the acetolactate synthase Ilv2, which catalyzes the first step of valine and leucine biosynthesis [START_REF] Cullin | Functional analysis of YCL09C: evidence for a role as the regulatory subunit of acetolactate synthase[END_REF]. The allelic variants of ILV6 differ in two non-synonymous SNPs (Table 19). SNP L4S lies in the N-terminal signal peptide domain of the protein, whereas SNP P56A is within a non-cytoplasmic domain.

Another SNP is located in the 1000-bp upstream region and causes a loss of the predicted binding motifs for Msn2, Msn4, Nrg1 and Rph1 in strain MTF2621 (Additional file 6). The MTF2621 allele of ILV6 leads to an increase in the formation of 2-methylpropanol and 3methylbutanol by a factor of 1.24 (Figure 21). Therefore, we can hypothesize that the MTF2621 allele of ILV6 stimulates a higher synthesis rate of acetolactate, which could result in higher synthesis of α-keto-isovalerate, including its degradation products.

The formation of propanol is influenced by Alp1 and Nrg1

We assessed the three partly interacting genomic regions that were detected with the single, double and multiple QTL mapping to affect the production of propanol and related compounds (Table 18). NRG1 and ALP1 were validated in QTL chr4@177.5 and QTL chr14@43.9, respectively. As no clear candidate gene was identified in QTL chr12@226.9, we assessed the whole region by RHA; however, no significant impact could be detected for the production of propanol or propyl acetate. The QTL is likely to interact with chr4@177.5, and the negative validation of chr12@226.9 might indicate a possible epistatic interaction. Furthermore, chr12@226.9 is the weakest QTL of the three assessed loci, with an LOD score of 4.08 (Table 16), which could have hindered the validation.

Propanol and propanoic acid derive from the decarboxylation of α-keto-butyrate and the oxidation or reduction of the resulting propionaldehyde [START_REF] Hazelwood | The Ehrlich Pathway for Fusel Alcohol Production: a Century of Research on[END_REF]. α-ketobutyrate is produced from the transamination of threonine, which is taken up from the medium as a nitrogen source or can be metabolized from pyruvate via aspartate through the amino acid pathway (Figure 22). It was shown, however, that the formation of propanol is mainly limited to the beginning of wine fermentation when nitrogen is present in the must and is dependent on the initial amount of available nitrogen [START_REF] Mouret | Kinetic analysis and gas-liquid balances of the production of fermentative aromas during winemaking fermentations: effect of assimilable nitrogen and temperature[END_REF].

The protein Nrg1 is a transcriptional regulator of glucose repressed genes [START_REF] Park | Nrg1 Is a Transcriptional Repressor for Glucose Repression of STA1 Gene Expression in Saccharomyces cerevisiae[END_REF][START_REF] Zhou | NRG1 is required for glucose repression of the SUC2 and GAL genes of Saccharomyces cerevisiae[END_REF] and mediates a set of stress responsive genes [START_REF] Vyas | Repressors Nrg1 and Nrg2 regulate a set of stress-responsive genes in Saccharomyces cerevisiae[END_REF]. The parental allelic variants of NRG1 differ in two non-synonymous SNPs (Table 19), which are both located in the transcriptional repressor protein "yy" domain. The MTF2621 variant of the gene leads to an increase in propanol production by a factor of 1.10, whereas no significant effect could be detected in the formation of related compounds (Figure 22). The repressive function of Nrg1 is inhibited by Snf1; therefore, it is suspected to have a role in the response to nitrogen limitation [START_REF] Kuchin | Snf1 protein kinase and the repressors Nrg1 and Nrg2 regulate FLO11, haploid invasive growth, and diploid pseudohyphal differentiation[END_REF]. Furthermore, Nrg1 was found to influence the expression of BAT1, a mitochondrial aminotransferase involved in branched amino acid synthesis and Ehrlich pathway catabolism [START_REF] Costanzo | The genetic landscape of a cell[END_REF]. With regard to this finding, we propose that the allelic variants of Nrg1 show a different response to nitrogen limitation, which affects the expression of BAT1, leading to a lower transamination rate of valine, leucine and isoleucine taken up from the medium. In this scenario, the overall availability of nitrogen for metabolism would be influenced, which therefore influences the synthesis of propanol.

The gene ALP1 encodes a permease for cationic amino acids [START_REF] Regenberg | Substrate specificity and gene expression of the amino-acid permeases in Saccharomyces cerevisiae[END_REF][START_REF] Sychrova | Yeast sequencing reports. APL1, a yeast gene encoding a putative permease for basic amino acids[END_REF]. The parental variants differ in one non-synonymous SNP (Table 19), which is located in a transmembrane domain. The MTF2621 variant increases the production of propanol by a factor of 1.07 (Figure 22). We can suggest that this variant of Alp1 leads to an increased uptake of amino acids from the medium at the beginning of the fermentation, which explains higher propanol formation. This hypothesis is supported by a significant decrease in fermentative aromas derived from α-keto-isovalerate and α-keto-isocaproate for the MTF2621 allele of the gene (Figure 21). The opposite correlation is reported in the literature, in which a lower overall intracellular concentration of nitrogen leads to a higher level of fermentative aroma production due to a lower transamination rate of α-keto-acids derived from central carbon metabolism [START_REF] Oshita | Clarification of the relationship between fusel alcohol formation and amino acid assimilation by brewing yeast using 13C-labeled amino acid[END_REF][START_REF] Vilanova | Assimilable nitrogen utilisation and production of volatile and non-volatile compounds in chemically defined medium by Saccharomyces cerevisiae wine yeasts[END_REF].

Conclusions

In this study, we confirm the potential of QTL analysis for deciphering the impact of genetic variation on the production of volatile metabolites by Saccharomyces cerevisiae during alcoholic fermentation. We were able to enlarge the analytical power of the approach compared to previous studies by using a comparatively large number of 130 segregants originating from a cross of two wine strains and by increasing the recombination rate of the segregants. This approach enabled us to perform single and multiple QTL mapping strategies, leading to the detection of 65 QTLs with an influence on the formation of volatile metabolites, the production of extracellular main metabolites and general fermentation parameters. Our results confirm that multiple QTL mapping offers the possibility to detect additional, particularly minor loci. We were furthermore able to detect interacting QTLs for three evaluated traits, i.e., the formation of ethyl lactate, ethyl octanoate and propanol. However, it could be seen that an even larger number of segregants is required for a thorough and significant determination of QTL interactions.

We validated 13 genes in 9 QTLs, and of these genes, five (AGP1, ALP1, FAS1, ILV6 and LEU9) have well described roles in metabolic pathways leading to yeast fermentative aroma formation. We could confirm their contribution to volatile production and characterized allelic variants that explain variations in these traits between the parent strains. Furthermore, the previously described fructophilic character of the MTF2621 allele of HXT3 was confirmed in this study. For the other 7 validated genes (AGP2, IXR1, MAE1, NRG1, RGS2, RGT1 and SIR2), we revealed contributions to the formation of fermentative aromas that were not previously reported. The fact that 5 of the 12 validated genes involved in volatile formation have broad regulatory functions on gene expression reveals the significant role of gene regulation in fermentative aroma production. These results demonstrate that QTL mapping is an effective and advisable approach for detecting the impact of globally acting genes on individual traits.

In summary, our findings of QTLs, their interactions and underlying gene variants emphasize the complexity of yeast fermentative aroma formation and provide the most extensive analysis of the links between genetic variation and the fermentative production of sensorial important volatiles to date. The results of this study will lead to the improvement of commercial S. cerevisiae starter cultures for the production of fermented food and beverages by non-GMO methods, such as breeding via marker-assisted selection. As many of the described secondary metabolites are additionally used as biofuel additives or building blocks for chemical syntheses, improved knowledge about allelic variation may also open paths for improving strains in a wide range of biotechnological applications. Additional file 5: Marker map. Graphic representation of marker positions that were used for linkage analysis.

List of Abbreviations

Additional file 6: SNPs in predicted regulatory binding sites of validated genes. Detected SNPs in the 1000-bp upstream region of evaluated target genes that affect binding motifs for regulatory proteins as predicted with YEASTRACT [START_REF] Teixeira | The YEASTRACT database: an upgraded information system for the analysis of gene and genomic transcription regulation in Saccharomyces cerevisiae[END_REF]. Comparison of the strains MTF2621 and MTF2622 with the S. cerevisiae reference strain S288C.

Gene Nucleotide position S288C MTF2621 MTF2622 Affected binding motifs

AGP1 -523 A G A Ume6 ILV6 -814 C G C Msn2, Msn4, Nrg1, Rph1 LEU9 -709 T T C Gcr1, Nrg1 MAE1 -989 T C T Stp1, Stp2 -707 A A G Rtg1, Rtg3 -694 G G A Mot3 -667 T T A Azf1 -351 C C G Rtg1, Rtg3 RGT1 -640 A G A Rtg1, Rtg3, Stb5 -562 C T C Ash1 -438 C A C Rtg1, Rtg3 -241 A C T Ste12
Chapter 2: Development of a QTL mapping approach to detect loci that influence intracellular fluxes (f-QTLs) of the yeast central carbon metabolism.

The second part of the thesis assesses the possibility to extend QTL mapping to the detection of loci with an influence on intracellular metabolic fluxes (f-QTLs). In a wider view, another motivation behind the approach was the fact that the formation of fermentative aroma is connected to pathways of the yeast central carbon metabolism. The possibility to detect QTLs that influence flux distributions could therefore lead to the identification of allelic variants impacting aroma formation.

Extracellular metabolites were determined during the exponential phase of fermentation for all 130 F2-segregant strains. Intracellular fluxes were estimated by feeding the obtained metabolite concentrations into a constraint-based model of yeast central carbon metabolism previously developed in the lab. The estimated fluxes were then used as phenotype data for QTL mapping, relying on the marker map that was obtained during the first part of the project.

The chapter is composed as a research article, which will be submitted before the defense of this thesis.

QTL mapping of modeled metabolic fluxes reveals gene variants impacting yeast central carbon metabolism

Abstract

With the increasing availability of genomics data, elucidating the genotype-phenotype links has become a driving question today. We recently demonstrated great variations in metabolic fluxes of the central carbon metabolism between yeast strains of different origin. However, due to the complexity of flux regulatory mechanisms, we have a limited understanding of how fluxes are modulated. Here, we investigated the potential of the quantitative trait locus (QTL) mapping approach to elucidate the genetic variations responsible for differences in metabolic fluxes (f-QTL), using a population of 130 F2-segregants from a cross of two wine yeast strains. Intracellular metabolic fluxes were estimated by constraint-based modeling and used as quantitative phenotypes. Differences in fluxes were linked to genomic variations in the progeny population. Using this approach, we detected four main QTLs that influence metabolic pathways. The molecular dissection of these QTLs revealed the contribution of two allelic gene variants, PDB1 and VID30, which have an influence on glycolysis, glycerol synthesis, ethanol synthesis, tricarboxylic acid cycle fluxes and transport and excretion of main metabolites.

Our study proves the feasibility of using model-estimated metabolic fluxes to decipher metabolic traits and increases the value of QTL mapping to elucidate the impact of genomic variation. We report the first elucidation of genetic determinants influencing metabolic fluxes. Deducing the mechanisms that control a metabolic network will allow the development of strains for producing food and beverages with optimized metabolite profiles using metabolic engineering or breeding strategies.

Keywords: f-QTL mapping, metabolic fluxes, central carbon metabolism, metabolic model, yeast

Introduction

Many phenotypic traits of the yeast Saccharomyces cerevisiae are dependent on the functional and regulatory properties of central carbon metabolism (CCM), and most of these traits are relevant for industrial processes such as the production of biofuels or the fermentation of foods and beverages. This includes fermentation rate, ethanol yield or the formation of other extracellular metabolites such as glycerol, acetic acid, succinic acid or pyruvic acid. Besides these main metabolites, some volatile aroma molecules are formed from intermediates of yeast CCM. Higher alcohols, fusel acids and their esters, e.g., originate from α-keto-acids, which can derive from pyruvate, phosphoenolpyruvate or oxaloacetate. Medium chain fatty acids and their esters are synthesized from acetyl-CoA. The composition and concentrations of main and volatile metabolites are an essential quality criterion for many fermented foods and beverages such as wine, where they play an important role for its sensorial perception.

Main and volatile metabolites are transformed from substrates of the fermentation medium by a large number of intracellular reactions. The formation of these metabolites is governed by metabolic fluxes, which refer to the turnover rate of substances through metabolic pathways. They reflect the integration of complex regulation on various biological levels, including genetic (transcription, translation, protein modifications, protein-protein interactions) and metabolic levels. Thus, they represent an interesting phenotypic trait, close to the cellular phenotype, which is of interest to study in order to better understand the genetic determinants contributing to the control of these fluxes.

While metabolite turnover rates are difficult to determine experimentally, they can be estimated by modeling with a limited set of data (reviewed by [START_REF] Österlund | Fifteen years of large scale metabolic modeling of yeast: Developments and impacts[END_REF][START_REF] Van Gulik | A metabolic network stoichiometry analysis of microbial growth and product formation[END_REF]. These constraint-based models (CBM) that formulate the metabolic network as a stoichiometry matrix exist in a wide range, from small models focusing on specific subtypes of cellular metabolism to genome-scale networks representing all metabolic reactions.

The first step of predicting fluxes from networks is to add constraints on input and output fluxes. Depending on network size and number of constraints, this approach, which is termed metabolic flux analysis (MFA), can already be sufficient to estimate fluxes. However, in most cases adding constraints on input and output data is not sufficient and there are two ways to deal with it, 13 C-MFA and flux balance analysis (FBA). The 13 C-MFA approach uses 13 C labeled substrates and tracking of 13 C across cellular metabolites generates information to also constraint intracellular fluxes in order to subsequently estimate them. The FBA applies the assumption that cellular functions of biochemical networks in a steady state are limited by physico-chemical constraints (reviewed by [START_REF] Palsson | The challenges of in silico biology[END_REF]. Through linear optimization, FBA chooses the best fitting solution out of a narrowed solution frame defined by the stoichiometry matrix of the CBM [START_REF] Varma | Stoichiometric flux balance models quantitatively predict growth and metabolic by-product secretion in wild-type Escherichia coli W3110[END_REF]. The outcome of this flux prediction depends on an applied objective function. Commonly used objective functions are the maximization of ATP production, minimalization of metabolic adjustment or, as in most cases, maximization of biomass production. The effectivity of these objective functions depends on experimental conditions, constraints and models.

A central issue for understanding cellular physiology is to understand the modulation of metabolic fluxes by genetic or environmental determinants and the application of CBM is a suited approach for that. In a previous study, our group used a combined 13 C-MFA/FBA approach to estimate intracellular fluxes of S. cerevisiae CCM in conditions of modified intracellular redox balance (Celton et al., 2012b). This model was used to assess the sensitivity of flux distribution to environmental conditions for S. cerevisiae wine yeast (Celton et al., 2012a). Another example for the application of FBA is the study of [START_REF] Quirós | Metabolic flux analysis during the exponential growth phase of Saccharomyces cerevisiae in wine fermentations[END_REF], who used a model developed by [START_REF] Vargas | Expanding a dynamic flux balance model of yeast fermentation to genome-scale[END_REF] to evaluate changes of yeast metabolism in high sugar must. In both studies, glycolytic fluxes were demonstrated to show the least variation, while fluxes of the pentose phosphate pathway (PPP) were highly variable. 13 C-MFA, on the other hand, was used to study network robustness [START_REF] Blank | Metabolic-flux and network analysis in fourteen hemiascomycetous yeasts[END_REF] or the effects of deletion mutants [START_REF] Velagapudi | Metabolic flux screening of Saccharomyces cerevisiae single knockout strains on glucose and galactose supports elucidation of gene function[END_REF]. The later study demonstrated interesting links between networks, e.g., a positive correlation between the PPP and biomass yield.

In recent years, research brought vast amounts of information about the genotypic and phenotypic diversity of S. cerevisiae [START_REF] Fay | Evidence for domesticated and wild populations of Saccharomyces cerevisiae[END_REF][START_REF] Legras | Bread, beer and wine: Saccharomyces cerevisiae diversity reflects human history[END_REF][START_REF] Legras | Selection of hypervariable microsatellite loci for the characterization of Saccharomyces cerevisiae strains[END_REF]Liti et al., 2009;[START_REF] Strope | The 100-genomes strains, an S. cerevisiae resource that illuminates its natural phenotypic and genotypic variation and emergence as an opportunistic pathogen[END_REF][START_REF] Warringer | Trait variation in yeast is defined by population history[END_REF]. In these studies, the phenotypic diversity was mainly assessed by comparison of growth parameters in different media. However, several studies started to extend the characterization of diversity to a greater number of phenotypic traits. [START_REF] Spor | Niche-driven evolution of metabolic and life-history strategies in natural and domesticated populations of Saccharomyces cerevisiae[END_REF] evaluated the diversity of six life-history traits and three metabolic traits among S. cerevisiae strains and could separate them into two groups of different life-history strategies. By performing wider phenotypic screening of 72 S. cerevisiae strains from different origins for seven life-history traits and eleven metabolic traits, [START_REF] Camarasa | Phenotypic landscape of Saccharomyces cerevisiae during wine fermentation: evidence for origin-dependent metabolic traits[END_REF] showed that origin has a broad phenotypic impact.

In a previous study, our group assessed the diversity of flux distributions between S. cerevisiae strains from different origins [START_REF] Nidelet | Diversity of flux distribution in central carbon metabolism of S. cerevisiae strains from diverse environments[END_REF]. This was done by using the constraintbased model developed by Celton et al. (2012a) to estimate CCM flux distributions between 43 strains of different ecological origins that were grown under wine fermentation conditions.

The study showed a contrasted image regarding flux variability with quasi-constancy of glycolysis and ethanol synthesis on the one hand, but high variation for other fluxes such as the PPP or acetaldehyde production on the other hand. In addition, the fluxes showed multimodal distributions that could be linked to ecological origin, which reveals an association between genetic origin and metabolic flux manifestation. Wine strains, e.g., exhibit relative increase of PPP and tricarboxylic acid cycle (TCA) cycle fluxes, while the production of acetaldehyde is strongly diminished. Flor strains, on the other hand, show a relative reduction in PPP and TCA cycle fluxes with a higher production of acetic acid [START_REF] Nidelet | Diversity of flux distribution in central carbon metabolism of S. cerevisiae strains from diverse environments[END_REF].

Results gained from flux analysis have also been used for strain improvement by metabolic engineering based on flux predictions [START_REF] Agren | Genome-scale modeling enables metabolic engineering of Saccharomyces cerevisiae for succinic acid production[END_REF][START_REF] Patil | Uncovering transcriptional regulation of metabolism by using metabolic network topology[END_REF]. Possible metabolic engineering strategies to increase ethanol yield where evaluated using CBM by [START_REF] Bro | In silico aided metabolic engineering of Saccharomyces cerevisiae for improved bioethanol production[END_REF], which led to the development of a strain with ethanol yield increased by 3% and glycerol yield decreased by 40%. Further examples of genome scale model guided engineering strategy predictions include the optimization of yields of purine [START_REF] Burgard | Optimization-based framework for inferring and testing hypothesized metabolic objective functions[END_REF], succinic acid [START_REF] Agren | Genome-scale modeling enables metabolic engineering of Saccharomyces cerevisiae for succinic acid production[END_REF][START_REF] Otero | Industrial systems biology of Saccharomyces cerevisiae enables novel succinic acid cell factory[END_REF] or proline [START_REF] Bundy | Evaluation of predicted network modules in yeast metabolism using NMR-based metabolite profiling[END_REF]. Therefore, more knowledge about the impact of genomic variation on metabolic flux distributions has potential for the selection or improvement of strains with diverse applications.

With numerous existing studies, QTL mapping has become an important approach to deeper understand genomic complexity of S. cerevisiae and to decipher the impact of genomic variation on yeast complex traits [START_REF] Swinnen | Genetic mapping of quantitative phenotypic traits in Saccharomyces cerevisiae[END_REF]. This includes investigations of genetic determinants influencing enological important traits, which led to the discovery of allelic variants accounting for variations of these traits [START_REF] Ambroset | Deciphering the molecular basis of wine yeast fermentation traits using a combined genetic and genomic approach[END_REF]Brice et al., 2014a;[START_REF] Eder | QTL mapping of volatile compound production in Saccharomyces cerevisiae during alcoholic fermentation[END_REF][START_REF] Martí-Raga | Genetic causes of phenotypic adaptation to the second fermentation sparkling wines in[END_REF][START_REF] Marullo | Single QTL mapping and nucleotide-level resolution of a physiologic trait in wine Saccharomyces cerevisiae strains[END_REF][START_REF] Noble | Identification of new Saccharomyces cerevisiae variants of the MET2 and SKP2 genes controlling the sulfur assimilation pathway and the production of undesirable sulfur compounds during alcoholic fermentation[END_REF][START_REF] Salinas | The genetic basis of natural variation in oenological traits in Saccharomyces cerevisiae[END_REF][START_REF] Steyer | QTL mapping of the production of wine aroma compounds by yeast[END_REF]. All these studies have in common that the assessed traits were straightforward to quantify. However, difficulties remain to detect QTLs for traits with small variations or more complex to determine, such as intracellular metabolic fluxes.

Thus, the possibility of using QTL mapping to decipher genomic variation impacting metabolic profiles rather than single metabolites would open ways to understand the mechanisms behind metabolic flux distributions and to engineer strains with superior metabolic properties for various applications.

To achieve this, we phenotyped 130 meiotic F2-segregants from a cross of two wine yeast strains for their production of extracellular main metabolites during exponential phase. We modeled intracellular fluxes of the yeast CCM by feeding these experimentally determined metabolite concentrations into a constraint-based model. Subsequently, we used these estimated fluxes as phenotypic data to perform QTL mapping on metabolic flux distributions.

With this approach we were able to detect four QTLs with an influence on various metabolic fluxes. By performing reciprocal hemizygosity analysis (RHA), we confirmed the robustness of the method by validating the role of two genes, PDB1 and VID30, within two QTLs. The allelic variants of these genes show a different effect on fluxes of the glycolysis, ethanol synthesis, glycerol synthesis, TCA cycle and the excretion of TCA cycle metabolites.

2 Materials and methods

Media

Yeast strains were cultured at 28 °C in yeast extract peptone dextrose (YPD) media, containing 10 g/L yeast extract, 20 g/L peptone and 20 g/L glucose. Solid YPD media contained 1.5% agar.

Selective YPD media contained 200 µg/mL geneticin (G418), 200 µg/mL nourseothricin (clonNAT) or 200 µg/mL hygromycin B were used.

Wine fermentations were carried out in synthetic grape must (SM200) described by [START_REF] Bely | Automatic detection of assimilable nitrogen deficiencies during alcoholic fermentation in oenological conditions[END_REF]. It contains 100 g/L of each sugar, glucose and fructose, and 200 mg/L of assimilable nitrogen. The amino acid composition hereby mimics the nitrogen content of standard grape juice.

Strain selection and generation

The haploid S. cerevisiae strains MTF2621 (4CAR1 [ΔHO::Neo r ]) and MTF2622 (T73 [ΔHO::Nat r ]) were selected for the study according to their different need for nitrogen during wine fermentation. The requirement of nitrogen was estimated by using an approach based on the addition of nitrogen to keep the CO2 production rate constant during nitrogen limitation (Brice et al., 2014b). The strain T73 belongs to the phylogenetic clade of wine strains. The strain 4CAR1, however, belongs to the group of champagne strains, which originated through crossings between strains of the wine clade and the flor clade [START_REF] Coi | A set of haploid strains available for genetic studies of Saccharomyces cerevisiae flor yeasts[END_REF]. From a crossing of both parent cells, 130 F2-segregants were generated as described in [START_REF] Eder | QTL mapping of volatile compound production in Saccharomyces cerevisiae during alcoholic fermentation[END_REF].

Phenotyping of strains

Segregants were fermented in duplicates with the parent strains as controls. The strains were grown overnight in 50 mL YPD media. The cell density was determined using a Multisizer™ 3 Coulter Counter (Beckman Coulter). Sterilized 300-mL glassware mini fermenters were filled with 280 mL of SM200 and closed with an air lock. The fermenters were inoculated to a cell density of 1 × 10 6 cells/mL, weighed and left at 24 °C under stirring (300 rpm).

In order to determine the concentration of extracellular metabolites during the exponential phase, a sample was taken when approximately 10 g/L of CO2 were produced. This was determined by weighting the fermenters regularly to draw the weight decrease caused by the release of CO2. The dry weight was determined in duplicate by filtering 10 mL of cell suspension through a nitrocellulose membrane with a porosity of 0.45 μm (Millipore, France)

and known dry weight. The membrane was rinsed twice with 10 mL of distilled water, dried for 48 h and weighed to determine the dry biomass of the sample.

Modeling of metabolic fluxes

Extracellular metabolite concentrations (in mmol/mL) and dry mass weight (in g/L) obtained by the phenotyping of segregant strains were used to constrain a model of yeast central carbon metabolism previously developed (DynamoYeast, Celton et al., 2012a). The model covers 68 reactions (Additional file 10), 61 metabolites (Additional file 9) and distinguishes three compartments, the extracellular medium, cytoplasm and mitochondria. All predictions were performed using the programming language R v3.2.3 with the R/sybil v2.0.0 and R/sybilSBML v2.0.11 libraries [START_REF] Gelius-Dietrich | sybil-Efficient constraint-based modelling in R[END_REF]. Due to inexplicable block effects concerning the determination of succinate concentrations, succinate fluxes were constraint by setting a fixed range, corresponding to the maximum succinate flux variation between S. cerevisiae strains determined in our previous study [START_REF] Nidelet | Diversity of flux distribution in central carbon metabolism of S. cerevisiae strains from diverse environments[END_REF]. The error margin for the flux boundaries of the model was set to ±2.5%. We obtained the flux distribution throughout the metabolic network for each segregant by FBA with minimization of glucose input as objective function. For the modeling approach, fructose was hereby also considered as glucose, as this did not impact flux predictions. Finally, estimated fluxes were normalized to sugar uptake.

QTL mapping

QTL mapping of modeled metabolic fluxes was performed using a marker map that we obtained by whole genome sequencing of the individual segregant strains during a previous study [START_REF] Eder | QTL mapping of volatile compound production in Saccharomyces cerevisiae during alcoholic fermentation[END_REF]. The statistical analyses were carried out using the programming language R v3.2.3 (www.r-project.org) with the R/qtl v1.40-8 and R/eqtl v1.1-7 libraries [START_REF] Broman | R/qtl: QTL mapping in experimental crosses[END_REF]. QTL mapping was performed with two different phenotype models, the normal model using Haley-Knott regression and a non-parametric analysis, resulting in logarithm of odds (LOD) scores for each marker and pseudo-marker every 2.5 cM (interval mapping method). An interval estimate of the location of each QTL was obtained as the 1-LOD support interval, the region in which the LOD score is within 1 unit of the peak LOD score. If the same locus was detected with both models, the results with the higher LOD score were selected.

Reciprocal hemizygosity analysis

Molecular dissection of found QTLs was performed using RHA [START_REF] Steinmetz | Dissecting the architecture of a quantitative trait locus in yeast[END_REF][START_REF] Warringer | Yeast reciprocal hemizygosity to confirm the causality of a quantitative trait loci-associated gene[END_REF]. Target genes in QTLs were chosen according to a biological function associated with metabolic processes and the gene's proximity to the determined QTL peak.

The gene sequences were deleted in both parent strains by homologous recombination with a disruption cassette containing the hygromycin B resistance gene (hph r ). Positive integration was selected by plating the transformed cells on YPD-agar plates containing hygromycin B.

Correct gene deletion was verified by PCR using a primer that binds in the upstream region of the deleted gene and a primer that binds within the deletion cassette. Deleted parent strains were subsequently mated with the opposite undeleted parent to form a heterozygote that is hemizygous for the target gene. The obtained strains were phenotyped in triplicate (2.3).

Significant influence of the allelic gene variant on the trait was evaluated by student's t-test.

3 Results

Phenotyping of strains

Using the obtained metabolite data, metabolic fluxes of the CCM were predicted for all strains.

As the single fluxes within main metabolic pathways strongly coincide with each other, a selection of fluxes representative for main metabolic pathways was made to facilitate the following analyses (Additional file 11). Principal component analysis (PCA) of these selected fluxes was performed to assess flux correlations and to evaluate the variation between parent and segregant strains (Figure 23). With the first two dimensions explaining 69.1% of trait variation, the PCA of estimated fluxes well depicts the variation among strains. A positive correlation between fluxes of the PPP, TCA cycle oxidative branch and biomass formation can be seen. Together, these fluxes are negatively correlated to the upper glycolysis. Negative correlation can furthermore be found between fluxes of the lower glycolysis/ethanol synthesis and glycerol formation. It can be seen that the parent strains behave similar and show only small difference in their flux profile, while the segregant strains are more divergent. This is confirmed by the visualization of trait distributions (Additional file 12). The parental strains are located within the population of segregants for the majority of traits. To further assess variation among strains, coefficients of variation for estimated fluxes were separately calculated for parent and segregant strains (Figure 24). We show that the variation between segregant strains regarding the determined fluxes exceeds the variation between the parent strains, ranging from a factor of 2.4 for acetic acid production (Acald_Ac) to a factor of 268.8 for ethanol excretion (Eth_t). These results confirm the conclusion drawn by the PCA (Figure 23), explicitly that the parent strains do not show as much differences in flux distribution as the segregant population. However, differences in variation can be seen between single fluxes. To better visualize trait variation, the distributions for each flux were plotted around the mean value (Figure 25). While fluxes of the glycolysis and ethanol synthesis only vary about ±2.5% around the mean value, fluxes of the PPP or the production of metabolites such as glycerol or acetic acid diverge up to 200% around the mean. Most fluxes are normally distributed, however, few outliers can be seen, particularly for glycerol, pyruvate and ethanol excretion. The distributions of three fluxes differ from a normal distribution, the synthesis of acetyl-CoA from acetate (Ac_Accoa), the NADP dependent mitochondrial flux from isocitrate to AKG (Icit_Akg_m_nadp) of the TCA cycle oxidative branch and the succinate excretion (Succ_t). In the case of Ac_Accoa, a subpopulation of segregant strains shows a strongly reduced flux towards acetyl-CoA. In the case of Icit_Akg_m_nadp, the analyses indicate that this flux is inactive in the majority of segregant strains, while this is not the case for the parental strains. In the case of Succ_t, two populations can be distinguished, which indicates a major influence of one allele on the trait. However, the flux of succinate excretion can be subject to greater inaccuracy since the flux was not constraint by experimental data but by an experimentally determined possible range (2.4).

Genome wide identification of QTLs influencing metabolic carbon fluxes

In a first step, QTL mapping was performed on obtained metabolite yields during exponential phase using the segregant marker map that we obtained from our previous study [START_REF] Eder | QTL mapping of volatile compound production in Saccharomyces cerevisiae during alcoholic fermentation[END_REF] (Table 20). This analysis led to the detection of 8 QTLs on 5 chromosomes influencing 7 traits. This included the majority of determined metabolite production yields as well as differences in sugar uptake (G/F ratio). The highest detected LOD-score was 4.71 for QTL chr4@152.6 influencing the succinate yield. Therefore, almost 16% of trait variation can be explained by the locus. The identification of two similar likely QTLs with an influence on succinate production does not confirm the previous assumption that one locus has a major impact on the trait. In a second step, QTL mapping was performed on estimated intracellular carbon fluxes (Table 21). A total of 4 QTLs influencing 7 traits could be detected on chromosomes II, V, VII and VIII. This included fluxes of the glycolysis/ethanol synthesis, glycerol synthesis, TCA cycle oxidative branch, biomass formation and metabolite transport/excretion. No QTLs could be detected for fluxes of the PPP, TCA cycle reductive branch and glutamate cycle, although these fluxes showed the most substantial variation among the segregant strains (Figure 24). The highest LOD-score of 4.63 was found for the influence of QTL chr7@18.0 on glycolysis and ethanol synthesis, meaning that 15.7% of the trait variation can be explained by the locus. The region of the QTL furthermore influences the most traits. Besides glycolysis and ethanol synthesis, fluxes of the biomass formation, TCA cycle oxidative branch and metabolite transport/excretion are affected.

Validation of detected QTLs

The 2 QTLs with the highest LOD-score detected to influence estimated intracellular fluxes were selected for validation and candidate genes were chosen according to their distance to the QTL peak and biological function related to central carbon metabolism (Table 22). The impact of these genes and their allelic variants was evaluated by RHA. The constructed strains were phenotyped for their formation of extracellular metabolites during exponential phase, intracellular fluxes were estimated using these metabolite concentrations and the differences between the alleles regarding flux distributions were assessed (Table 22). In QTL chr2@222.9, detected to influence glycerol synthesis, PDB1 was assessed by RHA and a significant influence of the allelic variants on the trait was detected. The allelic variants of the gene differ in three non-synonymous SNPs (Table 23). The MTF2621 allele of PDB1 increases glycerol synthesis fluxes by 5% (Figure 26).

In region chrVII:40,689..82,449, detected to influence glycolysis, ethanol synthesis, biomass formation, TCA cycle fluxes and transport/excretion of TCA cycle metabolites, two genes were selected for validation, HAP2 and VID30. While the variants of HAP2 did not show significant difference regarding the metabolic fluxes, the contribution of VID30 to the detected phenotype variations could be validated (Table 22). The allelic variants of VID30 differ in three non-synonymous SNPs (Table 23). One SNP lies in the 1000-bp upstream region of the gene, however, no recognized binding site is affected according to the YEASTRACT database [START_REF] Teixeira | The YEASTRACT database: an upgraded information system for the analysis of gene and genomic transcription regulation in Saccharomyces cerevisiae[END_REF]. The MTF2621 allele of Vid30 was found to decrease fluxes of glycolysis and ethanol synthesis by 1% (Figure 26). As the total variation regarding ethanol synthesis is only 6% among the segregant strains (Figure 25), this decrease by 1% was considered significant. In addition, the excretion of pyruvate, α-ketoglutarate and succinate was reduced up to 27%. A significant influence of the alleles on the TCA cycle oxidative branch could not be detected. In contrast, the reductive branch of the TCA cycle was significantly affected, with the MTF2621 allele leading to a decrease in fluxes from malate to succinate by 10%.

Distribution of PDB1 and VID30 alleles in S. cerevisiae population

To visualize the natural variation of validated target genes within the S. cerevisiae population and to potentially link the variants to strain origins, phylogenetic trees were drawn using public available PDB1 and VID30 gene sequences (Figure 27 and Additional file 13). Regarding PDB1, the allelic variants from yeast strains of different origin do not show much nucleotidic variation. Two main clusters can be seen, one consisting of mostly laboratory strains and the other of mostly wine strains. The allelic variants of the parental strains of this study are comparatively close. In contrast to this, the phylogenetic tree of VID30 variants displays more variation between the strains. A probable explanation is the larger gene size. Several clusters can be distinguished, a laboratory strain cluster, a cluster consisting of African and sake strains and a wine strain cluster with a subcluster of mixed strains. The parental variants are more separated. While the MTF2621 allele is similar to the allele of strain EC1118, a genotypically close wine x flor strain, the MTF2622 allele is located within the mixed cluster.

Discussion

Many S. cerevisiae traits of interest for industrial applications are dependent on flux distributions within the CCM. We recently showed a pathway-dependent variability of flux distributions between S. cerevisiae strains, which was linked to the strain origin for some fluxes [START_REF] Nidelet | Diversity of flux distribution in central carbon metabolism of S. cerevisiae strains from diverse environments[END_REF]. These findings suggest the existence of a stock of genetic resources that can help to understand the genetic basis of flux distribution and to identify relevant targets for yeast strain improvement. In recent years, powerful methods such as QTL mapping have been developed to link phenotypic and genomic variations. Our objective was to assess the potential of QTL mapping to detect genomic regions influencing metabolic fluxes (f-QTLs).

To this end, we used a population of 130 F2-segregants obtained from a cross of two wine yeast strains. Intracellular carbon fluxes were estimated for these strains using a constraintbased stoichiometric model of yeast CCM. Analysis of the flux deviations among the population of segregants indicated a positive correlation between fluxes of the PPP, TCA cycle oxidative branch and biomass formation, while these fluxes are negatively correlated to the upper glycolysis. Negative correlations were furthermore found between fluxes of the lower glycolysis/ethanol synthesis and glycerol formation (Figure 23). These observations are consistent with previous studies on S. cerevisiae strains [START_REF] Heyland | Correlation between TCA cycle flux and glucose uptake rate during respiro-fermentative growth of Saccharomyces cerevisiae[END_REF][START_REF] Nidelet | Diversity of flux distribution in central carbon metabolism of S. cerevisiae strains from diverse environments[END_REF].

Although the parent strains do not display high variation for most modeled fluxes, a substantial variation among the segregants was observed (Figure 24). For some fluxes, for example PPP or main metabolite synthesis fluxes, the variation among segregants reached the variation among strains from different ecological origins determined by [START_REF] Nidelet | Diversity of flux distribution in central carbon metabolism of S. cerevisiae strains from diverse environments[END_REF].

This emphasizes the complex nature of intracellular flux determination and indicates a rich genomic resource for metabolic profile optimization.

With 8 detected QTLs, the number of regions influencing metabolite yields during exponential phase was higher than the detected 4 QTLs influencing modeled metabolic fluxes. However, the regions from both QTL mappings differ from each other, showing that the modeling step was crucial for f-QTL detection.

All QTLs were compared to 8 loci detected by our previous study to influence extracellular metabolite production after 80% of fermentation using the same yeast cross [START_REF] Eder | QTL mapping of volatile compound production in Saccharomyces cerevisiae during alcoholic fermentation[END_REF]. Only QTL chr7@18.0, which influences fluxes of the glycolysis, ethanol synthesis, biomass production, TCA cycle and transport/excretion of TCA cycle metabolites, was detected in our previous study to influence the pyruvate yield after 80% of fermentation. This indicates that the difference in flux distribution caused by QTL chr7@18.0 has a long-lasting effect on metabolite formation that can still be detected at the end of fermentation. However, regarding the concentrations of extracellular metabolites during exponential phase and after 80% of fermentation, all detected regions differ from each other and there are no common QTLs affecting metabolite production during both phases of fermentation. This indicates that different genomic regions could control metabolite production in growth phase and stationary phase, which actually corresponds to very different physiologic states of yeast during wine fermentation.

Within the two detected QTLs with the highest LOD-score, target genes with a potential role in CCM were identified and assessed by RHA. This study revealed the role of PDB1 and VID30 in the evaluated traits.

PDB1, shown to influence fluxes of the glycerol synthesis (Table 22, Figure 26), encodes the beta subunit of the pyruvate dehydrogenase (PDH), which is part of the large multienzyme PDH complex [START_REF] Miran | Characterization of PDH beta 1, the structural gene for the pyruvate dehydrogenase beta subunit from Saccharomyces cerevisiae[END_REF]. Together with the other components dihydrolipoamide acetyltransferase and dihydrolipoamide dehydrogenase, the PDH complex converts pyruvate into acetyl-CoA (Pronk et al., 1996b). The allelic variants of the gene differ in three nonsynonymous SNPs (Table 23) of which one, SNP V289I, lies in the pyruvate-ferredoxin oxidoreductase domain II of the protein. A possible explanation for the impact of Pdb1 variants on glycerol fluxes would be that the MTF2621 allele of Pdb1 shows an increased conversion rate of pyruvate to acetyl-CoA, which leads to a higher formation of the redox cofactor NADH. The resulting cofactor excess is then compensated through an increased glycerol synthesis, which maintains redox balance by NADH consumption (van Dijken and Scheffers, 1986). However, no significant difference in the estimated flux from pyruvate to acetyl-CoA (Pyr_Accoa_m) was detected by RHA for the alleles of PDB1.

The second validated gene, VID30, influences fluxes of the glycolysis/ethanol synthesis, TCA cycle reductive branch and excretion of TCA cycle metabolites (Table 22, Figure 26). Two functions of Vid30 could potentially account for these observed differences, the regulation of genes involved in glutamate/glutamine synthesis and the degradation of various metabolic enzymes.

The expression of VID30 is repressed by ammonia and upregulated in response to low ammonia levels, a characteristic limitation during wine fermentation. Vid30 regulates various nitrogen catabolic genes, including GDH1, GDH2, GDH3, GLN1 and GLT1. These genes express enzymes involved in the synthesis (and interconversion) of glutamate and glutamine from AKG and ammonia, therefore explaining the role of Vid30 for central carbon metabolism since AKG is part of fluxes of the TCA cycle oxidative branch. Gdh1, Gdh3 and Gln1 catalyze reactions from AKG to glutamine [START_REF] Avendaño | GDH3 encodes a glutamate dehydrogenase isozyme, a previously unrecognized route for glutamate biosynthesis in Saccharomyces cerevisiae[END_REF][START_REF] Mitchell | Biochemical and physiological aspects of glutamine synthetase inactivation in Saccharomyces cerevisiae[END_REF][START_REF] Moye | Nucleotide sequence of yeast GDH1 encoding nicotinamide adenine dinucleotide phosphate-dependent glutamate dehydrogenase[END_REF], whereas Gdh2 catalyzes the conversion of glutamate to AKG [START_REF] Miller | Role of NAD-linked glutamate dehydrogenase in nitrogen metabolism in Saccharomyces cerevisiae[END_REF]. Glt1 synthesizes glutamate from either AKG or glutamine [START_REF] Filetici | Sequence of the GLT1 gene from Saccharomyces cerevisiae reveals the domain structure of yeast glutamate synthase[END_REF]. In low ammonia environment, Vid30 behaves as a positive regulator for GDH1, GDH3 and GLT1, which increases the flux from AKG to glutamate (van der [START_REF] Van Der Merwe | Ammonia regulates VID30 expression and Vid30p function shifts nitrogen metabolism toward glutamate formation especially when Saccharomyces cerevisiae is grown in low concentrations of ammonia[END_REF]. Since a decreased AKG production was detected for the MTF2621 allele of Vid30 (Figure 26), we suggest that this variant could stimulate an increased flux from AKG to glutamate through positive regulation of GDH1, GDH3 and GLT1.

Another potential role of Vid30 in central carbon metabolism is its regulation of metabolic enzymes through degradation. When glucose-starved yeast is again transferred to glucoserich medium, e.g., during inoculation, the metabolism increases the expression of glycolytic enzymes and simultaneously inactivates gluconeogenetic enzymes through catabolite inactivation. Vid30 possesses two functions in this process. It acts as a subunit of the glucose induced degradation (GID) protein complex that performs the ubiquitination of enzymes, which leads to their proteasome dependent inactivation [START_REF] Hämmerle | Proteins of newly isolated mutants and the amino-terminal proline are essential for ubiquitin-proteasome-catalyzed catabolite degradation of fructose-1, 6-bisphosphatase of Saccharomyces cerevisiae[END_REF][START_REF] Menssen | Exploring the topology of the Gid complex, the E3 ubiquitin ligase involved in cataboliteinduced degradation of gluconeogenic enzymes[END_REF][START_REF] Regelmann | Catabolite degradation of fructose-1, 6-bisphosphatase in the yeast Saccharomyces cerevisiae: a genome-wide screen identifies eight novel GID genes and indicates the existence of two degradation pathways[END_REF][START_REF] Santt | The yeast GID complex, a novel ubiquitin ligase (E3) involved in the regulation of carbohydrate metabolism[END_REF]. Furthermore, Vid30 plays an important role for the formation of vesicles of the vacuole import and degradation pathway [START_REF] Alibhoy | Vid30 is required for the association of Vid vesicles and actin patches in the vacuole import and degradation pathway[END_REF], which carries out the degradation of enzymes expressed under growth on nonfermentable carbon sources [START_REF] Huang | Identification of novel vesicles in the cytosol to vacuole protein degradation pathway[END_REF][START_REF] Hung | Degradation of the gluconeogenic enzymes fructose-1, 6-bisphosphatase and malate dehydrogenase is mediated by distinct proteolytic pathways and signaling events[END_REF][START_REF] Shieh | In vitro reconstitution of glucose-induced targeting of fructose-1, 6-bisphosphatase into the vacuole in semi-intact yeast cells[END_REF]. Regulation peformed in this manner includes the turnover of hexose transporters Hxt3 and Hxt7 [START_REF] Snowdon | Components of the Vid30c are needed for the rapamycin-induced degradation of the high-affinity hexose transporter Hxt7p in Saccharomyces cerevisiae[END_REF][START_REF] Snowdon | Regulation of Hxt3 and Hxt7 turnover converges on the Vid30 complex and requires inactivation of the Ras/cAMP/PKA pathway in Saccharomyces cerevisiae[END_REF]. Furthermore, various enzymes are regulated through degradation by Vid30 that catalyze gluconeogenesis reactions such as fructose-1,6-bisphosphatase, cytosolic malate dehydrogenase, isocitrate lyase and phosphoenolpyruvate carboxykinase [START_REF] Carlson | Glucose repression in yeast[END_REF][START_REF] Gancedo | Yeast carbon catabolite repression[END_REF][START_REF] Holzer | Proteolytic catabolite inactivation in Saccharomyces cerevisiae. Revis[END_REF][START_REF] López-Boado | Catabolite inactivation of isocitrate lyase from Saccharomyces cerevisiae[END_REF]. The reactions catalyzed by these enzymes strongly affect fluxes of the glycolysis and TCA cycle.

The allelic variants of VID30 differ in three non-synonymous SNPs (Table 23) of which one, SNP V882I, lies in the CTLH/CRA domain of the protein, a protein-protein interaction domain also found in other components of the GID complex. We propose that the SNPs in the MTF2621 variant of Vid30 influence the protein's ability to inactivate hexose transporters and gluconeogenesis enzymes by degradation, hypothetically by an altered affinity to other components of the GID complex. This hypothesis is supported by the observed influence of the allelic variants on fluxes of the TCA cycle reductive branch (Figure 26), as the cytosolic malate dehydrogenase, which catalyzes the reaction from malate to oxaloacetate, is among the enzymes inactivated by Vid30 [START_REF] Hung | Degradation of the gluconeogenic enzymes fructose-1, 6-bisphosphatase and malate dehydrogenase is mediated by distinct proteolytic pathways and signaling events[END_REF]. Furthermore, the reported SNPs could affect the role of Vid30 in regulation of enzymes involved in the synthesis of glutamate from AKG. This hypothesis is supported by the detected significant influence of the Vid30 alleles on AKG formation (Figure 26). On the other hand, significant difference between the alleles in the flux from AKG to glutamate could not be detected by RHA. The difference in AKG formation could also be explained by the role of Vid30 for the degradation of isocitrate lyase. The enzyme catalyzes the reaction from isocitrate to succinate, which could influence AKG synthesis.

Conclusion

In this study we prove the feasibility of using modeled phenotypic data to detect regions in the genome with an influence on the underlying traits. We used extracellular main metabolites to estimate intracellular fluxes of the central carbon metabolism of S. cerevisiae using a constraint-based model. This led us to the integration of otherwise independent quantifiable traits. With this approach we detected 4 QTLs with an influence on 4 main metabolic pathways and various metabolite transport and excretion fluxes. These QTLs could not be found by linkage analysis considering extracellular metabolite concentration alone. As reported in the literature and seen in our study, variances of certain intracellular fluxes, such as the glycolysis, are in general low. Therefore, increased statistical power is needed for a more thorough determination of the impact of genomic variation on these fluxes. This could be achieved by increasing the number of segregants or by performing multiple QTL mapping strategies, which has the potential to find QTLs with minor contributions.

The relevance of our approach was further confirmed by the validation of two target genes within found QTLs, PDB1 and VID30. The allelic variants of PDB1 cause differences in fluxes of the glycerol synthesis that we connected to redox imbalances as a result of altered pyruvate conversion. The variants of VID30 impact fluxes of the glycolysis, ethanol synthesis and TCA cycle, which we propose to be caused by different regulation of enzymes catalyzing glutamate formation or a different catabolite induced degradation of enzymes involved in sugar uptake, gluconeogenesis and TCA cycle.

Compared to strains of other origins, the parental variants of the evaluated target genes are comparatively close. The characterization of more distant variants and the evaluation of their influence on intracellular flux distributions will increase knowledge about genetic resources that bear further potential to shape the metabolic profile of strains.

In summary, our findings of QTLs and allelic variants impacting metabolic fluxes increase knowledge about the links between genomic variation and yeast metabolic properties. The fact that we could demonstrate the applicability of QTL mapping on modeled phenotypic data, especially modeled metabolic fluxes, will open ways to improve strains not only for fermented beverages but for manifold purposes, e.g., the production of biofuels or other bulk and fine chemicals.

Supplementary Information

Additional file 8: List of primers used in this study with nucleotide sequence (5' -> 3')

Primer name

Metabolite abbreviation Compartment

Metabolite description

13dpg[c] cytoplasm 3-Phospho-D-glyceroyl-phosphate 2pg[c] cytoplasm D-Glycerate-2-phosphate 3pg[c] cytoplasm 3-Phospho-D-glycerate 6pgc[c] cytoplasm 6-Phospho-D-gluconate 6pgl[c] cytoplasm 6-phospho-D-glucono-1,5-lactone ac[c] cytoplasm Acetate ac[m] mitochondria Acetate acald[c] cytoplasm Acetaldehyde acald[m] mitochondria Acetaldehyde accoa[c] cytoplasm Acetyl-CoA accoa[m] mitochondria Acetyl-CoA adp[c] cytoplasm ADP adp[m] mitochondria ADP akg[c] cytoplasm Alpha ketoglutarate akg[m] mitochondria Alpha ketoglutarate amp[m] mitochondria AMP atp[c] cytoplasm ATP atp[m] mitochondria ATP cit[m] mitochondria Citrate CO2[c] cytoplasm CO2 CO2[m] mitochondria CO2 coa[c] cytoplasm Coenzyme-A coa[m] mitochondria Coenzyme-A dhap[c] cytoplasm Dihydroxyacetone-phosphate e4p[c] cytoplasm D-Erythrose-4-phosphate etoh[c] cytoplasm Ethanol etoh[m] mitochondria Ethanol f6p[c] cytoplasm D-Fructose-6-phosphate fdp[c] cytoplasm D-Fructose-1,6-bisphosphate fum[c] cytoplasm Fumarate fum[m] mitochondria Fumarate g3p[c] cytoplasm Glyceraldehyde-3-phosphate g6p[c] cytoplasm D-Glucose-6-phosphate glc[c] cytoplasm D-Glucose gln[c] cytoplasm L-Glutamine glu[c] cytoplasm L-Glutamate glu[m] mitochondria L-Glutamate glyc[c] cytoplasm Glycerol glyc3p[c] cytoplasm Glycerol-3-phosphate icit[m] mitochondria Isocitrate mal[c] cytoplasm L-Malate mal[m] mitochondria L-Malate nad[c] cytoplasm Nicotinamide-adenine-dinucleotide nad[m] mitochondria Nicotinamide-adenine-dinucleotide nadh[c] cytoplasm Nicotinamide-adenine-dinucleotide-reduced nadh[m] mitochondria Nicotinamide-adenine-dinucleotide-reduced nadp[c] cytoplasm Nicotinamide-adenine-dinucleotide-phosphate nadp[m] mitochondria Nicotinamide-adenine-dinucleotide-phosphate nadph[c] cytoplasm Nicotinamide-adenine-dinucleotide-phosphate-reduced nadph[m] mitochondria Nicotinamide-adenine-dinucleotide-phosphate-reduced oaa[c] cytoplasm Oxaloacetate oaa[m] mitochondria Oxaloacetate pyr[c] cytoplasm Pyruvate pyr[m] mitochondria Pyruvate r5p[c] cytoplasm alpha-D-Ribose-5-phosphate ru5p[c] cytoplasm D-Ribulose-5-phosphate s7p[c] cytoplasm Sedoheptulose-7-phosphate succ[c] cytoplasm Succinate succ[m] mitochondria Succinate succoa[m] mitochondria Succinyl-CoA xu5p[c] cytoplasm D-Xylulose-5-phosphate
Additional file 10: Estimated metabolic reactions with their corresponding compartment. * flux abbreviations are encoded as substrate and product connected with "_". For mitochondrial reactions we added "_m". Extracellular transport and mitochondrial transport reactions are marked with "_t" and a "_tm" respectively. Metabolite abbreviations can be found in Additional file 10.

Flux abbrevation* Compartment

Reaction Glc_G6p cytoplasm glc[c] + atp[c]  g6p[c] + adp[c] G6p_F6p cytoplasm g6p[c] ⇌ f6p[c] F6p_Fdp cytoplasm f6p[c] + atp[c]  fdp[c] + adp[c] Fdp_Dhap cytoplasm fdp[c] ⇌ dhap[c] + g3p[c] Dhap_G3p cytoplasm dhap[c] ⇌ g3p[c] G3p_13dpg cytoplasm g3p[c] + nad[c] ⇌ 13dpg[c] + nadh[c] 13dpg_3pg cytoplasm 13dpg[c] + adp[c] ⇌ 3pg[c] + atp[c] 3pg_2pg cytoplasm 3pg[c] ⇌ 2pg[c] 2pg_Pep cytoplasm 2pg[c] ⇌ pep[c] Pep_Pyr cytoplasm pep[c] + adp[c]  pyr[c] + atp[c] G6p_6pgl cytoplasm g6p[c] + nadp[c] ⇌ 6pgl[c] + nadph[c] 6pgl_6pgc cytoplasm 6pgl[c]  6pgc[c] 6pgc_Ru5p cytoplasm 6pgc[c] + nadp[c]  CO2[c] + nadph[c] + ru5p[c] Ru5p_Xu5p cytoplasm ru5p[c] ⇌ xu5p[c] Ru5p_R5p cytoplasm ru5p[c] ⇌ r5p[c] R5p_S7p cytoplasm r5p[c] + xu5p[c] ⇌ g3p[c] + s7p[c] E4p_F6p cytoplasm e4p[c] + xu5p[c] ⇌ f6p[c] + g3p[c] S7p_E4p cytoplasm g3p[c] + s7p[c] ⇌ e4p[c] + f6p[c] Dhap_Glyc3p cytoplasm dhap[c] + nadh[c]  glyc3p[c] + nad[c] Glyc3p_Glyc cytoplasm glyc3p[c]  glyc[c] Pyr_Acald cytoplasm pyr[c]  acald[c] + CO2[c] Acald_Eth cytoplasm acald[c] + nadh[c]  etoh[c] + nad[c] Acald_Ac cytoplasm acald[c] + nadp[c]  ac[c] + nadph[c] Ac_Accoa cytoplasm ac[c] + 2 atp[c]  accoa[c] + 2 adp[c] Pyr_Oaa cytoplasm pyr[c] + atp[c] + CO2[c]  oaa[c] + adp[c] Acald_Eth_m mitochondria acald[m] + nadh[m] ⇌ etoh[m] + nad[m] Acald_Ac_m mitochondria acald[m] + nadp[m]  ac[m] + nadph[m] Oaa_Mal cytoplasm oaa[c] + nadh[c] ⇌ mal[c] + nad[c] Mal_Fum cytoplasm mal[c]  fum[c] Fum_Succ cytoplasm fum[c]  succ[c] Akg_Glu cytoplasm akg[c] + nadph[c] ⇌ glu[c] + nadp[c] Glu_Akg_m mitochondria glu[m] + nad[m]  akg[m] + nadh[m] Pyr_Accoa_m mitochondria pyr[m] + nad[m]  accoa[m] + nadh[m] + CO2[m] Oaa_Cit_m mitochondria accoa[m] + oaa[m]  cit[m] Cit_Icit_m mitochondria cit[m] ⇌ icit[m] Icit_Akg_m_nad mitochondria icit[m] + nad[m]  akg [m]+ CO2[m] + nadh[m] Icit_Akg_m_nadp mitochondria icit[m] + nadp[m]  akg[m] + CO2[m] + nadph[m] Akg_Succoa_m mitochondria akg[m] + nad[m]  succoa[m] + CO2[m] + nadh[m] Succoa_Succ_m mitochondria succoa[m] + adp[m]  succ[m] + atp[m] Oaa_Mal_m mitochondria oaa[m] + nadh[m]  mal[m] + nad[m] Mal_Fum_m mitochondria mal[m]  fum[m] Fum_Succ_m mitochondria fum[m]  succ[m] Mal_Pyr_m mitochondria mal[m] + nadp[m]  CO2[m] + nadph[m] + pyr[m] Acald_tm transport acald[c] ⇌ acald[m] Succ_tm transport succ[c] + atp[c]  succ[m] + adp[c] Mal_tm transport mal[c] + atp[c]  mal[m] + adp[c] Mal_Succ_tm transport mal[c] + succ[m] ⇌ mal[m] + succ[c] Pyr_tm transport pyr[c] + atp[c]  pyr[m] + adp[c] Akg_tm transport akg[c] ⇌ akg[m] Oaa_tm transport oaa[c] + atp[c]  oaa[m] + adp[c] Eth_tm transport etoh[c] ⇌ etoh[m] CO2_tm transport CO2[c] ⇌ CO2[m] Ac_tm transport ac[c] ⇌ ac[m] Accoa_tm transport accoa[c]  accoa[m] Glu_tm transport glu[c] + atp[c]  glu[m] + adp[c] Glc_t transport glc[c]  Eth_t transport etoh[c]  Ac_t transport ac[c]  Pyr_t transport pyr[c]  Akg_t transport akg[c]  Succ_t transport succ[c]  But_t transport but[c]  Aceto_t transport aceto[c]  Acald_t transport acald[c]  Glyc_t transport glyc[c]  CO2_t transport CO2[c] ⇌ ATP_Shuttle cytoplasm
Additional file 11: Used selection of 20 fluxes representative for main metabolic pathways.

Flux abbrevation* Pathway Reaction

G6p_F6p

Upper glycolysis g6p * flux abbreviations are encoded as substrate and product connected with "_". For mitochondrial reactions we added "_m". Extracellular transport and mitochondrial transport reactions are marked with "_t" and a "_tm" respectively. Metabolite abbreviations can be found in Additional file 10. Dimethylsulfide (DMS) is a potent aroma enhancer and contributor in wine. However, yeast can metabolize DMS precursors during alcoholic fermentation and therefore decreases the potential level of the volatile in wine. This chapter of the thesis applies the established QTL mapping strategy to the detection of genomic regions behind the yeast's ability to influence the level of the grape-derived DMS precursor S-methylmethionine (SMM). For this purpose, SMM was added to the synthetic must and all 130 F2-segregants were phenotyped for their ability to leave SMM in the medium at the end of fermentation. The results were used as phenotype data for QTL mapping, using the marker map that was obtained during the first part of the project.

[c] ⇌ f6p[c] Pep_Pyr Lower glycolysis pep[c] + adp[c]  pyr[c] + atp[c] G6p_6pgl PPP g6p[c] + nadp[c] ⇌ 6pgl[c] + nadph[c] Pyr_Acald Ethanol synthesis pyr[c]  acald[c] + CO2[c] Acald_Eth Ethanol synthesis acald[c] + nadh[c]  etoh[c] + nad[c] Acald_Ac Acetate metabolism acald[c] + nadp[c]  ac[c] + nadph[c] Ac_Accoa Ac-CoA metabolism ac[c] + 2 atp[c]  accoa[c] + 2 adp[c] Pyr_Oaa TCA reductive branch pyr[c] + atp[c] + CO2[c]  oaa[c] + adp[c] Acald_Eth_m Ethanol synthesis acald[m] + nadh[m] ⇌ etoh[m] + nad[m] Oaa_Cit_m TCA oxidative branch accoa[m] + oaa[m]  cit[m] Icit_Akg_m_nad TCA oxidative branch icit[m] + nad[m]  akg [m]+ CO2[m] + nadh[m] Icit_Akg_m_nadp TCA oxidative branch icit[m] + nadp[m]  akg[m] + CO2[m] + nadph[m] Eth_t Ethanol excretion etoh[c]  Ac_t Acetate excretion ac[c]  Pyr_t Pyruvate excretion pyr[c]  Akg_t AKG excretion akg[c]  Succ_t Succinate synthesis succ[c]  Glyc_t Glycerol synthesis glyc[c]  CO2_t CO2 synthesis CO2[c
During this thesis project, a secondment was done at the group for Industrial Microbiology of the TU Delft, The Netherlands, under supervision of Dr. Jean-Marc Daran. During this secondment, the CRISPR/Cas9 genome editing system was established in the yeast strains used in this study.

The chapter is composed as a research article for submission.

Genomic bases for the metabolism of the DMS precursor Smethylmethionine by Saccharomyces cerevisiae

Introduction

The perception of wine is tightly linked to its aroma, a complex blend of compounds with low boiling points, which are therefore volatile. These molecules can emerge the liquid in the wine glass to become detectable by the human nose [START_REF] Swiegers | Yeast and bacterial modulation of wine aroma and flavour[END_REF]. Over 1000 aroma compounds have been found and described in wine [START_REF] Tao | Active volatiles of cabernet sauvignon wine from Changli County[END_REF]. Depending on their origin these compounds can be divided into classes. Varietal aromas are contributed by the grapes and are distinctive for each grape variety. Fermentative aromas are produced by yeast and bacteria during alcoholic and malolactic fermentation. Post-fermentative aromas develop during conservation and aging of wine due to occurring transformations. However, these classes are not strictly separated. Varietal aroma contributors, for example, may exist as odorless precursors in the grape berry, which are then transformed to odorous compounds during harvesting, winemaking or ageing.

Dimethyl sulfide (DMS), a well described sulfur-containing odorant [START_REF] Anness | Dimethyl sulphide -a review[END_REF], is one of the most important aroma compounds released from grape derived precursors during the maturation of wine [START_REF] Simpson | Aroma composition of bottle aged white wine. Vitis-Berichte ueber Rebenforsch. mit Dokumentation der Weinbauforsch[END_REF]. The olfactory perception threshold of DMS was described to be 27 µg/L in red wines [START_REF] Beloqui | Determination of the content of dimethyl sulphide in some red wines[END_REF] and 25 µg/L in white wines [START_REF] Spedding | The influence of dimethyl sulphide and carbon disulphide in the bouquet of wines[END_REF]. DMS is known to confer pleasant notes to Cabernet-Sauvignon [START_REF] De Mora | Dimethyl sulphide in some Australian red wines[END_REF] and Shiraz red wines [START_REF] Segurel | Contribution des précurseurs glycosidiques et du sulfure de diméthyle des baies de Vitis vinifera L. Cv Grenache noir et Syrah à l'arôme des vins de la Vallée du Rhône[END_REF]. Furthermore, different white wines with small additions of DMS were preferred over their non-treated counterparts [START_REF] Spedding | The influence of dimethyl sulphide and carbon disulphide in the bouquet of wines[END_REF]. In low concentrations, DMS enhances the berry fruit aroma of wines [START_REF] De Mora | Dimethyl sulphide in some Australian red wines[END_REF][START_REF] Escudero | Analytical characterization of the aroma of five premium red wines. Insights into the role of odor families and the concept of fruitiness of wines[END_REF][START_REF] Lytra | How do esters and dimethyl sulphide concentrations affect fruity aroma perception of red wine? Demonstration by dynamic sensory profile evaluation[END_REF] and was reported to impart additional truffle and black olive notes [START_REF] Segurel | Contribution of dimethyl sulfide to the aroma of syrah and grenache noir wines and estimation of its potential in grapes of these varieties[END_REF]. In fact, DMS is a characteristic aroma molecule of black and summer truffles [START_REF] Culleré | Characterisation of aroma active compounds in black truffles (Tuber melanosporum) and summer truffles (Tuber aestivum) by gas chromatography-olfactometry[END_REF]. At higher concentrations and in certain wines, the presence of DMS is perceived less pleasant [START_REF] Spedding | The influence of dimethyl sulphide and carbon disulphide in the bouquet of wines[END_REF]. The compound can impart vegetal or molasses notes [START_REF] Mestres | Analysis of organic sulfur compounds in wine aroma[END_REF], and DMS was described as a characteristic flavor compound in many raw and processed vegetables, such as tomatoes, asparagus, broccoli or cooked corn [START_REF] Buttery | Characterization of additional volatile components of tomato[END_REF][START_REF] Dignan | DMS levels in the aroma of cooked frozen sweet corn as affected by cultivar, maturity, blanching and packaging[END_REF][START_REF] Tulio | Formation of methanethiol and dimethyl disulfide in crushed tissues of broccoli florets and their inhibition by freeze-thawing[END_REF][START_REF] Ulrich | Contribution of volatile compounds to the flavor of cooked asparagus[END_REF].

Different grape derived precursor molecules are able to form DMS in wine and therefore determine the DMS potential (pDMS). Besides from dimethyl sulfoxide (DMSO), yeast can generate DMS from the sulfur amino acids cysteine, cystine and glutathione [START_REF] Anness | Dimethyl sulphide -a review[END_REF][START_REF] De Mora | The formation of dimethyl sulphide during fermentation using a wine yeast[END_REF]. With a vapor pressure of 53 kPa at 20 °C DMS is relatively volatile [START_REF] Lestremau | Evaluation of solid-phase microextraction for time-weighted average sampling of volatile sulfur compounds at ppb concentrations[END_REF], and molecules produced during fermentation are mostly driven off by CO2, which results in low DMS concentrations observed just after the fermentation [START_REF] Dagan | Potentiel aromatique des raisins de Vitis Vinifera L. cv. Petit Manseng Gros Manseng[END_REF].

The main precursor of DMS, however, was identified to be S-methylmethionine (SMM), an amino acid derivative accounting for more than 70% of formed DMS [START_REF] Loscos | Identification of S-methylmethionine in Petit Manseng grapes as dimethyl sulphide precursor in wine[END_REF]. The synthesis of SMM is only reported in plants, where it is synthesized from methionine and AdoMet [START_REF] Mudd | The S-Methylmethionine Cycle in Lemna paucicostata[END_REF]. SMM plays an important role for the phloem sulfur transport [START_REF] Bourgis | S-methylmethionine plays a major role in phloem sulfur transport and is synthesized by a novel type of methyltransferase[END_REF] and in addition acts as a precursor for the biosynthesis of dimethylsulfoniopropionate, an osmoprotectant [START_REF] Trossat | Salinity promotes accumulation of 3-dimethylsulfoniopropionate and its precursor S-methylmethionine in chloroplasts[END_REF].

The uptake of SMM into the cell is achieved by the high affinity SMM permease, encoded by gene MMP1, and by another low affinity transporter system that could also account for the low affinity uptake of S-adenosyl methionine (AdoMet), an intermediate of methionine metabolism (Rouillon et al., 1999). Inside the cell, SMM is metabolized, together with homocystein, into two molecules of methionine by the SMM-homocystein methyl-transferase Mht1 (Figure 28) [START_REF] Thomas | Reverse methionine biosynthesis from Sadenosylmethionine in eukaryotic cells[END_REF]. SMM can therefore be used as a sulfur source by the yeast (Rouillon et al., 1999). The genes MMP1 and MHT1 are clustered in the genome of S. cerevisiae and oriented in opposite direction, being only separated by a cis-acting regulator sequence for the MET gene network [START_REF] Thomas | Reverse methionine biosynthesis from Sadenosylmethionine in eukaryotic cells[END_REF], a set of genes implicated in the steps of methionine biosynthesis (reviewed by [START_REF] Thomas | Metabolism of sulfur amino acids in Saccharomyces cerevisiae[END_REF]. MHT1 is regulated along with other MET genes and both genes, MMP1 and MHT1, are repressed by methionine and require the Met4p activator for activation [START_REF] Thomas | Reverse methionine biosynthesis from Sadenosylmethionine in eukaryotic cells[END_REF].

The presence of DMS in wine mainly depends on its release from conserved precursor molecules through a chemical process during wine aging [START_REF] Segurel | Ability of possible DMS precursors to release DMS during wine aging and in the conditions of heatalkaline treatment[END_REF]. Only a small proportion of pDMS is recovered in young wines. The recovery rate depends on numerous factors, such as the yeast strain, yeast assimilable nitrogen content in must and the general winemaking process [START_REF] Dagan | Le sulfure de diméthyle: quels moyens pour gérer ses teneurs dans les vins en bouteille[END_REF]. Nevertheless, the remaining pDMS leads to the release of DMS during maturation, and DMS levels far exceeding the olfactory threshold were detected in older vintages [START_REF] Dagan | Potentiel aromatique des raisins de Vitis Vinifera L. cv. Petit Manseng Gros Manseng[END_REF]de Mora et al., 1993). Although the release of DMS is dependent on the wine storage conditions, mostly storage temperature, the final level is determined by the pDMS content at bottling [START_REF] Fedrizzi | Aging effects and grape variety dependence on the content of sulfur volatiles in wine[END_REF][START_REF] Marais | Effect of storage time and temperature on the formation of dimethyl sulphide and on white wine quality[END_REF].

As the decrease of SMM is a metabolic process and dependent on several genes, the genetic properties of yeast are expected to account for trait variation, and differences between yeast strains in pDMS preservation could be observed [START_REF] Dagan | Le sulfure de diméthyle: quels moyens pour gérer ses teneurs dans les vins en bouteille[END_REF]. One approach to link genetic to phenotypic variation is QTL mapping, which has already been successfully applied in yeast to decipher enological important traits [START_REF] Ambroset | Deciphering the molecular basis of wine yeast fermentation traits using a combined genetic and genomic approach[END_REF]Brice et al., 2014a;[START_REF] Martí-Raga | Genetic causes of phenotypic adaptation to the second fermentation sparkling wines in[END_REF][START_REF] Marullo | Single QTL mapping and nucleotide-level resolution of a physiologic trait in wine Saccharomyces cerevisiae strains[END_REF][START_REF] Noble | Identification of new Saccharomyces cerevisiae variants of the MET2 and SKP2 genes controlling the sulfur assimilation pathway and the production of undesirable sulfur compounds during alcoholic fermentation[END_REF][START_REF] Salinas | The genetic basis of natural variation in oenological traits in Saccharomyces cerevisiae[END_REF][START_REF] Steyer | QTL mapping of the production of wine aroma compounds by yeast[END_REF]. In addition to investigate fermentative aroma formation, [START_REF] Steyer | QTL mapping of the production of wine aroma compounds by yeast[END_REF] used a QTL mapping approach to assess the role of yeast metabolism on the transformation of grape derived aroma molecules during wine fermentation, and they found 5 genomic regions influencing the alteration of grape terpenols. However, the impact of yeast metabolism on the degradation of pDMS has never been investigated until now.

The aim of this study was to understand the impact of genetic variation on the metabolism of pDMS. We performed a QTL mapping approach with a set of 130 F2-segregants previously used to detect genomic regions influencing fermentative aroma formation [START_REF] Eder | QTL mapping of volatile compound production in Saccharomyces cerevisiae during alcoholic fermentation[END_REF].

We detected a locus in the yeast genome with a dominant effect on SMM preservation and could validate two contained genes within the QTL, YLL058W and MMP1, to have an impact on the trait. The SMM-transporter gene MMP1 showed to have the major impact on SMM metabolism and an Mmp1 variant with superior preservation properties was identified and characterized. The detection of MMP1 and YLL058W variants will help to select commercially available yeast starter cultures for the production of more aromatic wines and other fermented foods, or to improve commercial S. cerevisiae strains through non-genetically modified organisms (GMO) methods, like breeding via marker-assisted selection.

2 Materials and methods

Media

Yeast strains were cultured at 28 °C in yeast extract peptone dextrose (YPD) media, containing 10 g/L yeast extract, 20 g/L peptone and 20 g/L glucose. Solid YPD media contained 1.5% agar.

Selective YPD media contained 200 µg/mL geneticin (G418), 200 µg/mL nourseothricin (clonNAT) or 200 µg/mL hygromycin B were used.

Wine fermentations were carried out in synthetic grape must (MS200) described by [START_REF] Bely | Automatic detection of assimilable nitrogen deficiencies during alcoholic fermentation in oenological conditions[END_REF]. It contains 100 g/L of each sugar, glucose and fructose, and 200 mg/L of assimilable nitrogen. The amino acid composition hereby mimics the nitrogen content of standard grape juice.

Strain selection, segregant generation and genotyping

The haploid S. cerevisiae strains MTF2621 (4CAR1 [ΔHO::Neo r ]) and MTF2622 (T73 [ΔHO::Nat r ]) were selected for the study according to their different need for nitrogen during wine fermentation. The requirement of nitrogen was estimated by using an approach based on the addition of nitrogen to keep the CO2 production rate constant during nitrogen limitation (Brice et al., 2014b). The strain T73 belongs to the phylogenetic clade of wine strains. The strain 4CAR1, however, belongs to the group of champagne strains, which originated through crossings between strains of the wine clade and the flor clade [START_REF] Coi | A set of haploid strains available for genetic studies of Saccharomyces cerevisiae flor yeasts[END_REF]. For this study, we used a population of 130 segregants of the F2 generation previously obtained from a cross between these two strains [START_REF] Eder | QTL mapping of volatile compound production in Saccharomyces cerevisiae during alcoholic fermentation[END_REF]. The strains had been genotyped by whole genome sequencing in order to generate a marker map for QTL mapping.

Phenotyping of strains

Segregants were fermented in duplicates with the parent strains as controls. Strains were precultured overnight in 50 mL YPD media and cell density was determined using a Multisizer™ 3 Coulter Counter (Beckman Coulter). Sterilized 300-mL glassware mini fermenters were filled with 280 mL of SM200, supplemented with 500 µg/L S-methylmethionine chloride and closed with an air lock. Fermenters were inoculated to a cell density of 1 × 10 6 cells/mL and incubated at 24 °C under stirring (300 rpm). Flasks were weighted twice daily to determine CO2 production, which directly reflects sugar consumption. Samples were taken when approx. 80% of the sugars were depleted in order to determine the concentration of S-methylmethionine near the end of the fermentation. These samples were then analyzed for the quantity of remaining pDMS by solid phase micro extraction and GC-MS (Nyseos in Montpellier, France). pDMS concentrations are given as equivalent of DMS formation.

QTL mapping

The phenotyping and genotyping data were used to identify QTLs in the yeast genome that influence pDMS metabolism during wine fermentation. The statistical analyses were carried out using the programming language R v3.2.3 (www.r-project.org) with the R/qtl v1.40-8 and R/eqtl v1.1-7 libraries [START_REF] Broman | R/qtl: QTL mapping in experimental crosses[END_REF]. QTL mapping was performed with two different phenotype models, the normal model using Haley-Knott regression and a non-parametric analysis, resulting in logarithm of odds (LOD) scores for each marker and pseudo-marker every 2.5 cM (interval mapping method). An interval estimate of the location of each QTL was obtained as the 1-LOD support interval, the region in which the LOD score is within 1 unit of the peak LOD score. If the same locus was detected with both models, the results with the higher LOD score were selected.

Reciprocal hemizygosity analysis

Molecular dissection of QTLs was performed using reciprocal hemizygosity analysis (RHA) [START_REF] Steinmetz | Dissecting the architecture of a quantitative trait locus in yeast[END_REF][START_REF] Warringer | Yeast reciprocal hemizygosity to confirm the causality of a quantitative trait loci-associated gene[END_REF]. The gene sequences of selected target genes were deleted in both parent strains by homologous recombination with a disruption cassette containing the hygromycin B resistance gene (hph r ). Positive integration was selected by plating the transformed cells on YPD-agar plates containing hygromycin B. Correct gene deletion was verified by PCR using the primer test_ [GENE]_fw which binds in the upstream region of the deleted gene and the primer Hygro_rv which binds within the deletion cassette. Deleted parent strains were subsequently mated with the opposite undeleted parent to form a heterozygote that is hemizygous for the target gene. The obtained strains were phenotyped in triplicate (2.3). Significance of the impact of allelic variation on the trait was evaluated by student's t-test.

Allelic swap

The impact of the allelic variants on the phenotype was validated by exchanging the corresponding sequences between the parent cells, using the CRISPR/Cas9 toolbox developed by [START_REF] Mans | CRISPR/Cas9: a molecular Swiss army knife for simultaneous introduction of multiple genetic modifications in Saccharomyces cerevisiae[END_REF]. This approach has the advantage that genes and even sole SNPs can be rapidly exchanged in a marker-and scarless way. The CAS9 expression cassette was transformed into both parent strains via homologous recombination replacing GAL1. For the allele swap, the allelic gene variants were amplified with the Phusion DNA polymerase according to the protocol (Thermo Fisher Scientific). An allele specific guide-RNA was designed and transformed into the parent cells where it induced the Cas9 mediated double-strand break within the variant sequence, while the corresponding allele of the other parent was provided as repair fragment. Positive sequence exchange was verified by allelic PCR, using a forward primer in the upstream region of the gene and one or more reverse primer on SNP positions within the gene. The obtained strains were phenotyped in triplicate (2.3) to validate the impact of the allelic variants on the trait.

Results and Discussion

Phenotyping of strains

First, the ability of both parental strains to metabolize SMM, the precursor of DMS, was compared. The content of pDMS in synthetic must before fermentation was determined at 282.2 ± 48.0 µg/L (as equivalent of DMS formation). The two parent strains consumed a major fraction of pDMS after 80% of the fermentation but differ significantly in their ability to reduce the precursor. While the residual concentration of pDMS was 73.04 ± 18.30 µg/L for strain MTF2621, almost no precursor was left by strain MTF2622 (6.54 ± 6.95 µg/L pDMS).

As DMS is mainly originating from amino acid related compounds, it was necessary to assess whether the consumption of pDMS is connected to the consumption of certain amino acids. This could indicate a common regulation or the involvement of an amino acid transporter in pDMS uptake. For both parent strains the uptake of nitrogen sources was compared to the decrease of pDMS in the medium during the first 28 hours of the fermentation (Figure 29). Interestingly, the parental strains show a different behavior in their consumption of nitrogen sources. Strain MTF2621 consumed all available nitrogen within 28 h, while MTF2621 achieved this within 23 h. A rapid decrease in the pDMS concentration could be observed for both strains in the beginning of the fermentation. In the first sample, taken after 20 min of the fermentation, the pDMS concentration was measured to be roughly 60% of the concentration determined in the initial must. A rapid decrease of pDMS after inoculation has been previously reported [START_REF] Dagan | Le sulfure de diméthyle: quels moyens pour gérer ses teneurs dans les vins en bouteille[END_REF]. For MTF2622 a second decrease of pDMS in the medium could be observed after 14 h. As the decrease occurs approximately with the depletion of methionine from the medium, it suggests that pDMS uptake is repressed by methionine. Strain MTF2622 had completely taken up all available pDMS after 23 h, which correlates to the depletion of all available nitrogen sources from the medium. However, the concentration of pDMS in the medium subsequently increased again and reached almost 30% of the initial concentration at the end of the measurement. This indicates that pDMS was actively taken up and that it was very likely not metabolized completely and released to the medium at the end of the fermentation. For MTF2621 a minor decrease to 50% of the initial pDMS concentration could be observed between 16.0 h and 24.0 h of the fermentation. In this case the decrease of pDMS did not correlate with the depletion of methionine. In addition, the concentration of pDMS in the medium rose again before the depletion of all nitrogen sources and reached its initial level at the end of the follow-up. This suggests that strains MTF2622 and MTF2622 differ in their ability to take up pDMS.

Genome wide identification of QTLs influencing pDMS metabolism

In order to discover the genetic basis behind the different abilities to metabolize pDMS, we performed a QTL analysis. 130 F2-segregants from a cross between the two parent strains were phenotyped for their ability to consume pDMS. The distribution of pDMS content at the end of fermentation between the strains revealed two populations (Figure 30). A large share of 45.4% of the segregants had metabolized 90-100% of the pDMS present in the medium while a smaller fraction of the strains had left approximately 70.0 µg/L pDMS. This matches the determined concentrations for the parental strains. For the segregant with the highest preservation of pDMS, a concentration of 123.9 µg/L could be measured, which still corresponds to a pDMS loss of 56.1% compared to the unfermented medium. These results indicate the major influence of one locus on the trait.

Variant of S-methylmethionine transporter Mmp1 causes pDMS preservation

By performing RHA we evaluated the allelic effects of the three candidate genes within the QTL region that contained non-synonymous SNPs, MMP1, GTT2 and YLL058W. While no difference could be seen for the allelic variants of GTT2, the other two candidate genes showed significant difference between the alleles (Figure 32). A smaller effect can be seen for the allelic variants of YLL058W, the MTF2621 allele causes the hemizygote to leave 2.2 times more pDMS at the end of fermentation. The variants differ in six non-synonymous SNPs within the coding region (Table 24), of which three lie in the pyridoxal phosphate-dependent transferase domain of the protein. This domain is shared with several proteins involved in amino acid metabolic processes (Saccharomyces genome database). YLL058W encodes a protein of unknown function that shows similarity to the cystathionine gamma-synthase Str2 (Giaever et al., 2002). Str2 converts cysteine into cystathionine and is involved in the regulation of sulfur assimilation genes (Hansen and Johannesen, 2000).

The parental variants of MMP1 showed to have a bigger impact on the metabolism of pDMS. The MTF2621 allele causes an increased pDMS preservation by factor 5.64 in comparison to the MTF2622 allele. The variants differ in one non-synonymous SNPs within the coding region (A536G), which causes the introduction of a STOP codon at position 179 of the 583 AA long polypeptide chain in strain MTF2622. This position is located within the amino acid permease sequence of the protein. Another SNP was found in the 1000bp upstream region of the gene although no recognized transcription factor binding site was affected according to the YEASTRACT database [START_REF] Teixeira | The YEASTRACT database: an upgraded information system for the analysis of gene and genomic transcription regulation in Saccharomyces cerevisiae[END_REF]. To assess whether the non-synonymous SNP in MMP1 is responsible for the increased preservation of pDMS, we exchanged nucleotide 536 between the parent strains. This allelic swap resulted in strain MTF2621 MMP1( 2622) with an eliminated STOP codon in MMP1 and strain MTF2622 MMP1(2621) with an introduced STOP codon in MMP1. The elimination of the STOP codon completely inversed the phenotype of MTF2621 to MTF2622 (Figure 33). Strain MTF2621 MMP1(2622) had consumed the same share of pDMS after 80% of the fermentation as MTF2622 and was not anymore able to preserve SMM. A similar result could be seen for the opposite allelic swap: the introduction of the STOP codon within MMP1 of MTF2622 almost completely inversed the phenotype of MTF2622 to MTF2621. However, the concentration of preserved pDMS by MTF2622 MMP1(2621) was slightly lower than for strain MTF2621, although the difference was not significant. This could be an indication that other alleles, like YLL058W, or environmental factors, like fermentation time, have an influence on the trait. During the fermentation, the concentration of pDMS decreased for all observed segregants, even for strains containing the truncated variant of Mmp1. This could indicate the existence of other transport mechanisms, either active through transporter proteins or passive through permeases. The MTF2622 strain showed marked uptake of pDMS at the beginning of the fermentation, although it contains the truncated Mmp1 transporter (Figure 29). For both strains a release of pDMS could be seen after an initial uptake, indicating diffusion of pDMS with the concentration gradient. Other possibilities for the observed precursor loss are yeastindependent influences, like degradation or evaporation.

To assess the occurrence of the truncated Mmp1 variant within S. cerevisiae strains of different origin, a blast of the nucleotide sequence was performed against 85 yeast genomes recently sequenced in our group [START_REF] Coi | Genomic signatures of adaptation to wine biological ageing conditions in biofilm-forming flor yeasts[END_REF]Legras et al., submitted) or available in the Saccharomyces genome database (SGD) (Table 25, Additional file 15). SNP G536A, resulting in the STOP codon at amino acid position 179 within MMP1, was found in 19 out of 85 strains. All 8 assessed flor strains contain the truncated Mmp1 variant, whereas 83% of wine strains possess the full-length variant. Apart from flor strains, considerable occurrence of the truncated Mmp1 variant can only be seen in cheese strains, with three of the six assessed strains containing the variant.

The significant concentration of the truncated Mmp1 allele in flor strains indicates a relation of this mutation and the origin. To further evaluate this potential link, a phylogenetic analysis of all complete MMP1 sequences (79) from the assessed strains was performed (Figure 34).

Conclusion

In this study we confirmed the potential of QTL analysis to decipher enological important traits of Saccharomyces cerevisiae, in particular the ability to metabolize grape derived aroma precursors during wine fermentation. By phenotyping and genotyping a set of 130 F2segregants deriving from a cross between two strains isolated from wine, we were able to detect a genomic region with a strong influence on the metabolism of SMM, the main precursor for DMS.

Two genes in the QTL, MMP1 and YLL058W, were found to influence SMM metabolism and the SMM transporter gene MMP1 was identified to be responsible for the major part of trait variation. The MTF2621 allele of MMP1 expresses a strongly decreased ability to take up SMM from the medium and by performing allelic swap of the gene between the parent strains of the study, we identified SNP 536 G -> A to be causative for this behavior. The nucleotide change leads to the introduction of a STOP codon at position 179 of the 583 AA long polypeptide chain, which results in the expression of a truncated SMM transporter Mmp1.

By comparing MMP1 variants from 85 yeast strains of different origin, we could demonstrate that the truncated Mmp1 variant predominantly occurs in flor strains, therefore indicating a link to this origin. Rare findings of the described SNP in wine strains could be attributed to gene flow from flor to wine yeast strains.

These findings offer new perspectives for the management of pDMS in grape must. The introduction of the truncated allele of MMP1 into industrial strains by non-GMO methods, like breeding via marker-assisted selection, will generate superior strains for enhancing the preservation of SMM during wine fermentation and will therefore lead to a more fruity and aromatic wine. As DMS is a flavor active volatile present in other fermented foods, like beer [START_REF] Anness | Dimethyl sulphide -a review[END_REF] and cheese [START_REF] Carbonell | Seasonal variation of volatile compounds in ewe raw milk La Serena cheese[END_REF], the discovered genetic variation may therefore be used to improve yeast starter cultures for a wide range of fermented products.

Supplementary information

Additional file 14: List of primers used in this study with nucleotide sequence (5' -> 3').

Primer name

Chapter 4: Preliminary results about the genetic bases of terpenol transformation by S. cerevisiae during alcoholic fermentation

This chapter describes additional results of this thesis that were obtained during experimental work on terpenol transformation during wine fermentation, but that could not be completed for publishing as a research article.

Terpenols represent a group of compounds that are important for the bouquet of certain wine styles. They are produced in grape vine and are introduced to must by the grapes. However, terpenols are transformed during yeast alcoholic fermentation and a strain dependency of this conversion was observed.

The population of 130 F2-segregants described in chapter 1 was phenotyped for the strains' ability to metabolize the terpenol precursor geraniol, which was added to the synthetic must. Samples were taken near the end of fermentation and terpenol content was determined by SPME-GC-MS. The results were used as phenotype data for QTL mapping, using the marker map that was obtained during the first part of the project.

This approach was successful and allowed to identify 21 QTLs with an influence on terpenol composition as well as several potential target genes within the QTL regions. The role of these allelic variants remains to be confirmed.

Introduction

Terpenols are volatile compounds that are produced by algae and higher plants from the common precursor geranyl pyrophosphate (reviewed by [START_REF] Jüttner | Physiology and biochemistry of odorous compounds from freshwater cyanobacteria and algae[END_REF]by Nagegowda, 2010, respectively). Terpenols such as geraniol, nerol, linalool, citronellol, α-terpineol and cisrose oxide are potent aroma compounds with a rose-flowery or citrusy scent. Geraniol can also be produced by some S. cerevisiae strains in generally very low amounts [START_REF] Carrau | De novo synthesis of monoterpenes by Saccharomyces cerevisiae wine yeasts[END_REF], although some mutant strains have gained the ability to produce significantly elevated levels of geraniol and linalool [START_REF] Javelot | Introduction of terpeneproducing ability in a wine strain of Saccharomyces cerevisiae[END_REF].

Muscat and Gewürztraminer wines contain up to 1.5 and 4 mg/L of linalool and geraniol respectively, in contrast to other wines where the contents are 10 to 20 fold lower (reviewed by [START_REF] Black | Terpenoids and their role in wine flavour: recent advances[END_REF][START_REF] Ribéreau-Gayon | Aroma of Muscat grape varieties[END_REF][START_REF] Simpson | Aroma composition of bottle aged white wine. Vitis-Berichte ueber Rebenforsch. mit Dokumentation der Weinbauforsch[END_REF][START_REF] Steyer | Genetic analysis of geraniol metabolism during fermentation[END_REF]. In addition, these grape varieties present a characteristic proportion between linalol and geraniol. While high concentrations of free linalool are found in muscat grapes, high amounts of glycosidically bound geraniol are present in gewürztraminer grapes [START_REF] Duchêne | Variation of linalool and geraniol content within two pairs of aromatic and non-aromatic grapevine clones[END_REF][START_REF] Ribéreau-Gayon | Aroma of Muscat grape varieties[END_REF].

Although the vast majority of terpenols found in wine are produced by grape vine, accumulated in the grape berry during its development and introduced to must by the grapes, they are modified during wine fermentation by S. cerevisiae and other yeasts [START_REF] Gamero | Monoterpene alcohols release and bioconversion by Saccharomyces species and hybrids[END_REF][START_REF] King | Biotransformation of monoterpene alcohols by Saccharomyces cerevisiae, Torulaspora delbrueckii and Kluyveromyces lactis[END_REF]. It could be demonstrated that the shape of these conversions is strain dependent, indicating an impact of yeast metabolic properties [START_REF] Furdíková | Influence of yeast strain on aromatic profile of Gewürztraminer wine[END_REF]. Some steps in the transformation of terpenols are mainly driven by simple chemical reactions such isomerization reactions at acidic pH [START_REF] Simpson | Aroma composition of aged Riesling wine[END_REF]. This is the case for the conversion of geraniol to linalool [START_REF] Zea | Content of free terpenic compounds in cells and musts during vinification with three Saccharomyces cerevisiae races[END_REF], although yeast might influence the conversion rate directly through additional enzymatic catalyzation or indirectly through affecting environmental conditions such as pH. From linalool, α-terpineol can be formed during fermentation [START_REF] King | Biotransformation of monoterpene alcohols by Saccharomyces cerevisiae, Torulaspora delbrueckii and Kluyveromyces lactis[END_REF]. The reduction of geraniol to citronellol was already elucidated to be enzymatically catalyzed and was reported to be a three step reaction involving two alcohol dehydrogenases and the oxidoreductase Oye2 [START_REF] Steyer | Genetic analysis of geraniol metabolism during fermentation[END_REF]. Some of the citronellol produced during fermentation is the basis for the synthesis of the highly potent aroma contributor cis-rose oxide [START_REF] Koslitz | Stereoselective formation of the varietal aroma compound rose oxide during alcoholic fermentation[END_REF]. Different yeast strains present varying abilities to produce this compound [START_REF] Furdíková | Influence of yeast strain on aromatic profile of Gewürztraminer wine[END_REF]. In addition, terpenols are primary alcohols and can therefore be acetylated like other aliphatic alcohols such as propanol or hexanol. Atf1 has been shown to be the main acetyltransferase involved in terpenol acetylation [START_REF] Steyer | Genetic analysis of geraniol metabolism during fermentation[END_REF]. Several studies report a loss in total terpenol concentrations during alcoholic fermentation [START_REF] King | Biotransformation of monoterpene alcohols by Saccharomyces cerevisiae, Torulaspora delbrueckii and Kluyveromyces lactis[END_REF][START_REF] Soares | Monitoring the evolution of volatile compounds using gas chromatography during the stages of production of Moscatel sparkling wine[END_REF][START_REF] Vaudano | Modulation of Geraniol Metabolism During Alcohol Fermentation[END_REF]. Possible explanations include evaporation or the adsorption to yeast cell wall structures [START_REF] Bishop | Microencapsulation in yeast cells[END_REF]. As the precursor of geraniol is geranyl pyrophosphate, an intermediate of the sterol pathway, this also led to the assumption that yeast can incorporate geraniol into sterol synthesis, especially during alcoholic fermentation under oxygen limitation [START_REF] Vaudano | Modulation of Geraniol Metabolism During Alcohol Fermentation[END_REF]. However, this hypothesis was never confirmed and sterol auxotrophic mutants with limitations in pathway steps before Erg20 were unable to use geraniol for growth. These aspects were not assessed in this study.

The conversion from geraniol to other terpenols during fermentation is presented in Figure 35. [START_REF] King | Biotransformation of monoterpene alcohols by Saccharomyces cerevisiae, Torulaspora delbrueckii and Kluyveromyces lactis[END_REF][START_REF] Zea | Content of free terpenic compounds in cells and musts during vinification with three Saccharomyces cerevisiae races[END_REF].

The enzymatic bases and principles of several reactions of terpenol transformation are still not yet understood. This includes the conversion of linalool to α-terpineol or of citronellol to cis-rose oxide. In a similar manner, no clear explanation has been given to the loss of overall terpenols during alcoholic fermentation. As terpenol compounds have a high impact on wine aroma, we have evaluated potential differences in the production of terpenols among a population of segregant strains to gain insights into the metabolism of these compounds during alcoholic fermentation. For this purpose, we have used the same experimental design as described above in order to correlate differences in terpenol formation to genetic variation among strains.

Materials and Methods

Strain selection, segregant generation and genotyping

This experiment is associated to the previous experiment described in chapter 1. The evaluated strains consisted in the haploid S. cerevisiae strains MTF2621 (4CAR1 [ΔHO::Neo r ]) and MTF2622 (T73 [ΔHO::Nat r ]). From a cross between these parents, a population of 130 segregants of the F2 generation was obtained. The strains had been genotyped by whole genome sequencing in order to generate a marker map for QTL mapping (Chapter 1).

Phenotyping of segregant population

The segregant strains were phenotyped during wine fermentation in 280 mL of synthetic grape must spiked with 2.0 mg/L geraniol. Samples were taken after 80% of fermentation, which was determined by following the weight loss caused by the production of CO2.

For the analysis of terpenol composition, the samples were extracted by solid phase micro extraction (SPME) and measured via GC-MS [START_REF] García | Systematic optimization of the analysis of wine bouquet components by solid-phase microextraction[END_REF]. In a 20-mL glass flacon, 2.3 g of sodium chloride were dissolved in 7 mL of MilliQ water and the solution was kept on ice. 30 µL of internal deuterated terpenol standard and 1 mL of sample were added, the tubes were closed and mixed well. A PDMS/DVB/carboxen fiber was inserted in the headspace of the vessel while agitating and incubating the sample for 30 min at 30 °C. Subsequently, the fiber was inserted in the GC-MS and terpenol content was determined.

QTL mapping

The phenotyping and genotyping data were used to identify QTLs in the yeast genome that influence terpenol transformation during wine fermentation. The statistical analyses were carried out using the programming language R v3.2.3 (www.r-project.org) with the R/qtl v1.40-8 and R/eqtl v1.1-7 libraries [START_REF] Broman | R/qtl: QTL mapping in experimental crosses[END_REF]. QTL mapping was performed with two different phenotype models, the normal model using Haley-Knott regression and a nonparametric analysis, resulting in logarithm of odds (LOD) scores for each marker and pseudomarker every 2.5 cM (interval mapping method). An interval estimate of the location of each QTL was obtained as the 1-LOD support interval, the region in which the LOD score is within 1 unit of the peak LOD score. If the same locus was detected with both models, the results with the higher LOD score were selected.

Results and Discussion

In order to obtain precise quantitative data on the conversion of geraniol to other terpenols for the QTL mapping, we first considered potential biases related to different fermentative behaviors of the segregants. Indeed, as higher fermentation rates lead to a more vigorous production and release of CO2, one possible cause for terpenol loss during fermentation could be the stripping of compounds from the medium by CO2. To evaluate the significance of this factor and its potential impact on QTL mapping, the influence of different CO2 production rates was evaluated for two segregant strains with different fermentation kinetics in synthetic must containing 10 mg/L of geraniol (Table 26). A decrease in geraniol concentration from 10.0 mg/L at the beginning to less than 2.0 mg/L at the end of fermentation can be observed for both strains. Total terpenol concentrations also declined during fermentation. This could either result from a loss of terpenols through evaporation, adsorption to cellular structures or conversion of terpenols into compounds not recorded by the phenotyping. Although differences exist between the strains regarding terpenol composition after fermentation, no significant difference in total terpenol content can be observed. This is in agreement with former observations, which reported low stripping of geraniol during fermentation [START_REF] Ferreira | Losses of volatile compounds during fermentation[END_REF][START_REF] Steyer | Genetic analysis of geraniol metabolism during fermentation[END_REF]. This indicates that fermentation kinetics should not affect the search for QTLs influencing terpenol metabolism.

Subsequently, the phenotyping of our 130 F2-segregant population was carried out for the content of terpenols after 80% of fermentation. PCA was performed to show the correlations between dfferent terpenols compounds (Figure 36). The first two dimensions of the PCA explain 62.1% of variation between the fermentations. Terpenol acetates are correlated with each other while they are negatively correlated to fermentation time. However, the fermentation time is only little explained by the PCA, as is the formation of cis-rose oxide and α-terpineol. The concentrations of geraniol and linalool are correlated. This can be explained by the chemical conversion of geraniol to linalool [START_REF] Zea | Content of free terpenic compounds in cells and musts during vinification with three Saccharomyces cerevisiae races[END_REF], which decreases with diminished geraniol concentrations. Geraniol is negatively correlated to nerol and citronellol formation. This was expected as citronellol was already shown to be synthesized from geraniol by enzymatic reactions [START_REF] Steyer | Genetic analysis of geraniol metabolism during fermentation[END_REF]. Nerol is the isomer of geraniol and the negative correlation between both suggests an influence of yeast on the isomerization reaction. Finally, the concentration of cis-rose oxide is correlated to the citronellol content, which is in agreement with previous reports [START_REF] Furdíková | Influence of yeast strain on aromatic profile of Gewürztraminer wine[END_REF][START_REF] Steyer | Genetic analysis of geraniol metabolism during fermentation[END_REF].

The phenotypic data set was then used to detect QTLs in the yeast genome that influence terpenol metabolism (Table 27). This approach revealed as many as 21 QTLs distributed over seven chromosomes that influence 8 traits. Traits influenced by these detected QTLs include the total amount of terpenols and terpenol acetates as well as concentrations of single compounds. The fact that several loci could be detected for the formation of linalool and α-terpineol indicates an influence of yeast on the corresponding reactions. However, no QTLs with an impact on citronellol or cis-rose oxide concentrations were found. The highest determined logarithm of odds (LOD)-score is 6.63 for QTL chr7@340.5 influencing terpenol acetate formation, meaning that up to 21 % of trait variation can be explained by the locus. Most QTLs (10) were detected to influence the formation of terpenol acetates. Two mechanisms may explain differences in acetate ester production, namely a different amount of terpenol substrates or a different general acetylation activity of the metabolism. As most QTLs for terpenol acetate formation showed no influence on single terpenol concentrations, it is therefore likely that these loci impact the acetylation reaction.

The detected regions were compared to four QTLs reported by [START_REF] Steyer | QTL mapping of the production of wine aroma compounds by yeast[END_REF] to influence the formation of citronellol and cis-rose oxide, but no overlap was found for any of these regions. This could be due to the different origin of strains. [START_REF] Steyer | QTL mapping of the production of wine aroma compounds by yeast[END_REF] used a cross from one S. cerevisiae wine and one lab strain to map QTLs for terpenol metabolism, therefore, the more divergent genomic background could have led to the discovery of different regions.

The QTLs detected in this study were furthermore compared with regions that influence the formation of main and other volatile metabolites, as well as estimated metabolic fluxes and fermentation parameters that were detected during the first parts of this project (Table 28). Out of the 21 QTLs detected for terpenol related compounds, 13 regions had been found in previous chapters of this thesis to influence other traits such as the formation of medium chain fatty acids, fatty acid ethyl esters or propanol and related compounds. In two cases, QTLs affecting the formation of terpenol acetates also influence the CO2 production rate after 80% of fermentation, a measure of fermentation kinetic. This confirms deductions drawn by the PCA, namely a negative correlation of terpenol acetate formation and fermentation time (Figure 36).

In five QTLs detected to influence both terpenol metabolism and fermentative aroma formation, target genes were previously validated for their influence on fermentative aroma formation (chapter 2). In QTL chr2@172.5, membrane sensor protein Agp2 was validated influencing several medium chain fatty acids and their ethyl esters. In QTL chr4@141.9, histone deacetylase Sir2 was identified to also have an influence on the formation of fatty acids and fatty acid ethyl esters. Hexose transporter Hxt3 was validated in QTL chr4@379.2 to influence CO2 production rate and sugar metabolism. In QTL chr7@26.5, Vid30, which is involved in vacuolar import and protein ubiquitylation, has been shown to impact metabolic fluxes. In QTL chr14@36.3, amino acid transporter Alp1 was identified to influence the production of propanol, dodecanoic acid and valeric acid. However, it is necessary to assess whether the validated genes are also responsible for variations of terpenol content in wine.

Candidate genes with a potential role in terpenol metabolism were also searched for in remaining QTLs with LOD-scores higher than 4.42. This corresponds to an explained variation of at least 15% by these loci (Table 29).

ZWF1

Glucose-6-phosphate dehydrogenase chr16@299.9 linalool terpenol acetates

GDB1

Glycogen debranching enzyme

HDA3

Subunit of histone deacetylase

In 6 of the evaluated loci, 8 genes with biological functions potentially involved in terpenol transformation could be detected. This includes enzymes with roles in yeast central carbon metabolism (CCM), as the production of terpenol acetates relies on the availability of acetyl-CoA, an intermediate of CCM. In the same manner, deacetylases could have an influence on terpenol acetate formation. Furthermore, dehydrogenases were chosen as candidate genes since the reaction from geraniol to citronellol was already proposed to be partly dependent on alcohol dehydrogenases [START_REF] Steyer | Genetic analysis of geraniol metabolism during fermentation[END_REF]. In addition, the chosen candidate genes contain non-synonymous SNPs (GDB1, HAP2, PDX1, SAY1, ZWF1) or SNPs in the upstream region (ERG24, HDA3, TDH3) between the parental variants, making these alleles potential targets for evaluation.

Conclusion

In this chapter, we used the QTL mapping approach established in the first part of the thesis to evaluate the ability of S. cerevisiae to transform terpenol compounds during alcoholic fermentation. We detected 21 QTLs affecting the total content of terpenol or concentrations of particular compounds. This indicates that yeast genomic properties have an influence on the shaping of wine terpenol profile. The QTLs could be separated in two groups, loci affecting the concentrations of terpenol compounds and loci with an influence on various terpenol acetates. We conclude that most QTLs found to influence terpenol acetates rather affect yeast's general acetylation capability since those regions did not influence the concentrations of primary terpenols.

Several QTLs were detected with an impact on single terpenols, particularly on linalool and αterpineol. This demonstrates that the formation of these compounds is influenced by yeast.

Although no QTL was detected to affect nerol concentration, a determined negative correlation between the isomers geraniol and nerol indicates an influence of yeast on the isomerization step.

Most of the detected QTLs were already found in previous experiments to influence the production of fermentative aromas or metabolic fluxes (chapters 1 and 2). Thus we can conclude that terpenol transformation during wine fermentation is very likely connected to yeast metabolism and as consequence to fermentative aroma formation. In some of these regions, target genes were already demonstrated to account for the influence on fermentative aromas or metabolic flux distribution. It has to be evaluated if these genes are also responsible for terpenol transformation and which mechanism is behind this process.

In several newly detected QTLs we identified target genes that have a probable function in terpenol transformation and contain non-synonymous SNPs or SNPs in the upstream region between the parent strains. The evaluation of these candidates using reciprocal hemizygosity analysis or allele swap is still pending. Assessing these genes will lead to a better understanding of the genomic bases and metabolic processes behind grape terpenol transformation by yeast during wine fermentation. The identification of allelic variants that account for differences in terpenol composition provides potential to shape the aromatic profile of yeast starter cultures for wine production. As hop-derived terpenols are also important contributors to beer flavor and biotransformation of terpenols was shown during ale and lager beer fermentation [START_REF] King | Biotransformation of hop aroma terpenoids by ale and lager yeasts[END_REF]Takoi et al., 2010aTakoi et al., , 2010b)), this knowledge can furthermore be applied to improve commercially used beer yeast strains.

Wine market is a big and global business, driven partially by consumers' expectations for wine flavor that is susceptible to change. The actual market attitude is a general priority for fruity wines and the background of health concerns should make light and alcohol reduced wines more popular. This is in conflict with a general increase in grape ripeness that is caused by global climate change and leads to elevated alcohol contents in finished wine. As vines can only be grown in limited latitudes, wine producing regions are generally also wine exporters and a competition between these countries for market share exists. This brings wine producers to constantly strive for improving their product in order to meet the customers' expectations in the context of a changing environment.

Besides viticultural and winemaking practices, yeast has significant impact on wine flavor. During fermentation, it changes and deepens the aromatic profile by producing volatile compounds from nutrients in the must, by transforming aroma compounds that are contributed by the grapes or by releasing potent aroma molecules from odor-less, grapederived precursor molecules. Among the most abundant aroma compounds de novo produced by yeast are higher alcohols, fusel acids, medium chain fatty acids and their corresponding ethyl and acetate esters. Depending on compound and concentration, these molecules can provide pleasant flavors, such as fruity or flowery, and unpleasant odors, such as rancid or solvent. Their production is closely linked to yeast metabolic properties and is driven by both, the carbon and nitrogen metabolism. Higher alcohols and fusel acids can be derived from the degradation of absorbed amino acids but also from intermediates of the CCM. The production of medium chain fatty acids relies on acetyl-CoA that emerges from the CCM. Acetate and ethyl esters of all listed compounds are formed enzymatically by acetylation or acylation. Yeast also affects grape derived flavors, either by metabolism of odor-less precursors to aromas or by alteration of grape aroma compounds. Terpenols, for example, are synthesized and contributed by grapes. Depending on the compound, they provide flavors ranging from piney to citrusy and flowery. Yeast enzymes were identified that catalyze conversions between different terpenols during fermentation. Furthermore, the influence of yeast on wine composition has an effect on the formation of post-fermentative aromas during wine aging. The most notable example is DMS, which increases general fruitiness and can impart notes of truffle and olives. The precursors of DMS are present in grapes and yeast can metabolize them to DMS, which is, however, driven off by the CO2 produced during fermentation. Only precursor molecules left in young wine are then transformed chemically during maturation.

As the shape of described aroma compounds relies on yeast metabolic properties, it is therefore governed by the underlying genomic bases. Human use and selection of yeast for winemaking has led to a wide yeast population containing high genomic variation.

Understanding the links between genomic and metabolic properties, but also the impact of genomic variation on metabolic traits is a requirement for the exploitation of yeast diversity.

In the end, this has the potential to improve wine aroma and quality and to adapt to consumers' demands.

The genomic basis of complex traits, meaning traits that are driven by several genes and are therefore quantitative, can be fathomed by QTL mapping. Different strategies were developed, which have in common that the presence of defined genetic markers, i.e., genotype differences, is connected to phenotypic variations. Starting from plant breeding, this approach was adapted to S. cerevisiae and has become a powerful tool for enological studies with numerous analyses published. QTL mapping is continuously further improved to enhance the outcomes of the analyses and to expand the methodology to new traits of interest.

Referring to the presented background, future research on wine strain development will pursue the following key points: (i) more profound determination and assessment of wine sensory characteristics; (ii) evaluation of consumers' preferences for aroma and flavor to predict wine choice behavior in key markets; (iii) development and application of methodologies to identify genomic and metabolic factors that influence both, the de novo production of volatile compounds and the release or transformation of grape flavor precursors by yeast; (iv) population analyses and characterization of superior alleles in terms of aroma formation and (v) improvement of existing commercially used wine strains by non-GMO methods to further shape and improve their aromatic properties in order to meet consumers' demands.

The research outlined in this thesis contributes to points (iii) and (iv). QTL mapping was applied to detect regions in the genome of S. cerevisiae that influence the production of fermentative aromas, the alteration of grape aroma compounds and the potential for post-fermentative aroma formation during wine maturation. The use of a comparatively high number of segregants with increased recombination rate led to an elevated analytic strength of the analysis. As result, several QTL mapping strategies could be applied, including the search for interacting QTLs using multiple QTL mapping. The outcome was the detection of over 80 QTLs, including loci with minor effects, and the identification of interacting QTLs for several traits. By validating candidate genes within found QTLs, the involvement and impact of several allelic variants of these genes was demonstrated. The connection of some genes to the observed traits has already been described before (AGP1, ALP1, ILV6, LEU9), but some genes could be newly connected to influenced traits (AGP2, IXR1, MAE1, NRG1, RGS2, RGT1, SIR2). In all cases, allelic variants with different impact were described.

Variants of the amino acid transporters Agp1 and Alp1 affect the formation of Ehrlich pathway products through proposed differences in expression or affinity. The involvement of nitrogen metabolism in the formation of fermentative aroma was further demonstrated by the identification of two other enzymes involved in amino acid synthesis pathways, Ilv6 and Leu9. The fact that fermentative aroma production is also linked to the CCM was underlined with the validation of the target genes MAE1 and FAS1. Mae1 catalyzes the reaction of malate to pyruvate and a higher conversion rate of this enzyme was proposed to increase the production of volatiles derived from pyruvate. Fas1 catalyzes reaction steps of the fatty acid synthesis starting from malonyl-CoA, which is formed by carboxylation of acetyl-CoA. In addition to that, the fact that a large share of validated candidate genes are transcriptional regulators (AGP2, IXR1, NRG1, RGS2, RGT1, SIR2) emphasizes the role of regulation for fermentative aroma formation, a relation that has been less investigated yet. Candidate genes could also be validated that influence pDMS preservation. In particular, the validation of SMM transporter MMP1 and identification of a truncated allele with superior pDMS preserving properties confirm both, the influence of yeast on grape-derived aroma precursors and the suitability of QTL mapping for elucidating this context.

The analytical methodology was further developed by extending the QTL mapping approach to the identification of genomic regions influencing modeled metabolic fluxes. This demonstrated that modeled phenotypic data, which was obtained with limited experimental data, could be used to detect QTLs with an influence on these modeled traits. The robustness of the approach was further proven by validation of two target genes, PDB1 and VID30, whose allelic variants differently influence glycolysis, ethanol and glycerol synthesis, fluxes of the TCA cycle and various metabolite transport and excretion fluxes. Better understanding of the impact of genomic variation on intracellular fluxes is crucial as the production of yeast fermentative aroma is closely linked to fluxes of the CCM. In addition, the approach also has the potential to gain further knowledge about yeast substrate utilization or metabolite production for other biotechnological applications.

All together, these results confirm and emphasize the role of genetic diversity for fermentative aroma formation. The complexity of the underlying genomic bases is demonstrated by the high number of detected QTLs and the indication of QTL interactions. The study demonstrated once more the tight link of fermentative aroma formation to nitrogen and carbon metabolism.

Besides confirming the value of QTL mapping for assessing enological traits, its utility for deciphering complex traits in general was demonstrated. Globally acting target genes could be validated for their impact on single traits and genes with influence on metabolic fluxes could be identified by flux QTL mapping.

In comparison to this study, previous QTL mapping studies on yeast aroma formation used a cross between an S. cerevisiae lab and wine strain. This had the advantage that the strains were more different from a genomic point of view. Greater differences between allelic gene variants might have helped to detect their general impact on the observed traits as they can result in more diverse phenotypes. On the other hand, the use of S. cerevisiae lab strains to investigate winemaking traits might generally bring the disadvantage to map SNPs that correspond to deleterious mutations accumulated in lab strains. In this study, two wine strains were used for the QTL mapping. Choosing this approach, alleles with an actual application for winemaking could be detected and were characterized. In addition to that, generating the F2generation increased the recombination rate of the segregants and a comparatively large number of strains was assessed. These measures led to the detection of QTLs with minor influence and QTL interactions.

One strain of this study, 4CAR1, is a hybrid between a wine and a flor strains and is used for champagne making. Some phenotypic traits could be connected to this genomic origin such as the sugar uptake, for which the causative allelic variant of hexose transporter HXT3 was found to be shared with other flor strains. In addition, an allelic variant of pDMS transporter MMP1 was characterized for its ability to preserve pDMS in must and was connected to the flor origin. However, compared to yeast strains with other applications such as the production of beer or sake, the parental strains are close from a genomic point of view. Nevertheless, variation could be seen for most traits and the cross of both parents generated an even greater variation among the segregant population. This did not only concern the aroma production, but the metabolic profile in general. It indicates the presence of a rich genetic resource for strain improvement by in-crossing of superior alleles. In addition, if these close strains already have the potential to generate great variety, the use of more distant yeasts such as beer, rum or sake strains offers even wider possibilities for breeding programs.

A general evaluation of the allele frequency among the yeast population can be made for the three validated genes with performed sequence alignments. The alleles of SMM transporter gene MMP1 are highly similar among wine and flor strains, which includes both parental alleles. However, the occurrence of the SNP causative for superior SMM preservation is rather rare and mainly connected to flor origin. Concerning pyruvate dehydrogenase subunit gene PDB1, besides an insertion of three nucleotides that is only found in three other strains (soil, wine and wine x flor origin), the MTF2621 allele is common among wine strains and is also found in strains of other niches such as beer or bread. Surprisingly, the PDB1 allele of wine strain MTF2622 is more different to other assessed wine strains. The allele of palm wine strain PW5 is the most divergent in comparison to all other assessed strains. For the gene of GID complex component Vid30, both parental alleles differ considerably from the majority of evaluated wine strains. The MTF2621 allele is located between most wine alleles and the genotypically similar wine x flor strain EC1118. The VID30 allele of MTF2622 shares similarity with a cluster of several strains with different origins, namely laboratory, African and soil. VID30 alleles of palm wine and sake strains are the most divergent to those of wine strains.

Universities and research institutes often face the prejudice that they produce theoretical and basic knowledge with little industrial or economic relevance. The results obtained by this study prove otherwise. Although the findings were not used to construct new wine yeast strains with novel aromatic properties for commercialization, the identification of superior allelic gene variants provides targets for future strain improvements. This could include the construction of strains with higher general production of fermentative aromas. However, this broad increase is likely to generate little added value, as a variety of strains producing different levels of fermentative aromas already exist. A more promising approach is the targeted shape of certain aroma characteristics. This could include an overproduction of single fermentative compounds, e.g., 2-phenylethyl acetate, which contributes floral and rose notes to alcoholic beverages. Allelic variants of Leu9 and Rgs2 were demonstrated to affect 2-phenylethyl acetate production and an impact of Agp1 and Mae1 on the formation of the underlying higher alcohol 2-phenylethanol could be shown. Another possibility would be the optimization of strains regarding their terpenol metabolism. As pointed out, different terpenol compounds contribute different flavors to wine. It was shown that the segregant strains differed in their terpenol profile and QTLs with an impact on this profile could be detected. However, target genes remain to be validated, which would facilitate the process of strain improvement if performed in future studies. A third possibility would be the selection or construction of strains with pDMS conserving properties. This is facilitated by the identification of the truncated Mmp1 variant that leads to the conservation of pDMS in the must. This variant could be identified in various strains of different origin, therefore enabling the selection of a suited strain. In addition, the information can also lead to the insertion of the allele in existent industrial strains by non-GMO methods. It was furthermore shown that the detected allelic variants of PDB1 and VID30 show differences in flux distribution between the glycerol and ethanol synthesis. Although the caused variation is relatively small, the redistribution of carbon to glycerol production plays an important role for the construction of wine yeast strains with reduced alcohol production.

The obtained information about target genes and superior alleles can also be used for other purposes. This includes the biosynthesis of bulk and fine chemicals, e.g., higher alcohols or organic acids, for applications such as biofuel or bioplastic production. In addition, knowledge about all presented target genes can be used to characterize other allelic variants in yeast strains from various origins regarding their impact on the described traits.

The presented results give answers to the majority of issues drawn in the objectives of the thesis. However, they also led to the rise of new questions:

o What proportion does the contribution of QTL interactions have for fermentative aroma formation and how important is their global influence? To fully answer this question, a much larger population of segregants is needed to significantly detect QTLs and QTL interactions with small impacts.

o Which characteristics do different allelic variants of identified target genes show and can superior variants be found in strains from other niches?

o Why does pDMS concentration rapidly decrease at the beginning of fermentation? This can be assessed by the inoculation with inactivated yeast and the subsequent determination of pDMS. An equal decrease in pDMS concentrations would indicate the physical adsorption of pDMS to cellular components.

o Which other genes are involved in pDMS consumption? One possibility to determine additionally involved genes would be repeated QTL mapping with newly generated or existing crosses of yeast strains that both contain an identical MMP1 allele.

o Why do overall terpenol levels decrease during fermentation? Are further compounds produced that were not determined during this study? Do these compounds have organoleptic properties for wine?

In the same manner as for pDMS, the use of inactivated cells can also indicate physical adsorption of terpenols to components of the cell.

o Which allelic variants are responsible for terpenol transformation in detected QTLs? o By which means do conversions between terpenols occur during fermentation? If target genes for terpenols are validated, this question can already be answered by considering the biological function of these genes.

o Can grape-derived terpenols be incorporated in the sterol pathway of yeast and therefore support cell proliferation? This hypothesis could be evaluated by fermenting yeast strains in synthetic medium spiked with 13 C-labeled geraniol. By extracting and determining synthesized sterols after fermentation, the presence of 13 C-labeled sterols would affirm the question.

It is expected that these questions are pursued in future studies for which this thesis lays the foundation. Furthermore, the generated segregant population with associated genotype information can be used to decipher the bases for additional traits. With the first two dimensions explaining 89.7% of variance for main metabolite production and 83.5% of variance for fermentative aroma formation, both PCAs well describe the differences between strains. Although differences between both parent strains have a stronger impact on the determined traits, a distinction between haploid and diploid cells can be made. This notably concerns the yields of glycerol and ethanol, the maximum fermentation rate (µ_max), the formation of some higher alcohols and the formation of medium chain fatty acid and derived volatiles.

The following table breaks down the changes in all observed traits when haploid parental strains are compared to their diploid counterpart (Table 30). It can be seen that most traits are strongly influenced by the ploidy of the strains. Fermentation time, succinate yield, ethanol yield and the formation of ethyl acetate and most medium chain fatty acids are reduced, while the production of some desirable flavor compounds such as 2-methylpropyl acetate and ethyl butanoate is increased for fermentations of haploid parents. Therefore, it seems to be of advantage to use haploid strains for wine fermentation. However, for both parent strains only 62% of all observed traits follow the same pattern by changes in ploidy, which indicates significant strain dependence. Nevertheless, it is evident that ploidy influences the strain's behavior in terms of fermentation kinetics and metabolite formation, which has to be taken in consideration when evaluating the characteristics of wine strains.

2

Evaluation of newly generated Saccharomyces eubayanus x Saccharomyces cerevisiae (wine) hybrids for the fermentation of beer

Introduction

Lager strains (S. pastorianus) occurred probably during beer brewing through hybridization of an S. cerevisiae ale strain and another parental yeast species that was long time unknown [START_REF] Dunn | Reconstruction of the genome origins and evolution of the hybrid lager yeast Saccharomyces pastorianus[END_REF]. Only recently, the second parent has been discovered and characterized as S. eubayanus [START_REF] Libkind | Microbe domestication and the identification of the wild genetic stock of lager-brewing yeast[END_REF]. The hybridizytion of new S. pastorianus strains is currently being performed by many research groups to extend the selection of lager beer strains for brewing.

The use of non-ale S. cerevisiae parent strains, like wine strains, could provide new aromatic characteristics to S. pastorianus beer strains. In addition, this approach can furthermore be used to create cold tolerant strains for wine fermentation, since the use of lower fermentation temperatures preserves volatiles produced by the yeast or present in the must (Magalhães et al., 2017a).

In this experiment, it was evaluated if an S. cerevisiae wine strain parent can impart new aromatic properties to an S. pastorianus hybrid strain used for the production of beer. Hybrid strains were generated by mating of the S. eubayanus isolate C902 and a natural lysine auxotrophic mutant of the S. cerevisiae wine strain 59A, a haploid derivate of the widely used strain EC1118 [START_REF] Krogerus | New lager yeast strains generated by interspecific hybridization[END_REF]. Six S. pastorianus hybrids were selected during fermentation at 37 °C in the absence of lysine supplementation. These strains have already been assessed in cider (Magalhães et al., 2017b) and wine making conditions (Magalhães et al., 2017a).

Materials and Methods

The assessment of the generated hybrids in beer making conditions was carried out at the Heineken brewery in Zoeterwoude, The Netherlands. The strains were fermented anaerobically at 22 °C in 400 mL of 12 °P Heineken wort with the addition of 9.2 µM ZnSO4 and 0.5 mL/L antifoam emulsion. The wort was inoculated to a cell density of 1.0 × 10 7 cells/mL and the fermentation was followed with the Alcoholic Fermentation Monitor (AFM, Applikon Biotechnology) by measuring the exhaust of produced CO2. Samples of 40 mL were taken after 18 h, 40 h and at the end of fermentation. The samples were submitted to the Heineken Sample Service Center and were analyzed for residual sugars, fermentative aromas, sulfur dioxide and 4-vinylguaiacol.

Results and Discussion

Both parents and the hybrid strains were fermented in 12°P Heineken wort at 22 °C. The corresponding fermentation kinetics are presented in Figure 39 and Figure 40. 10 h after the beginning of fermentation, a drop in CO2 production rate can be observed for all strains. Four of the hybrid strains (H11, H12, H24 and H54) even show a second lag phase up to 14 h. The S. eubayanus parent C902, as well as all hybrid strains except H16, did not produce the same total amount of CO2 as the S. cerevisiae parent 59A and hybrid H16. This indicates differences in the total consumption of present sugars (maltose, glucose, maltotriose, fructose, sucrose). Residual sugars after the end of fermentation were determined for both parents and the hybrid strains (Table 31). It can be seen that all strains fully metabolize the sugars glucose, fructose and sucrose. Differences exist between the strains in the metabolism of maltose, which is only fully consumed by the parent strains and the hybrids H16 and H27. This is contradictory to observable differences between the fermentation curves in Figure 40, where a lower CO2 production can be seen for parent C902 and H27, indicating that less sugars were consumed. A possible explanation could be the continuation of fermentation after the samples were taken, although samples were kept cold before submission to the Sample Service Center. Another explanation could be that not all fermenters were airtight sealed. Maltotriose is not consumed significantly by any of the strains. In some cases, the concentration of maltotriose seems higher than the initial concentration in wort. This was caused by inoculation with maltotriose rich preculture. The disablilty to consume maltotriose must not be a fault for beer brewing strains. Maltotriose does not have high sweetening power and a large share of S. cerevisiae ale strains does not fully metabolize the sugar [START_REF] Zastrow | Maltotriose fermentation by Saccharomyces cerevisiae[END_REF]. However, the incomplete consumption of maltose, as it was the case for several of the hybrid strains (Table 31), is more problematic as it leads to sweeter and less alcoholic beers that are more prone to contamination.

The strains show a sequential sugar metabolism, which could be demonstrated by measuring the sugar concentrations in samples taken during the fermentation (data not shown). Glucose is consumed first, then maltose. The switch between both sugar metabolisms causes the observed decline in fermentation rate after 10 h of fermentation, which is strain dependent (Figure 39 and Figure 40).

End point samples of the hybrid fermentations were submitted to the Heineken Sample Service Center and the concentrations of main fermentative aromas were determined. Unfortunately, no samples of the parent strain fermentations were submitted for aroma compound determination. The concentrations of volatile metabolites are given in Table 32. The hybrid strains show considerable variation regarding the production of fermentative aromas. The variation ranges from factor 1.1 for the production of 4-vinylguaiacol to factor 7.0 for the formation of the esters ethyl propionate and ethyl capronate. The production of 4vinylguaiacol was therefore uniform among the strains. With more than factor 5.0, the variation for acetate ester formation (ethyl acetate and isoamyl acetate) was also high. The formation of higher alcohols varied by factor 2.8 between the hybrid strains.

The olfactory properties of the hybrid strains were rudimentary assessed by smelling 20 mL of the fermentation supernatants in a 50 mL Falcon tube. The detected odors are listed in Table 33.

Table 33: Aroma characteristics of the hybrid strain fermentations gained by the olfactory analysis.

Strain

Odor H11 fruity H12 fresh, fruitier than H11, slightly sweet H16 unpleasant H24 sweet, candy like, sligh solvent odor, stronger perception of 4vinylguaiacol H27 odd, sulfuric, worty, unpleasant H54 similar to H24, also stronger perception of 4-vinylguaiacol

The perceived fruity aroma produced by strains H12 and H11 can be explained with higher levels of produced esters. The unpleasant aroma of strain H27 could occur due to the higher levels of produced higher alcohols (Table 32). The stronger perception of 4-vinylguaiacol, a clove-like aroma, for strains H24 and H54 can not be explained with the measured levels of the compound, which were similar among all strains (Table 32). It is more likely that the aroma is perceived stronger since less other fermentative aromas were produced due to the unfinished fermentation (Table 32). The aroma of the majority of fermented beer samples was considered pleasant.

Based on the olfactory analysis, it was decided to ferment strain H12 in a 200-L scale in the Heineken pilot brewery. The resulting beer was filtered, filled in bottles and offered to participants of the 33 rd International Specialized Symposium on Yeast (ISSY33 in Cork, Ireland).

The beer was well perceived and attributed commercial potential. The taste and flavor perception was generally described as medium-bodied, round, slightly sweet and fruity.

Conclusion

In conclusion, the novel hybridization of S. pastorianus can generate great variety between the resulting hybrid strains. The use of non-ale S. cerevisiae strains as parent additionally increases the potential for obtaining strains with new attributes. The resulting strains do not only provide new characteristics for the fermentation of lager beer, but also extend the properties of strains used for the production of other fermented foods, like wine or cider.

More studies are necessary to fully assess the potential of the evaluated hybrids. This includes the fermentation at lower temperatures and the comparison with existing commercialized strains in order to evaluate the novelty of obtained flavor profiles. Nevertheless, it was shown that the generation of new S. pastorianus hybrids has potential to add more variety to lager beer strains.

Abstract:

The yeast Saccharomyces cerevisiae plays a vital role in the production of aroma compounds, such as esters, higher alcohols and organic acids, and the conversion of grape-derived aroma precursors during wine fermentation. To identify the genomic and metabolic bases for these processes, a cross was performed between two wine yeast strains selected because of their different nitrogen requirement during fermentation. 130 F2-segregants were genotyped by whole genome sequencing and individually phenotyped during wine fermentation by measuring extracellular metabolites using HPLC and GC-MS. Intracellular metabolic fluxes were estimated by constraint-based modeling. Quantitative trait locus (QTL) mapping was used to identify allelic variants influencing variations in the aroma profile and metabolic fluxes. More than 80 QTLs explaining variation in 59 quantitative traits were detected. These traits consisted of general fermentation parameters, substrate consumption, the production of main metabolites and fermentative aromas and the metabolism of grape aroma compounds such as terpenols or dimethyl sulfide. The applicability of QTL mapping to detect regions influencing intracellular fluxes (f-QTLs) was furthermore demonstrated. Found QTLs were dissected and genes with allele specific contributions to the phenotype were identified. These results emphasize the genomic and metabolic complexity of yeast aroma formation.

In addition, the identification of genetic determinants increases knowledge about the links between genetic variation and industrial traits and provides a valuable foundation for the development of optimized strains by marker-assisted selection breeding strategies.

Title: Deciphering the genetic and metabolic bases of yeast aroma properties Keywords: Wine fermentation, industrial wine yeast, aroma production, fermentative aromas, terpenols, dimethyl sulfide, metabolic fluxes

Figure 1 :Figure 2 :Figure 3 :Figure 4 :Figure 5 :Figure 10 :Figure 11 :Figure 12 :Figure 13 :Figure 16 :Figure 17 :Figure 18 :

 1234510111213161718 Figure 1: Simplified pathway of alcoholic fermentation by Saccharomyces cerevisiae. ....................................... 31 Figure 2: Representative development of main fermentation parameters during anaerobic wine fermentation by yeast. The different fermentation phases are connected to the availability of nutrients and the accumulation of metabolites. Dark blue: fermentation rate expressed as CO2 release; light blue: ethanol concentration; red: yeast cell number; green: nitrogen concentration; purple: concentration of sugars; from Marsit and Dequin, (2015). .............................................................................................................................................................................. 34 Figure 3: Synthesis of monoterpenes and sesquiterpenes (based on[START_REF] Cheng | Plant terpenoids: biosynthesis and ecological functions[END_REF][START_REF] Mcgarvey | Terpenoid metabolism[END_REF][START_REF] Nagegowda | Plant volatile terpenoid metabolism: biosynthetic genes, transcriptional regulation and subcellular compartmentation[END_REF]. ..................................................................................................................................... 38 Figure 4: Schematic representation of the formation of terpenols during wine fermentation and the potential connection to yeast sterol pathway (based on King and Dickinson, 2000; Zea et al., 1995). ............................... 39 Figure 5: Schematic representation of the changes in aroma composition during wine fermentation, as pictured by chromatographic measurement. The yeast S. cerevisiae converts grape derived compounds and aroma precursors to volatile aromas; from Swiegers et al. (2005). ................................................................................. 41 Figure 6: Simplified depiction of the derivation and synthesis of flavor-active compounds from carbon, nitrogen and sulfur metabolism of wine yeast; from Swiegers et al. (2005). ...................................................................... 42 Figure 7: Production of higher alcohols in yeast (adapted from Hazelwood et al., 2008). ................................... 43 Figure 8: Synthesis of higher alcohols and acetate esters (adapted from Bell and Henschke, 2005). ................. 45 Figure 9: Fatty acid synthesis and their implication on ethyl esters formation (adapted from Saerens et al., 2010). .............................................................................................................................................................................. 48 Figure 10: Formation of DMS from DMSO by the yeast methionine-S-sulfoxide reductase (Mxr1) during fermentation. ........................................................................................................................................................ 49 Figure 11: Metabolism of SMM by yeast. Enzymatic transfer of methyl-group from SMM to homocystein forms two molecules of methionine (adapted from Thomas et al., 2000). .................................................................... 50 Figure 12: Development of DMS concentrations in wine stored over a 16-week period. Influence of wine grape variety (initial pDMS) and storage temperature on DMS formation (based on Marais, 1979). ........................... 51 Figure 13: Potential range of wine flavor characteristics (dashed arrows) caused by the presence and level of representative aroma compounds (solid arrows, boldness correlating to magnitude of impact). Examples of desirable flavor phenotype represented by position of yeast; from Cordente et al. (2012). ............................... 51 Figure 14 : Principle of QTL mapping used in this thesis. A yeast cross was sporulated to generate an F1-meiotic segregant population. Strains of this population were mated and sporulated again to obtain an F2-meiotic segregant population with increased recombination rate. 130 strains of this population were genotyped and phenotyped in order to perform linkage analysis on determined traits............................................................... 56 Figure 15: Simple depiction of linkage analysis. Phenotype distributions are evaluated at marker positions and a statistical analysis calculates the probability for each marker to have an influence on the trait, which therefore reveals the QTL positions with contained genes potentially involved. ................................................................. 58 Figure 16: Methods of QTLs dissection used in this thesis. For RHA (A), both target gene alleles are separately deleted in the parental heterozygote. The resulting hemizygous strains are compared with each other to assess the impact of the remaining homozygous allele. For allele swap (B) the target gene alleles are exchanged between the parental strains and the resulting strains are compared with the undeleted parents to evaluate the resulting change in phenotype............................................................................................................................................. 60 Figure 17: Experimental layout of small scale wine fermentations with tracking of fermentation kinetics by hand (left) and using a custom built robot (right). ......................................................................................................... 74 Figure 18: Simplified synthesis pathways of determined metabolites. Main and secondary metabolites determined in this study by HPLC (green) and GC-MS (red). ................................................................................ 96 Figure 19: Principle component analysis. PCA for the formation of extracellular metabolites by S. cerevisiae. Traits that are less than 2% explained by the first two dimensions of the PCA were excluded (2-methylbutanol, acetate yield, alpha-ketoglutarate yield, ethanol yield, ethyl acetate, glycerol yield, propyl acetate, and valeric acid). . 97

Figure 20 :Figure 21 :Figure 22 :Figure 25 :Figure 26 :Figure 27 :Figure 28 :Figure 32 :Figure 33 :Figure 34 :Figure 37 :

 2021222526272832333437 Figure 20: Effect of validated variants on medium chain fatty acid formation. Simplified pathway of fatty acid synthesis by the enzymes Fas1 and Fas2, which is dependent on intracellular acetyl transport (A). Allelic effect of the enzymes Agp2, Fas1 and Sir2 on the formation of fatty acids (B) and fatty acid ethyl esters (C) as determined by RHA. Concentrations are given in relation to the heterozygote of the parental strains MTF2621 and MTF2622. (p-value: * ≤ 0.05, ** ≤ 0.01, *** ≤ 0.001, **** ≤ 0.0001) ................................................................................. 107 Figure 21: Effect of validated variants on of higher alcohol and fusel acid formation. Amino acids are transported into the cell by Agp1 and Alp1. The expression of AGP1 is influenced by Sir2 (A). Simplified synthesis pathway of fermentative aromas connected to valine and leucine metabolism (B). Allelic effect of the involved enzymes Agp1, Alp1, Ilv6, Mae1 and Sir2 on the formation of volatiles deriving from α-keto-isovalerate (C) and α-ketoisocaproate (D) as determined by RHA. Concentrations are given in relation to the heterozygote of the parental strains MTF2621 and MTF2622. (p-value: * ≤ 0.05, ** ≤ 0.01, *** ≤ 0.001, **** ≤ 0.0001) .............................. 110 Figure 22: Effect of validated variants on propanol formation. Simplified synthesis pathway of fermentative aromas connected to threonine metabolism (A). Allelic effect of the involved enzymes Alp1 and Nrg1 on the formation of volatiles derived from α-keto-butyrate as determined by RHA (B). Concentrations are given in relation to the heterozygote of the parental strains MTF2621 and MTF2622. (p-value: * ≤ 0.05, ** ≤ 0.01). .. 112 Figure 23: PCA of selected modeled fluxes (left) and variation among parents (red, green) and segregant strains (grey) (right). ....................................................................................................................................................... 133 Figure 24: Coefficient of variation of selected representative fluxes among the parent strains of the study (blue) and among the resulting F2-segregants (red). Explanation of flux abbreviations is given in Additional file 9 and Additional file 10. ................................................................................................................................................ 134 Figure 25: Variation of selected fluxes around the mean value among the segregant strains and ±5% interval indicated with dashed lines. ............................................................................................................................... 135 Figure 26: Allelic effect of PDB1 (A) and VID30 (B) on different estimated fluxes of yeast CCM. (p-value: * ≤ 0.05, ** ≤ 0.01, **** ≤ 0.0001) .................................................................................................................................... 139 Figure 27: Phylogenetic trees of target genes (A) PDB1 and (B) VID30, made from variant sequences of strains with different origin. Gene sequences were obtained from the Saccharomyces genome database (SGD). Maximum likelihood trees were constructed by bootstrap method with 200 replications using MEGA v7.0.26 (MEGA software). ................................................................................................................................................ 140 Figure 28: Metabolism of SMM by S. cerevisiae. Two molecules of methionine are synthesized from SMM and homocysteine; One methionine molecule is recycled to homocysteine, the other methionine molecule can be used for protein synthesis; adapted from Thomas et al. (2000). ........................................................................ 158 Figure 29: Determined concentrations of pDMS over the first 28 hours of the fermentation for the parental strains MTF2621 (top) and MTF2622 (bottom). Values for methionine and the amount of total available nitrogen are shown for comparison. Concentrations are given in relation to the starting concentration. ...................... 162 Figure 30: Distribution of measured pDMS concentrations after 80% of the fermentation (as equivalent of DMS concentration) among the population of segregants, together with the concentration mean and interquartile range (boxplot). ................................................................................................................................................... 163 Figure 31: Detected QTL on chromosome XII with an influence on the metabolism of pDMS. ......................... 164 Figure 32: Allelic impact of the genes GTT2, MMP1 and YLL058W on the concentration of preserved pDMS after 80% of the fermentation, assessed by RHA. Concentrations are given in relation to the undeleted parental heterozygote. p-value: ns > 0.05, ** ≤ 0.01, *** ≤ 0.001. .................................................................................. 166 Figure 33: Effect on the concentration of residual pDMS after 80% of the fermentation for the allelic swap of MMP1 (nucleotide 536) between the parent cells. ............................................................................................ 167 Figure 34: Phylogenetic analysis of MMP1 alleles from 79 strains of different origin. Wine strains marked green and flor strains marked red. Wine trains with truncated Mmp1 transporter marked orange. Maximum likelihood tree was constructed by bootstrap method with 200 replications using MEGA v7.0.26 (MEGA software). ...... 169 Figure 35 : Schematic representation of the formation of terpenols during wine fermentation and the potential connection to yeast sterol pathway (based on[START_REF] King | Biotransformation of monoterpene alcohols by Saccharomyces cerevisiae, Torulaspora delbrueckii and Kluyveromyces lactis[END_REF][START_REF] Zea | Content of free terpenic compounds in cells and musts during vinification with three Saccharomyces cerevisiae races[END_REF]. ............................. 177 Figure 36: PCA of fermentation time (80%) and determined terpenol concentrations at this time point. ........ 180

Figure 1 :

 1 Figure 1: Simplified pathway of alcoholic fermentation by Saccharomyces cerevisiae.

Figure 2 :

 2 Figure 2: Representative development of main fermentation parameters during anaerobic wine fermentation by yeast. The different fermentation phases are connected to the availability of nutrients and the accumulation of metabolites. Dark blue: fermentation rate expressed as CO2 release; light blue: ethanol concentration; red: yeast cell number; green: nitrogen concentration; purple: concentration of sugars; from Marsit and Dequin, (2015).

Figure 3 :

 3 Figure 3: Synthesis of monoterpenes and sesquiterpenes (based on[START_REF] Cheng | Plant terpenoids: biosynthesis and ecological functions[END_REF][START_REF] Mcgarvey | Terpenoid metabolism[END_REF][START_REF] Nagegowda | Plant volatile terpenoid metabolism: biosynthetic genes, transcriptional regulation and subcellular compartmentation[END_REF].

Figure 4 :

 4 Figure 4: Schematic representation of the formation of terpenols during wine fermentation and the potential connection to yeast sterol pathway (based on[START_REF] King | Biotransformation of monoterpene alcohols by Saccharomyces cerevisiae, Torulaspora delbrueckii and Kluyveromyces lactis[END_REF][START_REF] Zea | Content of free terpenic compounds in cells and musts during vinification with three Saccharomyces cerevisiae races[END_REF].

Figure 5 :

 5 Figure 5: Schematic representation of the changes in aroma composition during wine fermentation, as pictured by chromatographic measurement. The yeast S. cerevisiae converts grape derived compounds and aroma precursors to volatile aromas; from Swiegers et al. (2005).

Figure 6 :

 6 Figure 6: Simplified depiction of the derivation and synthesis of flavor-active compounds from carbon, nitrogen and sulfur metabolism of wine yeast; from Swiegers et al. (2005).

Figure 7 :

 7 Figure 7: Production of higher alcohols in yeast (adapted from Hazelwood et al., 2008).

Figure 8 :

 8 Figure 8: Synthesis of higher alcohols and acetate esters (adapted from Bell and Henschke, 2005).

Figure 9 :

 9 Figure 9: Fatty acid synthesis and their implication on ethyl esters formation (adapted from Saerens et al., 2010).

Figure 10 :

 10 Figure 10: Formation of DMS from DMSO by the yeast methionine-S-sulfoxide reductase (Mxr1) during fermentation.

Figure 11 :

 11 Figure 11: Metabolism of SMM by yeast. Enzymatic transfer of methyl-group from SMM to homocystein forms two molecules of methionine (adapted from Thomas et al., 2000).

Figure 12 :

 12 Figure 12: Development of DMS concentrations in wine stored over a 16-week period. Influence of wine grape variety (initial pDMS) and storage temperature on DMS formation (based on Marais, 1979).

Figure 13 :

 13 Figure 13: Potential range of wine flavor characteristics (dashed arrows) caused by the presence and level of representative aroma compounds (solid arrows, boldness correlating to magnitude of impact). Examples of desirable flavor phenotype represented by position of yeast; from Cordente et al. (2012).

Figure 14 :

 14 Figure14: Principle of QTL mapping used in this thesis. A yeast cross was sporulated to generate an F1-meiotic segregant population. Strains of this population were mated and sporulated again to obtain an F2-meiotic segregant population with increased recombination rate. 130 strains of this population were genotyped and phenotyped in order to perform linkage analysis on determined traits.

Figure 15 :

 15 Figure 15: Simple depiction of linkage analysis. Phenotype distributions are evaluated at marker positions and a statistical analysis calculates the probability for each marker to have an influence on the trait, which therefore reveals the QTL positions with contained genes potentially involved.

Figure 16 :

 16 Figure16: Methods of QTLs dissection used in this thesis. For RHA (A), both target gene alleles are separately deleted in the parental heterozygote. The resulting hemizygous strains are compared with each other to assess the impact of the remaining homozygous allele. For allele swap (B) the target gene alleles are exchanged between the parental strains and the resulting strains are compared with the undeleted parents to evaluate the resulting change in phenotype.

Figure 17 :

 17 Figure 17: Experimental layout of small scale wine fermentations with tracking of fermentation kinetics by hand (left) and using a custom-built robot (right).

Figure 18 :

 18 Figure 18: Simplified synthesis pathways of determined metabolites. Main and secondary metabolites determined in this study by HPLC (green) and GC-MS (red).

Figure 19 :

 19 Figure 19: Principle component analysis. PCA for the formation of extracellular metabolites by S. cerevisiae. Traits that are less than 2% explained by the first two dimensions of the PCA were excluded (2-methylbutanol, acetate yield, alphaketoglutarate yield, ethanol yield, ethyl acetate, glycerol yield, propyl acetate, and valeric acid).

Figure 20 :

 20 Figure 20: Effect of validated variants on medium chain fatty acid formation. Simplified pathway of fatty acid synthesis by the enzymes Fas1 and Fas2, which is dependent on intracellular acetyl transport (A). Allelic effect of the enzymes Agp2, Fas1 and Sir2 on the formation of fatty acids (B) and fatty acid ethyl esters (C) as determined by RHA. Concentrations are given in relation to the heterozygote of the parental strains MTF2621 and MTF2622. (p-value: * ≤ 0.05, ** ≤ 0.01, *** ≤ 0.001, **** ≤ 0.0001)

Figure 21 :

 21 Figure 21: Effect of validated variants on of higher alcohol and fusel acid formation. Amino acids are transported into the cell by Agp1 and Alp1. The expression of AGP1 is influenced by Sir2 (A). Simplified synthesis pathway of fermentative aromas connected to valine and leucine metabolism (B). Allelic effect of the involved enzymes Agp1, Alp1, Ilv6, Mae1 and Sir2 on the formation of volatiles deriving from α-keto-isovalerate (C) and α-keto-isocaproate (D) as determined by RHA. Concentrations are given in relation to the heterozygote of the parental strains MTF2621 and MTF2622. (p-value: * ≤ 0.05, ** ≤ 0.01, *** ≤ 0.001, **** ≤ 0.0001)

Figure 22 :

 22 Figure 22: Effect of validated variants on propanol formation. Simplified synthesis pathway of fermentative aromas connected to threonine metabolism (A). Allelic effect of the involved enzymes Alp1 and Nrg1 on the formation of volatiles derived from α-keto-butyrate as determined by RHA (B). Concentrations are given in relation to the heterozygote of the parental strains MTF2621 and MTF2622. (p-value: * ≤ 0.05, ** ≤ 0.01).

Figure 23 :

 23 Figure 23: PCA of selected modeled fluxes (left) and variation among parents (red, green) and segregant strains (grey) (right).

Figure 24 :

 24 Figure 24: Coefficient of variation of selected representative fluxes among the parent strains of the study (blue) and among the resulting F2-segregants (red). Explanation of flux abbreviations is given in Additional file 9 and Additional file 10.

Figure 25 :

 25 Figure 25: Variation of selected fluxes around the mean value among the segregant strains and ±5% interval indicated with dashed lines.

Figure 26 :

 26 Figure 26: Allelic effect of PDB1 (A) and VID30 (B) on different estimated fluxes of yeast CCM. (p-value: * ≤ 0.05, ** ≤ 0.01, **** ≤ 0.0001)

Figure 27 :

 27 Figure 27: Phylogenetic trees of target genes (A) PDB1 and (B) VID30, made from variant sequences of strains with different origin. Gene sequences were obtained from the Saccharomyces genome database (SGD). Maximum likelihood trees were constructed by bootstrap method with 200 replications using MEGA v7.0.26 (MEGA software).

  Abbreviations for metabolites of the CCM and their compartmental localization.

  g6p[c] + 0.258 r5p[c] + 0.129 e4p[c] + 0.116 g3p[c] + 0.303 3pg[c] + 0.232 pep[c] + 0.775 oaa[c] + 1.084 pyr[m] + 0 pyr[c] + 0.176 accoa[m] + 0.252 accoa[c] + 0.106 akg[m] + 0.366 akg[c] + 0 CO2[c] + 0.136 glu[c] + 115 atp[c] + 0.106 atp[m] + 1.499 nad[c] + 0.176 nad[m] + 0.602 nadph[m] + 5.35 nadph[c]  115 adp[c] + 0.106 adp[m] + 1.499 nadh[c] + 0.176 nadh[m] + 0.602 nadp[m] + 5.35 nadp[c]

  g6p[c] + 0.258 r5p[c] + 0.129 e4p[c] + 0.116 g3p[c] + 0.303 3pg[c] + 0.232 pep[c] + 0.775 oaa[c] + 1.084 pyr[m] + 0 pyr[c] + 0.176 accoa[m] + 0.252 accoa[c] + 0.106 akg[m] + 0.366 akg[c] + 0 CO2[c] + 0.136 glu[c] + 115 atp[c] + 0.106 atp[m] + 1.499 nad[c] + 0.176 nad[m] + 0.602 nadph[m] + 5.35 nadph[c]  115 adp[c] + 0.106 adp[m] + 1.499 nadh[c] + 0.176 nadh[m] + 0.602 nadp[m] + 5.35 nadp[c]

Figure 28 :

 28 Figure 28: Metabolism of SMM by S. cerevisiae. Two molecules of methionine are synthesized from SMM and homocysteine; One methionine molecule is recycled to homocysteine, the other methionine molecule can be used for protein synthesis; adapted from Thomas et al. (2000).

Figure 29 :

 29 Figure 29: Determined concentrations of pDMS over the first 28 hours of the fermentation for the parental strains MTF2621 (top) and MTF2622 (bottom). Values for methionine and the amount of total available nitrogen are shown for comparison. Concentrations are given in relation to the starting concentration.

Figure 30 :

 30 Figure 30: Distribution of measured pDMS concentrations after 80% of the fermentation (as equivalent of DMS concentration) among the population of segregants, together with the concentration mean and interquartile range (boxplot).

Figure 32 :

 32 Figure 32: Allelic impact of the genes GTT2, MMP1 and YLL058W on the concentration of preserved pDMS after 80% of the fermentation, assessed by RHA. Concentrations are given in relation to the undeleted parental heterozygote. p-value: ns > 0.05, ** ≤ 0.01, *** ≤ 0.001.

Figure 33 :

 33 Figure 33: Effect on the concentration of residual pDMS after 80% of the fermentation for the allelic swap of MMP1 (nucleotide 536) between the parent cells.

Figure 34 :

 34 Figure 34: Phylogenetic analysis of MMP1 alleles from 79 strains of different origin. Wine strains are marked green and flor strains are marked red. Strains with truncated Mmp1 transporter are indicated with asterisks. Maximum likelihood tree was constructed by bootstrap method with 200 replications using MEGA v7.0.26 (MEGA software).

Figure 35 :

 35 Figure 35: Schematic representation of the formation of terpenols during wine fermentation and the potential connection to yeast sterol pathway (based on[START_REF] King | Biotransformation of monoterpene alcohols by Saccharomyces cerevisiae, Torulaspora delbrueckii and Kluyveromyces lactis[END_REF][START_REF] Zea | Content of free terpenic compounds in cells and musts during vinification with three Saccharomyces cerevisiae races[END_REF].

Figure 36 :

 36 Figure 36: PCA of fermentation time (80%) and determined terpenol concentrations at this time point.

Figure 37 :

 37 Figure 37: PCA of extracellular main metabolite yields, fermentation parameters and ratio of left sugars after 80% of fermentation, dependent on strain and ploidy.

Figure 38 :

 38 Figure 38: PCA of volatile compound formation (selection) after 80% of fermentation, dependent on strain and ploidy.

Figure 39 :Figure 40 :

 3940 Figure39: CO2 production rate of the fermented parent strains C902 and 59A as well as the hybrids H11 -H54 in 12 °P Heineken wort at 22 °C.

Table 2 :

 2 . ............................................. 36 List of intermediates and products of the Ehrlich pathway, as well as their precursor amino acids. ..... 44

Table 3 :

 3 Composition of YPD medium. ................................................................................................................. 65

Table 4 :

 4 Composition of yeast sporulation media. ............................................................................................... 66

Table 5 :

 5 Composition of the synthetic must SM200 used for fermentations. ...................................................... 67

Table 6 :

 6 Composition of micronutrient stock solution. ........................................................................................ 67

Table 7 :

 7 Composition of the vitamin stock solution. ............................................................................................ 67

Table 8 :

 8 Composition of the anaerobic factors stock solution. ............................................................................ 68

Table 9 :

 9 Composition of the iron chloride stock solution. .................................................................................... 68

Table 10 :

 10 Composition of the amino acid stock solution. ..................................................................................... 68

Table 11 :

 11 Reaction mix for the PCR using Taq polymerase (left) and Phusion polymerase (right). ...................... 70

Table 12 :

 12 List of buffers used for the analysis of amino acids. ............................................................................. 76

Table 13 :

 13 Parameters for the determination of volatile fermentative aromas by GC-MS. Target ions for peak quantification in bold. ........................................................................................................................................... 77

Table 14 :

 14 Parameters for the determination of terpenols by GC-MS. Target ions for peak quantification in bold.

.............................................................................................................................................................................. 79 Table

15

: Primers used for amplification of CAS9 integration cassette (1678-5981) and CRISPR plasmids (6005 and CasGuide_GENE), according to the protocol of

[START_REF] Mans | CRISPR/Cas9: a molecular Swiss army knife for simultaneous introduction of multiple genetic modifications in Saccharomyces cerevisiae[END_REF]

. ............................................................. 82 Table 16: QTLs detected with single QTL mapping. List of QTLs with an influence on fermentation parameters, the production of extracellular metabolites and volatile secondary metabolites that were detected with single QTL mapping. QTLs containing several single trait results with a peak distance < 10 cM are numbered in superscript. ........................................................................................................................................................... 98 Table 17: QTLs detected with double and multiple QTL mapping. QTLs with an influence on fermentation parameters and the production of extracellular metabolites and volatile secondary metabolites that were additionally detected with double and multiple QTL searches. QTLs containing several single trait results with a peak distance < 10 cM are numbered in superscript. ......................................................................................... 101 Table 18: Validated allelic variants in detected QTLs. Selected target genes for the verification of QTLs influencing fermentation kinetics, substrate consumption and the production of fermentative aromas; differences caused by the allelic gene variants regarding the influenced traits were detected by RHA and are given as the ratio of phenotype MTF2621 to phenotype MTF2622. (p-value: * ≤ 0.05, ** ≤ 0.01, *** ≤ 0.001) ................................ 103

Table 19 :

 19 Non synonymous SNPs between allelic variants. Differences in the amino acid (AA) sequence of the expressed protein resulting from non-synonymous SNPs between the allelic variants of the evaluated target genes. Comparison of the strains MTF2621 and MTF2622 with the S. cerevisiae reference strain S288C. ....... 104

Table 20 :

 20 Detected QTLs influencing metabolite yields and ratio of remaining sugars (G/F ratio) during exponential phase. .............................................................................................................................................. 137

Table 21 :

 21 Detected QTLs influencing modeled metabolic fluxes. ....................................................................... 137

Table 22 :

 22 Validated allelic variants in detected QTLs influencing modeled metabolic fluxes. (p-value: * ≤ 0.05, ** ≤ 0.01) ................................................................................................................................................................. 138

Table 23 :

 23 Differences in amino acids (AA) of validated gene variants caused by non-synonymous SNPs between parent strains. Comparison of SNP identity to S. cerevisiae type strain S288C. ................................................. 138

Table 24 :

 24 Differences in peptide chains of validated gene variants caused by non-synonymous SNPs between parent strains. Comparison of SNP identity to S. cerevisiae type strain S288C. ................................................. 165

Table 25 :

 25 Distribution

of the truncated SMM transporter Mmp1, resulting from SNP G536A in the gene, between 85 assessed S. cerevisiae strains of different origin. ........................................................................................... 168

Table 26 :

 26 Difference of terpenol formation for two segregant strains with extreme fermentation kinetics. Samples were taken after 80% of fermentation, which corresponded to 73h for the fast fermenting strain and to 261h for the slow fermenting strain. .................................................................................................................. 179

Table 27 :

 27 Detected QTLs that influence the metabolism of terpenols by S. cerevisiae. ..................................... 181 Table 28: QTLs for terpenol metabolism overlapping with regions detected by previous experiments to influence other traits. ......................................................................................................................................................... 182

Table 29 :

 29 Suggestions for target genes to validate in detected QTLs with LOD-scores of more than 4.42. ....... 183

Table 30 :

 30 Comparison of haploid and diploid parental strains in terms of fermentation parameters, main metabolite yields, ratio of remaining glucose/fructose (G/F ratio) and formation of volatile metabolites after 80% of fermentation. .................................................................................................................................................. 223

Table 31 :

 31 Concentration of residual sugars [g/L] after the fermentation of the hybrid strains in 12 °P Heineken wort at 22 °C. ...................................................................................................................................................... 226

Table 32 :

 32 Concentrations of fermentative aromas [mg/L] produced by the hybrid strains H11 -H54 during fermentation in 12 °P Heineken wort at 22 °C. ................................................................................................... 228

Table 33 :

 33 Aroma characteristics of the hybrid strain fermentations gained by the olfactory analysis. ............. 228

  De ce fait, nous avons pu appliquer plusieurs stratégies de cartographie QTL et notamment rechercher des interactions entre QTL. Ces analyses nous ont permis de détecter 65 QTL ayant une influence sur la formation de métabolites volatils et sur la production de métabolites extracellulaires et paramètres généraux de fermentation. Nous avons confirmé que la cartographie QTL multiple offre la possibilité de détecter des loci supplémentaires, en particulier mineurs. Des QTL interactifs ont pu être détectés pour trois caractères évalués : la formation de lactate d'éthyle, d'octanoate d'éthyle et de propanol. En validant les gènes Mae1 catalyse la réaction du malate au pyruvate. Nos travaux suggèrent qu'un taux de conversion enzymatique plus élevé conféré par un allèle variant augmenterait la production de substances volatiles dérivées du pyruvate. Fas1 catalyse les étapes de la synthèse des acides gras à partir de malonyl-CoA, qui est formé par carboxylation de l'acétyl-CoA, et un allèle variant permettant une synthèse réduite des acides gras a été caractérisé. Les concentrations en principaux métabolites extracellulaires mesurés pendant la phase de croissance exponentielle de la fermentation ont été utilisées pour estimer les flux intracellulaires du métabolisme central carboné de la levure en utilisant un modèle stoechiométrique à base de contraintes précédemment développé au laboratoire. Cela a conduit à l'intégration de traits quantifiables, par ailleurs indépendants. Les flux métaboliques estimés ont été utilisés comme données phénotypiques pour détecter les f-QTL. 4 QTLs ayant une influence sur 4 voies métaboliques principales et différents flux de transport de métabolites ont été détectés. Ces QTL n'ont pas pu être trouvés par analyse de liaison en considérant la concentration des métabolites extracellulaires seuls. Ceci démontre que des données phénotypiques modélisées, obtenues à partir d'un jeu de données expérimentales de taille limitée, peuvent permettre la détection de QTLs. La robustesse de l'approche a été confirmée par la validation de deux gènes cibles dans deux des QTL détectés, PDB1 et VID30. Mmp1 codant pour une protéine tronquée est principalement trouvé dans les souches de levure de voile, indiquant ainsi un lien avec cette origine. La présence de ce SNP dans quelques souches de vin pourrait être attribuée à des transferts de gènes entre les souches de levure de voile et les levures de vin.Dans le quatrième chapitre de la thèse, l'approche de cartographie QTL établie a été appliquée pour détecter les régions dans le génome qui influencent le métabolisme de la levure des terpénols dérivés du raisin. A cette fin, le géraniol, le précurseur de terpénols, a été ajouté au moût synthétique et tous les descendants ont été phénotypés pour leur capacité à transformer le géraniol pendant la fermentation oenologique. L'analyse de liaison a révélé 21 QTL dans le génome qui influencent la concentration de plusieurs terpénols en même temps ou la formation de composés uniques tels que le linalol ou l'α-terpinéol. Parmi les 21 QTL détectés, 13 régions correspondent à des QTL influençant d'autres traits déjà identifiés dans les chapitres précédents de la thèse. acétate de 2-phényléthyle et l'impact de Agp1 et de Mae1 sur la formation de l'alcool supérieur correspondant, le 2-phényléthanol, ont pu être montrés. Une autre possibilité serait l'optimisation des souches pour le métabolisme des terpénols. Comme indiqué précédemment, les différents terpénols apportent différentes notes aromatiques au vin et sont notamment à la base de la typicité des muscats. Nous avons montré que les ségrégants différaient dans leur profil de terpénols et que des QTLs ayant un impact sur ce profil pouvaient être détectés. Cependant, il reste à valider les gènes candidats, ce qui permettra d'identifier les cibles d'amélioration des souches les plus pertinentes. Une troisième possibilité serait la sélection ou la construction de souches ayant des propriétés de préservation de la SMM. L'identification du variant tronqué de Mmp1 qui conduit à la préservation de la SMM dans le moût offre des perspectives prometteuses dans ce contexte. Ce variant pourrait être recherché dans des souches d'origine différente, permettant ainsi la sélection de souches d'intérêt. Cette information peut également conduire à l'insertion de

candidats dans les QTL trouvés par analyse d'hemizygotie réciproque, nous avons démontré l'implication et l'impact de 13 variants alléliques au sein de 9 QTLs. Parmi ces allèles validés, 5 (AGP1, ALP1, FAS1, ILV6 et LEU9) ont des rôles bien décrits dans les voies métaboliques conduisant à la formation d'arômes fermentaires de levure. Leur contribution à la production de volatils a été confirmée et des allèles expliquant une variation des caractères observés entre les souches parentales ont été identifiés et caractérisés. En outre, le caractère fructophile précédemment décrit de l'allèle HXT3 de la souche MTF2621 a été confirmé dans cette étude. Pour les 7 autres gènes validés (AGP2, IXR1, MAE1, NRG1, RGS2, RGT1 et SIR2), des contributions à la formation d'arômes fermentaires ont été révélées, celles-ci n'étant pas connues auparavant. En outre, une grande partie des gènes candidats validés sont des régulateurs transcriptionnels (AGP2, IXR1, NRG1, RGS2, RGT1, et SIR2), ce qui souligne le rôle de la régulation dans la formation des arômes fermentaires, assez peu étudié jusqu'à maintenant. Pour tous les gènes validés, des variants alléliques ayant des impacts différents ont été décrits. Des variants des transporteurs d'acides aminés Agp1 et Alp1, affectent la formation des produits de la voie d'Ehrlich par des différences d'expression ou d'affinité. L'implication du métabolisme de l'azote dans la formation d'arômes fermentaires est appuyée par l'identification de deux autres enzymes impliquées dans les voies de synthèse des acides aminés, Ilv6 et Leu9. Le fait que la production d'arômes fermentaires soit également liée au métabolisme central carboné est souligné par la validation des gènes cibles MAE1 et FAS1.

La production d'arômes fermentaires de levure étant étroitement liée aux flux du métabolisme carboné central, une meilleure compréhension de l'impact des variations génomiques sur les flux intracellulaires est cruciale. Dans la deuxième partie de la thèse, nous avons cherché à évaluer la possibilité d'étendre la méthodologie de cartographie QTL à la détection de loci influençant les flux métaboliques intracellulaires, appelés flux-QTL (f-QTL). Les variants alléliques de PDB1 provoquent des différences dans les flux de synthèse du glycérol qui peuvent être liées aux déséquilibres redox engendrés par une modification de la conversion du pyruvate. Les variants de VID30 ont une influence sur les flux de la glycolyse, de la synthèse de l'éthanol et du cycle de Krebs, pouvant être liée à des différences dans la dégradation des enzymes impliquées dans les réactions du métabolisme central du carbone. Ces résultats apportent un nouveau regard sur l'implication de certains gènes et de leurs variants sur le réseau métabolique, qui pourrait avoir des répercussions sur les propriétés aromatiques des levures. L'extension de l'approche QTL pour la détection des f-QTL pourrait être appliquée à l'élucidation d'autres traits, comme par exemple l'utilisation efficace de substrats alternatifs (glycérol, xylose …) par la levure ou la production de métabolites pour d'autres applications biotechnologiques.

Le troisième chapitre de la thèse applique la stratégie de cartographie QTL à la détection des régions génomiques impliquées dans la capacité de la levure à influencer le niveau de la Sméthylméthionine (SMM), le précurseur du DMS dérivé du raisin. A cette fin, la SMM a été ajoutée au moût synthétique et les 130 ségrégants ont été phénotypés pour leur capacité à éliminer la SMM du milieu en fin de fermentation. Ces résultats ont été utilisés comme données phénotypiques pour l'analyse QTL. Cela a conduit à la détection d'un QTL dans le génome de la levure ayant une forte influence sur le métabolisme de la SMM. Trois gènes candidats de cette région ont été évalués et nous avons pu démontrer que deux d'entre eux, MMP1 et YLL058W, influencent le métabolisme de la SMM. Le gène du transporteur de la SMM, MMP1, a été identifié comme étant responsable de la majeure partie de la variation du phénotype. Ainsi, un allèle parental de MMP1 exprime une capacité fortement diminuée à éliminer la SMM du milieu. En effectuant un échange allélique du gène entre les souches parentales à l'aide de la technique CRISPR-Cas9, nous avons identifié la mutation ponctuelle (SNP) à l'origine de ce caractère. Cette mutation génère un codon STOP dans le gène MMP1, ce qui conduit à l'expression d'un transporteur de la SMM tronqué, entrainant une meilleure conservation de la SMM qui est moins utilisé par la levure. En étudiant la distribution des variants de MMP1 de 85 souches de levure d'origine différente, nous avons pu démontrer que le variant Ces QTL influencent les paramètres de fermentation ou la production des principaux métabolites, et également pour la plupart la formation d'arômes fermentaires. Cela indique une connexion potentielle entre le métabolisme des terpénols et la production de composés volatils, par exemple un mécanisme identique entraînant l'acétylation des alcools supérieurs formés et des terpénols présents.

En conclusion, les résultats obtenus confirment et soulignent le rôle de la diversité génétique dans la formation des arômes fermentaires. La complexité des bases génomiques sousjacentes est démontrée par le nombre élevé de QTL détectés et l'indication des interactions QTL. Nos travaux démontrent une fois de plus le lien étroit entre la formation d'arômes fermentaires et le métabolisme de l'azote et du carbone. En plus de confirmer l'intérêt de la cartographie QTL pour évaluer les traits oenologiques, les résultats de cette étude soulignent sa pertinence pour déchiffrer des traits complexes. En utilisant la cartographie QTL, il a été possible de valider des gènes cibles ayant une action globale sur des traits définis et d'identifier des gènes influençant les flux métaboliques (f-QTL).

Le secteur industriel perçoit parfois les universités et centres de recherche publics comme producteurs de connaissances théoriques peu pertinentes sur le plan économique. Les connaissances générées dans cette étude offrent des possibilités d'application à court terme. Bien que les allèles variants n'aient pas encore été introduits dans d'autres souches oenologiques ayant un fond génétique différent, l'identification d'allèles d'intérêt fournit clairement de nouvelles cibles pour construire des souches des levures de vin aux propriétés aromatiques améliorées pouvant être commercialisées. Cela peut inclure la construction de souches avec une production d'arômes fermentaires globalement plus élevée. Cependant, cette stratégie est susceptible de générer peu de valeur ajoutée, car des souches produisant différents niveaux d'arômes fermentaires existent déjà sur le marché. Une approche plus prometteuse serait de cibler certaines caractéristiques aromatiques. Par exemple, une surproduction d'acétate de 2-phényléthyle, qui apporte des notes florales et de rose aux boissons alcoolisées pourrait être envisagée. Des variants alléliques de Leu9 et Rgs2 affectant la production d'l'allèle d'intérêt dans des souches déjà commercialisées par hybridation et sélection assistée par marqueur. Nous avons en outre montré que les variants alléliques de PDB1 et VID30 présentent des différences de distribution de flux entre la synthèse du glycérol et de l'éthanol. Bien que la variation causée soit relativement faible, cet allèle pourrait être utile, en appui à une autre stratégie, pour favoriser la surproduction de glycerol dans l'optique de diminuer le rendement en alcool.

Les gènes cibles et allèles les plus performants identifiés pourraient également être utilisées pour d'autres applications, incluant la synthèse de produits de chimie fine, par exemple d'alcools supérieurs ou d'acides organiques, pour la production de biocarburants ou de bioplastiques. Ces connaissances peuvent également s peuvent être utilisées pour caractériser d'autres variants alléliques dans des souches de levure d'origines diverses, et prédire ainsi leur phénotype.

Concentration in wine (mg/L) Perception threshold (mg/L) Aroma description

	Ethyl acetate	22.5-63.5	7.5*	VA, nail polish, fruity
	2-phenylethyl acetate	0-18.5	0.25*	Flowery, rose, fruity
	Isoamyl acetate	0.1-3.4	0.03*	Banana, pear
	Isobutyl acetate	0.01-1.6	1.6****	Banana, fruity
	Hexyl acetate	0-4.8	0.7**	Sweet, perfume
	Ethyl butanoate	0.01-1.8	0.02*	Floral, fruity
	Ethyl hexanoate	0.03-3.4	0.05*	Green apple
	Ethyl octanoate	0.05-3.8	0.02*	Sweet soap
	Ethyl decanoate	0-2.1	0.2*****	Floral, soap
	Propanol	9.0-68	500**	Pungent, harsh
	Butanol	0.5-8.5	150*	Fusel, spiritous
	Isobutanol	9.0-174	40*	Fusel, spiritous
	Isoamyl alcohol	6.0-490	30*	Harsh, nail polish
	Hexanol	0.3-12.0	4**	Green, grass
	2-phenylethyl alcohol	4.0-197	10*	Floral, rose
	Acetic acid	100-1150	280*	VA, vinegar
	Acetaldehyde	10-75	100**	Sherry, nutty, bruised
				apple
	Diacetyl	<5	0.2** / 2.8***	Buttery
	Glycerol	5-14 g/L	5.2 g/L**	Odourless (slightly sweet
				taste)
	Linalool	0.0017-0.010	0.0015******/0.025*****	Rose
	Geraniol	0.001-0.044	5******/30*	Rose-like
	Citronellol	0.015-0.042	8******/100*	Citronella
	2-acetyl-1-pyrroline	Trace	0.0001******	Mousy
	2-acetyltetrahydropyridine	0.0048-0.1	0.0016******	Mousy
	4-ethylphenol	0.012-6.5	0.14*/0.6***	Medicinal, barnyard
	4-ethyl guaiacol	0.001-0.44	0.033*/0.11***	Phenolic, sweet

Table 2 :

 2 List of intermediates and products of the Ehrlich pathway, as well as their precursor amino acids.

	Amino acid	α-keto acid	Fusel aldehyde	Fusel alcohol	Fusel acid
	Leucine	α-ketoisocaproate	Isoamylaldehyde	Isoamyl alcohol	Isovalerate
	Valine	α-ketoisovalerate	Isobutanal	Isobutanol	Isobutyrate
	Isoleucine	α-ketomethylvalerate	Methylvaleraldehyde	Active amyl alchol	Methylvalerate
	Phenylalanine	Phenylpyruvate	2-phenylacetaldehyde		2-phenylacetate
	Tyrosine	p-hydroxy-	p-hydroxy-	p-hydroxy-	p-hydroxy-
		phenylpyruvate	phenylacetaldehyde	phenylethanol	phenylacetate
	Tryptophane	3-indole pyruvate	3-indole acetaldehyde	Tryptophol
	Methionine	α-keto-γ-	Methional	Methionol
		(methylthio)butyrate

Table 3 :

 3 Composition of YPD medium.

	Compound	Amount per L
	Yeast extract	10 g
	Peptone	20 g
	Glucose	20 g
	Solid YPD media contained 1.5% agar

Table 4 :

 4 Composition of yeast sporulation media.

	Compound	Amount per L.
	PRE5 medium
	Yeast extract	8 g
	Peptone	3 g
	Glucose	10 g
	Potassium acetate	10 g
	MiliQ water	to 1 L
	SPO2 medium
	Potassium acetate	5 g
	MiliQ water	to 1 L
	Micromanipulation solid medium
	Yeast extract	10 g
	Peptone	20 g
	Glucose	20 g
	Ultra pure agar	15 g
	MiliQ water	to 1 L

Table 5 :

 5 Composition of the synthetic must SM200 used for fermentations.

	Compound	Amount per L
	Glucose	100 g
	Fructose	100 g
	Malic acid	6 g
	Citric acid	6 g
	Potassium phosphate	0.75 g
	Potassium sulfate	0.5 g
	Magnesium sulfate	0.25 g
	Calcium chloride	0.155 g
	Sodium Chloride	0.2 g
	Ammonium chloride	0.22 g
	Micronutrient stock solution	1 mL
	Vitamins stock solution	10 mL
	Anaerobic Factor stock Solution	0.33 mL
	Amino acid stock solution	6.16 mL
	Iron chloride stock solution	1 mL
	The pH of the medium is adjusted to 3.3 with
	10M Sodium hydroxide

Table 6 :

 6 Composition of micronutrient stock solution.

	Compound	Amount per L
	Manganese sulfate monohydrate	4 g
	Zinc sulphate heptahydrate	4 g
	Copper sulfate pentahydrate	1 g
	Potassium iodide	1 g
	Cobalt chloride hexahydrate	0.4 g
	Boric acid	1 g
	Ammonium heptamolybdate	1 g
	The micronutrient stock solution is sterilized by 0.22 μm filtration and
	stored at 4 °C

Table 7 :

 7 Composition of the vitamin stock solution.

	Compound

Table 8 :

 8 Composition of the anaerobic factors stock solution.

	Compound

Table 9 :

 9 Composition of the iron chloride stock solution.

	Compound

Table 10 :

 10 Composition of the amino acid stock solution.

	Compound

Table 11 :

 11 Reaction mix for the PCR using Taq polymerase (left) and Phusion polymerase (right).

	Compound	Quantity	Volume (µL) Compound	Quantity	Volume (µL)
	Buffer potassium	1/10	2.5	Buffer HF 5×	1/5	5
	chloride 10×
	dNTPs 10 mM	200 µM	0.5	dNTPs 10 mM	200 µM	0.5
	Primer fw	0.4 µM	1	Primer fw	0.5 µM	1.25
	(10 µM)			(10 µM)
	Primer rv	0.4 µM	1	Primer rv	0.5 µM	1.25
	(10 µM)			(10 µM)
	Taq polymerase	0.035 U/µL	0.25	Phusion polymerase	0.02 U/µL	0.25
	DNA (25ng/µL)	50 ng	2	DNA (25ng/µL)	50 ng	2
	Magnesium chloride	2 mM	2.5
	25 mM
	MilliQ water		to 25	MilliQ water		to 25

Table 12 :

 12 List of buffers used for the analysis of amino acids.

	Buffer	Ion	Molarity (mM)	pH
	Load	Lithium citrate	200	2.2
	A	Lithium citrate	200	2.8
	B	Lithium citrate	300	3
	C	Lithium citrate	500	3.15
	D	Lithium citrate	900	3.5
	E	Lithium citrate	1650	3.55
	F	Lithium hydroxide	300

Table 13 :

 13 Parameters for the determination of volatile fermentative aromas by GC-MS. Target ions for peak quantification in bold.

	Compound	Retention	Qualifier ion m/z	Internal standard
		time [min]
	Ethyl acetate	2.85	61, 70, 88	d5-ethyl butanoate
	Ethyl propanoate	3.63	73, 75, 102	d5-ethyl butanoate
	Ethyl 2-methylpropanoate	3.77	71, 88, 116	d5-ethyl butanoate
	Propyl acetate	3.92	43, 61, 73	d5-ethyl butanoate
	2-Methylpropyl acetate	4.64	56, 73	d5-ethyl butanoate
	Ethyl butanoate	5.15	71, 88, 101	d5-ethyl butanoate
	Propanol	5.2	31, 42, 59	d5-ethyl butanoate
	Ethyl 2-methylbutanoate	5.52	85, 102, 115	d5-ethyl butanoate
	Ethyl 3-methylbutanoate	5.95	85, 88, 115	d5-ethyl butanoate
	2-Methylpropranol	6.52	43, 74	d5-ethyl butanoate
	2-Methylbutyl acetate	7.42	57, 72, 74	d5-ethyl butanoate
	3-Methylbutyl acetate	7.45	55, 70, 87	d5-ethyl butanoate
	Ethyl pentanoate	7.82	85, 88, 101	d5-ethyl butanoate
	3-Methylbutanol	10.08	55, 57, 70	d5-ethyl hexanoate
	Hexanol	10.59	45, 69, 87	d5-ethyl hexanoate
	Ethyl hexanoate	10.96	88, 99, 115	d5-ethyl hexanoate
	2-Methylbutanol	11.48	55, 57, 70	d5-ethyl hexanoate
	Hexyl acetate	12.25	56, 69, 84	d5-ethyl hexanoate
	Ethyl lactate	14.5	45, 75	d5-ethyl octanoate
	Ethyl octanoate	17.53	88, 101	d5-ethyl octanoate
	Propanoic acid	20.66	57, 73, 74	d5-ethyl octanoate
	2-Methylpropanoic acid	21.53	43, 73, 88	d7-butyric acid
	Butanoic acid	23.3	60, 73, 45	d7-butyric acid
	Ethyl decanoate	23.72	88, 101	d5-ethyl decanoate
	3-Methylbutanoic acid	24.49	157	d7-butyric acid
	2-Methylbutanoic acid	24.51	60, 61, 87	d7-butyric acid
	Diethyl succinate	24.79	73, 74, 87	d5-ethyl decanoate
	3-Methylthiopropanol	25.76	101, 129	d5-ethyl decanoate
	Pentanoic acid	26.4	61, 73, 106	d7-butyric acid
	2-Phenylethyl acetate	28.47	60, 73	d5-ethyl decanoate
	Hexanoic acid	29.3	91, 104	d7-butyric acid
	Ethyl dodecanoate	29.4	60, 73, 87	d5-ethyl decanoate
	2-Phenylethanol	30.9	88, 101	d4-phenylethanol
	Octanoic acid	34.3	91, 92, 122	d7-butyric acid
	Decanoic acid	36.84	60, 73, 101	d7-butyric acid
	Dodecanoic acid	38.89	60, 73, 129	d7-butyric acid

Table 14 :

 14 Parameters for the determination of terpenols by GC-MS. Target ions for peak quantification in bold.

	Compound	Retention	Qualifier ion m/z	Internal standard
		time [min]
	cis-rose oxide	22.71	total ion current	d5-linalool
	Linalool	29.13	55, 71, 93	d5-linalool
	Linallyl acetate	29.55	93, 69, 121	d5-linalool
	Citronellyl acetate	32.62	69, 81, 95	d5-linalool
	α-terpineol	33.64	59, 93, 121	d2-geraniol
	Neryl acetate	34.5	69, 93, 121	d2-nerol
	Geranyl acetate	35.28	total ion current	d2-geraniol
	Citronellol	35.46	total ion current	d5-linalool
	Nerol	36.45	41, 69, 93	d2-nerol
	Geraniol	37.87	41, 69, 139	d2-geraniol

Table 16 : QTLs detected with single QTL mapping.

 16 List of QTLs with an influence on fermentation parameters, the production of extracellular metabolites and volatile secondary metabolites that were detected with single QTL mapping. QTLs containing several single trait results with a peak distance < 10 cM are numbered in superscript.

		Trait	QTL name	Chromosome	Start position	End position	LOD
					[bp]	[bp]	Score
		Fermentation parameters
	8	CO2 production rate at t80%	chr4@385.5	IV	1134839	1173812	4.77
	9	CO2 production rate at t80%	chr4@410.0	IV	1198692	1246959	3.69
		CO2 production rate at t80%	chr10@241.5	X	717987	726938	4.67
	20	Fermentation time t80%	chr13@7.9	XIII	20503	25723	4.86
	20	CO2 production rate at t80%	chr13@7.9	XIII	20503	25723	3.81
		Extracellular metabolites after 80% of the fermentation
	2	Pyruvate yield	chr2@172.5	II	507274	527387	3.54
	8	G/F ratio	chr4@386.5	IV	1153678	1173812	10.49
	9	G/F ratio	chr4@412.2	IV	1205742	1243242	5.49
	10	Pyruvate yield	chr7@20.4	VII	56448	74414	6.15
	11	Pyruvate yield	chr7@156.9	VII	463981	503880	3.75
	14	Pyruvate yield	chr9@58.7	IX	173782	179168	4.00
	20	Pyruvate yield	chr13@7.9	XIII	20503	25723	4.80

Table 17 : QTLs detected with double and multiple QTL mapping.

 17 QTLs with an influence on fermentation parameters and the production of extracellular metabolites and volatile secondary metabolites that were additionally detected with double and multiple QTL searches. QTLs containing several single trait results with a peak distance < 10 cM are numbered in superscript.

	Trait	QTL name	Chromosome	Peak [bp]	LOD Score
	Diethyl succinate	chr1@64.7	I		4.23
	1 Ethyl 2-methylbutanoate	chr2@116.2	II		4.13
	1 Hexanoic acid	chr2@122.1	II		2.54
	2 Ethyl lactate	chr2@181.0	II		4.78
	Ethyl 2-methylpropanoate	chr3@78.8	III		2.84
	3 Pyruvate yield	chr4@91.9	IV		2.39
	3 Ethyl butanoate	chr4@92.0	IV		3.63
	4 Ethyl hexanoate	chr4@132.0	IV		2.59
	5 2-methylpropyl acetate	chr4@165.8	IV		2.42
	6 Diethyl succinate	chr4@175.0	IV		2.99
	6 Ethyl octanoate	chr4@175.0	IV		2.14
	Dodecanoic acid	chr4@298.0	IV		4.60
	Valeric acid	chr4@324.9	IV		2.50
	7 2-methylpropanol	chr4@348.9	IV	1046700	3.36
	7 3-methylbutanol	chr4@348.9	IV	1046730	3.66
	9 Pyruvate yield	chr4@411.9	IV	1235730	2.54
	G/F ratio	chr6@62.0	VI		3.90
	Ethyl 3-methylbutanoate	chr6@85.2	VI		4.14
	12 Decanoic acid	chr7@198.0	VII		3.65
	13 2-methylpropyl acetate	chr9@16.9	IX	50700	2.09
	13 Dodecanoic acid	chr9@17.6	IX	52800	3.20
	2-methylbutyl acetate	chr9@32.6	IX	97800	1.75
	14 Dry weight	chr9@60.0	IX		3.67
	14 Ethyl propanoate	chr9@67.5	IX		2.88
	14 Propanol	chr9@67.5	IX		1.85
	2-phenylethyl acetate	chr9@101.1	IX		2.25
	Propyl acetate	chr10@27.2	X	81600	1.53
	15 Ethyl hexanoate	chr10@61.3	X		2.97
	15 Hexanoic acid	chr10@61.3	X		2.78
	Ethyl octanoate	chr10@88.5	X		5.24
	Propanol	chr10@104.7	X		2.08
	16 2-methylpropanol	chr10@213.1	X		2.29
	16 2-methylpropyl acetate	chr10@221.7	X		4.04
	16 2-methylbutyl acetate	chr10@228.1	X		2.34
	16 3-methylbutyl acetate	chr10@228.8	X		2.97
	17 Decanoic acid	chr11@29.2	XI	87600	3.95
	17 Ethyl decanoate	chr11@34.2	XI		2.65
	17 t80%	chr11@43.2	XI		3.10
	17 Ethyl octanoate	chr11@51.7	XI		4.95
	Glycerol yield	chr11@70.0	XI		2.72
	G/F ratio	chr11@85.6	XI		2.65
	Ethyl propanoate	chr11@107.7	XI		3.78
	18 3-methylbutanoic acid	chr11@134.4	XI		3.68
	2-methylpropyl acetate	chr12@98.3	XII		3.15

Table 18 : Validated allelic variants in detected QTLs

 18

	MTF2621 to

. Selected target genes for the verification of QTLs influencing fermentation kinetics, substrate consumption and the production of fermentative aromas; differences caused by the allelic gene variants regarding the influenced traits were detected by RHA and are given as the ratio of phenotype

Table 19 : Non synonymous SNPs between allelic variants

 19

	Gene	AA position	S288C	MTF2621	MTF2622
	AGP1	7	P	P	L
		24	G	E	G
		142	N	S	N
		316	V	V	A
		370	F	L	F
		466	I	L	I
		540	L	I	L
		597	D	N	D
	AGP2	256	H	Y	H
	ALP1	126	V	V	A
	FAS1	1504	V	A	V
		1715	V	A	V
		1970	V	I	V
	ILV6	4	S	L	S
		56	A	P	A

. Differences in the amino acid (AA) sequence of the expressed protein resulting from non-synonymous SNPs between the allelic variants of the evaluated target genes. Comparison of the strains MTF2621 and MTF2622 with the S. cerevisiae reference strain S288C.

: Table of primers used in this study.

	test_ALP1_fw Ethyl 2-methylbutanoate CAGCTCCACTCCACATAAG 0.03 ± 0.00	0.03 ± 0.00	0	77,06
	Primer name test_12@227_fw ATATGGGCGATGGTTAGGG Ethyl 3-methylbutanoate 0.07 ± 0.00 Nucleotide sequence (5' -> 3') del_ACP1_fw test_FAS1_fw ATTCGCCACACCTAACTG Hexanoic acid 2.34 ± 0.33 ACAAACAACACAACTAACTCAATACAGCACCTTCCTTGCCTTCGTACGCTGCAGGTCGAC 0.07 ± 0.00 0 1.76 ± 0.29 1,8 del_ACP1_rv test_FAT3_fw TTTCTGCTTCGCTGTCTTCC Methionol 4.39 ± 1.96 10.53 ± 2.82 4,4 GGGGTGACACGATACAATATAATAGAGCGGGGACGGACACGCATAGGCCACTAGTGGATCTG del_AGP1_fw test_HXT3_fw CGCGGAACATTCTAGCTCG Octanoic acid 5.67 ± 1.33 4.48 ± 1.13 2,51 AGAAGAAGCACGCTAATATAGACAAAGATAGCTTCGCACATTCGTACGCTGCAGGTCGAC del_AGP1_rv test_HXT6_fw GATGTCTCGGATCTGTATGC Phenylethanol 455.13 ± 105.33 711.57 ± 106.55 267,48 CAAAAATGAATAAATATAAAAGAAGTAAATGCTTTTTTTTGCATAGGCCACTAGTGGATCTG del_AGP2_fw test_HXT7_fw CCCACCATCTTTCGAGATCC Propanoic acid 1.09 ± 0.25 0.53 ± 0.08 0,34 AAGCTGCACTTACATTTTGCTTCCATAACTTTTGCCAAGCTTCGTACGCTGCAGGTCGAC del_AGP2_rv test_ILV6_fw CAGACCGTCATGCAAGAATC Propanol 21.41 ± 3.36 7.23 ± 0.96 6,81 GCAGTCAATTTAAATTTGTGAATATAACGACATAATTGCAGCATAGGCCACTAGTGGATCTG del_ALP1_fw test_IXR1_fw ATGGGTGGAACGGTTACTGAC Propyl acetate 0.07 ± 0.01 0.04 ± 0.00 0,02 GTAGTGTTGCGATTATTGCCATGGATGAAACTGTGAACATTTCGTACGCTGCAGGTCGAC del_ALP1_rv del_12@227_fw del_12@227_rv del_FAS1_fw del_FAS1_rv del_FAT3_fw del_FAT3_rv del_HXT3_fw del_HXT3_rv TAAAATACACTATTATTCAGCACTACGGTTTAGCGTGAAAGCATAGGCCACTAGTGGATCTG test_YLR278C_fw ACCTGAATCGCATCGTGTG ATAGAATCACAAACAAAATTTACATCTGAGTTAAACAATCTTCGTACGCTGCAGGTCGAC test_YDL124W_fw TCCGCTTTCCAGTCGAACAC CAAGAAGGTCTGAGGGTTTTCTTGAGCCCAGGAAGTCAGCGCATAGGCCACTAGTGGATCTG test_SIR2_fw GGCCTGCTATTTCTGTATCG AAGGCGTTTGCTGCCTTAACCCAATTGATGGAAAATTCGGTTCGTACGCTGCAGGTCGAC test_RGT1_fw GCTTATGAGCACCAGAGAC AAGTTAAATATTTCTTACGGTTATATAATCACTTAAGAAAGCATAGGCCACTAGTGGATCTG test_RGS2_fw ACGCCGTTGCAGCTAAAGTC ATTTATTCGCCACACCTAACTGCTCTATTATTCGCTCATCTTCGTACGCTGCAGGTCGAC test_PXA2_fw AGATTAAGGGCGGCAATG GACGTGAAAAGCGGATCGTGTGTGTCTTGTATTTACGATGGCATAGGCCACTAGTGGATCTG test_NRG1_fw CCTCAGGGAAAGCCAAGAAATG TATTATTGTAATATGGGCGATGGTTAGGGTGACGGCGACTTTCGTACGCTGCAGGTCGAC test_MAE1_fw CTTGGCTTCGACTCATCATC GTGGTATGGAGTATTATTCTAAAATTATGAAAGGACATCCGCATAGGCCACTAGTGGATCTG test_LEU9_fw TTCTGGCTAGTTCCCTCGTTTC Valeric acid 0.28 ± 0.03 0.06 ± 0.02 0,21	59,66 91,33 65,71 41,46 76,64 18,98 63,61 70,62 93,21
	del_HXT6_fw	GGCTTGCAGACAATGGAGAGCAAATGGGTATACAATATAGTTCGTACGCTGCAGGTCGAC
	del_HXT6_rv	CAGAATTAGAGTGCATTTCAAATGCACAAATTAGAGCGTGGCATAGGCCACTAGTGGATCTG
	del_HXT7_fw	CTTCACAATGTTCGAATCTATTCTTCATTTGCAGCTATTGTTCGTACGCTGCAGGTCGAC
	del_HXT7_rv	GAGTACATTTCAAATGCACAAATTAGAGCGTGATCATGAAGCATAGGCCACTAGTGGATCTG
	del_ILV6_fw del_ILV6_rv Compound	AATCTTTAGAACATCTGAGCTCACTAACCCAGTCTTTCTATTCGTACGCTGCAGGTCGAC [mg/L] [mg/L] [mg/L] AGGAGAGTCCCGAGGGCGATCGCAAGGCCGAGAGACTAACGCATAGGCCACTAGTGGATCTG MTF2621 MTF2622 Trait variety as IQR Heritability
	del_IXR1_fw 2-methylbutanoic acid CTGTGATATACGTACGACGCTAACAGTACCCACAACTGCATTCGTACGCTGCAGGTCGAC 1.60 ± 0.19 2.65 ± 0.33 0,54	75,71
	del_IXR1_rv 2-methylbutanol	TGGGATAATGTTACAGTGGAAAACTAAAGTTGTTTATTTGGCATAGGCCACTAGTGGATCTG 0.05 ± 0.00 0.05 ± 0.00 0	58,14
	del_LEU9_fw 2-methylbutyl acetate GGATAATACTATCAGCACATTATCATTTAGCCGCGTAGCCTTCGTACGCTGCAGGTCGAC 0.26 ± 0.03 0.43 ± 0.09 0,14	92,87
	del_LEU9_rv 2-methylpropanoic acid TATATATATAACATGAGTAATCATAAGCTACTCCTTTCTAGCATAGGCCACTAGTGGATCTG 2.80 ± 0.30 3.7 ± 0.51 1,61	94,35
	del_MAE1_fw 2-methylpropanol	AGTGCACATAAATACCAAGACAAAAGGTAGAAATACGGTTTTCGTACGCTGCAGGTCGAC 31.06 ± 4.63 21.6 ± 3.03 20,51	87,95
	del_MAE1_rv 2-methylpropyl acetate TTTTTTTTTTTAAGTGCAGGCGTTGGTTATGCTTCGTCTAGCATAGGCCACTAGTGGATCTG 0.17 ± 0.02 0.10 ± 0.02 0,11	93,72
	del_NRG1_fw 2-phenylethyl acetate CTCGACCAGCATATTACTACCCTTCGCAAACTTTCAGGCATTCGTACGCTGCAGGTCGAC 5.11 ± 1.15 5.65 ± 0.80 2,95	80,47
	del_NRG1_rv 3-methylbutanoic acid AGTAGTACTGCTAATGAGAAAAACACGGGTATACCGTCAAGCATAGGCCACTAGTGGATCTG 2.01 ± 0.23 1.98 ± 0.23 0,65	78,27
	del_PXA2_fw 3-methylbutanol	ATAATAATACAATTAAAAGTTACCGAAGAAAGATTTTATATTCGTACGCTGCAGGTCGAC 234.41 ± 53.93 244.13 ± 46.85 60,79	-3,21
	del_PXA2_rv 3-methylbutyl acetate CAATTTATACATGATTTGGATTCTCCTTTGGCTATGTATGGCATAGGCCACTAGTGGATCTG 1.13 ± 0.11 1.93 ± 0.3 0,54	93,68
	del_RGS2_fw Butanoic acid	CCTTTGATACATAAAACGAAGAAAATTCAGCACATGCCAGTTCGTACGCTGCAGGTCGAC 1.69 ± 0.36 0.95 ± 0.26 0,5	15,83
	del_RGS2_rv Decanoic acid	TGAAGATATTTGTGTCTCCACAGATGATGAAGAGGCTATCGCATAGGCCACTAGTGGATCTG 1.59 ± 0.90 1.66 ± 1.05 0,93	-56,77
	del_RGT1_fw Diethyl succinate	GAAGCTGTACTCTTCTCTCAAACTCCAATATATTTCAAATTTCGTACGCTGCAGGTCGAC 0.02 ± 0.02 0.03 ± 0.03 0,02	0,19
	del_RGT1_rv Dodecanoic acid	GGAGAACCTGACCTACAGGAGAAGGGAGCATAGTTACCTGGCATAGGCCACTAGTGGATCTG 0.29 ± 0.20 0.62 ± 0.48 0,25	-4,48
	del_SIR2_fw Ethyl 3-methylthiopropanoate 1.99 ± 0.95 ×10 -3 CATTCAAACCATTTTTCCCTCATCGGCACATTAAAGCTGGTTCGTACGCTGCAGGTCGAC 3.97 ± 0.99 ×10 -3 0	69,38
	FAS: Fatty acid synthetase; LOD: logarithm of odds; PCA: Principal component analysis; QTL: del_SIR2_rv TATTAATTTGGCACTTTTAAATTATTAAATTGCCTTCTACGCATAGGCCACTAGTGGATCTG Ethyl acetate 33.54 ± 16.01 74.83 ± 94.73 17,83 72,04
	del_YDL124W_fw TAAACGGTTGTGTTACCCTAAAGAAACAGAGGTCAGTTAATTCGTACGCTGCAGGTCGAC Ethyl butanoate 0.11 ± 0.02 0.09 ± 0.02 0,07 Quantitative trait locus; RHA: Reciprocal hemizygosity analysis; SM: Synthetic must; SNP: 86,17 del_YDL124W_rv ATACAAAACAAATATGACTCGTACATAAATTGTCCGGTATGCATAGGCCACTAGTGGATCTG Ethyl decanoate 0.61 ± 0.29 0.55 ± 0.31 0,38 28,8 Single-nucleotide polymorphism; YPD: Yeast extract peptone dextrose. Hygro_rv TGTTATGCGGCCATTGTC Ethyl dodecanoate 0.40 ± 0.27 0.6 ± 0.27 0,35 1,98
	tal_12@227_1 Ethyl hexanoate	CGCCAGCATCAACATTAC 0.43 ± 0.09	0.27 ± 0.05	0,24	69,56
	tal_12@227_2 Ethyl 2-methylpropanoate CGCCGGCATCAACATTAC 0.04 ± 0.00	0.04 ± 0.01	0,01	88,05
	test_ACP1_fw Ethyl lactate	GATAAGGCCGGTGCAACTTC 0.79 ± 0.36	1.21 ± 0.53	0,57	42,24
	test_AGP1_fw Ethyl octanoate	TTCTTCAGTGCCGCTTGAG 0.64 ± 0.19	0.53 ± 0.16	0,33	36,06
	test_AGP2_fw Ethyl propanoate	CGCCAAACCGGACCATTTAGATTC 0.10 ± 0.01	0.11 ± 0.02	0,03	84,15

Additional file 2

Additional file 3: Additional phenotypic information. Concentrations of determined secondary metabolites produced by the parental strains used in this study with trait variety among the segregant population given as Interquartile range (IQR) and heritability of evaluated traits.

Table 20 :

 20 Detected QTLs influencing metabolite yields and ratio of remaining sugars (G/F ratio) during exponential phase.

	Trait	QTL name	Chromosome	QTL start [bp]	QTL end [bp]	LOD
	Succinate yield	chr4@122.8	IV	356071	380035	4.42
	G/F ratio	chr4@125.4	IV	356071	400864	3.67
	G/F ratio	chr4@139.3	IV	409433	448045	3.63
	CO2 yield	chr4@139.3	IV	410742	448045	4.05
	Succinate yield	chr4@152.6	IV	426649	488205	4.71
	G/F ratio	chr4@160.0	IV	448242	505548	3.76
	CO2 yield	chr4@160.0	IV	448242	505548	4.33
	Glycerol yield	chr7@90.3	VII	252047	273771	3.76
	AKG yield	chr10@242.3	X	717987	648141	4.38
	Ethanol yield	chr13@208.9	XIII	622064	660267	4.22
	G/F ratio	chr13@214.5	XIII	624189	648141	3.62
	CO2 yield	chr13@214.5	XIII	624189	648141	3.7
	Acetate yield	chr13@237.7	XIII	710548	726277	3.84
	AKG yield	chr15@31.8	XV	67745	111309	3.52

Table 21 :

 21 Detected QTLs influencing modeled metabolic fluxes.

	Trait	QTL name	Chromosome	QTL start [bp]	QTL end [bp]	LOD
	Glycerol synthesis	chr2@222.9	II	662795	701771	4.58
	Malate transport	chr5@128.3	V	354177	400836	4.01
	Glycolysis & ethanol synthesis	chr7@18.0	VII	40689	58851	4.63
	Biomass	chr7@25.5	VII	52412	82449	3.73
	TCA cycle oxidative branch	chr7@25.5	VII	52412	82449	4.05
	Transport & excretion TCA cycle	chr7@25.5	VII	52412	82449	4.05
	metabolites
	Ethanol transport	chr8@155.7	VIII	443664	483121	3.45

Table 22 :

 22 Validated allelic variants in detected QTLs influencing modeled metabolic fluxes. (p-value: * ≤ 0.05, ** ≤ 0.01)

	QTL name	Trait	Evaluated genes	Different impact of allele on trait
				as MTF2621/MTF2622 [factor]
	chr2@222.9	glycerol synthesis	PDB1	1.05* glycerol synthesis
	chr7@25.5	biomass	HAP2	no effect
		glycolysis & ethanol synthesis	VID30	0.99* glycolysis & ethanol
		TCA oxidative branch		synthesis
		TCA cycle metabolite transport		0.92* -0.73** TCA cycle
		& excretion		metabolite excretion
				(0.90* TCA reductive branch)

Table 23 :

 23 Differences in amino acids (AA) of validated gene variants caused by non-synonymous SNPs between parent strains. Comparison of SNP identity to S. cerevisiae type strain S288C.

	Gene	Length in AA AA position	S288C	MTF2621	MTF2622
	PDB1	366	14	A	A	T
			26	-	A	-
			289	V	V	I
	VID30	958	37	H	Y	H
			672	E	E	G
			882	I	V	I

Table 24 :

 24 Differences in peptide chains of validated gene variants caused by non-synonymous SNPs between parent strains. Comparison of SNP identity to S. cerevisiae type strain S288C.

	Gene	AA position	S288C	MTF2621	MTF2622
	MMP1	179	W	W	STOP
	YLL058W	98	K	K	E
		107	T	T	R
		131	A	A	T
		207	G	G	D
		213	P	P	S
		252	S	S	L

Table 25 :

 25 Distribution of the truncated SMM transporter Mmp1, resulting from SNP G536A in the gene, between 85 assessed S. cerevisiae strains of different origin.

	Strain origin	Full lenght Mmp1	Truncated Mmp1
	African	4	0
	Beer	3	0
	Bread	2	1
	Cheese	3	3
	Clinical	1	0
	Flor	0	8
	Laboratory	6	0
	Oak Mediterranean	3	0
	Oak North American	7	1
	Others	8	0
	Rum	8	2
	Wine	19	4
	Wine x flor	2	0
	Total	66	19

  Occurrence of truncated SMM transporter Mmp1, caused by SNP G536A within the gene, in 85 strains of different origin. Genome sequences were obtained from data bases Evolya, Genowine and the Saccharomyces genome database (SGD).

		CBS1171 CBS7957		beer rum	full truncated	Evolya Evolya
		ForstersB CBS7959		beer rum	full full	SGD Evolya
		ForstersO CBS7960		beer rum	full full	SGD SGD
	1678_GAL1DisB 3093_tagA-pUG	6464 EDV493 Nucleotide sequence (5' -> 3') bread rum CLIB215 bread JAY291 rum AATGAGAAGTTGTTCTGAACAAAGTAAAAAAAAGAAGTATACTTACATAGGCCACTAGTGGATCTGTAT full Evolya full Evolya full Evolya full SGD CLIB215-3B bread truncated Evolya 1014-F5 wine full Genowine AG ACTATATGTGAAGGCATGGCTATGGCACGGCAGACATTCCGCCAGATCATCAATAGGCACCTTCGTACG KS11 cheese full Evolya 20B2 wine truncated Genowine
		KS18 22A4	CTGCAGGTCGA	cheese wine	truncated full	Evolya Genowine
	4653_A-CYC1t-rv	GTGCCTATTGATGATCTGGCGGAATGTCTGCCGTGCCATAGCCATGCCTTCACATATAGTCCGCAAATTA MUCL42908 cheese full Evolya 6320-A7 wine full Genowine
		AAGCCTTCGAG NRRLY1545 AWRI1631	cheese wine	full full	Evolya SGD
	5981_Cas9_GAL1_fw TL229-A TTCACCGGTCGCGTTCCTGAAACGCAGATGTGCCTCGCGCCGCACACCGTATTACCGCCTTTGAGTG cheese truncated Evolya AWRI796 wine full SGD
	6005_p426-CRISP_rv VKMY373 GATCATTTATCTTTCACTGCGGAGAAG cheese BC187 wine	truncated full	Evolya SGD
	6209_Cas9ORF_rv	CTGCGCCGTGCTGTTCTTTTGAG YJM789 clinical D47-6-4A wine	full full	SGD Genowine
	962_GAL1_fw	7-7 F12-3B	TACGGATTAGAAGCCGCCGAGC flor wine	truncated full	Genowine Genowine
	CasGuide_MMP1_1 F25 GE7-4A CasGuide_MMP1_2 FS2D K1-28-1A TGCGCATGTTTCGGCGTTCGAAACTTCTCCGCAGTGAAAGATAAATGATCCGATGGTGCCTAATAAGAG flor truncated Genowine wine full Genowine AGTTTTAGAGCTAGAAATAGCAAGTTAAAATAAG TGCGCATGTTTCGGCGTTCGAAACTTCTCCGCAGTGAAAGATAAATGATCCGATGGTGCCCAATAAGAG flor truncated Genowine wine full Genowine
		AGTTTTAGAGCTAGAAATAGCAAGTTAAAATAAG GUF54-A1 flor L1414 wine	truncated full	Genowine Genowine
	CasRep_MMP1_fw	GGTAGGATGTTCGCTTACCATC flor Lava32-15 P3-D5 wine	truncated truncated	Genowine Evolya
	CasRep_MMP1_rv	CCACCACCTGCAATGAGAAC flor Lava32-6 TA12-2 wine	truncated truncated	Genowine Evolya
	del_GTT2_fw	AGAAACTTGGCGCTCTATATAAAGTACCTACAAAGGATACTTCGTACGCTGCAGGTCGAC TS12-A7 flor truncated Genowine M2ONO800-1 wine full Evolya
	del_GTT2_rv	GTTTCCACGTACCACGCAAAACTTGTCTCAAGTAGCCACTGCATAGGCCACTAGTGGATCTG flor truncated Evolya MCN1500-10 VPDN wine full Evolya
	del_MMP1_fw	TCAAGAGTTCTCCATCAGGATACCATGAAAGATACCCGAATTCGTACGCTGCAGGTCGAC laboratory full SGD MCN1500-3B BY4741 wine full Evolya
	del_MMP1_rv	ATTATTATTAATTAATATTCAAATGAAGTACTTCACTAAGGCATAGGCCACTAGTGGATCTG laboratory full SGD MCN1500-3C BY4742 wine full Evolya
	del_YLL058W_fw	FL100 N15-4	AACCACCACTGAACAACTATTAATCATCGTGGAAATTATCTTCGTACGCTGCAGGTCGAC laboratory full SGD wine truncated Evolya
	del_YLL058W_rv	AAAAAATTCTTGATTTTTGATATTCTACTCATGATCTGCTGCATAGGCCACTAGTGGATCTG laboratory full SGD REDSTAR FY1679 wine full SGD
	Hygro_rv	TGTTATGCGGCCATTGTC laboratory RM11-1a JK9-3D wine	full full	SGD SGD
	tal_MMP1_1	CCGATGGTGCCTAATAAGAG SEY6210 laboratory VL3 wine	full full	SGD SGD
	tal_MMP1_2	CCGATGGTGCCCAATAAGAG laboratory YJM269 W303 wine	full full	SGD SGD
	test_GTT2_fw	TCGATGGATGCCGTATCAC X2180-1A laboratory QA23 wine x flor	full full	SGD SGD
	test_MMP1_fw	CTACCACCTTTCGCTCTTG YPH499 laboratory Vin13 wine x flor	full full	SGD SGD
	test_YLL058W_fw	ACACCAGGATGACTAACC OakRom3-2 oak MED	full	Evolya
		ZP848			oak MED	full	Evolya
		ZP851			oak MED	full	Evolya
	OakArd11-2-2 OakBord21-1 OakGri7-1 Additional file 15: Strain T7		oak NA oak NA oak NA Origin oak NA	full full truncated Mmp1 variant full	Evolya Evolya Evolya Source SGD
		DBVPG6044 YPS128		african oak NA	full full	SGD SGD
		PW5 YPS163			african oak NA	full full	SGD SGD
		SK1 ZP1050			african oak NA	full full	SGD Evolya
		Y55 ZP611			african oak NA	full full	SGD Evolya
		YQ5 245			atypical bread rum	full full	Evolya Evolya
		YA3 309		atypical bread (sake like) rum	full full	Evolya Evolya
		NRRY1791 376	atypical cheese (sake like) rum	full full	Evolya Evolya
		CLIB219 390-D2		atypical grape rum	full truncated	Evolya Evolya
		Lava38-1 460		atypical grape rum	full full	Evolya Evolya

Table 26 :

 26 Difference of terpenol formation for two segregant strains with extreme fermentation kinetics. Samples were taken after 80% of fermentation, which corresponded to 73h for the fast fermenting strain and to 261h for the slow fermenting strain.

			Concentration [µmol/L]
	Compound	Must	Slow fermenter	Fast fermenter
	cis-rose oxide	0.13	0.12 ± 0.0	0.12 ± 0.01
	Citronellol	0.33	10.58 ± 0.43	8.38 ± 0.50
	Citronellyl acetate	0.07	1.38 ± 0.31	2.97 ± 0.12
	Geraniol	61.55	7.94 ± 0.78	5.58 ± 0.14
	Geranyl acetate	0.03	0.61 ± 0.07	1.69 ± 0.06
	Linalool	0.32	0.86 ± 0.06	0.66 ± 0.04
	Nerol	0.51	0.70 ± 0.01	0.68 ± 0.05
	Neryl acetate	0.02	0.04 ± 0.01	0.09 ± 0.01
	α-terpineol	0.00	0.01 ± 0.0	0.01 ± 0.0
	Total	63.07	22.24 ± 1.68	20.18 ± 0.32
		100%	35.27 ± 2.67%	32.00 ± 0.50%

Table 27 :

 27 Detected QTLs that influence the metabolism of terpenols by S. cerevisiae.

	Trait	QTL name	Chromosome QTL start [bp] QTL end [bp] LOD-Score
	Linalool	chr2@172.5	II	493459	527387	3.97
	α-Terpineol	chr4@141.1	IV	410742	426649	4.00
	α-Terpineol	chr4@160.0	IV	448242	512368	3.62
	Terpenol acetates	chr4@379.2	IV	1132305	1141711	4.19
	Linallyl acetate	chr7@16.8	VII	32927	56448	4.13
	Neryl acetate	chr7@16.8	VII	47026	56448	5.36
	Linalool	chr7@198.0	VII	589048	608880	4.75
	Neryl acetate	chr7@294.6	VII	867733	896533	4.81
	Terpenol acetates	chr7@340.5	VII	1013880	1025828	6.63
	α-Terpineol	chr8@134.5	VIII	386376	412965	3.55
	Linallyl acetate	chr13@7.9	XIII	20503	25723	3.50
	Linalool	chr13@7.9	XIII	20503	30565	4.15
	Geraniol	chr13@53.3	XIII	130890	190371	3.79
	Total terpenols	chr13@69.8	XIII	193006	217967	3.93
	Geraniol	chr14@36.3	XIV	78386	119900	3.76
	Total terpenols	chr14@36.3	XIV	78386	119900	4.65
	Geraniol	chr14@65.6	XIV	194351	201973	3.98
	Linalool	chr14@65.6	XIV	194351	201973	5.54
	Total terpenols	chr14@78.6	XIV	204725	247403	4.22
	Citronellyl acetate	chr14@102.4	XIV	290197	314885	4.30
	α-Terpineol	chr15@27.9	XV	67745	93027	3.96
	Citronellyl acetate	chr15@299.1	XV	873008	904240	3.73
	Terpenol acetates	chr15@336.2	XV	1004482	1011768	3.77
	Terpenol acetates	chr16@286.3	XVI	840264	876279	3.85
	Terpenol acetates	chr16@299.9	XVI	896990	904961	4.64
	Linalool	chr16@300.7	XVI	896990	917224	4.01

Table 28 :

 28 QTLs for terpenol metabolism overlapping with regions detected by previous experiments to influence other traits.

	QTL name	Influenced terpenol traits	Influenced other traits
	chr2@172.5	linalool	ethyl butanoate
			ethyl hexanoate
			pyruvate yield
	chr4@141.9	α-terpineol	ethyl hexanoate
			higher alcohol acetates (2x)
	chr4@161.9	α-terpineol	higher alcohol acetates (4x)
	chr4@379.2	terpenol acetates	CO2 production rate t80
			ratio glucose/fructose
	chr7@26.5	terpenol acetates	pyruvate yield
			glycolysis and ethanol synthesis
			biomass
			TCA cycle oxidative
			TCA cycle metabolites
			ethyl esters (2x)
	chr7@198.0	linalool	decanoic acid
			dodecanoic acid
	chr7@294.6	neryl acetate	2-phenylethyl acetate
	chr13@7.9	linalool	CO2 production rate t80
		linallyl acetate	pyruvate yield
			t80
	chr14@36.3	geraniol	dodecanoic acid
		total terpenols	propanoic acid
			propanol
			propyl acetate
			valeric acid
	chr14@65.6	geraniol	propanoic acid
		linalool
	chr14@78.6	total terpenols	valeric acid
	chr15@299.1	citronellyl acetate	ethyl esters (2x)
	chr16@299.9	linalool	ethyl esters (4x)
		terpenol acetates

Table 29 :

 29 Suggestions for target genes to validate in detected QTLs with LOD-scores of more than 4.42.

	QTL name	Trait	Taget gene	Function
	chr7@16.8	linallyl acetate	HAP2	Subunit of DNA-binding complex involved in activation and
		neryl acetate		repression of respiratory gene expression
	chr7@198.0	linalool	no clear candidate
	chr7@294.6	neryl acetate	PDX1	Part of pyruvate dehydrogenase complex
			TDH3	Glyceraldehyde-3-phosphate dehydrogenase
	chr7@340.5	terpenol acetates	SAY1	Sterol deacetylase
	chr14@36.3	geraniol	ERG24	C-14 sterol reductase
		total terpenols
	chr14@65.6	geraniol
		linalool

Table 30 :

 30 Comparison of haploid and diploid parental strains in terms of fermentation parameters, main metabolite yields, ratio of remaining glucose/fructose (G/F ratio) and formation of volatile metabolites after 80% of fermentation.

	Phenotype	Trait	haploid/diploid	haploid/diploid
			MTF2621 [%]	MTF2622 [%]
	Fermentation parameters	µ max	3.9	-9.1
		tµ max	0.9	5.0
		t80%	-16.6	-10.7
	Main metabolite yields	Acetate	12.7	37.5
		AKG	-33.0	15.2
		Ethanol	-0.7	-0.6
		Glycerol	-8.0	-4.3
		Pyruvate	34.3	0.8
		Succinate	-13.0	-9.5
	Sugar consumption	G/F ratio	13.0	12.3
	Acetate esters	2-methylbutyl acetate	15.2	-7.6
		2-methylpropyl acetate	59.0	21.2
		3-methylbutyl acetate	-3.9	-14.9
		2-phenylethyl acetate	2.3	-20.4
		ethyl acetate	-25.1	-25.2
		propyl acetate	25.2	-0.6
	Ethyl esters	diethyl succinate	-16.1	-23.3
		ethyl 2-methylbutanoate	-31.8	-26.3
		ethyl 2-methylpropanoate	-11.5	-26.4
		ethyl 3-methylbutanoate	189.1	-17.0
		ethyl 3-methylthiopropanoate	-0.8	4.8
		ethyl butanoate	4.8	8.4
		ethyl decanoate	-4.4	-24.3
		ethyl dodecanoate	10.8	-11.9
		ethyl hexanoate	-18.6	-20.7
		ethyl lactate	17.9	-11.6
		ethyl octanoate	-23.3	-25.6
		ethyl propanoate	-23.2	-37.6
	higher alcohols	2-methylbutanol	55.8	-29.2
		2-methylpropanol	3.3	0.8
		2-phenylethanol	-16.4	-20.4
		3-methylbutanol	-9.0	-3.2
		hexanol	-3.4	3.7
		methionol	11.2	-17.7
		propanol	4.1	-4.9

Table 31 :

 31 Concentration of residual sugars [g/L] after the fermentation of the hybrid strains in 12 °P Heineken wort at 22 °C.

	Strain	Glucose	Fructose	Sucrose	Maltose Maltotriose	Total
	Wort	1.23	0.30	0.28	5.22	1.42	8.44
	59A	0.00	0.01	0.00	0.00	1.47	1.53
	C902	0.00	0.01	0.00	0.00	1.44	1.50
	H11	0.00	0.00	0.00	0.35	1.36	1.71
	H12	0.00	0.00	0.00	1.77	1.44	3.21
	H16	0.00	0.00	0.00	0.00	1.33	1.33
	H24	0.00	0.00	0.00	3.72	1.38	5.10
	H27	0.00	0.00	0.00	0.00	1.40	1.40
	H54	0.00	0.00	0.00	4.00	1.46	5.46

Table 32 :

 32 Concentrations of fermentative aromas [mg/L] produced by the hybrid strains H11 -H54 during fermentation in 12 °P Heineken wort at 22 °C.

	Compound	H11	H12	H16	H24	H27	H54
	Acetaldehyde	3.37	1.66	0.68	2.34	2.42	2.07
	DMS [µg/L]	19.33	10.93	13.08	15.40	14.40	15.60
	Acetone	0.52	0.36	0.68	0.68	0.57	0.67
	Ethyl formiate	0.09	0.06	0.07	0.05	0.09	0.05
	Ethyl acetate	18.65	14.98	23.31	6.50	30.07	5.35
	Methanol	2.56	2.26	2.53	2.19	2.37	2.18
	Ethyl propionate	0.07	0.04	0.14	0.02	0.09	0.02
	Propanol	30.57	19.94	27.67	12.13	26.86	11.58
	Isobutanol	34.48	31.61	43.67	13.08	39.47	10.23
	Isoamyl acetate	1.82	1.52	4.06	0.95	3.58	0.80
	Amyl alcohols	98.46	82.20	141.10	56.12	100.46	53.16
	Ethyl capronate	0.12	0.11	0.49	0.09	0.39	0.07
	Total higher alc.	163.60	133.70	212.50	81.30	166.90	75.00
	4-vinylguaiacol	2.04	2.14	2.08	1.94	2.07	2.03

  Résumé : La levure Saccharomyces cerevisiae joue un rôle essentiel dans la production de composés aromatiques, tels que les esters, les alcools supérieurs et les acides organiques, ainsi que dans la transformation de précurseurs d'arômes du raisin pendant la fermentation du vin. Afin d'identifier les bases génomiques et métaboliques de ces propriétés, un croisement a été réalisé entre deux souches de levures de vin, sélectionnées pour leurs besoins en azote différents lors de la fermentation. 130 ségrégants de génération F2 ont été génotypés par séquençage complet du génome et individuellement phénotypés pendant la fermentation en mesurant les métabolites extracellulaires par HPLC et GC-MS. Les flux métaboliques intracellulaires ont été estimés à l'aide d'un modèle à base de contraintes. Une analyse QTL (quantitative trait locus) a été utilisée pour identifier les allèles influençant les variations d'arômes et de flux métaboliques. Plus de 80 QTL expliquant la variation de 59 caractères quantitatifs ont été détectés. Ces caractères comprennent des paramètres fermentaires, de consommation de substrat, la production de principaux métabolites et d'arômes fermentaires, ainsi que le métabolisme de composés aromatiques du raisin comme de terpénols ou le sulphur de diméthyle. L'intérêt de la cartographie QTL pour identifier les déterminants génétiques de variations de flux intracellulaires (f-QTLs) a par ailleurs été démontrée. Les QTL détectés ont été disséqués et des gènes dont les allèles contribuent spécifiquement aux variations phénotypiques ont été identifiés. Ces résultats soulignent la complexité génomique et métabolique de la synthèse et de la transformation d'arômes par la levure. L'identification de ces déterminants génétiques permet de mieux comprendre les liens entre variation génétique des levures et traits technologiques et fournit une base précieuse pour le développement de souches optimisées par des stratégies génétiques de croisement assisté par marqueurs.

Titre : Décrypter les bases génétiques et métaboliques des propriétés aromatiques de la levure Mots clés : Fermentation oenologique, levure de vin industrielle, production d'arômes, arômes fermentaires, terpénols, sulfure de diméthyle, flux métaboliques

Acknowledgments

This thesis was carried out in the mixed research unit Sciences for Oenology (UMR 1083 SPO) of INRA, SupAgro, Montpellier. The research project was part of the YEASTCELL initial training network (ITN) within the EU Marie Curie Actions.

Acknowledgements:

We are grateful to Bruno Blondin for his contribution to the design of the QTL strategy. We thank Anna Hagstrom for her technical help in the construction of hemizygotes.

Funding: This work was supported by the People Programme (Marie Curie Actions) of the European Union's Seventh Framework Programme FP7/2007-2013/ under REA grant agreement n° 606795, project acronym: YEASTCELL.

Availability of data and material:

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

List of Additional Files

Additional file 1: Genomic background of parent strains. Location of the S. cerevisiae strains used in this study, MTF2621 (4CAR1) and MTF2622 (T73), within the genotypic subgroups of champagne strains (light green lines) and wine strains (dark green lines). Phylogenetic tree constructed with data from and as described by [START_REF] Legras | Bread, beer and wine: Saccharomyces cerevisiae diversity reflects human history[END_REF] Authors' contributions: ME conducted the experiments, contributed to the linkage analysis and wrote the manuscript. IS established and performed the linkage analysis. CB contributed to the selection of parent cells and generation of F2-segregants. CC contributed to the phenotyping of strains. JLL analyzed the genotyping data. SD conceived and planned the study, with contribution of CC and JLL. All authors revised the manuscript and read and approved the final manuscript.

Supplementary Information

Additional file 1: Genomic background of parent strains. Location of the S. cerevisiae strains used in this study, MTF2621 (4CAR1) and MTF2622 (T73), within the genotypic subgroups of champagne strains (light green lines) and wine strains (dark green lines). Phylogenetic tree constructed with data from and as described by [START_REF] Legras | Bread, beer and wine: Saccharomyces cerevisiae diversity reflects human history[END_REF].

Abstract

Dimethyl sulfide (DMS) is a well described sulfur containing flavor active volatile that enhances general fruity aroma and imparts aromatic notes in wine. The most important precursor of DMS is S-methylmethionine (SMM), which is synthesized by grapes and can be metabolised by the yeast S. cerevisiae during wine fermentation. Precursor molecules left after fermentation are chemically converted to DMS during wine maturation, meaning that the level of DMS in wine is determined by the amount of remaining precursors at bottling.

To elucidate SMM metabolism in yeast we performed a quantitative trait locus (QTL) mapping approach using a population of 130 F2-segregants obtained from a cross between two wine yeast strains and we detected one major QTL explaining almost 30% of the trait variation. Within the QTL, the gene YLL058W and SMM transporter gene MMP1 were found to have an influence on SMM metabolism, from which MMP1 has the bigger impact. We identified and characterized a variant coding for a truncated transporter with superior SMM preserving attributes. A population analysis with 85 yeast strains from different origins revealed a significant association of the variant to flor strains and minor occurrence in cheese and wine strains.

These results will help in the selection and improvement of S. cerevisiae strains for the production of wine and other fermented foods containing DMS such as cheese or beer.

Keywords: QTL mapping, DMS, SMM metabolism, varietal aroma, yeast The detected QTL had an LOD-score of 9.46, meaning that 29.4% of the trait variation can be explained by the locus. The detected region chrXII:17,035..24,535 has a size of 7.5 kB and contains four genes: MHT1, MMP1, GTT2, and YLL058W. Interestingly, MHT1 and MMP1 are described to be involved in pDMS metabolism. MHT1 is the only gene that does not contain non-synonymous SNPs between the parent strains. Two SNPs are located in the 1000-bp upstream region of the gene at positions -878 and -848, however, they are already located in MMP1.

The high impact of the detected region chrXII:17,035..24,535 could have masked the detection of additional QTLs with low influence on the trait. For this reason, QTL mapping was repeated with the marker chr12_20766 as covariate. No additional QTL was found, although a region on chromosome XVI around 656700 bp was close to being significant with an LOD of 3.59. The region was closer observed, but no promising target gene with a potential function for the trait was identified.

Concluding Remarks

Bibliography

Annex Additional experiments

During the experimental work of this thesis, additional results were obtained related to yeast alcoholic fermentation or fermentative aroma. As the haploid parent strains of this study showed spontaneous mating type switch and self-diploidization, the influence of differences in ploidy on several wine fermentation traits was assessed for these strains. Furthermore, a secondment was done in the course of the thesis at the Heineken brewery, The Netherlands, under supervision of Dr. Jan Maarten Geertman. This opportunity was used to perform a study in cooperation with Frederico Magalhães, PhD student at VTT Finnland, to evaluate the suitability of newly generated Saccharomyces pastorianus strains for beer brewing.

Effect of yeast ploidy on wine fermentation traits

Saccharomyces cerevisiae can survive and grow in two different genomic states, haploid and diploid. While the majority of isolated S. cerevisiae wine strains is diploid [START_REF] Legras | Bread, beer and wine: Saccharomyces cerevisiae diversity reflects human history[END_REF], research on wine yeast phenotypes is mainly performed on haploid spores. This raises the question of the impact of wine yeast ploidy on aroma formation and fermentation kinetics. To evaluate this context, diploid strains of parental spores that had formed through selfdiploidization after spontaneous mating type switch were isolated. This behavior could be observed in both parent strains, although the HO gene had been deleted in beforehand. Ploidy of strains was verified by mating type PCR on the MAT-locus. Haploid and diploid strains were phenotyped in triplicate during small scale wine fermentation in 280 mL of synthetic grape must. After 80% of fermentation, samples were taken and the concentrations of extracellular main metabolites and volatile metabolites were determined by HPLC and GC-MS.

Principle component analyses (PCAs) were performed on fermentation parameters, sugar consumption and main metabolite yields (Figure 37), as well as on the formation of volatile metabolites (Figure 38).

Contribution to other publications

Publication 2

The contribution to this publication consisted in the qualitative analysis of high resolution mass spectrometry data and its graphic representation.

Targeted filtering reduces the complexity of UHPLC-Orbitrap-HRMS data to decipher polyphenol polymerization

Publication 3

The contribution to this publication consisted in the qualitative analysis of high resolution mass spectrometry data.

The hidden face of wine polyphenol polymerization highlighted by high resolution mass spectrometry