**Abstract**

In the world of winemaking, tradition and innovation have always been side by side, on the one hand a culture of several centuries and on the other the need to constantly improve and answer new challenges. Consumers' preferences, climate changes, and fermentation efficiency are some of the modern questions that winemakers have to consider. Yeast, at the center of the fermentation, has revealed itself as the perfect platform to answer many of these challenges. By understanding the metabolism and the genetic basis that modulate specific phenotypes of yeast during fermentation, an era of yeast optimization has surfaced in the last decades and pushed research even further. In this chapter we will focus the attention on two of the most successful techniques to that end, quantitative trait locus (QTL) and evolutionary engineering. QTL relies on a highly precise identification of the genome regions that control a phenotype of interest. The transfer of these regions to selected wine yeasts is then possible by a technique called backcrossing. Evolutionary engineering induces the yeast itself to modify its genetic background to adapt to a selective pressure and improve its fitness. The right choice of pressure leads to the improvement of its performances in enological conditions.

**Keywords:** winemaking, yeast optimization, QTL, backcrossing, evolutionary engineering

## **1. Introduction**

Nowadays, most of the enological fermentations are performed using selected wine yeast strains. Historically, and to some extent to this day, the selected wine yeasts have been found exploring the microbial flora present on the grapes, in the cellar, or in the vineyards. Next, a long process of characterization and selection is conducted in order to identify the yeast strain corresponding to a specific demand [1–5]. Since many years, the knowledge about wine yeasts has exponentially increased thanks to numerous scientific studies as well as the immense gain in the understanding of their metabolism. Consumption and requirements in nutrients (sugars, lipids, nitrogen, sulfur, vitamins, minerals), synthesis and production of biomass and metabolites (ethanol, glycerol, acids, alcohols, esters, sulfur compounds), resistance to stresses, and deficiencies have been well characterized. At the same time, the market trend and consumers' preference evolution results in a growing demand for new wine yeast strains combining different properties of interest or adapted to specific winemaking conditions and to global climate change. Consequently, meeting those steadily increasing requirements started to be a challenge. It becomes harder to find a strain combining all the properties

of interest [3, 6]. The development of wine yeast strain optimization strategies provided then a possible way out [7–9].

Optimization strategies of wine yeasts can be divided in two categories: the first exploits the existing diversity inside the *Saccharomyces cerevisiae* genus that has been recently demonstrated to be immense [10, 11] and the second allows to go further creating new phenotypes.

The exploitation of the natural diversity can be done by breeding. Breeding strategies of wine yeasts to combine properties of the parental strains have been implemented for many years [12, 13]. This can be done by sexual breeding or protoplast fusion for strains impaired in sporulation or mating. However, breeding without prior knowledge of the genetic basis of the properties of interest may lead to aleatory results, as most of the phenotypes are governed by complex regulations and often involve interactions. Additionally, a major drawback is that wine yeast strains are particularly difficult to mate due to the low spore viability and homothallism typical of this group [7]. Nowadays, more rational and powerful methods supported by the rise of the "omics" (genomic, transcriptomic, metabolomic studies, etc.), such as directed hybridization, can be carried out. Directed hybridization takes advantage of the knowledge of gene(s) of interest to follow and direct their transfer from one wine yeast strain to another [14].

On the other hand, going beyond the existing phenotypes can be performed by inducing new mutations. Mutagenesis by chemical or physical ways can be used to induce aleatory mutations inside the genome of wine yeasts [9]. Although simple to perform, this approach delivers quite random results with potential deleterious effects and requires massive clone screening, which can be unpractical depending on the phenotype being tested [15].

Evolutionary engineering also allows to go further the common phenotypes by continuously applying specific stressful conditions to a population of yeasts and selecting natural mutants presenting a higher fitness under those conditions [16–19]. This strategy is particularly powerful when the genetic bases of the phenotypes are not known.

Finally, the GMO strategy, also called genetic engineering, can be considered. This strategy exploits a set of molecular tools in order to manipulate the genetic characteristics of yeasts. In comparison to conventional improvement strategies that can transfer a large number of both specific and nonspecific genes to the recipient or may be responsible for some nontargeted variations in the genome, genetic engineering only transfers a small block of desired genes. Thus, this strategy is less time-consuming and yields more reliable products. However, the use of GMOs in food is strictly regulated in the EU and requires a heavy declaration, traceability procedures, and mandatory labeling even if no trace of the GMO can be found in the final product [20]. Although in some countries, the use of GMOs in food applications can be more easily allowed, the lack of background and studies to assess their impact on food safety, public health, and environment led to the creation of strict regulations and legislation during the 1990s. Several European regulations (e.g., EC258/97, EC1829/2003, 65/2004) have been issued to regulate every aspect of GMO use in the EU [21]. In enology, different strains were genetically modified, for instance, to obtain better aromatic profiles [22–24] or to overproduce glycerol and reduce ethanol yield [25, 26]. However their use in the EU and other countries is far from simple: long and costly administrative procedures, international and local regulations, consumer distrust, and the desire to keep the process within traditional boundaries point to a future in the wine industry without GMO [15]. More recently, the development of a marker-free, high-throughput, and multiplexed genome editing approach, the clustered regularly interspaced short palindromic repeats and CRISPR-associated protein 9 (Cas9) (CRISPR-Cas9) immune system, an easier and

**135**

*Yeast Strain Optimization for Enological Applications DOI: http://dx.doi.org/10.5772/intechopen.86515*

**2. QTL mapping and backcrossing**

phenotypes is performed (**Figure 1**).

current context.

winemaking.

**mapping**

traceless method of genome editing, has also been classified by the European Court of Justice as a GMO and is subject to the same controls [27]. It becomes clear that this kind of approaches cannot be reasonably developed for wine applications in the

In this chapter, we will develop more in depth the two most widely used approaches for wine yeast improvement, directed hybridization through quantitative trait locus (QTL) mapping combined with backcrossing cycles and evolutionary engineering. These approaches currently provide very efficient, GMO-free strategies that have been greatly contributing for yeast optimization, particularly in

**2.1 Identification of the molecular basis of technological properties: QTL** 

*Arabidopsis thaliana* [30], in crops [31], and in yeast [32, 33].

Numerous properties and phenotypes of wine yeasts are quantitative traits. These present continuous variations among individuals, in opposition to qualitative ones showing discrete variations. Those quantitative traits are due to complex genetic mechanisms, often linked to interactions between several loci. It is possible to identify the genetic determinants of such phenotypes using a QTL mapping. A quantitative trait locus is defined as a region of the genome, often scattered, associated with the phenotypic variation of a quantitative trait. The first study using the principles of QTL was done almost 100 years ago [28]. At the time, Sax [28] performed a genetic analysis correlating the size of beans with the color of pigmentation. Shortly after, the concept was applied to agriculture and since then has been widely used in many different organisms such as *Drosophila melanogaster* [29] and

Thanks to those approaches, chromosomic regions, genes, or even mutations, responsible for several wine yeast properties, have been deciphered. These include traits like acetic acid production, sporulation, ethanol tolerance, growth at high temperature, flocculation, wine aroma production, amino acid consumption, nitrogen requirement, fermentative performances, and sulfur compound production [34–45]. These studies have shown some phenotypes to be particularly complex. The QTL mapping method is divided into three steps. First, a recombinant population is constituted, second, this population is then phenotyped and genotyped, and, lastly, a statistical analysis to link the regions of the genome to the

The recombinant population is usually constituted from a hybrid obtained by crossing two parental strains, selected based on their phenotypic diversity. We can note that it is also possible to start directly with a highly heterozygous diploid parental strain, e.g., selected after evolutionary engineering. The hybrid is induced to sporulate to generate a population of meiotic segregants. The meiotic segregants passed through recombination so that each segregant possesses a random distribution of the alleles of the two parents. As the recombination rate is a crucial point in the accuracy of the final mapping of the QTL, it is also possible to generate an F2 segregant population to increase the allelic mixing. In that case, the initial meiotic segregant population, F1, is submitted to random crossing before a second sporula-

The phenotyping of the segregant population is a crucial step that can be limiting in the QTL approach. Each segregant has to be phenotyped individually for the trait of interest. The higher the number of segregants that are phenotyped, the

tion round to constitute the F2 haploid segregant population [42, 43].

*Yeast Strain Optimization for Enological Applications DOI: http://dx.doi.org/10.5772/intechopen.86515*

*Advances in Grape and Wine Biotechnology*

provided then a possible way out [7–9].

transfer from one wine yeast strain to another [14].

on the phenotype being tested [15].

types are not known.

creating new phenotypes.

of interest [3, 6]. The development of wine yeast strain optimization strategies

The exploitation of the natural diversity can be done by breeding. Breeding strategies of wine yeasts to combine properties of the parental strains have been implemented for many years [12, 13]. This can be done by sexual breeding or protoplast fusion for strains impaired in sporulation or mating. However, breeding without prior knowledge of the genetic basis of the properties of interest may lead to aleatory results, as most of the phenotypes are governed by complex regulations and often involve interactions. Additionally, a major drawback is that wine yeast strains are particularly difficult to mate due to the low spore viability and homothallism typical of this group [7]. Nowadays, more rational and powerful methods supported by the rise of the "omics" (genomic, transcriptomic, metabolomic studies, etc.), such as directed hybridization, can be carried out. Directed hybridization takes advantage of the knowledge of gene(s) of interest to follow and direct their

On the other hand, going beyond the existing phenotypes can be performed by inducing new mutations. Mutagenesis by chemical or physical ways can be used to induce aleatory mutations inside the genome of wine yeasts [9]. Although simple to perform, this approach delivers quite random results with potential deleterious effects and requires massive clone screening, which can be unpractical depending

Evolutionary engineering also allows to go further the common phenotypes by continuously applying specific stressful conditions to a population of yeasts and selecting natural mutants presenting a higher fitness under those conditions [16–19]. This strategy is particularly powerful when the genetic bases of the pheno-

Finally, the GMO strategy, also called genetic engineering, can be considered. This strategy exploits a set of molecular tools in order to manipulate the genetic characteristics of yeasts. In comparison to conventional improvement strategies that can transfer a large number of both specific and nonspecific genes to the recipient or may be responsible for some nontargeted variations in the genome, genetic engineering only transfers a small block of desired genes. Thus, this strategy is less time-consuming and yields more reliable products. However, the use of GMOs in food is strictly regulated in the EU and requires a heavy declaration, traceability procedures, and mandatory labeling even if no trace of the GMO can be found in the final product [20]. Although in some countries, the use of GMOs in food applications can be more easily allowed, the lack of background and studies to assess their impact on food safety, public health, and environment led to the creation of strict regulations and legislation during the 1990s. Several European regulations (e.g., EC258/97, EC1829/2003, 65/2004) have been issued to regulate every aspect of GMO use in the EU [21]. In enology, different strains were genetically modified, for instance, to obtain better aromatic profiles [22–24] or to overproduce glycerol and reduce ethanol yield [25, 26]. However their use in the EU and other countries is far from simple: long and costly administrative procedures, international and local regulations, consumer distrust, and the desire to keep the process within traditional boundaries point to a future in the wine industry without GMO [15]. More recently, the development of a marker-free, high-throughput, and multiplexed genome editing approach, the clustered regularly interspaced short palindromic repeats and CRISPR-associated protein 9 (Cas9) (CRISPR-Cas9) immune system, an easier and

Optimization strategies of wine yeasts can be divided in two categories: the first exploits the existing diversity inside the *Saccharomyces cerevisiae* genus that has been recently demonstrated to be immense [10, 11] and the second allows to go further

**134**

traceless method of genome editing, has also been classified by the European Court of Justice as a GMO and is subject to the same controls [27]. It becomes clear that this kind of approaches cannot be reasonably developed for wine applications in the current context.

In this chapter, we will develop more in depth the two most widely used approaches for wine yeast improvement, directed hybridization through quantitative trait locus (QTL) mapping combined with backcrossing cycles and evolutionary engineering. These approaches currently provide very efficient, GMO-free strategies that have been greatly contributing for yeast optimization, particularly in winemaking.
